Multitemporal series of satellite SPOT-VEGETATION Normalized Difference of Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) data from 1998 to 2003 were exploited for studying persistence in Mediterranean ecosystems of the Sardinia Region (southern Italy). Three different veget...

This site allows you to combine the NDVI vegetation index with Net Solar Radiation values. The Normalized Difference Vegetation Index, or NDVI, is an index of green leaf density. The higher the value, the more luxuriant the vegetation. This is but one of many animated datasets that can be combined to introduce correlations and interactions between radiant energy and the biosphere.

A shrub savannah landscape in Niger was optically characterized utilizing blue, green, red and near-infrared wavelengths. Selected vegetation indices were evaluated for their performance and sensitivity to describe the complex Sahelian soil/vegetation canopies. Bidirectional reflectance factors (BRF) of plants and soils were measured at several view angles, and used as input to various vegetation indices. Both soil and vegetation targets had strong anisotropic reflectance properties, rendering all vegetation index (VI) responses to be a direct function of sun and view geometry. Soil background influences were shown to alter the response of most vegetation indices. N-space greenness had the smallest dynamic range in VI response, but the n-space brightness index provided additional useful information. The global environmental monitoring index (GEMI) showed a large VI dynamic range for bare soils, which was undesirable for a vegetation index. The view angle response of the normalized difference vegetation index (NDVI), atmosphere resistant vegetation index (ARVI) and soil atmosphere resistant vegetation index (SARVI) were asymmetric about nadir for multiple view angles, and were, except for the SARVI, altered seriously by soil moisture and/or soil brightness effects. The soil adjusted vegetation index (SAVI) was least affected by surface soil moisture and was symmetric about nadir for grass vegetation covers. Overall the SAVI, SARVI and the n-space vegetation index performed best under all adverse conditions and were recommended to monitor vegetation growth in the sparsely vegetated Sahelian zone

not support the same insect and animal populations that the original, native ... Normalized Difference Vegetation Index (NDVI) phenology layers were acquired ... aquatic vegetation degradation, fish kills, and decreases in shellfish populations.

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single ...

We investigated the fire-induced variability in the 1998-2003 time series of Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION sensor for two different kinds of vegetation sites: fire un-affected and fire-affected. The statistical analysis, performed by using the detrended fluctuat...

There is an increasing need to monitor the dynamics of green LAI of field crops through the growing season. A simple approach is to use a regression model to estimate crop LAI from a vegetation index derived from optical remote sensing data. However, variations of interference factors in the signal path could induce variations in spectral reflectance, leading to uncertainty in LAI estimation. A semi-empirical equation was implemented to estimate green LAI of field crops from Landsat-5/7 data using a few vegetation indices, including the normalized difference vegetation index (NDVI), the optimized soil adjusted vegetation index (OSAVI), the two band enhanced vegetation index (EVI2) and the modified triangular vegetation index (MTVI2). Data were collected during several growing seasons, from...

Fires induce dynamical trends in vegetation covers. In order to investigate the effects of fires in dynamical patterns of vegetation covers, a time series 1998 to 2003 of Normalized Difference Vegetation Index (NDVI) from SPOT-VGT sensor was analyzed for burned and unburned test sites located in th...

Traditional remote sensing methods for yield estimation rely on broadband vegetation indices, such as the Normalized Difference Vegetation Index, NDVI. Despite demonstrated relationships between such traditional indices and yield, NDVI saturates at larger leaf area index (LAI) values, and it is affe...

Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained using the Fourier analysis.

Phenological changes are closely related to the carbon cycle of terrestrial ecosystems, and satellite data have been widely used in large scale phenological research. Numerous methods have been developed to reconstruct distinct satellite derived vegetation signals from continuous vegetation index time series and to track the points corresponding to important phenological events. In this study, we perform a multiple-method investigation of the spring vegetation growth onset phenology in temperate China north of 30^oN with NDVI (normalized difference vegetation index) data produced from SPOT satellites. The results indicated that the spring onset dates estimated from five different methods show similar spatial pattern along latitudinal or altitudinal gradients, but with significant variances...

8.4.2.3 Subseasonal-to-Decadal Climate Variability and Prediction ............ 75 ...... quality, health, transportation, agriculture, fisheries, water, energy, construction, tourism, and ...... Normalized Difference Vegetation Index (NDVI) data set since ...

Eurasian Reindeer Pastoralism in a Changing Climate: Indigenous .... encroachment from development, tourism, damming of rivers, cultivation, oil and gas ..... to Normalized Difference Vegetation Index (NDVI<0.45), when soil is dry and ...

The normalized difference vegetation index (NDVI) has many applications in agricultural management, including monitoring real-time crop coefficients for estimating crop evapotranspiration (ET). However, frequent monitoring of NDVI as needed in such applications is generally not feasible from aerial ...

Management zones for various crops have been delineated using NDVI (Normalized Difference Vegetation Index), apparent bulk soil electrical conductivity (ECa - Veris), and yield data; however, estimations of uncertainty for these data layers are equally important considerations. The objective of this...

Abstract Aim- Regional movements of tropical birds are among the least understood patterns of migration, generally assumed to be related to seasonality of vegetation and food resources. We used readily available remotely sensed data to analyse this relationship in the three-wattled bellbird (Procnias tricarunculatus), an IUCN Vulnerable canopy frugivorous bird that undergoes localized altitudinal and latitudinal movements between several non-breeding and breeding ranges. Location- Central America. Methods- We generated ecological niche models (GARP and Maxent) based on remotely sensed vegetation indices (enhanced vegetation index, EVI; red index, RI; and normalized difference water index, NDWI) that were used as proxies for canopy characteristics and phenological changes between seasons. T...

Consistent and long-term measurements from satellite sensors are important in assessing the spatial and temporal variability of the earth's terrestrial vegetation and in studying how global ecosystems are changing and how the earth's vegetation is being transformed. In this paper we present and evaluate one year of vegetation index product availability from the Moderate Resolution Imaging Spectroradiometer (MODIS). Two MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are produced at 1 km, 500 m, and 250 m resolutions and 16-day compositing periods. Multitemporal, seasonal profiles of the MODIS VI's are presented for numerous biome types in North and South America which depicted quite well their respective phenologies. The dynamic range of the MODIS VI's are presented and their sensitivities in discriminating vegetation differences are evaluated over sparsely vegetated areas as well as high biomass, densely vegetated areas. We found the NDVI to asymptotically saturate in high biomass areas while the EVI remained sensitive to vegetation variations in higher biomass regions such as in the Amazon. Validation campaigns were made at test sites representing semi-arid grass and shrub, savanna, and tropical forest biomes. Results show a good correspondence between airborne-measured, top-of-canopy, reflectances and VI values with those from the MODIS sensor over the test sites. Simultaneously derived field biophysical measures also demonstrated the science utility of the VI's.

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt wi...

Introduction. In their paper, Zhou et al. [2001] analyzed advanced very high resolution radiometer-normalized difference vegetation index (AVHRR-NDVI) satellite data relative to the vegetated areas of the Northern Hemisphere for the period July 1981 to December 1999, at 8 km resolution. They conclud...

We present a method for identifying dominant surface type and estimating high spectral resolution surface albedo at the Atmospheric Radiation Measurement (ARM) facility at the Southern Great Plains (SGP) site in Oklahoma for use in radiative transfer calculations. Given a set of 6-channel narrowband visible and near-infrared irradiance measurements from upward and downward looking multi-filter radiometers (MFRs), four different surface types (snow-covered, green vegetation, partial vegetation, non-vegetated) can be identified. A normalized difference vegetation index (NDVI) is used to distinguish between vegetated and non-vegetated surfaces, and a scaled NDVI index is used to estimate the percentage of green vegetation in partially vegetated surfaces. Based on libraries of spectral albedo measurements, a piecewise continuous function is developed to estimate the high spectral resolution surface albedo for each surface type given the MFR albedo values as input. For partially vegetated surfaces, the albedo is estimated as a linear combination of the green vegetation and non-vegetated surface albedo values. The estimated albedo values are evaluated through comparison to high spectral resolution albedo measurements taken during several Intensive Observational Periods (IOPs) and through comparison of the integrated spectral albedo values to observed broadband albedo measurements. The estimated spectral albedo values agree well with observations for the visible wavelengths constrained by the MFR measurements, but have larger biases and variability at longer wavelengths. Additional MFR channels at 1100 nm and/or 1600 nm would help constrain the high resolution spectral albedo in the near infrared region.

A detailed understanding of the spatial patterns of burning is valuable for managing biodiversity and ecosystems. This research assesses the performance of several spectral indices derived from Landsat data when modelling fire occurrence probability by means of logistic regression. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR) and the greenness and wetness components of the Tasseled Cap Transformation were tested. Landscape variables (topography, accessibility and structural vegetation) were also included as predictors in models development. Although fire risk is closely related to weather and vegetation status at a given time, it is also strongly linked to fire history, and changes in predictor values in years previ...

Most remote sensing estimations of vegetation variables such as Leaf Area Index (LAI), Absorbed Photosynthetically Active Radiation (APAR), and phytomass are made using broad band sensors with a bandwidth of approximately 100 nm. However, high resolution spectrometers are available and have not been fully exploited for the purpose of improving estimates of vegetation variables. A study directed to investigate the use of high spectral resolution spectroscopy for remote sensing estimates of APAR in vegetation canopies in the presence of nonphotosynthetic background materials such as soil and leaf litter is presented. A high spectral resolution method defined as the Chlorophyll Absorption Ratio Index (CARI) was developed for minimizing the effects of nonphotosynthetic materials in the remote estimates of APAR. CARI utilizes three bands at 550, 670, and 700 nm with bandwidth of 10 nm. Simulated canopy reflectance of a range of LAI were generated with the SAIL model using measurements of 42 different soil types as canopy background. CARI obtained from the simulated canopy reflectance was compared with the broad band vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR)). CARI reduced the effect of nonphotosynthetic background materials in the assessment of vegetation canopy APAR more effectively than broad band vegetation indices.

The improved Temperature Vegetation Dryness Index (iTVDI) can be used as an indicator of transpiration rates in mountainous areas. We investigated the influence of vegetation cover types on differences observed in iTVDI, together with NDVI in vegetation covers around Lake Mashu in a summer day. Based on the results of comparing NDVI and iTVDI values among 14 vegetation cover types, it was shown that the vegetation cover type differences could cause significant differences in iTVDI values. Shrub and grassland categories showed lower NDVI but higher iTVDI values, whereas tall trees except Erman's birch showed relatively higher NDVI but lower iTVDI values. The Erman's birch iTVDI values were higher than the other tall trees. These results suggest that the difference of vegetation cover types could be one of the factors that influence iTVDI values.   

Abstract Increase in rainfall variability has important consequences for organisms in arid and semiarid regions around the world. In South American and Australian deserts, the El Nino/Southern Oscillation (ENSO) phenomenon greatly influences rainfall patterns, and therefore the dynamics of plant communities. However, the field data needed to assess the effect of climate change on vegetational patterns is difficult to obtain because of the large spatial scale required for such studies. Normalized Difference Vegetation Index (NDVI) characteristics allow the use of several indexes related to vegetational structure. Due to its direct relationship with primary productivity, it is possible to obtain several measures of annual productivity. These include annual plant yield, annual maximum yield, ...

Semi-arid rangelands are very sensitive to global climatic change; studies of their biophysical attributes are crucial to understanding the dynamics of rangeland ecosystems under human disturbance. In the Santa Rita Experimental Range (SRER), Arizona, the vegetation has changed considerably and there have been many management activities applied. This study calculates seven surface variables: the enhanced vegetation index (EVI), the normalized difference vegetation index (NDVI), surface albedos (total shortwave, visible and near-infrared), leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FPAR) from the Enhanced Thematic Mapper (ETM+) data. Comparison with the MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index and albedo products indicate they agree well with our estimates from ETM+ while their LAI and FPAR are larger than ETM+. Human disturbance has significantly changed the cover types and biophysical conditions. Statistical tests indicate that surface albedos increased and FPAR decreased at all sites. The recovery will require more than 67 years, and is about 50% complete within 40 years at the higher elevation. Grass cover, vegetation indices, albedos and LAI recovered from cutting faster at the higher elevation. Woody plants, vegetation indices and LAI have recovered to their original characteristics after 65 years at the lower elevation. More studies are needed to examine the spectral characteristics of different ground components.

A 3D radiative transfer model is used to investigate the relationship between spectral indices and fraction of absorbed photosynthetically active radiation (PAR) in horizontally heterogeneous vegetation canopies. Canopy reflection at optical wavelengths and PAR absorption are simulated. Data obtained indicate that the leaf area index of a canopy is less of an instructive parameter than the ground cover and clump leaf area index for these canopies. It is found that the relationship between the normalized difference vegetation index and fraction of absorbed PAR is almost linear and independent of spatial heterogeneity.

The objective of this research is to develop KDD (knowledge discovery in databases) techniques for spatio-temporal geo-data, and use these techniques to examine seasonal and inter-annual vegetation health signals. The underlying hypothesis of the research is that the signatures of inter-annual variability of climate on vegetation dynamics as represented by the statistical descriptors of vegetation index variations depend upon a variety of attributes related to the climate, physiography, topography, and hydrology. Several scientific questions related to the identification and characterization of the inter-annual variability ensue as a consequence of this hypothesis. Various vegetation indices will be enlisted to represent vegetation health, such as NDVI (normalized difference vegetation index), EVI (enhanced vegetation index), LAI (leaf area index), FPAR (fraction of photosynthetically active radiation), PSN (photosynthesis), and NPP (net primary product). Relationships between these indices and topography and its derivatives (slope, aspect, etc.), nearness to water bodies, precipitation, temperature, etc. will be analyzed. Preliminary investigations were performed using 13 years of 1 km resolution NDVI data from the AVHRR instrument on NOAA's POES (polar-orbiting operational environmental satellite). Deviations from the 13-year average were utilized in order to identify anomalous behavior. The pilot KDD technique includes distance-based clustering algorithms and Apriori association rule algorithms adapted for spatial-temporal data. Future work will incorporate more complex algorithms such as density-based clustering and constraint-based association mining algorithms.

Long-term trends in vegetation phenology indicate ecosystem change due to the combined impacts of human activities and climate. In this study we used 1982 to 2006 Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index (AVHRR NDVI) imagery across China and the TIMESAT program to quantify annual vegetation production and its changing trend. Results showed great spatial variability in vegetation growth and its temporal trend across the country during the 25-year study period. Significant decreases in vegetation production were detected in the grasslands of Inner Mongolia, and in industrializing regions in southern China, including the Pearl River Delta, the Yangtze River Delta, and areas along the Yangtze River. Significant increases in vegetation production were foun...

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large fires on the Peloponnese peninsula in southern Greece. In this context, 13 red-near infrared (R-NIR) vegetation indices (VIs) were evaluated. Some of these indices, the so called Soil-Adjusted VIs (SAVIs), attempt to minimize the influence of background variability, however, so far the impact of the variability in spectral response between different vegetation species on index performance has not yet been rigorously assessed. Using a combination of field and simulation techniques this study accounts for the impact of both background and vegetation variability on index performance. The field data included a spectral library (59 vegetation and 29 substrate signals) and 78 line transect plots. One Landsat Thematic Mapper (TM) scene of July 2010, 3 years after the fire event, was employed in the study. Results based on simulated mixtures of in situ measured reflectance showed that (i) SAVIs outperformed the Normalized Difference Vegetation Index (NDVI) in environments with a single vegetation type, (ii) the NDVI more accurately estimated vegetation cover in environments with heterogeneous vegetation layers and a single soil type and (iii) overall, when both vegetation and background variability is incorporated in the model, the NDVI was the most optimal index. Findings from the simulation experiment corroborated with the results from the Landsat application. The Landsat NDVI showed the highest correlation with the line transect field data of recovery (R2 = 0.68) and the rank in performance of the Landsat-based indices was similar to that of the simulation experiment in which both vegetation and substrate variability was introduced. Results depend on the initial variability present in the study area, however, some trends can be generalized. Firstly, results support the use of SAVIs in environments with a single vegetation type. Secondly, for applications in environments to which natural vegetation variability is inherent, such as the post-fire recovery landscape of this study, we, however, recommend the use of the NDVI because its normalizing capacity minimizes the impact of vegetation variability on fractional cover estimates.

Forest inventory data can be used along with remotely sensed data to estimate biomass and carbon stocks over large and inaccessible forested areas. In this study, the relationship between satellite-derived multispectral data and forest variables from intervened and non-intervened Nothofagus pumilio forest stands located in the Magellan region of Chile was examined, in order to quantify the over bark volume (OBV) and aboveground tree biomass (AGTB). Four vegetation parameters - the green normalised difference vegetation index (GNDVI), normalised difference vegetation index (NDVI), simple ratio (SR) and vegetation cover fraction (VCF) - were retrieved from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image of the study area. The results indicate that only the VCF...

Modeling and analyzing dynamic changes of land thermal radiance scenes play an important role in thermal remote sensing. In this paper, the diurnal variation of ground surface thermal scene is mainly discussed. Firstly, based on the land surface energy balance equation, the diurnal variation of land surface temperatures (LSTs) over bare land covers were simulated by an analytical thermal model with second harmonic terms, and the diurnal LST variation of vegetation canopy was simulated using the Cupid model. Secondly, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and ratio resident-area index (RRI) were used to evaluate the endmember abundance of four land cover types including vegetation, bare soil, impervious and water area, which were calculated...

Based on the 1982-2006 NDVI remote sensing data and meteorological data of Southwest China, and by using GIS technology, this paper interpolated and extracted the mean annual temperature, annual precipitation, and drought index in the region, and analyzed the correlations of the annual variation of NDVI in different vegetation types (marsh, shrub, bush, grassland, meadow, coniferous forest, broad-leaved forest, alpine vegetation, and cultural vegetation) with corresponding climatic factors. In 1982-2006, the NDVI, mean annual temperature, and annual precipitation had an overall increasing trend, and the drought index decreased. Particularly, the upward trend of mean annual temperature was statistically significant. Among the nine vegetation types, the NDVI of bush and mash decreased, and the downward trend was significant for bush. The NDVI of the other seven vegetation types increased, and the upward trend was significant for coniferous forest, meadow, and alpine vegetation, and extremely significant for shrub. The mean annual temperature in the areas with all the nine vegetation types increased significantly, while the annual precipitation had no significant change. The drought index in the areas with marsh, bush, and cultural vegetation presented an increasing trend, that in the areas with meadow and alpine vegetation decreased significantly, and this index in the areas with other four vegetation types had an unobvious decreasing trend. The NDVI of shrub and coniferous forest had a significantly positive correlation with mean annual temperature, and that of shrub and meadow had significantly negative correlation with drought index. Under the conditions of the other two climatic factors unchanged, the NDVI of coniferous forest, broad-leaved forest, and alpine vegetation showed the strongest correlation with mean annual temperature, that of grass showed the strongest correlation with annual precipitation, and the NDVI of mash, shrub, grass, meadow, and cultural vegetation showed the strongest correlation with drought index. There existed definite correlations among the climatic factors. If the correlations among the climatic factors were ignored, the significant level of the correlations between NDVI and climatic factors would be somewhat reduced. PMID:21608242

Considerable work has been done to examine the relationship between environmental constraints and vegetation activities represented by the remote sensing-based Normalized Difference Vegetation Index (NDVI). However, the relationships along either environmental or vegetation type gradients are rarely examined. The aim of this paper was to identify the vegetation types that are potentially susceptible to climate change through examining the interaction between vegetation activity and water deficit. We selected 12 major vegetation types along the north-south transect of Eastern China (NSTEC), examined their time trends from 1982 to 2006 with respect to climate change, vegetation activity and water deficit. The results showed that all vegetation types experienced warming during the study period, and the majority of them experienced precipitation decline. Warming and growing season water deficit exert counteracting controls on vegetation activity. Our study found insignificant greening trends in the northernmost cold temperate coniferous forest (CTCF), three temperate herbaceous types including the meadow steppe (TMS), grass steppe (TGS) and grassland (TG), where the growing season warming exerted more than offset effect on vegetation activity (phenology) than growing season water deficit. For the three temperate forest including the coniferous (TCF), mixed (TMF) and deciduous-broadleaved (TDBF), growing season water deficit was the main constraint on vegetation activity. Differently, the growing season browning in subtropical or tropical forests of coniferous (STCF), deciduous-broadleaved (SDBF) and evergreen-broadleaved (SEBF) and subtropical grasslands (STG) were likely attributed to decline in sunshine duration due to increased summer cloudiness. Poor water status in TDS, TG, TMS and severe drought in TGS have been identified by using growing season water deficit index (GWDI), suggested these ecosystems were subjected to severe progressing drought that may create greening trend reversal in future. The emerging water deficit in CTCF, TCF and SDBF suggested their rising susceptibility to future climate change.

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T s) from MODIS 8-day composite data during cloud-free period (September?October) were adopted to construct an NDVI?T s space, fro...

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation simulations to: (i) input mean climatologies of different length, (ii) the choice of observed data for evaluating the model results, and (iii) the method used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example. BIOME4 was run under 19 climatologies of different lengths derived from the CRU TS 2.0 data set. The 19 climate-driven scenarios extend backward in time from December of 1992 for 2, 5, 10, ~{!-~}, 90 years respectively. The year 1992 was chosen as the common end point for the climatologies because three global land- and tree-cover data sets, i.e. the Global Land Cover Characteristic (GLCC) data, the Global Land Cover Facility (GLCF) data, and the Ramankutty and Foley global Potential Natural Vegetation data (PNV) used to evaluate the model results were developed from remotely sensed images taken over the period of April 1992 to March 1993. The Kappa statistic, Fuzzy Kappa and a newly developed map comparison method, the Nomad index, were used to quantify the agreement between the BIOME4 biomes simulated under each climate scenario and those derived from three land- and tree-cover data sets. The results indicate that 30- and 35-year mean climatologies for the time period immediately preceding the origin date of the observed data produce the most consistent and accurate vegetation simulations when compared with all three observed data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, different methods of map comparisons can lead to differing judgment on the accuracy of a vegetation model to simulate terrestrial vegetation. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.

Abstract This study investigates how abundance, diversity and composition of understorey spiders were influenced by four different forest habitats in a southern Brazilian Araucaria forest. The study area encompasses a landscape mosaic comprised of Araucaria forest, Araucaria plantation, Pinus plantation, and Eucalyptus plantation. Understorey spiders were collected by beating the vegetation inside three patches of each forest habitat. To assess possible predictors of spider assemblage structure, several patch features were analysed: potential prey abundances, estimation of vegetation cover, diversity index of vegetation types, patch ages, patch areas, and geographical distance between patches. To assess the influence of high-level taxa approaches on spider assemblage patterns, analyses wer...

Thirty-six soil samples were collected and their hyperspectral data used to calculate vegetation indices such as a normalised difference vegetation index (NDVI) and a difference vegetation index (DVI). These were evaluated for typical surface object features within the wastelands around Haizhou Opencast Coal Mine in Fuxin city. A principal component analysis to the hyperspectral data was performed, and the result showed that the first and the second principal components satisfactorily accounted for the multi-spectral image information. The panchromatic and multi-spectral images of SPOT5 were then merged. The panchromatic image replaced the first principal component to improve spatial resolution of the image. In addition, the multispectral images and the NDVI image were classified into six types using the unsupervised classification method. The linear quantitative models were built up and the highest correlation coefficients were obtained between the hyperspectral vegetation index and the vegetation index data from the SPOT5 image. The results show that the hyperspectral data and remote sensing images can be used for quantitative estimation of soil nutrients in coal mine wasteland. They can also provide large area surface information for fast and effective decision making regarding revegetation and the monitoring of dynamic change.

This research explores the sensitivity of vegetation in China to El-Niño/Southern Oscillation (ENSO) events from 1982 to 2006. The ENSO events are defined by the Multivariate ENSO Index (MEI), and variation in vegetation cover is captured by the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI). Pearson's ?2 test was used to identify the areas where the variation in vegetation was sensitive to El Niño and La Niña events. The difference in the sensitivity of various ecosystems was investigated using the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product in 2000. Composite NDVI graphs during El Niño, La Niña and non-ENSO years were also produced to investigate the ENSO relationship with the six vegetation ecosystems during El Niño, La Niña and normal phases. The results show that most of the ENSO-sensitive land in China is only affected by one of the two phases of ENSO events, and the area of El Niño-sensitive vegetation is much larger than that of La Niña-sensitive vegetation. North China and the Hengduan Mountains are the two cores of the El Niño-sensitive areas, while the La Niña-sensitive areas are mainly distributed in the central, northwest and northeast regions of China. The sensitivity of vegetation varies across ecosystems: grassland and shrubland had the largest share of El Niño-sensitive areas, and sparse vegetation and savanna were the most sensitive to La Niña events. Overall, the impacts of El Niño events on vegetation in China had regular seasonal variation, while the impacts of La Niña events had regular zonal distribution.

This research describes the accuracy assessment process for a land-cover dataset developed for the Great Lakes Basin (GLB). This land-cover dataset was developed from the 2007 MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data. Tr...

The Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) 16-day composite data product (MOD12Q) was used to develop annual cropland and crop-specific map products (corn, soybeans, and wheat) for the Laurentian Great Lakes Basin (GLB). Th...

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250-m time-series data to develop a cropland mapping capability throughout the 480 000 km2 Great Lakes Basin (GLB). Cropland mapping was conducted usi...

The response of photosynthetic activity to interannual rainfall variations in Africa South of the Sahara is examined using 20 years (1981-2000) of Normalised Difference Vegetation Index (NDVI) AVHRR data. Linear correlations and regressions were computed between annual NDVI and annual rainfall at a ...

The normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) data are influenced by cloud contamination, which is common in individual AVHRR scenes. Maximum value compositing (MVC) of NDVI data has been employed to minimize cloud contamination....

Using satellite measurements to detect elevated sea surface temperatures (SSTs) and subsequent elevated normalized difference vegetation index (NDVI) data in Africa, we predicted an outbreak of Rift Valley fever (RVF) in humans and animals in the Horn of Africa during September 2006-May 2007. We det...

The sensitivity of global and regional climate to changes in vegetation density is investigated using a coupled biosphere-atmosphere model. The magnitude of the vegetation changes and their spatial distribution are based on natural decadal variability of the normalized difference vegetation index (ndvi). Different scenarios using maximum and minimum vegetation cover were derived from satellite records spanning the period 1982-1990. Albedo decreased in the northern latitudes and increased in the tropics with increased ndvi. The increase in vegetation density revealed that the vegetation's physiological response was constrained by the limits of the available water resources. The difference between the maximum and minimum vegetation scenarios resulted in a 46% increase in absorbed visible solar radiation and a similar increase in gross photosynthetic C02 uptake on a global annual basis. This caused the canopy transpiration and interception fluxes to increase, and reduced those from the soil. The redistribution of the surface energy fluxes substantially reduced the Bowen ratio during the growing season, resulting in cooler and moister near-surface climate, except when soil moisture was limiting. Important effects of increased vegetation on climate are : (1) A cooling of about 1.8 K in the northern latitudes during the growing season and a slight warming during the winter, which is primarily due to the masking of high albedo of snow by a denser canopy. and (2) A year round cooling of 0.8 K in the tropics. These results suggest that increases in vegetation density could partially compensate for parallel increases in greenhouse warming . Increasing vegetation density globally caused both evapotranspiration and precipitation to increase. Evapotranspiration, however increased more than precipitation resulting in a global soil-water deficit of about 15 %. A spectral analysis on the simulated results showed that changes in the state of vegetation could affect the low-frequency modes of the precipitation distribution and might reduce its low frequency variability in the tropics while increasing it in northern latitudes.

Drought indices (DI) are an useful tool for assessing different sectarian droughts. Standardized Precipitation Index (SPI) has been used worldwide to assess/monitor the onset, active phase, cessation and severity of drought. Normalized Difference Vegetation Index (NDVI) provides a comprehensive vegetation dynamics, which directly linked with rainfall received in a particular region. Indo-Gangetic Region (IGR), providing employment and livelihood to tens of millions of rural families directly or indirectly and rice (Oryza sativa L.)-wheat (Triticum aestivum L.) (RW) system of the Indo-Gangetic Plains (IGP) contributes 80% of the total cereal production and is critical to food security of the region. This study tries to verify the applicability of water-vegetative indices viz., SPI, Rainfall...

The strong dependence of Mediterranean vegetation on water availability has been for long known. Drought events are relatively frequent in Mediterranean countries and prolonged intense drought episodes are responsible for the most negative impacts on vegetation, such as losses in crop yields, increases of fire risk, declines of forest growth and land degradation and desertification. The aim of the present work is to analyze in detail the impact of drought episodes on vegetation behavior in the Mediterranean region during the last three decades. For this purpose we use the Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset, as obtained from NOAA-AVHRR sensor and the recently developed multi-scale drought index Standardised Precipitation-Evapotranspiration Index (SPEI, Vicente-Serrano et al, 2010). The study aims to analyze the drought impacts on vegetation dynamics since the early 1980s over the entire Mediterranean region, with the purpose of determining the most sensitive areas and land cover types. Additionally we need to evaluate this impact on a seasonal basis and identify which drought-time scales are more prone to cause negative effects on vegetation. Thus, correlation maps between fields of monthly NDVI and SPEI for time scales ranging between 1 and 24 months were computed in order to identify the regions and seasons most affected by climatic droughts. The role played by vegetation density and aridity on drought impacts on vegetation were also analyzed for different regions of the Mediterranean basin. Vegetation affected by drought presents high spatial and seasonal variability, with a maximum in summer and a minimum in winter. During February half of these affected pixels correspond to time scale of 6 months, while in November the most frequent time scale corresponds to just 3 months, representing more than 40% of the pixels affected by drought. While in February sparse vegetation is the most affected land cover type, rainfed crops are the most affected land cover during summer and autumn. Furthermore, for Iberian rainfed crops, we found a clear dependence of drought impacts with aridity and annual mean of NDVI. Vicente-Serrano S.M., Beguería S., López-Moreno J.I., 2010: A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index - SPEI. Journal of Climate 23(7), 1696-1718, DOI: 10.1175/2009JCLI2909.1

Field studies and radiative transfer model calculations have shown that brightness temperature at high microwave frequencies is strongly affected by vegetation. The daytime observations for six consecutive years (1979 to 1984) over the Sahara, Senegalese Sahel, Burkina Fasso (Upper Volta), and U.S. Southern Great Plains at 37 GHz frequency of the Sanning Multichannel Microwave Radiometer (SMMR) on board the Nimbus-7 satellite are analyzed, and a high correlation with the normalized difference vegetation index derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite is found. The SMMR data appear to provide a valuable new long-term global data set for monitoring vegetation. In particular, the differing responses of vegetation (for example, annual grasses versus woody plants) to drought and the stability of the desert/steppe boundary of northern Africa might be studied using the time series data.

Abstract in portuguese Métodos têm sido propostos visando à melhoraria da administração de nitrogênio (N) e, simultaneamente, ao aumento de produtividade com a proteção do meio ambiente, diminuindo a concentração de nitratos no solo e na água, em que um deles, a agricultura de precisão, consiste na aplicação localizada dos insumos agrícolas em função da necessidade específica local. Neste contexto, a medição da reflectância espectral foliar da planta se apresenta como méto (more) do promissor para o sensoriamento instantâneo da deficiência de N em milho, através do cálculo de índices de vegetação; entretanto, não são bem conhecidas as características da interação das plantas com a radiação solar. Avaliou-se, neste trabalho, o comportamento dos índices de vegetação em relação à hora do dia e da taxa de nitrogênio aplicada. Seis índices diferentes foram estudados: relação infravermelho próximo/vermelho (IVP/V), relação infravermelho próximo/verde (IVP/Verde), índice de vegetação de diferença normalizada (IVDN), índice verde de vegetação da diferença normalizada (IVVDN), índice de vegetação ajustado ao solo (IVAS) e índice aperfeiçoado de vegetação ajustado ao solo (IAVAS). Quando analisados apenas os dados coletados em torno do meio dia solar, os índices que apresentaram a menor dispersão dos resultados foram o IVDN e o IAVAS, enquanto para dados tomados durante todo o dia os índices que indicaram melhor explicação da variabilidade foram o IVAS e o IAVAS. Abstract in english Methods have been proposed seeking to improve the application of nitrogen (N) simultaneously with yield increase and environmental protection, reducing the nitrate concentration in the soil and in the water. One of these methods, precision agriculture consists of the site-specific application of the agricultural inputs as a function of the local need. In this context, the measurement of the spectral leaf reflectance shows itseif as a promising method for the instantaneous (more) remote sensing of deficiency of N in corn through the calculations of vegetation indexes. However, the characteristics of the interaction of the plants with the solar radiation are not well known. This work evaluated the behavior of the vegetation indexes in relation to the hour of the day and the applied nitrogen rate. Six different indexes were studied: relationship near-infrared/red (NIR/red), relationship near-infrared/green (NIR/green), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), soil adjusted vegetation index (SAVI) and optimized soil adjusted vegetation index (OSAVI). When only the data collected around the half solar day weve analized, the indexes that presented the lowest dispersion results were NDVI and OSAVI. For data collected during the whole day the indexes that presented better explanation of the variability were SAVI and OSAVI.

Urban green volume is an important indicator for analyzing urban vegetation structure, ecological evaluation, and green-economic estimation. This paper proposes an object-based method for automated estimation of urban green volume combining three-dimensional (3D) information from airborne Light Detection and Ranging (LiDAR) data and vegetation information from high resolution remotely sensed images through a case study of the Lujiazui region, Shanghai, China. High resolution airborne near-infrared photographs are used for identifying the urban vegetation distribution. Airborne LiDAR data offer the possibility to extract individual trees and to measure the attributes of trees, such as tree height and crown diameter. In this study, individual trees and grassland are identified as the independent objects of urban vegetation, and the urban green volume is computed as the sum of two broad portions: individual trees volume and grassland volume. The method consists of following steps: generating and filtering the normalized digital surface model (nDSM), extracting the nDSM of urban vegetation based on the Normalized Difference Vegetation Index (NDVI), locating the local maxima points, segmenting the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. The results show the quantity and distribution characteristics of urban green volume in the Lujiazui region, and provide valuable parameters for urban green planning and management. It is also concluded from this paper that the integrated application of LiDAR data and image data presents an effective way to estimate urban green volume.

Mountains are a good study area for monitoring and modeling vegetation changes in both the past and future climates because various landscapes from hot and dry lands in lowlands to cold and wet highlands are located across a rather small area. Our goal was to model vegetation redistribution during the Holocene - from 10000 years before present (B.P.) to the year 2100 AD over the Altai-Sayan Mountains using different climate change scenarios and identify how similar/dissimilar was the past vegetation versus future vegetation. We used our mountain bioclimatic vegetation model (MontBioCliM) to predict the paleo and future vegetation distribution coupling MontBioCliM with different climate change scenarios. Our model is an envelope-type model that predicts a vegetation type from three climatic indices: growing degree days, base 5 deg. C; negative degree days below 0 deg. C; and annual moisture index (a ratio between growing degree days and annual precipitation). The past climate change scenarios were constructed by comparing current and past vegetation. The past vegetation was reconstructed from fossil data in 10 sites for 3200 B.P.(the Subboreal), 5300 B.P.(the mid-Holocene), 8 000 B.P. (the Boreal), and 10 000 B.P. (the Pre-Boreal). We inversely used MontBioCliM to predict climatic indices for a vegetation type in a paleo time. Paleo vegetation was mapped by coupling MontBioCliM with each of four paleo climate change scenario. To predict future vegetation we coupled MontBioCliM with Hadley HadCM3 A1FI and B1 climate change scenarios for 2020, 2050 and 2080. An agreement between pairs of vegetation maps for different time slices was found based on kappa statistics. The kappa statistics matrix showed that the vegetation structure in the Altai-Sayan Mountains was similar during the mid-Holocene and the Boreal with warmer and moister climates than nowadays, and the Last Glacial and Subboreal with colder and dryer climates than nowadays. No analogs between future and paleo vegetation distribution were found although in literature the mid-Holocene is suggested to consider as an analog of the current mid-century climate. Climates 5300-8000 B.P. were warmer but also moist compared to dry climates across the 21st century suggested by climate change scenarios from general circulation model projections.

Abstract in english This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor (more) and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.

Abstract Water samples and vegetation data in 1-m--1-m vegetation quads (vegetation density, biomass, and size were investigated) were collected from Panyu District and reclaimed regions with different reclamation histories in Nansha District throughout the Pearl River Estuary in August 2010. The spatial distribution characteristics of Fe, Cu, and Mn in the surface water system were measured based on ordinary Kriging method of ArcGis Geostatistical Analyst. Metal index (MI) was selected to assess the pollution levels and principal correlation analysis (PCA) was chose to interpret the effects of these heavy metals on vegetation. Results showed that (i) there were higher concentrations of Fe and Cu appearing in the up west than that in the other parts of the whole region, meanwhile, a simila...

Vegetation phenology affects energy and mass exchanges between Earth's surface and atmosphere and is critical in understanding biosphere-atmosphere interactions. Recently, satellite observations have been found to be useful in monitoring and investigating vegetation dynamics at regional and global scale. In this study, we investigated the interaction of climate and vegetation physiognomy on phenology observed along Brazilian cerrado and transition forest gradient. We extracted five years of Moderate Resolution Imaging Spectroradiometer (MODIS), Vegetation Index (VI) time-series data along north-south ecoclimatic gradient in which the major factors controlling vegetation activity were rainfall and temperature. We found significant differences in phenology patterns between the cerrado and transition forests. The cerrado temporal profiles depicted very pronounced dry-wet seasonal contrast while decreasing dry-wet contrasts were observed in the transition forests with deeper rooted vegetation that had prolonged access to soil moisture. We also observed seasonal shifts in VI phenologies due to different in vegetation physiognomic response to rainfall. This yielded important phonology information useful in land cover characterization and parameterization for biosphere-climate models.

This study evaluates the capabilities of the NASA Experimental Advanced Airborne Research Lidar (EAARL) system in delineating vegetation assemblages in Jean Lafitte National Park, Louisiana. Five-meter-resolution grids of bare earth (BE), canopy height (CH), canopy-reflection ratio (CRR), and height of median energy (HOME) were derived from EAARL data acquired in September 2006. Ground-truth data were collected along transects to assess species composition, canopy cover, and ground cover. Comparisons of the capabilities of general linear models (GLM) and generalized additive models (GAM) were conducted using conventional evaluation methods (sensitivity, specificity, kappa statistics, area under the curve) and two new indexes, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). GAMs were superior to GLMs in modeling the vegetation training data, but no statistically significant differences between the two models were achieved for predicting vegetation validation data using conventional evaluation methods, although statistically significant improvements in net reclassifications were observed. The goodness-of-fit and prediction accuracy for both models are influenced by data prevalence and occurrence, although GAM models perform much better in the case of training data for all vegetation communities. Vegetation communities with less than 60% prevalence (e.g., coarse woody debris, herbs, shrubs, floating aquatics, and palms) have less than 40% maximum deviance explained, with the exception of bare ground for which the deviance explained by the GAM model is 64%. Midstory and canopy trees that have over 80% prevalence and over 10% occurrence have 99% deviance explained by the GAM model. For the validation dataset with vegetation community prevalence above 35%, GAM models show improvements in NRI only for vegetation categories with occurrences above 10%, and improvements in IDI for vegetation categories with occurrences above 20%. Different vegetation categories may be defined by the same structural characteristics, since LiDAR metrics are more closely related to vegetation structure rather than to species alone.

The fluctuation of vegetation water condition around desert area is one of most important parameters to interpret the desertification expansion. United Nations reported that about 35 million square kilometers of land are subject to desertification. Historically, many parts of China have been suffered from severe desertification. This paper attempts an analysis for spatio-temporal variation characteristics of vegetation drought status around China and Mongolia desert with remotely sensed data. Time series images (1 January, 1999 - 31 December 2006) obtained from SPOT/VEGETATION were used to monitor inter-annual variability of water condition. SPOT/VEGETATION satellite, which has a fine temporal resolution and sensitive to vegetation growth, could be very useful to detect large scale dynamics of environmental changes and desertification progress. The main objective of the study is analyzing water status around China and Mongolia desert and predicting a risk area of desertification. In this study, NDWI (Normalized Difference Water Index) is used to monitor vegetation water condition (drought status) over the study area. To interpret the relationship between vegetation drought status and vigor, NDVI (Normalized Difference Vegetation Index) was employed in ensemble with NDWI. Annual total precipitation from NCEP/NCAR reanalysis data is used as subsidiary data. The study area from 73°36´E to 120°41´E longitude and from 30°81´N to 52°13´N longitude in northern China and whole Mongolia. NDWI value around desert has a range from -0.05 to -0.35 and NDWI values are decreased during the study period. Each year precipitation patterns are similar to yearly mean NDWI value. The study detected several areas where NDWI is dramatically decreased for 8 years, especially northeast part of Mongolian Gobi desert and southeast part of China Taklamakan desert.

Three levels of scale for determining leaf area index (LAI) were explored within an almond orchard of alternating rows of Nonpareil and Monterey varieties using hemispherical photography and mule lightbar (MLB) at ground level up to airborne and satellite imagery. We compared LAI estimates of 56 fisheye photos strategically placed in the orchard to validate 500,000 MLB point scans of a small portion of the aisles between tree rows to water and vegetation indexes of MASTER (MODIS/ASTER simulator) and Landsat 5 imagery. The high correlation of fisheye photo LAI to MLB LAI estimates establishes this new method against the measurement standard within the plant community while significantly increasing sample size. MLB LAI and MASTER vegetation indexes, such as NDWI (normalized difference water ...

Vegetation has long been recognized as a fundamental factor in soil formation, but vegetation and soils commonly covary in response to other environmental factors, confounding the specific effects of vegetation on soil properties. The lysimeter installation at the San Dimas Experimental Forest in southern California offers a rarely found opportunity for quantifying cation-cycling processes in a setting where all factors except vegetation are kept constant. The lysimeters were filled in 1937 with homogenized, fine sandy loam and planted in 1946 with chamise (Adenostoma fasciculatum Hook, and Arn.) and ceanothus (Ceanothus crassifolius Torr.). Comparison of the chamise and ceanothus lysimeters was best achieved by using the Ca/Mg ration of the different cation pools and fluxes as an index. In 1987, the ceanothus exchangeable soil pool contained proportionally more Ca than Mg compared with chamise; that is, the Ca/Mg ratio in the ceanothus exchangeable soil pool was higher than that in chamise. Strong evidence supports vegetation influence on intra-system fluxes (weathering and biocycling) as the basis for these differences. First, more Ca than Mg was released by weathering under ceanothus than under chamise. Second, the ceanothus aboveground biomass exhibited a higher Ca/Mg ration that the chamise. Third, differences between vegetation types widened with time since construction of the lysimeter installation in both the aboveground biomass and exchangeable soil pools. Differences in cation storage measured for the lysimeter chamise and ceanothus stands appear representative of natural chaparral communities throughout California, and may result in distinct Ca and Mg biogeochemical processes in associated ecosystems.

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.

Drought is a complex phenomenon, which is difficult to define. The term is used to refer to deficiency in rainfall, soil moisture, vegetation greenness, ecological conditions or socio economic conditions, and different drought types can be inferred. In this study, drought is considered as a period when the pasture growth is low in regard to long-term average conditions. The extensive livestock production is based on the natural resources available. The good management practices concurs the maximum livestock nutrition needs with the maximum pasture availability. Therefore, early drought detection and impact assessment on the amount of pasture biomass are important in several areas in Spain, whose economy strongly depends on livestock production. The use of remote sensing data presents a number of advantages when determining drought impact on vegetation. The information covers the whole of a territory and the repetition of images provides multi-temporal measurements. In addition, vegetation indexes, being NDVI (normalized difference vegetation index) and SAVI (soil-adjusted vegetation index) the most common ones, obtainedfrom satellite data allow areas affected by droughts to be identified. These indices are being used for estimation of vegetation photosynthesis activity and monitoring drought. The present study shows the application of these vegetation indices for pasture drought monitoring in three places in Spain and their correlation with several field measurements. During 2010 and 2011 three locations, El Cubo de Don Sancho (Salamanca), Trujillo (Cáceres) and Pozoblanco (Córdoba), were selected and a periodic pasture monitoring and botanic composition were achieved. Daily precipitation, temperature and monthly soil water content were measurement as well as fresh and dry pasture weight. At the same time, remote sensing images were capture by DEIMOS-1 of the chosen places.This satellite is based on the concept Microsat-100 from Surrey. It is conceived for obtaining Earth images with a good enough resolution to study the terrestrial vegetation cover (20x20 m), although with a great range of visual field (600 km) in order to obtain those images with high temporal resolution and at a reduced cost. It has 6 cameras in red, green and near infrared bands, equivalent to Landsat ones. A discussion on the correlations found between field measurements and both vegetation index considering seasonal pattern and location are presented. Acknowledgements. This work was partially supported by ENESA under project P10 0220C-823. Funding provided by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no. AGL2010-21501/AGR is greatly appreciated.

The fraction of photosynthetically active radiation (FPAR) is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index (VI) and neural network (NN) methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance (SWIR) bands are stronger than the positive correlations between FPAR and near-infrared band reflectan...

The strong correlation between environmental conditions and abundance and spatial spread of the tick Ixodes ricinus is widely documented. I. ricinus is in Europe the main vector of the bacterium Borrelia burgdorferi, the pathogen causing Lyme borreliosis (LB). Humidity in vegetated systems is a major factor in tick ecology and its effects might translate into disease incidence in humans. Time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture were tested as explanatory variables of LB incidence. Wavelet-based multiresolution analysis allowed the examination of these signals at different temporal scales in study sites in Belgium, where increases in LB incidence were reported in recent years. The analysis showed the potential of the tested indices for disease monitoring, the usefulness of analyzing the signal in different time frames and the importance of local characteristics of the study area for the selection of the vegetation index.

Consider the computer science literature indexed in the. ACM Digital ..... HORTICULTURAL PRODUCTS VEGETABLE PRODUCTS AGRICULTURAL ECONOMICS. 9: 0.09 ... trieval.," Journal of the American Society for Information. Science ...

Crop yield estimation has an important role on economy development and its accuracy and speed influence yield price and helps in deciding the excess or deficit production conditions. The water productivity evaluates the irrigation command through water use efficiency (WUE). Remote sensing (RS) and geographical information system (GIS) techniques were used for crop yield and water productivity estimation of wheat crop (Triticum aestivum) grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. One IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The temporally and spatially ...

Within the ImpactMin project, funded by the Framework Programme 7 of the European Commission, new methods for the environmental impact monitoring of mining operations are being developed. The objective of this study is to analyze the impact of mining on soil properties through assessment of the vegetation status using time series analysis of low resolution Normalized Difference Vegetation Index (NDVI) images derived from SPOT-Vegetation. The study focuses on the surroundings of mining areas in the Orenburg region in the Russian Urals. Karabash has been a centre for mining and metal production for well over 3000 years, and environmental impact of (historical) mining in the area is extremely severe. The area was characterized as an 'ecological disaster zone', based on chemical analysis of soil samples in the area [1]. The mining activities were intensified in the early to mid-20th century, but the old smelter was modernized in the 1990s. A time series of 10-daily NDVI images from SPOT-Vegetation (S10 April/1998-December/2010 at 1km2 resolution, http://www.vgt.vito.be/) is analyzed. Different land cover types clearly show different phenology. To remove seasonal vegetation changes and thus to facilitate the interpretation through the historical record, a Standardized Difference Vegetation Index (SDVI) was calculated for each pixel and for each record of the time series. The first results of trend analyses indicate a strong recovery of open forests in the Karabash region in the last decade. To what extent this can be related to reduced mining impact or climate factors, still needs to be assessed. Further research will also focus on the spatial heterogeneity of phenological parameters, in relation to distance to and wind direction of the smelters and soil properties. [1] V. Nestersnko, "Urban associations of elements- environmental pollutants in Karabash city (Chelyabinsk oblast) as a reflection of ore-chemical descriptions of mineral raw material", Proceedings of the Chelyabinsk Scientific Center, vol. 3, pp. 58-62, 2006.

Two NOAA AVHRR images are presented which provide a comparison of the geographic distribution of an integration of the normalized difference vegetation index (NDVI) for the Sahel zone in Niger for the growing seasons of 1986 and 1987. The production of the images and the application of the images for resource management are discussed. Daily large area coverage with a spatial resolution of 1.1 km at nadir were transformed to the NDVI and geographically registered to produce the images.

Reflected radiation recorded by satellite sensors is a common procedure to estimate the leaf area index (LAI) of boreal forest. The normalized difference vegetation index (NDVI), derived from measurements of visible and near infrared radiation were commonly used to estimate LAI. But research in tropical forest has shown that LAI is more closely related to radiation of middle infrared wavelengths than that of visible wavelengths. This research calculated a vegetation index (VI3) using radiation from vegetation recorded at near and middle infrared wavelengths. In the case of boreal forest, VI3 and LAI displayed a closer relationship than NDVI and LAI. Also, the use of VI3 explained approximately 76 per cent of the variation in field estimates of LAI, versus approximately 46 per cent for NDVI. The authors concluded that consideration should be given to information provided by middle infrared radiation to estimate the leaf area index of boreal forest. The research area was located in the Southern Study Area (SSA) of the BOReal Ecosystem-Atmospher Study (BOREAS), situated on the southern edge of the Canadian boreal forest, 40 km north of Prince Albert, Saskatchewan. 1 tab., 4 figs., 46 refs.

To assess ongoing changes in high latitude vegetation productivity we compared spatiotemporal patterns in remotely sensed vegetation productivity in the tundra and boreal zones of North America and Eurasia. We compared the long-term GIMMS (Global Inventory Modeling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) to the more recent and advanced MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI data set, and mapped circumpolar trends in a gross productivity metric derived from the former. We then analyzed how temporal changes in productivity differed along an evergreen-deciduous gradient in boreal Alaska, along a shrub cover gradient in Arctic Alaska, and during succession after fire in boreal North America and northern Eurasia. We find that the earlier reported contrast between trends of increasing tundra and decreasing boreal forest productivity has amplified in recent years, particularly in North America. Decreases in boreal forest productivity are most prominent in areas of denser tree cover and, particularly in Alaska, evergreen forest stands. On the North Slope of Alaska, however, increases in tundra productivity do not appear restricted to areas of higher shrub cover, which suggests enhanced productivity across functional vegetation types. Differences in the recovery of post-disturbance vegetation productivity between North America and Eurasia are described using burn chronosequences, and the potential factors driving regional differences are discussed.

Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R2?=?0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information. PMID:16922309

The aim of this study was to investigate the effects of vegetable tablets containing Gamma-Aminobutyric Acid (GABA) intake on cardiovascular response and the autonomic nervous system in young adults. In a double-blind, randomized controlled trial, 7 healthy subjects were assigned to take vegetable tablets (10 g/trial) or control tablets (10 g/trial). We measured heart rate (HR), systolic and diastolic blood pressure, stroke volume, cardiac output, total peripheral resistance index, and the low- and high-frequency oscillatory components of heart rate variability (HRV). Two major spectral components were examined at low-frequency (LF: 0.04–0.15 Hz) and high-frequency (HF: 0.15–0.4 Hz) bands to indicate HRV. There were significant interactions in HR (p<0.01) and in LF/HF of HRV (p<0.05). HR increased after intake of control tablets, but not after that of vegetable tablets. LF/HF increased rapidly after intake of control tablets and rose slightly after vegetable tablet intake. There was no significant difference between the vegetable and control tablet trials in stroke volume, cardiac output, total peripheral resistance, systolic or diastolic blood pressure, HF, or LF. In conclusion, these results suggest the possibility that single administration of vegetable tablets containing GABA suppresses the sympathetic nervous activity leading to an elevation of blood pressure.   

The joint Japan-Mongolia-USA project DUVEX (Dust-Vegetation Interaction Experiment) was designed to develop a biogeophysical model which can simulate dust emission and ecosystem processes over the vegetated land surface. Dust emission processes have been investigated mostly on bare land, and there is very little information about vegetated land. Thus, intensive observations were conducted of a dust event that occurred on the Mongolian steppe on 24 April 2008. Meteorological and dust elements (e.g., saltation flux, visibility, dust concentration) and land-surface parameters (e.g., roughness length, vegetation cover, and the ground-based normalized difference vegetation index) were measured. During the event (from 13:00 to 18:00 LST on 24 April), the threshold wind speed at 1.54 m height, which is the minimum wind speed inducing saltation of particles ranging from 30 to 667 ?m in diameter, was 8.9 m s-1 on a land surface with 7.2% vegetation cover with dead brown leaves, a small roughness length (0.0058 m), and a very dry sandy soil at 0-5 mm depth (water content, 0.002 g g-1). For comparison with previous studies, the threshold wind speed value was converted to the values at the heights in each study by using the logarithmic law of wind profile. Our value is close to the SYNOP-derived values for the same area, but larger than ground-observed and SYNOP-derived values for East Asian deserts.   

The capability to automatically detect vegetation changes using multitemporal remotely sensed image data is of upmost importance to many global-change research projects. A procedure to automatically map vegetation changes within arid and semi-arid regions of the southwestern United States is presented. Multitemporal Landsat Multispectral Scanner (MSS) images were the primary data source, but some preliminary work was also done using same-date Visible-Infrared Spin-Scan Radiometer (VISSR) data for comparison with the MSS results. The change-detection procedure includes multitemporal image calibration using a hybrid method that we developed for the project; the hybrid calibration allows a radiometric calibration to be applied to historical data by using field-radiance information rather than a modeling procedure. The results indicate that a calibrated visible band is more sensitive than the widely used Normalized Difference Vegetation Index (NDVI) in detecting vegetation changes in the arid and semi-arid environments of the southwestern United States. Changes were detected in the desert environment, where the vegetation density is relatively low, with both Landsat MSS and GOES VISSR images. Some changes detected by the automatic procedure were confirmed in the field during two of the Landsat overpasses. The changes corresponded mostly to the blooming of ephemeral or annual vegetation.

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T (s)) from MODIS 8-day composite data during cloud-free period (September-October) were adopted to construct an NDVI-T (s) space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut. PMID:22200944

The chlorophyll content of plant has relative correlation with photosynthetic capacity and growth levels of plant. It affects the plant canopy spectra, so the authors can use hyperspectral remote sensing to monitor chlorophyll content. By analyzing existing mature vegetation index model, the present research pointed out that the TCARI model has deficiencies, and then tried to improve the model. Then using the PROSPECT+SAIL model to simulate the canopy spectral under different levels of chlorophyll content and leaf area index (LAI), the related constant factor has been calculated. The research finally got modified transformed chlorophyll absorption ratio index (MTCARI). And then this research used optimized soil background adjust index (OSAVI) to improve the model. Using the measured data for test and verification, the model has good reliability. PMID:23156785

The Tibetan Plateau is a region sensitive to climate change, due to its high altitude and large terrain. This sensitivity can be measured through the response of vegetation patterns to climate variability in this region. Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery and correlation analyses are effective tools to study land cover changes and their response to climatic variations. This is especially important for regions like the Tibetan Plateau, which has a complex ecosystem but lacks a lot of detailed in-situ observation data due to its remoteness, vastness and the severity of its climatic conditions. In this research a time series of 315 SPOT VEGETATION scenes, covering the period between 1998 and 2006, has been processed with the Harmonic ANalysis of Time...

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close co...

The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992-93, 1995) and SPOT Vegetation (1998-2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroundings. We then summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analysed using Landsat TM imagery and extensive ground knowledge to assess the results. This technique identified anomalies that can be linked to numerous anthropogenic impacts including agricultural and urban expansion, maintenance of protected areas and increased fallow. Local variance analysis is a reliable method for assessing vegetation degradation resulting from human pressures or increased land productivity from natural resource management practices. ?? 2004 Published by Elsevier Ltd.

The U.S. Geological Survey, using NOAA-11 Advanced Very High Resolution Radiometer (AVHRR) 1-km data, has produced a time series of 19 biweekly maximum normalized difference vegetation index (NDVI) composites of the conterminous United States for the 1990 growing season. Each biweekly composite included data from approximately 20 calibrated and georegistered daily overpasses. The output is a data set which includes all five calibrated AVHRR channels, NDVI values, three satellite/solar viewing angles, and date of observation pointer for each biweekly composite. The data set is intended for assessing seasonal variations in vegetation condition and provides a foundation for studying long-term changes in vegetation resulting from human interactions or global climate alterations. 12 refs.

Abstract Vegetation stress or mortality can be the result of many factors including drought-induced water deficit, insect infestations and failures of, or fluctuations in, precipitation sources typical to an area. Reduction of cover and reduced health are identifiable in remotely-sensed multispectral satellite images. A suite of images from NASA's MODIS sensor was used to calculate the Normalised Difference Vegetation Index (NDVI) during the 2000-2006 North American growing seasons. Fluctuations in NDVI over this period show a significant decline in vegetative health in the region with specific areas showing changes linked to moisture sources, prevailing wind patterns, slope aspect and solar radiation receipt. Ground-truthing of these areas has confirmed the extent and magnitude of the die...

Monitoring the growth and distribution of Arctic tundra vegetation is important for understanding changes in early growing season conditions in Arctic ecosystems in response to a warming climate. The primary objective of this study is to examine the utility of computed Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) products relative to 16-day maximum value composite (MVC) datasets for observing early season green-up dynamics of Arctic tundra vegetation across the North Slope of Alaska. Greening in the Arctic typically occurs shortly after snowmelt and can potentially be captured by using satellite observations that are available on a daily basis. Daily MODIS Snow Cover products were employed to retrieve dates of complete snowmelt (...

We describe a pragmatic approach for evaluating the spatial representativeness of flux tower measurements based on footprint climatology modeling analyses of land cover and remotely sensed vegetation indices. The approach was applied to the twelve flux sites of the Canadian Carbon Program (CCP) that include grassland, wetland, and temperate and boreal forests across an east-west continental gradient. The spatial variation within the footprint area was evaluated by examining the spatial structure of Normalized Difference Vegetation Index (NDVI) and land cover using geostatistical analyses of frequency distribution, variogram and window size. The results show that at most sites (i) the percentages of the target vegetation functional type (dominant land cover) observed by the CCP towers were ...

Abstract in english Stomach contents of Holoshesthes heterodon Eigenmann, 1915 (Teleostei, Cheirodontinae), collected in the depletion zone of Cajuru reservoir when it was at its maximum water level in two stations with different vegetal densities, were studied in order to investigate the influence of the flooded vegetal biomass on the food quantity and quality ingested by fish. Eighteen individuals from each station were examined. The standard length was l.53±0.05 cm and l.52±0.05 cm, res (more) pectively at the lower biomass (8.19 kg diy weight/ha) and higher biomass (38.10 kg diy weight/ha) sampling stations. The stomach repletion Index (SRI) was applied for the quantitative analysis. The alimentary index (IAi) was used for the quali-quantitative analysis, with the volume of the items obtained through the points method. SRI did not show values significatively different between the two stations, p>0.05, by applying the Mann-Whitney test. In both situations, Cladocera was the most important item. There were no correlation between the flooded vegetal biomass in the depletion zone and the intake of food by H. heterodon. However, as there were no empty stomachs, possibly even the lower vegetal biomass was enough to provide abundant feeding resources.

Analyses of vegetation properties along climatic gradients provide first order approximations as to how vegetation might respond to a temporally dynamic climate. Until recently, no systematic study of tundra vegetation had been conducted along bioclimatic transects that represent the full latitudinal extent of the arctic tundra biome. Since 1999, we have been collecting data on arctic tundra vegetation and soil properties along two such transects, the North American Arctic Transect (NAAT) and the Yamal Arctic Transect (YAT). The NAAT spans the arctic tundra from the Low Arctic of the North Slope of Alaska to the polar desert of Cape Isachsen on Ellef Ringnes Island in the Canadian Archipelago. The Yamal Arctic Transect located in northwest Siberia, Russia, presently ranges from the forest-tundra transition at Nadym to the High Arctic tundra on Belyy Ostrov off the north coast of the Yamal Peninsula. The summer warmth indices (SWI - sum of mean monthly temperatures greater than 0°C) range from approximately 40 °C months to 3 °C months from south to north. For largely zonal sites along these transects, we systematically collected leaf area index (LAI-2000 Plant Canopy Analyzer), normalized difference vegetation index (NDVI - PSII hand-held spectro-radiometer), and vegetation biomass (clip harvests). Site-averaged LAI ranges from 1.08 to 0 along the transects, yet can be highly variable at the landscape scale. Site-averaged NDVI ranges from 0.67 to 0.26 along the transects, and is less variable than LAI at the landscape scale. Total aboveground live biomass ranges from approximately 700 g m-2 to NDVI are highly correlated logarithmically (r = 0.80) for the entire dataset. LAI is significantly related to total aboveground (live plus dead) vascular plant biomass, although there is some variability in the data (r = 0.63). NDVI is strongly correlated as a power function with photosynthetic biomass (r = 0.81). In general, for the same bioclimate subzone, total aboveground live biomass is substantially greater on the YAT compared to the NAAT. Some of this difference can be accounted for by the differences in measured non-vascular biomass. Since reindeer grazing on the Yamal Peninsula should reduce vegetation biomass to a greater extent than caribou grazing in North America, grazing differences are likely not responsible for biomass differences. However, different glacial and disturbance histories, soil substrates, and the resultant nutrient cycling processes could be hypothesized to yield these differences in vegetation biomass.

Frequency and severity of droughts were projected to increase in many regions. However, their effects of temporal dynamics on the terrestrial carbon cycle remain uncertain, and hence deserve further investigation. In this paper, the droughts that occurred in China during 2001-2010 were identified by using the standardized precipitation index (SPI). Standardized anomaly index (SAI), which has been widely employed in reflecting precipitation, was extended to evaluate the anomalies of net primary productivity (NPP). In addition, influences of the droughts on vegetation were explored by examining the temporal dynamics of SAI-NPP along with area-weighted drought intensity at different time scales (1, 3, 6, 9 and 12 months). Year-to-year variability of NPP with several factors, including droughts, NDVI, radiation and temperature, was analyzed as well. Consequently, the droughts in the years 2001, 2006 and 2009 were well reconstructed. This indicates that SPI could be applied to the monitoring of the droughts in China during the past decade (2001-2010) effectively. Moreover, strongest correlations between droughts and NPP anomalies were found during or after the drought intensities reached their peak values. In addition, some droughts substantially reduced the countrywide NPP, whereas the others did not. These phenomena can be explained by the regional diversities of drought intensity, drought duration, areal extents of the droughts, as well as the cumulative and lag responses of vegetation to the precipitation deficits. Besides the drought conditions, normalized difference vegetation index (NDVI), radiation and temperature also contribute to the interannual variability of NPP. PMID:23164540

Amazon rainforests play a dominant role in the global climate system by exerting a strong control on the exchanges of carbon, water and the energy. A comprehensive understanding of the seasonal and interannual dynamics of these forests, however, is still lacking. While field measurements have contributed greatly to our understanding of Amazon forests, they represent a small fraction of Amazonia. Satellite data, representing near daily synoptic views of these forests, are probably one of the best ways to study them. Current efforts at using optical satellite data in the Amazon have generated significant interest as well as controversy. Persistent cloud cover during the wet season and aerosols during the dry season have made the use of optical satellite data rather difficult. AQUA, with sensors collecting information in both optical and microwave wavelengths, provides a unique opportunity to address some of the long-standing issues in applying remotely sensed data in the Amazon. We will discuss the differences and similarities among optical, microwave products and ecosystem model results for representing seasonal and inter-annual variability of Amazon forests. Using products such as the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) from MODIS, and Vegetation Optical Depth (VOD) from AMSR-E, we will demonstrate the unique capabilities that AQUA brings for studying tropical rainforests.

In recent years, land use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. This research utilizes the integrated remote sensing and geographic information systems (GIS) in the southern part of Iraq (Basrah Province was taken as a case) to monitor, map, and quantify the environmental change using a 1:250,000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation land, sand land, urban area, unused land, and water bodies. Supervised classification and normalized difference buildup index, normalized difference vegetation index, normalized difference bare land index, the normalized differential water index, crust index (CI) algorithms, and change detection techniques were adopted in this research and used, respectively, to retrieve its class boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. The results showed that the urban area, sand lands, and bare lands had increased by the rate of 1.2%, 0.8%, and 0.4% per year, with area expansion from 3,299.1, 4,119.1 km2, and 3,201.9 km2 in 1990 to 3,794.9, 4,557.7, and 3,351.7 km2 in 2003, respectively. While the vegetation cover and water body classes were about 43.5% in 1990, the percentage decreased to about 39.6% in 2003. This study demonstrates the effectiveness of the remote sensing and GIS technologies in detecting, assessing, mapping, and monitoring the environmental changes. PMID:19565344

Contaminated soil particles and food are established routes of exposure. We investigated the relations between biomarkers of exposure to cadmium and lead, and the metal loading rates in house dust in the adult residents of an area with a soil cadmium concentration of >=3mg/kg (n=268) and a reference area (n=205). We determined the metal concentrations in house dust allowed to settle for 3 months in Petri dishes placed in the participants' bedrooms. The continuously distributed vegetable index was the first principal component derived from the metal concentrations in six different vegetables. The biomarkers of exposure (blood cadmium 9.2 vs. 6.2nmol/L; 24-h urinary cadmium 10.5 vs. 7.0nmol; blood lead 0.31 vs. 0.24{mu}mol/L), the loading rates of cadmium and lead in house dust (0.29 vs. 0.12 and 7.52 vs. 3.62ng/cm{sup 2}/92 days), and the vegetable indexes (0.31 vs. -0.44 and 0.13 vs. -0.29 standardized units) were significantly higher in the contaminated area. A two-fold increase in the metal loading rate in house dust was associated with increases (P<0.001) in blood cadmium (+2.3%), 24-h urinary cadmium (+3.0%), and blood lead (+2.0%), independent of the vegetable index and other covariates. The estimated effect sizes on the biomarkers of internal exposure were three times greater for house dust than vegetables. In conclusion, in the adult population, house dust is potentially an important route of exposure to heavy metals in areas with contaminated soils, and should be incorporated in the assessment of health risks.

Two sets of numerical experiments using the coupled National Center for Environmental Prediction General Circulation Model (NCEP/GCM T42L18) and the Simplified Simple Biosphere land surface scheme (SSiB) were carried out to investigate the climate impacts of fractional vegetation cover (FVC) and leaf area index (LAI) on East Asia summer precipitation, especially in the Yellow River Basin (YRB). One set employed prescribed FVC and LAI which have no interannual variations based on the climatology of vegetation distribution; the other with FVC and LAI derived from satellite observations of the International Satellite Land Surface Climate Project (ISLSCP) for 1987 and 1988. The simulations of the two experiments were compared to study the influence of FVC, LAI on summer precipitation interannual variation in the YRB. Compared with observations and the NCEP reanalysis data, the experiment that included both the effects of satellite-derived vegetation indexes and sea surface temperature (SST) produced better seasonal and interannual precipitation variations than the experiment with SST but no interannual variations in FVC and LAI, indicating that better representations of the vegetation index and its interannual variation may be important for climate prediction. The difference between 1987 and 1988 indicated that with the increase of FVC and LAI, especially around the YRB, surface albedo decreased, net surface radiation increased, and consequently local evaporation and precipitation intensified. Further more, surface sensible heat flux, surface temperature and its diurnal variation decreased around the YRB in response to more vegetation. The decrease of surface-emitting longwave radiation due to the cooler surface outweighed the decrease of surface solar radiation income with more cloud coverage, thus maintaining the positive anomaly of net surface radiation. Further study indicated that moisture flux variations associated with changes in the general circulation also contributed to the precipitation interannual variation.

Spatial and temporal variability of new shoots (number of shoots, dry mass and nitrogen concentration) were investigated under several conditions using precision agriculture technology. The growth and spatial variability of new shoots were both determined using the normalized difference vegetation index (NDVI). At harvest, there were differences in new shoots growth depending on variety, severe shading, and nitrogen fertilizer type. There were differences in new shoot for “Ten-cya” compared to that for “Sen-cya,” and temporal variability of growth had a different tendency compared to spatial variability at harvest depending on several conditions. Coefficients of determination (R2) and root mean square error (RMSE) were established by the NDVI model. The accuracy was R2?0.826 with RMSE?15.0 g/m2 for “Sen-cya” and R2?0.877 with RMSE?13.6 g/m2 (vegetation coverage ratio ? 100%) for “Ten-cya”.   

Extreme climatic events like droughts, floods, heat waves and ice storms impact ecosystems as well as human societies. There is wide concern about how terrestrial ecosystems respond to extreme climatic events in the context of global warming. In this study, we used satellite-derived vegetation greenness data (Normalized Difference Vegetation Index; NDVI), in situ weather station data (temperature and precipitation) and the Palmer Drought Severity Index (PDSI) to analyze the spatio-temporal change of the area experiencing vegetation greenness anomalies and extreme climatic events in China from 1982 to 2009. At the national scale, we found that China experienced an increasing trend in heat waves and drought events during the study period. The average fraction of climate stations with drought events (defined by growing season PDSI <- 2) detected increased from 8% in the 1980s, to nearly 20% in the 2000s, at a rate of 0.6% yr-1 (R2 = 0.61, P < 0.001). In contrast, the area showing negative anomalies of vegetation greenness decreased at the rate of 0.9% yr-1 from 1982 to 2009 (R2 = 0.29, P = 0.003), although this trend stalled or reversed during the 2000s, particularly in northern China. The decrease in vegetation growth during the last decade over northern China was accompanied by the increase in extreme drought events in the 2000s. In southern China, although both precipitation and PDSI data suggest a greater area experiencing drought events during the 2000s than in the 1980s, the area showing negative vegetation greenness decreased consistently during the whole study period.

ABSTRACT: BACKGROUND: Monitoring inequalities in noncommunicable disease risk factor prevalence can help to inform and target effective interventions. The prevalence of current daily smoking, low fruit and vegetable consumption, physical inactivity, and heavy episodic alcohol drinking were quantified and compared across wealth and education levels in low- and middle-income country groups. METHODS: This study included self-reported data from 232,056 adult participants in 48 countries, derived from the 2002-2004 World Health Survey. Data were stratified by sex and low- or middle-income country status. The main outcome measurements were risk factor prevalence rates reported by wealth quintile and five levels of educational attainment. Socioeconomic inequalities were measured using the slope index of inequality, reflecting differences in prevalence rates, and the relative index of inequality, reflecting the prevalence ratio between the two extremes of wealth or education accounting for the entire distribution. Data were adjusted for confounding factors: sex, age, marital status, area of residence, and country of residence. RESULTS: Smoking and low fruit and vegetable consumption were significantly higher among lower socioeconomic groups. The highest wealth-related absolute inequality was seen in smoking among men of low- income country group (slope index of inequality 23.0 percentage points; 95% confidence interval 19.6, 26.4). The slope index of inequality for low fruit and vegetable consumption across the entire distribution of education was around 8 percentage points in both sexes and both country income groups. Physical inactivity was less prevalent in populations of low socioeconomic status, especially in low-income countries (relative index of inequality: (men) 0.46, 95% confidence interval 0.33, 0.64; (women) 0.52, 95% confidence interval 0.42, 0.65). Mixed patterns were found for heavy drinking. CONCLUSIONS: Disaggregated analysis of the prevalence of non-communicable disease risk factors demonstrated different patterns and varying degrees of socioeconomic inequalities across low- and middle-income settings. Interventions should aim to reach and achieve sustained benefits for high-risk populations. PMID:23102008

Abstract in portuguese Efeitos direcionais introduzem variabilidade na reflectância e na determinação de índices de vegetação, especialmente quando sensores de amplo campo de visada são usados (p.ex., Moderate Resolution Imaging Spectroradiometer - MODIS). Neste estudo, nós avaliamos os efeitos direcionais sobre a reflectância e quatro índices de vegetação (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI1640 e (more) NDWI2120), calculados a partir de dados MODIS, em função do crescimento da soja em duas estações agrícolas (2004-2005 e 2005-2006). Para manter o estádio reprodutivo de uma dada variedade como um fator constante, variando a geometria de visada, pares de imagens obtidas em datas próximas e com ângulos de visada opostos foram analisados. Usando uma abordagem estatística não-paramétrica com análise bootstrapping e normalizando os índices para suas diferenças angulares entre as direções de visada, sua sensibilidade para os efeitos direcionais foi estudada. Os resultados mostraram que a variação da reflectância do MODIS entre estádios fenológicos consecutivos foi geralmente menor do que aquelas resultantes da geometria de visada para dosséis fechados. O contrário foi observado para dosséis esparsos. A reflectância das primeiras sete bandas do MODIS foi maior na direção de retro-espalhamento. Exceto o EVI, os demais índices de vegetação tiveram maiores valores na direção de espalhamento frontal. Os efeitos direcionais diminuíram com o fechamento do dossel. O NDVI foi menos afetado pelos efeitos direcionais do que os demais índices, apresentando as menores diferenças entre as direções de visada para os mesmos estádios fenológicos. Abstract in english Directional effects introduce a variability in reflectance and vegetation index determination, especially when large field-of-view sensors are used (e.g., Moderate Resolution Imaging Spectroradiometer - MODIS). In this study, we evaluated directional effects on MODIS reflectance and four vegetation indices (Normalized Difference Vegetation Index - NDVI; Enhanced Vegetation Index - EVI; Normalized Difference Water Index - NDWI1640 and NDWI2120) with the soybean development (more) in two growing seasons (2004-2005 and 2005-2006). To keep the reproductive stage for a given cultivar as a constant factor while varying viewing geometry, pairs of images obtained in close dates and opposite view angles were analyzed. By using a non-parametric statistics with bootstrapping and by normalizing these indices for angular differences among viewing directions, their sensitivities to directional effects were studied. Results showed that the variation in MODIS reflectance between consecutive phenological stages was generally smaller than that resultant from viewing geometry for closed canopies. The contrary was observed for incomplete canopies. The reflectance of the first seven MODIS bands was higher in the backscattering. Except for the EVI, the other vegetation indices had larger values in the forward scattering direction. Directional effects decreased with canopy closure. The NDVI was lesser affected by directional effects than the other indices, presenting the smallest differences between viewing directions for fixed phenological stages.

Estimation of surface reflectance is essential for an accurate retrieval of aerosol optical thickness (AOT) by satellite remote sensing approach. Due to the variability of surface reflectance over land surfaces, a surface model is required to take into account the crucial factor controlling this variability. In the present study, we attempted to simulate surface reflectance in the short-wave channels with two methods, namely the land cover type dependent method and a two-source linear model. In the two-source linear model, we assumed that the spectral property can be described by a mixture of vegetated and non-vegetated area, and both the normalized difference vegetation index (NDVI), and the vegetation continuous field (VCF) was applied to summarize this surface characteristic. By comparing our estimation with surface reflectance data derived from Moderate Resolution Imaging Spectroradiometer (MODIS), it indicated that the land cover type approach did not provide a better estimation because of inhomogeneous land cover pattern and the mixing pixel properties. For the two-source linear method, the study suggested that the use of NDVI as parameterization for vegetation fraction can reflect the spectral behavior of shortwave surface reflectance, despite of some deviation due to the averaging characteristics in our linear combination process. A channel-dependent offset and scalar factor could enhance reflectance estimation and further improve AOT retrieval by the current Bremen AErosol Retrieval (BAER) approach.

Practical geologic CO{sub 2} sequestration will require long-term monitoring for detection of possible leakage back into the atmosphere. One potential monitoring method is multi-spectral imaging of vegetation reflectance to detect leakage through CO{sub 2}-induced plant stress. A multi-spectral imaging system was used to simultaneously record green, red, and near-infrared (NIR) images with a real-time reflectance calibration from a 3-m tall platform, viewing vegetation near shallow subsurface CO{sub 2} releases during summers 2007 and 2008 at the Zero Emissions Research and Technology field site in Bozeman, Montana. Regression analysis of the band reflectances and the Normalized Difference Vegetation Index with time shows significant correlation with distance from the CO{sub 2} well, indicating the viability of this method to monitor for CO{sub 2} leakage. The 2007 data show rapid plant vigor degradation at high CO{sub 2} levels next to the well and slight nourishment at lower, but above-background CO{sub 2} concentrations. Results from the second year also show that the stress response of vegetation is strongly linked to the CO{sub 2} sink-source relationship and vegetation density. The data also show short-term effects of rain and hail. The real-time calibrated imaging system successfully obtained data in an autonomous mode during all sky and daytime illumination conditions.

Systematic evaluation of food security throughout the Sahel has been attempted for nearly two decades. Food security analyses have used both food prices to determine the ability of the population to access food, and satellite-derived vegetation indices that measure vegetation production to establish how much food is available each year. The relationship between these two food security indicators is explored here using correspondence analysis and through the use of Markov chain models. Two sources of quantitative data were used: 8 km normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometers (AVHRR) carried on the NOAA series of satellites, and monthly millet prices from 445 markets in Mali, Niger and Burkina Faso. The results show that the growing season vegetation production is related to the price of millet at the annual and the seasonal time scales. If the growing season was characterized by erratic, sparse rainfall, it resulted in higher prices, and well-distributed, abundant rainfall resulted in lower prices. The correspondence between vegetation production and millet prices is used to produce maps of millet prices for West Africa.

When determining the water status of leafy vegetables with oven-drying techniques, consumption of time and resources is inevitable. The aim of this work was to evaluate the performance of both relative water content (RWC) and free water and bound water content (FW?BW) techniques to obtain the water content index (WC) of different leafy vegetables in order to dispense with the traditional methodology for water content determination. By doing this, not only consumption of time is reduced, but also skilled labor and laboratory resources. Butterhead lettuces harvested at different growing stages and stored at different postharvest conditions were evaluated. In all cases, water content values obtained with RWC technique were statistically equivalent to those obtained with the traditional method...

The use of remote sensing at different spatio-temporal resolutions is being common during the last decades since sensors offer many inputs to water budget estimation. Various water balance models use the LAI as a parameter for accounting water interception, evapotranspiration, runoff and available ground water. The objective of the present work is to improve vegetation stress monitoring at regional scale for a natural forested ecosystem. LAI-MODIS and spatialized vegetation, soil and climatic data have been integrated in a water budget model that simulates evapotranspiration and soil water content at daily step. We first explore LAI-MODIS in the specific context of Mediterranean natural ecosystem. Results showed that despite coarse resolution of LAI-MODIS product (1 km), it was possible to discriminate evergreen and coniferous vegetation and that LAI values are influenced by underlying soil capacity of water holding. The dynamic of vegetation has been integrated into the water budget model by weekly varying LAI-MODIS. Results of simulations were analysed in terms of actual evapotranspoiration, deficit of soil water to field capacity and vegetation stress index based on actual and potential evapotranspiration. Comparing dynamic LAI variation, afforded by MODIS, to a hypothetic constant LAI all over the year correspond to 30% of fAPAR increase. A sensitivity analysis of simulation outputs to this fAPAR variation reveals that increase of both deficit of soil water to field capacity and stress index are respectively 18% and 27%, (in terms of RMSE, these variations are respectively 1258 mm yr-1 and 11 days yr-1). These results are consistent with previous studies led at local scale showing that LAI increase is accompanied by stress conditions increase in Mediterranean natural ecosystems. In this study, we also showed that spatial modelisation of drought conditions based on water budget simulations is an adequate tool for quantifying expositions of different species to stress and for analysing most influent factors on ecosystem vulnerability to drought.

A case study for the use of an artificial neural network (ANN) model for landslide susceptibility mapping in Koyulhisar (Sivas-Turkey) is presented. Digital elevation model (DEM) was first constructed using ArcGIS software. Relevant parameter maps were created, including geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index and distance from roads. Finally, a landslide susceptibility map was constructed using the neural networks. The drawbacks of the method are discussed but as the validation procedures used confirmed the quality of the map produced, it is recommended the use of ANN may be helpful for planners and engineers in the initial assessment of landslide susceptibil...

Abstract in portuguese Embora os índices de vegetação MODIS estejam sendo extensivamente investigados quanto ao seu potencial para o mapeamento e monitoramento biofísico do bioma Cerrado, em particular no que diz respeito à sazonalidade e fenologia da cobertura vegetal, pouco se sabe sobre o comportamento espacial desses índices em escalas regionais. Assim, neste estudo foram avaliados, à escala adotada em estudos de macroecologia (Resolução de 1º x 1º), os padrões de autocorrelaç? (more) ?o espacial do EVI (índice de vegetação realçado) e NDVI (índice de vegetação da diferença normalizada), utilizando-se índices I de Moran obtidos em diferentes classes de distância geográfica (correlogramas espaciais). Em média, os valores apresentados por esses índices são autocorrelacionados até uma distância aproximada de 800 km, que pode revelar um padrão de manchas afetado por variação ambiental e conversão da vegetação nativa. No entanto, esses padrões de similaridade espacial são principalmente influenciados pelo contraste sazonal encontrado no bioma Cerrado, bem como em função dos padrões de cobertura da terra e do tipo de índice considerado (i.e., EVI ou NDVI). Abstract in english While the MODIS vegetation indices have been extensively investigated regarding their potential for mapping and biophysical monitoring of the Cerrado biome, particularly with respect to the seasonality and phenology of the vegetative cover, very little is known about the spatial behavior of these indices. Thus, this study assessed, at the "macroecology" scale (1º x 1º spatial resolution), the autocorrelation patterns of both the EVI (enhanced vegetation index) and NDVI (more) (normalized difference vegetation index), using Moran's I coefficients obtained for several geographic distance classes (spatial correlogram). On average, the values presented by these two indices are correlated up to a distance of about 800 km, possibly revealing a patch pattern affected by environmental variables and native vegetation conversion. On the other hand, these spatial similarity patterns are mainly influenced by the marked seasonal contrast of the cerrado biome, as well as by the land cover classes and the vegetation index considered (i.e. EVI or NDVI).

Abstract:- Protected areas cover over 12% of the terrestrial surface of Earth, and yet many fail to protect species and ecological processes as originally envisioned. Results of recent studies suggest that a critical reason for this failure is an increasing contrast between the protected lands and the surrounding matrix of often highly altered land cover. We measured the isolation of 114 protected areas distributed worldwide by comparing vegetation-cover heterogeneity inside protected areas with heterogeneity outside the protected areas. We quantified heterogeneity as the contagion of greenness on the basis of NDVI (normalized difference vegetation index) values, for which a higher value of contagion indicates less heterogeneous land cover. We then measured isolation as the difference betw...

In 2006-2010, effects of four different cattle stocking rates (0, 14.4, 28.8, and 43.2 animal unit months) were compared, representing 0%, 20%, 32%, and 46% utilization of vegetation by domestic livestock, on vegetation structure (as indexed by visual obstruction), and songbird population and apparent nest density, community composition, and diversity in a Pacific Northwest bunchgrass prairie in northeastern Oregon, USA. Overall paddock-level visual obstruction decreased and structural heterogeneity increased with increasing stocking rates, and those effects carried over 1year after grazing had ceased. Most species were able to locate nesting sites regardless of differences in visual obstruction, except western meadowlark and vesper sparrow, for which obstruction was lower in paddocks with...

The use of normalized difference vegetation index (NDVI) data acquired with multiple satellite sensors has become a necessity in research fields such as agriculture, land-use and land-cover change and changes in the natural environment, where fast changes are taking place. A good understanding of these changes is a strong requirement of long-time-series monitoring programmes. In this paper, VEGETATION 10-day composite (VGT-S10) NDVI data with a 1 × 1 km resolution, covering the period from April 1998 to December 2006 and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data with a 8 × 8 km resolution, covering the period form April 1998 to December 2003 are used. The differences between the datasets were analysed to enabl...

We discuss our experience in application of the normalized difference vegetation index (NDVI), which we use as a basis for proposing methods for automated recognition, classification, and assessment of the condition of land utilized for different agricultural purposes (agriculture, pasture, peat soils, water bodies, etc.), with visualization of the results as color-coded raster maps. The objective is realized utilizing the specialized ERDAS Imagine software, using multispectral Ikonos satellite data (1.2 m resolution), based on standardization against 84 reference areas from the Polesskaja experimental station. The results were tested on landscape analogs.

Images using reflected visible and NIR data and images using passive microwave data were compared in terms of their usefulness for characterizing land-cover types in South America and Africa. The former images are of the normalized difference vegetation index (NDVI), and the latter images are of the microwave polarization difference temperature (MPDT). The combined use of MPDT and NDVI data sets show clear synergistic benefits in using the two data sets. However, the evidence suggests that for most cover types, increasing the temporal frequency of the NDVI images is more advantageous than incorporating MPDT data sets.

We discuss our experience in application of the normalized difference vegetation index (NDVI), which we use as a basis for proposing methods for automated recognition, classification, and assessment of the condition of land utilized for different agricultural purposes (agriculture, pasture, peat soils, water bodies, etc.), with visualization of the results as color-coded raster maps. The objective is realized utilizing the specialized ERDAS Imagine software, using multispectral Ikonos satellite data (1.2 m resolution), based on standardization against 84 reference areas from the Polesskaja experimental station. The results were tested on landscape analogs.

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean–atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982–2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central andeastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East–West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST–NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, we evaluated the impact of sensor degradation on trend detection using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001-0.004/yr decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002-2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in NDVI trends over vegetation.

Spectral reflectances of Produra wheat were measured at 13 different times of the day at Phoenix, Arizona, during April 1979 using a nadir-oriented hand-held 4-band radiometer which had bandpass characteristics similar to those on LANDSAT satellites. Different Sun altitude and azimuth angles caused significant diurnal changes in radiant return in both visible and near-IR regions of the spectrum and in several vegetation indices derived from them. The magnitude of these changes were related to different canopy architecture, percent cover and green leaf area conditions. Spectral measurements taken at each time period were well correlated with green leaf area index but the nature of the relationship changed significantly with time of day. Thus, a significant bias in the estimation of the green leaf area index from remotely sensed spectral data could occur if sun angles are not properly accounted for.

In recent years, the demand for vegetables has been decreasing. However, due to the development of externalization and the requirement for simplicity and ease in meal preparation, the market size of ready cut vegetables has increased. In addition, the suppliers of ready cut vegetables are interested in domestic vegetables for security-/- relief of food storage. Therefore, the suppliers become an important point of sale for vegetables, but, in the case of ready cut vegetables, the stipulated quality and standards are different from that for normal vegetables. Therefore, this report clarifies the correspondence with and the problems faced by the production district in dealing with contract farming of ready cut vegetables.   

This dissertation is aimed at evaluating and improving remote sensing techniques for vegetation moisture and fire risk estimation. Empirical retrievals of vegetation moisture using liquid water absorption based spectral indices such as the NDWI (Normalized Difference Water Index) and NDII (Normalized Difference Infrared Index) may have uncertainties, since these indices cannot fully normalize the reflectance variability due to other biophysical, biochemical, soil and illumination viewing geometry factors. Coupled leaf-canopy reflectance models, National Fire Danger Rating System data and the FARSITE fire behavior model were used to estimate the effect of Live Fuel Moisture Content (LFMC) retrieval uncertainties on fire spread rate predictions. The uncertainty estimation was focused on the Okefenokee National Wildlife Refuge where errors in LFMC retrievals using NDWI and NDII were shown to result in considerable fire spread rate prediction errors at lower LFMC levels. Soil reflectance contamination driven by soil moisture variability was identified as a problem causing errors in Vegetation Water Content (VWC) retrievals over low vegetation conditions. Analysis of canopy reflectance simulations from coupled soil-leaf-canopy reflectance models revealed that VWC isolines were curved and did not converge at the origin of the 1.64mum--0.86mum space. These were identified as causes for the soil moisture contamination of the spectral index NDII. As an improvement strategy an origin transformed NDII, called the SANDII (Soil Adjusted NDII) was designed to minimize soil contamination. Further separate regression models between VWC and the SANDII for different soil moisture classes were proposed to account for the curved nature of VWC isolines. The new technique which requires categorical soil moisture information was shown to reduce VWC estimation errors by about 20% over grassland conditions. The approach was supported using data collected over pastures during the Soil Moisture Experiment 2003 field campaign. Finally as an alternative to current subjective fire risk indices a new Fire Susceptibility Index (FSI) based on physical concept of pre-ignition energy was proposed. FSI uses remotely sensed estimations of fuel temperature and LFMC. Its physical basis is expected to allow computations of ignition probabilities and fire spread rates. FSI can be used compare fire risk across ecoregions and yet has the flexibility to be localized for an ecoregion for improved performance. A good agreement with the well tested FPI (Fire Potential Index) over Georgia, suggests the validity of FSI as a fire risk estimator. These new approaches would be helpful in fire risk monitoring, agriculture and climate studies.

Karst regions are typically ecological fragile zones constrained by geological setting, which resulted in high heterogeneity of vegetation standing conditions. The karst vegetation was featured with stone, dry and high calcium carbonate content growth conditions. Based on vegetation spectral analysis and canonical correspondence analysis (CCA), the present study aimed to examine the feasibility of using vegetation spectra to monitor the heterogeneous karst standing conditions. The results showed that there were significant differences between karst vegetation and non-karst vegetation within the spectral range of 1 300-2 500 nm reflectance and 400 - 680 nm first-derivative spectra. It was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst regions. Ordination diagrams of CCA could distinguish the differences of karst vegetation and non-karst vegetation. Our study demonstrates that vegetation spectra are highly related to karst standing conditions and it is feasible to monitor karst standing conditions with vegetation spectral features. PMID:23016347

In this study, the changes in desert boundaries in Asia (Gobi, Karakum, Lut, Taklimakan, and Thar deserts) during the growing season (April-October) in the years 1982-2008 were investigated by analyzing the normalized difference vegetation index (NDVI), precipitation, and temperature. In the desert boundary regions, the domain mean NDVI values increased by 7.2% per decade in 1982-1998 but decreased by 6.8% per decade thereafter. Accordingly, the bare soil areas (or nonvegetated areas) of the inside of the desert boundaries contracted by 9.8% per decade in the 1990s and expanded by 8.7% per decade in the 2000s. It is noted that the five deserts experience nearly simultaneous NDVI changes although they cover a very diverse area of Asia. In contrast, changes in temperature and precipitation in the deserts show rather diverse results. In desert boundaries located along 40°N (Gobi, Taklimakan, and Karakum), the decadal changes in vegetation greenness were mainly related to regional climate during the entire analysis period. Precipitation increased in the 1990s, providing favorable conditions for vegetation growth (i.e., greening), but precipitation reduced (19 mm per decade) and warming intensified (0.7°C per decade) in the 2000s, causing less moisture to be available for vegetation growth (i.e., browning). In desert boundaries below 40°N (Lut and Thar), although an increase in precipitation (8 mm per decade) led to greening in the 1990s, local changes in precipitation and temperature did not necessarily cause browning in the 2000s. Observed multidecadal changes in vegetation greenness in the present study suggest that under significant global and/or regional warming, changes in moisture availability for vegetation growth in desert boundaries are an important factor when understanding decadal changes in areas vulnerable to desertification over Asia.

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E?~?1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period. PMID:22972316

Abstract in spanish Se realizó el análisis de la distribución de 40 géneros y 113 especies de pteridofitas del valle de México de acuerdo a los tipos de vegetación de Rzedowski (2001). Se encontró que el 54% de las especies se encuentran en el bosque mesófilo de montaña; 45% en el bosque de coníferas; 33.6% en bosque de Quercus; 34.5% en pastizales; 28% en matorral xerófilo; 22% en bosque de pino-encino; 3.5% en matorral de encino y 1.8% de vegetación acuática así como en veget (more) ación halófila. Las afinidades entre las pteridofitas de las diferentes comunidades fueron analizadas usando datos sobre la presencia/ausencia basadas en el índice de similitud de Sörensen y el método de construcción UPGMA para el dendrograma, encontrándose mayor similitud entre el bosque de Pinus y el de Abies. El bosque de Quercus se relaciona más estrechamente con el bosque mesófilo, y el matorral xerófilo es muy afín al pastizal. La vegetación halófita guarda escasa relación con las otras comunidades y la vegetación acuática no se relaciona en absoluto con ninguna otra. Abstract in english An analysis of distribution for 40 genera and 113 species of pteridophytes from the Valley of Mexico according vegetation's types of Rzedowski (2001) is presented. We found 54% of the species in cloud forest; 45% in conifers forests; 33.6% in Quercus forest; 34.5% in grasslands; 28% in xero-philous scrubs; 22% in pine-oak forest; 3.5% in Quercus scrub and 1.8% in aquatic vegetation as in halophyllous vegetation. The affinities between the pteridophytes of the different co (more) mmunities were analyzed using presence/absence data with the Sörensen's similarity index and UPGMA method to construct the dendrogram, and we found more similarity between Pinus forest and the Abies forest. Quercus forest is related to the cloud forest, and the xerophilous scrubs is similar with glassland. The hallophyllous vegetation has little relation with another communities and the aquatic vegetation has not relation with any other.

Natural and anthropogenic sources of CO2 around the globe contribute to mid-tropospheric concentrations, yet it remains unknown how measurements of mid-tropospheric CO2 relate to regional ecosystem dynamics. NASA Atmospheric Infrared Sounder (AIRS) measurements of CO2 concentrations in the mid-troposphere from 2002 to 2010 were examined in relation to precipitation and vegetation phenology across the US Great Plains. Wavelet multi-resolution analysis and the information theory metric of relative entropy were applied to assess regional relationships between mid-tropospheric CO2, Normalized Difference Vegetation Index (NDVI), and precipitation (PPT). Results show that AIRS observations of mid-tropospheric CO2 exchange greater amounts of information with regional PPT and NDVI at seasonal, ann...

We investigate methods for geospatiotemporal data mining of multi-year land surface phenology data (250 m2 Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectrometer (MODIS) in this study) for the conterminous United States (CONUS) as part of an early warning system for detecting threats to forest ecosystems. The approaches explored here are based on k-means cluster analysis of this massive data set, which provides a basis for defining the bounds of the expected or normal phenological patterns that indicate healthy vegetation at a given geographic location. We briefly describe the computational approaches we have used to make cluster analysis of such massive data sets feasible, describe approaches we have explored for distinguishing between normal and abnormal phenology, and present some examples in which we have applied these approaches to identify various forest disturbances in the CONUS.

Multispectral imagery (MSI) taken with high-spatial resolution systems provides a powerful tool for mapping kelp in water. MSI are not always available, however, and there are systems which provide only panchromatic imagery which would be useful to exploit for the purpose of mapping kelp. Kelp mapping with MSI is generally done by use of the standard Normalized Difference Vegetation Index (NDVI). In broadband panchromatic imagery, the kelp appears brighter than the water because of the strong response of vegetation in the NIR, and can be reliably detected by means of a simple threshold; overall brightness is generally proportional to the NDVI. Confusion is caused by other bright pixels in the image, including sun glint. This research seeks to find ways of mitigating the number of false alarms using spatial image processing techniques. Methods developed in this research can be applied to other water target detection tasks.

A process-based crop growth model (Vegetation Interface Processes (VIP) model) is used to estimate crop yield with remote sensing over the North China Plain. Spatial pattern of the key parameter-maximum catalytic capacity of Rubisco (Vcmax) for assimilation is retrieved from Normalized Difference of Vegetation Index (NDVI) from Terra-MODIS and statistical yield records. The regional simulation shows that the agreements between the simulated winter wheat yields and census data at county-level are quite well with R^2 being 0.41-0.50 during 2001-2005. Spatial variability of photosynthetic capacity and yield in irrigated regions depend greatly on nitrogen input. Due to the heavy soil salinity, the photosynthetic capacity and yield in coastal region is less than 50mmolCm^-^2s^-^1 and 3000kgha^-...

Abstract Background:- Information on the role of family dietary behaviours is needed to enable the design of effective interventions for treatment of childhood obesity. The present study aimed to analyse differences in consumption and predictors of fruit, berries and vegetables (FBV) between normal-weight and overweight treatment-seeking children and their parents. Methods:- Fifty-four treatment-seeking overweight and 65 normal-weight 8-year-old children and their parents participated in the present study. Children-s and parent-s consumption of FBV were assessed by a food frequency questionnaire. Availability of vegetables at home meals, child-s preference for FBV and parent-s control over portion size were determined. Weight and height were measured and the standardised body mass index of...

We investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250~m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an early warning system to identify threats to forest ecosystems. Cluster analysis of this massive data set, using high-performance computing, provides a basis for several possible approaches to defining the bounds of ``normal'' phenological patterns, indicating healthy vegetation in a given geographic location. We demonstrate the applicability of such an approach, using it to identify areas in Colorado, USA, where an ongoing mountain pine beetle outbreak has caused significant tree mortality.

Abstract Many studies of mammalian herbivores have employed remotely sensed vegetation greenness, in the form of Normalized Difference Vegetation Index (NDVI) as a proxy for forage quality. The assumption that reflected greenness represents forage quality often goes untested, and limited data exist on the relationships between remotely sensed and traditional forage nutrient indicators. We provide the first study connecting NDVI and forage nutrient indicators within a free-ranging African herbivore ecosystem. We examined the relationships between fecal nutrient levels (nitrogen and phosphorus), forage nutrient levels, body condition, and NDVI for African buffalo (Syncerus caffer) in a South African savanna ecosystem over a 2-year period (2001 and 2002). We used an information-theoretic appr...

We present an approach to regional environmental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index (NDVI) data over the period 2001-2008 and land cover change (LCC) analysis of the 2001 and 2008 MODIS Global Land Cover product (MCD12Q1). NDVI trends were overwhelmingly negative across the grain belt with statistically significant (p?0.05) positive trends covering only 1% of the land surface. LCC was dominated by transitions between three classes; cropland, grassland, and a mixed cropland/natural vegetation mosaic. Combining our analyses of NDVI trends and LCC, we found a pattern of agricultural abandonment (cropland to grassland) in the southern range of the grain belt coinciding with statistically significant (p?0...

Lake Okeechobee, located in southern Florida, encompasses approximately 1,700 sq km and is a vital part of the Lake Okeechobee and Everglades ecosystem. Major cyanobacterial blooms have been documented in Lake Okeechobee since the 1970s and have continued to plague the ecosystem. Similarly, hydrilla, water hyacinth, and water lettuce have been documented in the lake and continue to threaten the ecosystem by their rapid growth. This study examines invasive aquatic vegetation occurrence through the use of the Normalized Difference Vegetation Index (NDVI) calculated on MOD09 surface reflectance imagery. Occurrence during 2008 was analyzed using the Time Series Product Tool (TSPT), a MATLAB-based program developed at John C. Stennis Space Center. This project tracked spatial and temporal variability of cyanobacterial blooms, and overgrowth of water lettuce, water hyacinth, and hydrilla. In addition, this study presents an application of Moderate Resolution Imaging Spectroradiometer (MODIS) data to assist in water quality management.

Using the Regional Atmospheric Modeling System (RAMS) we show that agricultural intensification and irrigation can modify the surface moisture and energy distribution, which alters the boundary layer and regional convergence, mesoscale convection, and precipitation patterns over the Indian monsoon region. Four experiments were conducted to simulate a rain event from 16 to 20 July 2002 over the Indian region: (i) a control with Global Land Cover land use and observed Normalized Difference Vegetation Index, (ii) an irrigated crop scenario, (iii) a non-irrigated crop scenario, and (iv) a scenario for potential (natural) vegetation. Results indicate that even under active monsoon conditions, the simulated surface energy and moisture flux over the Indian monsoon region are sensitive to the irri...

Little information is available about shell ridge ecosystems. Vegetative distribution and shell sand properties were investigated on a shell ridge in the Binzhou National Shell Ridge and Wetland Nature Reserve. 21 plant species were observed in the study area and, according to the Shannon?Weiner Index and species evenness, vegetative cover, and abundance varied significantly at different sites (P?

The effect of soil-water content ? in dust-source regions on the Asian dust event was examined using the AMSR-E soil-water product from 2003 to 2011. Soil water content ? ranging from 100 to 150 (g/cm3×103) has the highest correlation with Asian dust events in dust-source regions and over Japan. The NDVI-? relationship was proposed to determine the effect of surface conditions on the Asian dust event. Actual relationships in 2005 (2006), when the Asian dust events were less frequent (more frequent), were far from the assumption. It can be considered that there are some issues in using the AMSR-E soil-water product when the Normalized Difference Vegetation Index (NDVI) is high because of difficulties in monitoring the soil-water content itself independent of the vegetation structure.   

The objectives of a biogeochemical study of sagebrush ecosystems in Wyoming and their interactions with terrain are as follows: to describe the vegetational pattern on the landscape and elucidate controlling variables, to measure the soil properties and chemical cycling properties associated with the vegetation units, to associate soil properties with vegetation properties as measured on the ground, to develop remote sensing capabilities for vegetation and surface characteristics of the sagebrush landscape, to develop a system of sensing snow cover and indexing seasonal soil to moisture; and to develop relationships between temporal Thematic Mapper (TM) data and vegetation phenological state.

Information from images of Railroad Valley, Nevada captured on August 17,2001 by the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER) may provide a powerful tool for monitoring crop health and maintenance procedures.These images cover an area of north central Nevada. The top image shows irrigated fields, with healthy vegetation in red. The middle image highlights the amount of vegetation. The color code shows highest vegetation content in red, orange, yellow, green, blue, and purple and the lowest in black. The final image is a thermal infrared channel, with warmer temperatures in white and colder in black.In the thermal image, the northernmost and westernmost fields are markedly colder on their northwest areas, even though no differences are seen in the visible image or the second, Vegetation Index image. This can be attributed to the presence of excess water, which can lead to crop damage.The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)is an imaging instrument that is flying on Terra, a satellite launched in December 1999 as part of NASA's Earth Observing System (EOS). The instrument is being used to obtain detailed maps of land surface temperature, emissivity, reflectance and elevation. The Earth Observing System (EOS) platforms are part of NASA's Earth Science Enterprise, whose goal is to obtain a better understanding of the interactions between the biosphere, hydrosphere, lithosphere and atmosphere.NASA's Jet Propulsion Laboratory is a division of the California Institute of Technology, Pasadena.

The relationships between environmental exposure to risk agents and health conditions have been studied with the aid of remote sensing imagery, a tool particularly useful in the study of vegetation cover. This study aims to evaluate the influence of environmental variables on the spatial distribution of the abundance of Lutzomyia longipalpis and the reported canine and human visceral leishmaniasis (VL) cases at an urban area of Campo Grande, state of Mato Grosso do Sul. The sandfly captures were performed in 13 residences that were selected by raffle considering four residences or collection station for buffer. These buffers were generated from the central house with about 50, 100 and 200 m from it in an endemic area of VL. The abundance of sandflies and human and canine cases were georreferenced using the GIS software PCI Geomatica. The normalized difference vegetation index (NDVI) and percentage of land covered by vegetation were the environmental variables extracted from a remote sensing IKONOS-2 image. The average NDVI was considered as the complexity of habitat and the standard deviation as the heterogeneity of habitat. One thousand three hundred sixty-seven specimens were collected during the catch. We found a significant positive linear correlation between the abundance of sandflies and the percentage of vegetation cover and average NDVI. However, there was no significant association between habitat heterogeneity and the abundance of these flies. PMID:22510836

Vegetation net primary production (NPP) derived from a carbon model (Carnegie-Ames-Stanford Approach, CASA) and its interannual change in the Qinghai-Xizang (Tibetan) Plateau were investigated in this study using 1982-1999 time series data sets of normalized difference vegetation index (NDVI) and paired ground-based information on vegetation, climate, soil, and solar radiation. The 18-year averaged annual NPP over the plateau was 125 g C m-2 yr-1, decreasing from the southeast to the northwest, consistent with precipitation and temperature patterns. Total annual NPP was estimated between 0.183 and 0.244 Pg C over the 18 years, with an average of 0.212 Pg C (1 Pg = 1015 g). Two distinct periods (1982-1990 and 1991-1999) of NPP variation were observed, separated by a sharp reduction during 1990-1991. From 1982 to 1990, annual NPP did not show a significant trend, while from 1991 to 1999 a marked increase of 0.007 Pg C yr-2 was observed. NPP trends for most vegetation types resembled that of the whole plateau. The largest annual NPP increase during 1991-1999 appeared in alpine meadows, accounting for 32.3% of the increment of the whole region. Changes in solar radiation and temperature significantly influenced NPP variation, suggesting that solar radiation may be one of the major factors associated with changes in NPP.

Global data sets of land cover are a significant requirement for global biogeochemical and climate models. Remotely sensed satellite data is an increasingly attractive source for deriving these data sets due to the resulting internal consistency, reproducibility, and coverage in locations where ground knowledge is sparse. Seasonal changes in the greenness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index (NDVI) throughout the year, have been the basis for discriminating between cover types in previous attempts to derive land cover from AVHRR data at global and continental scales. This study examines the use of metrics derived from the NDVI temporal profile, as well as metrics derived from observations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability measures calculated from Bhattacharya distances, average separabilities improved by using 12 of the 16 metrics tested (1.97) compared to separabilities using 12 monthly NDVI values alone (1.88). Overall, the most robust metrics for discriminating between cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Band 2 (near-infrared reflectance) and Band 1 (red reflectance) corresponding to the time of maximum NDVI, and maximum land surface temperature. Deciduous and evergreen vegetation can be distinguished by mean NDVI, maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mean NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.

The Community Land Model version 4 (CLM4) is applied to explore the spatial temporal patterns of spring (April May) vegetation growth trends over the northern mid high latitudes (NMH) (>25 N) between 1982 and 2004. During the spring season through the 23 yr period, both the satellite-derived and simulated normalized difference vegetation index (NDVI) anomalies show a statistically significant correlation and an overall greening trend within the study area. Consistently with the observed NDVI temperature relation, the CLM4 NDVI shows a significant positive association with the spring temperature anomaly for the NMH, North America and Eurasia. Large study areas experience temperature discontinuity associated with contrasting NDVI trends. Before and after the turning point (TP) of the temperature trends, climatic variability plays a dominant role, while the other environmental factors exert minor effects on the NDVI tendencies. Simulated vegetation growth is broadly stimulated by the increasing atmospheric CO2. Trends show that nitrogen deposition increases NDVI mostly in southeastern China, and decreases NDVI mainly in western Russia after the temperature TP. Furthermore, land use-induced NDVI trends vary roughly with the respective changes in land management practices (crop areas and forest coverage). Our results highlight how non-climatic factors mitigate or exacerbate the impact of temperature on spring vegetation growth, particularly across regions with intensive human activity.

The exchange of the trace gases between the land and atmosphere is highly influenced by vegetation. Therefore, the representation of phenological properties in global carbon models plays a key role in understanding and predicting the global carbon cycle. Phenological parameters such as Leaf Area Index (LAI) and fraction of photosynthetically active radiation absorbed (fPAR) are often calculated or estimated based on remote sensing measurements, which can be biased by clouds, aerosols, or snow. Alternatively, we can prognose vegetation phenology through the use of models that predict vegetation status based on meteorological conditions. Here our goal is to provide better understanding of carbon dynamics as a function of phenological parameters and their dependence on meteorological forcing. We evaluate phenological characteristics and their influence on carbon dynamics at several grassland sites. Modeled carbon flux, as a function of both diagnosed (from satellite) and prognosed phenological state are confronted with data from flux towers. Remotely-sensed phenology has a tendency to expand the growing season, and does not reflect the rapid response of vegetation in rain-green biomes as well as the prognostic phenology model does. These differences in phenology are reflected in modeled fluxes of energy, moisture, and carbon.

The use of vegetable biodegradable basestocks for lubricant oils present several advantages over the much more extended mineral bases. These advantages refer to biodegradability, a renewable feedstock of local production, lubricant and viscosity index and lower costs than synthetic lubricant bases. Despite these benefits, their use in industry and motor vehicles is not yet extensive due their lower stability and higher pour points. Vegetable oils are esters of fatty acids and glycerol, and their physicochemical properties rely mainly on the composition of their acyl moieties. Thus, to assure the maximum levels of stability while maintaining acceptable behavior at low temperatures, monounsaturated fatty acids are preferred for this purpose. The presence of natural antioxidants also improves the properties of these vegetable based stocks as lubricants. These oils usually require additives to improve their viscosity value, oxidative stability and properties at low temperatures. In the present work, the different sources of vegetable oils appropriate for biolubricant production were reviewed. Their properties and the future improvement of the oil bases, oil based stock production, uses and additives are discussed. (Author).

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R2 of 0.85; there were no significant differences...

Satellite data offer unique perspectives for monitoring and quantifying land cover change, however, the radiometric consistency among co-located multi-temporal images is difficult to maintain due to variations in sensors and atmosphere. To detect accurate landscape change using multi-temporal images, we developed a new relative radiometric normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on 9 June 1990 (Landsat 4), 20 June 2000, and 26 August 2001 (Landsat 7) for analyses over boreal forests near the Siberian city of Krasnoyarsk. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Reduced Simple Ratio (RSR) were investigated in the normalization study. The temporally invariant cluster (TIC) centers were identified through a point density map of the base image and the target image and a normalization regression line was created through all TIC centers. The target image digital data were then converted using the regression function so that the two images could be compared using the resulting common radiometric scale. We found that EVI was very sensitive to vegetation structure and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. NDVI was a very effective vegetation index to reduce the influence of shadow, while EVI was very sensitive to shadowing. After normalization, correlations of NDVI and EVI with field collected total Leaf Area Index (LAI) data in 2000 and 2001 were significantly improved; the r-square values in these regressions increased from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI ¡°cancellation effect¡± where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared with some previous relative normalization methods, this new method can avoid subjective selection of a normalization regression line. It does not require high level programming and statistical analyses, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare.

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST)...

In water-deficient areas, water resource management requires evapotranspiration at high spatiotemporal resolution - an impossible situation given the trade-off between spatial and temporal resolutions in space-borne systems. Some researchers have suggested sharpening the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature product with a resolution from 1 km to 250 m and a functional relationship between surface temperature (Tr) and normalized difference vegetation index (NDVI). Evapotranspiration at 250 m resolution can be obtained once every few days using this technique. Based on the interpretation of the triangular Tr-NDVI space and assuming uniform soil moisture conditions in a coarse pixel, this paper suggests an alternative algorithm - the triangl...

The communities of benthic microalgae that form dense biofilms at the surface of aquatic sediments, or microphytobenthos, are important primary producers in estuarine intertidal flats and shallow coastal waters. The microalgal biomass present in the photic zone of the sediment is a key parameter for ecological and photophysiological studies on microphytobenthos, and has been routinely estimated using hyperspectral reflectance indices based on the chlorophyll (Chl) a red absorption peak at 675?nm, usually the Normalised Difference Vegetation Index (NDVI). This study reports that red region-based biomass indices measured on microphytobenthos biofilms can be significantly affected by the enrichment of reflected light with solar-induced Chl fluorescence emitted by the microalgae. Chl fluoresce...

Data from the advanced very high resolution radiometer on board the National Oceanic and Atmospheric Administration's polar-orbiting meteorological satellites have been used to infer ecological parameters associated with Rift Valley fever (RVF) viral activity in Kenya. An indicator of potential viral activity was produced from satellite data for two different ecological regions in Kenya, where RVF is enzootic. The correlation between the satellite-derived green vegetation index and the ecological parameters associated with RVF virus suggested that satellite data may become a forecasting tool for RVF in Kenya and, perhaps, in other areas of sub-Saharan Africa.

Geoland2 project is the FP7 project which intends to prepare, validate and demonstrate pre-operational service chains and products of the GMES Land Monitoring Service. The architecture of geoland2 is made of 3 Core Mapping Services (CMS) providing "basic" land products to7 Core Information Services (CIS) acting on various applications in spatial planning, water quality, forest monitoring, agriculture and food security, land carbon monitoring, and natural resources monitoring. We focus here on the BioPar CMS products related to soil and vegetation variables: the surface albedo, the Leaf Area Index (LAI), the Fraction of green Vegetation Cover (FCover), the fraction of absorbed photosynthetically active radiation (FAPAR) and the Normalized Differential Vegetation Index (NDVI). These products are derived from SPOT/VEGETATION sensor data and are currently distributed on the geoland2 portal (http://www.geoland2.eu). During the last year, the French Space Agency (CNES) has processed the 12 years of VGT archive data and generated a long term time series of biophysical variables, from 1999 to 2010. Since 2011, the production is running continuously at VITO (Belgium). The products provide a global coverage with a spatial resolution of 1 km and a temporal resolution of 10 days. CNES is currently processing this archive to produce a climatology : the vegetation variables are averaged over the 12 years to get a reference on vegetation variables with a 10 days step. In the next months, the SPOT/VGT time series will be completed by consistent LAI, FAPAR and FCover products derived from the AVHRR long term data archive covering the period from 1982 to 2000. This 30-years time series will provide a unique view of the evolution of ecosystems due to natural changes or human pressure. The poster will briefly describe the organization set-up to build the BioPar CMS and the product portfolio. Then the emphasis will be put on the content of the 12-year archive of vegetation products delivered for free to the users : technical content of the products, validation protocol, examples of maps on different phenomena (drought, burnt area, …).

Leaf Area Index (LAI) is one of a key variables in studying and understanding biogeochemical cycle mechanisms and ecosystem functionalities and, then, one of a main inputs for ecological modeling. Leaf area surface is related to the main interactions between leaves and the atmosphere as water interception, radiation extinction, energy, mass and gas exchange. Therefore LAI reduction, consequently the loss of productivity, is expression of any physiological and biochemical change of plant status due for example to summer water stress in Mediterranean areas. A good knowledge of seasonal trend and spatial variability of LAI can helps not only modelers but also local farmer to manage grasslands in a sustainable way (grazing, harvesting). In situ LAI measurements are often limited to relatively small areas whit a small number of samplings that can be sporadic, destructive and time-consuming. Nowadays an interesting alternative to estimate LAI is provided by a large variety of radiometric sensors (ground, airborne and satellite based) whit several spatial, temporal and spectral resolutions. However, few studies shown the effect of different radiometers set-up on VIs-LAI relationships that are also differently sensible to different ranges of LAI, management and to which method is used for LAI measurements. In this work, we analyzed the relations between several spectral vegetation indexes (VIs) and LAI for the Mediterranean grassland of Amplero, in the Abruzzo Region, Italy. In situ measurements were carried out in 2005 and 2006. Contemporaneously to destructive LAI measurements, radiometric measurements over the grass herbage were made by two different radiometric sensors: by hyperspectral Hand Held ASD spettroradiometer (HYS) field samplings and by broad band measurements (BNR) of incoming and outgoing global (shortwave) solar radiation components and of incident and reflected photosintetically active radiation (PAR). In addition we included in this analysis VIs calculated from MODIS Surface Reflectance (MOD-09) bands and MODIS Vegetation Indexes (MOD-13) product. Among all calculated spectral indexes, NPVI (Normalized Parabolic Vegetation Index), a new index that we proposed, showed best fit with LAI for HYS (R2 = 0.81), BNR (R2 = 0.79) and MOD-13 (R2 = 0.63) while MOD-09 correlates better with NDVI (R2 = 0.65). Moreover LAI-NPVI relationship seams not to be affected by saturation at LAI values higher than 1.5 m2 m-2 as it happens for other indexes as hyperspectral NDVI. LAI shows also a significant exponential relation with GPP (Gross Primary Production)(R2 = 0.69) saturating for LAI values higher than 1 m2 m-2. Moreover several studied vegetation indexes appear to correlate whit GPP offering thus the possibility to predict gross productivity both continuously by BNR radiometer and over a large area by MOD-09 and MOD-13 data. Finally, up-scaling the best LAI-VI relations we created LAI maps that can be helpful to local farmers to understand yield productivity and to modelers to assimilate in their models indirect estimation of leaf area index.

Dynamical effects of vegetation on the 2003 summer heat waves Marc Stéfanon(1), Philippe Drobinski(1), Fabio D'Andrea(1), Nathalie de Noblet(2) (1) IPSL/LMD, France; (2) IPSL/LSCE, France The land surface model (LSM) in regional climate models (RCMs) plays a key role in energy and water exchanges between land and atmosphere. The vegetation can affect these exchanges through physical, biophysical and bio-geophysical mechanisms. It participates to evapo-transpiration process which determines the partitioning of net radiation between sensible and latent heat flux, through water evaporation from soil throughout the entire root system. For seasonal timescale leaf cover change induced leaf-area index (LAI) and albedo changes, impacting the Earth's radiative balance. In addition, atmospheric chemistry and carbon concentration has a direct effect on plant stomatal structure, the main exchange interface with the atmosphere. Therefore the surface energy balance is intimately linked to the carbon cycle and vegetation conditions and an accurate representation of the Earth's surface is required to improve the performance of RCMs. It is even more crucial for extreme events as heat waves and droughts which display highly nonlinear behaviour. If triggering of heat waves is determined by the large scale, local coupled processes over land can amplify or inhibit heat trough several feedback mechanism. One set of two simulation has been conducted with WRF, using different LSMs. They aim to study drought and vegetation effect on the dynamical and hydrological processes controlling the occurrence and life cycle of heat waves In the MORCE plateform, the dynamical global vegetation model (DGVM) ORCHIDEE is implemented in the atmospheric module WRF. ORCHIDEE is based on three different modules. The first module, called SECHIBA, describes the fast processes such as exchanges of energy and water between the atmosphere and the biosphere, and the soil water budget. The phenology and carbon dynamics of the terrestrial biosphere are simulated by the STOMATE module. STOMATE essentially simulates processes as photosynthesis, carbon allocation, litter decomposition, soil carbon dynamics, maintenance and growth respiration, and phenology. Finally, the long-term processes, including vegetation dynamics, fire, sapling establishment, light competition, and tree mortality are simulated according to the global vegetation model LPJ. Two MORCE simulations are performed at 15-km grid resolution, driven by ERA-INTERIM for 2002-2003. The first, called CTL, was conducted using an LAI prescribed after that of year 2002. The second simulation called MORCE, uses LAI explicitly calculated. These simulations are inter-compared to provide an estimate of the dynamical vegetation contribution to the two distinct heat wave during the summer 2003.

SeaWiFS Ocean Chlorophyll & Land Vegetation Index ... invasions of exotic species, nitrogen deposition, and acidification of the surface ocean, is unclear. ... biodiversity, sustainable resource management, and the maintenance of a healthy, ...

The EVI gains its heritage primarily from the soil adjusted vegetation index (SAVI) and is an ... minimizes residual cloud and atmosphere effects and constrains view angles. This is .... seasonally moist tropical evergreen forest, Remote Sens.

PM2.5 Mass Concentration and particulate matter Air Quality Index are ... product is accurate over oceans and dark vegetated surfaces under typical AOD loading. ... Year-specific emissions; Dust, sea salt, sulfate-ammonium-nitrate system, ...

Coronado Island--together with nearby Isla Mitlan and Isla Calavera--has an arid climate and is sparsely vegetated. ... and tourism, local groups developed a management and conservation plan for the islands in the bay, ... View Images Index ...

Jul 30, 2003 ... vineyard canopy density, expressed both as leaf area index (LAI) and leaf area per vine. Within-field .... promote fruit production. ... in vertically shoot positioned vineyards: Determining optimal vegetation indices. Austr. J.

Sep 19, 2012 ... New technologies improve the quality and reliability of fiber-optic assemblies ... This new active vegetation index measurement technique enables remote ... Goddard's method uses a nanoscale material as a reinforcing phase ...

Data-mining is a technique that it can be used to interact with large databases and to help in the discovery relations between parameters by extracting information from massive and multiple data archives. Drought affects many economic and social sectors, from agricultural to transportation, going through urban water deficit and the development of modern industries. With these problems and drought geographical and temporal distribution it's difficult to find a single definition of drought. Improving the understanding of the knowledge of climatic index is necessary to reduce the impacts of drought and to facilitate quick decisions regarding this problem. The main objective is to analyze drought periods from 1950 to 2009 in Spain. We use several kinds of information, different formats, sources and transmission mode. We use satellite-based Vegetation Index, dryness index for several temporal periods. We use daily and monthly precipitation and temperature data and soil moisture data from numerical weather model. We calculate mainly Standardized Precipitation Index (SPI) that it has been used amply in the bibliography. We use OLAP-Mining techniques to discovery of association rules between remote-sensing, numerical weather model and climatic index. Time series Data- Mining techniques organize data as a sequence of events, with each event having a time of recurrence, to cluster the data into groups of records or cluster with similar characteristics. Prior climatological classification is necessary if we want to study drought periods over all Spain.

Satellite image data have become an important source of information for monitoring vegetation and mapping land cover at several scales. Beside this, the distribution and phenology of vegetation is largely associated with climate, terrain characteristics and human activity. Various vegetation indices have been developed for qualitative and quantitative assessment of vegetation using remote spectral measurements. In particular, sensors with spectral bands in the red (RED) and near-infrared (NIR) lend themselves well to vegetation monitoring and based on them [(NIR - RED) / (NIR + RED)] Normalized Difference Vegetation Index (NDVI) has been widespread used. Given that the characteristics of spectral bands in RED and NIR vary distinctly from sensor to sensor, NDVI values based on data from different instruments will not be directly comparable. The spatial resolution also varies significantly between sensors, as well as within a given scene in the case of wide-angle and oblique sensors. As a result, NDVI values will vary according to combinations of the heterogeneity and scale of terrestrial surfaces and pixel footprint sizes. Therefore, the question arises as to the impact of differences in spectral and spatial resolutions on vegetation indices like the NDVI. The aim of this study is to establish a comparison between two different sensors in their NDVI values at different spatial resolutions. Scaling analysis and modeling techniques are increasingly understood to be the result of nonlinear dynamic mechanisms repeating scale after scale from large to small scales leading to non-classical resolution dependencies. In the remote sensing framework the main characteristic of sensors images is the high local variability in their values. This variability is a consequence of the increase in spatial and radiometric resolution that implies an increase in complexity that it is necessary to characterize. Fractal and multifractal techniques has been proven to be useful to extract such complexities from remote sensing images and will applied in this study to see the scaling behavior for each sensor in generalized fractal dimensions. The studied area is located in the provinces of Caceres and Salamanca (east of Iberia Peninsula) with an extension of 32 x 32 km2. The altitude in the area varies from 1,560 to 320 m, comprising natural vegetation in the mountain area (forest and bushes) and agricultural crops in the valleys. Scaling analysis were applied to Landsat-5 and MODIS TERRA to the normalized derived vegetation index (NDVI) on the same region with one day of difference, 13 and 12 of July 2003 respectively. From these images the area of interest was selected obtaining 1024 x 1024 pixels for Landsat image and 128 x 128 pixels for MODIS image. This implies that the resolution for MODIS is 250x250 m. and for Landsat is 30x30 m. From the reflectance data obtained from NIR and RED bands, NDVI was calculated for each image focusing this study on 0.2 to 0.5 ranges of values. Once that both NDVI fields were obtained several fractal dimensions were estimated in each one segmenting the values in 0.20-0.25, 0.25-0.30 and so on to rich 0.45-0.50. In all the scaling analysis the scale size length was expressed in meters, and not in pixels, to make the comparison between both sensors possible. Results are discussed. Acknowledgements This work has been supported by the Spanish MEC under Projects No. AGL2010-21501/AGR, MTM2009-14621 and i-MATH No. CSD2006-00032