Inhaltszusammenfassung

Accurate estimation of Above-Ground Biomass Density (AGBD) is crucial for assessing vegetation structure, carbon accounting, and forest productivity, as well as for supporting climate change mitigation and sustainable forest management. Protected forest areas, such as those in Gilan Province in northern Iran, represent ecologically valuable ecosystems. However, due to limited access to field and airborne-based LiDAR data, spatially continuous monitoring of these areas remains a challenge. Rec...Accurate estimation of Above-Ground Biomass Density (AGBD) is crucial for assessing vegetation structure, carbon accounting, and forest productivity, as well as for supporting climate change mitigation and sustainable forest management. Protected forest areas, such as those in Gilan Province in northern Iran, represent ecologically valuable ecosystems. However, due to limited access to field and airborne-based LiDAR data, spatially continuous monitoring of these areas remains a challenge. Recent advancements in remote sensing, particularly the availability of spaceborne LiDAR from missions like Global Ecosystem Dynamics Investigation (GEDI), have opened new possibilities for monitoring forest structure over large and remote areas. The GEDI mission provides point-based measurements of canopy height and biomass, which can help overcome the limitations of traditional field-based methods. In this study, we propose an approach for estimating AGBD in the protected forests of Gilan using GEDI Level 4A-derived vertical structure data, combined with wall-to-wall multispectral data from Sentinel-2 and terrain information from SRTM. Based on machine learning algorithms, we extend the point-based biomass estimates to generate spatially continuous AGBD maps across the study area. The results highlight the potential of freely available spaceborne LiDAR, in conjunction with satellite data and modeling techniques, for mapping local-scale biomass. This study offers a valuable baseline for future research in forest ecosystem monitoring and opens the door for utilizing other satellite-based LiDAR resources such as ICESat-2 and incorporating advanced machine learning algorithms for biomass estimation and carbon stock assessment.» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
3.43-02 - Geodäsie, Photogrammetrie, Fernerkundung, Geoinformatik, Kartographie

DDC Sachgruppe:
Geowissenschaften