Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches
2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, California, Amerika Birleşik Devletleri, 16 - 21 Temmuz 2023, cilt.2023-July, ss.2661-2664, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 2023-July
- Doi Numarası: 10.1109/igarss52108.2023.10282899
- Basıldığı Şehir: California
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.2661-2664
- Anahtar Kelimeler: Artificial Intelligence, Forest, global, machine learning, map
- İstanbul Medipol Üniversitesi Adresli: Hayır
Özet
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 R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.