Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach

Mosomtai Gladys, Odindi John, Abdel-Rahman Elfatih M., Babin Régis, Pinard Fabrice, Mutanga Onisimo, Tonnang Henri E.Z., David Guillaume, Landmann Tobias. 2020. Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach. Journal of Applied Remote Sensing, 14 (4):044513, 20 p.

https://doi.org/10.1117/1.JRS.14.044513

Article de revue ; Article de recherche ; Article de revue à facteur d'impact

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Quartile : Q4, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Q4, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q4, Sujet : REMOTE SENSING

Résumé : Smallholder agroecological subzones (AEsZs) produce an array of crops occupying large areas throughout Africa but remain largely unmapped. We explored multisource satellite datasets to produce a seamless land-use and land-cover (LULC) and fragmentation dataset for upper midland (UM1 to UM4) AEsZs in central Kenya. Specifically, the utility of PlanetScope, Sentinel 2, and Landsat 8 images for mapping coffee-based landscape were tested using a random forest (RF) classifier. Vegetation indices, texture variables, and wavelength bands from all satellite data were used as inputs in generating four RF models. A LULC baseline map was produced that was further analyzed using FRAGSTAT to generate landscape metrics for each AEsZs. Wavelength bands model from Sentinel 2 had the highest overall accuracy with shortwave near-infrared and green bands as the most important variables. In UM1 and UM2, coffee was the dominant cover type, whereas annual and other perennial crops dominated the landscape in UM3 and UM4. The patch density for coffee was five times higher in UM4 than in UM1. Since Sentinel 2 is freely available, the approach used in our study can be adopted to support land-use planning in smallholder agroecosystems.

Mots-clés Agrovoc : paysage agricole, agroécosystème, cartographie de l'utilisation des terres, télédétection, apprentissage machine, Coffea arabica

Mots-clés géographiques Agrovoc : Kenya

Mots-clés complémentaires : Bioagresseur

Mots-clés libres : Agroecosystem, Remote Sensing, Machine learning, Coffea arabica, Landscape fragmentation

Classification Agris : F40 - Écologie végétale
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

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