| 西双版纳橡胶林地的遥感识别与数字制图 |
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| 引用本文： | 刘晓娜,封志明,姜鲁光,张景华.西双版纳橡胶林地的遥感识别与数字制图[J].资源科学,2012,34(9):1769-1780. |
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| 作者姓名： | 刘晓娜 封志明 姜鲁光 张景华 |
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| 作者单位： | 1. 中国科学院地理科学与资源研究所,北京100101 中国科学院研究生院,北京100049
2. 中国科学院地理科学与资源研究所,北京,100101 |
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| 基金项目： | 科技部基础性工作专项:“澜沧江中下游与大香格里拉地区科学考察”(编号:2008FY110300)。 |
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| 摘 要： | 橡胶林地是西双版纳人工景观的主导类型，橡胶林地的遥感识别与动态监测对于促进西双版纳的经济发展和生态保护具有重要意义。研究基于MODIS—NDVI数据，通过分析各植被覆盖地类的物候特征，判定橡胶林提取的时间窗口：以2010年TM为主要数据源，根据橡胶林在不同树龄所表现的光谱差异，采用面向对象分类方法，按照橡胶幼林（〈10年）、橡胶成林（≥10年）系统分析其光谱、纹理、地形以及类相关特征，完成了2010年橡胶林地的提取与制图。精度评价结果表明，总分类精度达到85．20％，较基于像元的决策树分类精度提高5．20％；其中橡胶成林分类精度达到92．50％，橡胶幼林分类精度在76．42％。统计结果表明，橡胶幼林与橡胶成林种植面积比重为1．04：1，与目前民营橡胶种植面积超过国营橡胶种植面积现状相吻合。研究提出来一种验证和提高橡胶林分类精度的新方法，即采用相同分类方法提取2000年的橡胶林地，通过2000年和2010年两期橡胶林地变化的叠加分析，发现橡胶成林提取方法较为可靠，而橡胶幼林误分率略大，可通过提取更晚年份的橡胶成林来进一步改善和提高提取精度。
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| 关 键 词： | 遥感识别 数字制图 橡胶幼林 橡胶成林 MODIS—NDVI 西双版纳 |
| 收稿时间： | 5/8/2012 12:00:00 AM |
Rubber Plantations in Xishuangbanna: Remote Sensing Identification and Digital Mapping |
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| LIU Xiaon,FENG Zhiming,JIANG Luguang and ZHANG Jinghua.Rubber Plantations in Xishuangbanna: Remote Sensing Identification and Digital Mapping[J].Resources Science,2012,34(9):1769-1780. |
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| Authors: | LIU Xiaon FENG Zhiming JIANG Luguang and ZHANG Jinghua |
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| Institution: | Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;Graduate University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Rubber plantations are the dominant artificial landscape across Xishuangbanna. Extraction and dynamic monitoring of rubber plantations is important to the region’s economic development and ecological protection. Based on MODIS-NDVI data we analyzed phenological characteristics of vegetation cover type and determined the temporal window for rubber forest detection. According to spectral differences between rubber forests at different growth stages we used object-oriented classification to discriminate spatial patterns with TM imagery. We found that the optimum temporal window for rubber detection was from January to February or from early June to late October. Using times series NDVI analysis we found that mature rubber forest has obvious differences compared with other land cover types and that young forest rubber was often confused with fallow farmland and tea garden. The NDVI characteristic of rubber forests is not the sole classification feature. Based on GPS and visual sampling total classification accuracy was 85.20%, about 5.20% higher than the decision tree method based on pixel classification. The accuracy of young rubber forest (<10 years old) was 92.50% and mature rubber forest (10 years old) was 76.42%, a ratio of 1.04∶]1. Remote sensing results were very close to the real condition, namely the area of private rubber plantation more than state-owned plantation. We propose a new method for the validation and improvement of rubber classification accuracy. Change analysis showed that the extraction method of mature rubber forest was more credible than for young rubber forest; mature rubber forest seldom transformed into other land use types. Rubber forest extraction and dynamic monitoring is important for rubber yield estimation, spatial distribution, environmental protection and local government decision-making. This is an effective method for monitoring rubber plantations dynamically and can be extended to other perennial vegetation extraction. |
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| Keywords: | Remote sensing identification Digital mapping Young rubber forest Mature rubber forest MODIS-NDVI Xishuangbanna |
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