SPOT-VEGETATION Normalized Difference Vegetation Index Research Articles

Multi-source remote sensing data shows a significant increase in vegetation on the Tibetan Plateau since 2000

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 ( p < .01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively ( p < .05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions.

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory (FRI) data, and SPOT Vegetation (SPOT-VGT) normalized difference vegetation index (NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China (with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg (1 Pg = 1015 g), with an average of 77.2 Mg ha−1, during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map.

Grassland conservation programs, vegetation rehabilitation and spatial dependency in Inner Mongolia, China

Three nationwide grassland conservation programs have been implemented in Inner Mongolia since 2000. All aim to relieve grazing pressure, hence to reverse the grassland degradation trend. The different timings and spatial configurations of these programs present an unusual setting of quasi-natural policy experiment for exploring their effectiveness and interactions with other drivers on a regional scale. In this paper, a spatial panel model was developed to examine the effects of the programs on vegetation rehabilitation, meanwhile to detect the spatial interdependent relationship among grassland management units occurring in the process of program implementation. The methodology used a panel dataset of SPOT-VEGETATION NDVI data, multi-station surface meteorological observations, and socio-economic statistics across 88 counties from 2000 to 2013. The modeling results suggested that these programs in general significantly facilitated grassland vegetation rehabilitation. Enrollment in the Beijing–Tianjin Wind/Sand Source Control Program and in the Grazing Withdrawal Program was predicted to increase the normalized difference vegetation index (NDVI) value by an amount equivalent to the effects of 136mm and 56mm additional annual precipitation, respectively. The positive and significant coefficient of spatial lag term indicated that there was a synergistic relationship in the vegetation variations of neighboring counties, and a unit increase in the weighted sum of all neighboring counties’ NDVI values could approximately increase a target county's NDVI value by 0.2, after controlling for other factors’ effects. Certain spatial spillover mechanisms may function to generate this effect, such as benign competition, mutual cooperation and coordination, or sharing of successful experiences among neighboring counties in carrying out the programs. Nevertheless, the actual mechanisms need to be confirmed by field surveys in future studies.

Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China

The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China.

Extracting grassland vegetation phenology in North China based on cumulative SPOT-VEGETATION NDVI data

Plant phenology is one of the main indicators of climate or other environmental processes. This paper assesses the detection accuracy of start of season (SOS) and end of season (EOS) for grassland vegetation in north China from 2001 to 2010 using SPOT-VEGETATION normalized difference vegetation index (NDVI) data sets and in situ observations. The cumulative NDVI is calculated and fitted using a logistic model to identify phenological transition dates. The curvature of the fitted logistic models predicts phenological transition dates that correspond to the times at which the curvature in the yearly integrated NDVI exhibits local minimums or maximums. Validating with in situ observations, phenological dates are extracted from satellite time series data and are accurate to within 10 days. The spatial trends of SOS and EOS are very similar for 2001–2010. SOS mainly occurs from the day of year (DOY) 110 to DOY 170, and EOS occurs from DOY 240 to DOY 300. SOS displays a marked delay from south to north, while EOS gradually advances, indicating regional differences in climate and terrain. However, the effect of latitude and longitude on the average EOS of alpine grasslands is not significantly different, while SOS at low latitude and high longitude is 10 days earlier than at high-latitude and high-longitude regions. We detected an overall advance in SOS of 3.1 days over 10 years, and a 1.3-day delay in EOS. However, the amplitude is low (about 5 days) and the changes in most regions are not significant (close to zero). The results in this paper are concordant with many reported studies that explored the phenology of grasslands in North China, which is an important component of global grasslands science.

Fisher–Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance

The time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher–Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher–Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1km×1km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers.

What multiscale environmental drivers can best be discriminated from a habitat index derived from a remotely sensed vegetation time series?

Understanding which environmental conditions are critical for species survival is a critical, ongoing question in ecology. These conditions can range from climate, at the broadest scale, through to elevation and other local landscape conditions, to fine scale landscape patterns of land cover and use. Remote sensing is an ideal technology to monitor and assess changes in these environmental conditions at a variety of spatial and temporal scales, with many studies focusing on the physiological state of vegetation derived from time series of satellite measurements. As vegetation occurs within specific climatic zones, over certain soil, terrain, and land cover types, it can be difficult to decipher the influence of the underlying role of climate, topography, soil, and land cover on the observed vegetation signal. In this article, we specifically addressed this problem by asking the question: what is the relative impact and importance of these different scales of environmental drivers on the temporal and spatial patterns observed on a habitat index derived from remotely sensed data? To find the solution, we utilized a SPOT VEGETATION-normalized difference vegetation index time series of Europe to create a remote-sensing-derived habitat index, which incorporates aspects of productivity, seasonality, and cover. We then compared the observed temporal and spatial variations in the index to a pan-Europe terrestrial classification system, which explicitly incorporates variations in climate, terrain, soil parent material, land cover, and use. Results indicated that the most accurate level of discrimination from the habitat index was at the broadest level of the hierarchy, climate, while the poorest degree of discrimination was associated with elevation. In terms of similarity on the index across time and space, we found that arable and forest cover classes were more similar across elevation and parent materials than across other land cover types within them. Analyzing the remote-sensing index, at multiple scales, provides significant insights into the drivers of satellite-derived greenness indices, as well as highlights the benefit and cautions associated with linking satellite-derived indirect indicators to species distribution modeling and biodiversity.

Vegetation dynamics and their response to freshwater inflow and climate variables in the Yellow River Delta, China

Based on SPOT Vegetation NDVI data, streamflow data and meteorological data, the variation of vegetation cover, measured by the Normalized Difference Vegetation Index (NDVI), and its response to freshwater inflow, precipitation and temperature in the Yellow River Delta and its buffer zones have been investigated for the period 1998–2009. The results show that NDVI has a remarkable regional and seasonal difference. The farther from the Yellow River Channel and the nearer to the Bohai Sea Coastline, the smaller the NDVI value, as influenced by the interaction between freshwater and saltwater on vegetation. Seasonally, high NDVI values appear in summer (August) and low in spring (April). From 1998 to 2009, growing season NDVI significantly increases in the Yellow River Delta. Summer and autumn NDVI have a similar trend pattern to growing season NDVI, while spring NDVI significantly decreases. NDVI shows different strengths of correlation with freshwater inflow, precipitation and temperature respectively and these correlations vary in different seasons and months. Freshwater inflow is a key factor for vegetation dynamics and NDVI variation. Climate features play a dominant role in seasonal variation in vegetation cover. However, the impacts of freshwater inflow and climate variables on vegetation have been greatly modified by a range of human activities such as land use pattern and land use change as well as water diversion from the Yellow River. Overall, the results of this study can be helpful for decision-making of regional ecological protection and economic development.

Evaluation of Aridity Indices Using SPOT Normalized Difference Vegetation Index Values Calculated Over Different Time Frames on Iberian Rain-Fed Arable Land

The objective of this study was to find the best-performing aridity index and time-frame in the Iberian Peninsula characterizing the effect of dryness on agricultural production. To achieve this goal time-series of 5 aridity indices for 1998 October–2009 December time-period were calculated on a 25 × 25 km grid, and the closest relationship with plant biomass was determined. Plant biomass was represented by the SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) satellite data masked out for rain-fed arable land for the period between 1998 and 2009, and also by official yield statistics of Spain and Portugal between 1999 and 2009. Aridity indices calculated for time frames matching the entire vegetative period resulted in the highest correlation coefficients with NDVI and with the crop yield. There was a difference between the two time frames covering twelve months. In contrast with the calendar year, using the hydrological year (1 October–30 September) ensured a very strong correlation between NDVI data and most aridity indices, with UNEP and Water Deficit aridity indices outperforming the others. Among the shorter time frames of April-October, January–October, and October–June, the latter provided very strong correlation between vegetation, UNEP and Water Deficit indices surpassing Budyko, De Martonne, and Thornthwaite aridity indices.

Vegetation change of ecotone in west of Northeast China plain using time-series remote sensing data

Multi-temporal series of satellite SPOT-VEGETATION normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data from 1998 to 2007 were used for analyzing vegetation change of the ecotone in the west of the Northeast China Plain. The yearly and monthly maximal values, anomalies and change rates of NDVI and NDWI were calculated to reveal the interannual and seasonal changes in vegetation cover and vegetation water content. Linear regression method was adopted to characterize the trends in vegetation change. The yearly maximal NDVI decreased from 0.41 in 1998 to 0.37 in 2007, implying the decreasing trend of vegetation activity. There was a significant decrease of maximal NDVI in spring and summer over the study period, while an increase trend was observed in autumn. The vegetation-improved regions and vegetation-degraded regions occupied 17.03% and 20.30% of the study area, respectively. The maximal NDWI over growing season dropped by 0.027 in 1998–2007, and about 15.15% of the study area showed a decreasing trend of water content. Vegetation water stress in autumn was better than that in spring. Vegetation cover and water content variations were sensitive to annual precipitation, autumn precipitation and summer temperature. The vegetation degradation trend in this ecotone might be induced by the warm-drying climate especially continuous spring and summer drought in the recent ten years.

Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data

AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference vegetation Index) data is available from 1981 to present time. The global coverage 8 km resolution 15-day composite data set has been used for numerous local to global scale vegetation time series studies during recent years. Several aspects however potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. More recent NDVI data sets from both Terra MODIS and SPOT VGT data are considered an improvement over AVHRR and these products in theory provide a possibility to evaluate the accuracy of GIMMS NDVI time series trend analysis for the overlapping period of available data. In this study the accuracy of the GIMMS NDVI time series trend analysis is evaluated by comparison with the 1 km resolution Terra MODIS (MOD13A2) 16-day composite NDVI data, the SPOT Vegetation (VGT) 10-day composite (S10) NDVI data and in situ measurements of a test site in Dahra, Senegal. Linear least squares regression trend analysis on eight years of GIMMS annual average NDVI (2000–2007) has been compared to Terra MODIS (1 km and 8 km resampled) and SPOT VGT NDVI data 1 km (2000–2007). The three data products do not exhibit identical patterns of NDVI trends. SPOT VGT NDVI data are characterised by higher positive regression slopes over the 8-year period as compared to Terra MODIS and AVHRR GIMMS NDVI data, possibly caused by a change in channels 1 and 2 spectral response functions from SPOT VGT1 to SPOT VGT2 in 2003. Trend analysis of AVHRR GIMMS NDVI exhibits a regression slope range in better agreement with Terra MODIS NDVI for semi-arid areas. However, GIMMS NDVI shows a tendency towards higher positive regression slope values than Terra MODIS in more humid areas. Validation of the different NDVI data products against continuous in situ NDVI measurements for the period 2002–2007 in the semi-arid Senegal revealed a good agreement between in situ measurements and all satellite based NDVI products. Using Terra MODIS NDVI as a reference, it is concluded that AVHRR GIMMS coarse resolution NDVI data set is well-suited for long term vegetation studies of the Sahel–Sudanian areas receiving < 1000 mm rainfall, whereas interpretation of GIMMS NDVI trends in more humid areas of the Sudanian–Guinean zones should be done with certain reservations.