Integration of Digital Cover Photography and Multi-Source Remote Sensing Approaches for Forest Canopy Cover Estimation in Southwest Ethiopia

Integration of Digital Cover Photography and Multi-Source Remote Sensing Approaches for Forest Canopy Cover Estimation in Southwest Ethiopia

 

Mohammed S Ozigis¹, Byongjun Hwang¹, Thierno Bachir Sy², Matthew Snell² and Desyalew Fantaye³, Adrian Wood²

¹Department of Biological and Geographical Sciences, School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield HD1 3HD, UK

²Department of Management, Huddersfield Business School, University of Huddersfield, Queensgate, Huddersfield HD1 3HD, UK

³Ethio-Wetlands and Natural Resource Association, Ethiopia.

 

Abstract

Forest loss through deforestation and degradation is an important factor shaping the global climate change and its attendant short- and long-term impacts. Forest canopy cover (FCC) estimation has evolved to become an important and essential parameter for establishing degraded forest. Recent advances in Earth Observation (EO) satellite sensors have opened a new frontier in estimating, mapping and monitoring forest cover using high-resolution imagery and machine learning (ML). These have typically relied on canopy cover extracted from aerial or satellite images to establish baseline reference data. While several studies have alluded to the suitability of field-based Digital Cover Photography (DCP) for forest canopy characterization, none have explored their potential in predicting forest canopy cover through its integration with EO satellite data in-combination with ML methods. This study explores the integration of multi-sensor EO data from Sentinel-1 and Sentinel-2, along with topographic information (Digital Surface Model, DSM) and field-based DCP canopy cover measurements, to enhance the accuracy of EO-derived forest canopy cover estimates in Southwest Ethiopia. Over 1,000 DCP measurements were obtained during a field campaign conducted from January to February 2025 in southwest Ethiopia. The DCP data were then used to train both simple linear regression and advanced ML regression models to predict and map canopy cover. Initial results suggest that the integration of Sentinel-2 raw spectral bands with DSM produced the most accurate canopy cover estimates, with Random Forest (RF) model achieving the highest R² (0.63) and lowest RMSE (7.4%). In addition, the XGBoost model achieved R² of 0.59 and an RMSE of 7.9%, while the Generalized Additive Model (GAM) outperformed the other linear models tested, producing a higher R² (0.52) and a lower RMSE (8.63%). This study demonstrates that integrating field-based DCP measurements with EO data provides a more accurate approach for estimating baseline forest canopy cover, thereby advancing existing knowledge and methodologies for EO-based canopy cover mapping.

 Keywords: Forest Canopy Cover, Deforestation, Forest Degradation, XGBoost, Random Forest, Digital Cover Photography, Sentinel-1, Sentinel-2