Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades

Descripción del Articulo

Land use and land cover (LULC) changes in the Piura River Basin, Peru, were analyzed from 2001 to 2022 using global MODIS and ESA-CCI datasets harmonized into six major land cover classes (Forest, Non-Forest Vegetation, Cropland, Bare Soil, Water and Urban) for comparative analysis. Pearson correlat...

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Detalles Bibliográficos
Autores: Castillón, Fiorela, Rau, Pedro, Bourrel, Luc, Frappart, Frédéric
Formato: artículo
Fecha de Publicación:2025
Institución:Instituto Geofísico del Perú
Repositorio:IGP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.igp.gob.pe:20.500.12816/5718
Enlace del recurso:http://hdl.handle.net/20.500.12816/5718
https://doi.org/10.3389/frsen.2025.1529044
Nivel de acceso:acceso abierto
Materia:Landuse-landcover
Spatio-temporal dynamics
ENSO impact
Pacific slope
Coast Peru
Piura River Basin
https://purl.org/pe-repo/ocde/ford#1.05.11
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dc.title.none.fl_str_mv Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
title Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
spellingShingle Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
Castillón, Fiorela
Landuse-landcover
Spatio-temporal dynamics
ENSO impact
Pacific slope
Coast Peru
Piura River Basin
https://purl.org/pe-repo/ocde/ford#1.05.11
title_short Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
title_full Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
title_fullStr Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
title_full_unstemmed Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
title_sort Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades
author Castillón, Fiorela
author_facet Castillón, Fiorela
Rau, Pedro
Bourrel, Luc
Frappart, Frédéric
author_role author
author2 Rau, Pedro
Bourrel, Luc
Frappart, Frédéric
author2_role author
author
author
dc.contributor.author.fl_str_mv Castillón, Fiorela
Rau, Pedro
Bourrel, Luc
Frappart, Frédéric
dc.subject.none.fl_str_mv Landuse-landcover
Spatio-temporal dynamics
ENSO impact
Pacific slope
Coast Peru
Piura River Basin
topic Landuse-landcover
Spatio-temporal dynamics
ENSO impact
Pacific slope
Coast Peru
Piura River Basin
https://purl.org/pe-repo/ocde/ford#1.05.11
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#1.05.11
description Land use and land cover (LULC) changes in the Piura River Basin, Peru, were analyzed from 2001 to 2022 using global MODIS and ESA-CCI datasets harmonized into six major land cover classes (Forest, Non-Forest Vegetation, Cropland, Bare Soil, Water and Urban) for comparative analysis. Pearson correlation analyses with hydroclimatic variables, including precipitation (PP), maximum (Tx) and minimum (Tn) temperatures, and El Niño Southern Oscillation (ENSO) indices (Eastern Pacific, Central Pacific, and Coastal El Niño), complemented the intensity analysis to explore environmental drivers. The analyses focused on the lower-middle and upper basin regions during wet (December-May) and dry (June-November) seasons. MODIS detected more dynamic LULC transitions, with 32.8% of pixels showing changes, compared to 6.8% detected by the ESA-CCI product. These differences reflect the distinct sensitivities of MODIS and ESA-CCI products to short-term fluctuations and long-term variations, respectively. Specifically, MODIS identified higher annual change intensities and more frequent transitions, especially in the upper basin, whereas ESA-CCI provided a more conservative view of land cover trends. Both datasets consistently indicated a decline in cropland areas and an increase in bare soil, suggesting agricultural degradation and potential desertification processes. Correlation analyses revealed significant relationships between vegetation dynamics and climatic variables, notably ENSO events, precipitation, and temperature extremes, highlighting how hydroclimatic factors drive vegetation variability. The upper basin experienced notable urban expansion and deforestation dynamics linked to temperature fluctuations and intensified El Niño events, particularly after 2011. These findings underscore the critical influence of climatic extremes and human activities on vegetation dynamics, emphasizing the need for integrated, adaptive management strategies to mitigate desertification in lowlands and enhance forest conservation in highlands.
publishDate 2025
dc.date.accessioned.none.fl_str_mv 2025-06-12T21:30:15Z
dc.date.available.none.fl_str_mv 2025-06-12T21:30:15Z
dc.date.issued.fl_str_mv 2025-05-15
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.none.fl_str_mv Castillón, F., Rau, P., Bourrel, L., y Frappart, F. (2025). Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades.==$Frontiers in Remote Sensing, 6$==. https://doi.org/10.3389/frsen.2025.1529044
dc.identifier.govdoc.none.fl_str_mv index-oti2018
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12816/5718
dc.identifier.journal.none.fl_str_mv Frontiers in Remote Sensing
dc.identifier.doi.none.fl_str_mv https://doi.org/10.3389/frsen.2025.1529044
identifier_str_mv Castillón, F., Rau, P., Bourrel, L., y Frappart, F. (2025). Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades.==$Frontiers in Remote Sensing, 6$==. https://doi.org/10.3389/frsen.2025.1529044
index-oti2018
Frontiers in Remote Sensing
url http://hdl.handle.net/20.500.12816/5718
https://doi.org/10.3389/frsen.2025.1529044
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv urn:issn: 2673-6187
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
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instname:Instituto Geofísico del Perú
instacron:IGP
instname_str Instituto Geofísico del Perú
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institution IGP
reponame_str IGP-Institucional
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spelling Castillón, FiorelaRau, PedroBourrel, LucFrappart, Frédéric2025-06-12T21:30:15Z2025-06-12T21:30:15Z2025-05-15Castillón, F., Rau, P., Bourrel, L., y Frappart, F. (2025). Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decades.==$Frontiers in Remote Sensing, 6$==. https://doi.org/10.3389/frsen.2025.1529044index-oti2018http://hdl.handle.net/20.500.12816/5718Frontiers in Remote Sensinghttps://doi.org/10.3389/frsen.2025.1529044Land use and land cover (LULC) changes in the Piura River Basin, Peru, were analyzed from 2001 to 2022 using global MODIS and ESA-CCI datasets harmonized into six major land cover classes (Forest, Non-Forest Vegetation, Cropland, Bare Soil, Water and Urban) for comparative analysis. Pearson correlation analyses with hydroclimatic variables, including precipitation (PP), maximum (Tx) and minimum (Tn) temperatures, and El Niño Southern Oscillation (ENSO) indices (Eastern Pacific, Central Pacific, and Coastal El Niño), complemented the intensity analysis to explore environmental drivers. The analyses focused on the lower-middle and upper basin regions during wet (December-May) and dry (June-November) seasons. MODIS detected more dynamic LULC transitions, with 32.8% of pixels showing changes, compared to 6.8% detected by the ESA-CCI product. These differences reflect the distinct sensitivities of MODIS and ESA-CCI products to short-term fluctuations and long-term variations, respectively. Specifically, MODIS identified higher annual change intensities and more frequent transitions, especially in the upper basin, whereas ESA-CCI provided a more conservative view of land cover trends. Both datasets consistently indicated a decline in cropland areas and an increase in bare soil, suggesting agricultural degradation and potential desertification processes. Correlation analyses revealed significant relationships between vegetation dynamics and climatic variables, notably ENSO events, precipitation, and temperature extremes, highlighting how hydroclimatic factors drive vegetation variability. The upper basin experienced notable urban expansion and deforestation dynamics linked to temperature fluctuations and intensified El Niño events, particularly after 2011. These findings underscore the critical influence of climatic extremes and human activities on vegetation dynamics, emphasizing the need for integrated, adaptive management strategies to mitigate desertification in lowlands and enhance forest conservation in highlands.Este trabajo fue financiado por el Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (Fondecyt - Perú) en el marco del proyecto ePiura [número de contrato 084-2018-FONDECYT-BM-IADT-MU PROCIENCIA].Por paresapplication/pdfengFrontiers Mediaurn:issn: 2673-6187info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/Landuse-landcoverSpatio-temporal dynamicsENSO impactPacific slopeCoast PeruPiura River Basinhttps://purl.org/pe-repo/ocde/ford#1.05.11Dynamics and patterns of land cover change in the Piura River Basin (Peruvian Pacific slope and coast) in the last two decadesinfo:eu-repo/semantics/articlereponame:IGP-Institucionalinstname:Instituto Geofísico del Perúinstacron:IGPLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.igp.gob.pe/bitstreams/e5a4fae2-4836-4031-9900-20b391012b6e/downloadbb9bdc0b3349e4284e09149f943790b4MD51ORIGINALCastillón_et_al_2025_Frontiers_Media.pdfCastillón_et_al_2025_Frontiers_Media.pdfapplication/pdf4160498https://repositorio.igp.gob.pe/bitstreams/7259a38d-7a8a-480b-996a-cfd60da7d0d2/download4a9cefd9b46e66d82a4439c9872101ecMD52TEXTCastillón_et_al_2025_Frontiers_Media.pdf.txtCastillón_et_al_2025_Frontiers_Media.pdf.txtExtracted texttext/plain89095https://repositorio.igp.gob.pe/bitstreams/efaf3e09-c895-43ce-a553-048ea64bb613/download06f251b5d573283e5bfe7df191c8a1e8MD53THUMBNAILCastillón_et_al_2025_Frontiers_Media.pdf.jpgCastillón_et_al_2025_Frontiers_Media.pdf.jpgGenerated Thumbnailimage/jpeg32221https://repositorio.igp.gob.pe/bitstreams/20e1a625-9386-4f51-be02-2299cc17eabc/downloadfafcf27f4c04d095d884268e177360bcMD5420.500.12816/5718oai:repositorio.igp.gob.pe:20.500.12816/57182025-06-12 17:00:39.125https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.igp.gob.peRepositorio Geofísico Nacionalbiblio@igp.gob.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