TUBITAK Destekli Projelerden Çıkan Yayınlar Topluluğu
Yayınlanmış 1 Ocak 2023 | Sürüm v1
Konferans bildirisi Açık

FOREST BIOPHYSICAL PARAMETER ESTIMATION VIA MACHINE LEARNING AND NEURAL NETWORK APPROACHES

Oluşturanlar

  • 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

Açıklama

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.

Dosyalar

bib-dedb6641-4629-4ffa-9475-33a444c065bf.txt

Dosyalar (311 Bytes)

Ad Boyut Hepisini indir
md5:fac8404543df1c4a8c57469bfe47f5fb
311 Bytes Ön İzleme İndir