A review of forest aboveground biomass estimation based on remote sensing data

doi:10.17521/cjpe.2023.0008

Abstract

Abstract:

Forests are crucial terrestrial ecosystems with wide distribution and substantial biomass, playing a vital role in the global carbon cycle. The estimation of aboveground biomass (AGB) in forests serves as a significant indicator of ecosystem productivity and is pivotal for studying material cycles and global climate change. Traditional methods for AGB estimation rely on individual tree-scale or forest stand-scale tree physical structural information measurements, which are often time-consuming and labor-intensive to obtain. Remote sensing technology offers a solution for comprehensively and multi-temporally obtaining forest structural information in large scale, making it indispensable for forest AGB estimation. Therefore, it is important to review and summarize recent advancements in remote sensing techniques for estimating forest AGB to promote their application and guide the development of related industries. This paper presents a comprehensive overview of the principles and methods used for estimating forest AGB using optical data, synthetic aperture radar (SAR) data, and light detection and ranging (LiDAR) data. It also analyzes the current status of synergistic estimation of forest AGB using multiple remote sensing data sources. The study highlights three key findings: (1) The use of novel remote sensing data, such as high-resolution satellite imagery and Global Ecosystem Dynamics Investigation LiDAR data, is expanding the boundaries of spatial and temporal resolutions, providing enhanced data sources for forest AGB research. (2) Synergistic approaches that combine multiple remote sensing data sources show promise in improving the accuracy of forest AGB estimation, but further optimization of related models is needed. (3) Machine learning, artificial intelligence, and deep learning techniques have been widely applied in forest AGB estimation, but continuous research on remote sensing mechanisms remains essential for innovation. Improvements in models and methodologies should revolve around a better understanding of these mechanisms.

Key words: forest aboveground biomass, optical remote sensing, synthetic aperture radar, light detection and ranging, multi-source remote sensing data collaboration

HAO Qing, HUANG Chang. A review of forest aboveground biomass estimation based on remote sensing data[J]. Chin J Plant Ecol, 2023, 47(10): 1356-1374.

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References 128

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植被指数 Vegetation index	计算公式 Calculation formula	特点 Characteristic
归一化植被指数 Normalized differential vegetation index (NDVI)	NDVI = (NIR - Red)/(NIR + Red)	应用广泛、反映植被空间分布与生长状况 It is widely used and reflects the spatial distribution and growth of vegetation
增强型植被指数 Enhanced vegetation index (EVI)	EVI = 2.5 × (NIR - Red)/(NIR + 6Red - 7.5Blue + 1)	可纠正大气和土壤背景的影响, 不易饱和 It can correct the influence of atmospheric and soil background and is not easily saturated
比值植被指数 Ratio vegetation index (RVI)	RVI = NIR/Red	计算简单, 在植被密集区域灵敏度高 It’s easy to calculate and has high sensitivity in densely vegetated areas
差值植被指数 Differential vegetation index (DVI)	DVI = NIR - Red	对土壤背景变化敏感, 易区分土壤和植被 It’s sensitive to soil background changes and easy to distinguish between soil and vegetation
重归一化植被指数 Re-normalized differential vegetation index (RDVI)	RDVI = (NIR – Red)/$\sqrt{(\text{NIR}+\text{Red})}$	可区分土壤和植被, 也可以反映植被信息 It can distinguish between soil and vegetation and reflect vegetation information
土壤调节植被指数 Soil-adjusted vegetation index (SAVI)	SAVI = 1.5 × (NIR - Red)/(NIR + Red + 0.5)	考虑土壤光学性质, 适用于稀疏植被区域 It can take into account soil optical properties and is suitable for areas of sparse vegetation
修正土壤调节植被指数 Modified soil-adjusted vegetation index (MSAVI)	MSAVI = 1/2 × (2NIR + 1 – $\sqrt{[{{(2\text{NIR}+1)}^{2}}-8\times (\text{NIR}-\text{Red})]}$)	可消除土壤背景 It can remove the soil background
垂直植被指数 Perpendicular vegetation index (PVI)	PVI = (NIR – 0.791Red – 0.043)/$\sqrt{({{0.791}^{2}}+1)}$	用于地表植被参数的反演 It can be used for inversion of surface vegetation parameters

植被指数 Vegetation index	计算公式 Calculation formula	特点 Characteristic
归一化植被指数 Normalized differential vegetation index (NDVI)	NDVI = (NIR - Red)/(NIR + Red)	应用广泛、反映植被空间分布与生长状况 It is widely used and reflects the spatial distribution and growth of vegetation
增强型植被指数 Enhanced vegetation index (EVI)	EVI = 2.5 × (NIR - Red)/(NIR + 6Red - 7.5Blue + 1)	可纠正大气和土壤背景的影响, 不易饱和 It can correct the influence of atmospheric and soil background and is not easily saturated
比值植被指数 Ratio vegetation index (RVI)	RVI = NIR/Red	计算简单, 在植被密集区域灵敏度高 It’s easy to calculate and has high sensitivity in densely vegetated areas
差值植被指数 Differential vegetation index (DVI)	DVI = NIR - Red	对土壤背景变化敏感, 易区分土壤和植被 It’s sensitive to soil background changes and easy to distinguish between soil and vegetation
重归一化植被指数 Re-normalized differential vegetation index (RDVI)	RDVI = (NIR – Red)/$\sqrt{(\text{NIR}+\text{Red})}$	可区分土壤和植被, 也可以反映植被信息 It can distinguish between soil and vegetation and reflect vegetation information
土壤调节植被指数 Soil-adjusted vegetation index (SAVI)	SAVI = 1.5 × (NIR - Red)/(NIR + Red + 0.5)	考虑土壤光学性质, 适用于稀疏植被区域 It can take into account soil optical properties and is suitable for areas of sparse vegetation
修正土壤调节植被指数 Modified soil-adjusted vegetation index (MSAVI)	MSAVI = 1/2 × (2NIR + 1 – $\sqrt{[{{(2\text{NIR}+1)}^{2}}-8\times (\text{NIR}-\text{Red})]}$)	可消除土壤背景 It can remove the soil background
垂直植被指数 Perpendicular vegetation index (PVI)	PVI = (NIR – 0.791Red – 0.043)/$\sqrt{({{0.791}^{2}}+1)}$	用于地表植被参数的反演 It can be used for inversion of surface vegetation parameters

国家或机构 Country or agency	卫星名称 Satellite name	年份 Year	极化方式 Polarization mode
中国 China	HJ-1	2012	VV (S波段) VV (S band)
	GF-3	2016	可选单极化(C波段) Optional unipolarization (C band)
	Qilu-1	2021	Ku谱段 Ku band
	海丝一号 Hisea-1	2022	VV (C波段) VV (C band)
	LT-1A	2022	全极化(L波段) Full polarization (L band)
	LT-1B	2022	全极化(L波段) Full polarization (L band)
	巢湖一号 Chaohu-1	2022	(C波段) (C band)
美国 USA	Seasat-A	1978	HH (L波段) HH (L band)
	Capalla-1	2018	HH (X波段) HH (X band)
	Capalla-2	2020	HH (X波段) HH (X band)
	Capalla-3	2021	HH (X波段) HH (X band)
	Capalla-4	2021	HH (X波段) HH (X band)
欧洲航天局 European Space Agency (ESA)	ENVISAT	2002	双极化(C波段) Bipolarization (C band)
	Sentinel-1A	2014	双极化(C波段) Bipolarization (C band)
	Sentinel-1B	2016	双极化(C波段) Bipolarization (C band)
加拿大 Canada	RADARSAT-2	2006	全极化(L、C波段) Full polarization (L, C band)
德国宇航局 Deutsches Zentrum für Luft- und Raumfahrt (DLR)	TerraSAR-X	2007	单极化、双极化、全极化(X波段) Unipolarization, bipolarization, full polarization (X band)
德国宇航局 Deutsches Zentrum für Luft- und Raumfahrt (DLR)	TanDEM-X	2010	单极化、双极化、全极化(X波段) Unipolarization, bipolarization, full polarization (X band)
日本 Japan	ALOS-PALSAR1	2006	全极化(L、C波段) Full polarization (L, C band)
日本 Japan	ALOS2-PALSAR2	2014	全极化(L、C波段) Full polarization (L, C band)

国家或机构 Country or agency	卫星名称 Satellite name	年份 Year	极化方式 Polarization mode
中国 China	HJ-1	2012	VV (S波段) VV (S band)
	GF-3	2016	可选单极化(C波段) Optional unipolarization (C band)
	Qilu-1	2021	Ku谱段 Ku band
	海丝一号 Hisea-1	2022	VV (C波段) VV (C band)
	LT-1A	2022	全极化(L波段) Full polarization (L band)
	LT-1B	2022	全极化(L波段) Full polarization (L band)
	巢湖一号 Chaohu-1	2022	(C波段) (C band)
美国 USA	Seasat-A	1978	HH (L波段) HH (L band)
	Capalla-1	2018	HH (X波段) HH (X band)
	Capalla-2	2020	HH (X波段) HH (X band)
	Capalla-3	2021	HH (X波段) HH (X band)
	Capalla-4	2021	HH (X波段) HH (X band)
欧洲航天局 European Space Agency (ESA)	ENVISAT	2002	双极化(C波段) Bipolarization (C band)
	Sentinel-1A	2014	双极化(C波段) Bipolarization (C band)
	Sentinel-1B	2016	双极化(C波段) Bipolarization (C band)
加拿大 Canada	RADARSAT-2	2006	全极化(L、C波段) Full polarization (L, C band)
德国宇航局 Deutsches Zentrum für Luft- und Raumfahrt (DLR)	TerraSAR-X	2007	单极化、双极化、全极化(X波段) Unipolarization, bipolarization, full polarization (X band)
德国宇航局 Deutsches Zentrum für Luft- und Raumfahrt (DLR)	TanDEM-X	2010	单极化、双极化、全极化(X波段) Unipolarization, bipolarization, full polarization (X band)
日本 Japan	ALOS-PALSAR1	2006	全极化(L、C波段) Full polarization (L, C band)
日本 Japan	ALOS2-PALSAR2	2014	全极化(L、C波段) Full polarization (L, C band)

遥感类型 Sensor type	优势 Advantage	不足 Disadvantage
光学遥感 Optical remote sensing	光谱信息丰富, 易得多种时空分辨率影像, 可用于不同尺度的生物量估算研究。数据提取方法较简便, 结果可视化程度较高 Spectral information is abundant, and various spatial and temporal resolution images are easily available, which can be used for biomass estimation research at different scales. The data extraction methods are relatively straightforward, and the results can be visualized to a high degree	光学传感器易受天气影响, 遥感信号难以到达植被冠层之下, 不能有效反映森林的垂直结构信息, 且受植被密度影响而易导致光饱和现象 Optical sensors are susceptible to weather conditions, and remote sensing signals struggle to penetrate beneath the vegetation canopy, thus failing to effectively capture vertical structural information of forests. Additionally, optical sensors are prone to saturation effects due to variations in vegetation density
SAR	能与树叶、树干和树冠发生作用, 成像受云雨影响小, 可快速获取大区域、全覆盖的影像, 对生物量敏感 SAR can interact with leaves, tree trunks, and canopies, with minimal impact from clouds and rain. It can rapidly acquire large-area, full-coverage images and is highly sensitive to biomass measurements	SAR影像来源相对较少, 数据处理较为复杂, 受地形和土壤条件影响较大, 后向散射系数估算存在饱和性 SAR images have relatively limited data sources and require more complex data processing. They are significantly influenced by terrain and soil conditions. Estimating the backscattering coefficient in SAR images can be subject to saturation effects
LiDAR	LiDAR数据的空间分辨率较高, 不仅能够获取森林的垂直结构信息, 而且还克服了信号饱和的局限性 LiDAR data possesses a high spatial resolution, enabling the acquisition of vertical structural information of forests. Moreover, LiDAR data overcomes the limitations of signal saturation	成本较高, 缺乏历史数据, 具体模型方法受研究区域限制; 机载LiDAR在大尺度空间上采样不连续, 无法达到无缝覆盖, 波形受林下地形和树木空间结构影响较大 LiDAR technology is associated with higher costs and lacks historical data. The specific models and methods may be limited by the research area. Airborne LiDAR suffers from discontinuous sampling at large spatial scales, making it challenging to achieve seamless coverage. The LiDAR waveform is greatly affected by the understory terrain and spatial structure of trees