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Background: Carbon emissions have received increasing international attention in recent years and countries have undertaken different measures to control emissions. Research into carbon emissions has also increased year by year, but remains mainly focused on national total carbon emissions, and per-capita carbon emissions have received less attention. This paper investigates spatial and temporal changes in per-capita carbon emissions and uses the Mann-Kendall test to assess the directions and magnitudes of trends, and to investigate abrupt changes in per-capita carbon emissions. Results: We found the highest positive growth rate in Oman, at 0.439 mts/yr, and the highest negative growth rate in United Arab Emirates, at -0.462 mts/yr. If no further mitigation measures are taken, we estimate that the current emissions trends will persist into the future for 86% of countries. The number of change-points was greatest in 1994 and smallest in 1999. The shift in the center of gravity for per-capita carbon emissions changed direction in 2004. From 1992 to 2004, the gravity center for per-capita carbon emissions gradually shifted to the southwest, and, after 2004, it gradually shifted to the southeast until 2018. Conclusions: Europe has entered the carbon reduction phase, but it is not yet significant, and carbon emissions are still increasing in the rest of the continent. The main driver for the westward shift in the gravity center before 2004 was that carbon emissions grew more strongly in the West than in the East before 2004, while the driver for the eastward shift in the gravity center after 2004 was a combination of emission reductions in the West and emission increases in the East.

Modelling in the changes of land use studies is essential towards investigating the regional land cover dynamics as well as for preparing sustainable land use planning and management. In considering the physiographic, climatic, and geospatial development factors, this study simulated and predicted the changes of land use over the southeastern region of Bangladesh integrated of non-spatially demand module and geospatially explicit distribution using CLUE-S model. Globally validated FROM-GLC land cover products of the year 2010, 2015, and 2017 were used as datasets. The simulated maps for 2015 and 2017 have been confirmed to be generally accurate with the actual land use using an error matrix and Kappa indices as of 62.38% (0.6101) and 71.64% (0.7106), respectively, to ensure model simulations success. However, the land-use scenarios between 2017 and 2025 were predicted assuming three modes of development as of existing trends, under forests protection, and of croplands protection. All three scenarios primarily predicted that urbanization, as well as built-up areas, would expand by 100%, 65.3%, and 34.2%, respectively in accumulated all other land-use types. This expansion is predicted as the leading land-use conversions in the study area and expected to an extensive loss of forest in hilly areas and water and croplands in the flat areas. The rate of uneven expansion might be controlled under strict forestland or cropland protection or as well inclusive implementations. The conversion trends of grasslands, wetlands, and others land use in the simulation processes were more subtle. Scientific information derived from simulations revealed the model approach is similarly suitable in formulating relevant land-use policies.

Backgrounds Vegetation dynamic plays a dominant role in the global carbon cycle and climate, especially in vulnerable karst ecosystem. Many studies have examined past several decades changes in vegetation greenness and the associated with climate drivers. Yet, few studies have analyzed the vegetation change in global karst regions particularly in the last decades when climate change and anthropogenic disturbance widely occurred. Methods In this study, we investigated the spatio-temporal variations of vegetation dynamic using the Seasonally Integrated Normalized Difference Vegetation Index (SINDVI) and examined their relationship to climate changes by correlation analysis, the ordinary least squares method investigate the variation trends and the Mann-Kendal test to detect the turning point from 2001 to 2020. Results As expected, there have greening trend in global karst SINDVI from 2001 to 2020, with significant increasing trend in China (range = 0.836, P

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

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

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 &deg, C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000&ndash, 2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 &deg, C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment.