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• Remote sensing was used to monitor plateau wetland ecological degradation. • A composite wetland degradation index was constructed to assess wetland degradation quantitatively. • We analyzed ecological degradation process with three administrative levels. The Zoige Plateau Wetland is the largest high-altitude swamp wetland in China. It plays a key role in ecological balance and the global change. Climate change and improper utilization have consequently resulted in decreased wetland area, disrupted ecosystem structure, and wetland function degradation and function loss. The Zoige Plateau Wetland thus faces severe ecological degradation problems. In this study, selecting the Zoige Plateau Wetland as the study area, remote sensing was used to track the ecological degradation process during 2000–2015 from three aspects (area degradation, structure degradation and function degradation) and at three administrative levels (study area, county and key township). To assess the wetland degradation quantitatively, an analytic hierarchy process (AHP) was used to construct a composite wetland degradation index based on five indicators: wetland area change rate, landscape fractal dimension, landscape fragmentation, vegetation biomass and vegetation coverage. The results showed the following. (1) In terms of area change rate, the total wetland area had continuously decreased during 2000–2015 compared to that in the 1990s, and 2010 was the most significant. The frequency of negative change in the wetland area reached 87.5% in all four counties and across four time periods (2000, 2005, 2010 and 2015), and 85% at the township level. At the spatial scale, Hongyuan County had the smallest wetland area change rate. (2) In terms of structure change, the variations in landscape fractal dimension and landscape fragmentation generally exhibited similar trends at different levels. Specifically, the ecosystem structure was seriously affected in 2000, then gradually recovered in 2005 and 2010, and was again under aggravated interference in 2015. (3) In terms of function change, the above-ground biomass yield in the northeastern region was higher than that in the southern region. A low value was detected in 2000, and then the value increased and remained essentially the same in 2005 and 2010. However, the value decreased in 2015, indicating deteriorated vegetation condition. The vegetation coverage in the study area remained steady, without deterioration in a particular year. (4) In terms of the whole study area, the wetland degradation grades in the four periods were mild (2000), moderate (2005), mild (2010) and mild (2015). In brief, the wetland degradation was the most severe in 2005. Over the 2000–2015 period, the spatial degradation pattern of the Zoige Plateau Wetland progressed from the northeast to the southwest. The model built in this study could be widely applicable. And this study has also some reference value for monitoring and management of wetland degradation by remote sensing. [ABSTRACT FROM AUTHOR]

Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [ABSTRACT FROM AUTHOR]

• The MP-Net model aggregates context information. • The MP-Net model reduces the feature losses. • The MP-Net model efficiently and precisely classifies crops. Accurate crop classification map is of great significance in various fields such as the survey of agricultural resource, the analysis of existing circumstance on land application, the yield estimation of crop and the disaster warning. The methods based on machine learning and deep learning are popularly used in crop classification and recognition of remote sensing images. However, the crop classification task based on neural networks still faces significant challenges due to the spatial and temporal distribution of crops and the inherent characteristics of remote sensing images. Therefore, this study proposes the multi-layer pyramid crop classification network (MP-Net) to solve the above problems. To reduce the feature loss during the crop extraction, the proposed model uses the pyramid pooling module to improve the ability of global information acquisition, and the information concatenation module to retain the upper features. Using the GF-6 and Sentinel-2 satellite data, the proposed model was tested in Erhai Lake Basin and Beian City. Compared with other five deep learning models, such as FCN, SegNet, U-Net, PSPNet and DeepLabv3+, the experimental results indicate that the proposed model achieves the highest accuracy in both study areas. Meanwhile, the proposed model has the advantages of short training time and high efficiency under the same running conditions. Overall, this study is beneficial to improve the efficiency and accuracy of crop classification task in the unbalanced temporal and spatial distribution. It also brings a feasible scheme for crop classification tasks in complex growing areas. The code has been publicly available at https://github.com/Xu-Chang-Hong/MP-Net. [ABSTRACT FROM AUTHOR]

Abstract: A standard methodology is needed to recognize potentially suitable areas for sustainable bioenergy crop production. This facilitates better identification of promising crops and cropping systems, logistical and economic studies, and work needed to meet regulatory criteria. A possible approach is built upon three layers of internationally available spatial data: (1) degrading and abandoned areas, (2) potentially suitable land cover classes, (3) exclusion zones such as nature reserves and areas of high biodiversity. For China, areas identified as potentially suitable range from 1.2 to 6.0% of the national territory, depending on different levels of statistical confidence in degrading area status and allowable limits of terrestrial carbon. Verification on the ground showed that about 60% of points tested conformed to the remote suitability assessment in the scenario, which represents the results for the combination of all degrading areas and a terrestrial carbon stock limit of 200 t ha−1. A top-down approach is useful in framing potentially suitable locations, but a complementary bottom-up analysis is still required to ultimately identify areas for sustainable bio-fuel production. [Copyright &y& Elsevier]