三维辐射传输模型在森林生态系统研究中的应用与展望

doi:10.17521/cjpe.2022.0247

植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1200-1218.DOI: 10.17521/cjpe.2022.0247

所属专题：全球变化与生态系统；生态学研究的方法和技术；生态遥感及应用

三维辐射传输模型在森林生态系统研究中的应用与展望

王嘉童¹^,², 牛春跃¹, 胡天宇¹^,², 李文楷³, 刘玲莉¹^,², 郭庆华⁴, 苏艳军¹^,²^,^*()

¹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
²中国科学院大学, 北京 100049
³中山大学地理科学与规划学院, 广州 510275
⁴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:2022-06-14 接受日期:2022-08-17 出版日期:2022-10-20 发布日期:2022-09-28
通讯作者: ^*苏艳军 ORCID:0000-0001-7931-339X(ysu@ibcas.ac.cn)
基金资助:
国家自然科学基金(41871332);国家自然科学基金(41901358);中国科学院战略性先导科技专项(A类)(XDA23080301)

Three-dimensional radiative transfer modeling of forest: recent progress, applications, and future opportunities

WANG Jia-Tong¹^,², NIU Chun-Yue¹, HU Tian-Yu¹^,², LI Wen-Kai³, LIU Ling-Li¹^,², GUO Qing-Hua⁴, SU Yan-Jun¹^,²^,^*()

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
²University of Chinese Academy of Sciences, Beijing 100049, China
³School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
⁴Institution of Remote Sensing and Geographical Information System, College of Urban and Environmental Sciences, Institute of Ecology, Peking University, Beijing 100871, China

Received:2022-06-14 Accepted:2022-08-17 Online:2022-10-20 Published:2022-09-28
Contact: ^*SU Yan-Jun ORCID:0000-0001-7931-339X(ysu@ibcas.ac.cn)
Supported by:
National Natural Science Foundation of China(41871332);National Natural Science Foundation of China(41901358);Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080301)

摘要/Abstract

摘要：

太阳辐射是森林生态系统功能与服务得以维持并发展的基础, 对森林中的辐射传输过程进行建模对于理解森林生态系统过程具有重要意义。近年来, 三维辐射传输模型的迅速发展使得冠层辐射能量分布格局与动态的准确模拟成为可能。为更好地理解三维辐射传输模型以使其更有效地服务于森林生态系统研究, 该文从模型的原理、应用及发展趋势3个角度展开论述。首先简要介绍了辐射度方法、光线追踪法等森林三维辐射传输模型常用的原理及目前代表性的模型, 然后总结了三维辐射传输模型在森林生态系统研究中的应用, 最后对模型未来如何通过提高易用性、增加多模型耦合等方式更好地应用于森林生态系统研究进行了展望。随着森林生态系统大数据的积累与过程模型的不断完善, 三维辐射传输模型将在未来森林生态理论研究与实践中发挥更加重要的作用。

关键词: 激光雷达, 三维辐射传输模型, 森林生态系统过程, 森林结构

Abstract:

Solar radiation is fundamental to the maintenance and development of forest ecosystem functions and services. Therefore, modeling the radiation transfer process in forest is of great significance for understanding forest ecosystem processes. In recent years, the rapid development of three-dimensional radiative transfer models makes it possible to accurately simulate the distribution and dynamics of radiation within forest canopies. In order to better understand three-dimensional radiative transfer models and make them better serve forest ecosystem research, we review the principles, applications and future prospects of these models. Firstly, common principles of three-dimensional radiative transfer models such as radiosity and ray tracing are briefly introduced, and then the applications of three-dimensional radiative transfer models in forest ecosystem research are summarized. Finally, future opportunities of integrating multiple datasets and models to better facilitate forest ecosystem research, such as model coupling and making various models easier to use, are discussed. With the accumulation of ecological big data and improvement of ecosystem progress models, three-dimensional radiative transfer models will play a more important role in theoretical research and practices of forest ecology in the future.

Key words: light detection and ranging, three-dimensional radiative transfer model, forest ecosystem progress, forest structural attributes

王嘉童, 牛春跃, 胡天宇, 李文楷, 刘玲莉, 郭庆华, 苏艳军. 三维辐射传输模型在森林生态系统研究中的应用与展望. 植物生态学报, 2022, 46(10): 1200-1218. DOI: 10.17521/cjpe.2022.0247

WANG Jia-Tong, NIU Chun-Yue, HU Tian-Yu, LI Wen-Kai, LIU Ling-Li, GUO Qing-Hua, SU Yan-Jun. Three-dimensional radiative transfer modeling of forest: recent progress, applications, and future opportunities. Chinese Journal of Plant Ecology, 2022, 46(10): 1200-1218. DOI: 10.17521/cjpe.2022.0247

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图/表 6

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原理 Principle	模型 Model	基本单元 Component	参考文献 Reference
比尔定律 Beer’s law	MIXLIGHT	几何体 Geometric primitives	Stadt & Lieffers, 2000
	OLTREE	几何体 Geometric primitives	Mariscal et al., 2004
	SILVI-STAR	几何体 Geometric primitives	Koop & Sterck, 1994
	tRAYci	几何体 Geometric primitives	Brunner, 1998
	FOREST	几何体 Geometric primitives	Cescatti, 1997
光线追踪 Ray-tracing	FLIGHT	体素 Voxel	North, 1996
	FLiES	几何体 Geometric primitives	Kobayashi & Iwabuchi, 2008
	Librat	面元 Facet	Lewis, 1999
	Raytran	面元、几何体 Facet, geometric primitives	Govaerts & Verstraete, 1998
	Rayspread	面元、几何体 Facet, geometric primitives	Widlowski et al., 2006a
	DART	体素、面元 Voxel, facet	Gastellu-Etchegorry et al., 1996
	DIRSIG	体素、面元 Voxel, facet	Schott et al., 1999
	VBRT	体素 Voxel	Li et al., 2018
	LESS	面元 Facet	Qi, et al., 2019a
辐射度 Radiosity	DIANA	面元 Facet	Goel et al., 1991
	RGM, TRGM	面元 Facet	Qin & Gerstl, 2000
	RAPID	面元 Facet	Huang et al., 2013

原理 Principle	模型 Model	基本单元 Component	参考文献 Reference
比尔定律 Beer’s law	MIXLIGHT	几何体 Geometric primitives	Stadt & Lieffers, 2000
	OLTREE	几何体 Geometric primitives	Mariscal et al., 2004
	SILVI-STAR	几何体 Geometric primitives	Koop & Sterck, 1994
	tRAYci	几何体 Geometric primitives	Brunner, 1998
	FOREST	几何体 Geometric primitives	Cescatti, 1997
光线追踪 Ray-tracing	FLIGHT	体素 Voxel	North, 1996
	FLiES	几何体 Geometric primitives	Kobayashi & Iwabuchi, 2008
	Librat	面元 Facet	Lewis, 1999
	Raytran	面元、几何体 Facet, geometric primitives	Govaerts & Verstraete, 1998
	Rayspread	面元、几何体 Facet, geometric primitives	Widlowski et al., 2006a
	DART	体素、面元 Voxel, facet	Gastellu-Etchegorry et al., 1996
	DIRSIG	体素、面元 Voxel, facet	Schott et al., 1999
	VBRT	体素 Voxel	Li et al., 2018
	LESS	面元 Facet	Qi, et al., 2019a
辐射度 Radiosity	DIANA	面元 Facet	Goel et al., 1991
	RGM, TRGM	面元 Facet	Qin & Gerstl, 2000
	RAPID	面元 Facet	Huang et al., 2013

参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
结构参数 Structure parameter	叶面积指数 Leaf area index	光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 Deciduous broad- leaved forest (DBF)	DART	R² = 0.99 RMSE = 0.2	Kimes et al., 2002
		全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.68 RMSE = 1.23	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	混交林 Mixed forest	DART	R² = 0.6 RMSE = 0.47	Banskota et al., 2013
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.8 RMSE = 0.22	Miraglio et al., 2020
		多光谱影像 Multispectral image	美国、加拿大 USA, Canada	落叶阔叶林、常绿针叶林 DBF, evergreen coniferou forest (ECF)	LIBERTY, GeoSAIL	R² = 0.98 RMSE = 0.24	Fang & Liang, 2005
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	-	模拟场景 Simulated scenes	POVRAY	R² = 0.99 RMSE = 0.046 7	Morsdorf et al., 2009
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.74 MAE = 3.8	Bye et al., 2017
	森林覆盖度 Canopy cover	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.84 RMSE = 0.07	Koetz et al., 2007
		全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.5 MAE = 0.1	Bye et al., 2017
		光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 DBF	DART	R² = 0.99 RMSE = 0.011	Kimes et al., 2002
		多光谱影像 Multispectral image	中国 China	针叶林 Coniferous forest	RAPID	R² = 0.832 RMSE = 0.121	Jin et al., 2021
生化参数 Biochemical parameter	叶绿素含量 Chlorophyll content	高光谱影像 Hyperspectral image	加拿大 Canada	针叶林 Coniferous forest	PROSPECT, SPRINT	R² = 0.4 RMSE = 8.1	Zarco-Tejada et al., 2004
		全波形激光雷达数、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.86 RMSE = 5.06	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.73 RMSE = 5.21	Miraglio et al., 2020
	叶片含水量 Leaf water content	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.79 RMSE = 0.005 3	Koetz et al., 2007
		多光谱影像 Multispectral image	西班牙 Spain	常绿阔叶林、落叶阔叶林、常绿针叶林 Evergreen broad-leaved forest (EBF), DBF, ECF	PROSPECT, GeoSAIL	R² = 0.5	Jurdao et al., 2013
		多光谱影像 Multispectral image	瑞士 Swiss	针叶林 Coniferous forest	GeoSAIL	RMSE = 0.013	K?tz et al., 2004
能量参数 Energy parameter	光合有效辐射吸收比例 Fraction of absorbed photosynthetic active radiation	光谱反射率数据 Spectral reflectance data	-	模拟场景 Simulated scenes	PARCINOPY	RMSE = 0.008 3	Combal et al., 2003
		多光谱影像 Multispectral image	美国、丹麦、德国 USA, Denmark, Germany	常绿阔叶林、落叶阔叶林、常绿针叶林 EBF, DBF, ECF	4SAIL2	R² = 0.72 RMSE = 0.07	Li & Fang, 2015
		实测数据 In-situ data	圭亚那 Guinea	稀树草原 Savanna	RIRI-3D	R² = 0.99 RMSE = 0.057	Le Roux et al., 1997
参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
	冠层截获光合有效辐射 Fraction of intercepted photosynthetically active radiation	多光谱影像 Multispectral image	西班牙 Spain	橄榄园 Olea europaea grove	ORIM, FLIGHT	R² = 0.83 RMSE = 0.05	Guillen- Climent et al., 2012b
		多光谱影像 Multispectral image	西班牙 Spain	桃园、柑橘园 Prunus persica and Citrus reticulata grove	FLIGHT	R² = 0.88 RMSE = 0.09	Guillen- Climent et al., 2012a
生态功能参数 Ecological function parameter	日光诱导叶绿素荧光 Solar-induced chlorophyll fluorescence	实测数据 In-situ data	美国 USA	混交林 Mixed forest	FluorWPS	R²= 0.998 RMSE = 1.85	Tong et al., 2021
			美国 USA	虚拟场景、常绿针叶林 Simulated scenes, ECF	CliMA-RT	R² = 0.85 RMSE = 0.18	Braghiere et al., 2021
			西班牙 Spain	阔叶林-草地 Mixed broad-leaved forest and grass	FluorFLIGHT	R² = 0.83 RMSE = 0.03	Hornero et al., 2021

参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
结构参数 Structure parameter	叶面积指数 Leaf area index	光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 Deciduous broad- leaved forest (DBF)	DART	R² = 0.99 RMSE = 0.2	Kimes et al., 2002
		全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.68 RMSE = 1.23	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	混交林 Mixed forest	DART	R² = 0.6 RMSE = 0.47	Banskota et al., 2013
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.8 RMSE = 0.22	Miraglio et al., 2020
		多光谱影像 Multispectral image	美国、加拿大 USA, Canada	落叶阔叶林、常绿针叶林 DBF, evergreen coniferou forest (ECF)	LIBERTY, GeoSAIL	R² = 0.98 RMSE = 0.24	Fang & Liang, 2005
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	-	模拟场景 Simulated scenes	POVRAY	R² = 0.99 RMSE = 0.046 7	Morsdorf et al., 2009
	树高 Tree height	全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.74 MAE = 3.8	Bye et al., 2017
	森林覆盖度 Canopy cover	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.84 RMSE = 0.07	Koetz et al., 2007
		全波形激光雷达数据 Full waveform lidar data	英国、加拿大、瑞典 UK, Canada, Sweden	混交林、山杨、针叶林 Mixed forest, Populus tremuloides, coniferous forest	FLIGHT	R² = 0.5 MAE = 0.1	Bye et al., 2017
		光谱反射率数据 Spectral reflectance data	法国 France	落叶阔叶林 DBF	DART	R² = 0.99 RMSE = 0.011	Kimes et al., 2002
		多光谱影像 Multispectral image	中国 China	针叶林 Coniferous forest	RAPID	R² = 0.832 RMSE = 0.121	Jin et al., 2021
生化参数 Biochemical parameter	叶绿素含量 Chlorophyll content	高光谱影像 Hyperspectral image	加拿大 Canada	针叶林 Coniferous forest	PROSPECT, SPRINT	R² = 0.4 RMSE = 8.1	Zarco-Tejada et al., 2004
		全波形激光雷达数、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.86 RMSE = 5.06	Koetz et al., 2007
		高光谱影像 Hyperspectral image	美国 USA	稀树草原 Savanna	PROSPECT, DART	R² = 0.73 RMSE = 5.21	Miraglio et al., 2020
	叶片含水量 Leaf water content	全波形激光雷达数据、高光谱影像 Full waveform lidar data, hyperspectral image	-	模拟场景 Simulated scenes	GeoSAIL	R² = 0.79 RMSE = 0.005 3	Koetz et al., 2007
		多光谱影像 Multispectral image	西班牙 Spain	常绿阔叶林、落叶阔叶林、常绿针叶林 Evergreen broad-leaved forest (EBF), DBF, ECF	PROSPECT, GeoSAIL	R² = 0.5	Jurdao et al., 2013
		多光谱影像 Multispectral image	瑞士 Swiss	针叶林 Coniferous forest	GeoSAIL	RMSE = 0.013	K?tz et al., 2004
能量参数 Energy parameter	光合有效辐射吸收比例 Fraction of absorbed photosynthetic active radiation	光谱反射率数据 Spectral reflectance data	-	模拟场景 Simulated scenes	PARCINOPY	RMSE = 0.008 3	Combal et al., 2003
		多光谱影像 Multispectral image	美国、丹麦、德国 USA, Denmark, Germany	常绿阔叶林、落叶阔叶林、常绿针叶林 EBF, DBF, ECF	4SAIL2	R² = 0.72 RMSE = 0.07	Li & Fang, 2015
		实测数据 In-situ data	圭亚那 Guinea	稀树草原 Savanna	RIRI-3D	R² = 0.99 RMSE = 0.057	Le Roux et al., 1997
参数类型 Parameter type	参数 Parameter	数据源 Original data	研究区域 Study area	植被类型 Vegetation type	模型 Model	精度 Accuracy	参考文献 Reference
	冠层截获光合有效辐射 Fraction of intercepted photosynthetically active radiation	多光谱影像 Multispectral image	西班牙 Spain	橄榄园 Olea europaea grove	ORIM, FLIGHT	R² = 0.83 RMSE = 0.05	Guillen- Climent et al., 2012b
		多光谱影像 Multispectral image	西班牙 Spain	桃园、柑橘园 Prunus persica and Citrus reticulata grove	FLIGHT	R² = 0.88 RMSE = 0.09	Guillen- Climent et al., 2012a
生态功能参数 Ecological function parameter	日光诱导叶绿素荧光 Solar-induced chlorophyll fluorescence	实测数据 In-situ data	美国 USA	混交林 Mixed forest	FluorWPS	R²= 0.998 RMSE = 1.85	Tong et al., 2021
			美国 USA	虚拟场景、常绿针叶林 Simulated scenes, ECF	CliMA-RT	R² = 0.85 RMSE = 0.18	Braghiere et al., 2021
			西班牙 Spain	阔叶林-草地 Mixed broad-leaved forest and grass	FluorFLIGHT	R² = 0.83 RMSE = 0.03	Hornero et al., 2021

三维辐射传输模型在森林生态系统研究中的应用与展望

Three-dimensional radiative transfer modeling of forest: recent progress, applications, and future opportunities

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