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Tian, L.; Wu, X.; Tao, Y.; Li, M.; Qian, C.; Liao, L.; Fu, W. Principles of Aboveground Biomass Estimation via Remote Sensing. Encyclopedia. Available online: (accessed on 16 June 2024).
Tian L, Wu X, Tao Y, Li M, Qian C, Liao L, et al. Principles of Aboveground Biomass Estimation via Remote Sensing. Encyclopedia. Available at: Accessed June 16, 2024.
Tian, Lei, Xiaocan Wu, Yu Tao, Mingyang Li, Chunhua Qian, Longtao Liao, Wenxue Fu. "Principles of Aboveground Biomass Estimation via Remote Sensing" Encyclopedia, (accessed June 16, 2024).
Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L., & Fu, W. (2023, August 14). Principles of Aboveground Biomass Estimation via Remote Sensing. In Encyclopedia.
Tian, Lei, et al. "Principles of Aboveground Biomass Estimation via Remote Sensing." Encyclopedia. Web. 14 August, 2023.
Principles of Aboveground Biomass Estimation via Remote Sensing

Quantifying forest aboveground biomass (AGB) is essential for elucidating the global carbon cycle and the response of forest ecosystems to climate change.  Remote-sensing techniques have played a vital role in forest AGB estimation at different scales.

forest aboveground biomass optical remote sensing remote sensing

1. Introduction

The rapidly changing climate has severely impacted natural ecosystems and human societies worldwide, resulting in a series of ecological issues, including rising global sea levels [1], accelerated glacial melting at high latitudes and elevations [2][3][4], extremely severe weather [5][6], reduced food production [7], species extinction [8], and deleterious human health effects [9] which directly threaten the survival and security of human beings [10]. Global climate change imposes serious, long-term challenges to the sustainable development of human societies and has evolved into a political, economic, and environmental issue of global concern [11][12].
Forest ecosystems comprise the largest terrestrial carbon pool, storing approximately 76%–98% of terrestrial organic carbon (approximately 80% of above- and 40% of belowground carbon) [13][14]. Moreover, forest ecosystems play a crucial role in the global carbon cycle by absorbing greenhouse gases (GHG) such as atmospheric CO2, thereby reducing GHG concentrations and mitigating global climate change [15][16][17][18].
Forest aboveground biomass (AGB, the aboveground part of forest biomass) reflects the complicated relationship between the nutrient cycle and energy flow [19], providing the necessary nutrient sources and energy base for the functionality of the entire forest ecosystem [20]. In addition, AGB is a key indicator of forest ecosystem carbon sequestration capacity [21][22], productivity, structural function [23][24], and carbon sources and sinks [21][25][26]. Forest AGB estimates have been used as surrogates for aboveground carbon measurements [27]. Accordingly, AGB variations reflect the quality and condition of forest ecosystems [28][29], as well as the effects of ecological succession, natural disturbances, human activities, and climate change on forests [21][30][31]. Therefore, forest AGB estimations in the context of climate change could provide a theoretical basis for the study of the carbon cycle in terrestrial ecosystems and global climate change [18][22][32], which plays a crucial role in understanding and monitoring the response of forest ecosystems to GHG emissions [13][28][33]. In addition, the estimation of forest AGB has contributed to providing strategic guidelines for sustainable forest management [24][34], as well as rational utilization of forest resources and improvement of the forest ecological environment [14][28][29][35].
A rapid and accurate estimation of forest AGB remains challenging in forestry research [25][30]. In general, forest AGB estimation methods can be categorized as field measurements, remote sensing-based approaches, and ecological model simulations [25][36]. Field measurements entail the construction of allometric equations using tree height and diameter data measured via National Forest Inventory (NFI) data or auxiliary field plots [19][37][38][39]. To date, field measurements are considered the most accurate means of obtaining forest biomass data [19][40]; however, these measurements are also the most challenging on a regional scale because of the lengthy and arduous nature of ground-based measurements [30][41]. In addition, the actual amount of land inventoried tends to be quite small; for example, the United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis program uses 1 plot per 2400 ha of land. Alternatively, remote sensing-based approaches combine ground measurement data with remotely sensed data to estimate parameters that highly correlate with AGB. This is achieved by obtaining spectral features and vegetation metrics from multi-source remotely sensed data, such as vegetation indices (VIs), canopy cover and height, texture, shaded fraction, leaf and basal area, and timber volume [31][42][43][44][45], whereafter an AGB estimation model is constructed for regional AGB mapping [37]. In addition to the two aforementioned methods, ecological model simulation is a promising tool for the dynamic assessment of regional AGB [46]. However, this approach is often location-specific, has poor applicability, and requires a large number of input parameters for which appropriate values may be difficult to obtain [47][48]. Therefore, remote sensing-based approaches remain the dominant data source for AGB mapping and estimation in different environments [30][36][49][50].
Optical remote sensing images with varying spatial, spectral, and temporal resolutions are freely available and are widely used to estimate AGB at different scales [49][51][52]. For example, moderate- and coarse-resolution data, such as those obtained from the moderate resolution imaging spectroradiometer (MODIS), are typically employed in large-scale AGB estimates for global, continental, or national regions [53][54]. Conversely, medium-resolution data, such as those obtained from Sentinel-2 and Landsat, are mainly used to estimate AGB at local scales [55][56][57][58]. Finer-resolution commercial satellite data, such as those obtained from IKONOS, QuickBird, and WorldView-2, have been employed for AGB estimation at the forest stand scale [59][60][61]. In addition, microwave radar remotely sensed data, including synthetic aperture radar (SAR), interferometric SAR (InSAR), and polarimetric InSAR (PolInSAR) data, have also been used to estimate AGB at regional scales with moderate spatial resolutions [62][63][64][65][66]. However, optical and radar data generally suffer from signal saturation at high AGB, which limits their ability to estimate AGB in dense tropical and subtropical forests [40][67]. Recently, measurements collected using LiDAR, which is capable of acquiring tree height, canopy area, and stand density data, have improved AGB estimations in dense forests [68][69]. Furthermore, unmanned aerial vehicles (UAVs) have emerged as promising remote sensing tools over the last few decades, demonstrating enhanced applications in forest AGB estimation [70][71][72][73].

2. Principles of AGB Estimation via Remote Sensing

As opposed to direct forest biomass estimations, remote sensing techniques generally evaluate forest AGB through the construction and use of parameters such as optical sensor-derived surface reflectance, VIs, leaf area index (LAI), coverage, and tree and canopy height, with the aim to establish relationships that serve as a proxy for the AGB [36]. The remote sensing techniques used to estimate forest AGB are illustrated in Figure 1. Over the past decades, remote sensing has played a critical role in estimating forest AGB at various spatial and temporal scales [18].
Figure 1. Illustration of forest aboveground biomass estimation using remote sensing techniques. Note: UAVs, unmanned aerial vehicles.
In addition to single-band information obtained via optical remote sensing, AGB estimations are commonly obtained using VIs based on live green vegetation absorbing solar radiation in red wavelengths to support photosynthesis, which include the normalized difference vegetation index (NDVI), difference vegetation index (DVI), and enhanced vegetation index (EVI) [18][74][75]. However, as green vegetation increases, the strong absorption of red wavelengths leads to a saturation effect, thereby decreasing the AGB estimation accuracy [36]. Nonetheless, several VIs, such as the renormalized DVI (RNDVI) and modified simple ratio (MSR), have been developed to improve the accuracy of biomass estimation in dense vegetation areas [76][77]. For sparse vegetation covers, the orthogonal transform-based perpendicular VI (PVI), soil-adjusted VI (SAVI), and modified SAVI (MSAVI) are used to minimize interference from the atmosphere and soil background [78][79][80]. Moreover, remote sensing-derived texture information has been increasingly used in the estimation of forest AGB [81][82].
Additional parameters that are essential for AGB estimation include those describing the forest structure, such as tree height, diameter at breast height (DBH), and canopy height. Tree height not only reflects the biological characteristics and growth capacity of trees, but also indicates the stand quality [83]. Previous studies have demonstrated a constant AGB-to-tree height ratio (10.6 t/(hm2·m)) in closed-canopy forests using global sampling site survey data on forest age and average tree height [84]. In other words, the density of AGB per forest space was constant (1.0 kg/m3). This phenomenon (called constant aboveground biomass per forest space, BPS) was more pronounced between regions, demonstrating a small mean variation of 9.4–10.5 Mg/(hm2·m) and a global mean value of 9.9 Mg/(hm2·m) [84]. However, it is difficult to determine tree height at the plot scale, especially in tall and closed-canopy forests; therefore, it is often more practical to determine the tree height of only some individuals and then estimate the overall tree height by establishing a growth correlation between tree height and DBH [83]. Furthermore, the power law allometric equation of AGB and tree height constructed at the plot scale remains applicable on a large scale [85], which is a significant advantage of estimating AGB using remote sensing combined with ground measurements [25][36][50]. In recent years, microwave and LiDAR remote sensing have been widely used to estimate AGB. Tree height can be accurately and conveniently obtained from InSAR and LiDAR data, which has significant potential for advancement [86][87][88]. In addition, canopy height has been shown to provide accurate AGB estimations [89][90]. Notably, canopy height is not tree height; it depends not only on tree height, but also on the canopy and stand density of each tree [36].
LAI and forest coverage are also valuable indicators for estimating AGB [52][91]. LAI refers to the total area of plant leaves per unit land area as a multiple of the land area; thus, it mainly reflects leaf biomass [92]. At large scales, LAI-based estimation of AGB requires preliminary establishment of the relationship between leaf biomass and AGB, whereafter AGB is obtained via extrapolation [93]. At small scales, however, total AGB usually refers to the sum of the wood and foliage biomass [94]. Forest coverage refers to the vertical projection of the aboveground portion of trees as a percentage of the sample area and is commonly used for closed-canopy forests to express the tree layer coverage, which is the ratio of the area covered by the forest canopy to the ground surface area [83]. Generally, in uniform forests, a higher coverage represents a higher biomass [52]. Nevertheless, as forest coverage approaches saturation (reaching 1), biomass may continue to increase, which, to some extent, reduces the biomass estimation accuracy in high forest coverage areas [36].
After solar radiation-induced excitation, green leaves emit solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal in the red and far-red spectral portions, from the core of their photosynthetic machinery. Therefore, SIF is mechanically connected to photosynthesis and thus provides a better representation of vegetation growth conditions compared to other biophysical parameters or VIs [95]. Previous studies have found that SIF is strongly related to the gross primary production (GPP) [96][97][98], and GPP can provide direct AGB estimations. Therefore, GPP can be obtained via SIF–GPP correlation analyses, whereafter forest AGB can be estimated from the GPP data [18][98][99][100]. Currently, several global SIF products are available that permit forest biomass estimations [101], including the SIF datasets of Global Monitoring Ozone Experiment 2 (GOME-2) [102][103][104], Orbiting Carbon Observatory 2 (OCO-2) [105] and the Chinese Carbon Dioxide Observation Satellite (TanSat) [106]. Despite the low spatial resolution (40 km × 40 km at best) of the GOME-2 SIF dataset, it is the most widely used owing to its continuous spatial sampling, global coverage, and long time series. For example, Hu et al. [107] developed a method to upscale SIF from instantaneous clear-sky observations to all-sky sums, adopting the absorbed photosynthetically active radiation (APAR) to correct for the effect of clouds on SIF, thereby deriving all-sky SIF (ASSIF) products from GOME-2 in 8-day and monthly intervals during 2007 and 2018. Moreover, a good correspondence between the temporal trajectories of SIF and GPP [108] has been demonstrated on a global scale by Li et al. [109], who assessed OCO-2-detected SIF data and flux tower GPP data. They showed that a strong relationship between SIF and GPP exists at the ecosystem level and is nearly universal across various biomes. Nevertheless, when using SIF data to estimate GPP and AGB, the influence of environmental conditions and vegetation structure must be considered. Accordingly, SIF yields may be less sensitive than photosynthetic yields under stress conditions [110]; and the relationship between photosynthesis and top-of-canopy SIF measurements is complicated by leaf and plant structural effects [111].
Overall, forest AGB is estimated using remotely sensed data acquired over a broad electromagnetic wavelength range, from visible light to microwaves. In addition to the above ecological process parameters, environmental (e.g., precipitation, temperature, and atmospheric pressure), topographic (e.g., elevation and slope), and biotic (e.g., species diversity) factors also affect forest AGB estimates. Specifically, factors such as precipitation, temperature, elevation, and slope drive tree species distribution patterns, while soil resources and radiation intensity determine vegetation growth conditions, all of which influence forest AGB [112]. In addition, by taking succession, disturbance, and ecosystem processes into account, forest AGB estimation accuracy may be improved [36][113].


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