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… Show more
“…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.…”
Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.
“…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.…”
Solar-induced chlorophyll fluorescence (SIF) is a novel approach to gain information about plant activity from remote sensing observations. However, there are currently no continuous SIF data produced at high spatial resolutions. Many previous studies have discussed the relationship between SIF and gross primary production (GPP) and showed a significant correlation between them, but few researchers have focused on forests, which are one the most important terrestrial ecosystems. This study takes Greater Khingan Mountains, a typical boreal forest in China, as an example to explore the feasibility of using MODerate resolution Imaging Spectroradiometer (MODIS) products and Orbiting Carbon Observatory-2 (OCO-2) SIF data to simulate continuous SIF at higher spatial resolutions. The results show that there is no significant correlation between SIF and MODIS GPP at a spatial resolution of 1 km; however, significant correlations between SIF and the enhanced vegetation index (EVI) were found during growing seasons. Furthermore, the broadleaf forest has a higher SIF than coniferous forest because of the difference in leaf and canopy bio-chemical and structural characteristic. When using MODIS EVI to model SIF, linear regression models show average performance (R2 = 0.58, Root Mean Squared Error (RMSE) = 0.14 from Julian day 145 to 257) at a 16-day time scale. However, when using MODIS EVI and temperature, multiple regressions perform better (R2 = 0.71, RMSE = 0.13 from Julian day 145 to 241). An important contribution of this paper is the analysis of the relationships between SIF and vegetation indices at different spatial resolutions and the finding that the relationships became closer with a decrease in spatial resolution. From this research, we conclude that the SIF of the boreal forest investigated can mainly be explained by EVI and air temperature.
“…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].…”
An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982-2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.
“…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.…”
Section: Satellite Datamentioning
confidence: 99%
“…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].…”
Section: Satellite Datamentioning
confidence: 99%
“…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].…”
The negative impact of the reduction of vegetation cover is already being felt in the Zambezi Region in northeastern Namibia. The region has been undergoing various land cover changes in the past decades. To understand the historical trend of vegetation cover (increase or decrease), we analyzed 8-km resolution Global Inventory Monitoring and Modeling Studies (GIMMS) from the Advanced Very High Resolution Radiometer (AVHRR) and 0.25° × 0.25° (resampled to 8 km) resolution Global Precipitation Climatology Center (GPCC). We used the Time Series Segmented Residual Trends (TSS-RESTREND) method. We found that the general trajectory of vegetation cover was negative. Pixel-wise analysis and visual interpretation of historical images both revealed clear signs of vegetation cover change. We observed a single breakpoint in the vegetation trajectory which correlated to the 1991–1992 drought in southern Central Africa. Potential drivers of land cover change are the (il)legal expansion of subsistence farming, population growth, and wood extraction. These findings will serve as a reference for decision makers and policymakers. To better understand the human-induced land cover change at the micro scale and sub-regional level, we recommend using higher resolution remote sensing datasets and historical documents to assess the effect of demographic change, disease, civil unrest, and war.
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