Inference for lidar-assisted estimation of forest growing stock volume

By Ronald E. McRoberts, Erik Næsset, Terje Gobakken. 2013

184 citations recorded by the source corpus.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/acea842b50f56c4a41c94d79596c9d88fe896385

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Hybrid estimators for mean aboveground carbon per unit area (2016, methodology): “Comparisons for wood densities The estimates of the population means were compared for the wood densities used for this study and the American wood densities reported by Miles and Smith (2009) (Table 1).”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “Previous research has shown that remote sensing-based models provide more accurate biomass estimation than other models (e.g. process-based ecosystem models, GIS-based empirical models; e.g. McRoberts et al. 2013).”
Effects of uncertainty in model predictions of individual tree volume on large area volume estimates (2014, methodology): “…and monitoring programs to increase the precision of large area volume estimates should focus on reducing the effects of sampling variability by increasing sample sizes, using more efficient sampling designs, and using remotely sensed data to enhance inferences (McRoberts et al. 2012, 2013).”
Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations (2017, methodology): “Model-based methodologies used to link field and lidar data to estimate and map AGB typically require laser altimetry information for the entire spatial domain of interest (Babcock et al., 2015, 2016; McRoberts et al., 2013).”
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014, methodology): “The use of non-linear regression (as e.g. applied in Næsset et al. 2013; McRoberts et al., 2013), as well as the further optimization of the KNN approach by applying efficient weighting procedures of the predictor variables (e.g., with a genetic algorithm or an optimized distance metric) could…”