...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Sustainability >Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand
【24h】

Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand

机译：Landsat TM-5和Landsat OLI-8影像的地形校正，以提高泰国东北山区的森林分类性能

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The accurate mapping and monitoring of forests is essential for the sustainable management of forest ecosystems. Advancements in the Landsat satellite series have been very useful for various forest mapping applications. However, the topographic shadows of irregular mountains are major obstacles to accurate forest classification. In this paper, we test five topographic correction methods: improved cosine correction, Minnaert, C-correction, Statistical Empirical Correction (SEC) and Variable Empirical Coefficient Algorithm (VECA), with multisource digital elevation models (DEM) to reduce the topographic relief effect in mountainous terrain produced by the Landsat Thematic Mapper (TM)-5 and Operational Land Imager (OLI)-8 sensors. The effectiveness of the topographic correction methods are assessed by visual interpretation and the reduction in standard deviation (SD), by means of the coefficient of variation (CV). Results show that the SEC performs best with the Shuttle Radar Topographic Mission (SRTM) 30 m × 30 m DEM. The random forest (RF) classifier is used for forest classification, and the overall accuracy of forest classification is evaluated to compare the performances of the topographic corrections. Our results show that the C-correction, SEC and VECA corrected imagery were able to improve the forest classification accuracy of Landsat TM-5 from 78.41% to 81.50%, 82.38%, and 81.50%, respectively, and OLI-8 from 81.06% to 81.50%, 82.38%, and 81.94%, respectively. The highest accuracy of forest type classification is obtained with the newly available high-resolution SRTM DEM and SEC method.
机译：准确地绘制森林图和进行监测对于森林生态系统的可持续管理至关重要。 Landsat卫星系列的进步对于各种森林测绘应用非常有用。但是，不规则山脉的地形阴影是准确进行森林分类的​​主要障碍。在本文中，我们测试了五种地形校正方法：改进的余弦校正，Minnaert，C校正，统计经验校正（SEC）和可变经验系数算法（VECA），以及多源数字高程模型（DEM）以减少地形起伏效应由Landsat Thematic Mapper（TM）-5和Operational Land Imager（OLI）-8传感器产生的山区地形。通过视觉解释和标准偏差（SD）的减少（通过变异系数（CV））来评估地形校正方法的有效性。结果表明，SEC在30 m×30 m DEM的航天飞机雷达地形任务（SRTM）下表现最佳。随机森林（RF）分类器用于森林分类，并评估森林分类的​​整体准确性以比较地形校正的性能。我们的结果表明，C校正，SEC和VECA校正的图像能够将Landsat TM-5的森林分类准确度分别从78.41％提高到81.50％，82.38％和81.50％，而OLI-8的森林分类准确度则从81.06％分别达到81.50％，82.38％和81.94％。使用最新可用的高分辨率SRTM DEM和SEC方法可获得最高的森林类型分类精度。

著录项

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