Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure

Vincent, Grégoire; Sabatier, Daniel; Blanc, Lilian; Chave, Jérôme; Weissenbacher, Emilien; Pélissier, Raphaël; Fonty, Émile; Molino, Jean‐François; Couteron, Pierre

doi:10.1016/j.rse.2012.06.019

Cited by 58 publications

(37 citation statements)

References 37 publications

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“…Similar lack of fit has been reported in studies from areas with high biomass values (e.g., [44,45]). The plots of the grouped means of observations versus predictions (Figures 5-7), however, showed small differences.…”

Section: Resultssupporting

confidence: 87%

“…The Pólya-urn resampling scheme generates a design-consistent posterior predictive distribution of the property in interest, given that the sample is reasonably large and representative of the population ( [43], pp. [44][45][46]. We consider our field sample of u=30 observations as representative of the population, and the Pólya-urn resampling generated posterior predictive distributions of biomass for U = 60, 120, and 180 observations based on the sample.…”

Section: Relative Efficiencymentioning

confidence: 99%

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Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Hansen

Gobakken

Solberg³

et al. 2015

Remote Sensing

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Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m 2 , sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5-6.0 times as many observations for plot sizes of 700-1900 m 2 to achieve the same precision as an inventory supported by ALS data. OPEN ACCESSRemote Sens. 2015, 7 9866

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Section: Resultssupporting

confidence: 87%

Section: Relative Efficiencymentioning

confidence: 99%

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Hansen

Gobakken

Solberg³

et al. 2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In this study, both multivariate linear regression (MLR) model and logistic regression (LR) model were used to account for the relationship of forest ecosystem AGB with the spectral variables selected by stepwise regression analysis [61,62]. MLR is the most widely used method, but many studies have shown that the MLR has several shortcomings such as leading to negative and extremely large estimates.…”

Section: Multivariate Linear Regression (Mlr) and Logistic Regressionmentioning

confidence: 99%

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

et al. 2017

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Abstract:The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin.

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“…We expected that vegetation structure and species composition would vary with climate and anthropogenic disturbance regimes in the studied area [27] and that relationships between LiDAR metrics and forest characteristics are site dependent [34]. It is important to evaluate the accuracy of a general model encompassing different vegetation types, for possible applications at a regional level.…”

Section: Introductionmentioning

confidence: 99%

Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

et al. 2014

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The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

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Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure

Cited by 58 publications

References 37 publications

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

Improving Species Diversity and Biomass Estimates of Tropical Dry Forests Using Airborne LiDAR

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