SPOT-VGT Data Research Articles

Comparative evaluation of NDVI strata and livelihood zones as spatial units of the practiced crop calendars

ABSTRACT Properly mapping the spatial variability of the practiced crop calendars is useful for precise planning of agricultural activities. A seasonal calendar based on the livelihood zone (LHZ) map is widely employed as a crop calendar in Ethiopia. However, using the LHZ-based crop calendar for agricultural purposes may cause problems of doubtful accuracy, since non-ecological parameters are included in the LHZ map. This research was conducted in the central and southeastern parts of Ethiopia to compare the hypertemporal normalized difference vegetation index (NDVI) and LHZ-based stratification methods in explaining the spatial variability of the farmers’ actual crop calendar. A NDVI strata generated from SPOT-VGT and PROBA-V NDVI data of 16 years and the 2008 LHZ map of Ethiopia were processed to select sampling sites that represented three NDVI strata and four LHZs in common. Interview data were collected randomly from individual farmers on ploughing, planting and expected harvesting dates of teff (Eragrostis tef), wheat and barley (fields), and were analysed using non-parametric tests. The results revealed that the NDVI strata had greater spatial quality and precision than the LHZs in terms of uniqueness and internal homogeneity of the spatial units, indicating its suitability as a preferable alternative crop calendar.

Using remote sensing to identify the peak of the growing season at globally-distributed flux sites: A comparison of models, sensors, and biomes

The peak of the growing season (POS) is known to play an important role in regulating the interannual variability of terrestrial carbon sequestration. Recent research has developed several models for POS estimation; however, a comprehensive understanding of the predictive ability of these models remains lacking, especially taking various biomes and satellite data from different sensors into account. Using data from 54 eddy covariance (EC) flux sites (434 site-years in total) from the FLUXNET2015 dataset, we extracted POS from the normalized difference vegetation index (NDVI), derived from two sensors (MODIS and SPOT-VGT), using four different methods. We then compared the model outputs when data from the different sensors were used, and across different biomes. Our results show that the model predictions correlated weakly (R2 < 0.4) with the flux-based POS when multiple biomes were considered together. However, the performance of the models varied significantly between the models, the sensors that provided the data, and different biomes. Firstly, the more recently proposed methods did not perform as expected, and some of them performed even worse than the commonly used approach. Secondly, POS modeled from MODIS data performed slightly better than that from SPOT-VGT data. Thirdly, when the models are combined, they can reliably estimate POS for grasslands, deciduous broadleaf forests, and open shrublands, but not necessarily for other biomes. Lastly, our results indicate that NDVI-based POS is not a good proxy of flux-based POS. The study suggests that both biomes and sensor properties should be taken into account when estimating POS, and a rigorous validation is necessary before different models are implemented at regional, or larger scales. Therefore, this study provides insights that are helpful for improving our understanding of the impacts of algorithms, sensors, and biomes on model estimates of POS.

A global approach to estimate irrigated areas – a comparison between different data and statistics

Abstract. Agriculture is the largest global consumer of water. Irrigated areas constitute 40 % of the total area used for agricultural production (FAO, 2014a) Information on their spatial distribution is highly relevant for regional water management and food security. Spatial information on irrigation is highly important for policy and decision makers, who are facing the transition towards more efficient sustainable agriculture. However, the mapping of irrigated areas still represents a challenge for land use classifications, and existing global data sets differ strongly in their results. The following study tests an existing irrigation map based on statistics and extends the irrigated area using ancillary data. The approach processes and analyzes multi-temporal normalized difference vegetation index (NDVI) SPOT-VGT data and agricultural suitability data – both at a spatial resolution of 30 arcsec – incrementally in a multiple decision tree. It covers the period from 1999 to 2012. The results globally show a 18 % larger irrigated area than existing approaches based on statistical data. The largest differences compared to the official national statistics are found in Asia and particularly in China and India. The additional areas are mainly identified within already known irrigated regions where irrigation is more dense than previously estimated. The validation with global and regional products shows the large divergence of existing data sets with respect to size and distribution of irrigated areas caused by spatial resolution, the considered time period and the input data and assumption made.

High resolution spatiotemporal analysis of erosion risk per land cover category in Korçe region, Albania

Some recent land use changes in Albania, such as deforestation, cropland abandonment, and urban sprawl, have caused serious increase of erosion risk. The main objective of this study was to map erosion risk in Korce region and assess the degree at which every land use is concerned. The G2 erosion model was applied, which can provide erosion maps and statistical figures at month-time intervals using input from free European and global geodatabases. The mapping results in Korce region were derived at a 30-m cell size, which is an innovation for G2. Autumn-winter months were found to be the most erosive, with average erosion rates reaching the maximum in November and December, i.e. 2.62 and 2.36 t/ha, respectively, while the annual rate was estimated at 10.25 t/ha/yr. Natural grasslands, shurblands, mixed forests, and vineyards showed to exhibit the highest mean erosion rates, while shrublands, broad-leaved forests and natural grasslands were found to be the most extended land covers risky for non-sustainable erosion rates (i.e. >10 t/ha/yr). A detailed examination of the detected hot spots is now necessary by the competent authorities, in order to apply appropriate, site-specific conservation measures. Notably, use of SPOT VGT data did not prevent the maps from having extended gaps due to cloudiness. Sentinel-2 time series, freely available by the European Space Agency (ESA), have the potential to improve spatiotemporal coverage of V-factor, thus further empowering the G2 model, in the near future.

Circumpolar vegetation dynamics product for global change study

Abstract Land surface phenology (LSP) and vegetation growth of the circumpolar north are changing in response to more pronounced warming in the region. We here introduce the first phenology index (PI) based vegetation dynamics product, comprising start (SOS), end (EOS), length of growing season (LOS), and growing season integrated annual normalized difference vegetation index (NDVI), specifically designed for the entire circumpolar north (> 45°N) using SPOT VGT data starting from 1999. PI combines the merits of NDVI and normalized difference infrared index (NDII) by taking the difference of squared greenness (from NDVI) and wetness (from NDII) to remove the soil and snow cover dynamics from key vegetation LSP cycles. The results show that the circumpolar vegetation dynamics and their spatial distributions are realistically detected. Further validation based on North American and European deciduous broadleaf, evergreen needleleaf and mixed forests, and wetland flux tower sites shows good agreements between the LSP dates from the circumpolar vegetation dynamics and ground phenology estimates from CO 2 flux measurements. The validation also proves that the circumpolar vegetation dynamics product is an improvement over the operational global MODIS Combined Land Cover Dynamics (MCD12Q2) product for the circumpolar region. The results are further compared with the interannual variability of sea ice extent and leading teleconnection patterns in the region. The circumpolar averaged results show that, the growing season integrated annual NDVI is significantly increasing (0.68% year − 1 , p = 0.006) and well correlated with the growing season sea ice extent trend ( p = 0.007). The circumpolar vegetation dynamics is more related to Polar/Eurasia pattern (i.e., indicator of circumpolar vortex) than to Scandinavian Pattern (SCA) and North Atlantic Oscillation (NAO). In view of the considerable scientific and policy importance of the circumpolar region, particularly the arctic ecosystems, the presented circumpolar vegetation dynamics product will greatly contribute to study changes in plant growth, phenology, photosynthetic capacity, and associated feedbacks under climate change.

Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data

In this study, we have used four methods to investigate the start of the growing season (SGS) on the Tibetan Plateau (TP) from 1982 to 2012, using Normalized Difference Vegetation Index (NDVI) data obtained from Global Inventory Modeling and Mapping Studies (GIMSS, 1982–2006) and SPOT VEGETATION (SPOT-VGT, 1999–2012). SGS values estimated using the four methods show similar spatial patterns along latitudinal or altitudinal gradients, but with significant variations in the SGS dates. The largest discrepancies are mainly found in the regions with the highest or the lowest vegetation coverage. Between 1982 and 1998, the SGS values derived from the four methods all display an advancing trend, however, according to the more recent SPOT VGT data (1999–2012), there is no continuously advancing trend of SGS on the TP. Analysis of the correlation between the SGS values derived from GIMMS and SPOT between 1999 and 2006 demonstrates consistency in the tendency with regard both to the data sources and to the four analysis methods used. Compared with other methods, the greatest consistency between the in situ data and the SGS values retrieved is obtained with Method 3 (Threshold of NDVI ratio). To avoid error, in a vast region with diverse vegetation types and physical environments, it is critical to know the seasonal change characteristics of the different vegetation types, particularly in areas with sparse grassland or evergreen forest.

Vegetation Activity in the Upper Oueme Basin (Benin, Africa) Studied from SPOT-VGT (2002-2012) According to Land Cover

Signs of climate change in West Africa over the last few decades are among the significant observed in the tropics, in particular the decrease in mean annual precipitation. Although such change has brought about new eco-climatic constraints on vegetation forms, it has not proven easy to determine interannual and intra-seasonal variations on a regional scale for major vegetation forms, whether natural or highly artificial as a result of human activities. The present study analyzes vegetation activity via a Normalized Difference Vegetation Index (NDVI) defined from ten-day SPOT-VGT data (one-km resolution, covering the period from 2002 to 2012) in the African Monsoon Multidisciplinary Analysis (AMMA) Program observation zone, located in the Upper Oueme River Basin in Benin. The statistical analysis is mainly based on a multifactor approach allowing approximately 54% of interannual NDVI variations to be accounted for. Results show that spatio-temporal NDVI variability in the River Basin is highly dependent on land use, be it forest, wooded savanna, farmland or areas undergoing conversion.

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.

COMPARISON OF GOSAT CAI AND SPOT VGT NDVI DATA WITH DIFFERENT SEASON AND LAND COVER IN EAST ASIA

Abstract. The Normalized Difference Vegetation Index (NDVI) has become one of the most widely used indices in remote sensing applications in a variety of fields. Many studies have compared the NDVI values for different satellite sensors. Nowadays, the Greenhouse Gases Observing Satellite (GOSAT) was successfully launched on January 23, 2009. It is used to monitor greenhouse gases on the Earth's surface and also has a sensor, the Cloud Aerosol Imager (CAI), that senses red and near infrared spectrums. It can also process NDVI data. Therefore, we are first compare GOSAT CAI and SPOT VGT NDVI data in different seasonal and land cover in East Asian, to explore the relationship between the two types of datasets, and to discuss the possibility of extending SPOT VGT data using GOSAT CAI NDVI data for the same area. We used GOSAT CAI Level 3 data to derive 10–day composite NDVI values for the East Asia region for November 2009 and January, April and July 2010 using the maximum value composite (MVC) method. We compared these values with 10–day composite SPOT VGT NDVI data for the same period. The results show that the correlation coefficients of regression analysis generally revealed a strong correlation between NDVI from the two sensors in November 2009 and January, April and July 2010 (0.88, 0.85, 0.77 and 0.74, respectively). The differences place may be affected by cloud cover. From the combined analysis of seasonal changes and land cover, we found that the correlations between the SPOT VGT and the GOSAT CAI NDVI data are less affected by seasonal change and the SPOT VGT data is more sensitive to high vegetation coverage than the GOSAT CAI data. In the future, through continued monitoring and processing by cloud removal technology, the accuracy of GOSAT CAI NDVI data will be further improved and thus be more widely used.

Climate change and local adaptation strategies in the middle Inner Mongolia, northern China

According to records of 17 meteorological stations distributed in the study area, climate change of the middle Inner Mongolia in northern China was analyzed in this paper. Based on SPOT VGT data, combined with field investigation, local vegetation change was detected in the last 10 years. The results show that annual mean air temperature obviously rose, while precipitation slightly decreased in fluctuation in the study area during the last 50 years. Air temperature increasing rates are +0.318°C 10 year−1 during 1960-2009 and +0.423°C 10 year−1 during 1980–2009, while precipitation decreasing rates are −2.91 mm 10 year−1 during 1960–2009. There were five different dry or wet periods from the 1960s to the 2000s in order, and the wetter 1990s and the drier 2000s changed dramatically in the study area. Local climate totally tend to warm–dry conditions during the last 50 years. According to coefficient of variation (Cv) of yearly growing-season cumulative NDVI value and yearly NDVI maximum in pixel scale, vegetation had experienced huge temporal and spatial variation during the last 10 years. Recently, frequent droughts and dust storms seriously affected local agriculture and grazing activities, and resulted in heavy economic loss, especially over the drought period of 1999–2001. Faced with those drought disasters accompanied with strong dust storms, the local authorities proposed the enclosing-transferring strategy and made great efforts to adapt overt climate change and improve environment, including making selective emigration, decreasing livestock numbers, fencing grasslands and building forage production bases with irrigation instruments and actively adjusting industry structure. However, some effects and potential problems of this adaptation strategy still need to be comprehensively assessed further in longer time scales and aimed at different sub-regions.

Leaf area index mapping in northern Canada

Leaf area index (LAI) is an important structural vegetation parameter that is commonly derived from remotely sensed data. It has been used as a reliable indicator for vegetation's cover, status, health and productivity. In the past two decades, various Canada-wide LAI maps have been generated by the Canada Centre for Remote Sensing (CCRS). These products have been produced using a variety of very coarse satellite data such as those from SPOT VGT and NOAA AVHRR satellite data. However, in these LAI products, the mapping of the Canadian northern vegetation has not been performed with field LAI measurements due in large part to scarce in situ measurements over northern biomes. The coarse resolution maps have been extensively used in Canada, but finer resolution LAI maps are needed over the northern Canadian ecozones, in particular for studying caribou habitats and feeding grounds. In this study, a new LAI algorithm was developed with particular emphasis over northern Canada using a much finer resolution of remotely sensed data and in situ measurements collected over a wide range of northern arctic vegetation. A statistical relationship was developed between the in situ LAI measurements collected over vegetation plots in northern Canada and their corresponding pixel spectral information from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. Furthermore, all Landsat TM and ETM+ data have been pre-normalized to NOAA AVHRR and SPOT VGT data from the growing season of 2005 to reduce any seasonal or temporal variations. Various spectral vegetation indices developed from the Landsat TM and ETM + data were analysed in this study. The reduced simple ratio index (RSR) was found to be the most robust and an accurate estimator of LAI for northern arctic vegetation. An exponential relationship developed using the Theil–Sen regression technique showed an R 2 of 0.51 between field LAI measurement and the RSR. The developed statistical relationship was applied to a pre-existing Landsat TM 250 m resolution mosaic for northern Canada to produce the final LAI map for northern Canada ecological zones. Furthermore, the 250 m resolution LAI estimates, per ecological zone, were almost generally lower than those of the CCRS Canada-wide VGT LAI maps for the same ecozones. Validation of the map with LAI field data from the 2008 season, not used in the derivation of the algorithm, shows strong agreement between the in situ LAI measurement values and the map-estimated LAI values.

Erratum to: Vegetation change based on SPOT-VGT data from 1998–2007, northern China

Erratum to: Vegetation change based on SPOT-VGT data from 1998–2007, northern China

Vegetation change based on SPOT-VGT data from 1998 to 2007, northern China

Dynamic change of vegetation in northern China from 1998–2007 was explored, based on SPOT-VGT data. The results showed that the NDVI can effectively monitor vegetation change, but also the mean multi-year NDVI maximum of 0.10 basically can be considered as the threshold of vegetation cover in northern China, and those places with smaller than or equal to the threshold value mainly were covered by deserts, Gobi and lakes, salinization lands, glaciers, snow patches and bare mountains, etc. The change trends of vegetation where sub-region C and D were affected by east-Asia monsoon were similar with the characteristics of first decreasing, later increasing and finally slowly decreasing; the difference lies in that sub-region C maintained a lower vegetation state, and sub-region D reached a higher state. The vegetation change of sub-region A was anomalous with smaller fluctuation. The change trend of vegetation of sub-region B was not only extremely unstable, but also the change extent was huge. As far as the whole of northern China is concerned, vegetation has not demonstrated obvious improvement since these ecological construction and protection projects, and the area of vegetation degradation still is much larger than that of vegetation improvement from 1998 to 2007, but there was a bigger spatial difference. Field investigation and remote sensing monitoring reveal that vegetation has obviously been improved in the agro-grazing mixing zone and to the south, as well as many oases in the northwestern arid zone, while vegetation still has worsened in the steppes, especially the Ulanqab steppe, Hunshandake Sandy Land, the region along the Sino–Mongolia border within Xilingol Meng, Hulunbeir Sandy Land and the northern Tianshan mountain region in Xinjiang. Human activities only obviously showed success in local regions. The compelling challenges of restoring the eco-environment are still immense, especially in the northern grazing zone of China.

A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data

Mapping urban areas at regional and global scales has become an urgent task because of the increasing pressures from rapid urbanization and associated environmental problems. Satellite imaging of stable anthropogenic lights from DMSP-OLS provides an accurate, economical, and straightforward way to map the global distribution of urban areas. To address problems in the thresholding methods that use empirical strategies or manual trial-and-error procedures, we proposed a support vector machine (SVM)-based region-growing algorithm to semi-automatically extract urban areas from DMSP-OLS and SPOT NDVI data. Several simple criteria were used to select SVM training sets of urban and non-urban pixels, and an iterative classification and training procedure was adopted to identify the urban pixels through region growing. The new method was validated using the extents of 25 Chinese cities, as classified by Landsat ETM+ images, and then compared with two common thresholding methods. The results showed that the SVM-based algorithm could not only achieve comparable results to the local-optimized threshold method, but also avoid its tedious trial-and-error procedure, suggesting that the new method is an easy and simple alternative for extracting urban extent from DMSP-OLS and SPOT NDVI data.

Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004)

Measurements of spring phenological dates in boreal regions using NDVI can be affected by snowmelt. This impacts the analysis of interannual variations in phenology and the estimates of annual carbon fluxes. For these two objectives, snowmelt effect must be removed from the phenological detection. We propose a methodology for determining the date of onset of greening in the 1982–2004 period using SPOT-VEGETATION (VGT) and NOAA Advanced Very High Resolution Radiometer (AVHRR) data. From 1998 onwards, the date of onset of greening is taken as the date at which the Normalized Difference Water Index (NDWI), calculated from SPOT-VGT near and short-wave infrared bands, starts increasing. This index decreases with snowmelt but increases with vegetation greening. For the 1982–2001 period, the date of onset of greening is the date at which AVHRR-NDVI equals a pixel specific threshold (PST), determined using the results of the NDWI method in the years common to the two datasets. The methods are validated using in situ measurements of the dates of leaf appearance. RMSE of 6.7 and 7.8 days, respectively, is found using NDWI-VGT and PST-NOAA methodologies, and the difference between the two methodologies in the common years is small. Very importantly, the dates are not biased. The interannual variations of the 23-year spring phenology dataset on the study area in northern Eurasia are analysed. In average over the study area, an advance of 8 days and a delay of 3.6 days are, respectively, found over the periods 1982–1991 and 1993–2004. These results confirm and complete previous studies about the greening trend, remove the uncertainty due to snow, and may improve carbon budget calculations.