Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds

Lu, Jinbo; Wang, Hong; Qin, Shuhong; Cao, Lin; Pu, Ruiliang; Li, Guilin; Sun, Jing

doi:10.1016/j.jag.2019.102014

Cited by 61 publications

(53 citation statements)

References 57 publications

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“…RF, being a bagging ensemble algorithm, can handle multicollinearity issues due to random predictor subset selection [42,43]. Previous studies have confirmed RF's stability [44] and relative resistance to overfitting as well as its effectiveness in AGB estimation [18,20].…”

Section: Random Forest Modeling and Assessmentmentioning

confidence: 98%

“…Data obtained by active sensors, such as Light Detection and Ranging (LiDAR), for biomass estimation over plantations through direct tree size measurements, have, so far, limited use due to the associated (high) costs. Only recently, Lu et al [18] estimated forest biomass in black locust plantations in the Yellow River delta based on LiDAR point clouds. On the other hand, active data from Synthetic-Aperture Radar (SAR) satellite sensors have been extensively employed for plantation biomass estimation, taking advantage of the fact that SAR can operate unaffected by daylight and cloud conditions; it is also able to penetrate the tree canopy [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

Theofanous

Chrysafis

Mallinis

et al. 2021

Forests

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Plantations of fast-growing forest species such as black locust (Robinia Pseudoacacia) can contribute to energy transformation, mitigate industrial pollution, and restore degraded, marginal land. In this study, the synergistic use of Sentinel-2 and Sentinel-1 time series data is explored for modeling aboveground biomass (AGB) in black locust short-rotation plantations in northeastern Greece. Optimal modeling dates and EO sensor data are also identified through the analysis. Random forest (RF) models were originally developed using monthly Sentinel-2 spectral indices, while, progressively, monthly Sentinel-1 bands were incorporated in the statistical analysis. The highest accuracy was observed for the models generated using Sentinel-2 August composites (R2 = 0.52). The inclusion of Sentinel-1 bands in the spectral indices’ models had a negligible effect on modeling accuracy during the leaf-on period. The correlation and comparative performance of the spectral indices in terms of pairwise correlation with AGB varied among the phenophases of the forest plantations. Overall, the field-measured AGB in the forest plantations plots presented a higher correlation with the optical Sentinel-2 images. The synergy of Sentinel-1 and Sentinel-2 data proved to be a non-efficient approach for improving forest biomass RF models throughout the year within the geographical and environmental context of our study.

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Section: Random Forest Modeling and Assessmentmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

Theofanous

Chrysafis

Mallinis

et al. 2021

Forests

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“…Several studies have demonstrated the potential for using remote sensing as a complementary alternative for overcoming these limitations [5][6][7]. Predictive relationships can be established between structural variables and remotely sensed data [1,3,6,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Prediction models that have been generally used are multiple linear regressions (MLR), which link vegetation structural parameters to FOTO indices that are derived from satellite images [6,8,14,18,21]. The major disadvantage of MLR methods is that they do not account for non-linearities, which are frequent when ecological variables are considered [7,22,23]. Several studies that are based upon MLRs have reported significant results in estimating AGB of forests [6,16,24] and oil palm plantations [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

Migolet

Goı̈ta

2020

Remote Sensing

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The present study developed methods using remote sensing for estimation of total dry aboveground biomass (AGB) of oil palm in the Congo Basin. To achieve this, stem diameters at breast height (DBH, 1.3 m) and stem heights were measured in an oil palm plantation located in Gabon (Congo Basin, Central Africa). These measurements were used to determine AGB in situ. The remote sensing approach that was used to estimate AGB was textural ordination (FOTO) based upon Fourier transforms that were applied, respectively, to PlanetScope and FORMOSAT-2 satellite images taken from the area. The FOTO method is based on the combined use of two-dimensional (2D) Fast Fourier Transform (FFT) and Principal Component Analysis (PCA). In the context of the present study, it was used to characterize the variation in canopy structure and to estimate the aboveground biomass of mature oil palms. Two types of equations linking FOTO indices to in situ biomass were developed: multiple linear regressions (MLR); and multivariate adaptive spline regressions (MARS). All best models developed yielded significant results, regardless of whether they were derived from PlanetScope or from FORMOSAT-2 images. Coefficients of determination (R2) varied between 0.80 and 0.92 (p ≤ 0.0005); and relative root mean-square-errors (%RMSE) were less than 10.12% in all cases. The best model was obtained using MARS approach with FOTO indices from FORMOSAT-2 (%RMSE = 6.09%).

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“…There have been many studies on point cloud data as well as point cloud classification algorithms proposed by many scientists in recent years (Lorenzo et al, 2018;Yumin et al, 2018;Ning et al, 2019;Geunsang et al, 2019;Mustafa et al 2019;Jinbo et al, 2020). Han et al (2017) have a detailed and complete overview of point cloud filtering algorithms.…”

Section: Introductionmentioning

confidence: 99%

Method of Defining the Parameters for UAV Point Cloud Classification Algorithm

Quy¹,

Hien²,

Long³

et al. 2020

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Image data from Drones/Unmanned Aerial Vehicles (UAVs) has been studied and used extensively for establishing maps. The process of UAV data provides three main products including (Digital Surface Model) DSM, Point cloud and Ortho-photos, in which point cloud is a valuable data source in building 3D models and topographic surfaces as well. However, processing point cloud separately to achieve secondary products has not been received much attention from researchers. This study determines parameters to develop a method for classifying point cloud data constructed from UAV images. Consequently, A 3D surface of the ground is built by applying a developed algorithm for the point cloud data for an open-pit mine. The temporal or non-ground objects such as trees, houses, vehicles are automatically subtracted from the point cloud by the algorithms. According to this line, it is possible to calculate and analyze the amount of reserves, the exploited volume to evaluate the efficiency for each mine during operation with the support of UAV integrated camera.

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Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds

Cited by 61 publications

References 57 publications

Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

Aboveground Biomass Estimation in Short Rotation Forest Plantations in Northern Greece Using ESA’s Sentinel Medium-High Resolution Multispectral and Radar Imaging Missions

Evaluation of FORMOSAT-2 and PlanetScope Imagery for Aboveground Oil Palm Biomass Estimation in a Mature Plantation in the Congo Basin

Method of Defining the Parameters for UAV Point Cloud Classification Algorithm

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