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

doi:10.17521/cjpe.2022.0158

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

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)

1. 附录中国森林结构参数验证数据集使用的无人机激光雷达数据基础描述信息.pdf(430KB)

摘要/Abstract

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

刘兵兵, 魏建新, 胡天宇, 杨秋丽, 刘小强, 吴发云, 苏艳军, 郭庆华. 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. 植物生态学报, 2022, 46(10): 1305-1316. DOI: 10.17521/cjpe.2022.0158

LIU Bing-Bing, WEI Jian-Xin, HU Tian-Yu, YANG Qiu-Li, LIU Xiao-Qiang, WU Fa-Yun, SU Yan-Jun, GUO Qing-Hua. Validation and uncertainty analysis of satellite remote sensing products for monitoring China’s forest ecosystems—Based on massive UAV LiDAR data. Chinese Journal of Plant Ecology, 2022, 46(10): 1305-1316. DOI: 10.17521/cjpe.2022.0158

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0158

https://www.plant-ecology.com/CN/Y2022/V46/I10/1305

图/表 7

图1 无人机激光雷达数据采集样地分布以及按高程渲染的三维点云示例图。A, 吉林省塔子沟林场。B, 山东省天麻林场。C, 海南省吊罗山国家森林公园。

Fig. 1 Spatial distribution of unmanned aerial vehicle (UAV) LiDAR data used in this study, and examples of 3D point cloud rendered by elevation. A, Tazigou Forest Farm, Jilin Province. B, Tianma Forest Farm, Shandong Province. C, Diaoluo Mountain National Forest Park, Hainan Province.

表1 无人机激光雷达系统相关参数

Table 1 Parameters of unmanned aerial vehicle (UAV) LiDAR system

无人机激光雷达系统 UAV LiDAR system	激光传感器 Laser sensor	最大扫描频率 Maximum scanning frequency (kHz)	测量精度 Measurement accuracy (mm)	视场角 Field of view	回波次数 Number of returns
LiAir 220	HESAI Pandar40	720	±20	360°	2次 Dual returns
LiAir Pro	Riegl VUX-1	550	±10	360°	多次 Multi-returns

表2 卫星遥感监测产品基本信息

Table 2 Basic information of satellite remote sensing products

产品 Product	版本 Version	覆盖范围 Spatial coverage	覆盖时段 Time period	时空分辨率 Spatiotemporal resolution	坐标系统及投影基准 Coordinate system and projection datum	数据来源 Data source
GLASS LAI	V50	全球 Global	2017	8 d, 500 m	正弦投影 Sinusoidal projection	http://glass-product.bnu.edu.cn/
GLCF TCC	V4	全球 Global	2015	1 a, 30 m	UTM投影 UTM projection	https://lpdaac.usgs.gov/products/gfcc30tcv003/
GFCH	-	全球 Global	2019	1 a, 30 m	UTM投影 UTM projection	https://glad.umd.edu/dataset/gedi/

图2 无人机激光雷达获取的3个森林结构参数分布直方图。

Fig. 2 Histograms of three forest structural attributes derived from unmanned aerial vehicle (UAV) LiDAR data. LAI, leaf area index.

图3 不同不确定性水平下无人机激光雷达获取的冠层覆盖度与全球冠层覆盖度产品(GLCF TCC)的散点图。虚线为1:1线, 实线为拟合线; 散点图右侧的色带表示数据点的概率密度, 颜色越黄, 点密度越大。A, 不确定性0-5%。 B, 不确定性5%-10%。C, 不确定性10%-15%。D, 不确定性≥15%。Bias, 偏差; R2, 决定系数; RMSE, 均方根误差。

Fig. 3 Scatter plots of canopy cover estimated from unmanned aerial vehicle (UAV) LiDAR data and Global Land Cover Facility-Tree Canopy Cover (GLCF TCC) under different uncertainty levels provided by GLCL TCC. The dotted lines are 1:1 lines, the solid lines are fitted lines, and color bars represent the probability density of observations with dark blue for low density and yellow for high density. A, Uncertainty: 0-5%. B, Uncertainty: 5%-10%. C, Uncertainty: 10%-15%. D, Uncertainty: above 15%. R2, determinant coefficient; RMSE, root mean square error.

图4 3种卫星遥感监测产品精度验证结果散点图。虚线为1:1线, 实线为拟合线; 散点图右侧的色带表示数据点的概率密度, 颜色越黄, 点密度越大。GFCH, 全球冠层高度产品; GLASS LAI, 全球叶面积指数产品; GLCF TCC, 全球冠层覆盖度产品。

Fig. 4 Accuracy of three satellite remote sensing products against unmanned aerial vehicle (UAV) LiDAR observations. The dotted line is the 1:1 line, the solid line is the fitted line, and the color bar represents the probability density of observations. GFCH, Global Forest Canopy Height; GLASS LAI, Global Land Surface Satellite Products System-Leaf Area Index; GLCF TCC, Global Land Cover Facility-Tree Canopy Cover.

表3 不同因子对3种卫星遥感监测产品精度的影响分析

Table 3 Influences of different factors on the accuracy of three satellite remote sensing products

产品 Product		林型 Forest type			坡度 Slope (°)				冠层覆盖度 Canopy cover (%)
产品 Product		a	b	c	0-10	10-20	20-30	≥30	0-30	30-60	60-80	≥80
GLCF TCC	R²	0.64	0.29	0.60	0.39	0.54	0.48	0.52	0.10	0.03	0.02	0.05
	Bias (%)	18.13	19.96	21.37	22.81	21.72	19.54	14.49	-4.78	11.55	25.39	33.35
	RMSE (%)	28.40	34.16	29.45	31.70	31.17	31.63	28.69	17.02	25.72	32.57	36.87
	像元数量 N	15 584	37 167	51 969	39 301	23 792	23 441	18 186	21 231	16 446	23 837	43 206
GLASS LAI	R²	0.73	0.15	0.36	0.29	0.38	0.28	0.34	0.17	0.3	0.04	0.11
	Bias (m²·m^-2)	-1.46	-1.39	-1.86	-1.81	-1.98	-1.63	-0.72	-1.22	-1.68	-2.05	-1.45
	RMSE (m²·m^-2)	1.76	2.01	2.23	2.16	2.30	2.11	1.62	1.44	2.12	2.35	2.03
	像元数量 N	52	196	204	136	87	162	67	24	64	112	252
GFCH	R²	0.35	0.40	0.34	0.45	0.37	0.31	0.37	0.09	0.14	0.27	0.25
	Bias (m)	0.64	2.87	1.23	1.67	0.79	2.09	3.22	-1.73	0.20	1.36	2.72
	RMSE (m)	3.91	6.29	4.22	4.10	4.40	5.84	6.72	5.16	4.83	4.40	5.61
	像元数量 N	16 794	85 571	74 047	58 565	34 454	41 776	41 617	9 291	19 420	32 156	115 545

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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

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

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