Modeling forest above-ground biomass using freely available satellite and multisource datasets

Hojo, Ai; Avtar, Ram; Nakaji, Tatsuro; Tadono, Takeo; Takagi, Kentaro

doi:10.1016/j.ecoinf.2023.101973

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…Among these selected articles (22), eleven regression models were used to integrate space-and airborne datasets with the field inventory or field data. Among these selected articles (22), random forest (8) [110][111][112][113][114][115][116][117] and linear regression (5) [118][119][120][121][122] were the most often used models, and the other models were employed once [123][124][125][126][127][128][129][130][131].…”

Section: Space-and Airborne Datasetsmentioning

confidence: 99%

“…An accuracy assessment of the space-and airborne platform datasets was carried out, and a regression model grounded on the article's coefficient of determination (R 2 ) was used. Fourteen articles [111][112][113][114]116,[118][119][120][121][124][125][126][127][128] were employed for further analysis, as they had R 2 values among the total classified articles (22). The error bar plot of R 2 and the regression models used, shown in Figure 18, showed that random forest was the most used model, with five articles for analysis, followed by linear regression (4).…”

Section: Space-and Airborne Analysismentioning

confidence: 99%

“…The random forest model was employed with parameters (annual precipitation, TDX-CHM, forest type, HV). R 2 = 0.70 [113]…”

Section: Study Area Data Source Research Highlights Accuracy Referencementioning

confidence: 99%

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Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Khan,

Tan,

Gul

et al. 2024

Forests

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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 integration methods to map and estimate forest AGB is still needed. This article reviews the integrated remote sensing approaches and discusses significant advances in estimating the AGB from space- and airborne sensors. This review covers the research articles published during 2015–2023 to ascertain recent developments. A total of 98 peer-reviewed journal articles were selected under the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Among the scrutinized studies, 54 were relevant to spaceborne, 22 to airborne, and 22 to space- and airborne datasets. Among the empirical models used, random forest regression model accounted for the most articles (32). The highest number of articles utilizing integrated dataset approaches originated from China (24), followed by the USA (15). Among the space- and airborne datasets, Sentinel-1 and 2, Landsat, GEDI, and Airborne LiDAR datasets were widely employed with parameters that encompassed tree height, canopy cover, and vegetation indices. The results of co-citation analysis were also determined to be relevant to the objectives of this review. This review focuses on dataset integration with empirical models and provides insights into the accuracy and reliability of studies on AGB estimation modeling.

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Section: Space-and Airborne Datasetsmentioning

confidence: 99%

Section: Space-and Airborne Analysismentioning

confidence: 99%

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Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Khan,

Tan,

Gul

et al. 2024

Forests

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“…However, this method requires complex numerical calculations and model building, demanding simulation software and computer hardware performance. Having taken into account the strengths and weaknesses of the above methods, a majority of researchers have opted for the freely available satellite-based global DEM [22][23][24], which is released by some agencies and organizations, such as the Shuttle Radar Topography Mission of the US National Aeronautics and Space Administration (NASA SRTM) [25]. However, various factors can reduce the reliability of urban waterlogging models.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Urban Waterlogging Simulation: A Novel Computer Vision-Based Digital Elevation Model Refinement Approach for Roads and Densely Built-Up Areas

Yang,

Huang,

Wang

et al. 2023

Remote Sensing

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Urban waterlogging is a natural disaster that occurs in developed cities globally and has inevitably become severe due to urbanization, densification, and climate change. The digital elevation model (DEM) is an important component of urban waterlogging risk prediction. However, previous studies generally focused on optimizing hydrological models, and there is a potential improvement in DEM by fusing remote sensing data and hydrological data. To improve the DEM accuracy of urban roads and densely built-up areas, a multisource data fusion approach (MDF-UNet) was proposed. Firstly, Fuzhou city was taken as an example, and the satellite remote sensing images, drainage network, land use, and DEM data of the study area were collected. Secondly, the U-Net model was used to identify buildings using remote sensing images. Subsequently, a multisource data fusion (MDF) method was adopted to reconstruct DEM by fusing the buildings identification results, land use, and drainage network data. Then, a coupled one-dimensional (1D) conduit drainage and two-dimensional (2D) hydrodynamic model was constructed and validated. Finally, the simulation results of the MDF-UNet approach were compared with the raw DEM data, inverse distance weighting (IDW), and MDF. The results indicated that the proposed approach greatly improved the simulation accuracy of waterlogging points by 29%, 53%, and 12% compared with the raw DEM, IDW, and MDF. Moreover, the MDF-UNet method had the smallest median value error of 0.08 m in the inundation depth simulation. The proposed method demonstrates that the credibility of the waterlogging model and simulation accuracy in roads and densely built-up areas is significantly improved, providing a reliable basis for urban waterlogging prevention and management.

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Mapping of Temporally Dynamic Tropical Forest and Plantations Canopy Height in Borneo Utilizing TanDEM-X InSAR and Multi-sensor Remote Sensing Data

Suab,

Supe,

Louw

et al. 2024

J Indian Soc Remote Sens

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Modeling forest above-ground biomass using freely available satellite and multisource datasets

Cited by 6 publications

References 55 publications

Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Improving the Accuracy of Urban Waterlogging Simulation: A Novel Computer Vision-Based Digital Elevation Model Refinement Approach for Roads and Densely Built-Up Areas

Mapping of Temporally Dynamic Tropical Forest and Plantations Canopy Height in Borneo Utilizing TanDEM-X InSAR and Multi-sensor Remote Sensing Data

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