基于Sentinel 1 & 2主被动遥感数据和冠层高度的森林净初级生产力估测

doi:10.12302/j.issn.1000-2006.202205014

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (4): 132-140.doi: 10.12302/j.issn.1000-2006.202205014

所属专题：专题报道Ⅲ：智慧林业之森林可视化研究

• 专题报道Ⅲ：智慧林业之森林可视化研究（执行主编李凤日、张怀清、曹林） • 上一篇下一篇

基于Sentinel 1 & 2主被动遥感数据和冠层高度的森林净初级生产力估测

田春红(), 李明阳^*(), 李陶, 李登攀, 田雷

南京林业大学林草学院,江苏南京 210037

收稿日期:2022-05-11 修回日期:2022-08-07 出版日期:2024-07-30 发布日期:2024-08-05
通讯作者: *李明阳(lmy196727@126.com),教授。
作者简介:
田春红(1786767729@qq.com)。
基金资助:
国家自然科学基金项目(31770679)

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

1. 基于Sentinel 1 & 2主被动遥感数据和冠层高度的森林净初级生产力估测.zip(7626KB)

摘要/Abstract

摘要：

【目的】利用Sentinel-1 & 2主被动遥感数据结合森林冠层高度,对区域森林净初级生产力(NPP)进行高效、准确的估测,为森林精准经营措施以及“双碳”目标的制定提供科学依据。【方法】以南方重点林区资兴市为研究区,基于Sentinel-1和Sentinel-2主被动遥感数据,采用了多元逐步回归、人工神经网络、K最邻近、随机森林4种模型估算NPP。在此基础上加入了Sentinel-1通过InSAR与SRTM DEM差分得到的冠层高度,分析其对NPP估测精度的影响。【结果】①研究区2019年的森林NPP均值为7.79 t/hm²,呈中南部高、西北低的空间分布特征。②4种模型中,主被动遥感结合估测NPP的精度均高于单源遥感方式;随机森林估测区域森林NPP的精度最高,模型表现最好。③加入冠层高度可一定程度提高森林NPP估测精度,R²从0.70提高到了0.75。【结论】基于Sentinel 1 & 2主被动遥感数据并结合DEM差分法获取的冠层高度因子,可有效提高NPP估测精度。

关键词: 森林净初级生产力, Sentinel-1, Sentinel-2, ICESat-2, 森林冠层高度, 资兴市, 智慧林业, 森林精准经营

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

中图分类号:

S757

田春红,李明阳,李陶,等. 基于Sentinel 1 & 2主被动遥感数据和冠层高度的森林净初级生产力估测[J]. 南京林业大学学报（自然科学版）, 2024, 48(4): 132-140.

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 (Natural Science Edition), 2024, 48(4): 132-140.DOI: 10.12302/j.issn.1000-2006.202205014.

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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

基于Sentinel 1 & 2主被动遥感数据和冠层高度的森林净初级生产力估测

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

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