Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data

Migolet, Pierre; Goı̈ta, Kalifa; Pambo, Aurélie Flore Koumba; Mambimba, Aboubakar Ndjoungui

doi:10.1080/15481603.2022.2026636

Cited by 11 publications

(8 citation statements)

References 106 publications

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“…However, the visible bands (B, G, and R), which were the top three ranking parameters, are typically used to assess vegetation health and classify vegetation types; therefore, the validity of the three parameters for estimating the AGB in young spruce plantations deserves further investigation. Meanwhile, as texture features have performed well in estimating the AGB in mature forests [64][65][66], this study also included texture features for AGB estimation in young spruce forests. However, the accuracy of the model decreased after only adding the texture feature that had a high importance ranking.…”

Section: Variable Contribution For Measuring Agb In Spruce Plantationsmentioning

confidence: 99%

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

Wang

Yi²,

et al. 2023

Sustainability

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More refined and economical aboveground biomass (AGB) monitoring techniques are needed because of the growing significance of spruce plantations in climate change mitigation programs. Due to the challenges of conducting field surveys, such as the potential inaccessibility and high cost, this study proposes a convenient and efficient alternative to traditional field surveys that integrates Gaofen-2 (GF-2) satellite optical images and unmanned aerial vehicle (UAV)-acquired optical and point cloud data to provide a reliable and refined estimation of the aboveground biomass (AGB) in spruce plantations. The feasibility of using data produced from the semiautomatic processing of UAV-based images and photogrammetric point clouds to replace conventional field surveys of sample plots in a young spruce plantation was evaluated. The AGB in 53 sample plots was estimated using data extracted from the UAV imagery. The UAV plot data and GF-2 optical data were used in four regression models to estimate the AGB in the study area. The coefficient of determination (R2), root-mean-square error (RMSE), mean percent standard error (MPSE), and Lin’s concordance correlation coefficient (LCCC) were calculated through five-fold cross-validation and stratified random sampling to evaluate the models’ efficacies. In the end, the most accurate model was used to generate the spatial distribution map of the AGB. The results revealed the following: (1) the individual-tree height (R2 = 0.90) and crown diameter (R2 = 0.74) extracted from UAV data were accurate enough to replace field surveys used to obtain the AGB at the plot levels; (2) the random forest (RF) model (R2 = 0.86; RMSE = 1.75 t/ha; MPSE = 15.75%; LCCC = 0.91) outperformed the ordinary least-squares (OLS) model (R2 = 0.68; RMSE = 2.49 t/ha; MPSE = 22.94%; LCCC = 0.81), artificial neural network (ANN) model (R2 = 0.67; RMSE = 2.54 t/ha; MPSE = 21.48%; LCCC = 0.80), and support vector machine (SVM) model (R2 = 0.60; RMSE = 2.84 t/ha; MPSE = 31.73%; LCCC = 0.76) in terms of the estimation accuracy; (3) an AGB map generated by the random forest model was in good agreement with field surveys and the age of the spruce plantations. Therefore, the method proposed in this study can be used as a refined and cost-effective way to estimate the AGB in young spruce plantations.

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Section: Variable Contribution For Measuring Agb In Spruce Plantationsmentioning

confidence: 99%

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

Wang

Yi²,

et al. 2023

Sustainability

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“…Though radar images can potentially capture vegetation information across seasons, radar or SAR image processing workflows have been largely unreported for the myriad potential applications in tropical forests. Using satellite-based radar data, there are increasing efforts in delineating and quantifying sectoral anthropogenic actions and land use [14,15] and aboveground biomass [16,17] in tropical forests. 13 Notwithstanding these growing efforts, most estimates of forest biomass and GHG emission for project-based and national assessments still rely largely on extrapolations from often scant field inventories using either allometric models or application of remote sensing data at spatial scales that mask variability across geographies and local details-the scale of most anthropogenic activities.…”

Section: Tropical Forest Biomass and Ghg Monitoringmentioning

confidence: 99%

“…The increasing in human-induced CO 2 emissions indirectly implies that forest worldwide will grow faster and reduce the amount of atmospheric CO 2 which stays airborne-an effect known carbon fertilization, which is high in the tropics. There has been increasing carbon sink on land since the 1980s; living woody plants were responsible for more than 80% of the sources and sinks on land 16,17 [20]. Globally, vegetation is locking away more carbon as atmospheric CO 2 levels rise.…”

Section: Carbon Fertilization and Tropical Forestry Nppmentioning

confidence: 99%

CDR and Tropical Forestry: Fighting Climate Change One Cubic Meter a Time

Augusto Zanetti,

N. Numbisi,

Karoshi

et al. 2023

Tropical Forests - Ecology, Diversity and Conservation Status

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In the coming decades, there will be a global increase in demand for biomass and in advocating GHG emission removal technology and practices. In the agriculture and forestry context, intensification of land use is the most promising solution—together with processing efficiency—in balancing consumption, rated as human appropriation of net primary production (HANPP), with Net Primary Production (NPP) from atmospheric CO2 fertilization. Forest plantations, croplands, cultivated pastures, lianas, palms and other secondary vegetation have shown yield gains from CO2 fertilization, while native forest (trees) experience short-lived increases in growth rates and are out-competed by fast-growing components—secondary vegetation. There is evident path of degradation in non-managed, native tropical forests fueled by atmospheric CO2 fertilization. Following such BAU scenario, tropical forests would experience important dwindling in tree cover on a temporal scale. An alternative IFM scenario is proposed combining contemporary silviculture techniques, adapted land use intensification and HWP increase. This would contribute additional atmospheric CO2 removals, certifiable as CDR goods able to generate carbon credits and financial incentive for cultivation of improved native tree species. These CDR credits can be included in tropical countries’ NDC and presented at UNFCCC as an ITMO for fighting global climate change.

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“…However, due to the weak canopy penetration of optical remote sensing, the signal is easily saturated, so it cannot obtain vertical structural information (Zhu & Liu, 2015). Although radar remote sensing has stronger canopy penetration ability and is not limited by weather, it also has a saturation issue and is easily affected by terrain (Migolet et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments

Feng,

Nie,

Wang

et al. 2024

The Photogrammetric Record

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Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV‐lidar) and terrestrial lidar (T‐lidar) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV‐lidar and T‐lidar data in forest scenes. It employs density‐based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both lidar sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst‐pattern‐based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimisation with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T‐lidar and UAV‐lidar registration over forest environments.

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Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data

Cited by 11 publications

References 106 publications

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

CDR and Tropical Forestry: Fighting Climate Change One Cubic Meter a Time

A novel method based on a starburst pattern to register UAV and terrestrial lidar point clouds in forest environments

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