Forest biomass reflects the material cycle of forest ecosystems and is an important index to measure changes in forest structure and function. The accurate estimation of forest biomass is the research basis for measuring carbon storage in forest syst...
In tropical and sub-tropical regions, biomass carbon (C) losses through forest degradation are recognized as central to global terrestrial carbon cycles. Accurate estimation of forest biomass C is needed to provide information on C fluxes and balance...
While considerable research has focused on using either L-band or P-band SAR (Synthetic Aperture Radar) on their own for forest biomass retrieval, the use of the two bands simultaneously to improve forest biomass retrieval remains less explored. In t...
Accurate forest biomass estimation is essential for quantifying carbon stocks, guiding sustainable forest management, and informing climate change mitigation strategies. Kenya’s forests are diverse, ranging from Afromontane and mangrove ecosyst...
Combining remote sensing data with field inventory data is a common practice in estimation of forest Aboveground Biomass (AGB). However, in areas with well-established ground monitoring systems, the actual added value of this combination has not been...
Research on the contribution of understory components to the total above ground biomass (AGB) has to date received very little attention because most prior biomass estimation studies have ignored small regenerating trees beneath the main canopy with...
Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested b...
Low-cost UAV aerial photogrammetry and airborne lidar scanning have been widely used in forest biomass survey and mapping. However, the feature dimension after multisource remote sensing fusion is too high and screening key features to achieve featur...
In the field of quantitative remote sensing of forest biomass, a prominent phenomenon is the increasing number of explanatory variables. Then how to effectively select explanatory variables has become an important issue. Linear regression model is on...
Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its het...
Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. Accurate regional-scale AGB estimation is crucial for advancing research on global climate change, ecosystem carbon cycles, and...
Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models ar...
Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensin...
Estimation of forest biomass and surface fuels is essential for wildfire risk assessment, carbon accounting, and ecosystem management in semi-arid forests. This study evaluated Sentinel-2 spectral indices and Gamma generalized linear models (GLMs) fo...
Enhanced methods are required for mapping the forest aboveground biomass (AGB) over a large area in Chinese forests. This study attempted to develop an improved approach to retrieving biomass by combining PALSAR (Phased Array type L-band Synthetic Ap...
This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first...
With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to...
Effective forest management and conservation are increasingly critical in addressing the challenges posed by climate change. Advances in remote sensing technologies, such as terrestrial laser scanning, offer promising tools for more accurate assessme...
Forest biomass is a major store of carbon and plays a crucial role in the regional and global carbon cycle. Accurate forest biomass assessment is important for monitoring and mapping the status of and changes in forests. However, while remote sensin...
The disturbance of mangrove forests could affect climate regulation, hydrological cycles, biodiversity, and many other unique ecological functions and services. Proper biomass estimation and carbon storage potential are needed to improve forest refer...
Forest biomass is an important indicator of forest ecosystem productivity, and it plays vital roles in the global carbon cycling, global climate change mitigating, and ecosystem researches. Multiscale, rapid, and accurate extraction of forest biomass...
Accurate biomass and carbon estimation in tropical plantation forests requires species-specific allometric models. Silver Oak (Grevillea robusta A. Cunn. ex R. Br.), cultivar “AVAONE,” is widely planted in northeastern Thailand, yet local...
Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitiga...
Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing...
Monitoring of changes in forest biomass requires accurate transfer functions between remote sensing-derived changes in canopy height (ΔH) and the actual changes in aboveground biomass (ΔAGB). Different approaches can be used to accomplish...
To effectively further research the regional carbon sink, it is important to estimate forest aboveground biomass (AGB). Based on optical images, the AGB can be estimated and mapped on a regional scale. The Landsat 8 Operational Land Imager (OLI) has,...
This study develops a modeling framework for utilizing the large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and Land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-Rad...
Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height...
Forests are important in the global carbon cycle and it is necessary to quickly and accurately measure forest volume to estimate forest aboveground biomass (AGB) and aboveground carbon storage (AGC). In this paper, we used data from the eighth forest...
Background: While forests are pivotal for climate change mitigation, robust CO2 accounting is required to quantify their climate benefits. However, varying current methodologies complicate this process for practitioners. This study addresses the need...
The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to...
Estimation of forest aboveground biomass (AGB) has become one of the main challenges of remote sensing science for global observation of carbon storage and changes in the past few decades. We examine the impact of plot size at different spatial resol...
To estimate the responses of forest ecosystems, most relationships in biological systems are described by allometric relationships, the parameters of which are determined based on field measurements. The use of existing observed data errors may occur...
Above-ground forest biomass is a significant variable in the terrestrial carbon budget, but is still estimated with relatively large uncertainty. Remote sensing methods can improve the characterization of the spatial distribution and estimation accur...
Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detail...
Multi-angle optical reflectance measurements such as those from the NASA moderate resolution imaging spectroradiometer (MODIS) are sensitive to forest 3D structures, potentially serving as a useful proxy to estimate forest structural variables such a...
Estimation of forest biomass with synthetic aperture radar (SAR) and interferometric SAR (InSAR) observables has been surveyed in 186 peer-reviewed papers to identify major research pathways in terms of data used and retrieval models. Research evalua...
In forestry studies, remote sensing has been widely used to monitor deforestation and estimate biomass, and it has contributed to forest carbon stock management. A major problem when estimating biomass from optical and SAR remote sensing images is th...
Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by en...
As the largest and most important natural terrestrial ecosystem, forest plays a crucial role in reducing the concentrations of greenhouse gases in the atmosphere, mitigating global warming, maintaining the global ecological balance, and promoting glo...
The traditional classical sampling statistics method ignores the spatial location relationship of survey samples, which leads to many problems. This study aimed to propose a spatial sampling method for sampling estimation and optimization of forest b...
Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Synthetic Aperture Radar (SAR), especially the polarimetric SAR with the capability of ident...
Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observ...
The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation...
Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artifi...
Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past...
Estimating forest aboveground biomass (AGB) in mountainous forest ecosystems remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing-b...
Continuous monitoring of forest biomass is indispensable for establishing transparent carbon budgets and ensuring sustainable forest management toward achieving carbon neutrality. While satellite data has traditionally been used for wide-area biomass...
Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing forest aboveground biomass estimation using remote sensing approaches, a comprehensive synthesis of synergetic in...
This paper was aimed at estimating the forest aboveground biomass (AGB) in the Central Kalimantan tropical peatland forest, Indonesia, using polarimetric parameters extracted from RadarSAT-2 images. Six consecutive acquisitions of RadarSAT-2 full pol...
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