| 新疆地区土地覆被遥感数据的一致性研究 |
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| 引用本文： | 徐泽源,罗庆辉,许仲林. 新疆地区土地覆被遥感数据的一致性研究[J]. 地球信息科学学报, 2019, 21(3): 427-436. DOI: 10.12082/dqxxkx.2019.180596 |
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| 作者姓名： | 徐泽源 罗庆辉 许仲林 |
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| 作者单位： | 新疆大学,乌鲁木齐 830046 |
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| 基金项目： | 中国科学院战略性先导科技专项（XDA20040400） |
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| 摘 要： | 鉴于新疆地区对中国乃至中亚有着特殊的战略意义,本文针对不同数据源及分类系统在土地覆被数据的空间分布上缺乏互通性问题,结合2010年目视解译土地利用现状遥感监测数据、GlobeLand30和GlobCover2009共3种土地覆被数据,采用类型相似分析、类型混淆分析、混淆矩阵分析、空间一致性分析4种方法开展精度评价及一致性分析,以期对土地覆被数据在中国西北干旱区的适用性及适用范围提供有效建议。结果表明,3种土地覆被数据对新疆地区土地覆被类型构成基本一致,且对裸地类型的辨识度最高;新疆地区中高度一致区域占新疆总面积的95%;3种数据两两对比时,总体精度在64.11%~72.57%之间,其中目视解译数据/GlobeLand 30组合表现出最高水平,且仍有提高空间,反映出目前相同卫星传感器是提升精度评价结果的重要因素之一,且不同分类系统、分类方法、空间分辨率及卫星过境时间等因素对精度评价结果也会产生巨大影响。为解决此类问题,利用多源土地覆被遥感数据的融合技术提高数据精度,或是利用深度学习对遥感影像资料进行精确地解译和判读,将是今后全球土地覆被制图及应用领域的主要发展趋势。
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| 关 键 词： | 土地覆被 精度评价 混淆矩阵 空间一致性分析 新疆地区 |
| 收稿时间： | 2018-11-20 |
Consistency of Land Cover Data Derived from Remote Sensing in Xinjiang |
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| Zeyuan XU,Qinghui LUO,Zhonglin XU. Consistency of Land Cover Data Derived from Remote Sensing in Xinjiang[J]. Geo-information Science, 2019, 21(3): 427-436. DOI: 10.12082/dqxxkx.2019.180596 |
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| Authors: | Zeyuan XU Qinghui LUO Zhonglin XU |
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| Affiliation: | Xinjiang University, Urumqi 830046, China |
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| Abstract: | Xinjiang region is of strategic significance to China and Central Asia. This study aimed to effectively combine different data sources and classification systems to mitigate the lack of their interoperability regarding spatial distribution of land cover data. For this purpose, three types of land cover data were included. They were the visual interpretation of land use status in 2010 remote sensing monitoring data, GlobeLand30, and GlobCover2009. Four methods including category similarity analysis, category confusion analysis, confusion matrix analysis, and spatial consistency analysis were used to evaluate their accuracies and consistencies. We expect that this study would provide recommendations for the applicability of land cover data in the arid region of northwest China. The results showed that the three types of land cover data exhibited a good consistency for describing land cover categories in Xinjiang, with similarity higher than 0.9. Particularly, bare land identification demonstrated the highest consistency, followed by grassland, cultivated land, and forest. About 95% of the land area in Xinjiang showed a relatively high consistency, and the overall accuracy for land cover data ranged from 64.11% to 72.57%. Data from the group of visual interpretation/GlobCover2009 demonstrated the lowest accuracy, followed by the group of GlobeLand30/GlobCover2009. The group of visual interpretation data/GlobeLand30 had the highest accuracy, but it still had room for improvement. These results demonstrated that using the same satellite sensor plays an integral role in enhancing the accuracy of evaluation results. Moreover, classification systems, classification methods, spatial resolution, and satellite passage time used would also have a huge impact on the accuracy of evaluation results. In order to solve this problem more effectively, multi-source remote sensing data integration technology or deep learning will become more promising for accurately interpreting remote sensing image data in the near future, for further improving data accuracy in global land cover mapping and application fields. Depending on the distinctive landscape pattern of Xinjiang region, this research analyzed the accuracy of three different kinds of data for different land cover categories to provide reliable information which shall be proved to be useful in resource development, environment protection and sustainable development of Xinjiang. Additionally, it initiated a framework for providing basic data for China’s significant development strategy "the Belt and Road". Moreover, the results demonstrated the better performance of GlobeLand30 in accuracy assessment. As compared to other land cover data within the same category, the GlobeLand30 data is overwhelming in spatial resolution. |
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| Keywords: | land cover accuracy evaluation confusion matrix spatial consistency analysis Xinjiang region |
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