Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km². AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.     

Characterizing post‐fire vegetation recovery of California chaparral using TM/ETM+ time‐series data

A time series of normalized difference vegetation index (NDVI) data derived from 11 TM/ETM+ images was used to examine the recovery characteristics of chaparral vegetation in a small watershed near Santa Barbara, California following a fire event in 1985. The NDVI recovery trajectory was compared to a generalized recovery trajectory of leaf area index (LAI) for the same region, which was established using a chronosequence approach and TM/ETM+ data. Post‐fire NDVI recovery trajectories were derived for the entire catchment and for individual vegetation types. Post‐fire NDVI spatial patterns on each image date were compared to the pre‐fire pattern to determine the extent to which the pre‐fire pattern was re‐established, and the rate of this recovery. Results indicated that the post‐fire recovery trajectory for the catchment area average NDVI was similar to the previously established regional LAI trajectory based on a chronosequence approach. The NDVI recovery was disrupted by drought stress and attained pre‐fire levels approximately 10 years after the fire. Individual vegetation types did not exhibit different rates of recovery and the recovery trajectories were only distinguished by the maximum post‐fire NDVI observed after 10 years. The post‐fire NDVI spatial pattern also showed a systematic return to pre‐fire conditions, but exhibited a more substantial disruption due to drought stress than was the case for the average NDVI recovery trajectory.     

Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas

Wildfires are a major cause of land degradation in the Mediterranean region due to their frequent recurrence in the same areas. The evaluation of fire risk is therefore of high practical importance, particularly during the summer arid season, when fires are most frequent and harmful. Recent studies have demonstrated that the evaluation of dynamic fire risk can be carried out by the use of remotely sensed images, and specifically of NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. This use relies on the sensitivity of the index to vegetation dryness, which is a major predisposing factor for fire occurrence. Several problems, however, remain linked to the spatial variability of the risk in environmentally heterogeneous areas, which requires the application of suitable processing techniques to the low-resolution imagery.The current work reports on the development and testing of different methodologies for estimating dynamic fire risk by the use of NOAA-AVHRR data. The investigation was conducted in Tuscany (Central Italy) using a large archive of fires that occurred in the region and NOAA-AVHRR NDVI data of 16 years (1985-2000). Relying on previous methodological achievements of our group and other research groups, several procedures were tested to extract information related to fire risk from the remotely sensed images. These trials led to define an optimum method which is based on the identification of pixels where the accordance between interyear variations in fire probabilities and NDVI values is maximum. The accuracy of the risk estimates from this optimum method was finally evaluated by a leave-one-out cross-validation strategy. In this way, the performance of the methodology was assessed, together with its potential for operational fire risk monitoring and forecasting in Mediterranean areas.     

On the suitability of the use of normalized difference vegetation index for forest fire risk assessment

The NOAA‐AVHRR normalized difference vegetation index (NDVI) has often been used for forestry applications, including forest fire risk estimation. The decrement of NDVI values over a particular area has been considered an indicator of vegetation stress and linked to high fire risk. In the Mediterranean region, a large number of fires occurred in areas where the NDVI values were low. Consequently, the link between low NDVI values and fire occurrences was established. However, studies supporting this hypothesis were based only on analysis in areas that suffered fires and information over similar areas where fires did not occur was not considered. This study investigates the ability of NDVI to discriminate levels of fire risk in Spain using a very large dataset of satellite sensor images and fire events. The relative frequency distribution of NDVI values in both areas that suffered fires and those where fires did not occur, was compared in a 10 year period. The results highlight that NDVI values in areas where fires took place were similar to NDVI values in areas in which fire did not occur, showing the limitations of using the NDVI as an index of fire risk.     

The relation between Normalized Difference Vegetation Index and vegetation moisture content at three grassland locations in Victoria,Australia

The supposition that, for most practical purposes, a single, generic, widely applicable relation exists between Normalized Difference Vegetation Index (NDVI) and grassland vegetation moisture content is tested. An experiment is described in which the vegetation moisture content at three Victorian grassland sites of varying composition is measured over the course of a complete curing episode. For each site, corresponding satellite radiation measurements are used to extract surface reflectances corrected for atmospheric and view-angle effects, and NDVI values based on these. On relating NDVI so obtained to the field measurements of vegetation moisture expressed in terms of a parameter commonly employed in assessing grassland fire risk, namely Fuel Moisture Content (FMC), separate relations for each site are clearly identified. When the relation appropriate to each site is used to derive FMC for that site, accurate estimates are obtained. Accuracy decreases markedly if the relation appropriate to one site is used to derive estimates of FMC at the other sites. When FMC values are transformed to another commonly employed parameter of grassland vegetation moisture content, namely Grassland Curing Index (GCI), the loss of accuracy becomes much greater. More accurate estimates of GCI are obtained using a direct relation between NDVI and GCI.     

NDVI time series for monitoring RUSLE cover management factor in a tropical watershed

Land cover, an important factor for monitoring changes in land use and erosion risk, has been widely monitored and evaluated by vegetation indices. However, a study that associates normalized difference vegetation index (NDVI) time series to climate parameters to determine soil cover has yet to be conducted in the Atlantic Rainforest of Brazil, where anthropogenic activities have been carried out for centuries. The objective of this paper is to evaluate soil cover in a Brazilian Atlantic rainforest watershed using NDVI time series from Thematic Mapper (TM) Landsat 5 imagery from 1986 to 2009, and to introduce a new method for calculating the cover management factor (C-factor) of the Revised Universal Soil Loss Equation (RUSLE) model. Twenty-two TM Landsat 5 images were corrected for atmospheric effects using the 6S model, georeferenced using control points collected in the field and imported to a GIS database. Contour lines and elevation points were extracted from a 1:50,000-scale topographic map and used to construct a digital elevation model that defined watershed boundaries. NDVI and RUSLE C-factor values derived from this model were calculated within watershed limits with 1 km buffers. Rainfall data from a local weather station were used to verify NDVI and C-factor patterns in response to seasonal rainfall variations. Our proposed method produced realistic values for RUSLE C-factor using rescaled NDVIs, which highly correlated with other methods, and were applicable to tropical areas exhibiting high rainfall intensity. C-factor values were used to classify soil cover into different classes, which varied throughout the time-series period, and indicated that values attributed to each land cover cannot be fixed. Depending on seasonal rainfall distribution, low precipitation rates in the rainy season significantly affect the C-factor in the following year. In conclusion, NDVI time series obtained from satellite images, such as from Landsat 5, are useful for estimating the cover management factor and monitoring watershed erosion. These estimates may replace table values developed for specific land covers, thereby avoiding the cumbersome field measurements of these factors. The method proposed is recommended for estimating the RUSLE C-factor in tropical areas with high rainfall intensity.     

Influence of fire severity on plant regeneration by means of remote sensing imagery

In this paper we analyse the interactions between fire severity (plant damage) and plant regeneration after fire by means of remote sensing imagery and a field fire severity map. A severity map was constructed over a large fire (2692 ha) occurring in July 1994 in the Barcelona province (north-east of Spain). Seven severity classes were assigned to the apparent plant damage as a function of burning intensity. Several Landsat TM and MSS images from dates immediately before and after the fire were employed to monitor plant regeneration processes as well as to evaluate the relationship with fire severity observed in situ . Plant regeneration was monitored using NDVI measurements (average class values standardized with neighbour unburned control plots). Pre-fire NDVI measurements were extracted for every plant cover category (7), field fire severity class (7), and spatial cross-tabulation of both layers (33) and compared to post-fire values. NDVI decline due to fire was positively correlated with field fire severity class. Results show different patterns of recovery for each dominant species, severity class and combination of both factors. For all cases a significant negative correlation was found between damage and regeneration ability. This work leads to a better understanding of the influence of severity, a major fire regime parameter on plant regeneration, and may aid to manage restoration on areas burned under different fire severity levels.     

Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary,Argentina

In arid and semi-arid ecosystems, salinisation and desertification are the most common processes of land degradation, and satellite data may provide a valuable tool to assess land surface condition and vegetation status. The aim of this study was to evaluate the capability of Landsat 8 OLI (Operational Land Imager) remote sensing information and broadband indices derived from it, to monitor above ground biomass (AGB) and salinity in two different semiarid saline environments (unit a and unit b) in the Bahía Blanca Estuary. Unit a (Ua) is composed of bushes of Cyclolepis genistoides in association with Atriplex undulata and 41% of bare soil. Unit b (Ub) is composed of dense thickets of Allenrolfea patagonica in association with C. genistoides and 34% of bare soil. Pearson’s correlation analyses were performed between field estimates of AGB and salinity (soil salinity and interstitial water salinity) and remote sensing estimates. Satellite data include surface reflectance of individual bands, vegetation indices (NDVI [normalised difference vegetation index], SAVI [soil-adjusted vegetation index], MSAVI2 [modified soil-adjusted vegetation index], NDII [normalised difference infrared index], GNDVI [green normalised difference vegetation index], GRNDI [green-red normalised difference index], OSAVI [optimised soil-adjusted vegetation index], SR [simple ratio]), and salinity indices (SI1, SI2, SI3 [salinity index 1, 2 and 3, respectively] and BI [brightness index]). Correlation analyses involving AGB were performed twice; first considering all months and then again excluding the months with higher soil salinities. In Ua, soil adjusted vegetation indices SAVI and MSAVI2 showed to be suitable to detect changes in the total green AGB and C. genistoides green AGB (the major contributor to total green AGB). After excluding data from December and January (the months with the highest soil salinity), green AGB of A. undulata also showed a significant positive correlation with soil adjusted indices SAVI, MSAVI2 and OSAVI. Although proportionally this species was not a large contributor to the total biomass, it is characterised by a high leaf reflectance, which makes it suitable for biomass retrieval. In Ub, significant positive correlations were obtained between NDVI, SAVI, NDII, OSAVI and SR indices and the AGB green ratio, but significant negative correlations were obtained between A. patagonica red AGB and these vegetation indices. When December and January were excluded from the analysis the negative correlations between vegetation indices NDVI, OSAVI and SR and red AGB remained significant (r = ?0.68, ?0.76 and ?0.7, respectively). The positive correlations between these indices and AGB green ratio (r = 0.73, 0.78 and 0.75, respectively) remained significant as well. Significant negative correlations were also found between NDVI, NDII, GNDVI, OSAVI and SR indices and field salinity estimates. As soil salinisation induces A. patagonica reddening, red AGB and soil salinity covariate in the field, and the negative correlation with vegetation indices may be useful to retrieve information on both variables combined, which are indicative of water stress. Correlation analysis between field estimates of salinity and spectral salinity indices showed significant positive correlation for all the tested indices. The obtained results highlight the importance of a thoughtful selection of remote sensing indices to account for changes in vegetation biomass, especially in arid and semiarid environments particularly sensitive to desertification and salinisation. Also, ground truth cannot be overlooked, and field work is necessary to test index performance in every case.     

Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling

Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R² 0.21–0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R² 0.33–0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling.     

Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation.Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties.     

Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats

Dry grassland sites are amongst the most species-rich habitats of central Europe and it is necessary to design effective management schemes for monitoring of their biomass production. This study explored the potential of hyperspectral remote sensing for mapping aboveground biomass in grassland habitats along a dry-mesic gradient, independent of a specific type or phenological period. Statistical models were developed between biomass samples and spectral reflectance collected with a field spectroradiometer, and it was further investigated to what degree the calibrated biomass models could be scaled to Hyperion data. Furthermore, biomass prediction was used as a surrogate for productivity for grassland habitats and the relationship between biomass and plant species richness was explored. Grassland samples were collected at four time steps during the growing season to capture normally occurring variation due to canopy growth stage and management factors. The relationships were investigated between biomass and (1) existing broad- and narrowband vegetation indices, (2) narrowband normalized difference vegetation index (NDVI) type indices, and (3) multiple linear regression (MLR) with individual spectral bands. Best models were obtained from the MLR and narrowband NDVI-type indices. Spectral regions related to plant water content were identified as the best estimators of biomass. Models calibrated with narrowband NDVI indices were best for up-scaling the field-developed models to the Hyperion scene. Furthermore, promising results were obtained from linking biomass estimations from the Hyperion scene with plant species richness of grassland habitats. Overall, it is concluded that ratio-based NDVI-type indices are less prone to scaling errors and thus offer higher potential for mapping grassland biomass using hyperspectral data from space-borne sensors.     

Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour

Millennia of intensive use have led to severe land degradation in the Mediterranean basin, much of which is now under serious risk of desertification. Very large extents are presently covered with scrub dominated by plants of the genus Cistus. The level of soil protection provided by these shrubs is proportional to their biomass, so it is important to be able to quantify this variable. The potential for mapping Cistus scrub biomass using optical satellite imagery was investigated in this study. It was found that the peculiar adaptations of the Cistus to summer drought strongly influence its spectral behaviour, which, during this season, is similar to that of vegetation of arid and semi-arid regions. During the rainy winter this pattern changes and becomes very similar to that of active green vegetation. In summer images, individual Landsat Thematic Mapper (TM) bands, including the near-infrared band, presented negative correlations with biomass. However, in spite of these inverse relationships, the Normalized Difference Vegetation Index (NDVI) had a high positive correlation with biomass, so this variable can be adequately mapped using satellite images. Seventy-four per cent of the biomass values estimated with linear regression models, using the NDVI as predictor, were within 50% of the ground measurements.     

Assessing woody biomass in African tropical savannahs by multiscale remote sensing

Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R² = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate

Drylands cover about 41% of the globe's surface and provide important ecosystem services, but land use and climate change exert considerable pressure on these ecosystems. Both of these drivers frequently result in gradual vegetation change and landscape-scale trend analysis based on yearly vegetation estimates can capture such changes. Such trend analyses based on high-resolution time series of satellite imagery have so far not widely been used and existing studies in drylands relied on different vegetation measures. Spectral mixture analysis (SMA) has been chosen due to its superiority to simpler vegetation estimates in quantifying vegetation cover in single-date studies, however SMA can be challenging to implement for large areas. Here, we quantify the trade-off involved when using simple vegetation estimates instead of SMA fractions for subsequent trend analyses. We calculated NDVI, SAVI and Tasseled Cap Greenness, as well as SMA green vegetation fractions for a time series of Landsat images from 1984-2005 for a study region in Crete. Linear trend analysis showed that trend coefficients and the spatial patterns of trends were similar across all vegetation estimates and the entire study region, especially for areas where vegetation changed gradually. On average, trends based on simple measures differed less than 5% from SMA-based trends with decreasing similarity in trend results from Tasseled Cap Greenness to SAVI and NDVI. Vegetation estimates differed markedly in their response to disturbance events such as fires. Trend analyses based on qualitative measures can easily be applied across very large areas and using multi-sensor time series based on high-resolution data. While the subtle differences between vegetation estimates may still be important for some applications, the robustness of trend analyses regarding the choice of vegetation estimate bears considerable promise to reconstruct fine-scale vegetation dynamics and land use histories and to assess climate change impacts on the world's drylands.     

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.     

Estimating tree‐cover change in Australia: challenges of using the MODIS vegetation index product

Time series of the vegetation index product MOD13Q1 from the Moderate Resolution Imagery Spectroradiometer (MODIS) were assessed for estimating tree foliage projective cover (FPC) and cover change from 2000 to 2006. The MOD13Q1 product consists of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). There were four challenges in using the MOD13Q1 product to derive tree FPC: assessing the impact of the sensor's varying view geometry on the vegetation index values; decoupling tree and grass cover contributions to the vegetation index signal; devising a method to relate the temporally composited vegetation index pixels to Lidar estimates of tree FPC for calibration; and estimating the accuracy of the FPC and FPC change measurements using independently derived Lidar, Landsat and MODIS cover estimates. The results show that, for complex canopies, the varying view geometry influenced the vegetation indices. The EVI was more sensitive to the view angle than the NDVI, indicating that it is sensitive to vegetation structure. An existing time series method successfully extracted the evergreen vegetation index signal while simultaneously minimizing the impact of varying view geometry. The vegetation indices were better suited to monitoring tree cover change than deriving accurate single‐date estimates of cover at regional to continental scales. The EVI was more suited to monitoring change in high‐biomass regions (cover >50%) where the NDVI begins to saturate.     

Assessing rangeland degradation using multi temporal satellite images and grazing pressure surface model in Upper Mustang, Trans Himalaya, Nepal

This study aims to map and discriminate rangeland degradation from the effects of precipitation variability and thereby identify the driving forces of degradation in the grazing areas of Ghiling in Upper Mustang, Nepal. Landsat MSS, TM, ETM, and SPOT images covering the years 1976-2008 were analyzed. 8 km resolution NOAA NDVI from 1981to 2006 were used to identify the long term interrelationship between vegetation greenness and precipitation variability. The use of time series residual of the NDVI/precipitation linear regression to normalize the precipitation effect on vegetation productivity and identify the long term degradation was extended at the local scale. A weighted grazing pressure surface model was developed combining information from satellite images, topography, forage availability and detailed field work data on points of livestock concentration, herders' ranking of forage quality and grazing pattern in each pasture unit. The grazing pressure of a given site was defined as the product of annual net stocking density and the inverse of the total friction of livestock movement. While annual precipitation was found as the dominant factor for the interannual vegetation variability, degradation in Upper Mustang was the result of grazing induced change and some localized natural processes.     

Estimating burned area for Tropical Africa for the year 1990 with the NOAA-NASA Pathfinder AVHRR 8 km land dataset

The international scientific community recognizes the long-term monitoring of biomass burning as important for global climate change, vegetation disturbance and land cover change research on the Earth's surface. Although high spatial resolution satellite images may offer a more detailed view of land surfaces, their limited area coverage and temporal sampling have restricted their use to local research rather than global monitoring. Low spatial resolution images provide an invaluable source for the detection of burned areas in vegetation cover (scars) at global scale along time. However, the automated burned area mapping algorithm applicable at continental or global scale must be sufficiently robust to accommodate the global variation in burned scar signals. Here, the estimation of the percentage of a pixel area affected by a fire is crucial. In a first step, an empirical method is used which is based on a function between the change in Normalized Difference Vegetation Index (NDVI) and the surface area affected by fire. Next, a new statistical method, based on the Monte Carlo algorithm, is applied to compute probabilities of burned pixels percentages in different neighbourhood conditions.     

Regression and analytical models for estimating mangrove wetland biomass in South China using Radarsat images

Mangrove wetlands have been rapidly diminishing because of human pressures worldwide. The Guangdong Province in South China, which has the largest area of mangrove wetlands in the nation, is under severe threat as a result of rapid urbanization and economic development. In this paper, comparisons were made between optical Landsat TM images and Radarsat fine‐mode images for estimating wetland biomass. Regression and analytical models were used to establish the relationships between remote sensing data and wetland biomass. The optimal parameter values for the analytical model were determined using genetic algorithms. Experiments indicate that the models using Radarsat fine‐mode images have significant accuracy improvement in terms of Root Mean‐Square Error (RMSE) whereas the use of the single Normalized Difference Vegetation Index (NDVI) may produce serious errors in biomass estimation. The Radarsat images can obtain more accurate trunk information about mangrove forests because of higher resolution and side‐looking geometry. The use of genetic algorithms can help to decompose backscatter into vegetation and soil backscattering, which is very useful for ecological modelling.