Integrating MODIS-derived indices for eucalyptus stand volume estimation: an evaluation of MODIS gross primary productivity - Rajab Pourrahmati, Manizheh; Le Maire, Guerric; Baghdadi, Nicolas; Alvares, Clayton Alcarde; Stape, José Luis; Scolforo, Henrique Ferraco; Campoe, Otávio Camargo; Nouvellon, Yann; Guillemot, Joannès (Frontiers in Remote Sensing, 2025)
Accurate estimates of stand volume dynamics in Eucalyptus plantations is critical for sustainable forest management and wood production. This study investigates the integration of MODIS-derived indices, such as gross primary productivity (GPP), net photosynthesis (PSN) and normalized difference vegetation index (NDVI), with traditional age-based methods to improve stand volume estimation in Eucalyptus plantations. MODIS GPP was first evaluated against flux tower measurements, showing moderate agreement and systematic biases, particularly during periods of highest and lowest productivity in the first years after planting, with an RMSE of 19.65 gC m-2 8day-1 and R2 of 0.38. Multiple linear regression (MLR) and two machine learning models, including random forest (RF) and stochastic gradient boosting (SGB), were used to estimate stand volume by incorporating cumulative MODIS indices (Cgpp, Cpsn and Cndvi) and stand age. The SGB model showed the best performance using the full dataset, including stands aged from 1.6 to 8.4 years, with an RMSE of 22.63 m3 ha-1, an rRMSE of 17.15% and an R2 of 0.90. We showed that including cumulative indices from the first two years of growth significantly improved the model’s ability to predict growth dynamics in middle-aged to mature stands. These results highlight the utility of MODIS productivity products for medium to large-scale plantation management, providing scalable and cost-effective monitoring of stand volume.