SPOT VEGETATION Data Research Articles

Normalised Difference Vegetation Index Based Drought Monitoring Using Remote Sensing and GIS

The objective of this research is to analyse vegetation’s reactions to precipitation. Three Pinups sp. combinations and water tension conditions (pine) forests and NDVI’s assessment of the capacity to identify drought and water stress in this sort of ecosystem. For the purposes of this analysis, a 30-year time series of NDVI indices, derived from SPOT-Vegetation data, is built for all three study sites and compared with field measured precipitation data for the same time span. Results indicate a good relationship between the two indices suggesting a stronger capacity for investigating the vegetation water status. In general, fluctuations in high seasonal precipitation tend to have a strong influence on both NDVI ratios, while a smoother spread of precipitation results in a poorer relationship with the two indices.

Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests

Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics. This study is the first comprehensive comparison of vegetation variables and methods to retrieve land surface phenology for 1999–2017 time series of Copernicus Global Land products derived from SPOT-VEGETATION and PROBA-V data. We investigated the sensitivity of phenology to (I) the input vegetation variable: normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER); (II) the smoothing and gap filling method for deriving seasonal trajectories; and (III) the method to extract phenological metrics: thresholds based on a percentile of the annual amplitude of the vegetation variable, autoregressive moving averages, logistic function fitting, and first derivative methods. We validated the derived satellite phenological metrics (start of the season (SoS) and end of the season (EoS)) using available ground observations of Betula pendula, B. alleghaniensis, Acer rubrum, Fagus grandifolia, and Quercus rubra in Europe (Pan-European PEP725 network) and the USA (National Phenology Network, USA-NPN). The threshold-based method applied to the smoothed and gap-filled LAI V2 time series agreed best with the ground phenology, with root mean square errors of ˜10 d and ˜25 d for the timing of SoS and EoS respectively. This research is expected to contribute for the operational retrieval of land surface phenology within the Copernicus Global Land Service.

Comparison of Two Satellite‐Based Evapotranspiration Models of the Nagqu River Basin of the Tibetan Plateau

AbstractEvapotranspiration (ET) is one of the major uncertain components of the energy and water cycle and was derived for the Nagqu River Basin based on remote sensing data and atmospheric surface layer observations under cloudless conditions. Two process‐based models were used to determine the ET: a Priestley‐Taylor (PT)‐based model and a topographical enhanced surface energy balance system (TESEBS) model. Improved broadband albedo, downward shortwave radiation flux, and reconstructed normalized difference vegetation index (NDVI) were coupled into the TESEBS model and PT‐based model to estimate the actual ET. The atmospheric surface layer meteorological data, SPOT Vegetation (VGT) data, and Moderate Resolution Imaging Spectroradiometer data were used as inputs for 10‐day ET calculations. The model‐estimated results were compared with ground truths calculated via the combinatory method. The results indicated that the ET determined by both models well fit the actual ET, with correlation coefficients (R) of 0.88 and 0.82, respectively. However, the TESEBS model showed a better performance than the PT model, with a lower mean bias error (−0.02 mm/hr) and lower root–mean‐square error (0.08 mm/hr). Although the PT model is computationally simple and requires few parameters, the strong weighting of the NDVI may lead to some overestimations, especially during the growing season.

Vegetation Dynamics and Their Response to Climatic Variability in China

Based on SPOT VEGETATION data and meteorological data, NDVI (Normalized Difference Vegetation Index) and its response to temperature and precipitation in China and its different regions were investigated over the period 1998–2013 by using the maximum value composite and linear regression methods. The results showed that NDVI presented significant increase (0.0046/a) for all of China and all the regions over the last 16 years. Meanwhile, annual mean temperature of China presented a slightly increasing trend, while the annual precipitation showed a slightly decreasing trend over the last 16 years. Nevertheless, there were differences between temperature and precipitation in the subregions of China. The Annual NDVI had better relationships with precipitation (r=0.126) compared to temperature (r=-0.094), and NDVI also had a good correlation with precipitation rather than temperature in different subregions of China. Additionally, human activities also made a difference to the trends of NDVI in some regions. This study is conductive to the effects of climate change on vegetation activity in future research.

Dynamic self-learning water-masking algorithm for AATSR, MERIS, and SPOT VEGETATION

Within the ESA CCI 'Fire Disturbance' project (Guenther et al., 2012), a dynamic self-learning water masking approach was developed for AATSR, MERIS-FR(S), MERIS-RR, and for SPOT VEGETATION (SPOT-VGT) data. The primary goal of the development was to find for all sensors a generic algorithm by combining static water masks on a global scale with a self-learning algorithm. Our approach results in the generation of a dynamic water mask which helps to distinguish burned areas from other dark areas as, e.g., cloud or topographic shadows or coniferous forests. The use of static water masks as training areas for the learning algorithm must take into account that small and shallow water bodies may change in time and that a precise geo-location of the static water mask and the scene under investigation is mandatory. The comparison of the water masks derived from all sensors for a region in Kazakhstan demonstrates the quality of the new dynamic water masks. In addition, the advantages to other water masking algorithms (MOD44W, Hansen_GFC or IDEPIX) are shown. Furthermore, the dynamic water masks of AATSR, MERIS and SPOT-VGT for the same region are presented and discussed together with the use of more detailed static water masks.

Analysis of the Effects of Drought on Vegetation Cover in a Mediterranean Region through the Use of SPOT-VGT and TERRA-MODIS Long Time Series

The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION (between September 1998 and August 2013) and TERRA-MODIS satellite data (between September 2000 and August 2013), was used to analyze the vegetation dynamics over the central region of Tunisia in North Africa, which is characterized by a semi-arid climate. Products derived from these two satellite sensors are generally found to be coherent. Our analysis of land use and NDVI anomalies, based on the Vegetation Anomaly Index (VAI), reveals a strong level of agreement between estimations made with the two satellites, but also some discrepancies related to the spatial resolution of these two products. The vegetation’s behavior is also analyzed during years affected by drought through the use of the Windowed Fourier Transform (WFT). Discussions of the dynamics of annual agricultural areas show that there is a combined effect between climate and farmers’ behavior, leading to an increase in the prevalence of bare soils during dry years.

Cropping intensity and seasonality parameters across Asia extracted by multi-temporal SPOT vegetation data

Cropping intensity and seasonality parameters across Asia extracted by multi-temporal SPOT vegetation data

Operational Monitoring of the Desert Locust Habitat with Earth Observation: An Assessment

Desert locust swarms intermittently damage crops and pastures in sixty countries from Africa to western Asia, threatening the food security of 10% of the world’s population. During the 20th century, desert locust control operations began organizing, and nowadays, they are coordinated by the Food and Agriculture Organization (FAO), which promotes a preventative strategy based on early warning and rapid response. This strategy implies a constant monitoring of the populations and of the ecological conditions favorable to their development. Satellite remote sensing can provide a near real-time monitoring of these conditions at the continental scale. Thus, the desert locust control community needs a reliable detection of green vegetation in arid and semi-arid areas as an indicator of potential desert locust habitat. To meet this need, a colorimetric transformation has been developed on both SPOT-VEGETATION and MODIS data to produce dynamic greenness maps. After their integration in the daily locust control activities, this research aimed at assessing those dynamic greenness maps from the producers’ and the users’ points of view. Eight confusion matrices and Pareto boundaries were derived from high resolution reference maps representative of the temporal and spatial diversity of Mauritanian habitats. The dynamic greenness maps were found to be accurate in summer breeding areas (F-score = 0.64–0.87), but accuracy dropped in winter breeding areas (F-score = 0.28–0.40). Accuracy is related to landscape fragmentation (R2 = 0.9): the current spatial resolution remains too coarse to resolve complex fragmented patterns and accounts for a substantial (60%) part of the error. The exploitation of PROBA-V 100-m images at the finest resolution (100-m) would enhance by 20% the vegetation detection in fragmented habitat. A survey revealed that end-users are satisfied with the product and find it fit for monitoring, thanks to an intuitive interpretation, leading to more efficiency.

Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance

Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method ( $\mathbf{R}^{2}=0.809$ , $\mathbf{RMSE} =0.157$ ) was comparable with that of the GEOV1 FVC product ( $\mathbf{R}^{2}=0.775$ , $\mathbf{RMSE} =0.166$ ) , which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Space-time LAI variability in Northern Puglia (Italy) from SPOT VGT data.

The vegetation space–time variability during 1999–2010 in the North of the Apulian region (Southern Italy) was analysed using SPOT VEGETATION (VGT) sensor data. Three bands of VEGETATION (RED, NIR and SWIR) were used to implement the vegetation index named reduced simple ratio (RSR) to derive leaf area index (LAI). The monthly average LAI is an indicator of biomass and canopy cover, while the difference between the annual maximum and minimum LAI is an indicator of annual leaf turnover. The space–time distribution of LAI at the catchment scale was analysed over the examined period to detect the consistency of vegetation dynamics in the study area. A diffuse increase of LAI was observed in the examined years that cannot be directly explained only in terms of increasing water availability. Thus, in order to explain such a general behaviour in terms of climatic factors, the analysis was performed upon stratification of land cover classes, focusing on the most widespread species: forest and wheat. An interesting ascending–descending behaviour was observed in the relationship between inter-annual increments of maximum LAI and rainfall, and in particular, a strong negative correlation was found when the rainfall amount in January and February exceeded a critical threshold of about 100 mm.

Comparing land surface phenology with leafing and flowering observations from the PlantWatch citizen network

Annual maps of the remote sensing green-up date derived from SPOT-VEGETATION data were compared to the phenological observations collected by the PlantWatch citizen science project across Canada between 1998 and 2012. Green-up dates were found to relate to the leaf-out dates for four woody species (Populus tremuloides, Acer rubrum, Syringa vulgaris, Larix laricina), with a RMSE from 13.6 to 15.6days. This was true for all landcover types except in pixels where agriculture or water bodies were dominant. This is less accurate than the results from previous studies for boreal Eurasia (RMSE=8.7days), with phenology data from an operational network. When data were aggregated at a regional level, the remote sensing green-up date matched well with the interannual variations in leafing and also in flowering of most of the recorded species. These included spring events for trees, shrubs and non-woody plants which were either native to Canada or introduced. For most plants, spring flowering and leafing times are functions of accumulated temperature. For this reason, plant species develop in a predictable sequence, and interannual variations in this cohort of species leafing and flowering are correlated. This explains the correlation with remote sensing green-up. Data from this volunteer PlantWatch network proved consistent with independent satellite data, suggesting that combining the two will strengthen the future capacity to monitor vegetation changes.

Analysis of Vegetation Cover Changing Trend in Pearl River Basin

The vegetation cover changing trend in Pearl River Basin during 1998-2012 are analyzed based on sequential SPOT VEGETATION data with Maximum Value Composites (MVC) method and linear regression technique. Results show: (1) From 1998 to 2012, the seasonal maximum NDVI changes in Pearl River Basin are obvious, peak appears in September and minimum in February, average is 0.578, the mean maximum NDVI is on the rise during the recent 15 years; the average of maximum NDVI is 0.740, the vegetation cover condition is good, but there is an obvious spatial difference. (2) In recent 15 years, the mean maximum NDVI trend shows that vegetation overall improved, deteriorated partly. Improved area, deteriorated area and invariant area account for 69.80 percent, 2.10 percent and 28.10 percent of the total area respectively.

Carbon density and accumulation in agroecosystem of Indo-Gangetic Plains and Vindhyan highlands, India

In agroecosystems, the annual carbon (C) inputs to soil are one of the most promising greenhouse gas mitigation options. Net primary productivity (NPP) provides the inputs of C in ecosystems that can potentially be sequestered in soil organic matter. In this study, we estimates the C density and accumulation rate in rice-wheat agroecosystem at four sites of Indo-Gangetic Plains and Vindhyan highlands through amalgamation of ground truth (GT) and remote sensing (RS) approach. In addition to this, we validated field-measured aboveground net production (ANP) with remotely sensed SPOT-Vegetation data. ANP of the sites ranged from 6.8 to 11.1 and 3.3 to 8.8t-Cha(-1)year(-1) for GT and RS, respectively. Both estimates (GT and RS) were linearly and significantly related with each other (y = 1.33x - 5.82, R (2) = 0.93, P = 0.04; where x = GT ANP and y = RS ANP). For the whole region, total NPP (rice + wheat) was ranged from 7.9 to 12.5t-Cha(-1)year(-1). The C accumulation potential of the present agroecosystems was 9.1 and 1.9t-Cha(-1)year(-1) in the form of ANP and belowground NPP (BNP), respectively. The aboveground C stock of agroecosystem allocated in foliage (7.8%), stem (57.6%), and grain (35.1%). Survey about the fate of post-harvested materials indicated that aboveground portion of the crop is almost used completely within the year for different purposes including human consumption and cattle feeding. Therefore, only BNP and litter contribute to long-term C sequestration. Since agroecosystem has enormous potential to sequester C, better management of aboveground portion NPP of the crop may enhance C sequestration potential of agroecosystem.

PROBA-V mission for global vegetation monitoring: standard products and image quality

PROBA-V is a new global vegetation monitoring mission, to be launched in the second quarter of 2013. PROBA-V has been developed to show a consistent performance with SPOT-VEGETATION (SPOT-VGT) data, with similar spectral bands but with an improved spatial resolution of 1/3 km. The innovative mission concept has led to several key research topics related to image quality, which are discussed in this article. To support the existing VEGETATION user community, the data products for PROBA-V continue to provide daily top of canopy synthesis (S1-TOC) and 10 day synthesis products (S10-TOC). In addition, the new top of atmosphere daily synthesis (S1-TOA) products and a radiometrically/geometrically corrected (level 1C) product in raw resolution will also be provided for scientific users.

Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain

Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region.

The Extraction and Dynamic Monitoring of Leaf Area Index in Northeast of China Based on SPOT VEGETATION Data

Using SPOT VEGETATION data, the extraction method of experience model of LAI (leaf area index), we estimate and dynamic analysis the LAI of the northeast in 2000, 2004 and 2008. The results showed that: the LAI values of the Northeast region from 2000 to 2008 appears gradually increasing trend, the lowest average of 3.10 throughout the region in 2000, the highest in 2008, reaching 4.97. Judging from the different regions, changes in Liaoning province is the largest one which increases 136.40 points between 8 years, while Heilongjiang Province is the lowest, increased at only 21.53%. LAI values of Heilongjiang province are all the highest in 2000, 2004 and 2008 mainly because of the rich forest resources, vegetation coverage are relatively good; while in Liaoning province and Inner Mongolia, vegetation coverage is relatively poor. Therefore, the LAI is relatively low.

The Extraction and Dynamic Monitoring of Leaf Area Index in Northeast of China Based on SPOT VEGETATION Data

Using SPOT VEGETATION data, the extraction method of experience model of LAI (leaf area index), we estimate and dynamic analysis the LAI of the northeast in 2000, 2004 and 2008. The results showed that: the LAI values of the Northeast region from 2000 to 2008 appears gradually increasing trend, the lowest average of 3.10 throughout the region in 2000, the highest in 2008, reaching 4.97. Judging from the different regions, changes in Liaoning province is the largest one which increases 136.40 points between 8 years, while Heilongjiang Province is the lowest, increased at only 21.53%. LAI values of Heilongjiang province are all the highest in 2000, 2004 and 2008 mainly because of the rich forest resources, vegetation coverage are relatively good; while in Liaoning province and Inner Mongolia, vegetation coverage is relatively poor. Therefore, the LAI is relatively low.

Characterizing land condition variability in Ferlo, Senegal (2001–2009) using multi-temporal 1-km Apparent Green Cover (AGC) SPOT Vegetation data

The ecosystem state or ‘land condition’ can be characterized by a set of attributes, which show variations at different temporal scales. A multi-resolution analysis (MRA) based on the wavelet transform (WT) has been implemented to examine the land condition of a dryland region in Ferlo (Senegal) over the period 2001–2009. This methodology has proven to be useful for smoothing time series while considering those temporal resolutions that incorporate information about the vegetation dynamics. For this purpose, time series of the 1-km Apparent Green Cover (AGC) from the 10-day composites SPOT Vegetation (VGT) data are analyzed. Two relevant outputs from the MRA, A 1 (de-noised) and the A 6 (inter-annual) components have served us for characterizing the annual vegetation production and assess the long-term variation, respectively. In a first stage, the vegetation seasonality (or intra-annual variation) over the area is described by using several metrics related to vegetation phenology derived from the de-noised time series ( A 1). In a second phase, the temporal variability of the inter-annual component series ( A 6) is accomplished to detect potential vegetation changes over the considered period. A Mann–Kendall test has been applied to confirm the significance of the observed inter-annual changes. A higher number of significant pixels (86%) are obtained when considering the inter-annual component in the trend analysis instead of the original time-series (47%). The results confirm a general greening up over the period 2001–2009, not fully explained by precipitation, as well as rather local negative trends. The Rain-Use Efficiency (RUE) ratio computed using the AGC as a proxy of vegetation production has been considered to further analyze the detected changes. Inter-annual changes in RUE provide a potential method of separating vegetation declines due to lack of rainfall from declines associated with degradation. Some spots of negative values in inter-annual RUE changes are found in certain areas for which several evidence of land degradation have been documented.

Mapping of summer crops in the State of Paraná, Brazil, through the 10-day spot vegetation NDVI composites

The search for low subjectivity area estimates has increased the use of remote sensing for agricultural monitoring and crop yield prediction, leading to more flexibility in data acquisition and lower costs comparing to traditional methods such as census and surveys. Low spatial resolution satellite images with higher frequency in image acquisition have shown to be adequate for cropland mapping and monitoring in large areas. The main goal of this study was to map the Summer crops in the State of Paraná, Brazil, using 10-day composition of NDVI SPOT Vegetation data for 2005/2006, 2006/2007 and 2007/2008 cropping seasons. For this, a supervised digital classification method with Parallelepiped algorithm in multitemporal RGB image composites was used, in order to generate masks of Summer cultures for each 10-day composition. Accuracy assessment was performed using Kappa index, overall accuracy and Willmott's concordance index, resulting in good levels of accuracy. This methodology allowed the accomplishment, with free and low resolution data, of the mapping of Summer cultures at State level.

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Multitemporal Principal Component Analysis (MPCA) was used for processing Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) satellite images. MPCA was able to merge spectral data corresponding to TM-1996 (pre-fire in 1997), ETM-2000 (post-fire 1997 and pre-fire 2002) and ETM-2003 (post-fire in 2002), which was crucial for detecting the fire impact and vegetation recovery. Results indicate that the burnt areas of 1997 and 2002 were 89,086 ha (16.5%) and 31,859 ha (5.9%), respectively, within the study area of 540,000 ha. Satellite Pour 1’Observation de la Terre (SPOT)-VEGETATION 10-day Maximum Value Composite (MVC) data were also used and compared with Normalized Difference Vegetation Index (NDVI) from ground-based NDVI. Our research demonstrates the strong relationship between Landsat- TM/ETM+, SPOT-VEGETATION data and ground-based NDVI in identifying land-cover changes and vegetation recovery over the tropical peat swamp forest area in Central Kalimantan, Indonesia that is affected by forest fires that occurred in 1997 and 2002.