Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass

Chen, Qi; Laurin, Gaia Vaglio; Battles, John J.; Saah, David

doi:10.1016/j.rse.2012.01.021

Cited by 102 publications

(74 citation statements)

References 39 publications

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“…However, White et al [67] and Naesset [72] found that leaf-off LiDAR data correlate better to field observed AGB than leaf-on data, so we recommend further study for sites with leaf-off data to reevaluate the performance of generic model. Considering the limitations of remotely sensed data (e.g., limited spatial coverage and incapability for species mapping with LiDAR, and signal saturation of radar or passive optical imagery in high biomass areas), several studies have underscored the need for data fusion from different sources to accurately estimate AGB over a large area (e.g., [30,[73][74][75]). …”

Section: Discussionmentioning

confidence: 99%

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Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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Section: Discussionmentioning

confidence: 99%

“…However, the form of the models and importance rankings of predictors can vary with forest types [30]. In any case, formulation of a generic model requires selection of an optimal set of predictors [29].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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“…In contrast to Swatantran et al (2011) andChen et al (2012), other studies suggest that integrating Lidar data and optical or radar imagery does not produce better biomass predictions. For example, Hyde et al (2006) found that adding Quickbird and SAR/InSAR forest structure metrics to Lidar resulted in no improvement for estimating biomass across 120 circular 0.40-acre (1-hectare) plots in the Sierra National Forest; this was explained by the fact that the structure metrics from SAR/InSAR and Quickbird were very similar to those of Lidar.…”

Section: Sierra Nevada Adaptive Management Projectmentioning

confidence: 88%

“…A different approach to biomass mapping was adopted by Chen et al (2012), who used mixed-effects modeling to integrate airborne Lidar data and vegetationtype data derived from aerial imagery. Incorporating vegetation type improved biomass estimation (R 2 improved from 0.77 to 0.83) and decreased RMSE by 10% from 199.6 to 178.4 megagrams (Mg) per acre (80.8 to 72.2 Mg per hectare).…”

Section: Sierra Nevada Adaptive Management Projectmentioning

confidence: 99%

Mapping forests with Lidar provides flexible, accurate data with many uses

Kelly¹,

Tommaso²

2015

Cal Ag

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“…Field AGB measurements usually are not achievable at forests with dense canopies, and cannot cover extensive spatial area. Light detection and ranging (lidar) is a remote sensing technology that has been widely used to estimate forest biomass at plot and stand levels (Lefsky et al 1999;Hyde, Nelson et al 2007;Chen et al 2012). Canopy vertical structural indices can be extracted from lidar measurements.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Tree Height-DBH Allometry on Lidar-Based Tree Aboveground Biomass Modeling

Fang¹

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG VIII/7 KEY WORDS: tree aboveground biomass, lidar, allometric equations, simulation, regression ABSTRACT:Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in R 2 and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.

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Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass

Cited by 102 publications

References 39 publications

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Mapping forests with Lidar provides flexible, accurate data with many uses

Impacts of Tree Height-DBH Allometry on Lidar-Based Tree Aboveground Biomass Modeling

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