Improving forest above-ground biomass estimation using genetic-based feature selection from Sentinel-1 and Sentinel-2 data (case study of the Noor forest area in Iran)
Biomass holds great importance in the environment, as it not only allows us to measure the carbon stored in forests but also facilitates the assessment of biodiversity and the evaluation of ecological integrity within these crucial ecosystems. In this study, we employed a Genetic Algorithm (GA) to e...
Saved in:
| Main Authors: | Armin Moghimi, Ava Tavakoli Darestani, Nikrouz Mostofi, Mahdiyeh Fathi, Meisam Amani |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-04-01
|
| Series: | Kuwait Journal of Science |
| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410823002006 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud
by: Chiranjit Singha, et al.
Published: (2025-04-01) -
Estimation of Amorphophallus Konjac Above-Ground Biomass by Integrating Spectral and Texture Information from Unmanned Aerial Vehicle-Based RGB Images
by: Ziyi Yang, et al.
Published: (2025-03-01) -
Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data
by: Win Sithu Maung, et al.
Published: (2025-03-01) -
Exploring optimal combinations of multi-frequency polarimetric SAR observations to estimate forest above-ground biomass
by: Yongjie Ji, et al.
Published: (2025-03-01) -
Species richness is not a good predictor for above-ground biomass in a warm temperate deciduous broadleaf forest
by: Chunmei He, et al.
Published: (2025-01-01)