植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1305-1316.DOI: 10.17521/cjpe.2022.0158

所属专题: 全球变化与生态系统 生态遥感及应用

• 研究论文 • 上一篇    

卫星遥感监测产品在中国森林生态系统的验证和不确定性分析——基于海量无人机激光雷达数据

刘兵兵1,2, 魏建新1,2,3,*(), 胡天宇4,5, 杨秋丽4,5, 刘小强4,5, 吴发云6, 苏艳军4,5, 郭庆华7   

  1. 1新疆大学地理与遥感科学学院, 乌鲁木齐 830017
    2新疆激光雷达应用工程技术研究中心, 乌鲁木齐 830002
    3新疆维吾尔自治区自然资源信息中心, 乌鲁木齐 830002
    4中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093
    5中国科学院大学, 北京 100049
    6国家林业和草原局调查规划设计院, 北京 100714
    7北京大学遥感与地理信息系统研究所, 北京大学城市与环境学院, 北京大学生态研究中心, 北京 100871
  • 收稿日期:2022-04-24 接受日期:2022-09-05 出版日期:2022-10-20 发布日期:2022-09-28
  • 通讯作者: 魏建新
  • 作者简介:*(wjxlr@126.com)
  • 基金资助:
    中国科学院战略性科技先导专项(A类)(XDA23080301);国家自然科学基金(31971575);国家林业和草原局2020年行业管理专项业务经费(2020-21-89);国家林草局自主研发计划项目(LC-1-01)

Validation and uncertainty analysis of satellite remote sensing products for monitoring China’s forest ecosystems—Based on massive UAV LiDAR data

LIU Bing-Bing1,2, WEI Jian-Xin1,2,3,*(), HU Tian-Yu4,5, YANG Qiu-Li4,5, LIU Xiao-Qiang4,5, WU Fa-Yun6, SU Yan-Jun4,5, GUO Qing-Hua7   

  1. 1College of Geography and Remote Sensing Sciences, Xinjiang University, Ürümqi 830017, China
    2Xinjiang Laser Radar Application Engineering Technology Research Center, Ürümqi 830002, China
    3Xinjiang Uygur Autonomous Regions Natural Resources Information Center, Ürümqi 830002, China
    4State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    5University of Chinese Academy of Sciences, Beijing 100049, China
    6Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
    7Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
  • Received:2022-04-24 Accepted:2022-09-05 Online:2022-10-20 Published:2022-09-28
  • Contact: WEI Jian-Xin
  • Supported by:
    Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080301);National Natural Science Foundation of China(31971575);2020 Industry Management Special Business Fund of National Forestry and Grassland Administration(2020-21-89);Independent Research and Development Plan Project of National Forestry and Grassland Administration(LC-1-01)

摘要:

准确获取森林结构参数对森林生态系统研究及其保护有着重要意义。卫星遥感数据作为获取大尺度森林结构参数的重要数据源, 已被制作成各种植被监测产品并被应用于森林质量状况变化评估、森林生物量估算以及森林干扰和生物多样性监测等研究。然而, 这些卫星遥感植被监测产品针对中国复杂多样的森林区域缺乏有效验证, 在不同林况和地形条件下的不确定性也不明确。激光雷达具备高精度三维信息采集的优势, 在国内外已被广泛用于森林生态系统监测和卫星遥感产品验证。为此, 该研究利用在中国114个样地收集的153 km2的无人机激光雷达数据, 构建了我国森林结构参数验证数据集, 并以此为基础对3套全球遥感监测产品(全球叶面积指数(GLASS LAI)、全球冠层覆盖度(GLCF TCC)、全球冠层高度(GFCH))进行了像元尺度的验证, 并分析了其在不同坡度、覆盖度和林型条件下的不确定性。研究结果表明: 与无人机激光雷达获取的叶面积指数、覆盖度以及冠层高度相比, GLASS LAI、GLCF TCC、GFCH在中国森林区域均存在一定的不确定性, 且受林况和地形因素影响的程度不一致。对GLASS LAI和GLCF TCC影响的最大因素分别为林型和覆盖度; 而GFCH则更易受地形坡度和覆盖度的影响。

关键词: 精度验证, 无人机激光雷达, 叶面积指数, 冠层覆盖度, 冠层高度

Abstract:

Aims Accurately obtaining forest structural attributes is important for forest ecosystem research and protection. As a key data source, satellite remote sensing data are used to derive various regional and global products of forest structure and conditions, which are widely used in forest condition evaluation, forest biomass estimation, and forest disturbance and biodiversity monitoring. However, these products derived from satellite remote sensing data lack verification for Chinaʼs forested areas, and their accuracy and uncertainty under different forest structure and terrain conditions is not clear. Light detection and ranging (LiDAR) has the advantage of acquiring high-precision three-dimensional information. It has been widely used in monitoring forest ecosystems and validating various datasets of forest structure derived from remote sensing data. This study focused on evaluating the accuracy of Global Land Surface Satellite Products System-Leaf Area Index (GLASS LAI), Global Land Cover Facility-Tree Canopy Cover (GLCF TCC), and Global Forest Canopy Height (GFCH) products in China based on massive unmanned aerial vehicle (UAV) LiDAR data.

Methods We collected nationwide LiDAR point cloud data at 114 sites in China’s forested areas to build the benchmark validation dataset including canopy cover, canopy height and LAI. The corresponding pixel values of the above three products were extracted using the geolocation from UAV LiDAR data. The coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the accuracy and uncertainty of the three products. The uncertainty under different forest types, canopy cover and terrain conditions were also analyzed.

Important findings The results indicate that compared to the LAI, canopy cover and canopy height derived from UAV LiDAR data, GLASS LAI (R2 = 0.29, RMSE= 2.1 m2·m-2), GLCF TCC (R2= 0.47, RMSE= 31%), GFCH (R2= 0.37, RMSE = 5 m) all exhibit large uncertainties and suffer from saturation problems in China’s forested areas, and their accuracy varies significantly across forest types, canopy cover and terrain conditions. In general, the GLASS LAI and GLCF TCC are mainly influenced by forest types and canopy cover, respectively. In contrast, both slope and canopy cover have large influences on the accuracy of GFCH.

Key words: validation, UAV lidar, leaf area index, canopy cover, canopy height