| 喀斯特山区居民地多尺度遥感信息提取精度对比 |
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| 引用本文： | 安霞霞,杨广斌,许元红,陈智虎,张 凯,张 斌.喀斯特山区居民地多尺度遥感信息提取精度对比[J].中国岩溶,2017,36(4):501-511. |
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| 作者姓名： | 安霞霞 杨广斌 许元红 陈智虎 张 凯 张 斌 |
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| 作者单位： | 贵州师范大学地理与环境科学学院/贵州省山地资源与环境遥感应用重点实验室 |
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| 基金项目： | 国家自然科学基金（41361091）；贵阳市白云区科技计划项目（白科合同[2016]61号） |
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| 摘 要： | 以贵州省典型喀斯特山区晴隆县为研究区，以分辨率分别为2.1 m的资源三号（ZY3）影像、10 m的哨兵二号（Sentinel-2）影像、16 m的高分一号（GF1）影像以及30 m的Landsat8影像为数据源，利用面向对象分类和人机交互相结合的方法提取研究区城市居民地、乡镇居民地和农村居民地，并将4种不同分辨率影像上提取的3种居民地信息与从0.5 m分辨率的普莱亚(Pleiades)影像通过目视解译且经实地核查得到的数据进行对比，计算出4种分辨率影像上3种居民地信息的提取精度，对提取结果进行精度对比分析。结果表明:（1）同一分辨率影像中，城镇居民地提取误差小于农村居民地；不同分辨率影像中，随影像分辨率大小的降低3种居民地中城市居民地的精度变化最小，为23.99%，农村居民地的数据精度变化最大，达到35.3%。（2）从2.1 m到30 m分辨率影像，居民地信息错分总误差快速增加，总误差比依次为:2.56%、15.58%、24.50%、32.72%，城市居民地错分误差比明显小于农村居民地，且3种居民地错分为其他地类面积最多的均是裸地；（3）随着影像分辨率大小的降低，居民地漏分总误差分别为2.86%，18.60% ，27.99%，37.49%，其中分散式的农村居民地识别更易受到周围环境的影响，漏分误差随影像分辨率大小降低而显著增加，4种分辨率影像中3种居民地信息漏分误差最小的均为水体，最大的是裸地和耕地。
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| 关 键 词： | 喀斯特 居民地 面向对象分类 精度分析 |
Comparative of resident-information extraction accuracy from multi-scale remote sensing data in karst mountainous areas |
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| AN Xiaxi,YANG Guangbin,XU Yuanhong,CHEN Zhihu,ZHANG Kai and ZHANG Bin.Comparative of resident-information extraction accuracy from multi-scale remote sensing data in karst mountainous areas[J].Carsologica Sinica,2017,36(4):501-511. |
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| Authors: | AN Xiaxi YANG Guangbin XU Yuanhong CHEN Zhihu ZHANG Kai and ZHANG Bin |
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| Affiliation: | School of Geography and Environmental Science, Guizhou Normal University/Guizhou Mountain Resources and Environmental Remote Sensing Application Laboratory |
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| Abstract: | Guizhou Province hosts a typical karst landform in China, where nearly 65% of the land is karst regions. It leads to the complexity, vulnerability and diversity of Guizhou’s ecological environment because of its unique mountainous plateau terrain and the existence of karst geological conditions. The spatial distribution of the karst mountainous areas is quite different from that of other plateaus mountainous areas, which seriously affects the survival and development of the mountainous residents. Therefore, studying the satellite images with suitable resolution in the karst areas to obtain the information of the residents is very significant. In this paper, the Qinglong county is selected as the study area. The county is under the jurisdiction of Qianxi’nan Prefecture, Guizhou Province. It is located in the middle of the Yunnan-Guizhou plateau and the west of the Miaoling mountains. The main karst forms include peak-cluster depression, sinkholes, dry gutters and karst caves in Qinglong. The author selected four different resolution images as the data source, which are Y3 image (2.1 m), sentinel-2 image(10 m), GF1 image(16 m) and Landsat8 image (30 m), respectively and extracted the residential area, township and rural regions of the study area using the object-oriented classification and human-computer interaction methods. Furthermore, the accuracy of the results that three kinds of residential information extracted from the four different resolution were compared with the geographical census data obtained from the 0.5 m resolution Pleiades image through visual interpretation and field verification. The results show that, (1) In the same resolution image, the extraction error of the urban residents is smaller than that of the rural residents. Among the different resolution images, the accuracy change of the resolution of urban residential areas is the smallest, which is 23.99%, whereas the accuracy change of the rural residential areas is the biggest, up to 35.3%; (2) The total commission errors of residential information increases rapidly from 2.1 m to 30 m resolution images, which are, in order, 2.56%, 15.58%,24.50%, 32.72%. The total omission errors in the urban residential areas are obviously smaller than that in the rural residential areas, and the most wrongly type divided into other classes in three residential areas is bare lands; (3) With the reduction of image resolution, the total omission errors of residential areas are 2.86%, 18.60%, 27.99%, 37.49%, respectively, among which non-central rural residential areas are more vulnerable to the environmental impact from their surroundings, and the total omission errors of residents increase significantly with the reduction of image resolution. The final results obtained are consistent with the conclusion that the spatial resolution of the satellite sensor is proportional to the spatial resolution of the satellite sensor. |
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| Keywords: | karst residential land object-oriented classification accuracy analysis |
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