Estimation of forest biomass from light detection and ranging data by using machine learning

Abstract

The use of data driven predictive systems is becoming widespread as innovations in machine learning techniques have allowed the training of increasingly sophisticated models via the available data. The light detection and ranging (LiDAR) remote sensing technique is being increasingly applied to obtain informative terrain maps, due to its ability to collect large amounts of data with satisfactory accuracy. This paper focuses on the application of machine‐learning‐based predictive systems for the extraction of biomass information from LiDAR data. Biomass information has inmense ecological and economical value. We demonstrate the estimation of the Pinus radiata biomass in the Arratia‐Nervión region (Spain). Biomass estimation is considered a regression problem in which the ground truth for some specific sample sites is available. The promising results obtained in this study indicate that LiDAR data can be used to carry out detailed biomass mappings by the extrapolation of the models trained in this study.

Soft computing applications in the field of industrial and environmental enterprises

“…Torre‐Tojal, Lopez‐Guede, and Graña Romay () propose the application of machine‐learning‐based predictive systems for the extraction of biomass information from Light Detection and Ranging (LiDAR) data. LiDAR is a remote sensing tool that retrieves surface elevation measurements at high spatial resolutions, even in rough terrains and heavily forested areas.…”

Suitability Assessment Method of Red Tourism Development Using Geospatial and Social Humanity Data: A Case Study of Ruijin City, East China

“…Remote sensing (RS) image classification is a common tool for land use survey, which has become more robust with the introduction of machine learning algorithms [41][42][43][44][45][46]. Supervised machine learning algorithms have obtained promising results in mineral prospectivity mapping [47][48][49][50], geo-hazard mapping and geo-risk assessment [51][52][53][54], biomass estimation [55][56][57][58], and dust source susceptibility mapping [59,60].…”