Sample records for digital vegetation maps
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Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel
2017-09-11
Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.
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Vegetation inventory, mapping, and classification report, Fort Bowie National Historic Site
Studd, Sarah; Fallon, Elizabeth; Crumbacher, Laura; Drake, Sam; Villarreal, Miguel
2013-01-01
A vegetation mapping and characterization effort was conducted at Fort Bowie National Historic Site in 2008-10 by the Sonoran Desert Network office in collaboration with researchers from the Office of Arid lands studies, Remote Sensing Center at the University of Arizona. This vegetation mapping effort was completed under the National Park Service Vegetation Inventory program which aims to complete baseline mapping inventories at over 270 national park units. The vegetation map data was collected to provide park managers with a digital map product that met national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and even national context. Work comprised of three major field phases 1) concurrent field-based classification data collection and mapping (map unit delineation), 2) development of vegetation community types at the National Vegetation Classification alliance or association level and 3) map accuracy assessment. Phase 1 was completed in late 2008 and early 2009. Community type descriptions were drafted to meet the then-current hierarchy (version 1) of the National Vegetation Classification System (NVCS) and these were applied to each of the mapped areas. This classification was developed from both plot level data and censused polygon data (map units) as this project was conducted as a concurrent mapping and classification effort. The third stage of accuracy assessment completed in the fall of 2010 consisted of a complete census of each map unit and was conducted almost entirely by park staff. Following accuracy assessment the map was amended where needed and final products were developed including this report, a digital map and full vegetation descriptions. Fort Bowie National Historic Site covers only 1000 acres yet has a relatively complex landscape, topography and geology. A total of 16 distinct communities were described and mapped at Fort Bowie NHS. These ranged from lush riparian woodlands lining the
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VEGETATION MAPPING IN WETLANDS
Directory of Open Access Journals (Sweden)
F. PEDROTTI
2004-01-01
Full Text Available The current work examines the main aspects of wetland vegetation mapping, which can be summarized as analysis of the ecological-vegetational (ecotone gradients; vegetation complexes; relationships between vegetation distribution and geomorphology; vegetation of the hydrographic basin lo which the wetland in question belongs; vegetation monitoring with help of four vegetation maps: phytosociological map of the real and potential vegetation, map of vegetation dynamical tendencies, map of vegetation series.
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Remote sensing and vegetation mapping in South Africa
Directory of Open Access Journals (Sweden)
M. L. Jarman
1983-12-01
Full Text Available The kinds of imagery, types of data and general relationships between scale of study, scale of mapping and scale of remote sensing products that are appropriate to the South African situation for visual and digital analysis are presented. The type of remote sensing product and processing, the type of field exercise appropriate to each, and the purpose of producing maps at each scale are discussed. Lack of repetitive imagery to date has not allowed for the full investigation of monitoring potential and careful planning at national level is needed to ensure availability of imagery for monitoring purposes. Map production processes which are rapid and accurate should be utilized. An integrated approach to vegetation mapping and surveying, which incorporates the best features of both visual and digital processing, is recommended for use.
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Vegetation classification and distribution mapping report Mesa Verde National Park
Thomas, Kathryn A.; McTeague, Monica L.; Ogden, Lindsay; Floyd, M. Lisa; Schulz, Keith; Friesen, Beverly A.; Fancher, Tammy; Waltermire, Robert G.; Cully, Anne
2009-01-01
The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted
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UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis
Directory of Open Access Journals (Sweden)
Quanlong Feng
2015-01-01
Full Text Available Unmanned aerial vehicle (UAV remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1 Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2 the inclusion of texture features improved classification accuracy significantly; (3 classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.
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Pre-LBA CABARE Mapped Land Surface and Vegetation Characteristics, Rondonia, Brazil
National Aeronautics and Space Administration — ABSTRACT: Surface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8...
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Pre-LBA CABARE Mapped Land Surface and Vegetation Characteristics, Rondonia, Brazil
National Aeronautics and Space Administration — Surface parameter digital maps of vegetation, soil, and topography were obtained for Rondonia, Brazil, covering the 5x5 degree region bounded by 13-8 degrees S and...
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Directory of Open Access Journals (Sweden)
Maggi Kelly
2016-10-01
Full Text Available Research efforts that synthesize historical and contemporary ecological data with modeling approaches improve our understanding of the complex response of species, communities, and landscapes to changing biophysical conditions through time and in space. Historical ecological data are particularly important in this respect. There are remaining barriers that limit such data synthesis, and technological improvements that make multiple diverse datasets more readily available for integration and synthesis are needed. This paper presents one case study of the Wieslander Vegetation Type Mapping project in California and highlights the importance of rescuing, digitizing and sharing historical datasets. We review the varied ecological uses of the historical collection: the vegetation maps have been used to understand legacies of land use change and plan for the future; the plot data have been used to examine changes to chaparral and forest communities around the state and to predict community structure and shifts under a changing climate; the photographs have been used to understand changing vegetation structure; and the voucher specimens in combination with other specimen collections have been used for large scale distribution modeling efforts. The digitization and sharing of the data via the web has broadened the scope and scale of the types of analysis performed. Yet, additional research avenues can be pursued using multiple types of VTM data, and by linking VTM data with contemporary data. The digital VTM collection is an example of a data infrastructure that expands the potential of large scale research through the integration and synthesis of data drawn from numerous data sources; its journey from analog to digital is a cautionary tale of the importance of finding historical data, digitizing it with best practices, linking it with other datasets, and sharing it with the research community.
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Riparian Vegetation Mapping Along the Hanford Reach
International Nuclear Information System (INIS)
FOGWELL, T.W.
2003-01-01
During the biological survey and inventory of the Hanford Site conducted in the mid-1990s (1995 and 1996), preliminary surveys of the riparian vegetation were conducted along the Hanford Reach. These preliminary data were reported to The Nature Conservancy (TNC), but were not included in any TNC reports to DOE or stakeholders. During the latter part of FY2001, PNNL contracted with SEE Botanical, the parties that performed the original surveys in the mid 1990s, to complete the data summaries and mapping associated with the earlier survey data. Those data sets were delivered to PNNL and the riparian mapping by vegetation type for the Hanford Reach is being digitized during the first quarter of FY2002. These mapping efforts provide the information necessary to create subsequent spatial data layers to describe the riparian zone according to plant functional types (trees, shrubs, grasses, sedges, forbs). Quantification of the riparian zone by vegetation types is important to a number of DOE'S priority issues including modeling contaminant transport and uptake in the near-riverine environment and the determination of ecological risk. This work included the identification of vegetative zones along the Reach by changes in dominant plant species covering the shoreline from just to the north of the 300 Area to China Bar near Vernita. Dominant and indicator species included Agropyron dasytachyudA. smithii, Apocynum cannabinum, Aristida longiseta, Artemisia campestris ssp. borealis var scouleriana, Artemisa dracunculus, Artemisia lindleyana, Artemisia tridentata, Bromus tectorum, Chrysothamnus nauseosus, Coreopsis atkinsoniana. Eleocharis palustris, Elymus cinereus, Equisetum hyemale, Eriogonum compositum, Juniperus trichocarpa, Phalaris arundinacea, Poa compressa. Salk exigua, Scirpus acutus, Solidago occidentalis, Sporobolus asper,and Sporobolus cryptandrus. This letter report documents the data received, the processing by PNNL staff, and additional data gathered in FY2002
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Riparian Vegetation Mapping Along the Hanford Reach
Energy Technology Data Exchange (ETDEWEB)
FOGWELL, T.W.
2003-07-11
During the biological survey and inventory of the Hanford Site conducted in the mid-1990s (1995 and 1996), preliminary surveys of the riparian vegetation were conducted along the Hanford Reach. These preliminary data were reported to The Nature Conservancy (TNC), but were not included in any TNC reports to DOE or stakeholders. During the latter part of FY2001, PNNL contracted with SEE Botanical, the parties that performed the original surveys in the mid 1990s, to complete the data summaries and mapping associated with the earlier survey data. Those data sets were delivered to PNNL and the riparian mapping by vegetation type for the Hanford Reach is being digitized during the first quarter of FY2002. These mapping efforts provide the information necessary to create subsequent spatial data layers to describe the riparian zone according to plant functional types (trees, shrubs, grasses, sedges, forbs). Quantification of the riparian zone by vegetation types is important to a number of DOE'S priority issues including modeling contaminant transport and uptake in the near-riverine environment and the determination of ecological risk. This work included the identification of vegetative zones along the Reach by changes in dominant plant species covering the shoreline from just to the north of the 300 Area to China Bar near Vernita. Dominant and indicator species included Agropyron dasytachyudA. smithii, Apocynum cannabinum, Aristida longiseta, Artemisia campestris ssp. borealis var scouleriana, Artemisa dracunculus, Artemisia lindleyana, Artemisia tridentata, Bromus tectorum, Chrysothamnus nauseosus, Coreopsis atkinsoniana. Eleocharis palustris, Elymus cinereus, Equisetum hyemale, Eriogonum compositum, Juniperus trichocarpa, Phalaris arundinacea, Poa compressa. Salk exigua, Scirpus acutus, Solidago occidentalis, Sporobolus asper,and Sporobolus cryptandrus. This letter report documents the data received, the processing by PNNL staff, and additional data gathered in FY
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Spectral features based tea garden extraction from digital orthophoto maps
Jamil, Akhtar; Bayram, Bulent; Kucuk, Turgay; Zafer Seker, Dursun
2018-05-01
The advancements in the photogrammetry and remote sensing technologies has made it possible to extract useful tangible information from data which plays a pivotal role in various application such as management and monitoring of forests and agricultural lands etc. This study aimed to evaluate the effectiveness of spectral signatures for extraction of tea gardens from 1 : 5000 scaled digital orthophoto maps obtained from Rize city in Turkey. First, the normalized difference vegetation index (NDVI) was derived from the input images to suppress the non-vegetation areas. NDVI values less than zero were discarded and the output images was normalized in the range 0-255. Individual pixels were then mapped into meaningful objects using global region growing technique. The resulting image was filtered and smoothed to reduce the impact of noise. Furthermore, geometrical constraints were applied to remove small objects (less than 500 pixels) followed by morphological opening operator to enhance the results. These objects served as building blocks for further image analysis. Finally, for the classification stage, a range of spectral values were empirically calculated for each band and applied on candidate objects to extract tea gardens. For accuracy assessment, we employed an area based similarity metric by overlapping obtained tea garden boundaries with the manually digitized tea garden boundaries created by experts of photogrammetry. The overall accuracy of the proposed method scored 89 % for tea gardens from 10 sample orthophoto maps. We concluded that exploiting the spectral signatures using object based analysis is an effective technique for extraction of dominant tree species from digital orthophoto maps.
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California Natural Resource Agency — Digital version of the 1945 California Vegetation Type Maps by A. E. Wieslander of the U.S. Forest Service. Source scale of maps are 1:100,000. These compiled maps...
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An object-based approach for tree species extraction from digital orthophoto maps
Jamil, Akhtar; Bayram, Bulent
2018-05-01
Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1 : 5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85 % for all sample images.
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MAPPING ALPINE VEGETATION LOCATION PROPERTIES BY DENSE MATCHING
Directory of Open Access Journals (Sweden)
R. Niederheiser
2016-06-01
Full Text Available Highly accurate 3D micro topographic mapping in mountain research demands for light equipment and low cost solutions. Recent developments in structure from motion and dense matching techniques provide promising tools for such applications. In the following, the feasibility of terrestrial photogrammetry for mapping topographic location properties of sparsely vegetated areas in selected European mountain regions is investigated. Changes in species composition at alpine vegetation locations are indicators of climate change consequences, such as the pronounced rise of average temperatures in mountains compared to the global average. Better understanding of climate change effects on plants demand for investigations on a micro-topographic scale. We use professional and consumer grade digital single-lens reflex cameras mapping 288 plots each 3 x 3 m on 18 summits in the Alps and Mediterranean Mountains within the GLORIA (GLobal Observation Research Initiative in Alpine environments network. Image matching tests result in accuracies that are in the order of millimetres in the XY-plane and below 0.5 mm in Z-direction at the second image pyramid level. Reconstructing vegetation proves to be a challenge due to its fine and small structured architecture and its permanent movement by wind during image acquisition, which is omnipresent on mountain summits. The produced 3D point clouds are gridded to 6 mm resolution from which topographic parameters such as slope, aspect and roughness are derived. At a later project stage these parameters will be statistically linked to botanical reference data in order to conclude on relations between specific location properties and species compositions.
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Directory of Open Access Journals (Sweden)
J. Arieira
2011-03-01
Full Text Available Development of efficient methodologies for mapping wetland vegetation is of key importance to wetland conservation. Here we propose the integration of a number of statistical techniques, in particular cluster analysis, universal kriging and error propagation modelling, to integrate observations from remote sensing and field sampling for mapping vegetation communities and estimating uncertainty. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relationship between remotely sensed data and vegetation patterns, captured in four factorial axes, were described using multiple linear regression models. There were then used in a universal kriging procedure to reduce the mapping uncertainty. Cross-validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty derived from Monte Carlo simulations revealed significant spatial variation in classification, but this had little impact on the proportion and arrangement of the communities observed. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including a larger number of digital images as explanatory variables in the model. Comparison of the resulting plant community map with a flood duration map, revealed that flooding duration is an important driver of vegetation zonation. This mapping approach is able to integrate field point data and high-resolution remote-sensing images, providing a new basis to map wetland vegetation and allow its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland
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California Natural Resource Agency — Digital version of potential natural plant communites as compiled and published on 'Map of the Natural Vegetation of California' by A. W. Kuchler, 1976. Source map...
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Digital technical map of Košice city
Directory of Open Access Journals (Sweden)
Štefan Kuzevič
2007-06-01
Full Text Available The digital map of a city is comprised complex of the map. Process of creation of the digital map of a city is time consuming and financially demanding. The digital map is created as co-operation of the local authority, technology network administrator, companies, and local government. The exact and current digital map of the city utilizable for multilateral applications is the result of this co-operation. The digital map of city catch all important phenomenon and objects which are needed for making decisions and planning to all grades controlling the local authority. The Geographic information systems tools make possible updating digital map, analyses and syntheses spatial phenomena and theirs relationships. The digital map of city is created step by step. The digital technical map of city is one of very important part of the digital map. Illustration of the part digital technical map of Košice is showed in the Fig. 1.
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Vegetation mapping with satellite data of the Forsmark and Tierp regions
Energy Technology Data Exchange (ETDEWEB)
Boresjoe-Bronge, Laine; Wester, Kjell [SwedPower, Stockholm (Sweden)
2002-04-01
SKB (Swedish Nuclear Fuel and Waste Management Co) performs a siting program for deep repository of spent nuclear fuel that includes survey of three potential sites. The SKB siting process has now reached the site investigation phase. There are several fields of investigations performed in this phase. One of them is description of the surface ecosystems. The surface ecosystems are mapped both on a regional (50-100 km{sup 2} ) and a local level (1 km{sup 2} ). Two inventory methods are used, remote sensing (satellite data/aerial photographs) for the regional level, and field inventory for the detailed level. As a part of the surface ecosystem characterisation on the regional level vegetation mapping using satellite data has been performed over the three potential deep depository sites, Forsmark, Tierp and Oskarshamn. The user requirements for the vegetation mapping of the potential sites are the following: Dominated species in the tree layer, shrub layer, field layer and ground layer shall be described both on regional and local level; Dominated species in all layers shall be quantified regarding share and percentage of ground cover, or absence of cover (vegetation free ground); The regional and the local inventory shall have identical or comparable classification systems; The classification system and the method used shall make it possible to scale the results from local to regional level and vice versa; The produced layers shall be presented in digital form and make it possible to model biomass and turnover of organic matter (carbon, nutrients, water); The produced information shall in a first phase be of use for planning and for making nature and environmental considerations. Data sources used in the study include geo-referenced SPOT4 XI data (20 m ground resolution), geo-referenced Landsat TM data (30 m ground resolution), soil type data, topographic map data and colour infrared aerial photographs. The production of vegetation layers has been carried out in two
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Vegetation mapping with satellite data of the Forsmark and Tierp regions
International Nuclear Information System (INIS)
Boresjoe-Bronge, Laine; Wester, Kjell
2002-04-01
SKB (Swedish Nuclear Fuel and Waste Management Co) performs a siting program for deep repository of spent nuclear fuel that includes survey of three potential sites. The SKB siting process has now reached the site investigation phase. There are several fields of investigations performed in this phase. One of them is description of the surface ecosystems. The surface ecosystems are mapped both on a regional (50-100 km 2 ) and a local level (1 km 2 ). Two inventory methods are used, remote sensing (satellite data/aerial photographs) for the regional level, and field inventory for the detailed level. As a part of the surface ecosystem characterisation on the regional level vegetation mapping using satellite data has been performed over the three potential deep depository sites, Forsmark, Tierp and Oskarshamn. The user requirements for the vegetation mapping of the potential sites are the following: Dominated species in the tree layer, shrub layer, field layer and ground layer shall be described both on regional and local level; Dominated species in all layers shall be quantified regarding share and percentage of ground cover, or absence of cover (vegetation free ground); The regional and the local inventory shall have identical or comparable classification systems; The classification system and the method used shall make it possible to scale the results from local to regional level and vice versa; The produced layers shall be presented in digital form and make it possible to model biomass and turnover of organic matter (carbon, nutrients, water); The produced information shall in a first phase be of use for planning and for making nature and environmental considerations. Data sources used in the study include geo-referenced SPOT4 XI data (20 m ground resolution), geo-referenced Landsat TM data (30 m ground resolution), soil type data, topographic map data and colour infrared aerial photographs. The production of vegetation layers has been carried out in two steps. In
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Parkes, Stephen
2016-10-25
Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.
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Parkes, Stephen; McCabe, Matthew; Al-Mashhawari, Samir K.; Rosas, Jorge
2016-01-01
Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.
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Using Vegetation Maps to Provide Information on Soil Distribution
José Ibáñez, Juan; Pérez-Gómez, Rufino; Brevik, Eric C.; Cerdà, Artemi
2016-04-01
Many different types of maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research has indicated that comparing the results of different but related maps (e.g., soil and geology maps) may aid in identifying deficiencies in those maps. Therefore, this study was undertaken in the Almería Province (Andalusia, Spain) to (i) compare the underlying map structures of soil and vegetation maps and (ii) to investigate if a vegetation map can provide useful soil information that was not shown on a soil map. To accomplish this soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis. Results of the spatial analysis were exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence (P/A): (i) climatophilous (climate is the only determinant of P/A) (ii); lithologic-climate (climate and parent material determine PNV P/A); and (iii) edaphophylous (soil features determine PNV P/A). The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophylous units (which demand more soil water than is supplied by other soil types in the surrounding landscape) were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity
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A vegetation map for eastern Africa
DEFF Research Database (Denmark)
Lillesø, Jens-Peter Barnekow; van Breugel, Paulo; Graudal, Lars
2015-01-01
The potential natural vegetation (PNV) map of eastern and southern Africa covers the countries Burundi, Ethiopia, Kenya, Uganda, Rwanda, Tanzania, and Zambia. The first version of the map was developed by various partners in East Africa and Europe in 2010 and has now reached version 2. The map...... is available in different formats and is accompanied by an extensive documentation of the floristic, physiognomic and other characteristics of the different vegetation types and useful woody species in the 8 countries. It is complemented by a species selection tool, which can be used to 'find the right tree...
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Classifying and mapping wetlands and peat resources using digital cartography
Cameron, Cornelia C.; Emery, David A.
1992-01-01
Digital cartography allows the portrayal of spatial associations among diverse data types and is ideally suited for land use and resource analysis. We have developed methodology that uses digital cartography for the classification of wetlands and their associated peat resources and applied it to a 1:24 000 scale map area in New Hampshire. Classifying and mapping wetlands involves integrating the spatial distribution of wetlands types with depth variations in associated peat quality and character. A hierarchically structured classification that integrates the spatial distribution of variations in (1) vegetation, (2) soil type, (3) hydrology, (4) geologic aspects, and (5) peat characteristics has been developed and can be used to build digital cartographic files for resource and land use analysis. The first three parameters are the bases used by the National Wetlands Inventory to classify wetlands and deepwater habitats of the United States. The fourth parameter, geological aspects, includes slope, relief, depth of wetland (from surface to underlying rock or substrate), wetland stratigraphy, and the type and structure of solid and unconsolidated rock surrounding and underlying the wetland. The fifth parameter, peat characteristics, includes the subsurface variation in ash, acidity, moisture, heating value (Btu), sulfur content, and other chemical properties as shown in specimens obtained from core holes. These parameters can be shown as a series of map data overlays with tables that can be integrated for resource or land use analysis.
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National Park Service Vegetation Inventory Program, Cuyahoga Valley National Park, Ohio
Hop, Kevin D.; Drake, J.; Strassman, Andrew C.; Hoy, Erin E.; Menard, Shannon; Jakusz, J.W.; Dieck, J.J.
2013-01-01
The National Park Service (NPS) Vegetation Inventory Program (VIP) is an effort to classify, describe, and map existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VIP is managed by the NPS Biological Resources Management Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey (USGS) Vegetation Characterization Program lends a cooperative role in the NPS VIP. The USGS Upper Midwest Environmental Sciences Center, NatureServe, and NPS Cuyahoga Valley National Park (CUVA) have completed vegetation classification and mapping of CUVA.Mappers, ecologists, and botanists collaborated to identify and describe vegetation types within the National Vegetation Classification Standard (NVCS) and to determine how best to map them by using aerial imagery. The team collected data from 221 vegetation plots within CUVA to develop detailed descriptions of vegetation types. Data from 50 verification sites were also collected to test both the key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 647 accuracy assessment (AA) sites were collected (of which 643 were used to test accuracy of the vegetation map layer). These data sets led to the identification of 45 vegetation types at the association level in the NVCS at CUVA.A total of 44 map classes were developed to map the vegetation and general land cover of CUVA, including the following: 29 map classes represent natural/semi-natural vegetation types in the NVCS, 12 map classes represent cultural vegetation (agricultural and developed) in the NVCS, and 3 map classes represent non-vegetation features (open-water bodies). Features were interpreted from viewing color-infrared digital aerial imagery dated October 2010 (during peak leaf-phenology change of trees) via digital onscreen three-dimensional stereoscopic workflow systems in geographic
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Developing digital vegetation for central hardwood forest types: A case study from Leslie County, KY
Bo Song; Wei-lun Tsai; Chiao-ying Chou; Thomas M. Williams; William Conner; Brian J. Williams
2011-01-01
Digital vegetation is the computerized representation, with either virtual images or animations, of vegetation types and conditions based on current measurements or ecological models. Digital vegetation can be useful in evaluating past, present, or future land use; changes in vegetation linked to climate change; or restoration efforts. Digital vegetation can be...
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Digital soil mapping with limited data
Hartemink, A.E.; McBratney, A.B.; Lourdes Mendonça-Santos, de M.
2008-01-01
There has been considerable expansion in the use of digital soil mapping technologies and development of methodologies that improve digital soil mapping at all scales and levels of resolution. These developments have occurred in all parts of the world in the past few years and also in countries
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A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data
Stibig, H.-J.; Belward, A.S.; Roy, P.S.; Rosalina-Wasrin, U.; Agrawal, S.; Joshi, P.K.; ,; Beuchle, R.; Fritz, S.; Mubareka, S.; Giri, C.
2007-01-01
Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub
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Automatically Generated Vegetation Density Maps with LiDAR Survey for Orienteering Purpose
Petrovič, Dušan
2018-05-01
The focus of our research was to automatically generate the most adequate vegetation density maps for orienteering purpose. Application Karttapullatuin was used for automated generation of vegetation density maps, which requires LiDAR data to process an automatically generated map. A part of the orienteering map in the area of Kazlje-Tomaj was used to compare the graphical display of vegetation density. With different settings of parameters in the Karttapullautin application we changed the way how vegetation density of automatically generated map was presented, and tried to match it as much as possible with the orienteering map of Kazlje-Tomaj. Comparing more created maps of vegetation density the most suitable parameter settings to automatically generate maps on other areas were proposed, too.
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Vegetation burn severity mapping using Landsat-8 and WorldView-2
Wu, Zhuoting; Middleton, Barry R.; Hetzler, Robert; Vogel, John M.; Dye, Dennis G.
2015-01-01
We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.
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Map of mixed prairie grassland vegetation, Rocky Flats, Colorado
Energy Technology Data Exchange (ETDEWEB)
Clark, S J.V.; Webber, P J; Komarkova, V; Weber, W A
1980-01-01
A color vegetation map at the scale of 1:12,000 of the area surrounding the Rocky Flats, Rockwell International Plant near Boulder, Colorado, provides a permanent record of baseline data which can be used to monitor changes in both vegetation and environment and thus to contribute to future land management and land-use policies. Sixteen mapping units based on species composition were identified, and characterized by two 10-m/sup 2/ vegetation stands each. These were grouped into prairie, pasture, and valley side on the basis of their species composition. Both the mapping units and these major groups were later confirmed by agglomerative clustering analysis of the 32 vegetation stands on the basis of species composition. A modified Bray and Curtis ordination was used to determine the environmental factor complexes controlling the distribution of vegetation at Rocky flats. Recommendations are made for future policies of environmental management and predictions of the response to environmental change of the present vegetation at the Rocky Flats site.
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Integrating Vegetation Classification, Mapping, and Strategic Inventory for Forest Management
C. K. Brewer; R. Bush; D. Berglund; J. A. Barber; S. R. Brown
2006-01-01
Many of the analyses needed to address multiple resource issues are focused on vegetation pattern and process relationships and most rely on the data models produced from vegetation classification, mapping, and/or inventory. The Northern Region Vegetation Mapping Project (R1-VMP) data models are based on these three integrally related, yet separate processes. This...
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Vegetation (MCV / NVCS) Mapping Projects - California [ds515
California Natural Resource Agency — This metadata layer shows the footprint of vegetation mapping projects completed in California that have used the Manual California of Vegetation ( MCV 1st edition)...
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Sampling for validation of digital soil maps
Brus, D.J.; Kempen, B.; Heuvelink, G.B.M.
2011-01-01
The increase in digital soil mapping around the world means that appropriate and efficient sampling strategies are needed for validation. Data used for calibrating a digital soil mapping model typically are non-random samples. In such a case we recommend collection of additional independent data and
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Vrabec, Marko; Dolžan, Erazem
2016-04-01
The undergraduate field course in Geological Mapping at the University of Ljubljana involves 20-40 students per year, which precludes the use of specialized rugged digital field equipment as the costs would be way beyond the capabilities of the Department. A different mapping area is selected each year with the aim to provide typical conditions that a professional geologist might encounter when doing fieldwork in Slovenia, which includes rugged relief, dense tree cover, and moderately-well- to poorly-exposed bedrock due to vegetation and urbanization. It is therefore mandatory that the digital tools and workflows are combined with classical methods of fieldwork, since, for example, full-time precise GNSS positioning is not viable under such circumstances. Additionally, due to the prevailing combination of complex geological structure with generally poor exposure, students cannot be expected to produce line (vector) maps of geological contacts on the go, so there is no need for such functionality in hardware and software that we use in the field. Our workflow therefore still relies on paper base maps, but is strongly complemented with digital tools to provide robust positioning, track recording, and acquisition of various point-based data. Primary field hardware are students' Android-based smartphones and optionally tablets. For our purposes, the built-in GNSS chips provide adequate positioning precision most of the time, particularly if they are GLONASS-capable. We use Oruxmaps, a powerful free offline map viewer for the Android platform, which facilitates the use of custom-made geopositioned maps. For digital base maps, which we prepare in free Windows QGIS software, we use scanned topographic maps provided by the National Geodetic Authority, but also other maps such as aerial imagery, processed Digital Elevation Models, scans of existing geological maps, etc. Point data, like important outcrop locations or structural measurements, are entered into Oruxmaps as
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Digital mapping techniques '06 - Workshop proceedings
Soller, David R.
2007-01-01
The Digital Mapping Techniques `06 (DMT`06) workshop was attended by more than 110 technical experts from 51 agencies, universities, and private companies, including representatives from 27 state geological surveys (see Appendix A of these Proceedings). This workshop was similar in nature to the previous nine meetings, which were held in Lawrence, Kansas (Soller, 1997), Champaign, Illinois (Soller, 1998), Madison, Wisconsin (Soller, 1999), Lexington, Kentucky (Soller, 2000), Tuscaloosa, Alabama (Soller, 2001), Salt Lake City, Utah (Soller, 2002), Millersville, Pennsylvania (Soller, 2003), Portland, Oregon (Soller, 2004), and Baton Rouge, Louisiana (Soller, 2005). This year?s meeting was hosted by the Ohio Geological Survey, from June 11-14, 2006, on the Ohio State University campus in Columbus, Ohio. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. It is with great pleasure that I note that the objective was successfully met, as attendees continued to share and exchange knowledge and information, and renew friendships and collegial work begun at past DMT workshops.Each DMT workshop has been coordinated by the Association of American State Geologists (AASG) and U.S. Geological Survey (USGS) Data Capture Working Group, the latter of which was formed in August 1996 to support the AASG and the USGS in their effort to build a National Geologic Map Database (see Soller, this volume, and http://ngmdb.usgs.gov/info/standards/datacapt/). The Working Group was formed because increased production efficiencies, standardization, and quality of digital map products were needed for the database - and for the State and Federal geological surveys - to provide more high-quality digital maps to the public.At the 2006 meeting, oral and poster presentations and special discussion sessions emphasized: 1) methods for creating and publishing map products (here, "publishing" includes Web-based release); 2) field data
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Rockwell, Barnaby W.
2010-01-01
Multispectral remote sensing data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were analyzed to identify and map minerals, vegetation groups, and volatiles (water and snow) in support of geologic studies of the Bodie Hills, Sweetwater Mountains, and Wassuk Range, California/Nevada. Digital mineral and vegetation mapping results are presented in both portable document format (PDF) and ERDAS Imagine format (.img). The ERDAS-format files are suitable for integration with other geospatial data in Geographic Information Systems (GIS) such as ArcGIS. The ERDAS files showing occurrence of 1) iron-bearing minerals, vegetation, and water, and 2) clay, sulfate, mica, carbonate, Mg-OH, and hydrous quartz minerals have been attributed according to identified material, so that the material detected in a pixel can be queried with the interactive attribute identification tools of GIS and image processing software packages (for example, the Identify Tool of ArcMap and the Inquire Cursor Tool of ERDAS Imagine). All raster data have been orthorectified to the Universal Transverse Mercator (UTM) projection using a projective transform with ground-control points selected from orthorectified Landsat Thematic Mapper data and a digital elevation model from the U.S. Geological Survey (USGS) National Elevation Dataset (1/3 arc second, 10 m resolution). Metadata compliant with Federal Geographic Data Committee (FGDC) standards for all ERDAS-format files have been included, and contain important information regarding geographic coordinate systems, attributes, and cross-references. Documentation regarding spectral analysis methodologies employed to make the maps is included in these cross-references.
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Vegetation mapping of the Mond Protected Area of Bushehr Province (south-west Iran).
Mehrabian, Ahmadreza; Naqinezhad, Alireza; Mahiny, Abdolrassoul Salman; Mostafavi, Hossein; Liaghati, Homan; Kouchekzadeh, Mohsen
2009-03-01
Arid regions of the world occupy up to 35% of the earth's surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.
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Spatial Digital Database for the Geologic Map of Oregon
Walker, George W.; MacLeod, Norman S.; Miller, Robert J.; Raines, Gary L.; Connors, Katherine A.
2003-01-01
Introduction This report describes and makes available a geologic digital spatial database (orgeo) representing the geologic map of Oregon (Walker and MacLeod, 1991). The original paper publication was printed as a single map sheet at a scale of 1:500,000, accompanied by a second sheet containing map unit descriptions and ancillary data. A digital version of the Walker and MacLeod (1991) map was included in Raines and others (1996). The dataset provided by this open-file report supersedes the earlier published digital version (Raines and others, 1996). This digital spatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information for use in spatial analysis in a geographic information system (GIS). This database can be queried in many ways to produce a variety of geologic maps. This database is not meant to be used or displayed at any scale larger than 1:500,000 (for example, 1:100,000). This report describes the methods used to convert the geologic map data into a digital format, describes the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet. Scanned images of the printed map (Walker and MacLeod, 1991), their correlation of map units, and their explanation of map symbols are also available for download.
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Vegetation Water Content Mapping for Agricultural Regions in SMAPVEX16
White, W. A.; Cosh, M. H.; McKee, L.; Berg, A. A.; McNairn, H.; Hornbuckle, B. K.; Colliander, A.; Jackson, T. J.
2017-12-01
Vegetation water content impacts the ability of L-band radiometers to measure surface soil moisture. Therefore it is necessary to quantify the amount of water held in surface vegetation for an accurate soil moisture remote sensing retrieval. A methodology is presented for generating agricultural vegetation water content maps using Landsat 8 scenes for agricultural fields of Iowa and Manitoba for the Soil Moisture Active Passive Validation Experiments in 2016 (SMAPVEX16). Manitoba has a variety of row crops across the region, and the study period encompasses the time frame from emergence to reproduction, as well as a forested region. The Iowa study site is dominated by corn and soybeans, presenting an easier challenge. Ground collection of vegetation biomass and water content were also collected to provide a ground truth data source. Errors for the resulting vegetation water content maps ranged depending upon crop type, but generally were less than 15% of the total plant water content per crop type. Interpolation is done between Landsat overpasses to produce daily vegetation water content maps for the summer of 2016 at a 30 meter resolution.
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Creation of a Cell-Based Digital Cadastral Mapping System (Digital ...
African Journals Online (AJOL)
Digital cadastre enhances land transaction activities to be conducted in a business manner. Similarly, land subdivision or boundary redefinition, land registration and land marketing are achieved with better accuracy. This paper discusses the need to introduce a national Cell-Based Digital Cadastral Mapping System model ...
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Vancutsem, C.; Pekel, J.-F.; Evrard, C.; Malaisse, F.; Defourny, P.
2009-02-01
The need for quantitative and accurate information to characterize the state and evolution of vegetation types at a national scale is widely recognized. This type of information is crucial for the Democratic Republic of Congo, which contains the majority of the tropical forest cover of Central Africa and a large diversity of habitats. In spite of recent progress in earth observation capabilities, vegetation mapping and seasonality analysis in equatorial areas still represent an outstanding challenge owing to high cloud coverage and the extent and limited accessibility of the territory. On one hand, the use of coarse-resolution optical data is constrained by performance in the presence of cloud screening and by noise arising from the compositing process, which limits the spatial consistency of the composite and the temporal resolution. On the other hand, the use of high-resolution data suffers from heterogeneity of acquisition dates, images and interpretation from one scene to another. The objective of the present study was to propose and demonstrate a semi-automatic processing method for vegetation mapping and seasonality characterization based on temporal and spectral information from SPOT VEGETATION time series. A land cover map with 18 vegetation classes was produced using the proposed method that was fed by ecological knowledge gathered from botanists and reference documents. The floristic composition and physiognomy of each vegetation type are described using the Land Cover Classification System developed by the FAO. Moreover, the seasonality of each class is characterized on a monthly basis and the variation in different vegetation indicators is discussed from a phenological point of view. This mapping exercise delivers the first area estimates of seven different forest types, five different savannas characterized by specific seasonality behavior and two aquatic vegetation types. Finally, the result is compared to two recent land cover maps derived from
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Digital mapping in extreme and remote environments
Andersson, Joel; Bauer, Tobias; Sarlus, Zimer; Zainy, Maher; Brethes, Anais
2017-04-01
During the last few years, Luleå University of Technology has performed a series of research projects in remote areas with extreme climatic conditions using digital mapping technologies. The majority of past and ongoing research projects focus on the arctic regions of the Fennoscandian Shield and Greenland but also on the Zagros fold-and-thrust belt in northern Iraq. Currently, we use the Midland Valley application FieldMove on iPad mini devices with ruggedized casings. As all projects have a strong focus on geological field work, harsh climatic conditions are a challenge not only for the geologists but also for the digital mapping hardware. In the arctic regions especially cold temperatures affect battery lifetime and performance of the screens. But also high temperatures are restricting digital mapping. From experience, a typical temperature range where digital mapping, using iPad tablets, is possible lies between -20 and +40 degrees. Furthermore, the remote character of field areas complicates access but also availability of electricity. By a combination of robust solar chargers and ruggedized batteries we are able to work entirely autarkical. Additionally, we are currently installing a drone system that allows us to map outcrops normally inaccessible because of safety reasons or time deficiency. The produced data will subsequently be taken into our Virtual Reality studio for interpretation and processing. There we will be able to work also with high resolution DEM data from Lidar scanning allowing us to interpret structural features such as post-glacial faults in areas that are otherwise only accessible by helicopter. By combining digital field mapping with drone technique and a Virtual Reality studio we are able to work in hardly accessible areas, improve safety during field work and increase efficiency substantially.
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Photogrammetry, Digital mapping and Land Informations Systems
DEFF Research Database (Denmark)
Frederiksen, Poul
1998-01-01
Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia.......Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia....
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Digital Mapping and Land Information Systems - Volume 6
DEFF Research Database (Denmark)
Frederiksen, Poul
1998-01-01
Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ. Develop......Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ...
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State Base Map for GIS – New Digital Topographic Map of the Republic of Macedonia
Directory of Open Access Journals (Sweden)
Zlatko Srbinoski
2009-12-01
Full Text Available The basic aim of the National Spatial Data Infrastructure (NSDI built in accordance with INSPIRE directive is to standardize spatial data infrastructure on national level. In that direction, topographic maps are a basic platform for acquiring spatial data within geoinformation systems and one of the most important segments of NSDI. This paper presents methodology of establishing the new digital topographic map of the Republic of Macedonia titled “State Base Map for GIS in Macedonia”. This paper analyzes geometrical accuracy of new digital topographic maps. Production of the new digital topographic map has been the most important cartographic project in the Republic of Macedonia since it became independent.
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The integration of GPS, vegetation mapping and GIS in ecological and behavioural studies
Directory of Open Access Journals (Sweden)
Steven Mark Rutter
2007-07-01
Full Text Available Global Positioning System (GPS satellite navigation receivers are increasingly being used in ecological and behavioural studies to track the movements of animals in relation to the environments in which they live and forage. Concurrent recording of the animal's foraging behaviour (e.g. from jaw movement recording allows foraging locations to be determined. By combining the animal GPS movement and foraging data with habitat and vegetation maps using a Geographical Information System (GIS it is possible to relate animal movement and foraging location to landscape and habitat features and vegetation types. This powerful approach is opening up new opportunities to study the spatial aspects of animal behaviour, especially foraging behaviour, with far greater precision and objectivity than before. Advances in GPS technology now mean that sub-metre precision systems can be used to track animals, extending the range of application of this technology from landscape and habitat scale to paddock and patch scale studies. As well as allowing ecological hypotheses to be empirically tested at the patch scale, the improvements in precision are also leading to the approach being increasing extended from large scale ecological studies to smaller (paddock scale agricultural studies. The use of sub-metre systems brings both new scientific opportunities and new technological challenges. For example, fitting all of the animals in a group with sub-metre precision GPS receivers allows their relative inter-individual distances to be precisely calculated, and their relative orientations can be derived from data from a digital compass fitted to each receiver. These data, analyzed using GIS, could give new insights into the social behaviour of animals. However, the improvements in precision with which the animals are being tracked also needs equivalent improvements in the precision with which habitat and vegetation are mapped. This needs some degree of automation, as
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Remote sensing application for delineating coastal vegetation - A case study
Digital Repository Service at National Institute of Oceanography (India)
Kunte, P.D.; Wagle, B.G.
Remote sensing data has been used for mapping coastal vegetation along the Goa Coast, India. The study envisages the use of digital image processing techniques for delineating geomorphic features and associated vegetation, including mangrove, along...
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Digital geologic map in the scale 1:50 000
International Nuclear Information System (INIS)
Kacer, S.; Antalik, M.
2005-01-01
In this presentation authors present preparation of new digital geologic map of the Slovak Republic. This map is prepared by the State Geological Institute of Dionyz Stur as a part of the project Geological information system GeoIS. One of the basic information geologic layers, which will be accessible on the web-site will be digital geologic map of the Slovak Republic in the scale 1: 50 000
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The genotype-phenotype map of an evolving digital organism
Fortuna, Miguel A.; Zaman, Luis; Ofria, Charles; Wagner, Andreas
2017-01-01
To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms fr...
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Directory of Open Access Journals (Sweden)
Waldir de Carvalho Junior
2014-06-01
Full Text Available Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR and geostatistical (ordinary kriging and co-kriging. The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap. Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI, soil wetness index (SWI, normalized difference vegetation index (NDVI, and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.
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LBA-ECO LC-15 Aerodynamic Roughness Maps of Vegetation Canopies, Amazon Basin: 2000
National Aeronautics and Space Administration — This data set, LBA-ECO LC-15 Aerodynamic Roughness Maps of Vegetation Canopies, Amazon Basin: 2000, provides physical roughness maps of vegetation canopies in the...
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Using Digital Mapping Tool in Ill-Structured Problem Solving
Bai, Hua
2013-01-01
Scaffolding students' problem solving and helping them to improve problem solving skills are critical in instructional design courses. This study investigated the effects of students' uses of a digital mapping tool on their problem solving performance in a design case study. It was found that the students who used the digital mapping tool…
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Nelson, P.; Paradis, D. P.
2017-12-01
The small stature and spectral diversity of arctic plant taxa presents challenges in mapping arctic vegetation. Mapping vegetation at the appropriate scale is needed to visualize effects of disturbance, directional vegetation change or mapping of specific plant groups for other applications (eg. habitat mapping). Fine spatial grain of remotely sensed data (ca. 10 cm pixels) is often necessary to resolve patches of many arctic plant groups, such as bryophytes and lichens. These groups are also spectrally different from mineral, litter and vascular plants. We sought to explore method to generate high-resolution spatial and spectral data to explore better mapping methods for arctic vegetation. We sampled ground vegetation at seven sites north or west of tree-line in Alaska, four north of Fairbanks and three northwest of Bethel, respectively. At each site, we estimated cover of plant functional types in 1m2 quadrats spaced approximately every 10 m along a 100 m long transect. Each quadrat was also scanned using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm range) and photographed from multiple perspectives. We then flew our small UAV with a RGB camera over the transect and at least 50 m on either side collecting on imagery of the plot, which were used to generate a image mosaic and digital surface model of the plot. We compare plant functional group cover ocular estimated in situ to post-hoc estimation, either automated or using a human observer, using the quadrat photos. We also compare interpolated lichen cover from UAV scenes to estimated lichen cover using a statistical models using Landsat data, with focus on lichens. Light and yellow lichens are discernable in the UAV imagery but certain lichens, especially dark colored lichens or those with spectral signatures similar to graminoid litter, present challenges. Future efforts will focus on integrating UAV-upscaled ground cover estimates to hyperspectral sensors (eg. AVIRIS ng) for better combined
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USING OF THERMAL STRUCTURE MAPS FOR VEGETATION MAPPING (CASE OF ALTACHEYSKY WILDLIFE AREA
Directory of Open Access Journals (Sweden)
L. A. Abramova
2014-01-01
Full Text Available Thermal infrared imagery contains considerable amount of qualitative information about ground objects and landscapes. In spite of it, this type of data is often used to derive quantitative information such as land or sea surface temperatures. This paper describes the examination of Altacheysky wildlife area situated in the southern part of Buryatia Republic, Mukhorshibirsky district based on Landsat imagery and ground observations. Ground observations were led to study the vegetation cover of the area. Landsat imagery were used to make multitemporal thermal infrared image combined of 7 ETM+ scenes and to make multispectral image combined of different zones of a OLI scene. Both images were classified. The multitemporal thermal infrared classification result was used to compose thermal structure map of the wildlife area. Comparison of the map, multispectral image classification result and ground observations data reveals that thermal structure map describes better the particularities of Altacheysky wildlife area vegetation cover.
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Digital Geological Mapping for Earth Science Students
England, Richard; Smith, Sally; Tate, Nick; Jordan, Colm
2010-05-01
This SPLINT (SPatial Literacy IN Teaching) supported project is developing pedagogies for the introduction of teaching of digital geological mapping to Earth Science students. Traditionally students are taught to make geological maps on a paper basemap with a notebook to record their observations. Learning to use a tablet pc with GIS based software for mapping and data recording requires emphasis on training staff and students in specific GIS and IT skills and beneficial adjustments to the way in which geological data is recorded in the field. A set of learning and teaching materials are under development to support this learning process. Following the release of the British Geological Survey's Sigma software we have been developing generic methodologies for the introduction of digital geological mapping to students that already have experience of mapping by traditional means. The teaching materials introduce the software to the students through a series of structured exercises. The students learn the operation of the software in the laboratory by entering existing observations, preferably data that they have collected. Through this the students benefit from being able to reflect on their previous work, consider how it might be improved and plan new work. Following this they begin fieldwork in small groups using both methods simultaneously. They are able to practise what they have learnt in the classroom and review the differences, advantages and disadvantages of the two methods, while adding to the work that has already been completed. Once the field exercises are completed students use the data that they have collected in the production of high quality map products and are introduced to the use of integrated digital databases which they learn to search and extract information from. The relatively recent development of the technologies which underpin digital mapping also means that many academic staff also require training before they are able to deliver the
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE,
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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ANALYSIS OF THE EFFECTS OF IMAGE QUALITY ON DIGITAL MAP GENERATION FROM SATELLITE IMAGES
Directory of Open Access Journals (Sweden)
H. Kim
2012-07-01
Full Text Available High resolution satellite images are widely used to produce and update a digital map since they became widely available. It is well known that the accuracy of digital map produced from satellite images is decided largely by the accuracy of geometric modelling. However digital maps are made by a series of photogrammetric workflow. Therefore the accuracy of digital maps are also affected by the quality of satellite images, such as image interpretability. For satellite images, parameters such as Modulation Transfer Function(MTF, Signal to Noise Ratio(SNR and Ground Sampling Distance(GSD are used to present images quality. Our previous research stressed that such quality parameters may not represent the quality of image products such as digital maps and that parameters for image interpretability such as Ground Resolved Distance(GRD and National Imagery Interpretability Rating Scale(NIIRS need to be considered. In this study, we analyzed the effects of the image quality on accuracy of digital maps produced by satellite images. QuickBird, IKONOS and KOMPSAT-2 imagery were used to analyze as they have similar GSDs. We measured various image quality parameters mentioned above from these images. Then we produced digital maps from the images using a digital photogrammetric workstation. We analyzed the accuracy of the digital maps in terms of their location accuracy and their level of details. Then we compared the correlation between various image quality parameters and the accuracy of digital maps. The results of this study showed that GRD and NIIRS were more critical for map production then GSD, MTF or SNR.
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Evaluation of using digital gravity field models for zoning map creation
Loginov, Dmitry
2018-05-01
At the present time the digital cartographic models of geophysical fields are taking a special significance into geo-physical mapping. One of the important directions to their application is the creation of zoning maps, which allow taking into account the morphology of geophysical field in the implementation automated choice of contour intervals. The purpose of this work is the comparative evaluation of various digital models in the creation of integrated gravity field zoning map. For comparison were chosen the digital model of gravity field of Russia, created by the analog map with scale of 1 : 2 500 000, and the open global model of gravity field of the Earth - WGM2012. As a result of experimental works the four integrated gravity field zoning maps were obtained with using raw and processed data on each gravity field model. The study demonstrates the possibility of open data use to create integrated zoning maps with the condition to eliminate noise component of model by processing in specialized software systems. In this case, for solving problem of contour intervals automated choice the open digital models aren't inferior to regional models of gravity field, created for individual countries. This fact allows asserting about universality and independence of integrated zoning maps creation regardless of detail of a digital cartographic model of geo-physical fields.
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Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping
Directory of Open Access Journals (Sweden)
Valerie Ussyshkin
2011-02-01
Full Text Available Conventional discrete return airborne lidar systems, used in the commercial sector for efficient generation of high quality spatial data, have been considered for the past decade to be an ideal choice for various mapping applications. Unlike two-dimensional aerial imagery, the elevation component of airborne lidar data provides the ability to represent vertical structure details with very high precision, which is an advantage for many lidar applications focusing on the analysis of elevated features such as 3D vegetation mapping. However, the use of conventional airborne discrete return lidar systems for some of these applications has often been limited, mostly due to relatively coarse vertical resolution and insufficient number of multiple measurements in vertical domain. For this reason, full waveform airborne sensors providing more detailed representation of target vertical structure have often been considered as a preferable choice in some areas of 3D vegetation mapping application, such as forestry research. This paper presents an overview of the specific features of airborne lidar technology concerning 3D mapping applications, particularly vegetation mapping. Certain key performance characteristics of lidar sensors important for the quality of vegetation mapping are discussed and illustrated by the advanced capabilities of the ALTM-Orion, a new discrete return sensor manufactured by Optech Incorporated. It is demonstrated that advanced discrete return sensors with enhanced 3D mapping capabilities can produce data of enhanced quality, which can represent complex structures of vegetation targets at the level of details equivalent in some aspects to the content of full waveform data. It is also shown that recent advances in conventional airborne lidar technology bear the potential to create a new application niche, where high quality dense point clouds, enhanced by fully recorded intensity for multiple returns, may provide sufficient
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Wallace, Cynthia S.A.; Villarreal, Miguel L.; Norman, Laura M.
2011-01-01
This report summarizes the development of a binational vegetation map developed for the Santa Cruz Watershed, which straddles the southern border of Arizona and the northern border of Sonora, Mexico. The map was created as an environmental input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued ecosystem services at multiple scales within a participatory decision-making process. Maps related to vegetation type and are needed for use in modeling wildlife habitat and other ecosystem services. Although detailed vegetation maps existed for the U.S. side of the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. We produced a binational vegetation classification of the Santa Cruz River riparian habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems (TES) units using Classification And Regression Tree (CART) modeling. Environmental layers used as predictor data were derived from a seasonal set of Landsat Thematic Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevation-model (DEM) grid. Because both sources of environmental data are seamless across the international border, they are particularly suited to this binational modeling effort. Training data were compiled from existing field data for the riparian corridor and data collected by the NM-GAP (New Mexico Gap Analysis Project) team for the original Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional training data were collected from core areas of the SWReGAP classification itself, allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the watershed without collecting additional training data.
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Mapping coastal vegetation using an expert system and hyperspectral imagery
Schmidt, K.S.; Skidmore, A.K.; Kloosterman, E.H.; Oosten, van H.; Kumar, L.; Janssen, J.A.M.
2004-01-01
Mapping and monitoring salt marshes in the Netherlands are important activities of the Ministry of Public Works (Rijkswaterstaat). The Survey Department (Meetkundige Dienst) produces vegetation maps using aerial photographs. However, it is a time-consuming and expensive activity. The accuracy of the
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Digital Field Mapping with the British Geological Survey
Leslie, Graham; Smith, Nichola; Jordan, Colm
2014-05-01
The BGS•SIGMA project was initiated in 2001 in response to a major stakeholder review of onshore mapping within the British Geological Survey (BGS). That review proposed a significant change for BGS with the recommendation that digital methods should be implemented for field mapping and data compilation. The BGS•SIGMA project (System for Integrated Geoscience MApping) is an integrated workflow for geoscientific surveying and visualisation using digital methods for geological data visualisation, recording and interpretation, in both 2D and 3D. The project has defined and documented an underpinning framework of best practice for survey and information management, best practice that has then informed the design brief and specification for a toolkit to support this new methodology. The project has now delivered BGS•SIGMA2012. BGS•SIGMA2012 is a integrated toolkit which enables assembly and interrogation/visualisation of existing geological information; capture of, and integration with, new data and geological interpretations; and delivery of 3D digital products and services. From its early days as a system which used PocketGIS run on Husky Fex21 hardware, to the present day system which runs on ruggedized tablet PCs with integrated GPS units, the system has evolved into a complete digital mapping and compilation system. BGS•SIGMA2012 uses a highly customised version of ESRI's ArcGIS 10 and 10.1 with a fully relational Access 2007/2010 geodatabase. BGS•SIGMA2012 is the third external release of our award-winning digital field mapping toolkit. The first free external release of the award-winning digital field mapping toolkit was in 2009, with the third version (BGS-SIGMAmobile2012 v1.01) released on our website (http://www.bgs.ac.uk/research/sigma/home.html) in 2013. The BGS•SIGMAmobile toolkit formed the major part of the first two releases but this new version integrates the BGS•SIGMAdesktop functionality that BGS routinely uses to transform our field
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Computer aided site management. Site use management by digital mapping
International Nuclear Information System (INIS)
Chupin, J.C.
1990-01-01
The logistics program developed for assisting the Hague site management is presented. A digital site mapping representation and geographical data bases are used. The digital site map and its integration into a data base are described. The program can be applied to urban and rural land management aid. Technical administrative and economic evaluations of the program are summarized [fr
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BGS·SIGMA - Digital mapping at the British Geological Survey
Smith, Nichola; Lawrie, Ken
2017-04-01
Geological mapping methods have evolved significantly over recent decades and this has included the transition to digital field data capture. BGS has been developing methodologies and technologies for this since 2001, and has now reached a stage where our custom built data capture and map compilation system (BGS·SIGMAv2015) is the default toolkit, within BGS, for bedrock and superficial mapping across the UK and overseas. In addition, BGS scientists also use the system for other data acquisition projects, such as landslide assessment, geodiversity audits and building stone studies. BGS·SIGMAv2015 is an integrated toolkit which enables assembly, interrogation and visualisation of existing geological information; capture of, and integration with, new data and geological interpretations; and delivery of digital products and services. From its early days as a system which used PocketGIS run on Husky Fex21 hardware, to the present day system, developed using ESRI's ArcGIS built on top of a bespoke relational data model, running on ruggedized tablet PCs with integrated GPS units, the system has evolved into a comprehensive system for digital geological data capture, mapping and compilation. The benefits, for BGS, of digital data capture are huge. Not only are the data being gathered in a standardised format, with the use of dictionaries to ensure consistency, but project teams can start building their digital geological map in the field by merging data collected by colleagues, building line-work and polygons, and subsequently identifying areas for further investigation. This digital data can then be easily incorporated into corporate databases and used in 3D modelling and visualisation software once back in the office. BGS is now at a stage where the free external release of our digital mapping system is in demand across the world, with 3000 licences being issued to date, and is successfully being used by other geological surveys, universities and exploration companies
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Digital bedrock geologic map of the Cavendish quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-203A Ratcliffe, NM, 1995,�Digital bedrock geologic map of the Cavendish quadrangle, Vermont: USGS Open-File Report 95-203, 2 plates, scale...
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Digital bedrock geologic map of the Weston quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG96-526A Ratcliffe, NM�and Burton, WC, 1996,�Digital bedrock geologic map of the Weston quadrangle, Vermont: USGS Open-File Report 96-526, 2...
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Digital bedrock geologic map of the Chester quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-576A Ratcliffe, N.M., 1995,�Digital bedrock geologic map of the Chester quadrangle, Vermont: USGS Open-File Report 95-576, 2 plates, scale...
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Vegetation Mapping - Tecolote Canyon, San Diego Co. [ds656
California Natural Resource Agency — Vegetation mapping has been conducted at various City of San Diego Park and Recreation Open Space lands in support of natural resource management objectives and the...
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Digital bedrock geologic map of the Andover quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG96-31A Ratcliffe, N.M., 1996,�Digital bedrock geologic map of the Andover quadrangle, Vermont: USGS Open-File Report 96-31-A, 2 plates, scale...
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Digital bedrock geologic map of the Johnson quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG98-2 Thompson, PJ�and Thompson, TB, 1998,�Digital bedrock geologic map of the Johnson quadrangle, Vermont: VGS Open-File Report VG98-2, 2 plates,...
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Digital bedrock geologic map of the Rochester quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG96-33A Walsh, GJ�and Falta, CK, 1996, Digital bedrock geologic map of the Rochester quadrangle, Vermont: USGS Open-File Report 96-33-A, 2 plates,...
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Digital bedrock geologic map of the Plymouth quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG94-654A Walsh, G.J., and Ratcliffe, N.M., 1994,�Digital bedrock geologic map of the Plymouth quadrangle, Vermont: USGS Open-File Report 94-654, 2...
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Digital bedrock geologic map of the Eden quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG98-3 Kim, J, Springston, G, and Gale, M, 1998,�Digital bedrock geologic map of the Eden quadrangle, Vermont: VGS Open-File Report VG98-3, 2...
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Aircraft route planning based on digital map pre-treatment
Directory of Open Access Journals (Sweden)
Ran ZHEN
2015-04-01
Full Text Available Aiming at the flight path project in low complicated airspace, the influence of terrain conditions and surface threatening to aircraft flight are studied. Through the analysis of digital map and static threat, the paper explores the processing method of the digital map, and uses the Hermite function to process the map smoothly, reducing the searching range of optimal trajectory. By designing the terrain following, terrain avoidance and the way of avoiding a threat, the safety of aircraft can be guaranteed. In-depth analysis of particle swarm optimization (PSO algorithm realizes the three dimensional paths project before the aircraft performs a task. Through simulation, the difference of the maps before and after processing is shown, and offline programming of the three dimensional optimal path is achieved.
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Insights into bird wing evolution and digit specification from polarizing region fate maps.
Towers, Matthew; Signolet, Jason; Sherman, Adrian; Sang, Helen; Tickle, Cheryll
2011-08-09
The proposal that birds descended from theropod dinosaurs with digits 2, 3 and 4 was recently given support by short-term fate maps, suggesting that the chick wing polarizing region-a group that Sonic hedgehog-expressing cells-gives rise to digit 4. Here we show using long-term fate maps that Green fluorescent protein-expressing chick wing polarizing region grafts contribute only to soft tissues along the posterior margin of digit 4, supporting fossil data that birds descended from theropods that had digits 1, 2 and 3. In contrast, digit IV of the chick leg with four digits (I-IV) arises from the polarizing region. To determine how digit identity is specified over time, we inhibited Sonic hedgehog signalling. Fate maps show that polarizing region and adjacent cells are specified in parallel through a series of anterior to posterior digit fates-a process of digit specification that we suggest is involved in patterning all vertebrate limbs with more than three digits.
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Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.
2008-01-01
A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area. ?? 2008 American Society for Photogrammetry and Remote Sensing.
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International Nuclear Information System (INIS)
D'Agnese, F.A.; Faunt, C.C.; Turner, A.K.
1995-01-01
Parts of four 1:250,000-scale geologic maps by the California Department of Natural Resources, Division of Mines and Geology have been digitized for use in hydrogeologic characterization. These maps include the area of California between lat. 35 degree N; Long. 115 degree W and lat. 38 degree N, long. 118 degree W of the Kingman Sheet (Jennings, 1961), Trona Sheet (Jennings and others, 1962), Mariposa Sheet (Strand, 1967), and Death Valley Sheet (Streitz and Stinson, 1974). These digital maps are being released by the US Geological Survey in the ARC/INFO Version 6.1 Export format. The digitized data include geologic unit boundaries, fault traces, and identity of geologic units. The procedure outlined in US Geological Survey Circular 1054 (Soller and others, 1990) was sued during the map construction. The procedure involves transferring hard-copy data into digital format by scanning manuscript maps, manipulating the digital map data, and outputting the data. Most of the work was done using Environmental Systems Research Institute's ARC/INFO software. The digital maps are available in ARC/INFO Rev. 6.1 Export format, from the USGS, Yucca Mountain Project, in Denver, Colorado
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, , USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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Digital bedrock geologic map of the Saxtons River quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG96-52A Ratcliffe, NM�and Armstrong, TR, 1996, Digital bedrock geologic map of the Saxtons River quadrangle, Vermont, USGS Open-File Report...
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New modified map for digital image encryption and its performance
Suryadi, MT; Yus Trinity Irsan, Maria; Satria, Yudi
2017-10-01
Protection to classified digital data becomes so important in avoiding data manipulation and alteration. The focus of this paper is in data and information protection of digital images form. Protection is provided in the form of encrypted digital image. The encryption process uses a new map, {x}n+1=\\frac{rλ {x}n}{1+λ {(1-{x}n)}2}\\quad ({mod} 1), which is called MS map. This paper will show: the results of digital image encryption using MS map and how the performance is regarding the average time needed for encryption/decryption process; randomness of key stream sequence with NIST test, histogram analysis and goodness of fit test, quality of the decrypted image by PSNR, initial value sensitivity level, and key space. The results show that the average time of the encryption process is relatively same as the decryption process and it depends to types and sizes of the image. Cipherimage (encrypted image) is uniformly distributed since: it passes the goodness of fit test and also the histogram of the cipherimage is flat; key stream, that are generated by MS map, passes frequency (monobit) test, and runs test, which means the key stream is a random sequence; the decrypted image has same quality as the original image; and initial value sensitivity reaches 10-17, and key space reaches 3.24 × 10634. So, that encryption algorithm generated by MS map is more resistant to brute-force attack and known plaintext attack.
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Digital bedrock geologic map of the Morrisville quadrangle,�Vermont
Vermont Center for Geographic Information — Digital Data from VG98-1 Springston, G., Kim, J., and Applegate, G.S., 1998,�Digital bedrock geologic map of the Morrisville quadrangle,�Vermont: VGS Open-File...
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Digital compilation bedrock geologic map of the Milton quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-8A Dorsey, R, Doolan, B, Agnew, PC, Carter, CM, Rosencrantz, EJ, and Stanley, RS, 1995, Digital compilation bedrock geologic map of the Milton...
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Digital compilation bedrock geologic map of the Lincoln quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-5A Stanley, R, DelloRusso, V, Haydock, S, Lapp, E, O'Loughlin, S, Prewitt, J,and Tauvers, PR, 1995, Digital compilation bedrock geologic map...
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Successful Teaching, Learning, and Use of Digital Mapping Technology in Mazvihwa, Rural Zimbabwe
Eitzel Solera, M. V.; Madzoro, S.; Solera, J.; Mhike Hove, E.; Changarara, A.; Ndlovu, D.; Chirindira, A.; Ndlovu, A.; Gwatipedza, S.; Mhizha, M.; Ndlovu, M.
2016-12-01
Participatory mapping is now a staple of community-based work around the world. Particularly for indigenous and rural peoples, it can represent a new avenue for environmental justice and can be a tool for culturally appropriate management of local ecosystems. We present a successful example of teaching and learning digital mapping technology in rural Zimbabwe. Our digital mapping project is part of the long-term community-based participatory research of The Muonde Trust in Mazvihwa, Zimbabwe. By gathering and distributing local knowledge and also bringing in visitors to share knowledge, Muonde has been able to spread relevant information among rural farmers. The authors were all members of Muonde or were Muonde's visitors, and were mentors and learners of digital mapping technologies at different times. Key successful characteristics of participants included patience, compassion, openness, perseverance, respect, and humility. Important mentoring strategies included: 1) instruction in Shona and in English, 2) locally relevant examples, assignments, and analogies motivated by real needs, 3) using a variety of teaching methods for different learning modalities, 4) building on and modifying familiar teaching methods, and 5) paying attention to the social and relational aspects of teaching and learning. The Muonde mapping team has used their new skills for a wide variety of purposes, including: identifying, discussing, and acting on emerging needs; using digital mapping for land-use and agropastoral planning; and using mapping as a tool for recording and telling important historical and cultural stories. Digital mapping has built self-confidence as well as providing employable skills and giving Muonde more visibility to other local and national non-governmental organizations, utility companies, and educational institutions. Digital mapping, as taught in a bottom-up, collaborative way, has proven to be both accessible and of enormous practical use to rural Zimbabweans.
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Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain
Directory of Open Access Journals (Sweden)
Stephen Grebby
2016-10-01
Full Text Available Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the “salt-and-pepper” artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects. Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the “salt-and-pepper” artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain.
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Condit, C. D.; Mninch, M.
2012-12-01
The Dynamic Digital Map (DDM) is an ideal vehicle for the professional geologist to use to describe the geologic setting of key sites to the public in a format that integrates and presents maps and associated analytical data and multimedia without the need for an ArcGIS interface. Maps with field trip guide stops that include photographs, movies and figures and animations, showing, for example, how the features seen in the field formed, or how data might be best visualized in "time-frame" sequences are ideally included in DDMs. DDMs distribute geologic maps, images, movies, analytical data, and text such as field guides, in an integrated cross-platform, web enabled format that are intuitive to use, easily and quickly searchable, and require no additional proprietary software to operate. Maps, photos, movies and animations are stored outside the program, which acts as an organizational framework and index to present these data. Once created, the DDM can be downloaded from the web site hosting it in the flavor matching the user's operating system (e.g. Linux, Windows and Macintosh) as zip, dmg or tar files (and soon as iOS and Android tablet apps). When decompressed, the DDM can then access its associated data directly from that site with no browser needed. Alternatively, the entire package can be distributed and used from CD, DVD, or flash-memory storage. The intent of this presentation is to introduce the variety of geology that can be accessed from the over 25 DDMs created to date, concentrating on the DDM of the Springerville Volcanic Field. We will highlight selected features of some of them, introduce a simplified interface to the original DDM (that we renamed DDMC for Classic) and give a brief look at a the recently (2010-2011) completed geologic maps of the Springerville Volcanic field to see examples of each of the features discussed above, and a display of the integrated analytical data set. We will also highlight the differences between the classic or
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Digital compilation bedrock geologic map of the Warren quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-4A Walsh, GJ, Haydock, S, Prewitt, J, Kraus, J, Lapp, E, O'Loughlin, S, and Stanley, RS, 1995, Digital compilation bedrock geologic map of the...
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Digital geologic map database of the Nevada Test Site area, Nevada
Wahl, R.R.; Sawyer, D.A.; Minor, S.A.; Carr, M.D.; Cole, J.C.; Swadley, W.C.; Laczniak, R.J.; Warren, R.G.; Green, K.S.; Engle, C.M.
1997-01-01
Forty years of geologic investigations at the Nevada Test Site (NTS) have been digitized. These data include all geologic information that: (1) has been collected, and (2) can be represented on a map within the map borders at the map scale is included in the map digital coverages. The following coverages are included with this dataset: Coverage Type Description geolpoly Polygon Geologic outcrops geolflts line Fault traces geolatts Point Bedding attitudes, etc. geolcald line Caldera boundaries geollins line Interpreted lineaments geolmeta line Metamorphic gradients The above coverages are attributed with numeric values and interpreted information. The entity files documented below show the data associated with each coverage.
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Vegetation Mapping of the Mond Protected Area of Bushehr Provice (SW Iran)
Mehrabian, Ahmadreza; Mahiny, Abdolrassoul Salman; Mostafavi, Hossein; Liaghati, Homan
2010-01-01
The current study is a new approach to vegetation mapping in Iran using remote sensing (RS) and the geographic information system (GIS). One of the most important problems in remote sensing of desert vegetation is that the reflectance from soil and rocks is often much greater than that of sparse vegetation and this makes it difficult to separate out the vegetation signal (Gates et al. 1965); and there is spectral variability within shrubs of the same species (Duncant et al., 1993). These prop...
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USGS QA Plan: Certification of digital airborne mapping products
Christopherson, J.
2007-01-01
To facilitate acceptance of new digital technologies in aerial imaging and mapping, the US Geological Survey (USGS) and its partners have launched a Quality Assurance (QA) Plan for Digital Aerial Imagery. This should provide a foundation for the quality of digital aerial imagery and products. It introduces broader considerations regarding processes employed by aerial flyers in collecting, processing and delivering data, and provides training and information for US producers and users alike.
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The place of digital technology on the IEA's energy road-maps
International Nuclear Information System (INIS)
Ben-Naceur, Kamel
2017-01-01
The International Energy Agency (IEA) has drafted road-maps for the next four decades in collaboration with public and private producers and consumers of energy. For each type of energy, these road-maps indicate the key elements for compliance with the objectives of the Paris Climate Agreement. They emphasize the role of digital technology, in particular smart grids, in the transition toward a more digital and more intelligent energy system. The conditions necessary for successfully transforming this sector are mapped out, while attention is called to the risks inherent in this transition
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White, D. C.; Lewis, M. M.
2012-07-01
The Australian Great Artesian Basin (GAB) supports a unique and diverse range of groundwater dependent wetland ecosystems termed GAB springs. In recent decades the ecological sustainability of the springs has become uncertain as demands on this iconic groundwater resource increase. The impacts of existing water extractions for mining and pastoral activities are unknown. This situation is compounded by the likelihood of future increasing demand for extractions. Hyperspectral remote sensing provides the necessary spectral and spatial detail to discriminate wetland vegetation communities. Therefore the objectives of this paper are to discriminate the spatial extent and distribution of key spring wetland vegetation communities associated with the GAB springs evaluating three hyperspectral techniques: Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF) and Spectrally Segmented PCA. In addition, to determine if the hyperspectral techniques developed can be applied at a number of sites representative of the range of spring formations and geomorphic settings and at two temporal intervals. Two epochs of HyMap airborne hyperspectral imagery were captured for this research in March 2009 and April 2011 at a number of sites representative of the floristic and geomorphic diversity of GAB spring groups/complexes within South Australia. Colour digital aerial photography at 30 cm GSD was acquired concurrently with the HyMap imagery. The image acquisition coincided with a field campaign of spectroradiometry measurements and a botanical survey. To identify key wavebands which have the greatest capability to discriminate vegetation communities of the GAB springs and surrounding area three hyperspectral data reduction techniques were employed: (i) Spectrally Segmented PCA (SSPCA); (ii) the Minimum Noise Transform (MNF); and (iii) the Pixel Purity Index (PPI). SSPCA was applied to NDVI-masked vegetation portions of the HyMap imagery with wavelength regions spectrally
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Ralston, Barbara E.; Davis, Philip A.; Weber, Robert M.; Rundall, Jill M.
2008-01-01
A vegetation database of the riparian vegetation located within the Colorado River ecosystem (CRE), a subsection of the Colorado River between Glen Canyon Dam and the western boundary of Grand Canyon National Park, was constructed using four-band image mosaics acquired in May 2002. A digital line scanner was flown over the Colorado River corridor in Arizona by ISTAR Americas, using a Leica ADS-40 digital camera to acquire a digital surface model and four-band image mosaics (blue, green, red, and near-infrared) for vegetation mapping. The primary objective of this mapping project was to develop a digital inventory map of vegetation to enable patch- and landscape-scale change detection, and to establish randomized sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). The vegetation base map was constructed through a combination of ground surveys to identify vegetation classes, image processing, and automated supervised classification procedures. Analysis of the imagery and subsequent supervised classification involved multiple steps to evaluate band quality, band ratios, and vegetation texture and density. Identification of vegetation classes involved collection of cover data throughout the river corridor and subsequent analysis using two-way indicator species analysis (TWINSPAN). Vegetation was classified into six vegetation classes, following the National Vegetation Classification Standard, based on cover dominance. This analysis indicated that total area covered by all vegetation within the CRE was 3,346 ha. Considering the six vegetation classes, the sparse shrub (SS) class accounted for the greatest amount of vegetation (627 ha) followed by Pluchea (PLSE) and Tamarix (TARA) at 494 and 366 ha, respectively. The wetland (WTLD) and Prosopis-Acacia (PRGL) classes both had similar areal cover values (227 and 213 ha, respectively). Baccharis-Salix (BAXX) was the least represented at 94 ha. Accuracy assessment of the
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A Double Perturbation Method for Reducing Dynamical Degradation of the Digital Baker Map
Liu, Lingfeng; Lin, Jun; Miao, Suoxia; Liu, Bocheng
2017-06-01
The digital Baker map is widely used in different kinds of cryptosystems, especially for image encryption. However, any chaotic map which is realized on the finite precision device (e.g. computer) will suffer from dynamical degradation, which refers to short cycle lengths, low complexity and strong correlations. In this paper, a novel double perturbation method is proposed for reducing the dynamical degradation of the digital Baker map. Both state variables and system parameters are perturbed by the digital logistic map. Numerical experiments show that the perturbed Baker map can achieve good statistical and cryptographic properties. Furthermore, a new image encryption algorithm is provided as a simple application. With a rather simple algorithm, the encrypted image can achieve high security, which is competitive to the recently proposed image encryption algorithms.
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Digital bedrock geologic map of the Mount Snow & Readsboro quadrangles, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-DM1 Ratcliffe, NM, 1995, Digital bedrock geologic map of the Mount Snow & Readsboro quadrangles, Vermont, scale 1:24000, The bedrock...
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Inception Report, Photogrammetry and Digital Mapping, LATIVA
DEFF Research Database (Denmark)
Frederiksen, Poul
1996-01-01
The report gives the current situation on photogrammetry and digital mapping by the end of 1996 in Latvia.Objectives and proposals are given for activities of the EU Phare project: Technical Assistance to Land Privatisation and Registration in Latvia.The project is executed by Kampsax Geoplan...
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An analysis on vegetation pattern of ecotone in North China
Energy Technology Data Exchange (ETDEWEB)
Jia, J.C.; Zhang, H.Y. [North China Electric Power Univ., Beijing (China). Energy and Environmental Research Center
2008-07-01
Vegetation pattern is influenced by several natural factors, including climatic elements, elevation factors and soil conditions. Since soil formation and soil types are influenced by water-temperature conditions, much can be learned about vegetation distribution patterns by studying the relationship between water-temperature conditions and vegetation distribution. This paper presented the results of a study whose purpose was to provide scientific evidence for exploiting natural resources, planting trees, and restoring grassland from cropland. A warmth index (WI ) and humidity index (HI) were used to examine the relation between the distribution of vegetation and the water-temperature condition in North China's ecotone, the transition area between two adjacent but different plant communities, including steppe, bush and forest ecosystems. A vegetation map of the study site was digitized and then converted into a vegetation grid map from which 17 different vegetation types were chosen as the study object. A monthly mean temperature grid map and precipitation grid map of the study site were made based on the method of spatial interpolation, by using 119 meteorological data for 50 years during the period from 1951 to 2000. The thermal distribution curves and humidity distribution curves of 17 vegetation types in North China, determined the whole range and optimum range of WI and HI of 17 vegetation types. The relative proportion of each vegetation type distributed in the optimum range of WI and HI were calculated. The vegetation pattern was analyzed according to the WI and HI standard, and was described by species and their relative amount. 10 refs., 5 tabs., 3 figs.
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Digital and preliminary bedrock geologic map of the Wallingford quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG98-335A Burton, WC, and Ratcliffe, NM, 2000, Digital and preliminary bedrock geologic map of the Wallingford quadrangle, Vermont: USGS Open-File...
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Small forest cuttings mapped with Landsat digital data
Bryant, E.; Dodge, A. G.; Eger, M. J. E.
1979-01-01
The Cooperative Landsat Applications Research Group used computer classification of Landsat digital data to map forest cuttings (clearcuts) in northern New Hampshire. Cuttings as small as 3 hectares were identified. Several ages or conditions of clearcuts could be distinguished. Progress in two methods of duplicating classification categories from one Landsat pass to another are discussed. One method was used in making maps of areas in 1973, 1975, and 1978.
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Dye, Dennis G.; Middleton, Barry R.; Vogel, John M.; Wu, Zhuoting; Velasco, Miguel G.
2016-01-01
We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C4) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI2) data from Landsat to develop the NDPI, and MSAVI2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI2 and summer maximum MSAVI2), and two simple, conventional VI metrics (summer average MSAVI2, summer maximum MSAVI2). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C4 grass cover as estimated from landscape photographs at 92 sites (R2 = 0.76, p landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.
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Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello
2013-01-01
Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.
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Digital Soil Mapping – A platform for enhancing soil learning.
The expansion of digital infrastructure and tools has generated massive data and information as well as a need for reliable processing and accurate interpretations. Digital Soil Mapping is no exception in that it has provided opportunities for professionals and the public to interact at field and tr...
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Digital and preliminary bedrock geologic map of the Chittenden quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG97-854A Ratcliffe, NM, 1997,�Digital and preliminary bedrock geologic map of the Chittenden quadrangle, Vermont: USGS Open-File Report 97-854, 1...
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Directory of Open Access Journals (Sweden)
Eva Husson
2016-09-01
Full Text Available Aquatic vegetation has important ecological and regulatory functions and should be monitored in order to detect ecosystem changes. Field data collection is often costly and time-consuming; remote sensing with unmanned aircraft systems (UASs provides aerial images with sub-decimetre resolution and offers a potential data source for vegetation mapping. In a manual mapping approach, UAS true-colour images with 5-cm-resolution pixels allowed for the identification of non-submerged aquatic vegetation at the species level. However, manual mapping is labour-intensive, and while automated classification methods are available, they have rarely been evaluated for aquatic vegetation, particularly at the scale of individual vegetation stands. We evaluated classification accuracy and time-efficiency for mapping non-submerged aquatic vegetation at three levels of detail at five test sites (100 m × 100 m differing in vegetation complexity. We used object-based image analysis and tested two classification methods (threshold classification and Random Forest using eCognition®. The automated classification results were compared to results from manual mapping. Using threshold classification, overall accuracy at the five test sites ranged from 93% to 99% for the water-versus-vegetation level and from 62% to 90% for the growth-form level. Using Random Forest classification, overall accuracy ranged from 56% to 94% for the growth-form level and from 52% to 75% for the dominant-taxon level. Overall classification accuracy decreased with increasing vegetation complexity. In test sites with more complex vegetation, automated classification was more time-efficient than manual mapping. This study demonstrated that automated classification of non-submerged aquatic vegetation from true-colour UAS images was feasible, indicating good potential for operative mapping of aquatic vegetation. When choosing the preferred mapping method (manual versus automated the desired level of
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Digital and preliminary bedrock geologic map of the Rutland quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG98-121A Ratcliffe, N.M., 1998,�Digital and preliminary bedrock geologic map of the Rutland quadrangle, Vermont: USGS Open-File Report 98-121-A, 1...
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Digital compilation bedrock geologic map of the Mt. Ellen quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-6A Stanley, RS, Walsh, G, Tauvers, PR, DiPietro, JA, and DelloRusso, V, 1995,�Digital compilation bedrock geologic map of the Mt. Ellen...
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Vegetation map and plant checklist of Ol Ari Nyiro ranch and the ...
African Journals Online (AJOL)
Ol Ari Nyiro is a 360 km2 ranch of the Laikipia Plateau, in a semi-arid part of Kenya. The vegetation of the ranch and nearby Mukutan Gorge was mapped, and a preliminary check-list of fungi and vascular plants compiled. The vegetation was classified in 16 different types. A total of 708 species and subspecies were ...
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Comparison between detailed digital and conventional soil maps of an area with complex geology
Directory of Open Access Journals (Sweden)
Osmar Bazaglia Filho
2013-10-01
Full Text Available Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000 of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs. For the Digital Soil Map (DSM, spectral data were extracted from Landsat 5 Thematic Mapper (TM imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS. To compare the conventional and digital (DSM soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.
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Digital compilation bedrock geologic map of the South Mountain quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG95-3A Stanley, R.S., DelloRusso, V., Tauvers, P.R., DiPietro, J.A., Taylor, S., and Prahl, C., 1995, Digital compilation bedrock geologic map of...
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A Visual Framework for Digital Reconstruction of Topographic Maps
Thabet, Ali Kassem
2014-09-30
We present a framework for reconstructing Digital Elevation Maps (DEM) from scanned topographic maps. We first rectify the images to ensure that maps fit together without distortion. To segment iso-contours, we have developed a novel semi-automated method based on mean-shifts that requires only minimal user interaction. Contour labels are automatically read using an OCR module. To reconstruct the output DEM from scattered data, we generalize natural neighbor interpolation to handle the transfinite case (contours and points). To this end, we use parallel vector propagation to compute a discrete Voronoi diagram of the constraints, and a modified floodfill to compute virtual Voronoi tiles. Our framework is able to handle tens of thousands of contours and points and can generate DEMs comprising more than 100 million samples. We provide quantitative comparison to commercial software and show the benefits of our approach. We furthermore show the robustness of our method on a massive set of old maps predating satellite acquisition. Compared to other methods, our framework is able to accurately and efficiently generate a final DEM despite inconsistencies, sparse or missing contours even for highly complex and cluttered maps. Therefore, this method has broad applicability for digitization and reconstruction of the world\\'s old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.
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Digital bedrock geologic map of the Gilson Mountain quadrangle,�Vermont
Vermont Center for Geographic Information — Digital Data from VG95-7A Doolan, B, 1995,�Digital bedrock geologic map of the Gilson Mountain quadrangle,�Vermont: VGS Open-File Report VG95-7A, 2 plates, scale...
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Radarsat Antarctic Mapping Project Digital Elevation Model, Version 2
National Aeronautics and Space Administration — The high-resolution Radarsat Antarctic Mapping Project (RAMP) Digital Elevation Model (DEM) combines topographic data from a variety of sources to provide consistent...
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Directory of Open Access Journals (Sweden)
Timothy G. Whiteside
2015-09-01
Full Text Available Vegetation plays a key role in the environmental function of wetlands. The Ramsar-listed wetlands of the Magela Creek floodplain in Northern Australia are identified as being at risk from weeds, fire and climate change. In addition, the floodplain is a downstream receiving environment for the Ranger Uranium Mine. Accurate methods for mapping wetland vegetation are required to provide contemporary baselines of annual vegetation dynamics on the floodplain to assist with analysing any potential change during and after minesite rehabilitation. The aim of this study was to develop and test the applicability of geographic object-based image analysis including decision tree classification to classify WorldView-2 imagery and LiDAR-derived ancillary data to map the aquatic vegetation communities of the Magela Creek floodplain. Results of the decision tree classification were compared against a Random Forests classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010. The decision tree classification method provided an overall accuracy of 78% which was significantly higher than the overall accuracy of the Random Forests classification (67%. Most of the error in both classifications was associated with confusion between spectrally similar classes dominated by grasses, such as Hymenachne and Pseudoraphis. In addition, the extent of the sedge Eleocharis was under-estimated in both cases. This suggests the method could be useful for mapping wetlands where statistical-based supervised classifications have achieved less than satisfactory results. Based upon the results, the decision tree method will form part of an ongoing operational monitoring program.
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County digital geologic mapping. Final report
Energy Technology Data Exchange (ETDEWEB)
Hess, R.H.; Johnson, G.L.; dePolo, C.M.
1995-12-31
The purpose of this project is to create quality-county wide digital 1:250,000-scale geologic maps from existing published 1:250,000-scale Geologic and Mineral Resource Bulletins published by the Nevada Bureau of Mines and Geology (NBMG). An additional data set, based on current NBMG research, Major and Significant Quaternary and Suspected Quaternary Faults of Nevada, at 1:250,000 scale has also been included.
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County digital geologic mapping. Final report
International Nuclear Information System (INIS)
Hess, R.H.; Johnson, G.L.; dePolo, C.M.
1995-01-01
The purpose of this project is to create quality-county wide digital 1:250,000-scale geologic maps from existing published 1:250,000-scale Geologic and Mineral Resource Bulletins published by the Nevada Bureau of Mines and Geology (NBMG). An additional data set, based on current NBMG research, Major and Significant Quaternary and Suspected Quaternary Faults of Nevada, at 1:250,000 scale has also been included
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Cosh, Michael H.; Jing Tao; Jackson, Thomas J.; McKee, Lynn; O'Neill, Peggy
2011-01-01
Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.
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Digital Mapping Techniques '11–12 workshop proceedings
Soller, David R.
2014-01-01
The Digital Mapping Techniques '11 (DMT'11) workshop was hosted by Virginia Division of Geology and Mineral Resources and The College of William & Mary, and coordinated by the National Geologic Map Database project. Conducted May 22-25 on the campus of The College of William & Mary, in Williamsburg, Virginia, it was attended by 77 technical experts from 30 agencies, universities, and private companies, including representatives from 19 State geological surveys (see "DMT'11 Presentations and Attendees" in these Proceedings).
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Lewis, Donna L.; Phinn, Stuart
2011-01-01
Aerial photography interpretation is the most common mapping technique in the world. However, unlike an algorithm-based classification of satellite imagery, accuracy of aerial photography interpretation generated maps is rarely assessed. Vegetation communities covering an area of 530 km2 on Bullo River Station, Northern Territory, Australia, were mapped using an interpretation of 1:50,000 color aerial photography. Manual stereoscopic line-work was delineated at 1:10,000 and thematic maps generated at 1:25,000 and 1:100,000. Multivariate and intuitive analysis techniques were employed to identify 22 vegetation communities within the study area. The accuracy assessment was based on 50% of a field dataset collected over a 4 year period (2006 to 2009) and the remaining 50% of sites were used for map attribution. The overall accuracy and Kappa coefficient for both thematic maps was 66.67% and 0.63, respectively, calculated from standard error matrices. Our findings highlight the need for appropriate scales of mapping and accuracy assessment of aerial photography interpretation generated vegetation community maps.
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Fonnesbeck, Brook B.
2015-01-01
Digital soil mapping typically involves inputs of digital elevation models, remotely sensed imagery, and other spatially explicit digital data as environmental covariates to predict soil classes and attributes over a landscape using statistical models. Digital imagery from Landsat 5, a digital elevation model, and a digital geology map were used as environmental covariates in a 67,000-ha study area of the Great Basin west of Fillmore, UT. A “pre-map” was created for selecting sampling locatio...
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Small scale digital soil mapping in Southeastern Kenya
Mora Vallejo, A.P.; Claessens, L.; Stoorvogel, J.J.; Heuvelink, G.B.M.
2008-01-01
Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether
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Digital Mapping Techniques '10-Workshop Proceedings, Sacramento, California, May 16-19, 2010
Soller, David R.; Soller, David R.
2012-01-01
The Digital Mapping Techniques '10 (DMT'10) workshop was attended by 110 technical experts from 40 agencies, universities, and private companies, including representatives from 19 State geological surveys (see Appendix A). This workshop, hosted by the California Geological Survey, May 16-19, 2010, in Sacramento, California, was similar in nature to the previous 13 meetings (see Appendix B). The meeting was coordinated by the U.S. Geological Survey's (USGS) National Geologic Map Database project. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. It is with great pleasure that I note that the objective was again successfully met, as attendees continued to share and exchange knowledge and information, and renew friendships and collegial work begun at past DMT workshops. At this meeting, oral and poster presentations and special discussion sessions emphasized (1) methods for creating and publishing map products ("publishing" includes Web-based release); (2) field data capture software and techniques, including the use of LiDAR; (3) digital cartographic techniques; (4) migration of digital maps into ArcGIS Geodatabase format; (5) analytical GIS techniques; and (6) continued development of the National Geologic Map Database.
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Directory of Open Access Journals (Sweden)
Y. Zheng
2015-08-01
Full Text Available Peng Xi county, Sichuan province, the Bao Fan temple mural digitization survey mapping project: we use three-dimensional laserscanning, multi-baseline definition digital photography, multi-spectral digital image acquisition and other technologies for digital survey mapping. The purpose of this project is to use modern mathematical reconnaissance mapping means to obtain accurate mural shape, color, quality and other data. Combined with field investigation and laboratory analysis results, and based on a comprehensive survey and study, a comprehensive analysis of the historical Bao Fan Temple mural artistic and scientific value was conducted. A study of the mural’s many qualities (structural, material, technique, preservation environment, degradation, etc. reveal all aspects of the information carried by the Bao Fan Temple mural. From multiple angles (archeology, architecture, surveying, conservation science and other disciplines an assessment for the Bao Fan Temple mural provides basic data and recommendations for conservation of the mural. In order to achieve the conservation of cultural relics in the Bao Fan Temple mural digitization survey mapping process, we try to apply the advantages of three-dimensional laser scanning equipment. For wall murals this means obtaining three-dimensional scale data from the scan of the building and through the analysis of these data to help determine the overall condition of the settlement as well as the deformation of the wall structure. Survey analysis provides an effective set of conclusions and suggestions for appropriate mural conservation. But before data collection, analysis and research need to first to select the appropriate scanning equipment, set the appropriate scanning accuracy and layout position of stations necessary to determine the scope of required data. We use the fine features of the three-dimensional laser scanning measuring arm to scan the mural surface deformation degradation to reflect
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Zheng, Y.
2015-08-01
Peng Xi county, Sichuan province, the Bao Fan temple mural digitization survey mapping project: we use three-dimensional laserscanning, multi-baseline definition digital photography, multi-spectral digital image acquisition and other technologies for digital survey mapping. The purpose of this project is to use modern mathematical reconnaissance mapping means to obtain accurate mural shape, color, quality and other data. Combined with field investigation and laboratory analysis results, and based on a comprehensive survey and study, a comprehensive analysis of the historical Bao Fan Temple mural artistic and scientific value was conducted. A study of the mural's many qualities (structural, material, technique, preservation environment, degradation, etc.) reveal all aspects of the information carried by the Bao Fan Temple mural. From multiple angles (archeology, architecture, surveying, conservation science and other disciplines) an assessment for the Bao Fan Temple mural provides basic data and recommendations for conservation of the mural. In order to achieve the conservation of cultural relics in the Bao Fan Temple mural digitization survey mapping process, we try to apply the advantages of three-dimensional laser scanning equipment. For wall murals this means obtaining three-dimensional scale data from the scan of the building and through the analysis of these data to help determine the overall condition of the settlement as well as the deformation of the wall structure. Survey analysis provides an effective set of conclusions and suggestions for appropriate mural conservation. But before data collection, analysis and research need to first to select the appropriate scanning equipment, set the appropriate scanning accuracy and layout position of stations necessary to determine the scope of required data. We use the fine features of the three-dimensional laser scanning measuring arm to scan the mural surface deformation degradation to reflect the actual state of
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Energy Technology Data Exchange (ETDEWEB)
Tanaka, S; Sugimura, T [Remote Sensing Technology Center of Japan, Tokyo (Japan)
1990-12-05
This paper describes the relationship between remote sensing and digital map. The relation between remote sensing and digital map is roughly classified into two kinds. One of them is utilization of remote sensing and digital map in combination to analyze phenomena, and the other is normalization of remote sensing data by use of digital map. For examples of utilizing remote sensing and digital map, there are the creation of a perspective image of ground scene from Landsat MSS data by use of a mesh type digital map of the orthogonal co-ordinates, and the creation of an image of the enviromental research along roads from satilite data by use of a vector type digital map. Furthermore, this paper introduces a procedure of correcting geographical strains by use of a digital map and converting a radar image to corrected plane image, and the use of a digital map in the global scale for the analysis of floods and other purposes. 20 refs., 5 figs., 1 tab.
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Directory of Open Access Journals (Sweden)
Claudia C. Nolasco-Carvalho
2009-02-01
Full Text Available Elaborou-se um mapa digital de solos de uma área na região de Mucugê, BA, com o objetivo de avaliar o uso de geotecnologias na cartografia de solos. A metodologia desenvolvida a partir do modelo de inferência para solos - SoLIM , requer o conhecimento prévio da área por um especialista em mapeamento e está alicerçada na equação dos fatores de formação do solo e no modelo de distribuição dos solos na paisagem. Os dados, advindos do Modelo Digital do Terreno - MDT, da vegetação e da geologia, foram associados ao conhecimento do pedólogo e integrados em ambiente SIG (Sistema de Informações Geográficas sob inferência fuzzy. A modelagem por lógica fuzzy permitiu apontar as incertezas e transições da cobertura pedológica e gerou um mapa digital de solo que, quando comparado com o mapa convencional da área, mostrou menor generalização no domínio de espaços e parâmetros, ou seja, um refinamento da escala, porém a aplicabilidade da metodologia depende da validação de campo e da repetição em outras áreas.A digital soil map was elaborated for an area in the region of Mucugê-BA using data integration derived from a digital elevation model (DEM of the vegetation and geology that was associated with a soil scientist's knowledge and correlated in a GIS environment (Geography Information System under fuzzy inference, as a methodological proposal. The methodology was developed and based on the soil-land inference model - SoLIM, on the soil factor equation and the soil-landscape model. The fuzzy logic is able to simulate the uncertainty and transitions that often appear in pedologic systems. The results show that the methodology allows the generation of digital soil maps with increased scale and to reduce soil classe generalizations in the space and parameter domain. However, this methodology is very dependent upon the soil expert's knowledge and accuracy of the data base. To verify the applicability of the methodology the
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OSCEOLA COUNTY, FL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HART COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE,GRAVES COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYON COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WOLFE COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAVIESS COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARLBORO COUNTY, SC
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCOTT COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUCAS COUNTY, OHIO
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARDIN COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SARPY COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, MASSACHUSETTS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAIRFIELD COUNTY, CONNECTICUT
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, NE
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wilcox COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALLARD COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWELL COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Eddy County, NM
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Winston COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Mitchell County, GA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ANGELINA COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATASCOSA COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLEMING COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEWARD COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCCRACKEN COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAUNDERS COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, KANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARLAN COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RENO COUNTY, KANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FURNAS COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLAMAKEE COUNTY, IOWA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASS COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCPHERSON COUNTY, KANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAWES COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALLEY COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLSWORTH COUNTY, KANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERMAN COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARVEY COUNTY, KANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLATTE COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, THURSTON COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAMA COUNTY, IOWA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BONNER COUNTY, IDAHO
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROBERTSON COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESSEX COUNTY, MA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, AR
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, ARKANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHAMBERS COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NELSON COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCLEAN COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSK COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PEMBINA COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Harris COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, FLORIDA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Marshall COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRIMES COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALLER COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARBON COUNTY, UTAH
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Liberty County, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TYLER COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Stafford County , VIRGINIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HILLSBOROUGH COUNTY, FLORIDA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, FLORIDA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Jefferson COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Kenton COUNTY, Kentucky
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSSELL COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. LOUIS, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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A Visual Framework for Digital Reconstruction of Topographic Maps
Thabet, Ali Kassem; Smith, Neil; Wittmann, Roland; Schneider, Jens
2014-01-01
, this method has broad applicability for digitization and reconstruction of the world's old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALPENA COUNTY, MI
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map for Vermillion County, IN
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Dougherty County, GA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCDONALD COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MERCER COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Crawford County, PA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Elbert County, Colorado
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JASPER COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cleburne COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE,CAMDEN COUNTY, GEORGIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MENDOCINO COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ETOWAH COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HUNTERDON CO., NJ
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FINNEY COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CULLMAN COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEBANON COUNTY, PENNSYLVANIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLIOTT COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GAGE COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, MASSACHUSETTS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESCAMBIA COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Baldwin COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SONOMA COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, ARKANSAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GORDON COUNTY, GEORGIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information And supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIN COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOYLE COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bell COUNTY, Kentucky
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, VIRGINIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIMPSON COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANDERA COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Washington COUNTY, NE
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Mercer County, PA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUKES COUNTY, MA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Terrell County, GA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GLOUCESTER, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cherokee COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NORFOLK COUNTY, MASSACHUSETTS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COWLEY COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TALBOT, MARYLAND, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTERFIELD, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MASSACHUSETTS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAND COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DILLON COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, OHIO
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KOOTENAI COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Accomack County, VIRGINIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYCOMING COUNTY, PENNSYLVANIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHATCOM COUNTY, WASHINGTON
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MATHEWS COUNTY, VIRGINIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HIGHLAND COUNTY, VIRGINIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALLS COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, Georgia
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YOLO COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Tuolumne County, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADERA COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOPKINS COUNTY, KY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Butts County, GA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAYES COUNTY, OK
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRADY COUNTY, OKLAHOMA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHASTA COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROSS COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE CITY, MARYLAND
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Chambers COUNTY, AL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROGERS COUNTY, OKLAHOMA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEDINA COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARK COUNTY, OHIO
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBEMARLE COUNTY, VIRGINIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Content Layer progressive Coding of Digital Maps
DEFF Research Database (Denmark)
Forchhammer, Søren; Jensen, Ole Riis
2002-01-01
A new lossless context based method is presented for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the World Wide Web. Progressive encoding is achieved by encoding the image in content layers based on color level or other predefined...... information. Information from already coded layers are used when coding subsequent layers. This approach is combined with efficient template based context bilevel coding, context collapsing methods for multilevel images and arithmetic coding. Relative pixel patterns are used to collapse contexts. Expressions...... for calculating the resulting number of contexts are given. The new methods outperform existing schemes coding digital maps and in addition provide progressive coding. Compared to the state-of-the-art PWC coder, the compressed size is reduced to 50-70% on our layered map test images....
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Program Merges SAR Data on Terrain and Vegetation Heights
Siqueira, Paul; Hensley, Scott; Rodriguez, Ernesto; Simard, Marc
2007-01-01
X/P Merge is a computer program that estimates ground-surface elevations and vegetation heights from multiple sets of data acquired by the GeoSAR instrument [a terrain-mapping synthetic-aperture radar (SAR) system that operates in the X and bands]. X/P Merge software combines data from X- and P-band digital elevation models, SAR backscatter magnitudes, and interferometric correlation magnitudes into a simplified set of output topographical maps of ground-surface elevation and tree height.
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Digital and preliminary bedrock geologic map of the Pico Peak quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG98-226A Walsh, G. J., and Ratcliffe, N.M., 1998,�Digital and preliminary bedrock geologic map of the Pico Peak quadrangle, Vermont: USGS...
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Digital and preliminary bedrock geologic map of the Mount Carmel quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG98-330A Ratcliffe, N.M., and Walsh, G. J., 1998,�Digital and preliminary bedrock geologic map of the Mount Carmel quadrangle, Vermont: USGS...
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Digital Subtraction Angiography (DSA) "Road Map": An Angiographic Tool
Turski, P. A.; Stieghorst, M. F.; Strother, C. M.; Crummy, A. B.; Lieberman, R. P.; Mistretta, C. A.
1982-12-01
Continuous Digital subtraction combined with intraarterial injections of contrast medium permits the display of arterial structures during real time fluoroscopy. This DSA "road map" facilitates selective catheterization and has proved useful in interventional procedures.
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Digital compilation bedrock geologic map of part of the Waitsfield quadrangle, Vermont
Vermont Center for Geographic Information — Digital Data from VG96-03�Digital compilation bedrock geologic map of part of the Waitsfield quadrangle, Vermont: VGS Open-File Report VG96-3A, 2 plates, scale...
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Norman, Laura M.; Middleton, Barry R.; Wilson, Natalie R.
2018-01-01
Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000 km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.
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Securing Digital Audio using Complex Quadratic Map
Suryadi, MT; Satria Gunawan, Tjandra; Satria, Yudi
2018-03-01
In This digital era, exchanging data are common and easy to do, therefore it is vulnerable to be attacked and manipulated from unauthorized parties. One data type that is vulnerable to attack is digital audio. So, we need data securing method that is not vulnerable and fast. One of the methods that match all of those criteria is securing the data using chaos function. Chaos function that is used in this research is complex quadratic map (CQM). There are some parameter value that causing the key stream that is generated by CQM function to pass all 15 NIST test, this means that the key stream that is generated using this CQM is proven to be random. In addition, samples of encrypted digital sound when tested using goodness of fit test are proven to be uniform, so securing digital audio using this method is not vulnerable to frequency analysis attack. The key space is very huge about 8.1×l031 possible keys and the key sensitivity is very small about 10-10, therefore this method is also not vulnerable against brute-force attack. And finally, the processing speed for both encryption and decryption process on average about 450 times faster that its digital audio duration.
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Digital bedrock geologic map of the Mount Holly and Ludlow quadrangles, Vermont
Vermont Center for Geographic Information — Digital Data from VG94-229A Walsh, G.J., Ratcliffe, N.M., Dudley, J.B., and Merrifield, T., 1994,�Digital bedrock geologic map of the Mount Holly and Ludlow...
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Gao, Tian; Qiu, Ling; Chen, Cun-gen
2010-09-01
Based on the biotope classification system with vegetation structure as the framework, a modified biotope mapping model integrated with vegetation cover continuity attributes was developed, and applied to the study of the greenbelts in Helsingborg in southern Sweden. An evaluation of the vegetation cover continuity in the greenbelts was carried out by the comparisons of the vascular plant species richness in long- and short-continuity forests, based on the identification of woodland continuity by using ancient woodland indicator species (AWIS). In the test greenbelts, long-continuity woodlands had more AWIS. Among the forests where the dominant trees were more than 30-year-old, the long-continuity ones had a higher biodiversity of vascular plants, compared with the short-continuity ones with the similar vegetation structure. The modified biotope mapping model integrated with the continuity features of vegetation cover could be an important tool in investigating urban biodiversity, and provide corresponding strategies for future urban biodiversity conservation.
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Butera, M. K.
1979-01-01
The success of remotely mapping wetland vegetation of the southwestern coast of Florida is examined. A computerized technique to process aircraft and LANDSAT multispectral scanner data into vegetation classification maps was used. The cost effectiveness of this mapping technique was evaluated in terms of user requirements, accuracy, and cost. Results indicate that mangrove communities are classified most cost effectively by the LANDSAT technique, with an accuracy of approximately 87 percent and with a cost of approximately 3 cent per hectare compared to $46.50 per hectare for conventional ground survey methods.
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Thompson, D. R.; Wehmanen, O. A. (Principal Investigator)
1978-01-01
The author has identified the following significant results. The Green Number Index technique which uses LANDSAT digital data from 5X6 nautical mile sampling frames was expanded to evaluate its usefulness in detecting and monitoring vegetative water stress over the Great Plains. At known growth stages for wheat, segments were classified as drought or non drought. Good agreement was found between the 18 day remotely sensed data and a weekly ground-based crop moisture index. Operational monitoring of the 1977 U.S.S.R. and Australian wheat crops indicated drought conditions. Drought isoline maps produced by the Green Number Index technique were in good agreement with conventional sources.
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Norman, Laura
2004-01-01
We have prepared a digital map of soil parameters for the international Ambos Nogales watershed to use as input for selected soils-erosion models. The Ambos Nogales watershed in southern Arizona and northern Sonora, Mexico, contains the Nogales wash, a tributary of the Upper Santa Cruz River. The watershed covers an area of 235 km2, just under half of which is in Mexico. Preliminary investigations of potential erosion revealed a discrepancy in soils data and mapping across the United States-Mexican border due to issues including different mapping resolutions, incompatible formatting, and varying nomenclature and classification systems. To prepare a digital soils map appropriate for input to a soils-erosion model, the historical analog soils maps for Nogales, Ariz., were scanned and merged with the larger-scale digital soils data available for Nogales, Sonora, Mexico using a geographic information system.
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Digital soil mapping: strategy for data pre-processing
Directory of Open Access Journals (Sweden)
Alexandre ten Caten
2012-08-01
Full Text Available The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM. Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.
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Comparing the performance of various digital soil mapping approaches to map physical soil properties
Laborczi, Annamária; Takács, Katalin; Pásztor, László
2015-04-01
Spatial information on physical soil properties is intensely expected, in order to support environmental related and land use management decisions. One of the most widely used properties to characterize soils physically is particle size distribution (PSD), which determines soil water management and cultivability. According to their size, different particles can be categorized as clay, silt, or sand. The size intervals are defined by national or international textural classification systems. The relative percentage of sand, silt, and clay in the soil constitutes textural classes, which are also specified miscellaneously in various national and/or specialty systems. The most commonly used is the classification system of the United States Department of Agriculture (USDA). Soil texture information is essential input data in meteorological, hydrological and agricultural prediction modelling. Although Hungary has a great deal of legacy soil maps and other relevant soil information, it often occurs, that maps do not exist on a certain characteristic with the required thematic and/or spatial representation. The recent developments in digital soil mapping (DSM), however, provide wide opportunities for the elaboration of object specific soil maps (OSSM) with predefined parameters (resolution, accuracy, reliability etc.). Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil map. This suggests the opportunity of optimization. For the creation of an OSSM one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). We started comprehensive analysis of the effects of the various DSM components on the accuracy of the output maps on pilot areas. The aim of this study is to compare and evaluate different
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Digital mapping techniques '00, workshop proceedings - May 17-20, 2000, Lexington, Kentucky
Soller, David R.
2000-01-01
Introduction: The Digital Mapping Techniques '00 (DMT'00) workshop was attended by 99 technical experts from 42 agencies, universities, and private companies, including representatives from 28 state geological surveys (see Appendix A). This workshop was similar in nature to the first three meetings, held in June, 1997, in Lawrence, Kansas (Soller, 1997), in May, 1998, in Champaign, Illinois (Soller, 1998a), and in May, 1999, in Madison, Wisconsin (Soller, 1999). This year's meeting was hosted by the Kentucky Geological Survey, from May 17 to 20, 2000, on the University of Kentucky campus in Lexington. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. When, based on discussions at the workshop, an attendee adopts or modifies a newly learned technique, the workshop clearly has met that objective. Evidence of learning and cooperation among participating agencies continued to be a highlight of the DMT workshops (see example in Soller, 1998b, and various papers in this volume). The meeting's general goal was to help move the state geological surveys and the USGS toward development of more cost-effective, flexible, and useful systems for digital mapping and geographic information systems (GIS) analysis. Through oral and poster presentations and special discussion sessions, emphasis was given to: 1) methods for creating and publishing map products (here, 'publishing' includes Web-based release); 2) continued development of the National Geologic Map Database; 3) progress toward building a standard geologic map data model; 4) field data-collection systems; and 5) map citation and authorship guidelines. Four representatives of the GIS hardware and software vendor community were invited to participate. The four annual DMT workshops were coordinated by the AASG/USGS Data Capture Working Group, which was formed in August, 1996, to support the Association of American State Geologists and the USGS in their effort
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Digital Mapping Techniques '05--Workshop Proceedings, Baton Rouge, Louisiana, April 24-27, 2005
Soller, David R.
2005-01-01
Intorduction: The Digital Mapping Techniques '05 (DMT'05) workshop was attended by more than 100 technical experts from 47 agencies, universities, and private companies, including representatives from 25 state geological surveys (see Appendix A). This workshop was similar in nature to the previous eight meetings, held in Lawrence, Kansas (Soller, 1997), in Champaign, Illinois (Soller, 1998), in Madison, Wisconsin (Soller, 1999), in Lexington, Kentucky (Soller, 2000), in Tuscaloosa, Alabama (Soller, 2001), in Salt Lake City, Utah (Soller, 2002), in Millersville, Pennsylvania (Soller, 2003), and in Portland, Oregon (Soller, 2004). This year's meeting was hosted by the Louisiana Geological Survey, from April 24-27, 2005, on the Louisiana State University campus in Baton Rouge, Louisiana. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. It is with great pleasure I note that the objective was successfully met, as attendees continued to share and exchange knowledge and information, and to renew friendships and collegial work begun at past DMT workshops. Each DMT workshop has been coordinated by the Association of American State Geologists (AASG) and U.S. Geological Survey (USGS) Data Capture Working Group, which was formed in August 1996, to support the AASG and the USGS in their effort to build a National Geologic Map Database (see Soller and Berg, this volume, and http://ngmdb.usgs.gov/info/standards/datacapt/). The Working Group was formed because increased production efficiencies, standardization, and quality of digital map products were needed for the database?and for the State and Federal geological surveys?to provide more high-quality digital maps to the public. At the 2005 meeting, oral and poster presentations and special discussion sessions emphasized: 1) methods for creating and publishing map products (here, 'publishing' includes Web-based release); 2) field data capture software and
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Digital Terroir Mapping in the Tokaj Historical Wine Region
Pásztor, László; Lukácsy, György; Szabó, József; László, Péter; Burai, Péter; Bakacsi, Zsófia; Koós, Sándor; Laborczi, Annamária; Takács, Katalin; Bekő, László
2015-04-01
Tokaj is a historical region for botritized dessert wine making, the famed Tokaji Wine Region has the distinction of being Europe's first classified wine region. Very recently the sustainable quality wine production in the region was targeted, which requires detailed and "terroir-based approach" characterization of viticultural land. Tokaj region consists of 27 villages, the total producing vineyard surface area is 5,500 hectares, and the total vineyard land exceeds 11,000 hectares. The Tokaj Kereskedőház Ltd. is the only state owned winery in Hungary. The company is integrating grapes for wine production from 1,100 hectares of vineyard, which consist of 3,500 parcels with average size of 0.3 hectares. In 2013 the Hungarian Government has decided to elaborate a sustainable quality wine production in the Tokaj region coordinated by the Tokaj Kereskedőház Ltd, the biggest wine producer. To achieve the target it is indispensable to assess the viticultural potential of the land. In 2013 the characterization of the vineyard land potential was started collecting detailed, up-to-date information on the main environmental factors (geology, geomorphology and soil) which comprise the terroir effect and combined with legacy data of climate. The Council of Wine Communities of Tokaj Region has decided to widen the survey for the whole wine region in the year 2014. The primary objective of our work was the execution of an appropriate terroir zoning, which was carried out by digital terroir mapping. As a start-up we adapted some concepts recently applied in French wine regions. The implementation was however carried out totally in spatial, digital environment. Four main sources of information have been used (i) airborne laser scanning, (ii) hyperspectral imaginary, (iii) digital soil maps compiled based on detailed soil survey and (iv) interpolated climatic data. Based on them pedoclimate, mesoclimate and soil water reservoir were spatially predicted. The operational spatial
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Directory of Open Access Journals (Sweden)
Ning Gao
2018-02-01
Full Text Available The era of “Internet + education” has caused reforms in teaching ideas, teaching modes, and learning styles. The emergence of micro-course technology provides new strategies for integrating learning styles. Task-driven digital mapping teaching, known as traditional classroom organization, has poor teaching effect due to single learning style and strategy. A new teaching mode for digital mapping was constructed in this study based on micro-course technology by combining interactive micro-course technology and digital mapping teaching to adapt to the demands of modern teaching. This teaching mode mainly included four modules, namely, micro-courseware, micro-video, micro-exercise, and micro-examination. It realized the hierarchical teaching of knowledge points in digital mapping course, simplification of basic principles, simulation of engineering cases, and self-evaluation of learning outcomes. The teaching mode was applied to 114 students from the Mapping Engineering Department of Henan University of Urban Construction. Results indicate that the proposed teaching mode based on interactive micro-course technology promoting the independent after-class learning of the students, stimulating their learning enthusiasm, enhancing their practical abilities of the students, and improving the effect of teaching. This mode of teaching provides a new concept for the teaching mode reform of other courses in mapping engineering.
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DEFF Research Database (Denmark)
Hansen, Birger Ulf; Mosbech, Anders
1994-01-01
Remote Sensing, vegetation mapping, SPOT, Landsat TM, aerial photography, Jameson Land, East Greenland......Remote Sensing, vegetation mapping, SPOT, Landsat TM, aerial photography, Jameson Land, East Greenland...
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Understanding Urban Watersheds through Digital Interactive Maps, San Francisco Bay Area, California
Sowers, J. M.; Ticci, M. G.; Mulvey, P.
2014-12-01
Dense urbanization has resulted in the "disappearance" of many local creeks in urbanized areas surrounding the San Francisco Bay. Long reaches of creeks now flow in underground pipes. Municipalities and water agencies trying to reduce non-point-source pollution are faced with a public that cannot see and therefore does not understand the interconnected nature of the drainage system or its ultimate discharge to the bay. Since 1993, we have collaborated with the Oakland Museum, the San Francisco Estuary Institute, public agencies, and municipalities to create creek and watershed maps to address the need for public understanding of watershed concepts. Fifteen paper maps are now published (www.museumca.org/creeks), which have become a standard reference for educators and anyone working on local creek-related issues. We now present digital interactive creek and watershed maps in Google Earth. Four maps are completed covering urbanized areas of Santa Clara and Alameda Counties. The maps provide a 3D visualization of the watersheds, with cartography draped over the landscape in transparent colors. Each mapped area includes both Present and Past (circa 1800s) layers which can be clicked on or off by the user. The Present layers include the modern drainage network, watershed boundaries, and reservoirs. The Past layers include the 1800s-era creek systems, tidal marshes, lagoons, and other habitats. All data are developed in ArcGIS software and converted to Google Earth format. To ensure the maps are interesting and engaging, clickable icons pop-up provide information on places to visit, restoration projects, history, plants, and animals. Maps of Santa Clara Valley are available at http://www.valleywater.org/WOW.aspx. Maps of western Alameda County will soon be available at http://acfloodcontrol.org/. Digital interactive maps provide several advantages over paper maps. They are seamless within each map area, and the user can zoom in or out, and tilt, and fly over to explore
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Data Sprints: A Collaborative Format in Digital Controversy Mapping
DEFF Research Database (Denmark)
Munk, Anders Kristian; Tommaso, Venturini; Meunier, Axel
2017-01-01
driven by a desire to provide navigational aids to actors faced with the challenge of making sense of complicated techno-scientific problems. Natively digital media technologies have thus been re-appropriated by STS researchers specifically for the purpose of mapping controversies in a way that would...... experiences with various forms of public engagement and participation. Through a concrete reappropriation of a collaborative format that is indeed native to the digital domain - namely the hackathon - we will show how digital methods can make a difference in participatory STS research. The data sprint, as we...... in amsterdam. Through a mix of digital methods ranging from web cartography and text mining to scientometrics and social media analysis we took on questions related to climate adaptation funding, vulnerability assessment, project management, and dynamics of the international negotiations. The sprints hardwired...
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Directory of Open Access Journals (Sweden)
Vivian Fróes Renó
2011-03-01
Full Text Available Este estudo apresenta um mapa da cobertura vegetal da planície de inundação do Rio Amazonas entre as cidades de Parintins (AM e Almeirim (PA, com base em imagens Landsat-MSS adquiridas entre 1975 e 1981. O processamento digital dessas imagens envolveu a transformação para imagens-fração de vegetação, solo e água escura (sombra, seguido da aplicação de técnicas de segmentação e classificação por região. O mapa resultante da classificação foi organizado em quatro classes de cobertura do solo: floresta de várzea, vegetação não-florestal de várzea, solo exposto e água aberta. A precisão do mapa foi estimada a partir de dois tipos de informações coletadas em campo: 1 pontos de descrição: para validação das classes de cobertura não sujeitas a grandes alterações, como é o caso dos corpos d'água permanentes, e identificação de indicadores dos tipos de cobertura original presentes na paisagem na ocasião da obtenção das imagens (72 pontos; 2 entrevistas com moradores antigos para a recuperação da memória sobre a cobertura vegetal existente há 30 anos (44 questionários. Ao todo foram coletadas informações em 116 pontos distribuídos ao longo da área de estudo. Esses pontos foram utilizados para calcular o Índice Kappa de concordância entre os dados de campo e o mapa resultante da classificação automática, cujo valor (0,78 indica a boa qualidade do mapa de cobertura vegetal da várzea. Os resultados mostram que a região possuía uma cobertura florestal de várzea de aproximadamente 8.650 km2 no período de aquisição das imagens.This study presents a vegetation map of the Amazon River floodplain between the towns of Parintins (AM and Almeirim (PA, based on Landsat-MSS scenes from 1975 to 1981. Digital processing involved the transformation of multispectral images into fraction-images of vegetation, soil and dark water (shadow, followed by the application of segmentation and region
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Influences of watershed geomorphology on extent and composition of riparian vegetation
Blake M. Engelhardt; Peter J. Weisberg; Jeanne C. Chambers
2011-01-01
Watershed (drainage basin) morphometry and geology were derived from digital data sets (DEMs and geologic maps). Riparian corridors were classified into five vegetation types (riparian forest, riparian shrub, wet/mesic meadow, dry meadow and shrub dry meadow) using high-resolution aerial photography. Regression and multivariate analyses were used to relate geomorphic...
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Why Map Issues? On Controversy Analysis as a Digital Method.
Marres, Noortje
2015-09-01
This article takes stock of recent efforts to implement controversy analysis as a digital method in the study of science, technology, and society (STS) and beyond and outlines a distinctive approach to address the problem of digital bias. Digital media technologies exert significant influence on the enactment of controversy in online settings, and this risks undermining the substantive focus of controversy analysis conducted by digital means. To address this problem, I propose a shift in thematic focus from controversy analysis to issue mapping. The article begins by distinguishing between three broad frameworks that currently guide the development of controversy analysis as a digital method, namely, demarcationist, discursive, and empiricist. Each has been adopted in STS, but only the last one offers a digital "move beyond impartiality." I demonstrate this approach by analyzing issues of Internet governance with the aid of the social media platform Twitter.
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Ugbaje, S.U.; Reuter, H.I.
2013-01-01
Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We investigated using digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC)
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Anderson Tavares de Melo; Eduardo Juan Soriano-Sierra; Luiz Antônio Paulino
2011-01-01
The implementation of a hydraulic landfill along the southern expressway (Via Expressa Sul), in the central-south region of Santa Catarina Island, started in 1995 and was completed in 1997. The landfill provided the mangrove vegetation a new environment to colonize, which has developed rapidly during this short period of time. This study mapped the vegetation cover of this region using aerial photographs from five years (1994, 1997, 2002, 2004 and 2007), which demonstrated the spatial-tempora...
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Thomas, N. M.; Simard, M.; Byrd, K. B.; Windham-Myers, L.; Castaneda, E.; Twilley, R.; Bevington, A. E.; Christensen, A.
2017-12-01
Louisiana coastal wetlands account for approximately one third (37%) of the estuarine wetland vegetation in the conterminous United States, yet the spatial distribution of their extent and aboveground biomass (AGB) is not well defined. This knowledge is critical for the accurate completion of national greenhouse gas (GHG) inventories. We generated high-resolution baselines maps of wetland vegetation extent and biomass at the Atchafalaya and Terrebonne basins in coastal Louisiana using a multi-sensor approach. Optical satellite data was used within an object-oriented machine learning approach to classify the structure of wetland vegetation types, offering increased detail over currently available land cover maps that do not distinguish between wetland vegetation types nor account for non-permanent seasonal changes in extent. We mapped 1871 km2 of wetlands during a period of peak biomass in September 2015 comprised of flooded forested wetlands and leaf, grass and emergent herbaceous marshes. The distribution of aboveground biomass (AGB) was mapped using JPL L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Relationships between time-series radar imagery and field data collected in May 2015 and September 2016 were derived to estimate AGB at the Wax Lake and Atchafalaya deltas. Differences in seasonal biomass estimates reflect the increased AGB in September over May, concurrent with periods of peak biomass and the onset of the vegetation growing season, respectively. This method provides a tractable means of mapping and monitoring biomass of wetland vegetation types with L-band radar, in a region threatened with wetland loss under projections of increasing sea-level rise and terrestrial subsidence. Through this, we demonstrate a method that is able to satisfy the IPCC 2013 Wetlands Supplement requirement for Tier 2/Tier 3 reporting of coastal wetland GHG inventories.
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCINTOSH COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAWAII COUNTY, HAWAII, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, KENTUCKY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, ILLINOIS USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, MINNESOTA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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FINAL DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENWOOD COUNTY, SC
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RICE COUNTY, MINNESOTA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KARNES COUNTY, TEXAS, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VOLUSIA COUNTY, FL, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, IA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POTTAWATTAMIE COUNTY, IOWA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MITCHELL COUNTY, IOWA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAYTON COUNTY, IOWA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWARD COUNTY, IOWA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABAES, LA PAZ COUNTY, AZ
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NASSAU COUNTY, NEW YORK
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, NEW YORK
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, PRINCE GEORGE, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, OHIO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAGONER COUNTY, OKLAHOMA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, SC
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Buchanan County, Iowa, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF SACRAMENTO, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KAUAI COUNTY, HAWAII, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PASCO COUNTY, FLORIDA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GILMER COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIAMI - DADE COUNTY, FLORIDA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, APPLING COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINCOLN COUNTY, ARKANSAS, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARROLL COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CONVERSE COUNTY, WYOMING, USA.
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, ILLINOIS USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas County, Oregon, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, New Mexico
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SOLANO COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, FL, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, INDIANA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, NEBRASKA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWPORT COUNTY, RHODE ISLAND
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Charles COUNTY, MD, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, OHIO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE ROCKLAND COUNTY, NY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Richmond County, Virginia, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASATCH COUNTY, UTAH, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Westmoreland County, Virginia, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TOM GREEN COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Berks County, Pennsylvania, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STONE COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VAL VERDE COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RED WILLOW COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE FOR HOWARD COUNTY, NEBRASKA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, NE, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FERGUS COUNTY, MONTANA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JOAQUIN COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LORAIN COUNTY, OHIO USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COSHOCTON COUNTY, OHIO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, OHIO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RAY COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, IDAHO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAWRENCE COUNTY, OHIO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, WISCONSIN, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Nelson County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. FRANCOIS COUNTY, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAFAYETTE COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDALL COUNTY, TX, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENVILLE COUNTY, SOUTH CAROLINA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, McCormick County, SC
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, McCURTAIN COUNTY, OK
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DICKENSON COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIPOSA_CO_CA, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Charles County, Maryland, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Essex County, Virginia, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Calvert County, Maryland, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Bradford County, Pennsylvania, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DESOTO COUNTY, FL, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FANNIN COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HEMPSTEAD COUNTY, AR
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHATHAM COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JACINTO COUNTY, TX
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, New London County, CT
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Westmoreland County, PA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Sussex County, Delaware, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELAWARE COUNTY, OK, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNION COUNTY, FLORIDA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, FLORIDA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, FL, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, SC
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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Digital Flood Insurance Rate Map Database, Allegheny County, Pennsylvania, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARTOW COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALDWELL PARISH, LOUISIANA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHIAWASSEE COUNTY, MICHIGAN, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, OHIO,USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EL DORADO COUNTY, CALIFORNIA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GADSDEN COUNTY, FL, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LACLEDE COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLINTON COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, AUGUSTA COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, OH, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Spartanburg County, South Carolina
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Rio Grande County, Colorado
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE,FREDERICK COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Roosevelt COUNTY, New Mexico
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Linn County, Oregon, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wythe County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, PA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KITSAP COUNTY, WASHINGTON, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northumberland County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CADDO PARISH, LOUISIANA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FORT BEND COUNTY, TEXAS
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEDGWICK COUNTY, KANSAS, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAWFORD COUNTY, AR ,USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONMOUTH COUNTY, NEW JERSEY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YORK COUNTY, PA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUSSEX COUNTY, NEW JERSEY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLEARFIELD COUNTY, PA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOPER COUNTY, MISSOURI, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAMERON COUNTY, PA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, NEW JERSEY
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAYETTE COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, NEW YORK
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, INDIAN RIVER COUNTY, FL
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAROLINE COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST JOSEPH COUNTY, MI
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERBURNE COUNTY, MINNESOTA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Delaware County, Pennsylvania, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HALL COUNTY, NE, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PATRICK COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDOLPH COUNTY, WV, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAYSON COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SURRY COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Buckingham County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GARRETT COUNTY, Maryland, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALEIGH COUNTY, WV, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Essex County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Caroline COUNTY, Maryland, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TUCKER COUNTY, WV, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Sussex County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WESTMORELAND COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLUVANNA COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Richmond County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Pulaski County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Scott County, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Upshur County, WV, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINN COUNTY, IA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARKE COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELTA COUNTY, COLORADO, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COBB COUNTY, GA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GULF COUNTY, FLORIDA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUNA COUNTY, New Mexico
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENE COUNTY, GEORGIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLATSOP COUNTY, OR, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, VA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDLAND COUNTY, MICHIGAN, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SISKIYOU COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLUMAS COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RIVERSIDE COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIMA COUNTY, ARIZONA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COCHISE COUNTY, ARIZONA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YUMA COUNTY, ARIZONA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTTE COUNTY, CALIFORNIA, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...
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DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EATON COUNTY, MICHIGAN, USA
Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...