Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada

The boreal forest biome is one of the largest on Earth, covering more than 14% of the total land surface. Fire disturbance plays a dominant role in boreal ecosystems, altering forest succession, biogeochemical cycling, and carbon sequestration. We used two time-series data sets of Advanced Very High Resolution Radiometer (AVHRR) Normalized Differenced Vegetation Index (NDVI) imagery for North America to analyze vegetation recovery after fire. The Canadian Forest Service Large Fire Database was used to identify the location of fires and calculate scaled NDVI statistics from the Pathfinder AVHRR Land (PAL) and the Global Inventory Modeling and Mapping Studies (GIMMS) AVHRR data sets. Unburned areas were also identified, based on interannual variability metrics, in order to reduce the effects of factors other than fire on the temporal behavior of scaled NDVI. Burned and unburned areas were stratified by ecoregion to ensure the presence of comparable land cover types and account for influences of local environmental variability. Temporal anomalies in NDVI for burned and unburned areas show the impacts of fire and the recovery of the forest to pre-burn levels, and indicate changes in variability that might be associated with vegetation compositional changes consistent with early successional species. The rate of recovery varied in the three episodic fire years on which we focused our analysis (1981, 1989, and 1995), but were consistently shorter than previous studies that emphasized the most impacted areas within fires. Temporal variability in the time series, represented by the difference of burned and unburned area anomalies, increased beyond the observed post-fire recovery period. This indicates residual effects of fire disturbance over the regrowth period, perhaps associated with early successional vegetation and increased susceptibility to drought. Distinct differences were noted between the PAL and GIMMS data sets, with evidence for systematic data processing artifacts remaining in the PAL time series.     

Short‐term response of arctic vegetation NDVI to temperature anomalies

The effects of climate change on northern vegetation productivity need to be fully understood in order to reduce uncertainties in predicting vegetation distributions under different climate warming scenarios. Knowledge of the relationship between northern climate and vegetation productivity will also help provide a better understanding of changes in vegetation distributions as an indicator of climate change and variability. Vegetation productivity and biomass have been monitored using long‐term satellite earth observations, mostly using the Normalized Difference Vegetation Index (NDVI), as a cumulative indicator of all effects resulting from processes related to climate change, including changes in temperature, precipitation, and disturbance. In this paper, the investigation is focused on the short‐term effect of temperature anomalies on arctic and tree‐line transition vegetation productivity in both dry and humid regions of Canada. The analysis shows that several land‐cover types composed mainly of trees and shrubs exhibit a significant increase in NDVI with higher‐than‐normal temperatures in the preceding 10–40‐day period, while land‐cover types consisting of lichen and moss growing on mostly barren surfaces show a significant NDVI decrease with increased temperature. These trends are consistent with results reported in plot‐warming experiments in the north, which have shown that certain vegetation communities increase, while others decrease in cover fraction and biomass in response to warming. When land cover is grouped into increasing and decreasing NDVI with temperature and stratified by dry and humid regions of Canada, much of the dry region of northern Canada does not exhibit significant NDVI response to preceding temperature anomalies. It is expected that in the absence of disturbance or other limiting factors, an increased frequency of elevated temperature anomalies may eventually contribute to changes in vegetation biomass. A map of land‐cover types that have the potential to increase in biomass with climate warming and those that are vulnerable to decline is presented.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

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.     

The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data

The preliminary results of Normalized Difference Vegetation Index (NDVI) change studies over India using data from Advanced Very High Resolution Radiometer Global Inventory Modeling and Mapping Studies (AVHRR GIMMS) between 1982 and 2003 are presented. The three methodologies of univariate differencing, temporal profiling and anomaly analysis were undertaken. Univariate differencing was used to determine overall NDVI change between 1982 and 2003. A persistence filter was used to filter out ephemeral changes. The temporal profile analyses were carried out over different meteorological subdivisions to compare changes in NDVI with rainfall patterns. In the anomaly analysis, the areas of change were analysed over different land cover categories derived from IRS‐WiFS data. The preliminary results indicate that positive trends in vegetation change occurred over most parts of the country and these changes appear not to be highly correlated with rainfall data, indicating that land cover transformations may be the major driving force behind the changes. The land cover classifications experiencing the greatest increasing NDVI were tropical thorn forests and intensive agriculture and the land cover experiencing very slow growth included current jhum, tropical moist deciduous and temperate evergreen forest. Five‐year moving averages indicate a general increase in NDVI from 1986 to 1998 and then declining thereafter. This is a concern in most of the meteorological subdivisions.     

Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery.The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.     

Landscape cover dynamics pattern in Meghalaya

Human interventions in natural systems have resulted in large changes in vegetation composition and distribution patterns. The Land Use Change and Climate Change (LUCC) study under the International Geosphere Biosphere Program (IGBP) is a major initiative in this regard. Changes in land use and hence in vegetation cover, due to climatic change and human activity, affect surface water and energy budgets directly through plant transpiration, surface albedo, emissivity and roughness. They also affect primary production and, therefore, the carbon cycle. Thus, there is a need for spatial and temporal characterization of vegetation cover at different scales, from the global and continental scale to the local patch scale. Satellite remote sensing provides detailed information regarding the spatial distribution and extent of land use changes in the landscape. Meghalaya, in north-east India, is one of the most important, biologically rich landscapes. Degradational activities, namely shifting cultivation, clear felling of forests for timber, and mining, have altered the natural landscape to a great extent. Because of these increased anthropogenic activities the natural landscape has been modified which has resulted in a fragmented landscape with poor species composition. These changes in the landscape were analysed using IRS 1A, 1B and Landsat Multi-Spectral Scanner (MSS) data during the period 1980-1995. The vegetation type maps were prepared by a visual interpretation technique in order to study the land cover dynamics pattern in Meghalaya.     

Analysis of phenological change patterns using 1982–2000 Advanced Very High Resolution Radiometer (AVHRR) data

Using the National Oceanic & Atmospheric Administration (NOAA) National Aeronautics & Space Administration (NASA) Pathfinder Land dataset (PAL data) from 1982–2000, vegetation phenology (onset, peak and offset) was defined and analysed with climate data. In areas of precipitation-dependent phenology such as Central Africa, it was found that Normalized Difference Vegetation Index (NDVI) is affected approximately 20–40 days after the occurrence of precipitation, depending on land cover types. In areas of temperature-dependent phenology such as Siberia, the relationship of phenology and latitude/elevation was investigated. Using temporal NDVI data of 1982–2000, changes in seasonal NDVI pattern were classified into 11 classes and mapped in the Northern Hemisphere. From this analysis, increasing trends of the annual sum of NDVI were found in Siberia, NE Europe and the northern part of North America where good correspondence with the increasing trend of air temperature was recognized. In contrast, some areas such as the east of the Aral Sea showed a decreasing trend of the annual sum of NDVI. It was found that, in the Northern Hemisphere, the area with increasing trend of the annual sum of NDVI is approximately 12 times larger than the area with the decreasing trend. Also, it was found that areas of increasing/decreasing trend of the annual sum of NDVI correspond roughly to areas with increasing/decreasing trend of air temperature from 1982 to 1995.     

Estimating above-ground net primary productivity of the tallgrass prairie ecosystem of the Central Great Plains using AVHRR NDVI

Above-ground net primary productivity (ANPP) is indicative of an ecosystem's ability to capture solar energy and convert it to organic carbon (or biomass), which may be used by consumers or decomposers, or stored in the form of living and nonliving organic matter. Annual and interannual variation in ANPP is often linked to climate dynamics and anthropogenic influences, such as fertilization, irrigation, above-ground biomass harvest, and so on. The Central Great Plains grasslands occupy over 1.5 million km² and are a primary resource for livestock production in North America. The tallgrass prairies are the most productive grasslands in this region, and the Flint Hills of North America represent the largest contiguous area of unploughed tallgrass prairie (1.6 million ha). Measurements of ANPP are of critical importance to the proper management and understanding of climatic and anthropogenic influences on tallgrass prairie. Yet, accurate, detailed, and systematic measurements of ANPP over large geographic regions do not exist for this ecosystem. For these reasons, this study was conducted to investigate the use of the normalized difference vegetation index (NDVI) to model ANPP of the tallgrass prairie. Many studies have established a positive relationship between the NDVI and ANPP, but the strength of this relationship is influenced by vegetation types and can vary significantly from year to year depending on land use and climatic conditions. The goal of this study was to develop a robust model using the Advanced Very High Resolution Radiometer (AVHRR) biweekly NDVI values to predict tallgrass ANPP. This study was conducted using ANPP measurements from a watershed within the Konza Prairie Biological Station (KPBS) as the primary study area, with additional measurements from the Rannells Flint Hills Prairie Preserve (RFHPP) and biennial ANPP measurements by Kansas State University (KSU) students from tallgrass areas near Manhattan, Kansas. Data from the primary study site covered the period of 1989–2005. The optimal period for estimating ANPP using AVHRR NDVI composite data sets was found to be late July. The Tallgrass ANPP Model (TAM) explained 54% (coefficient of determination, R ² = 0.54, p < 0.001) of the year-to-year variation in ANPP. The creation of 1.0 km × 1.0 km resolution ANPP maps for a four-county (～7000 ha) area for years 1989–2007 showed considerable variation in annual and interannual ANPP spatial patterns, suggesting complex interactions among factors influencing ANPP spatially and temporally.     

Recent land‐cover/use change associated with land degradation in the Lake Baringo catchment,Kenya, East Africa: evidence from Landsat TM and ETM+

Many parts of East Africa are experiencing dramatic changes in land‐cover/use at a variety of spatial and temporal scales, due to both climatic variability and human activities. Information about such changes is often required for planning, management, and conservation of natural resources. Several methods for land cover/change detection using Landsat TM/ETM+ imagery were employed for Lake Baringo catchment in Kenya, East Africa. The Lake Baringo catchment presents a good example of environments experiencing remarkable land cover change due to multiple causes. Both the NDVI differencing and post‐classification comparison effectively depicted the hotspots of land degradation and land cover/use change in the Lake Baringo catchment. Change‐detection analysis showed that the forest cover was the most affected, in some sections recording reductions of over 40% in a 14‐year period. Deforestation and subsequent land degradation have increased the sediment yield in the lake resulting in reduction in lake surface area by over 10% and increased turbidity confirmed by the statistically significant increase (t = ?84.699, p<0.001) in the albedo between 1986 and 2000. Although climatic variations may account for some of the changes in the lake catchment, most of the changes in land cover are inherently linked to mounting human and livestock population in the Lake Baringo catchment.     

基于NDVI序列影像的植被覆盖变化研究

归一化植被指数NDVI是地表植被覆盖特征的重要指标之一。以新疆石河子地区2003~2006年MODIS遥感数据反演的NDVI时间序列影像为例,分析研究了植被长势的年内和年际变化,将植被长势的年内变化和年际变化分为比前一年(月)好、比前一年(月)稍好、与前一年(月)持平、比前一年(月)稍差和比前一年(月)差5个等级,得到年内和年际间植被长势的动态分布图,从植被长势分布图中NDVI的变化可以看出年际和年内植被长势的变化。并应用变化矢量分析法对2003~2006年石河子地区NDVI的变化强度进行了分析,获得了植被覆盖变化强度分布情况,研究结果表明4 a内石河子地区植被覆盖未发生大的变化,植被系统基本稳定。     

Vegetation greening in Spain detected from long term data (1981–2015)

ABSTRACT

This study describes a newly developed high-resolution (1.1 km) Normalized Difference Vegetation Index dataset for the peninsular Spain and the Balearic Islands (Sp_1km_NDVI). This dataset is developed based on National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA–AVHRR) afternoon images, spanning the past three decades (1981–2015). After a careful pre-processing procedure, including calibration with post-launch calibration coefficients, geometric and topographic corrections, cloud removal, temporal filtering, and bi-weekly composites by maximum NDVI-value, we assessed changes in vegetation greening over the study domain using Mann-Kendall and Theil-Sen statistics. Our trend results were compared with those derived from some widely recognized global NDVI datasets [e.g. the Global Inventory Modelling and Mapping Studies 3rd generation (GIMMS3g), Smoothed NDVI (SMN) and Moderate-Resolution Imaging Spectroradiometer (MODIS)]. Results demonstrate that there is a good agreement between the annual trends based on Sp_1km_NDVI product and other datasets. Nonetheless, we found some differences in the spatial patterns of the NDVI trends at the seasonal scale. Overall, in comparison to the available global NDVI datasets, Sp_1km_NDVI allows for characterizing changes in vegetation greening at a more-detailed spatial and temporal scale. In specific, our dataset provides relatively long-term corrected satellite time series (>30 years), which are crucial to understand the response of vegetation to climate change and human-induced activities. Also, given the complex spatial structure of NDVI changes over the study domain, particularly due to the rapid land intensification processes, the spatial resolution (1.1 km) of our dataset can provide detailed spatial information on the inter-annual variability of vegetation greening in this Mediterranean region and assess its links to climate change and variability.     

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.     

Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, Kazakhstan has reportedly undergone extensive land cover/land use change. Were the institutional changes sufficiently great to affect land surface phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series (1985-1988 and 1995-1999) from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset, which consists of 10 days maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures were extracted from the NCEP Reanalysis Project and 10 days composites of accumulated growing degree-days (AGDD) were produced. We selected for intensive study seven agricultural areas ranging from regions with rain-fed spring wheat cultivation in the north to regions of irrigated cotton and rice in the south. We applied three distinct but complementary statistical analyses: (1) nonparametric testing of sample distributions; (2) simple time series analysis to evaluate trends and seasonality; and (3) simple regression models describing NDVI as a quadratic function of AGDD.The irrigated areas displayed different temporal developments of NDVI between 1985-1988 and 1995-1999. As the temperature regime between the two periods was not significantly different, we conclude that observed differences in the temporal development of NDVI resulted from changes in agricultural practices.In the north, the temperature regime was also comparable for both periods. Based on extant socioeconomic studies and our model analyses, we conclude that the changes in the observed land surface phenology in the northern regions are caused by large increases in fallow land dominated by weedy species and by grasslands under reduced grazing pressure. Using multiple lines of evidence allowed us to build a case of whether differences in land surface phenology were mostly the result of anthropogenic influences or interannual climatic fluctuations.     

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.     

Natural vegetation responses to warming climates in Qaidam Basin 1982–2003

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April–September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982–2003 and in 24.1% over the first half period, 1982–1992. In addition, few areas were shown to have negative trends during these periods. In 1993–2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May–August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions.     

Long‐term monitoring of land cover changes based on Landsat imagery to improve hydrological modelling in West Africa

The spatial and temporal variability of land cover changes is a fundamental parameter to integrate when modelling water resources in order to reproduce the relations between rainfall and surface flow more precisely. This is particularly important in West Africa, where the land cover has been changing for more than 40 years under the combined impact of climatic effects and human activities. In this study, we evaluated the potential of Landsat imagery to monitor the vegetation cover in the upper Niger watershed (120 000 km²) using archive images from MSS, TM and ETM+ sensors covering three periods of time around 1975, 1985, and 2000. Because of the heterogeneity of the acquisition dates, the spatial and spectral resolution of the images, and the scale of analysis, we chose a simple system of classification. Pretreatments were applied to reduce variations between the images. Vegetation indices (NDVI) were then calculated and subsequently thresholded using the same land‐cover classification system. The thresholds were then optimized by automated recursive calculations of confusion matrices and control parcels. Our results revealed that although the accuracy was not perfect, it was nevertheless possible to estimate changes using an unconventional spatio‐temporal scale. The resulting changes were characterized by a moderate trend to deforestation with a corresponding increase in bare soils, soils with sparse vegetation, and shrublands. The spatial layers produced were then combined with a soil map to incorporate changes in surface conditions in the hydrological modelling of the Niger River.     

Estimating spatio-temporal patterns of agricultural productivity in fragmented landscapes using AVHRR NDVI time series

The characteristics of Normalized Difference Vegetation Index (NDVI) time series can be disaggregated into a set of quantitative metrics that may be used to derive information about vegetation phenology and land cover. In this paper, we examine the patterns observed in metrics calculated for a time series of 8 years over the southwest of Western Australia—an important crop and animal production area of Australia. Four analytical approaches were used; calculation of temporal mean and standard deviation layers for selected metrics showing significant spatial variability; classification based on temporal and spatial patterns of key NDVI metrics; metrics were analyzed for eight areas typical of climatic and production systems across the agricultural zone; and relationships between total production and productivity measured by dry sheep equivalents were developed with time integrated NDVI (TINDVI). Two metrics showed clear spatial patterns; the season duration based on the smooth curve produced seven zones based on increasing length of growing season; and TINDVI provided a set of classes characterized by differences in overall magnitude of response, and differences in response in particular years. Frequency histograms of TINDVI could be grouped on the basis of a simple shape classification: tall and narrow with high, medium or low mean indicating most land is responsive agricultural cover with uniform seasonal conditions; broad and short indicating that land is of mixed cover type or seasonal conditions are not spatially uniform. TINDVI showed a relationship to agricultural productivity that is dependent on the extent to which crop or total agricultural production was directly reduced by rainfall deficiency. TINDVI proved most sensitive to crop productivity for Statistical Local Areas (SLAs) having rainfall less than 600 mm, and in years when rainfall and crop production were highly correlated. It is concluded that metrics from standardized NDVI time series could be routinely and transparently used for retrospective assessment of seasonal conditions and changes in vegetation responses and cover.