FOREST BIOPHYSICAL PARAMETER ESTIMATION VIA MACHINE LEARNING AND NEURAL NETWORK APPROACHES

被引：3

作者：

Aksoy, Samet ^{[1
]}

Al Shwayyat, Shouq Zuhter Hasan ^{[2
]}

Topgul, Sule Nur ^{[1
]}

Sertel, Elif ^{[1
]}

Unsalan, Cem ^{[2
]}

Salo, Jari ^{[3
]}

Holmstrom, Anton ^{[4
]}

Wallerman, Jorgen ^{[5
]}

Nilsson, Mats ^{[5
]}

Fransson, Johan E. S. ^{[6
]}

机构：

[1] Istanbul Tech Univ, Istanbul, Turkiye

[2] Marmara Univ, Istanbul, Turkiye

[3] Univ Helsinki, Helsinki, Finland

[4] Katam Technol, Lund, Sweden

[5] Swedish Univ Agr Sci, Uppsala, Sweden

[6] Linnaeus Univ, Vaxjo, Sweden

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

基金：

瑞典研究理事会; 芬兰科学院;

关键词：

Forest; map; global; machine learning; Artificial Intelligence;

D O I：

10.1109/IGARSS52108.2023.10282899

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R-2 metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.

引用

页码：2661 / 2664

页数：4

共 11 条

[1] Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data [J].