改性纤维素类聚合物固沙剂的吸附力学及崩解特性试验

山地植被信息在气候变化研究和生态环境保护等方面发挥着重要作用，遥感技术能够快速获取山地植被信息，但是存在山地地形阴影的影响以及山地植被信息混淆问题。该文以山地植被为研究对象，基于Landsat卫星遥感影像多光谱数据，分析山地植被的主要特点，借鉴阴影消除植被指数（SEVI）的构造原理及形式，提出了一种适用于山地植被覆盖遥感监测的植被指数算法--植被区分阴影消除植被指数（VDSEVI）。研究结果表明：相对于已有的其他植被指数，VDSEVI较好地消除了地形阴影的影响；VDSEVI的信息量大，植被覆盖的识别能力较强，较好地解决了植被信息混淆问题，能够更好地反映山地植被覆盖情况。不同土地覆盖类型的VDSEVI存在显著差异；阴影稀疏林地和相邻非阴影稀疏林地的相对误差较小，为3.428%；各土地覆盖类型样本VDSEVI标准差均小于0.06；植被覆盖样本VDSEVI与太阳入射角的余弦值（cosi）的相关系数为?0.800。为验证VDSEVI在其他地区的适用性，将VDSEVI应用于内蒙古阿尔山和福州市闽侯县，结果表明VDSEVI同样适用。新疆那拉提、内蒙古阿尔山和福州市闽侯县3个区域基于VDSEVI阈值法的植被信息提取总体精度分别为84.136%、87.339%、86.709%，Kappa系数分别为0.799、0.788、0.791。

Adsorption mechanics and disintegration characteristics of modified cellulose polymer sand fixing agent

Mountain vegetation information plays an important role in climate change research and ecological environment protection. Remote sensing technology can quickly acquire mountain vegetation information, but there are the influence of mountain terrain shadows and mountain vegetation information confusion. This paper took mountain vegetation as the research object and analyzed the main characteristics of mountain vegetation based on the multi-spectral data of Landsat satellite remote sensing image. Learn from the structural principle and form of the shadow elimination vegetation index (SEVI), a vegetation index algorithm - Vegetation distinguished and shadow eliminated vegetation index (VDSEVI) for mountain vegetation cover remote sensing monitoring was proposed. Samples for comparison and analysis were selected according to the main land cover types in the study area. The accuracy, validity and practicability of mountain vegetation information extraction with different vegetation indices were compared and analyzed. There are certain criteria which vegetation index of the same vegetation cover in shady and sunny should be equivalent, and the vegetation index values of different vegetation cover should be differentiated and compliance with actual vegetation coverage. Comparative analysis methods for different vegetation indices include: the images of different vegetation indices were directly compared; the vegetation index values of the same vegetation in shady and sunny cover were compared; the vegetation index values of the different land cover types were compared; the correlation between vegetation index and cosi was analyzed. The VDSEVI was compared with the ratio vegetation index(RVI), the normalized vegetation index(NDVI), the enhanced vegetation index(EVI2) and SEVI. There was a significant difference in the mean value of VDSEVI among different land cover types. The relative error of the sparse woodland in shady and sunny was small, which was 3.428%. The standard deviation of each land cover type sample was less than 0.060.The shady area of the Nalati in Xinjiang was dominated by woodland, and the sunny area was dominated by grassland. Therefore, the vegetation index and cosi should be negatively correlated. The correlation coefficient between VDSEVI and cosi is -0.800.The data results showed that compared with RVI, NDVI, EVI2 and SEVI, VDSEVI eliminated the influence of terrain shadows, and had a large amount of information and strong recognition of vegetation coverage. The problem of vegetation information confusion was solved, and the actual situation of mountain vegetation cover was reflected. To verify the suitability of VDSEVI in other regions, VDSEVI was applied to the area of Arxan in Inner Mongolia and Minhou County in Fuzhou City. The results showed that VDSEVI was equally effective. Vegetation information was extracted based on VDSEVI threshold method in Nalati in Xinjiang, Arxan in Inner Mongolia and Minhou County in Fuzhou City. The threshold was calculated by the VDSEVI value of samples from comparative analysis of various land cover types. Finally, the results of vegetation information extraction were evaluated by validating samples. The overall accuracy of vegetation information extraction in the three regions was 84.136%, 87.339%, 86.709% respectively, and the Kappa coefficients were 0.799, 0.788 and 0.791 respectively.