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

国家林草科技领军期刊
中国精品科技期刊
中国高校百佳科技期刊
江苏省新闻出版政府奖期刊奖
RCCSE林学权威期刊（A+）
CSCD核心期刊
Scopus数据库收录期刊
中文核心期刊
SCD核心期刊

作者加群：102861116

微信公众号：南京林业大学学报

高级检索

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

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.

图/表 6

表1

表2

表3

表4

表5

图1

参考文献 37

[1]	陈利军, 刘高焕, 励惠国. 中国植被净第一性生产力遥感动态监测[J]. 遥感学报, 2002, 6(2):129-135.
	CHEN L J, LIU G H, LI H G. Estimating net primary productivity of’ terrestrial vegetation in China using remote sensing[J]. J Remote Sens, 2002, 6(2):129-135, 164.
[2]	刘国华, 傅伯杰, 方精云. 中国森林碳动态及其对全球碳平衡的贡献[J]. 生态学报, 2000, 20(5):733-740.
	LIU G H, FU B J, FANG J Y. Carbon dynamics of Chinese forests and its contribution to global carbon balance[J]. Acta Ecol Sin, 2000, 20(5):733-740. DOI: 10.3321/j.issn:1000-0933.2000.05.004.
[3]	朱文泉, 潘耀忠, 张锦水. 中国陆地植被净初级生产力遥感估算[J]. 植物生态学报, 2007, 31(3):413-424.
	ZHU W Q, PAN Y Z, ZHANG J S. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing[J]. J Plant Ecol, 2007, 31(3):413-424. DOI: 10.17521/cjpe.2007.0050.
[4]	郭睿妍, 田佳, 杨志玲, 等. 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征[J]. 生态学报, 2022, 42(13):5437-5445.
	GUO R Y, TIAN J, YANG Z L, et al. Spatio-temporal variation characteristics of forest net primary productivity in the Yellow River basin based on Google Earth Engine cloud platform[J]. Acta Ecol Sin, 2022, 42(13):5437-5445. DOI: 10.5846/stxb202104271113.
[5]	RAFIQUE R, ZHAO F, DE JONG R, et al. Global and regional variability and change in terrestrial ecosystems net primary production and NDVI: a model-data comparison[J]. Remote Sens, 2016, 8(3):177. DOI: 10.3390/rs8030177.
[6]	SANNIGRAHI S. Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere region,India using seven ecosystem models[J]. Ecol Model, 2017, 356:73-90. DOI: 10.1016/j.ecolmodel.2017.03.003.
[7]	林文鹏, 王臣立, 赵敏, 等. 基于森林清查和遥感的城市森林净初级生产力估算[J]. 生态环境, 2008, 17(2):766-770.
	LIN W P, WANG C L, ZHAO M, et al. Estimation urban forests NPP based on forest inventory data and remote sensing[J]. Ecol Environ, 2008, 17(2):766-770. DOI: 10.16258/j.cnki.1674-5906.2008.02.076.
[8]	TAN K, ZHOU S Y, LI E Z, et al. Assessing the impact of urbanization on net primary productivity using multi-scale remote sensing data: a case study of Xuzhou, China[J]. Front Earth Sci. 2015, 9(2):319-329. DOI: 10.1007/s11707-014-0454-7.
[9]	REICH P B, TURNER D P, BOLSTAD P. An approach to spatially distributed modeling of net primary production (NPP) at the landscape scale and its application in validation of EOS NPP products[J]. Remote Sens Environ, 1999, 70(1):69-81. DOI: 10.1016/S0034-4257(99)00058-9.
[10]	SHANG E P, XU E Q, ZHANG H Q, et al. Analysis of spatiotemporal dynamics of the Chinese vegetation net primary productivity from the 1960s to the 2000s[J]. Remote Sens, 2018, 10(6):860. DOI: 10.3390/rs10060860.
[11]	李陶, 李明阳, 钱春花. 结合冠层密度的森林净初级生产力遥感估测[J]. 南京林业大学学报(自然科学版), 2021, 45(5):153-160.
	LI T, LI M Y, QIAN C H. Combining crown density to estimate forest net primary productivity by using remote sensing data[J]. J Nanjing For Univ (Nat Sci Ed), 2021, 45(5):153-160. DOI: 10.12302/j.issn.1000-2006.202008007.
[12]	MA H, SONG J L, WANG J D, et al. Comparison of the inversion ability in extrapolating forest canopy height by integration of LiDAR data and different optical remote sensing products[C]// 2012 IEEE International Geoscience and Remote Sensing Symposium.Munich, Germany: IEEE, 2012:3363-3366. DOI: 10.1109/IGARSS.2012.6350700.
[13]	李登秋. 亚热带森林碳收支变化特征及其成因模拟分析[D]. 南京: 南京大学, 2014.
	LI D Q. Variations of forest carbon budget and underlying driving forces in the subtropical area of China: a case study for Jiangxi Province[D]. Nanjing: Nanjing University, 2014.
[14]	史瑶. 基于MSPA和MCR模型的资兴市生态网络构建研究[D]. 长沙: 中南林业科技大学, 2019.
	SHI Y. Ecological network construction and research using MSPA and MCR models in Zixing City, Hunan Province, China[D]. Changsha: Central South University of Forestry & Technology, 2019.
[15]	李轲敏, 傅丽华, 郭湘. 资兴市自然保护地空间冲突与重构研究[J]. 湖南工业大学学报, 2022, 36(2):86-94.
	LI K M, FU L H, GUO X. Research on spatial conflict and reconstruction of protected natural areas in Zixing City[J]. J Hunan Univ Technol, 2022, 36(2):86-94. DOI: 10.3969/j.issn.1673-9833.2022.02.012.
[16]	余超, 王斌, 刘华, 等. 中国森林植被净生产量及平均生产力动态变化分析[J]. 林业科学研究, 2014, 27(4):542-550.
	YU C, WANG B, LIU H, et al. Dynamic change of net production and mean net primary productivity of China’s forests[J]. For Res, 2014, 27(4):542-550. DOI: 10.13275/j.cnki.lykxyj.2014.04.016.
[17]	温小荣, 蒋丽秀, 刘磊, 等. 江苏省森林生物量与生产力估算及空间分布格局分析[J]. 西北林学院学报, 2014, 29(1):36-40.
	WEN X R, JIANG L X, LIU L, et al. Estimation of forest biomass, net primary production and analysis on spatial distribution pattern for Jiangsu Province[J]. J Northwest For Univ, 2014, 29(1):36-40. DOI: 10.3969/j.issn.1001-7461.2014.01.07.
[18]	董佳臣, 倪文俭, 张志玉, 等. ICESat-2植被冠层高度和地表高程数据产品用于森林高度提取的效果评价[J]. 遥感学报, 2021, 25(6):1294-1307.
	DONG J C, NI W J, ZHANG Z Y, et al. Performance of ICESat-2 ATL08 product on the estimation of forest height by referencing to small footprint LiDAR data[J]. Natl Remote Sens Bull, 2021, 25(6):1294-1307. DOI: 10.11834/jrs.202194491.
[19]	孙美美. 黄土丘陵区三种典型森林类型生产力形成及影响因素[D]. 杨凌: 西北农林科技大学, 2021.
	SUN M M. Net primary productivity and its influencing factors in three typical forest types in the loess hilly region[D]. Yangling: Northwest A&F University, 2021.
[20]	黄昕. 高分辨率遥感影像多尺度纹理、形状特征提取与面向对象分类研究[D]. 武汉: 武汉大学, 2009.
	HUANG X. Multiscale texture and shape feature extraction and object-oriented classification for very high resolution remotely sensed imagery[D]. Wuhan: Wuhan University, 2009.
[21]	刘俊, 毕华兴, 朱沛林, 等. 基于ALOS遥感数据纹理及纹理指数的柞树蓄积量估测[J]. 农业机械学报, 2014, 45(7):245-254.
	LIU J, BI H X, ZHU P L, et al. Estimating stand volume of Xylosma racemosum forest based on texture parameters and derivative texture indices of ALOS imagery[J]. Trans Chin Soc Agric Mach, 2014, 45(7):245-254. DOI: 10.6041/j.issn.1000-1298.2014.07.038
[22]	陈琳. 基于光学和干涉雷达的森林地上生物量遥感估算模型研究[D]. 长春: 中国科学院大学(中国科学院东北地理与农业生态研究所), 2020.
	CHEN L. Modeling of forest aboveground biomass based on optical and interferometric synthetic aperture radar[D]. Changchun: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 2020.
[23]	KENYI L W, DUBAYAH R, HOFTON M, et al. Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics[J]. Int J Remote Sens, 2009, 30(11):2797-2811. DOI: 10.1080/01431160802555853.
[24]	NI W J, GUO Z F, SUN G Q, et al. Investigation of forest height Retrieval using SRTM-DEM and ASTER-GDEM[C]// 30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing-Global Vision for Local Action: 20102111- 2114. DOI: 10.1109/IGARSS.2010.5651443.
[25]	BALLHORN U, JUBANSKI J, SIEGERT F. ICESat/GLAS data as a measurement tool for peatland topography and peat swamp forest biomass in Kalimantan, Indonesia[J]. Remote Sens, 2011, 3(9):1957-1982. DOI: 10.3390/rs3091957.
[26]	范文义, 张海玉, 于颖, 等. 三种森林生物量估测模型的比较分析[J]. 植物生态学报, 2011, 35(4):402-410.
	FAN W Y, ZHANG H Y, YU Y, et al. Comparison of three models of forest biomass estimation[J]. Chin J Plant Ecol, 2011, 35(4):402-410. DOI: 10.3724/SP.J.1258.2011.00402.
[27]	陈尔学, 李增元, 武红敢, 等. 基于k-NN和Landsat数据的小面积统计单元森林蓄积估测方法[J]. 林业科学研究, 2008, 21(6):745-750.
	CHEN E X, LI Z Y, WU H G, et al. Forest volume estimation method for small areas based on k-NN and Landsat data[J]. For Res, 2008, 21(6):745-750. DOI: 10.3321/j.issn:1001-1498.2008.06.002.
[28]	LI T, LI M Y, REN F, et al. Estimation and spatio-temporal change analysis of NPP in subtropical forests: a case study of Shaoguan, Guangdong, China[J]. Remote Sens, 2022, 14(11):2541.DOI: 10.3390/rs14112541.
[29]	李欣海. 随机森林模型在分类与回归分析中的应用[J]. 应用昆虫学报, 2013, 50(4):1190-1197.
	LI X H. Using “random forest” for classification and regression[J]. Chin J Appl Entomol, 2013, 50(4):1190-1197.DOI: 10.7679/j.issn.2095-1353.2013.163.
[30]	BOLÓN-CANEDO V, SÁNCHEZ-MAROÑO N, ALONSO-BETANZOS A. Feature selection for high-dimensional data[J]. Prog Artif Intell, 2016, 5(2):65-75.DOI: 10.1007/s13748-015-0080-y.
[31]	潘磊, 孙玉军, 王轶夫, 等. 基于Sentinel-1和Sentinel-2数据的杉木林地上生物量估算[J]. 南京林业大学学报(自然科学版), 2020, 44(3):149-156.
	PAN L, SUN Y J, WANG Y F, et al. Estimation of aboveground biomass in a Chinese fir(Cunninghamia lanceolata)forest combining data of Sentinel-1 and Sentinel-2[J]. J Nanjing For Univ (Nat Sci Ed), 2020, 44(3):149-156.DOI: 10.3969/j.issn.1000-2006.201811012.
[32]	SERVIA H, PAREETH S, MICHAILOVSKY C I, et al. Operational framework to predict field level crop biomass using remote sensing and data driven models[J]. Int J Appl Earth Obs Geoinf, 2022, 108:102725.DOI: 10.1016/j.jag.2022.102725.
[33]	高志强, 刘纪远. 中国植被净生产力的比较研究[J]. 科学通报, 2008, 53(3):317-326.
	GAO Z Q, LIU J Y. Comparative study on net productivity of vegetation in China[J]. Chin Sci Bull, 2008, 53(3):317-326. DOI: 10.1360/csb2008-53-3-317.
[34]	陈晓玲, 曾永年. 亚热带山地丘陵区植被NPP时空变化及其与气候因子的关系:以湖南省为例[J]. 地理学报, 2016, 71(1):35-48.
	CHEN X L, ZENG Y N. Spatial and temporal variability of the net primary production (NPP) and its relationship with climate factors in subtropical mountainous and hilly regions of China: a case study in Hunan Province[J]. Acta Geogr Sin, 2016, 71(1):35-48.DOI: 10.11821/dlxb201601003.
[35]	闫妍, 覃金华, 房磊, 等. 湖南省植被净初级生产力时空动态及其与气候因素的关系[J]. 生态学杂志, 2022, 41(8):1535-1544.
	YAN Y, QIN J H, FANG L, et al. Spatiotemporal dynamics of vegetation net primary productivity and its relationships with climatic factors in Hunan Province[J]. Chin J Ecol, 2022, 41(8):1535-1544. DOI: 10.13292/j.1000-4890.202208.015.
[36]	江源通. 湘江流域植被NPP时空动态及影响因素分析[D]. 湘潭: 湖南科技大学, 2015.
	JIANG Y T. Analysis of spatio-temporal dynamics and factors influencing vegetation NPP in Xiangjiang River basin[D]. Xiangtan: Hunan University of Science and Technology, 2015.
[37]	张骏. 中国中亚热带东部森林生态系统生产力和碳储量研究[D]. 杭州: 浙江大学, 2008.
	ZHANG J. NPP and carbon storage in subtropical forest,eastern China[D]. Hangzhou: Zhejiang University, 2008.

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

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 37

相关文章 5

编辑推荐

Metrics

本文评价

作者

读者

审稿

[1]	韩森, 阮仁宗, 傅巧妮, 许捍卫, 衡雪彪. 基于Sentinel-1和Sentinel-2影像的洪泽湖国家湿地公园水生植被信息提取[J]. 南京林业大学学报（自然科学版）, 2024, 48(2): 19-26.
[2]	王艳芳, 谭露, 郭红丽, 吴芳, 齐斐, 蒙雯婷, 徐雁南. 基于多源遥感数据的溧阳市林地植被覆盖度时空差异研究[J]. 南京林业大学学报（自然科学版）, 2023, 47(6): 183-191.
[3]	曹林, 周凯, 申鑫, 杨晓明, 曹福亮, 汪贵斌. 智慧林业发展现状与展望[J]. 南京林业大学学报（自然科学版）, 2022, 46(6): 83-95.
[4]	李陶, 李明阳, 钱春花. 结合冠层密度的森林净初级生产力遥感估测[J]. 南京林业大学学报（自然科学版）, 2021, 45(5): 153-160.
[5]	赵颖慧, 郭新龙, 甄贞. 基于光学-ALS变量组合和非参数模型的天然次生林地上生物量估算[J]. 南京林业大学学报（自然科学版）, 2021, 45(4): 49-57.