The use of remote sensing tools for accurate charcoal kilns’ inventory and distribution analysis: Comparative assessment and prospective

Abstract

Historical charcoal production is one of the significant factors affecting today’s forest dynamics. A key challenge is to develop tools to investigate historical charcoal production over large areas, allowing a more comprehensive understanding of past impacts and history of charcoal production over a given landscape. In this study, high-resolution remote-sensing airborne LiDAR images over a large woodland area were used to compare manual on-screen versus algorithm-based automatic methods to inventory charcoal kilns with inputs of field-validated data. The results revealed that (1) the on-screen detection method provided less false-positives, (2) the automatic method detects a higher number of kilns and (3) kiln distribution seemed to be connected mostly to land ownership rather than to environmental variables. This study validates a new method of charcoal kilns’ inventory and spatial analysis that can be applied to other areas to better understand the effect of past biomass harvesting for charcoal production on forest dynamics.

Keywords

• Charcoal kiln
• LiDAR
• Automatic detection
• Spatial analysis
• Deep learning