Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques

Area of Science:

• Forestry
• Remote Sensing
• Geostatistics

Background:

• Accurate mapping of forest biomass is essential for carbon dioxide (CO₂) emission estimations, forest management, and monitoring ecosystem productivity.
• Forest biomass data supports climate change mitigation strategies and sustainable forest resource utilization.

Purpose of the Study:

• To map aboveground woody biomass (AGWB) using integrated forest inventory, remote sensing, and geostatistical techniques.
• To evaluate and compare the accuracy of different geostatistical methods for biomass mapping.
• To identify the most effective method for precise AGWB estimation in the study area.

Main Methods:

• Stratified random sampling was employed to collect biophysical data from 36 sample plots.
• Aboveground woody biomass (AGWB) was calculated using species-specific volumetric equations and specific gravity.
• Three geostatistical techniques—direct radiometric relationships (DRR), k-nearest neighbors (k-NN), and cokriging (CoK)—were applied and validated.

Main Results:

• The k-nearest neighbors (k-NN) method, specifically using Mahalanobis distance, demonstrated the highest accuracy with a root mean square error (RMSE) of 42.25 Mg ha⁻¹.
• Fuzzy distance and Euclidean distance within the k-NN framework also provided reliable estimates, with RMSEs of 44.23 and 45.13 Mg ha⁻¹, respectively.
• Direct radiometric relationships (DRR) proved to be the least accurate method, yielding an RMSE of 67.17 Mg ha⁻¹.

Conclusions:

• The integration of forest inventory, remote sensing, and geostatistical techniques offers a robust approach for accurate forest biomass mapping.
• The k-nearest neighbors (k-NN) method is recommended as the most effective geostatistical technique for aboveground woody biomass estimation.
• This study underscores the potential of advanced spatial analysis techniques for enhancing forest resource assessment and carbon stock monitoring.