Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Ahmad, Adeel; Gilani, Hammad; Ahmad, Sajid Rashid

doi:10.3390/f12070914

Cited by 21 publications

(6 citation statements)

References 67 publications

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“…It selects a random number of samples from the training dataset chosen by the analyst and develops decision trees based on the most important variables [56,57]. The RF algorithm has been widely used in forest AGB estimation with remote sensing data and proved to have good performance [58][59][60]. The parameters ntree and mtry are the two key factors affecting accuracies in RF models.…”

Section: Agb Approaches For Estimating Forest Agb Using Modis and Fy-...mentioning

confidence: 99%

Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest

et al. 2022

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The aboveground biomass (AGB) of a forest is an important indicator of the forest’s terrestrial carbon storage and its relation to climate change. Due to the advantage of extensive spatial coverage and low cost, coarse-resolution remote sensing data is the main data source for wall-to-wall mapping of forest AGB at the regional scale. Despite this, improving the accuracy and efficiency of forest AGB estimation is a major challenge. In this study, two optical imageries, Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m imagery and Fengyun-3C Visible and Infrared Radiometer (FY-3C VIRR) 1000 m imagery, were used and compared for forest AGB estimation in Yunnan Province, southwest China. One parametric approach, multiple linear regression (MLR), and two nonparametric approaches, k-nearest neighbor (KNN) and random forest (RF), were applied for the two imagery datasets, respectively. We evaluated the performance of the combination of remote sensing data and modeling approaches by comparing the accuracies and also explored the potential of FY-3C imagery data in forest AGB estimation at the regional scale as it was used for this purpose for the first time. We found that the machine learning models KNN and RF provided better results than MLR. From the three approaches for both MODIS and FY-3C imagery, RF performed best with R2 values of 0.84 and 0.81 and RMSE of 23.18 and 23.43, respectively. Estimation of forest AGB based on MODIS was marginally better than the estimation based on FY-3C. FY-3C imagery could therefore be an additional optical remote sensing data source of coarse spatial resolution, comparable to MODIS data which has been widely used for regional forest AGB estimation. Indices related to forest canopy moisture levels from both types of imagery were sensitive to forest AGB. The RF model and MODIS imagery were then applied to map the spatial variation of forest AGB of Yunnan Province. As a result of our study, we determined that Yunnan Province has a total forest AGB of 2123.22 Mt, with a mean value of 58.05 t/ha for forestland in 2016.

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Section: Agb Approaches For Estimating Forest Agb Using Modis and Fy-...mentioning

confidence: 99%

Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest

et al. 2022

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“…The three-dimensional structure of forests is an important basis for estimating changes in forest biomass due to human activities or natural disturbances, as well as a crucial component for evaluating forest habitat quality and biodiversity on a regional scale [41]. Investigators need to develop large-scale, region-wide forest biomass estimation models by remote sensing and costly surveys.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Shu

et al. 2023

Forests

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Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation accuracy in the alpine mountains of Yunnan Province, China. This paper uses GEDI data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Based on the pre-processing of light spots. A total of 38 parameters were extracted from the canopy and vertical profiles of 1307 light spots in the study area, and the polygon data of the whole study area were obtained from the light spot data through Kriging interpolation. Multiple linear regression, support vector regression, and random forest were used to establish biomass models. The results showed that the optimal model is selected using the semi-variance function for the Kriging interpolation of each parameter of GEDI spot, the optimal model of modis_nonvegetated is a linear model, and the optimal model for rv, sensitivity, and modis_treecover is the exponential model. Analysis of the correlation between 39 parameters extracted from GEDI L2B and three topographic factors with oak biomass showed that sensitivity had a highly significant positive correlation (p < 0.01) with Quercus biomass, followed by a significant negative correlation (p < 0.05) with aspect and modis_nonvegation. After variable selection, the estimation model of Quercus biomass established using random forest had R2 = 0.91, RMSE = 19.76 t/hm2, and the estimation accuracy was better than that of multiple linear regression and support vector regression. The estimated total biomass of Quercus in the study area was mainly distributed between 26.48 and 257.63 t/hm2, with an average value of 114.33 t/hm2 and a total biomass of about 1.26 × 107 t/hm2. This study obtained spatial consecutive information using Kriging interpolation. It provided a new research direction for estimating other forest structural parameters using GEDI data.

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“…A literature review serves the purpose of gathering data and arguments on research topics from researchers who have conducted relevant studies in support of research results based on phenomena that have occurred. The literature review was carried out by collecting several papers or papers with criteria adjusted by researchers, in this case related to mangrove forests and mangrove carbon [26]. The results of the paper collection are then studied more deeply to identify the desired issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Mangrove Ecosystem Services Based on Comparison of Stand Carbon Stock Estimates in Climate Change Mitigation

Safe’i,

Syahiib

2023

IJEI

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One form of mangrove ecosystem service is carbon sequestration. The issue of carbon is an important concern for the climate change response. The purpose of this study is to determine the comparison of stand carbon stock estimates in mangrove ecosystems on the coast of East Lampung Regency (Kuala Penet Resort Mangrove Forest, Lampung Mangrove Center (LMC) Mangrove Forest, and Register 15 Mangrove Forest). The stages of research include: determination of data collection based on purposive sampling and Forest Health Monitoring (FHM) techniques; stand biomass and carbon calculations using allometric equations and the Intergovernmental Panel on Climate Change (IPCC); descriptive data analysis; and a literature study. Based on the results of the study, the highest stand carbon was in LMC (144.56 tons per ha), and the lowest was in Kuala Penet Resort (19.19 tons/ha). These conditions are influenced by stand structure factors (growing conditions, tree age, and microclimate conditions) and management schemes. The partnership is a form of stakeholder commitment as a way to maintain mangrove ecosystem services. Thus, carbon stands in coastal mangrove ecosystems of East Lampung Regency, namely Kuala Penet Resort (19.19 tons/ha), Register 15 (9.66 tons/ha), and LMC (144.56 tons/ha).

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Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Cited by 21 publications

References 67 publications

Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest

Mapping Forest Aboveground Biomass with MODIS and Fengyun-3C VIRR Imageries in Yunnan Province, Southwest China Using Linear Regression, K-Nearest Neighbor and Random Forest

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Optimization of Mangrove Ecosystem Services Based on Comparison of Stand Carbon Stock Estimates in Climate Change Mitigation

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