About 80% of global wetland resources are degrading or disappearing; thus the wetland ecosystem has become one of the most seriously threatened ecosystems in the world. As an area sensitive to global changes and acting as a security barrier for the Asian ecosystem, the Tibetan Plateau has about 13.19×10⁴ km² of wetlands of special significance within China. With the increasing application of remote sensing technology to wetland research, Tibetan Plateau wetland research has entered a period of rapid development. This paper summarizes the remote sensing research literature of the Tibetan Plateau wetlands from 1992 to 2014, and is intended to provide references for future research into the wetlands of the Tibetan Plateau. We have reviewed monitoring methods, research topics, and existing problems. Our review has revealed the following characteristics: (1) Over the past 40 years, the research paradigm of the Tibetan Plateau wetlands has undergone dynamic changes in the monitoring of wetland areas, landscape patterns and the eco-environment based on remote sensing technology. Attention has also been focused on constructing models with an ecological system perspective and analyzing three patterns of change trends within the Tibetan Plateau wetlands. (2) The results of Tibetan Plateau wetland research based on remote sensing were as follows: (a) between 1970 and 2006, the Tibetan Plateau wetland area decreased overall at a rate of 0.23%/a, and the landscape diversity declined at a rate of 0.17%/a; (b) by contrast, between 1976 and 2009, the lake area of the inland river basins in the Tibetan Plateau increased at a rate of 0.83%/a; and (c) the change trend in the Tibetan Plateau wetlands was controlled by climate change. Current problems relating to remote sensing (RS)-based research in the Tibetan Plateau wetlands are computer interpretation accuracy and the processing precision of cloud removal, and the lack of a comprehensive overview of the Tibetan Plateau wetland system. Finally, based on the review, some key activities for future study have been proposed, as follows: (1) Strengthening the integration of the Tibetan Plateau wetland research with remote sensing research; (2) discussing the response and adaptation mechanisms of the Tibetan Plateau wetland ecosystem within the context of global change; (3) strengthening the integration of remote sensing (RS), geographic information system (GIS), and global positioning system (GPS), and promoting the construction of a Tibetan Plateau wetland information platform.

The Koshi River Basin is in the middle of the Himalayas, a tributary of the Ganges River and a very important cross-border watershed. Across the basin there are large changes in altitude, habitat complexity, ecosystem integrity, land cover diversity and regional difference and this area is sensitive to global climate change. Based on Landsat TM images, vegetation mapping, field investigations and 3S technology, we compiled high-precision land cover data for the Koshi River Basin and analyzed current land cover characteristics. We found that from source to downstream, land cover in the Koshi River Basin in 2010 was composed of water body (glacier), bare land, sparse vegetation, grassland, wetland, shrubland, forest, cropland, water body (river or lake) and built-up areas. Among them, grassland, forest, bare land and cropland are the main types, accounting for 25.83%, 21.19%, 19.31% and 15.09% of the basin's area respectively. The composition and structure of the Koshi River Basin land cover types...

With the increasing frequency of various uncertainties and disturbances faced by urban systems, urban resilience is one of the vital components of the sustainability of modern cities. An indicator system is constructed to measure the resilience levels of the Yichang–Jingzhou–Jingmen–Enshi (YJJE) urban agglomeration during 2010–2023 based on four domains—economy, ecology, society, and infrastructure. This paper analyzes the spatiotemporal differentiation of resilience in YJJE in conjunction with the entropy weight method, Getis–Ord Gi* model, and robustness testing. Then, the factor contribution model is used to discern key driving elements of urban resilience. Finally, the CA-Markov model is implemented to predict urban resilience in 2030. The results reveal that the values of resilience in YJJE increase at a rate of 3.25%/a and continue to rise, with the differences among cities narrowing over the examined period. Furthermore, the urban resilience exhibits a significant spatially heterogeneity distribution, with Xiling, Wujiagang, Xiaoting, Yidu, Zhijiang, Dianjun, Dangyang, Yuan’an, Yiling, and Duodao being the high-value agglomerations of urban resilience, and Hefeng, Jianli, Shishou, and Wufeng being the low-value agglomerations of urban resilience. The marked heterogeneity of resilience in the YJJE urban agglomeration reflects the disparity in economic progress across the study area. The total amount of urban social retail, financial expenditure per capita, GDP per capita, park green space area, urban disposable income per capita, and number of buses per 10,000 people surface as the key influencing factors in relation to urban resilience. Finally, the levels of resilience among cities within YJJE will reach the medium level or higher than medium level in 2030. Xiling, Wujiagang, Xiaoting, Zhijiang, Dianjun, Dangyang, and Yuan’an will remain significant hot spots of urban resilience, while Jianli will remain a significant cold spot. In a nutshell, this paper can provide scientific references and policy recommendations for policymakers, urban planners, and researchers on the aspects of urban resilience and sustainable city.