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China has experienced a rapid urbanization during recent decades, strongly affecting vegetation dynamics in areas undergoing a transformation from rural to urban areas. At the same time, national greening policies have been implemented to promote urban sustainability and urban greening in China in recent years. However, it is unclear how urban greening compensates vegetation losses from urban expansion at national scale. Here, we use Moderate Resolution Imaging Spectroradiometer and Landsat satellite normalized difference vegetation index time series to study 974 major cities (urban area > 20 km2) in China during 2000 to 2020 and develop an urban vegetation change typology including 5 types of vegetation dynamics (greening, browning, stable, reversal, and recovery). We document a rapid urban expansion associated with a browning in urban areas before 2011, followed by widespread regreening of the urban areas after 2011. This recovery in greenness was found in 63.45% of the cities, while 14.68% showed a continuous browning, and 8.13% a continuous greening. Our findings reveal to what extent, where, and when vegetation browning from urban expansion is balanced by urban greening in urban core areas, which may indicate that initial vegetation losses are offset by urban greening initiatives.

Mapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time-series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF-1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high-resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency.

China karst is a global hotspot of increasing vegetation cover, with ecological conservation projects being considered as the main driver. New research using global datasets also indicates that rural outmigration has contributed to increasing biomass at national scale. However, the link between rural outmigration and vegetation cover increase has not been established at regional scale, and it remains unclear as to whether increases in biomass do, in fact, improve the environmental conditions. In this study, we use local field and statistical data on population density and rocky desertification areas to study population movements and changes in aboveground biomass in relation to rocky desertification in South China karst during 2000–2017. Our results show that the urban population in this region increased by 8.3 million people between 2005 and 2015, and the rural population decreased by 4.8 million people. We find that aboveground biomass increased most in rural areas with low human pressure, and that there was an almost linear relationship between increase in biomass and rural outmigration, with the highest increase in aboveground biomass density (1.5 MgC ha−1 yr−1) observed in areas where rural outmigration was highest, and the lowest increase in aboveground biomass density (1.1 MgC ha−1 yr−1) where rural outmigration was lowest. Rocky desertification areas decreased with a higher level of rural outmigration. Using local field data, our study confirmed that rural outmigration can generate a carbon sink at regional scale by reducing rocky desertification.

The karst area in southwest China is, in recent decades, one of the world’s regions with the fastest vegetation growth and increase in aboveground biomass. The improvement in the ecological conditions within this region can, to a large extent, be attributed to ecological protection projects. However, it remains unclear whether the observed increase in vegetation cover could mitigate rocky desertification at large scale. In this study, we utilized information from large field campaigns in the years 2005, 2011, and 2016, supported by satellite imagery interpretation and statistics on ecological protection projects, to explore the impact of restoration efforts on rocky desertification mitigation and vegetation resilience/resistance. The results show that restoration efforts caused a net decrease of 26.14% in areas affected by rocky desertification from 2005 to 2016. The larger the restoration efforts, the stronger the decrease in rocky desertification areas, and also the vulnerability of landscapes towards desertification; the probability of rocky desertification occurring decreased almost linearly with increased restoration efforts. In addition, both vegetation resilience and resistance against climate variations increased with restoration efforts. Restoration efforts were mainly responsible for the recovery of the ecosystem, showing that human impact is the main driver for the reversal of rocky desertification. Our study provides scientific evidence on rocky desertification control being an important step towards a sustainable development of the landscapes of Southwest China.

The karst area in Yunnan-Guangxi-Guizhou region in southwest China is known for widespread rocky desertification but several studies report a greening trend since the year 2000. While the start of the greening trend seems to match with the implementation of ecological conservation projects, no statistical evidence on a relationship between vegetation greening and eco-engineering exists. Moreover, dominant factors influencing the spatial patterns of vegetation trends have rarely been investigated. Here we use six comprehensive factors representing the natural conditions and human activities of the study area, and several statistical models consistently show that eco-engineering explains large parts of the positive vegetation trends in the karst areas, while negative vegetation trends in non-karst areas of Yunnan were related with a decrease in rainfall. We further show that the interaction of eco-engineering with other factors leads to a heterogeneous pattern of different vegetation trends. Knowing and understanding these patterns is crucial when planning ecological restoration, especially in diverse landscapes like China karst and the methods can be reused in other restoration areas.

The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a “climate change hot-spot”. Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems.