...
首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data
【24h】

Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data

机译:使用坡度校正ICESat数据,SRTM,MODIS得出的气候数据绘制中国植被高度的图

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Vegetation height is an important parameter for biomass assessment and vegetation classification. However, vegetation height data over large areas are difficult to obtain. The existing vegetation height data derived from the Ice, Cloud and land Elevation Satellite (ICESat) data only include laser footprints in relatively flat forest regions (<5). Thus, a large portion of ICESat data over sloping areas has not been used. In this study, we used a new slope correction method to improve the accuracy of estimates of vegetation heights for regions where slopes fall between 5 and 15. The new method enabled us to use more than 20% additional laser data compared with the existing vegetation height data which only uses ICESat data in relatively flat areas (slope < 5) in China. With the vegetation height data extracted from ICESat footprints and ancillary data including Moderate Resolution Imaging Spectroradiometer (MODIS) derived data (canopy cover, reflectances and leaf area index), climate data, and topographic data, we developed a wall to wall vegetation height map of China using the Random Forest algorithm. We used the data from 416 field measurements to validate the new vegetation height product. The coefficient of determination (R-2) and RMSE of the new vegetation height product were 0.89 and 4.73 m respectively. The accuracy of the product is significantly better than that of the two existing global forest height products produced by Lefsky (2010) and Simard et al. (2011), when compared with the data from 227 field measurements in our study area. The new vegetation height data demonstrated clear distinctions among forest, shrub and grassland, which is promising for improving the classification of vegetation and above-ground forest biomass assessment in China. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译:植被高度是生物量评估和植被分类的重要参数。但是,很难获得大面积的植被高度数据。从冰,云和陆地高程卫星(ICESat)数据获得的现有植被高度数据仅包括相对平坦的森林区域(<5)中的激光足迹。因此,尚未使用倾斜区域上的大部分ICESat数据。在这项研究中,我们使用了一种新的坡度校正方法来提高坡度介于5和15之间的区域的植被高度估计的准确性。与现有的植被高度相比,新方法使我们能够使用20%以上的额外激光数据仅在中国相对平坦的区域(坡度<5)中使用ICESat数据的数据。利用从ICESat足迹中提取的植被高度数据以及包括中等分辨率成像光谱仪(MODIS)衍生数据(冠层覆盖率,反射率和叶面积指数),气候数据和地形数据的辅助数据,我们绘制了一个中国使用随机森林算法。我们使用来自416个现场测量的数据来验证新的植被高度积。新植被高度积的确定系数(R-2)和RMSE分别为0.89和4.73 m。该产品的精度明显优于Lefsky(2010)和Simard等人生产的两个现有的全球林高产品。 (2011),与我们研究区域的227个现场测量数据进行比较。新的植被高度数据显示出森林,灌木和草地之间的明显区别,这有望改善中国的植被分类和地上森林生物量评估。 (C)2017国际摄影测量与遥感学会(ISPRS)。由Elsevier B.V.发布。保留所有权利。

著录项

  • 来源
  • 作者单位

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;

    Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA;

    Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA;

    Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China;

    Tsinghua Univ, Dept Earth Syst Sci, Beijing 100084, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ICESat/GLAS; Vegetation height; Vegetation classification;

    机译:ICESat / GLAS;植被高度;植被分类;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号