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Land-use change in oases of arid zones play a significant role in the sustainable development and stability of oases. This paper presents a typical case of successful efforts to mitigate land-use change, its drivers and effects on the oasis eco-environment at Keriya Oasis in the western arid zone of China using remotely-sensed data, official statistics, and data collected by field investigation. Mathematical models were developed to quantify important elements related to land-use change, including net change and total change. The results indicate that: (1) approximately 17% of land-use types of Keriya Oasis changed between 1991 and 2002; (2) socioeconomic development, climate change, and economic polices contributed to land-use change in the oasis; (3) inappropriate human activities were the main cause of land-use change and eco-environmental degradation in the oasis; and (4) the stability of the oasis is threatened by land-use change and unexpected eco-environmental changes in the oasis and oasis-desert ecotone. The study suggests irrational human activity in arid zones, and that caution should be exercised to maintain stability and sustainable development of oases. [ABSTRACT FROM AUTHOR]

Semi-arid regions belong to important ecological transitional zones with a high ecosystem vulnerability and sensitivity. In recent years, due to the influence of climate change and human activities, the land covers of semi-arid regions have experienced dramatic changes. Therefore, timely and accurate land cover mapping of semi-arid regions is of great significance. Deep learning has been a hotspot in land cover mapping; however, it requires a large number of labelled samples. To tackle this data-hunger issue, we resolve to a multi-gate semi-supervised learning (SSL) method that could use a huge number of unlabelled samples to improve the classification performance. Specifically, the proposed method consists of a probability gate, an uncertainty gate and a consistency gate, aiming to generate high-quality pseudo-labels from the unlabelled datasets. Experimental results from Zhangbei, China indicate that the proposed method yields a good performance with an overall accuracy of 90.31%, which is 4.31% higher than that of traditional supervised learning (SL) methods. Ablation studies also verify the effectiveness of the gated mechanism in selecting unlabelled data. [ABSTRACT FROM AUTHOR]

The development of the Asian monsoonal climate sparked a latitudinal shift in vegetation zones from the Oligocene to the Miocene. However, little is known about the history of the megathermal floristic response to the geologic events in key regions like subtropical China. Here, a species of Fabaceae, Ormosia cyclocarpa sp. nov., is recognised based on well-preserved fruits from the Miocene of Zhejiang in middle-subtropical China. This species features dehiscent, compressed suborbicular single-seeded pods with an intramarginal vein. Extensive comparisons with extant/fossil fabaceous taxa confidently support the present taxonomic consideration. Considering the extant distribution, diversity centre and regional climate history, the known fossil occurrences in southern China imply that Ormosia extended northwards during the Miocene from probable tropics of origin into subtropical China, where a once broad latitudinal arid zone prevailed throughout much of the Palaeogene. Such a changing phytogeographic pattern across tropical-subtropical China probably resulted from a drastic East Asian climate change around the Oligo-Miocene boundary, and closely coincided with the early-middle Miocene warming. The spreading of Ormosia and other tropical genera (e.g. Dipterocarpus, Shorea, Bauhinia, Dacrycarpus) sharing similar floristic history in subtropical China indicates a palaeotropical and Miocene origin for some clades of the extant East Asian flora. [ABSTRACT FROM AUTHOR]