| 基于HJ-CCD影像数据的新疆喀纳斯自然保护区植被叶面积指数估算 |
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| 引用本文： | 昝梅,李登秋,居为民,王希群,陈蜀江.基于HJ-CCD影像数据的新疆喀纳斯自然保护区植被叶面积指数估算[J].冰川冻土,2013,35(4):892-903. |
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| 作者姓名： | 昝梅 李登秋 居为民 王希群 陈蜀江 |
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| 作者单位： | 1. 南京大学 国际地球系统科学研究所, 江苏 南京 210093;2. 新疆师范大学 地理科学与旅游学院, 新疆 乌鲁木齐 830054;3. 新疆干旱区湖泊环境与资源实验室, 新疆 乌鲁木齐 830054;4. 国家林业局 林产工业规划设计院, 北京 100010 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)项目(2010CB833503;2010CB950702);江苏高校优势学科建设工程资助项目;江苏高校优秀科技创新团队资助 |
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| 摘 要： | 以新疆喀纳斯自然保护区为研究区, 评价了HJ-CCD影像数据估算植被叶面积指数(LAI)的能力及其对大气订正方法的敏感性.分别利用6S和FLAASH两种大气订正模型对HJ1B-CCD2影像进行大气订正, 比较了大气订正前后不同植被(针叶林、阔叶林、针阔混交林和草地)反射率及5种植被指数(NDVI、SR、SAVI、MSR、ARVI)的变化, 进而建立了4种植被类型LAI的遥感估算模型, 分析了LAI的空间分布格局.结果表明: 大气订正后可见光波段的反射率降低, 6S模型订正后近红外波段的反射率上升, 而FLAASH模型订正后近红外波段的反射率下降.大气订正后NDVI、SR、SAVI(除针叶林)和MSR上升, 6S模型订正后所有植被类型的ARVI下降, FLAASH模型订正后针叶林和阔叶林的ARVI上升, 而针阔混交林和草地的ARVI下降.大气订正提高了植被指数与LAI之间的相关性, 对于针叶林、阔叶林、针阔混交林而言, 利用6S模型订正后的反射率建立的模型优于FLAASH模型订正后的反射率建立的模型, 而草地却相反.经过大气订正, HJ-CCD影像数据可应用于研究区植被LAI的估算.研究区LAI的高值集中在湖泊和河流附近, 低值分布在海拔较高处.山地森林草原带、亚高山森林带、高山灌丛草甸带、高山冻原、高山冰川带植被LAI的平均值分别为2.6、3.9、2.5、1.7和1.0.
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| 关 键 词： | HJ-CCD影像 大气订正 LAI遥感估算 6S模型 FLAASH模型 |
| 收稿时间： | 2012-12-21 |
| 修稿时间： | 2013-03-17 |
Retrieval of Vegetation Leaf Area Index in Kanas National Nature Reserve,Xinjiang, Based on HJ-CCD Remote Sensing Data |
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| ZAN Mei,LI Deng-qiu,JU Wei-min,WANG Xi-qun,CHEN Shu-jiang.Retrieval of Vegetation Leaf Area Index in Kanas National Nature Reserve,Xinjiang, Based on HJ-CCD Remote Sensing Data[J].Journal of Glaciology and Geocryology,2013,35(4):892-903. |
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| Authors: | ZAN Mei LI Deng-qiu JU Wei-min WANG Xi-qun CHEN Shu-jiang |
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| Institution: | 1. International Institute for Earth System Science, Nanjing University, Nanjing Jiangsu 210093, China;2. School of Geography Science and Tourism, Xinjiang Normal University, Ürümqi Xinjiang 830054, China;3. Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Ürümqi Xinjiang 830054, China;4. Planning and Design Academy of Forest Products Industry, State Forest Administration, Beijing 100010, China |
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| Abstract: | The ability of HJ-CCD remote sensing data to retrieve vegetation leaf area index (LAI) and its sensitivity to atmospheric correction methods were investigated in the Kanas National Nature Reserve, Xinjiang. The 6S and FLAASH models were employed to implement atmospheric corrections for the remote sensing data. The changes in the reflectance and vegetation indices (NDVI, SR, SAVI, MSR, ARVI) of different land cover types (needle leaved forests, broad leaved forests, mixed forests, and grasslands) with and without atmospheric correction were analyzed. Then, the best fitted models for estimating LAI were built with the field measurements of LAI and spatial distribution patterns of LAI were analyzed. The results show that after atmospheric correction, the reflectance in the visible bands decrease. Reflectance in the near infrared band increases with the atmospheric correction by the 6S model and decreases with the atmospheric correction implemented by the FLAASH model. Consequently, atmospheric correction causes NDVI, SR, MSR and SAVI (except needle leaved forests) to increase. As to ARVI, the influence of atmospheric correction is related to the correction model used and vegetation types. With the atmospheric correction by the 6S model, ARVI decreases for all land cover types. The atmospheric correction conducted by the FLAASH model causes ARVI to increase for need leaved and broad leaved forests and to decrease for mixed forests and grasslands. Atmospheric correction enhances the linkage between LAI and vegetation indices. The models based on reflectance with the atmospheric correction by the 6S model are better than the models base on reflectance with atmospheric correction by the FLAASH model for forests (needle leaved forests, broad leaved forests, and mixed forests). As to retrieval of LAI for grasslands, the model based on reflectance with the atmospheric correction by the FLAASH model outperforms the model based on reflectance with the atmospheric correction by the 6S. The results indicate that the HJ-CCD data can be used to estimate LAI in the study area with atmospheric correction properly implemented. In the locations near lakes and rivers, LAI of vegetation is high. Low LAI is distributed at locations with high elevation. The LAI averages of mountain forest-steppe zone, subalpine forest zone, alpine shrub meadow, alpine tundra and alpine glaciers are 2.6, 3.9, 2.5, 1.7 and 1.0, respectively. |
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| Keywords: | HJ-CCD remote sensing image atmospheric correction LAI estimation of remote sensing 6S model FLAASH model |
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