Predicting stem diameters and aboveground biomass of individual trees using remote sensing data

Dalponte, Michele; Frizzera, Lorenzo; Ørka, Hans Ole; Gobakken, Terje; Næsset, Erik; Gianelle, Damiano

doi:10.1016/j.ecolind.2017.10.066

Cited by 55 publications

(50 citation statements)

References 21 publications

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“…In the first part of the analysis, only H and CD were used as input in order to compare the obtained results with those reported in Dalponte et al (2018). Tab.…”

Section: Resultsmentioning

confidence: 99%

“…In order to evaluate our methods, each dataset (i.e., Hadeland, and Våler) was divided in two subsets (same as in Dalponte et al 2018). The first set was used for calibration and the second for the validation phase.…”

Section: Parameter Settingmentioning

confidence: 99%

“…Among this last group of studies, Jucker et al (2017) proposed new allometric models to predict DBH and AGB based on H and crown diameter (CD) extracted from ARS data. Dalponte et al (2018) in a recent study successfully linked field-reference DBH and AGB with H and CD extracted from ALS data using linear models.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of the current study is to analyze the use of machine learning meth-ods, such as Support Vector Machines for Regression (SVR) and Random Forest (RF) to predict DBH and AGB at the ITC level using metrics extracted from ALS data. In the first part of the experiment, only H and CD are used as input in order to compare with the work of Dalponte et al (2018). After that, additional ALS metrics are used as input in order to see their impact on the quality of the prediction.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Malek¹,

Miglietta²,

Gobakken³

et al. 2019

iForest

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Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.

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“…In the first part of the analysis, only H and CD were used as input in order to compare the obtained results with those reported in Dalponte et al (2018). Tab.…”

Section: Resultsmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Malek¹,

Miglietta²,

Gobakken³

et al. 2019

iForest

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show abstract

“…To improve the accuracy of mangrove biomass estimation and mapping, more remote sensing data and methods have been tested and applied to mangroves [4,[12][13][14][15][16][17][18][19][20]. Very high resolution images, such as WorldView-2 (Digitalglobe, Westminster, CO, USA), GeoEye (GeoEye, Herndon, VA, USA), IKONOS (Spacing Imaging, Herndon, VA, USA), QuickBird (Digitalglobe, Westminster, CO, USA), digital photographs, and light detection and ranging (LiDAR) point cloud data, can be used for extracting individual trees [21][22][23][24][25][26], as well as the accurate estimation of AGB or carbon stocks at the individual tree level [27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Qiu

Wang

Zou

et al. 2019

Forests

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To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%.(2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R 2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha −1 vs. 38.95 Mg ha −1 . However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

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Estimating individual‐level plant traits at scale

Marconi

Graves

Bohlman

et al. 2021

Ecological Applications

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Functional ecology has increasingly focused on describing ecological communities based on their traits (measurable features affecting individuals' fitness and performance). Analyzing trait distributions within and among forests could significantly improve understanding of community composition and ecosystem function. Historically, data on trait distributions are generated by (1) collecting a small number of leaves from a small number of trees, which suffers from limited sampling but produces information at the fundamental ecological unit (the individual), or (2) using remote-sensing images to infer traits, producing information continuously across large regions, but as plots (containing multiple trees of different species) or pixels, not individuals. Remote-sensing methods that identify individual trees and estimate their traits would provide the benefits of both approaches, producing continuous large-scale data linked to biological individuals. We used data from the National Ecological Observatory Network (NEON) to develop a method to scale up functional traits from 160 trees to the millions of trees within the spatial extent of two NEON sites. The pipeline consists of three stages:(1) image segmentation, to identify individual trees and estimate structural traits; (2) an ensemble of models to infer leaf mass area (LMA), nitrogen, carbon, and phosphorus content using hyperspectral signatures, and DBH from allometry; and (3) predictions for segmented crowns for the full remote-sensing footprint at the NEON sites. The R 2 values on held-out test data ranged from 0.41 to 0.75 on held-out test data. The ensemble approach performed better than single partial least-squares models. Carbon performed poorly compared to other traits (R 2 of 0.41). The crown segmentation step contributed the most uncertainty in the pipeline, due to over-segmentation. The pipeline produced good estimates of DBH (R 2 of 0.62 on held-out data). Trait predictions for crowns performed significantly better than comparable predictions on pixels, resulting in improvement of R 2 on test data of between 0.07 and 0.26. We used the pipeline to produce individual-level trait data for~5 million individual crowns, covering a total extent of~360 km 2 . This large data set allows testing ecological questions on landscape scales, revealing that foliar traits are correlated with structural traits and environmental conditions.

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Predicting stem diameters and aboveground biomass of individual trees using remote sensing data

Cited by 55 publications

References 21 publications

Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

Estimating individual‐level plant traits at scale

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