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Detection and characterization of agroforestry systems in the Colombian Andes using sentinel-2 imagery

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Abstract

In the Colombian Andes, agroforestry is a traditional form of agriculture, characterized by a heterogeneous and often diversified composition of trees and crops. This form of land use provides important ecosystem services, such as carbon sequestration, reduction of soil erosion and the maintenance of biodiversity by providing a structural complex habitat. Satellite remote sensing is widely used for studying land use patterns and forest cover, however the discrimination between agroforestry systems and forests is still a challenge, especially in heterogeneous landscapes and in rough terrain. Here, we aim to advance the remote sensing of agroforestry systems using field measurements of vegetation structure in combination with Sentinel-2 images. We use spectral and textural variables derived from Sentinel-2 imagery to predict above ground biomass (AGB), leaf area index (LAI) and canopy closure (CC). The relationship between predicted and observed values obtained from Random Forest regression models showed good fits: for AGB with an R2 = 0.92 and relative RMSE = 34%; for LAI with an R2 = 0.91 and relative RMSE = 19%; and for CC an R2 = 0.89 and relative RMSE = 9%. This allowed us to map these important ecosystem variables at landscape scale and establish empirical thresholds, with which a discrimination of agroforestry systems from forests was possible with an accuracy of 94%. Our results suggest that the relationship between vegetation structure and the spectral information obtained by Sentinel-2 can contribute to the detection and characterization of agroforestry systems and thus help quantifying the ecosystem services and biodiversity conservation potential provided by this type of tropical agriculture.

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Acknowledgements

The authors are grateful for the funding received by the GEF-Satoyama project (http://gef-satoyama.net/) and the Young Researcher Scholarship for SBS granted by Colciencias Call No. 812 of 2018. SBS and BR would also like to thank the people of Las Cruces for their hospitality and their way to share their time and knowledge. BR would like to thank the Vicerrectoria de Investigación y Extensión of the Industrial University of Santander for their support during the execution of the GEF-Satoyama subgrant project. SBS would like to thank Mateo Jaimes, Daniel Badillo, Valentin Fromm, Xaver Schenk, Alwin Säman, Mauricio Pabón, Yovanny Duran and René Ardila for their support during field sampling. Finally, we thank Prof. Dr. Hannes Feilhauer for his comments on this ms.

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  1. School of Biology, Universidad Industrial de Santander, UIS, Cra 27 Calle 9 Ciudad Universitaria, Bucaramanga, Colombia

    Sergio Bolívar-Santamaría & Björn Reu

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  1. Sergio Bolívar-Santamaría
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Bolívar-Santamaría, S., Reu, B. Detection and characterization of agroforestry systems in the Colombian Andes using sentinel-2 imagery. Agroforest Syst 95, 499–514 (2021). https://doi.org/10.1007/s10457-021-00597-8

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