Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data


  • Fardin Moradi Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran
  • Seyed Mohamad Moein Sadeghi Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov
  • Hadi Beygi Heidarlou Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran
  • Azade Deljouei Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov
  • Erfan Boshkar Natural Resources Department, Faculty of Agriculture, Razi University, Kermanshah, Iran
  • Stelian Alexandru Borz Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Transilvania University of Brasov




Sentinel-2, Above-ground biomass, Optical satellite data, Machine learning, Modeling, Estimation, Accuracy, Sparse-Coppice


Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: Multi-Layer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots.


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