Detecting Global Vegetation Changes Using Mann-Kendal (MK) Trend Test for 1982–2015 Time Period

Abstract

Vegetation is the main component of the terrestrial ecosystem and plays a key role in global climate change. Remotely sensed vegetation indices are widely used to detect vegetation trends at large scales. To understand the trends of vegetation cover, this research examined the spatial-temporal trends of global vegetation by employing the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) time series (1982-2015). Ten samples were selected to test the temporal trend of NDVI, and the results show that in arid and semi-arid regions, NDVI showed a deceasing trend, while it showed a growing trend in other regions. Mann-Kendal (MK) trend test results indicate that 83.37% of NDVI pixels exhibited positive trends and that only 16.63% showed negative trends (P < 0.05) during the period from 1982 to 2015. The increasing NDVI trends primarily occurred in tree-covered regions because of forest growth and re-growth and also because of vegetation succession after a forest disturbance. The increasing trend of the NDVI in cropland regions was primarily because of the increasing cropland area and the improvement in planting techniques. This research describes the spatial vegetation trends at a global scale over the past 30+ years, especially for different land cover types.

Feasibility of Using MODIS Products to Simulate Sun-Induced Chlorophyll Fluorescence (SIF) in Boreal Forests

“…To maintain the concordance between MODIS and OCO-2 data, we selected a 16-day temporal resolution at a spatial resolution of 1 km. FAPAR, LAI, and GPP data were resampled to 1 km and compiled to 16 days using the maximum value composition (MVC) technique [44]. As mentioned above, the re-visit cycle of OCO-2 is 16 days, and point data were obtained during its clear-sky overpass.…”

Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

“…A great number of studies have explored vegetation dynamics and its responses to climate change at different spatial scales [4,[8][9][10]. However, most of them focused mainly on the long-term trend and potential driving factors [11].…”

Historical Trajectory in Vegetation Cover in Northeastern Namibia Based on AVHRR Satellite Imagery (1982–2015)

“…We downloaded a total of 816 raster images (two images per month) of 8-km resolution Global Inventory Monitoring and Modeling Systems (GIMMS) normalized difference vegetation index (NDVI) data [38]. The current version of the 8-km GIMMS (NDVI3g.v1) is available from 1981 to 2015 [38,39]. Only raster time series from 1982 to 2015 (816 raster images) were used, because the 1981 dataset is incomplete.…”

“…The bi-monthly raster time series were resampled using a common 8-km grid with the nearest neighbor interpolation algorithm, and re-projected to the Universal Transverse Mercator (UTM) coordinate reference system. Finally, the bi-monthly rasters were aggregated to monthly rasters [38], creating 408 NDVI raster images with 12 images per year. During aggregation, the maximum value composite (MVC) technique was applied, and quality flags [38] were used to retain only good quality pixels [9].…”

“…Finally, the bi-monthly rasters were aggregated to monthly rasters [38], creating 408 NDVI raster images with 12 images per year. During aggregation, the maximum value composite (MVC) technique was applied, and quality flags [38] were used to retain only good quality pixels [9]. The MVC works by retaining the maximum NDVI value for each pixel, which is later used to create the final composite image [41].…”