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

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.

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Main Authors: Mosomtai, Gladys, Odindi, John, Abdel-Rahman, Elfatih M., Babin, Régis, Pinard, Fabrice, Mutanga, Onisimo, Tonnang, Henri E.Z., David, Guillaume, Landmann, Tobias
Format: article biblioteca
Language:eng
Subjects:F40 - Écologie végétale, U30 - Méthodes de recherche, paysage agricole, agroécosystème, cartographie de l'utilisation des terres, télédétection, apprentissage machine, Coffea arabica, http://aims.fao.org/aos/agrovoc/c_37277, http://aims.fao.org/aos/agrovoc/c_36669, http://aims.fao.org/aos/agrovoc/c_9000100, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_49834, http://aims.fao.org/aos/agrovoc/c_1721, http://aims.fao.org/aos/agrovoc/c_4086,
Online Access:http://agritrop.cirad.fr/597442/
http://agritrop.cirad.fr/597442/1/Mos2.pdf
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spelling dig-cirad-fr-5974422024-01-29T03:16:51Z http://agritrop.cirad.fr/597442/ http://agritrop.cirad.fr/597442/ 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. Journal of Applied Remote Sensing, 14 (4):044513, 20 p.https://doi.org/10.1117/1.JRS.14.044513 <https://doi.org/10.1117/1.JRS.14.044513> 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 eng 2020 Journal of Applied Remote Sensing F40 - Écologie végétale U30 - Méthodes de recherche paysage agricole agroécosystème cartographie de l'utilisation des terres télédétection apprentissage machine Coffea arabica http://aims.fao.org/aos/agrovoc/c_37277 http://aims.fao.org/aos/agrovoc/c_36669 http://aims.fao.org/aos/agrovoc/c_9000100 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_1721 Kenya http://aims.fao.org/aos/agrovoc/c_4086 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/597442/1/Mos2.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1117/1.JRS.14.044513 10.1117/1.JRS.14.044513 info:eu-repo/semantics/altIdentifier/doi/10.1117/1.JRS.14.044513 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1117/1.JRS.14.044513
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic F40 - Écologie végétale
U30 - Méthodes de recherche
paysage agricole
agroécosystème
cartographie de l'utilisation des terres
télédétection
apprentissage machine
Coffea arabica
http://aims.fao.org/aos/agrovoc/c_37277
http://aims.fao.org/aos/agrovoc/c_36669
http://aims.fao.org/aos/agrovoc/c_9000100
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_1721
http://aims.fao.org/aos/agrovoc/c_4086
F40 - Écologie végétale
U30 - Méthodes de recherche
paysage agricole
agroécosystème
cartographie de l'utilisation des terres
télédétection
apprentissage machine
Coffea arabica
http://aims.fao.org/aos/agrovoc/c_37277
http://aims.fao.org/aos/agrovoc/c_36669
http://aims.fao.org/aos/agrovoc/c_9000100
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_1721
http://aims.fao.org/aos/agrovoc/c_4086
spellingShingle F40 - Écologie végétale
U30 - Méthodes de recherche
paysage agricole
agroécosystème
cartographie de l'utilisation des terres
télédétection
apprentissage machine
Coffea arabica
http://aims.fao.org/aos/agrovoc/c_37277
http://aims.fao.org/aos/agrovoc/c_36669
http://aims.fao.org/aos/agrovoc/c_9000100
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_1721
http://aims.fao.org/aos/agrovoc/c_4086
F40 - Écologie végétale
U30 - Méthodes de recherche
paysage agricole
agroécosystème
cartographie de l'utilisation des terres
télédétection
apprentissage machine
Coffea arabica
http://aims.fao.org/aos/agrovoc/c_37277
http://aims.fao.org/aos/agrovoc/c_36669
http://aims.fao.org/aos/agrovoc/c_9000100
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_1721
http://aims.fao.org/aos/agrovoc/c_4086
Mosomtai, Gladys
Odindi, John
Abdel-Rahman, Elfatih M.
Babin, Régis
Pinard, Fabrice
Mutanga, Onisimo
Tonnang, Henri E.Z.
David, Guillaume
Landmann, Tobias
Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach
description 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.
format article
topic_facet F40 - Écologie végétale
U30 - Méthodes de recherche
paysage agricole
agroécosystème
cartographie de l'utilisation des terres
télédétection
apprentissage machine
Coffea arabica
http://aims.fao.org/aos/agrovoc/c_37277
http://aims.fao.org/aos/agrovoc/c_36669
http://aims.fao.org/aos/agrovoc/c_9000100
http://aims.fao.org/aos/agrovoc/c_6498
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_1721
http://aims.fao.org/aos/agrovoc/c_4086
author Mosomtai, Gladys
Odindi, John
Abdel-Rahman, Elfatih M.
Babin, Régis
Pinard, Fabrice
Mutanga, Onisimo
Tonnang, Henri E.Z.
David, Guillaume
Landmann, Tobias
author_facet Mosomtai, Gladys
Odindi, John
Abdel-Rahman, Elfatih M.
Babin, Régis
Pinard, Fabrice
Mutanga, Onisimo
Tonnang, Henri E.Z.
David, Guillaume
Landmann, Tobias
author_sort Mosomtai, Gladys
title Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach
title_short Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach
title_full Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach
title_fullStr Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach
title_full_unstemmed Landscape fragmentation in coffee agroecological subzones in central Kenya: A multiscale remote sensing approach
title_sort landscape fragmentation in coffee agroecological subzones in central kenya: a multiscale remote sensing approach
url http://agritrop.cirad.fr/597442/
http://agritrop.cirad.fr/597442/1/Mos2.pdf
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