Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images

Global Vegetation Index (GVI) data from the Advanced Very High Resolution Radiometer (AVHRR) was used to identify macro-scale vegetation/ land cover regions in the former Soviet Union (FSU). These regions are a better representation of surface vegetation and land cover than can be obtained from existing thematic maps of the FSU. Image classes were identified through cluster analysis using the ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative analysis of the image variants produced with different input parameters indicated that an image with 42 classes best represented significant details in vegetation and land cover patterns without producing uninterpretable levels of details that represent artefacts of the clustering algorithm. Initial identification of image classes has been made by considering the weight of evidence provided by quantitative and qualitative analysis of existing maps, analytical tools from class statistics, ancillary data from a variety of sources and expert assessment by Russian scientists with extensive field experience in the FSU. Overall, this method of image classification using GVI data appears to describe accurately regions with similar vegetation and hind cover across the FSU. Some questions regarding the identification of wetlands and potential problems with classification in the Russian high arctic are discussed. The products of this research will help improve carbon budget estimates of the FSU by providing accurate delineation and definition of carbon quantifiable regions.     

A multi-year composite of GVI data as a clear reflectance database

A method is developed to generate a top of the atmosphere clear reflectance from the Global Vegetation Index (GVI) product. Our goal is to use this dataset as a threshold to be applied to the forthcoming POLDER observations, for operational cloud detection. The method is based on the hypothesis that clouds add a high frequency signal to the slow variations of the surface reflectance in clear conditions. The validity of our algorithm is verified through an analysis of the temporal profile of the reflectance that it generates. We show that these profiles are better than those resulting from the simpler Maximum Value Composite (MVC) method. The method is applied to five years of GVI products and the results are used to derive a reference database which accounts for the interannual variability of the surface reflectance.     

Identifying land cover variability distinct from land cover change: Cheatgrass in the Great Basin

An understanding of land use/land cover change at local, regional, and global scales is important in an increasingly human-dominated biosphere. Here, we report on an under-appreciated complexity in the analysis of land cover change important in arid and semi-arid environments. In these environments, some land cover types show a high degree of inter-annual variability in productivity. In this study, we show that ecosystems dominated by non-native cheatgrass (Bromus tectorum) show an inter-annual amplified response to rainfall distinct from native shrub/bunch grass in the Great Basin, US. This response is apparent in time series of Landsat and Advanced Very High Resolution Radiometer (AVHRR) that encompass enough time to include years with high and low rainfall. Based on areas showing a similar amplified response elsewhere in the Great Basin, 20,000 km², or 7% of land cover, are currently dominated by cheatgrass. Inter-annual patterns, like the high variability seen in cheatgrass-dominated areas, should be considered for more accurate land cover classification. Land cover change science should be aware that high inter-annual variability is inherent in annual dominated ecosystems and does not necessarily correspond to active land cover change.     

Objective assessment of the NOAA global vegetation index data product

Abstract

For the last 10 years the U.S. National Oceanic and Atmospheric Administration has produced an experimental Global Vegetation Index (GVI) data set for terrestrial vegetation research. These data, sampled from advanced very high resolution radiometer (AVHRR) observations, have served as a primary stimulus for global-scale vegetation research but have, so far, not been adequately evaluated. This study reviews the GVI production procedures and compares the resultant observations with a more comprehensive compilation of the AVHRR data being produced at the NASA Goddard Space Flight Center. There are many aspects of the GVI production procedures which could be improved to achieve the desired objectives. In particular, the mapping and sampling procedures employed provide measurements which only approximate the observed GAC measurements. The GVI NDVI record varies more than ±NDVI units (～ 7 per cent of signal) from the GAC record and, in general, seriously underestimates the GAC NDVI measurements. The NDVI portion of the GVI record is compromised through use of digital numbers rather than calibrated reflectance. NDVI measurements from the calibrated channels of the GVI data set produces values that compare favourably with the GAC measurements, but with considerable residual variance. Calculation of a 3 by 3 pixel average of the GVI NDVI measurements reduces residual variance between the data sets to ±0.018 NDVI units (～3 per cent of signal). Decay of sensor calibration and orbital overpass time, experienced by all the AVHRR sensors, as well as differences in these parameters between the sensors are not addressed but the results suggest the importance of accounting for these factors. These results indicate that GVI data sets, following adequate reprocessing, provide reasonable estimates of major regional contrasts in vegetation activity but should not be employed to evaluate local or minor trends.     

A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data

Annual, inter-annual and long-term trends in time series derived from remote sensing can be used to distinguish between natural land cover variability and land cover change. However, the utility of using NDVI-derived phenology to detect change is often limited by poor quality data resulting from atmospheric and other effects. Here, we present a curve fitting methodology useful for time series of remotely sensed data that is minimally affected by atmospheric and sensor effects and requires neither spatial nor temporal averaging. A two-step technique is employed: first, a harmonic approach models the average annual phenology; second, a spline-based approach models inter-annual phenology. The principal attributes of the time series (e.g., amplitude, timing of onset of greenness, intrinsic smoothness or roughness) are captured while the effects of data drop-outs and gaps are minimized. A recursive, least squares approach captures the upper envelope of NDVI values by upweighting data values above an average annual curve. We test this methodology on several land cover types in the western U.S., and find that onset of greenness in an average year varied by less than 8 days within land cover types, indicating that the curve fit is consistent within similar systems. Between 1990 and 2002, temporal variability in onset of greenness was between 17 and 35 days depending on the land cover type, indicating that the inter-annual curve fit captures substantial inter-annual variability. Employing this curve fitting procedure enhances our ability to measure inter-annual phenology and could lead to better understanding of local and regional land cover trends.     

Geostatistically estimated image noise is a function of variance in the underlying signal

Estimation of noise contained within a remote sensing image is often a prerequisite to dealing with the deleterious effects of noise on the signal. Image based methods to estimate noise are attractive to researchers for a range of applications because they are in many cases automatic and do not depend on external data or laboratory measurement. In this paper, the geostatistical method for estimating image noise was applied to Compact Airborne Spectrographic Imager (CASI) imagery. Three CASI wavebands (0.46–0.49 μm (blue), 0.63–0.64 μm (red), 0.70–0.71 μm (near-infrared)) and four land covers (coniferous woodland, grassland, heathland and deciduous woodland) were selected for analysis. Five sub-images were identified per land cover resulting in 20 example cases per waveband. As in previous studies, the analysis showed that noise was related to land cover type. However, the noise estimates were not related to the mean of the signal in any waveband. Rather, the noise estimates were related to the square root of the semivariogram sill, which represents the variability in the underlying signal. These results suggest that the noise estimates produced using the geostatistical method may be inflated where the variance in the image is large. Regression of the noise estimates on the square root of the sill may lead to a stable noise estimate (i.e. the regression intercept), which is not affected by the variability in the image. This provides a refined geostatistical (GS) method that avoids the problems outlined above.     

Comparative analysis of urban reflectance and surface temperature

Urban environmental conditions are strongly dependent on the biophysical properties and radiant thermal field of the land cover elements in the urban mosaic. Observations of urban reflectance and surface temperature provide valuable constraints on the physical properties that are determinants of mass and energy fluxes in the urban environment. Consistencies in the covariation of surface temperature with reflectance properties can be parameterized to represent characteristics of the surface energy flux associated with different land covers and physical conditions. Linear mixture models can accurately represent Landsat ETM+ reflectances as fractions of generic spectral endmembers that correspond to land surface materials with distinct physical properties. Modeling heterogeneous land cover as mixtures of rock and/or soil Substrate, Vegetation and non-reflective Dark surface (SVD) generic endmembers makes it possible to quantify the dependence of aggregate surface temperature on the relative abundance of each physical component of the land cover, thereby distinguishing the effects of vegetation abundance, soil exposure, albedo and shadowing. Comparing these covariations in a wide variety of urban settings and physical environments provides a more robust indication of the global variability in these parameter spaces than could be inferred from a single study area. A comparative analysis of 24 urban areas and their non-urban peripheries illustrates the variability in the urban thermal fields and its dependence on biophysical land surface components. Contrary to expectation, moderate resolution intra-urban variations in surface temperature are generally as large as regional surface heat island signatures in these urban areas. Many of the non-temperate urban areas did not have surface heat island signatures at all. However, the multivariate distributions of surface temperature and generic endmember fractions reveal consistent patterns of thermal fraction covariation resulting from land cover characteristics. The Thermal-Vegetation (TV) fraction space illustrates the considerable variability in the well-known inverse correlation between surface temperature and vegetation fraction at moderate (< 100 m) spatial resolutions. The Thermal-Substrate (TS) fraction space reveals energetic thresholds where competing effects of albedo, illumination and soil moisture determine the covariation of maximum and minimum temperature with illuminated substrate fraction. The dark surface endmember fraction represents a fundamental ambiguity in the radiance signal because it can correspond to either absorptive (e.g. low albedo asphalt), transmissive (e.g. deep clear water) or shadowed (e.g. tree canopy shadow) surfaces. However, in areas where dark surface composition can be inferred from spatial context, the different responses of these surfaces may still allow them to be distinguished in the thermal fraction space.     

森林叶面积指数遥感反演模型构建及区域估算

基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。     

Land cover changes around a major east African wildlife reserve: The Mara Ecosystem (Kenya)

The dynamics of savannah vegetation are still poorly understood. This study aims at analysing land cover changes over the past 20 years in the rangelands area of Narok District, Kenya. To analyse the impact of inter-annual climate variability and human activities on land cover modifications in the area, change detection techniques based on remote sensing data at different spatial and temporal resolutions were used. Coarse spatial, high temporal resolution NOAA (National Oceanic and Atmospheric Administration) data were analysed to investigate the role of inter-annual climate variations on the ecosystem. A combination of time contextual and spatial contextual change detection approaches was used on a set of three high spatial resolution Landsat images to map land cover modifications over the past 20 years. Both datasets are highly complementary in the detection of land cover dynamics. On the one hand, the coarse spatial resolution data detected areas that are sensitive to inter-annual climate fluctuations, but are not subjected to land cover conversion. On the other hand, the high spatial resolution data allowed the detection of land cover conversions or modifications between two consecutive dates that have a more permanent character and are independent of climate-induced fluctuations in surface attributes.     

AVHRR channel selection for land cover classification

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.     

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.     

Interpreting image-based methods for estimating the signal-to-noise ratio

The signal-to-noise ratio (SNR) of remotely sensed imagery has been estimated directly using a variety of image-based methods such as the Homogeneous Area (HA) and Geostatistical (GS) methods. However, previous research has shown that such estimates may be dependent on land cover type. We examine this dependence on land cover type using Compact Airborne Spectrographic Imager (CASI) imagery of an agricultural region in Falmouth, Cornwall. The SNR was estimated using the GS method for six different land covers and a range of wavelengths. Large differences in the SNR existed between land cover types. It follows that single estimates of SNR (e.g. for one land cover) should not be associated with an image (as a whole). It is recommended that either (i) each statistic is reported per land cover type per wavelength or (ii) that an image of local statistics is reported per wavelength. The regression of noise on signal can be used to separate independent noise (intercept) from signal-dependent noise (slope). Variation in the noise and SNR estimates can be used to (i) allow more accurate prediction of the SNR and (ii) provide information on uncertainty.     

Spatiotemporal variability of snow cover on Tibet,China using MODIS remote-sensing data

ABSTRACT

Snow cover is an important component of the cryosphere, and the study on spatial and temporal variations of snow cover is essential for understanding the consequences and impacts of climate change and water resources management. In this study, the temporal variation of snow-covered area (SCA) and spatial variability of snow-cover frequency (SCF) on Tibet is analysed based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra snow cover product (MOD10A2) from 2000 to 2015, and relationships with main climate variables are investigated. Results are as follows: (1) there is a very weak decreasing trend in annual mean SCA, and a slight increasing trend in autumn and winter and a slight decreasing trend in spring and more robust decreasing trend in summer for SCA are found. (2) The temporal variation of SCA is negatively correlated with temperature, whereas it is little correlated with corresponding precipitation. (3) The general trend of spatial SCF variation on Tibet, predominated by snow-cover variations in spring and autumn, tends to decrease in spring while it tends to increase in autumn. (4) The spatial variability of SCF is attributed to snow-cover variations in autumn and spring, which is more obvious in higher latitudes in autumn while it is more noticeable in lower-latitude southeastern plateau in spring. (5) The regions with higher variability of snow cover are main pastoral land and more prone to snow-related disaster in Tibet, becoming key zone of snow-cover monitoring and disaster prevention and mitigation.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.     

Evaluation of MK-4 multispectral satellite photography in land cover classification of eastern Ontario

Russian MK-4 multispectral satellite photography has been investigated for potential in land cover classification. Thematic maps were generated using maximum likelihood, neural network and context classifiers. Classifications of the raw spectral data, of spectral transforms, and of combined spectral/textural data were evaluated. Low point-based class accuracies resulted for land cover types exhibiting high spatial variability at the given pixel spacing of 7.5m, while more spatially homogeneous cover types were well classified. Several issues arose which need to be addressed for effective future use of high-resolution satellite sensors in regional land cover mapping. They include the need for further research in techniques for classification and accuracy assessment which are sensitive to the spatial variance of such high resolution imagery, and optimization of class attribute definitions.     

Lowest-order correction to GVI data for solar zenith angle effect

Abstract

The aims of this presentation were (i) to simulate the solar zenith angle effect on the Global Vegetation Index (GVI), (ii) to derive an expression for removing such an effect from the GVI data by the above simulation procedure and (iii) to apply this relation to the GVI data obtained from the NOAA-AVHRR imagery.     

The effects of aggregated land cover data on estimating NPP in northern Wisconsin

Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km²), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products-two factors derived from remotely sensed data and strongly affect NPP estimates-in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m^− 2 year^− 1 (15 m data) to 431 gC m^− 2 year^− 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.     

Automatic Classification of Time Series (ACTS): A new clustering method for remote sensing time series

Automatic classification methods have often been used as a first step for land cover mapping. The principle of such methods is to determine clusters of pixels with similar radiometric temporal behaviour based on their statistical properties. This allows a segmentation of the image into regions with similar radiometric properties. Most automatic classification of remote sensing data are based on the K-mean or dynamical clustering method. The latter method has two limitations. (i) It is necessary to fix the number of clusters, but this parameter is, in general, unknown. (ii) It is very slow and does not work correctly when the dimension of the problem and the number of samples become large which is typically the case for classification of remote sensing data at large scale. To avoid these limitations we have developed a new method called ACTS (Automatic Classification of Time Series), which is based on both hierarchical and dynamical clustering principles. First of all, the method is really 'automatic' since it determines, automatically, the number of clusters. Secondly, the method is very fast and does not show a degradation of the results with large dimensions or data sets. Application to synthetic data sets shows that in most cases ACTS is able to retrieve all the clusters of the image independently of the dimensions of the problem. Comparison of classifications based on actual global 8 km NDVI (Normalized Differential Vegetation Index) composites using both ACTS and a K-mean method show very similar results but the convergence for ACTS is 20 times faster than the K-mean method using '10-day' composites. The ability of ACTS to work with problems of large dimensions enables clustering of multi-year time series of NDVI. ACTS is here applied to the clustering of a 12-year time series of 10-day composites (1982-1993). The results show that the seasonal signal is dominant. The clusters are mainly representative of seasonal land cover regions. Moreover, the regions are more clearly delineated in comparison with the classification based on only one year of data. Such improved clustering can help avoid some confusion between biomes. Finally, ACTS is applied to 'deseasonalized' time series to investigate the interannual variability of the NDVI. The areas of higher variability are located in the tropical regions with a strong influence of El Nino events. A small positive trend in NDVI is visible in high latitudes. However, several problems linked to the quality of the data are clearly visible. For instance, the decrease of NDVI following the Pinatubo eruption in 1991 and the drift in calibration with the change from NOAA 9 to NOAA 11 in 1988 are visible in most of the regions. This unfortunately limits the possible interpretation of the signal and emphasizes the need to improve the preprocessing of AVHRR (Advanced Very High Resolution Radiometer) data for interannual variability applications.     

Global land monitoring from AVHRR: potential and limitations

Global Vegetation Index ( GVI) time series of visible, near-IR and thermal IR Advanced Very High Resolution Radiometer (AVHRR)weekly composite data with a 015^° spatial resolution collected from NOAA-9 and -11 satellites have been used to develop a prototype global land monitoring system. The system is based on standardized anomalies of the Normalized Difference Vegetation Index (NDVI) and channel 4 brightness temperature ( T₄ )for the period April 1985-September 1994. Processing included: post-launch updated calibration; cloud screening; filling in the cloud induced data gaps by monthly averaging and spatial interpolation; suppressing residual noise by smoothing; calculating 5-year monthly means and standard deviations of NDVI and T₄and their standardized anomalies. The derived anomalies show potential for detecting and interpreting the seasonal cycle and statistically significant interannual variability. Yet, discontinuities and residua! trends can be traced in time series of NDVI and T4. Discontinuities result from the switch from NOAA-9 to NOAA-11 in 1988, and the Mount Pinatubo eruption in 1991. Trends are a combined effect of satellite orbit drift and a possible persistent error in post-launch calibration of solar channels. The orbit drift affects the solar and thermal IR channels through systematic variation of illumination geometry and diurnal heating/cooling of the surface and atmosphere, respectively. Examples are given to illustrate the magnitude of these effects, which reduce the ability to monitor small year-to-year surface changes. The present system yields more accurate results in geographic regions, where atmospheric, angular and diurnal variability effects have a lesser impact on the derived anomalies, i.e. over vegetated areas outside the tropics during local summers. For global-scale monitoring, angular, atmospheric and diurnal variability corrections must be incorporated in the present system.     

Identification of potential land cover changes on a continental scale using NDVI time-series from SPOT VEGETATION

The identification of land cover changes on a continental scale is a laborious and time-consuming process. A new methodology is proposed based exclusively on SPOT VGT data, illustrated for the African Continent using GLC2000 as reference to select 26 distinct land cover types (classes). For each class, the normalized difference vegetation index (NDVI) time-series are extracted from SPOT VGT images and a hierarchical aggregation is done using two different methods: one that preserves the initial signatures throughout the hierarchical process, and another that recalculates the signatures for each aggregation level. The average classification agreement was above 89% using 26 classes. Reducing the number of classes improves classification agreement. In order to study the influence of temporal variability in the classification results, the methodology was applied on data from 1999, 2001, 2008, and 2010. With 26 classes, the best average classification agreement obtained was 94.5% with annual data, against 74.1% with interannual data.