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Copyright of Chinese Journal of Applied Mechanics is the property of Chinese Journal of Applied Mechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Multipurpose Utilization of Mineral Resources / Kuangchan Zonghe Liyong is the property of Multipurpose Utilization of Mineral Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Natural Disasters is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land degradation (desertification) in arid region, as one of the global ecological challenges, is crucial to food security, environmental quality and regional natural resource management. Fortunately, the development of remote sensing technology greatly improves the ability of land degradation information extraction and assessment, which can effectively reveal conditions of land degradation in arid region and provide the scientific basis for land degradation trend prediction and formulating the corresponding preventing measures. In fact, the essence of land degradation is the decrease of ecological service following the change of type, quantity, structure and function of land use/cover which emerges from interactions of the coupled human-environment (H-E) factors at multiple spatio-temporal scales, leading the difficulty of classification and assessment. This prompts an attempt to reduce the complexity of the assessment by identifying a limited suite number of processes and variables which makes the problem tractable at particular scale like the interaction process of soil and vegetation, the core of the degradation. Based the standard spectral endmember space of GF-1 satellite, this paper innovatively established three levels evaluation system of land degradation state in arid areas, including land use/cover structure, degradation type and degradation degree. Secondly, we realized dryland multi-temporary linear spectral mixture analysis. The remote sensing images of GF-1/WFV endmember fractions time series were applied to characterize the quality attributes of the underlying surface and organize the classification knowledge, and then to complete the fine land cover/use classification of Minqin county in 2015. Based on land use/cover mapping in arid region, the time series EMs including vegetation (GV), sand land (SL), saline land (SA), dark surface (DA) were combined to obtain and express the variables reflecting the ecosystem function, and then, were organized with decision tree (DT) for degradation state mapping. Finally, the results of land degradation state were assessed using photos of the site landscape and the measured data of the sampling sites. The results showed that: 1) The MESMA method could decompose four stable EMs types, which were consistent with the physical components of the surface cover in study area. The model could effectively simulate the spectral information of study area. More importantly, it solved the problem that the bands number of GF-1 WFV was few and the information mining was insufficient. And which provides a basis for establishing knowledge for decision tree classification and assessment. 2) The time series EMs in standard spectral endmember space could perfectly highlight the temporal interaction characteristics details between vegetation types and habitat and the ability of remote sensing to identify land degradation types and degree, with corresponding accuracy of 87.5% and 78.7%, respectively. 3) For Minqin, a typical dryland system, the sandification process and the sand-salinization process were the dominant land degradation processes. Light sandification and moderate sandification were the dominant degradation degrees. Through the relative evaluation results of land degradation types and degree, it helps divide the key control areas, ecological conservation areas and the use of decompression areas. The research result contributes to the sustainable development of regional management and control of ecological fragile areas, land space development and ecological environment protection. General remote-sensing technical framework for land degradation assessment was confirmed that it have the potential to be applied to the study of land degradation assessment in arid regions across time and regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Land-cover/land-cover pattern of arid ecosystem in China, which determines ecosystem service value, is undergoing accelerated changes due to natural and anthropogenic disturbances. To provide a basis for ecosystem service assessment, in this article, we adopted 2015 multi-seasonal data of Landsat 8 for exploring the land cover/use classification method in arid region using the Minqin oasis as a case study area. We firstly conducted a principle component transform on the multi-seasonal remote data for identifying the inherent dimensionality of Landsat OLI spectral space. Then endmember classes and respective representative season were determined through the analysis of principal component feature space for each season. After extracting the endmember spectra by averaging the reflectance of 200~400 endmember pixels, fully-constrained linear spectral mixture model was performed on each seasonal image for yielding quantitative estimates of the areal abundance of endmembers within each pixel. At last, these endmember abundance estimates were used for land cover/use classification in Minqin study area by using the decision tree method. According to the natural environment and land-use characters of study area and given the resolution of remote sensing data and applicability for ecosystem service assessment, in this research, we developed the two-level classification system. Exposed surface, crop land, forest/shrub land, grassland, impervious surface and water area were defined as first-level classes. The exposed surface was subdivided into moving sand, Gobi/hill/bare-land, salinized moving sand, and saline-alkaline land. Crop land was subdivided into spring crop, summer crop, perennial crop based on seasonal growth characteristics. Similarly, forest/shrub land was subdivided into spring forest/shrub, summer forest/shrub, and evergreen forest/shrub. Decision tree was designed based on the seasonality pattern of feature endmember abundance of each target class. The first step in the classification procedure was to overlay the training data on the three-seasonal abundance composite images for identifying the seasonality pattern of each class. The second step was to measure the feature endmember abundance distribution of each class within training samples. Aided by the histogram distribution, the segmenting boundary of each node was established by an interactive process. The results showed that sand, salt, green vegetation, dark materials, and water were five endmember classes used for multi-seasonal linear spectral mixture analysis. But their representative seasons were different. So, we extracted endmember reflectance for sand, salt, and green vegetation from early winter, spring, and summer, respectively. The spectral reflectance of dark material and water endmembers were derived from spring as well as salt. The mean RMSE (root-mean-square error) values were all lower than 0.01, which meant good fitness of linear spectral mixture model for each seasonal image. From the statistics of endmember abundance estimates, we found sand and dark materials were matrix of the study area. The areal percentages of water and salt were relatively low, and their values were not uniform. The green vegetation cover was the lowest, and corresponding abundance varied most. Through analyzing the rate of endmember abundance change between seasons, it seemed that the abundances of sand and dark materials did not vary with seasons. Green vegetation increased significantly from spring to summer, and the overall amounts of water and salt somewhat decreased during the same period. The overall precision of the classification method developed in this study was 90.94%, and the Kappa coefficient was 0.90. It suggested the reliability of the 2015 land cover/use classification results of Minqin. The supplement use of multi-seasonal remote data can help acquire the comprehensive information of target surface feature, which was important for the stability of land cover/use classification results. Owing to the obvious physical meaning of endmember abundance, the design of decision tree can depend on the prior knowledge instead of vast training data. Thus, the classification method developed in this study may have the potential in the land cover/use classification of the whole drylands in China. [ABSTRACT FROM AUTHOR]