Spatial Assessment of Agroforestry Biomass Availability in Northern Portugal Using GIS and High-Resolution Remote Sensing Data

Agroforestry biomass residues remain underexploited in Northern Portugal, with around 1.7 million tons being produced yearly (considering pruning residues and forest residual biomass)¹, of which only 40-60% is currently valorised².  Existing national-scale GIS models overlook this fine-grained spatial dispersion, leading to inefficient management practices that continue to drive harmful open-air burning³. High-resolution spatial surveys can address these logistical barriers and map local availability¹. Addressing this gap, this work introduces a scripted, reproducible QGIS 3.44 workflow that converts remote sensing-derived land-cover data into fine-resolution bioenergy intelligence, overcoming the coarse scale and manual processing of most existing GIS-based assessments.

An automated PyQGIS workflow processed the COS2023v1 national land-cover dataset, derived from very-high-resolution imagery and LiDAR coverage; clipped regional vectors; and applied literature-based, crop- and forestry-specific residue coefficients to quantify local biomass and aggregate it into continuous 2 km hexagonal grids.

The workflow estimated a total residual biomass availability at approximately 2.14 million t/year across Northern Portugal (1.39 million agricultural; 0.75 million forestry). The resulting heatmap reveals marked spatial heterogeneity, identifying hotspots such as the Douro vineyard concentration (301,248 t/year). Notably, olive groves yielded more biomass (656,214 t/year) than vineyards (465,615 t/year) despite a comparable cultivated area (101,084 vs. 91,256 ha), showing that residue coefficients, not land area, govern local supply. This spatial detail supports siting decentralised processing units (pyrolysis, micro-scale biogas, composting), reducing transport distances and logistical carbon footprint versus centralised alternatives.

This QGIS-based methodology addresses the limitations of broad national assessments and is easily applicable to other agroforestry regions, providing a practical, replicable basis for regional bioenergy planning.

Conflict of Interest: The authors declare no conflicts of interest.

[1] Nunes & Silva. Logistics 2023, 7, 48, doi:10.3390/logistics7030048.

[2] Bastos et al. J. Clean. Prod. 2024, 467, 143041, doi:10.1016/j.jclepro.2024.143041.

[3] Nunes et al. Land 2022, 11, 1722, doi:10.3390/land11101722.