Forest biomass estimation from airborne LiDAR data using machine learning approaches

By Colin J. Gleason, Jungho Im. 2012

323 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/fec8f5359eff7d616823afcfbe5f44e6e257f871

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Florian Hartig
2. Jingfeng Huang
3. Qi Chen
4. J. Xiao
5. Dimitri Lague
6. Hooman Latifi
7. George Alan Blackburn
8. Barbara Koch

In Insect Science

1. Qi Chen
2. J. Xiao
3. Hooman Latifi
4. Barbara Koch
5. Xing Li
6. D. Bickford
7. S. Howard

In Nature and Landscape Conservation

1. Florian Hartig
2. Fabian Ewald Fassnacht
3. Qi Chen
4. J. Xiao
5. Hooman Latifi
6. Barbara Koch
7. Xing Li
8. S. Howard

Citations in the corpus

• Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, background): “…al., 36 AC C EP TE D M AN U SC R IP T 2009; Roering et al., 2009; Tarolli et al., 2012), river bed and floodplain mor- phology (Marcus and Fonstad, 2010; Belmont, 2011), and vegetation character-1005 istics (Lefsky et al., 2002; Brandtberg, 2007; Breidenbach et al., 2010; Gleason and Im, 2012b,a).”
• Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar (2015, background): “Machine-learning models are usually data-driven and thus produce implicit model structure (e.g., Chen and Hay, 2011; Gleason and Im, 2012; Li et al., 2014; Mascaro et al., 2014).”
• Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, methodology): “Airborne LiDAR data have been successfully used for forest biomass estimation [89,90] and the characterization of forest canopy structure [91].”
• Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves (2017, methodology): “111 Furthermore, SVMs have been used to estimate plant biophysical and biochemical 112 parameters such as LAI, biomass, pigments and nitrogen contents (Gleason & Im, 2012; 113 Verrelst et al., 2012; Yang et al., 2011; Zhai et al., 2013).”
• Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass (2014, background): “Although also earlier studies reported notable differences in the performance of prediction methods (Garcia-Gutierrez et al., 2011; Gleason & Im, 2012; Latifi et al., 2010), we were somewhat surprised that the effect was so pronounced.”
• A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data (2019, result): “Cubist has shorter run times while producing comparable results with other machine learning approaches [48], and it has been widely used for the estimation of biophysical variables and carbon fluxes, such as leaf area index (LAI) [49], forest biomass [50,51], net ecosystem exchange (NEE) [52], and G”