Estimation of forest net primary productivity based on sentinel active and passive remote sensing data and canopy height

doi:10.12302/j.issn.1000-2006.202205014

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (4): 132-140.doi: 10.12302/j.issn.1000-2006.202205014

Special Issue: 专题报道Ⅲ：智慧林业之森林可视化研究

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Estimation of forest net primary productivity based on sentinel active and passive remote sensing data and canopy height

TIAN Chunhong(), LI Mingyang^*(), LI Tao, LI Dengpan, TIAN Lei

College of Forestry and Grassland, Nanjing Forestry University, Nanjing 210037, China

Received:2022-05-11 Revised:2022-08-07 Online:2024-07-30 Published:2024-08-05
Contact: LI Mingyang E-mail:1786767729@qq.com;lmy196727@126.com

Abstract

Abstract:

【Objective】Using open source active and passive remote sensing data (Sentinel-1 & -2) combined with forest canopy height can improve the estimation accuracy of forest net primary productivity (NPP), to provide a scientific basis for the formulation of forest precision management stragtegies and a “carbon peaking and carbon neutrality” strategy.【Method】Zixing City, which is a key forest area in the south, was used as the research area. Based on Sentinel-1 and Sentinel-2 active and passive remote sensing data, four models, namely multiple stepwise regression, artificial neural network, K-nearest neighbor, and random forest, were used to estimate NPP. On this basis, the canopy height obtained by Sentinel-1 through the difference between InSAR and SRTM DEM was added to analyze its effect on the accuracy of NPP estimation.【Result】(1) The mean value of forest NPP in the study area in 2019 was 7.79 t/hm², showing the spatial distribution characteristics of high in the central southwest and low in the northwest. (2) Among the four models, the accuracy of NPP estimation by active and passive remote sensing was higher than that by single remote sensing; the accuracy of random forest estimation of regional forest NPP was the highest, and the model performed the best. (3) Adding canopy height improved the estimation accuracy of forest NPP to a certain extent, with R² increased from 0.70 to 0.75.【Conclusion】The accuracy of NPP estimation can be improved based on Sentinel active and passive remote sensing data and canopy height factor, which is obtained by DEM difference method.

Key words: forest net primary productivity, sentinel data, ICESat-2, forest canopy height, Zixing City, smart forestry and forest precision management

CLC Number:

S757

TIAN Chunhong, LI Mingyang, LI Tao, LI Dengpan, TIAN Lei. Estimation of forest net primary productivity based on sentinel active and passive remote sensing data and canopy height[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(4): 132-140.

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References 37

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数据集 dataset	说明 description	来源 source	空间分辨率/m×m spatial scale	日期 date
Sentinel-1	干涉宽幅下的地距多视产品 IW Level-1 GRD	欧洲航民局(欧空局)ESA (https://scihub.copernicus.eu/)	10×10	2019.08.05
	干涉宽幅下的地距单视产品 IW Level-1 SLC		2.3×14.1	2019.07.24、 2019.08.05
Sentinel-2	MAI Level-1C产品	欧空局ESA (https://scihub.copernicus.eu/)	10×10	2019.09.20
航天飞机雷达地形测绘任务SRTM	V4.1 DEM产品	美国地质调查局USGS (https://glovis.usgs.gov/)	30×30	2000.02.11—2000.02.22
冰、云和陆地海拔卫星数据ICESat-2	第5版全球定位光子数据ATL03	美国国家冰雪数据中心NSIDC (https://nsidc.org/data/ICESat-2/)	17(足印尺寸)	2018.10.13—2019.12.31
	第5版土地和植被高度数据ATL08			2018.10.13—2019.12.31
冠层高度产品 global forest canopy height	NASIA数据范围	全球陆地分析与发现实验室GLAD (https://glad.umd.edu/datasat/gedi/)	30×30	2019

森林类型 forest type	样地数 number of plots	森林净初级生产力/ (t·hm^-2·a^-1) NPP
森林类型 forest type	样地数 number of plots	最小值 min	均值 mean	最大值 max	标准差 SD
针叶林 coniferous forest	18	1.05	9.60	18.42	4.94
阔叶林 broad-leaved forest	25	4.84	15.50	36.87	8.89
针阔混交林 mixed coniferous and broad-leaved forest	6	7.14	11.93	15.47	2.70
竹林bamboo forest	6	7.51	15.62	28.69	8.29
灌木林shrubs forest	16	0.59	7.42	10.89	4.12
总计total	71	0.59	11.98	36.87	7.16

数据源 data source	模型 model	特征变量 characteristic variable
主动遥感 Sentinel-1	SWR	β、VH_DIS、VV_COR
	ANN、KNN、RF	α、VV_COR、β、VV_CON、VH_VAR、VH_CON、DEM、VV_HOM、VV_DIS、VH_MEA、VH_COR、VH_DIS
被动遥感 Sentinel-2	SWR	B8_VAR、B7_VAR、B5_MEA、B6_ASM、B1_COR、Cv、B5_COR、TSAVI、B9_ENT、B5_CON
	ANN、KNN、RF	B3、B12_MEA、B11_MEA、α、B2、MTCI、B8a_MEA、B3_MEA、B11_ENT、B4_MEA、ARVI、B1_ENT
主被动遥感结合 Sentinel-1 & 2	SWR	B8_VAR、B7_VAR、VH_DIS、B5_MEA、B6_ASM、TSAVI、VH
	ANN、KNN、RF	B12_MEA、VV_DIS、(VH-VV)/(VH+VV)、WDVI、B11_MEA、B5_MEA、B3、B3_MEA、VV_COR、VV_HOM、DVI、B2

数据源 data source	多元逐步回归(SWR) multiple stepwise regression		人工神经网络(ANN) artificial neural network		K最近邻算法(KNN) K nearest neighbors		随机森林(RF) random forest
数据源 data source	R²	RMSE/ (t·hm^-2·a^-1)	R²	RMSE/ (t·hm^-2·a^-1)	R²	RMSE/ (t·hm^-2·a^-1)	R²	RMSE/ (t·hm^-2·a^-1)
Sentinel-1	0.26	8.44	0.50	9.36	0.57	5.28	0.66	5.26
Sentinel-2	0.40	10.06	0.52	8.16	0.60	4.64	0.67	5.08
Sentinel-1 & 2	0.58	8.26	0.55	11.29	0.64	4.82	0.70	5.11

数据源 data source	没有冠层高度 without forest canopy height		加入冠层高度 with forest canopy height
数据源 data source	R²	RMSE/ (t·hm^-2·a^-1)	R²	RMSE/ (t·hm^-2·a^-1)
Sentinel-1	0.66	5.26	0.68	5.16
Sentinel-2	0.67	5.08	0.71	4.95
Sentinel-1 & 2	0.70	5.11	0.75	4.78

Estimation of forest net primary productivity based on sentinel active and passive remote sensing data and canopy height

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