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作 者:苏泳娴[1,2,3] 王重洋[1,2,3] 张虹鸥 陈修治[4] 林晖[5] 许喜逢
机构地区:[1]中国科学院广州地球化学研究所,广州510640 [2]广州地理研究所,广州510070 [3]中国科学院大学,北京100049 [4]中国科学院华南植物园,广州510650 [5]广东环境保护工程职业学院,广东佛山528216 [6]安徽省濉溪县第二中学,安徽淮北235100
出 处:《热带地理》2015年第2期193-201,共9页Tropical Geography
基 金:国家自然科学基金(41401055);惠州市科技计划项目(2013B020006005)
摘 要:为提高城镇建设用地提取的精度,文章提出一种基于DMSP/OLS夜间灯光数据邻近象元特征差异的提取城镇建设用地的邻域分析方法。该方法主要是通过栅格运算识别建设用地和非建设用地的突变,结合阈值法准确提取建设用地边界。将该方法应用于2009年中国大陆34个主要城市的建设用地提取,并与基于TM数据以及基于DMSP/OLS数据传统全局阈值法和局部优化法提取的建设用地进行比较。结果显示,该方法提取的建设用地象元数与TM影像提取的象元数呈较好的相关性(R2=0.966),均方根误差为191.64个象元,相对精度为82.79%,景观形状指数、聚集度指数、边缘面积比指数和连接度指数也具有较高的相似性(R2分别为0.475 4、0.366 2、0.858 9和0.915 3)。该方法不仅能准确识别大面积的城市建设用地斑块边界,而且不会漏掉小面积的城市建设用地斑块,克服了传统全局阈值法和局部优化法的缺点。Under the current rapid urbanization process, it is an urgent task to map the regional and global built-up urban areas timely and accurately. The DMSP/OLS night light data is one accurate, affordable and convenient dataset to reflect the urban distribution and built-up area boundaries. In order to promote the accurate level, this paper develops a Neighborhood Statistics Analysis method(NSA) for mapping the built-up area, which is based on the relative differences between neighborhood pixels. The proposed method indentifies the mutation region by raster calculating and accurately extracts the built-up area boundaries by combining the threshold method. Then, the proposed NSA method is applied to extract the built-up urban areas of China's 34 main cities in 2009 and the accuracy is validated by using the results extracted from TM data and the global-fixed and local-optimized threshold methods based on DMSP/OLS night light data. Results show that the pixel numbers of NSA-extracted built-up lands match well with those of TM-extracted built-up lands. The coefficient of determination R2 is 0.966, with root mean squared error RMSE=191.64 and relative accuracy RA=82.79%. The Landscape shape index, aggregation index, edge area ratio index and connectivity index also show highly consistent(R2=0.475 4, 0.366 2, 0.858 9 and 0.915 3, respectively). In addition, the proposed NSA method, which significantly overcomes the disadvantages that are associated with the global-fixed and local-optimized threshold methods, accurately maps both the large patches of built-up areas in urban regions and the small patches of built-up areas in surrounding towns.
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