UAV and satellite-based prediction of aboveground biomass in scots pine stands: a comparative analysis of regression and neural network approaches
Yazarlar (2)
Dr. Öğr. Üyesi Hasan AKSOY Sinop Üniversitesi, Türkiye
Prof. Dr. Alkan Günlü Çankırı Karatekin Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
|
||
| Dergi Adı | Earth Science Informatics (Q1) | ||
| Dergi ISSN | 1865-0473 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 18-12-2024 |
| Cilt / Sayı / Sayfa | 18 / 1 / 66–0 | DOI | 10.1007/s12145-024-01657-0 |
| Makale Linki | https://doi.org/10.1007/s12145-024-01657-0 | ||
| UAK Araştırma Alanları |
Orman Hasılatı ve Amenajmanı
|
||
| Özet |
| Forest ecosystems play a vital role in balancing the global climate through functions such as regulating carbon emissions, carbon sequestration, and energy and water cycles. Aboveground biomass (AGB) is a critical component in forest management to understand better and predict the global carbon cycle. However, traditional methods used in AGB measurement involve time-consuming, costly, and labor-intensive processes. Sentinel-1 (active), Sentinel-2, and Landsat (passive) satellite imagery, which is freely accessible and offers global coverage with frequent updates, and recently developed remote sensing platforms such as Unmanned Aerial Vehicle (UAV) serve as a valuable data source for consistent and continuous monitoring of aboveground biomass. This research focuses on modeling the relationships between AGB and data obtained from various remote sensing sources, including Sentinel-1, Sentinel … |
| Anahtar Kelimeler |
| ANNs | Forest stand metrics | Natural forest | UAV |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 6 |
| Scopus | 5 |
| Google Scholar | 9 |