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

doi:10.17521/cjpe.2022.0158

Abstract

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

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[J]. Chin J Plant Ecol, 2022, 46(10): 1305-1316.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0158

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

Figures/Tables 7

References 37

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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

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

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/

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/

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