Annual Total Gross Primary Production Estimation based on Vegetation Indices Seasonal Variation Curve

For the estimation of annual Gross Primary Productivity(GPP),it is proposed an estimation method with simple parameters and small errors.By taking each type of vegetation in the area of Three-North Shelterbelt Program(TNSP) as the research subject,the MODIS vegetation indices were obtained,and the seasonal variation curve of vegetation indices were built.Then,the fitting relation between the integral of time series vegetation indices(ΣVIs) and GPP products of MODIS was established,so as to realize a simple GPP estimation method and study the applicable ΣVIs for estimating the GPP of all vegetation types.The results show that：(1) ΣVIs is suitable for estimating the annual total GPP in research area and significantly correlated with MODIS GPP at the confidence level of p<0.01;(2) ΣEVI2 is applicable to estimate the GPP of evergreen needleleaf forest,decidious needleleaf forest,decidious broadleaf forest,mixed forest,woody savannas,savannas,permanent wetlands,croplands,croplands/natural vegetation mosaic,while the effect of ΣNDVI for estimating the GPP of closed shrublands,open shrublands,grasslands,croplands,and barren or sparsely vegetated is superior to ΣEVI andΣEVI2;(3) Since the NDVI itself is saturated in the area of high Leaf Area Index(LAI),the error of estimating the GPP of high LAI vegetation type by ΣNDVI is larger,while using ΣEVI and ΣEVI2 to estimate them has better accuracy,and the limitation from blue band of EVI2 reduces compared with EVI,which can be applied to the GPP research of long time series better.     

Integrating remote sensing and spatial statistics to model herbaceous biomass distribution in a tropical savanna

Modelling herbaceous biomass is critical for an improved understanding of wildlife feeding patterns and distribution as well as for the development of early warning systems for fire management. Most savannas in South Africa are characterized by complex stand structure and abundant vegetation species. This has prohibited accurate estimation of biomass in such environments. We investigated the possibility of improving biomass predictions in tropical savannas using cokriging. Individual bands and ratios computed from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery were correlated with field measurements of biomass covering the Kruger National Park, South Africa. The band that yielded the highest correlation with biomass was then used for further analysis using cokriging. Three variogram models were developed: one for the herbaceous biomass, one for the best MODIS band and a cross variogram between all pairs of variables involved in the estimation. The variogram models were then used in cokriging to predict biomass distribution over the whole study area. Results indicate that a combination of remotely sensed data with field biomass measurements through cokriging improves the estimation accuracy compared to ordinary kriging and stepwise linear regression. Given the high temporal resolution of the freely available MODIS imagery, the result is critical for the improved monitoring and management of wildlife habitats.     

Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems

Northern Arizona ecosystems are particularly sensitive to plant-available moisture and have experienced a severe drought with considerable impacts on ecosystems from desert shrub and grasslands to pinyon-juniper and conifer forests. Long-term time-series from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used to monitor recent regional vegetation activity and temporal patterns across various ecosystems. Surface air temperature, solar radiation and precipitation are used to represent meteorological anomalies and to investigate associated impacts on vegetation greenness. Vegetation index anomalies in the northern Arizona ecosystem have a decreasing trend with increasing surface air temperature and decreasing precipitation. MODIS NDVI and EVI anomalies are likely sensitive to the amount of rainfall for northern Arizona ecosystem conditions, whereas inter-annual variability of surface air temperature accounts for MODIS NDVI anomaly variation. The higher elevation area shows the slow vegetation recovery through trend analysis from MODIS vegetation indices for 2000–2011 within the study domain and along elevation.     

Testing a MODIS Global Disturbance Index across North America

Large-scale ecosystem disturbances (LSEDs) have major impacts on the global carbon cycle as large pulses of CO₂ and other trace gases from terrestrial biomass loss are emitted to the atmosphere during disturbance events. The high temporal and spatial variability of the atmospheric emissions combined with the lack of a proven methodology to monitor LSEDs at the global scale make the timing, location and extent of vegetation disturbance a significant uncertainty in understanding the global carbon cycle. The MODIS Global Disturbance Index (MGDI) algorithm is designed for large-scale, regular, disturbance mapping using Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Aqua/MODIS Enhanced Vegetation Index (EVI) data. The MGDI uses annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Here we apply the full Aqua/MODIS dataset through 2006 to the improved MGDI algorithm across the woody ecosystems of North America and test the algorithm by comparison with confirmed, historical wildfire events and the windfall areas of documented major hurricanes. The MGDI accurately detects the location and extent of wildfire throughout North America and detects high and moderate severity impacts in the windfall area of major hurricanes. We also find detections associated with clear-cut logging and land-clearing on the forest-agricultural interface. The MGDI indicates that 1.5% (195,580 km²) of the woody ecosystems within North America was disturbed in 2005 and 0.5% (67,451 km²) was disturbed in 2006. The interannual variability is supported by wildfire detections and official burned area statistics.     

Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid,saline rangelands in North-East Iran?

ABSTRACT

Satellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R². Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R² = 0.47 and R² = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands.     

A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas

Multi-temporal vegetation index (VI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are becoming widely used for large-area crop classification. Most crop-mapping studies have applied enhanced vegetation index (EVI) data from MODIS instead of the more traditional normalized difference vegetation index (NDVI) data because of atmospheric and background corrections incorporated into EVI's calculation and the index's sensitivity over high biomass areas. However, the actual differences in the classification results using EVI versus NDVI have not been thoroughly explored. This study evaluated time-series MODIS 250-m EVI and NDVI for crop-related land use/land cover (LULC) classification in the US Central Great Plains. EVI- and NDVI-derived maps classifying general crop types, summer crop types and irrigated/non-irrigated crops were produced for southwest Kansas. Qualitative and quantitative assessments were conducted to determine the thematic accuracy of the maps and summarize their classification differences. For the three crop maps, MODIS EVI and NDVI data produced equivalent classification results. High thematic accuracies were achieved with both indices (generally ranging from 85% to 90%) and classified cropping patterns were consistent with those reported for the study area (> 0.95 correlation between the classified and USDA-reported crop areas). Differences in thematic accuracy (< 3% difference), spatially depicted patterns (> 90% pixel-level thematic agreement) and classified crop areas between the series of EVI- and NDVI-derived maps were negligible. Most thematic disagreements were restricted to single pixels or small clumps of pixels in transitional areas between cover types. Analysis of MODIS composite period usage in the classification models also revealed that both VIs performed equally well when periods from a specific growing season phase (green, peak or senescence) were heavily utilized to generate a specific crop map.     

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.     

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).     

Monitoring and forecasting ecosystem dynamics using the Terrestrial Observation and Prediction System (TOPS)

We present an approach for monitoring and forecasting landscape level indicators of the condition of protected area (PA) ecosystems including changes in snowcover, vegetation phenology and productivity using the Terrestrial Observation and Prediction System (TOPS). TOPS is a modeling framework that integrates operational satellite data, microclimate mapping, and ecosystem simulation models to characterize ecosystem status and trends. We have applied TOPS to investigate trends and patterns in landscape indicators using test cases at both national and park-level scales to demonstrate the potential utility of TOPS for supporting efforts by the National Park Service to develop standardized indicators for protected area monitoring. Our analysis of coarse resolution satellite-derived normalized difference vegetation index (NDVI) measurements for North America from 1982-2006 indicates that all but a few PAs are located in areas that exhibited a sustained decline in vegetation condition. We used Yosemite National Park as our park-level test case, and while no significant trends in NDVI were detected during the same period, evidence of drought-induced vegetation mortality and recovery patterns dominated the 25-year record. In our Yosemite analysis, we show that analyzing MODIS (Moderate Resolution Imaging Spectro-radiometer) products (vegetation indices, absorbed radiation, land surface temperature and gross primary production) in conjunction with ground-based measurements, such as runoff, lends additional utility to satellite-based monitoring of ecosystems indicators, as together they provide a comprehensive view of ecosystem condition. Analyses of MODIS products from 2001-2006 show that year-to-year changes in the onset of spring at Yosemite were as large as 45 days, and this signal in the satellite data record is corroborated by observed changes in spring runoff patterns. Finally, we applied TOPS to assess long-term climate impacts on ecosystem condition at the scale of an individual park. When driven by projected climatic changes at Yosemite of 4-6 °C warming by 2100 with no changes in precipitation patterns, TOPS predicts significantly reduced winter snowpack and an earlier onset of the growing season, resulting in prolonged summer drought and reduced vegetation productivity.     

Leaf and stand-level carbon uptake of a Mediterranean forest estimated using the satellite-derived reflectance indices EVI and PRI

Various aspects of global environmental change affect plant photosynthesis, the primary carbon input in ecosystems. Thus, accurate methods of measuring plant photosynthesis are important. Remotely sensed spectral indices can monitor in detail the green biomass of ecosystems, which provides a measure of potential photosynthetic capacity. In evergreen vegetation types, however, such as Mediterranean forests, the amount of green biomass changes little during the growing season and, therefore, changes in green biomass are not responsible for changes in photosynthetic rates in those forests. This study examined the net photosynthetic rates and the diametric increment of stems in a Mediterranean forest dominated by Quercus ilex using three spectral indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and photochemical reflectance index (PRI)) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Average annual EVI accounted for 83% of the variability of the diametric increment of Q. ilex stems over a 10 year period. NDVI was marginally correlated with the diametric increment of stems. This study was the first to identify a significant correlation between net photosynthetic rates and radiation use efficiency at the leaf level using PRI derived from satellite data analysed at the ecosystem level. These results suggest that each spectral index provided different and complementary information about ecosystem carbon uptake in a Mediterranean Q. ilex forest.     

Using short-term MODIS time-series to quantify tree cover in a highly heterogeneous African savanna

Reliable mapping of tree cover and tree-cover change at regional, continental, and global scales is critical for understanding key aspects of ecosystem structure and function. In savannas, which are characterized by a variable mixture of trees and grasses, mapping tree cover can be especially challenging due to the highly heterogeneous nature of these ecosystems. Our objective in this article was to develop improved tools for large-scale classification of savanna tree cover in grass-dominated savanna ecosystems that vary substantially in woody cover over fine spatial scales. We used multispectral, low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to identify the bands and metrics that are best suited to quantify woody cover in an area of the Serengeti National Park, Tanzania. We first used 1-m resolution panchromatic IKONOS data to quantify tree cover for February 2010 in an area of highly variable tree cover. We then upscaled the classification to MODIS (250 m) resolution. We used a 2 year time series (IKONOS date ± 1 year) of MODIS 16 day composites to identify suitable metrics for quantifying tree cover at low resolution, and calculated and compared the explanatory power of three different variable classes for four MODIS bands using Lasso regression: longitudinal summary statistics for individual spectral bands (e.g. mean and standard deviation), Fourier harmonics, and normalized difference vegetation index (NDVI) green-up metrics. Longitudinal summary statistics showed better explanatory power (R ² = 73% for calibration data; R ² = 61% for validation data) than Fourier or green-up metrics. The mid-infrared, near-infrared, and NDVI bands were all important predictors of tree cover. Mean values for the time series were more important than other metrics, suggesting that multispectral data may be more valuable than within-band seasonal variation obtained from time series data for mapping tree cover. Our best model improved substantially over the MODIS Vegetation Continuous Fields product, often used for quantifying tree cover in savanna systems. Quantifying tree cover at coarse spatial resolution using remote-sensing approaches is challenging due to the low amount and high heterogeneity of tree cover in many savanna systems, and our results suggest that products that work well at global scales may be inadequate for low-tree-cover systems such as the Serengeti. We show here that, even in situations where tree cover is low (<10%) and varies considerably across space, satisfactory predictive power is possible when broad spectral data can be obtained even at coarse spatial resolution.     

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.     

Biophysical estimation in tropical forests using JERS‐1 SAR and VNIR imagery. II. Aboveground woody biomass

Accurate estimates of aboveground biomass in tropical forests are important in carbon sequestration and global change studies. Tropical forest biomass estimation with microwave remote sensing is limited because of the strong scattering and attenuation properties of the green canopy. In this study a microwave/optical synergistic model was developed to quantify these effects to Synthetic Aperture Radar (SAR) signals and to better estimate woody structures, which are closely related to aboveground biomass. With a Leaf Area Index (LAI) retrieved from Japan Earth Resources Satellite (JERS)‐1 Very Near Infrared Radiometer (VNIR) imagery, leaf scattering and attenuation to woody scattering were quantified and removed from the total backscatter in a modified canopy scattering model. Woody scattering showed high sensitivity to biomass >100 tonnes/ha in tropical forests. Tree height and stand density were derived from the JERS‐1 SAR image with a root mean square error (RMSE) of 4 m and 161 trees/ha, respectively. Aboveground biomass was calculated using a general allometric equation. Biomass in secondary dry dipterocarps (Dipterocarpaceae family of tropical lowland deciduous trees) was overestimated. The modelled biomass in mixed deciduous and dry evergreen forests fit better with ground measurements. In mountainous areas with steep slopes, the topographic effects in the SAR image could not be properly corrected and therefore the results are unreliable.     

Land cover assessment with MODIS imagery in southern African Miombo ecosystems

Global land use and land cover products in highly dynamic tropical ecosystems lack the detail needed for natural resource management and monitoring at the national and provincial level. The MODIS sensor provides improved opportunities to combine multispectral and multitemporal data for land use and land cover mapping. In this paper we compare the MODIS Global Land Cover Classification Product with recent land use and land cover maps at the national level over a characteristic location of Miombo woodlands in the province of Zambezia, Mozambique. The performances of three land cover-mapping approaches were assessed: single-date supervised classification, principal component analysis of band-pair difference images, and multitemporal NDVI analysis. Extensive recent field data were used for the definition of the test sites and accuracy assessment. Encouraging results were achieved with the three approaches. The classification results were refined with the help of a digital elevation model. The most consistent results were achieved using principal component analysis of band-pair difference images. This method provided the most accurate classifications for agriculture, wetlands, grasslands, thicket and open forest. The overall classification accuracy reached 90%. The multitemporal NDVI provided a more accurate classification for the dense forest cover class. The selection of the right image dates proved to be critical for all the cases evaluated. The flexibility of these alternatives makes them promising options for rapid and inexpensive land cover mapping in regions of high environmental variability such as tropical developing countries.     

Mapping shrubland biomass along Mediterranean climatic gradients: The synergy of rainfall-based and NDVI-based models

Two approaches to biomass mapping of shrublands across sub-humid and arid transition zones are integrated. The first generalizes relationships between biomass and precipitation from sites in the Mediterranean Basin, California, Namibia and Mongolia. The second represents existing Normalized Difference Vegetation Index (NDVI)-based models for biomass estimation on a regional scale. A new modified NDVI-based model is presented that uses relative rain availability as the ratio between the mean annual precipitation and the threshold rain level representing the transition from herbaceous growth to shrub dominance. While the data accounts for the actual vegetation cover, the relative rain parameter accounts for the potential biomass production. Implementation of the modified relative rain model with Landsat imagery of climatic gradients (the east-west gradient between the Judean Mountains and the Judean desert and the north-south gradient between the Judean Mountains and the Negev Desert) yielded realistic estimates of biomass in areas of high human disturbance to the natural ecosystems. These results support the possibility that the modified model can be used to map biomass across wide Mediterranean and desert-fringe ecosystems.     

Retrieving understorey dynamics in the Australian tropical savannah from time series decomposition and linear unmixing of MODIS data

Retrieval from remote sensing of separate temporal dynamics for the understorey layer in tropical savannahs would be beneficial for monitoring fuel loads, biomass for livestock, interrelationships between trees and grasses, and modelling of savannah systems. In this study, we combined unmixing of fractional cover with normalized difference vegetation index (NDVI) and the short wave infrared ratio (SWIR32) with time series decomposition of the NDVI to attempt to fully resolve the dynamics of the herbaceous understorey in the Australian tropical savannah based on the fractions of photosynthetic herbaceous vegetation (F_PVH) and non-photosynthetic vegetation (F_NPV), from the woody overstorey, represented by the fraction of photosynthetic vegetation in the tree canopy (F_PVW). Evaluation of F_PVH against field data gave moderate relationships between predicted and observed values (R² between 0.5 and 0.6); since semivariogram metrics of representativeness indicated that field sites were relatively unrepresentative of variation at the Moderate Resolution Imaging Spectroradiometer MODIS) pixel scale. Both F_PVW and F_PVH produced strong linear relationships (root mean square error < 0.1 units) with high-resolution Orbview 3 cover fractions classified from tasselled cap transformations. However, F_NPVH (non-photosynthetic herbaceous cover fraction) retrievals at southern arid locations produced an evaluation relationship with a greater deviation from the 1:1 line than for northern locations. This suggested that there may be limitations on the NDVI–SWIR32 unmixing approach in more sparsely vegetated savanna. Maps of average annual maximum F_PVH, F_NPVH, and total herbaceous cover fraction could be used as indicators of savannah productivity and landscape health. However, close examination of the limitations of the NDVI–SWIR32 response may be required for application of this method in other global savannahs.     

An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data

Monitoring vegetation condition is an important issue in the Mediterranean region, in terms of both securing food and preventing fires. The recent abundance of remotely sensed data, such as the daily availability of MODIS imagery, raises the issue of appropriate temporal sampling when monitoring vegetation: under‐sampling may not accurately describe the phenomenon under consideration, whilst over‐sampling would increase the cost of the project without additional benefit. The aim of this work is to estimate the optimum temporal resolution for vegetation monitoring on a nationwide scale using 250 m MODIS/Terra daily images and composites. Specific objectives include: (i) an investigation into the optimum temporal resolution for monitoring vegetation condition during the dry season on a nationwide scale using time‐series analysis of Normalized Difference Vegetation Index, NDVI, datasets, (ii) an investigation into whether this temporal resolution differs between the two major vegetation categories of natural and managed vegetation, and (iii) a quality assessment of multi‐temporal NDVI composites following the proposed optimum temporal resolution. A time‐series of daily NDVI data is developed for Greece using MODIS/Terra 250 m imagery. After smoothing to remove noise and cloud influence, it is subjected to temporal autocorrelation analysis, and its level of significance is the adopted objective function. In addition, NDVI composites are created at various temporal resolutions and compared using qualitative criteria. Results indicate that the proposed optimum temporal resolution is different for managed and natural vegetation. Finally, quality assessment of the multi‐temporal NDVI composites reveals that compositing at the proposed optimum temporal resolution could derive products that are useful for operational monitoring of vegetation.     

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.     

Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands

Considerable significance is placed on the mapping and monitoring of degraded areas in semi-arid regions of the world, including Botswana. Degraded areas include those suffering from bush encroachment, believed to result from heavy cattle grazing over a number of years. However, certain bush encroachment species have been found to be relatively nutrient-rich. The present work considers the extent to which a series of quantified layers through mainly bush encroachment canopies can be identified using conventional and newly derived vegetation indexes and transforms based on Thematic Mapper (TM) imagery. Field work involved the stratification of green biomass into firstly the herbaceous cover layer; secondly the 0.3-1.5 m browse layer; then the 1.5-2.5 m browse layer; and finally the >2.5 m browse layer. Biomass measurements from these layers were statistically associated with conventional vegetation indexes and transforms such as the Normalized Difference Vegetation Index (NDVI), brightness and greenness values, and relatively newly derived darkening indexes involving the mid-infrared bands. When green biomass and transformed pixel data were averaged per classified vegetation unit, weak negative correlations emerged between grass biomass and the transformed pixel data and no significant correlations developed with the woody biomass (browse) layers. However, when point data were used in the analyses, results showed that most indexes and the brightness transform were significantly correlated with the lower browse layer. Only the darkening indexes and brightness function were sensitive to the browse layers individually and the browse plus grass layers. This work shows the limitations of conventional indexes such as the NDVI in terms of browse and herbaceous layer assessment. New indexes for forage assessment based on relationships between the mid-infrared bands, such as those found in the new MODIS TERRA platform, are urgently required for semi-arid areas.     

Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors

Quantitative estimation of fractional cover of photosynthetic vegetation (f_PV), non-photosynthetic vegetation (f_NPV) and bare soil (f_BS) is critical for natural resource management and for modeling carbon dynamics. Accurate estimation of fractional cover is especially important for monitoring and modeling savanna systems, subject to highly seasonal rainfall and drought, grazing by domestic and native animals, and frequent burning. This paper describes a method for resolving f_PV, f_NPV and f_BS across the ~ 2 million km² Australian tropical savanna zone with hyperspectral and multispectral imagery. A spectral library compiled from field campaigns in 2005 and 2006, together with three EO-1 Hyperion scenes acquired during the 2005 growing season were used to explore the spectral response space for f_PV, f_NPV and f_BS. A linear unmixing approach was developed using the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Translation of this approach to MODerate resolution Imaging Spectroradiometer (MODIS) scale was assessed by comparing multiple linear regression models of NDVI and CAI with a range of indices based on the seven MODIS bands in the visible and shortwave infrared region (SWIR) using synthesized MODIS surface reflectance data on the same dates as the Hyperion acquisitions. The best resulting model, which used NDVI and the simple ratio of MODIS bands 7 and 6 (SWIR3/SWIR2), was used to generate a time series of fractional cover from 16 day MODIS nadir bidirectional reflectance distribution function-adjusted reflectance (NBAR) data from 2000-2006. The results obtained with MODIS NBAR were validated against grass curing measurement at ten sites with good agreement at six sites, but some underestimation of f_NPV proportions at four other sites due to substantial sub-pixel heterogeneity. The model was also compared with remote sensing measurements of fire scars and showed a good matching in the spatio-temporal patterns of grass senescence and posterior burning. The fractional cover profiles for major grassland cover types showed significant differences in relative proportions of f_PV, f_NPV and f_BS, as well as large intra-annual seasonal variation in response to monsoonal rainfall gradients and soil type. The methodology proposed here can be applied to other mixed tree-grass ecosystems across the world.