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Identifying the factors influencing wetland variations is crucial for understanding the relationship of climate change with wetland conservation and management. The wetland distribution is associated with multiple variables, and the interactions among these variables are complex. In this study, we aim to explore an interpretable and quantitative analysis of factors related to wetland spatiotemporal variations on the Tibetan Plateau (TP). By combining SHapley Additive exPlanations with a spatially stratified heterogeneity model, we propose a locally explained stratified heterogeneity (LESH) model that well reveals the effects of multiple variable interactions on the spatiotemporal variations of wetlands. The results show that topographic variables are the most important variables related to the spatial distribution of wetlands on the TP, and climatic variables are the most relevant factors for the increase in the wetland area on the TP from 2015 to 2019. In addition, the interactions among multiple variables strongly influence wetlands on the TP. Among them, when other geographic variables interact with the evaporation variable, its explanatory power on wetland distribution is significantly increased. Knowledge of wetland distribution determinants can help us understand the evolution of wetlands and the impacts of climate change on wetland variations. [ABSTRACT FROM AUTHOR]

Qinghai-Tibet Plateau lakes are important carriers of water resources in the 'Asian's Water Tower', and it is of great significance to grasp the spatial distribution of plateau lakes for the climate, ecological environment, and regional water cycle. However, the differences in spatial-spectral characteristics of various types of plateau lakes, and the complex background information of plateau both influence the extraction effect of lakes. Therefore, it is a great challenge to completely and effectively extract plateau lakes. In this study, we proposed a multiscale contextual information aggregation network, termed MSCANet, to automatically extract Plateau lake regions. It consists of three main components: a multiscale lake feature encoder, a feature decoder, and a Multicore Pyramid Pooling Module (MPPM). The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes. The MPPM module aggregated the contextual information of various lakes globally. We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data; additionally, comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity. Finally, we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet. [ABSTRACT FROM AUTHOR]

This article provides an introduction to a special collection of five articles showcasing the work of rising scholars in the geography and anthropology of Tibetan regions in China (Eveline Washul, Andrew Grant, Tsering Bum, Huatse Gyal and Duojie Zhaxi, published in Critical Asian Studies 50: 4 and Critical Asian Studies 51: 1). It contextualizes the authors' contributions in the recent promotion of planned urbanization in Tibetan regions as the key to achieving the "Chinese Dream" under President Xi Jinping. The paper calls attention to these authors' focus on Tibetan experiences of new urbanization policies and practices, as well as their less-appreciated entanglement with shifting education priorities. Providing brief summaries of each author's case study and arguments, it points to the ways in which all five articles address the relationship between space and subjectivity, as well as the issue of constrained agency (versus simple notions of "choice"), in statist urbanization processes. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau is primarily composed of alpine grasslands. Spatial distributions of alpine grasses, however, are not well documented in this remote, highly uninhabited region. Taking advantage of the frequently observed moderate resolution imaging spectroradiometer (MODIS) images (500-m, 8-day) in 2010, this study extracted the phenological metrics of alpine grasses from the normalized difference vegetation index time series. With the Support Vector Machine, a multistep classification approach was developed to delineate alpine meadows, steppes, and desert grasses. The lakes, permanent snow, and barren/desert lands were also classified with a MODIS scene acquired in the peak growing season. With ground data collected in the field and aerial experiments in 2011, the overall accuracy reached 93% when alpine desert grasses and barren lands were not examined. In comparison with the recently published national vegetation map, the alpine grassland map in this study revealed smoother transition between alpine meadows and steppes, less alpine meadows in the southwest, and more barren/deserts in the high-cold Kunlun Mountain in the northeast. These variations better reflected climate control (e.g. precipitation) of different climatic divisions on alpine grasslands. The improved alpine grassland map could provide important base information about this cold region under the pressure of rapidly changing climate. [ABSTRACT FROM AUTHOR]

Understanding the relationships between snow and vegetation is important for interpretation of the responses of alpine ecosystems to climate changes. The Qinghai-Tibetan Plateau is regarded as an ideal area due to its undisturbed features with low population and relatively high snow cover. We used 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) datasets during 2001–2010 to examine the snow–vegetation relationships, specifically, (1) the influence of snow melting date on vegetation green-up date and (2) the effects of snow cover duration on vegetation greenness. The results showed that the alpine vegetation responded strongly to snow phenology (i.e., snow melting date and snow cover duration) over large areas of the Qinghai-Tibetan Plateau. Snow melting date and vegetation green-up date were significantly correlated (p< 0.1) in 39.9% of meadow areas (accounting for 26.2% of vegetated areas) and 36.7% of steppe areas (28.1% of vegetated areas). Vegetation growth was influenced by different seasonal snow cover durations (SCDs) in different regions. Generally, the December–February and March–May SCDs played a significantly role in vegetation growth, both positively and negatively, depending on different water source regions. Snow's positive impact on vegetation was larger than the negative impact. [ABSTRACT FROM PUBLISHER]

Vegetation plays an important role in regulating the climate system and terrestrial carbon cycle. In recent decades, numerous studies have focused on monitoring vegetation greenness changes at different scales with long-term remote-sensing data. Most previous studies adopted a monotonic linear regression approach (one-piece linear regression) to detect vegetation greenness trends at global and regional scales. However, the monotonic linear regression approach is limited in its ability to detect abrupt changes in vegetation greenness trends because non-monotonic phenomena may exist in these trends. This study applied a piecewise linear regression (PLR) method to detect the non-monotonic trends of vegetation greenness in the Tibetan Plateau from 1982 to 2006. The vegetation greenness was indicated by the normalized difference vegetation index (NDVI) data derived from Global Inventory Modelling and Mapping Studies (GIMMS) Advanced Very High Resolution Radiometer (AVHRR) data. The results implied that the PLR method could detect an abrupt change in the trend with a break point (i.e. the year when the vegetation greenness trend changed abruptly) mainly concentrated in 1989. We chose three typical regions to compare the PLR and monotonic linear regression approach for vegetation trend detection to verify the effectiveness of the PLR method. The PLR method can detect different trends during different periods, but the one-piece linear regression method can only detect monotonic trends. After comparing the results of the two methods for three climatic zones, we found that the greenness trend detected by PLR can be better explained by temperature and precipitation variations. Our results illustrate that the PLR method is superior to the one-piece linear regression method due to its ability to detect non-monotonic trends. It can, therefore, delineate vegetation greenness trends in detail and be applied to other, similar studies. [ABSTRACT FROM AUTHOR]