Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools
Land degradation and regeneration are complex processes that greatly impact climate regulation, ecosystem service provision, and population well‐being and require an urgent and appropriate response through land use planning and interventions. Spatially explicit land change models can greatly help decision makers, but traditional regression approaches fail to capture the nonlinearity and complex interactions of the underlying drivers. Our objective was to use a machine learning algorithm combined with high‐resolution data sets to provide simultaneous and spatial forecasts of deforestation, land degradation, and regeneration for the next two decades. A 17,000‐km2 region in the south of Madagascar was taken as the study area. First, an empirical analysis of drivers of change was conducted, and then, an ensemble model was calibrated to predict and map potential changes based on 12 potential explanatory variables. These potential change maps were used to draw three scenarios of land change while considering past trends in intensity of change and expert knowledge. Historical observations displayed clear patterns of land degradation and relatively low regeneration. Amongst the 12 potential explanatory variables, distance to forest edge and elevation were the most important for the three land transitions studied. Random forest showed slightly better prediction ability compared with maximum entropy and generalized linear model. Business‐as‐usual scenarios highlighted the large areas under deforestation and degradation threat, and an alternative scenario enabled the location of suitable areas for regeneration. The approach developed herein and the spatial outputs provided can help stakeholders target their interventions or develop large‐scale sustainable land management strategies.
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dig-cirad-fr-5955142024-01-29T02:43:38Z http://agritrop.cirad.fr/595514/ http://agritrop.cirad.fr/595514/ Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools. Grinand Clovis, Vieilledent Ghislain, Razafimbelo Tantely Maminiana, Rakotoarijaona Jean-Roger, Nourtier Marie, Bernoux Martial. 2020. Land Degradation and Development, 31 (13) : 1699-1712.https://doi.org/10.1002/ldr.3526 <https://doi.org/10.1002/ldr.3526> Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools Grinand, Clovis Vieilledent, Ghislain Razafimbelo, Tantely Maminiana Rakotoarijaona, Jean-Roger Nourtier, Marie Bernoux, Martial eng 2020 Land Degradation and Development U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières K70 - Dégâts causés aux forêts et leur protection modélisation environnementale modélisation des cultures apprentissage machine déboisement dégradation des terres régénération conservation du paysage changement dans l'usage des terres analyse spatiale http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_15590 http://aims.fao.org/aos/agrovoc/c_34823 http://aims.fao.org/aos/agrovoc/c_6486 http://aims.fao.org/aos/agrovoc/c_35166 http://aims.fao.org/aos/agrovoc/c_fac4b794 http://aims.fao.org/aos/agrovoc/c_40da9d3b Madagascar http://aims.fao.org/aos/agrovoc/c_4510 Land degradation and regeneration are complex processes that greatly impact climate regulation, ecosystem service provision, and population well‐being and require an urgent and appropriate response through land use planning and interventions. Spatially explicit land change models can greatly help decision makers, but traditional regression approaches fail to capture the nonlinearity and complex interactions of the underlying drivers. Our objective was to use a machine learning algorithm combined with high‐resolution data sets to provide simultaneous and spatial forecasts of deforestation, land degradation, and regeneration for the next two decades. A 17,000‐km2 region in the south of Madagascar was taken as the study area. First, an empirical analysis of drivers of change was conducted, and then, an ensemble model was calibrated to predict and map potential changes based on 12 potential explanatory variables. These potential change maps were used to draw three scenarios of land change while considering past trends in intensity of change and expert knowledge. Historical observations displayed clear patterns of land degradation and relatively low regeneration. Amongst the 12 potential explanatory variables, distance to forest edge and elevation were the most important for the three land transitions studied. Random forest showed slightly better prediction ability compared with maximum entropy and generalized linear model. Business‐as‐usual scenarios highlighted the large areas under deforestation and degradation threat, and an alternative scenario enabled the location of suitable areas for regeneration. The approach developed herein and the spatial outputs provided can help stakeholders target their interventions or develop large‐scale sustainable land management strategies. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/595514/7/595514.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1002/ldr.3526 10.1002/ldr.3526 info:eu-repo/semantics/altIdentifier/doi/10.1002/ldr.3526 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1002/ldr.3526 info:eu-repo/grantAgreement/EC///// |
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U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières K70 - Dégâts causés aux forêts et leur protection modélisation environnementale modélisation des cultures apprentissage machine déboisement dégradation des terres régénération conservation du paysage changement dans l'usage des terres analyse spatiale http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_15590 http://aims.fao.org/aos/agrovoc/c_34823 http://aims.fao.org/aos/agrovoc/c_6486 http://aims.fao.org/aos/agrovoc/c_35166 http://aims.fao.org/aos/agrovoc/c_fac4b794 http://aims.fao.org/aos/agrovoc/c_40da9d3b http://aims.fao.org/aos/agrovoc/c_4510 U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières K70 - Dégâts causés aux forêts et leur protection modélisation environnementale modélisation des cultures apprentissage machine déboisement dégradation des terres régénération conservation du paysage changement dans l'usage des terres analyse spatiale http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_15590 http://aims.fao.org/aos/agrovoc/c_34823 http://aims.fao.org/aos/agrovoc/c_6486 http://aims.fao.org/aos/agrovoc/c_35166 http://aims.fao.org/aos/agrovoc/c_fac4b794 http://aims.fao.org/aos/agrovoc/c_40da9d3b http://aims.fao.org/aos/agrovoc/c_4510 |
spellingShingle |
U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières K70 - Dégâts causés aux forêts et leur protection modélisation environnementale modélisation des cultures apprentissage machine déboisement dégradation des terres régénération conservation du paysage changement dans l'usage des terres analyse spatiale http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_15590 http://aims.fao.org/aos/agrovoc/c_34823 http://aims.fao.org/aos/agrovoc/c_6486 http://aims.fao.org/aos/agrovoc/c_35166 http://aims.fao.org/aos/agrovoc/c_fac4b794 http://aims.fao.org/aos/agrovoc/c_40da9d3b http://aims.fao.org/aos/agrovoc/c_4510 U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières K70 - Dégâts causés aux forêts et leur protection modélisation environnementale modélisation des cultures apprentissage machine déboisement dégradation des terres régénération conservation du paysage changement dans l'usage des terres analyse spatiale http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_15590 http://aims.fao.org/aos/agrovoc/c_34823 http://aims.fao.org/aos/agrovoc/c_6486 http://aims.fao.org/aos/agrovoc/c_35166 http://aims.fao.org/aos/agrovoc/c_fac4b794 http://aims.fao.org/aos/agrovoc/c_40da9d3b http://aims.fao.org/aos/agrovoc/c_4510 Grinand, Clovis Vieilledent, Ghislain Razafimbelo, Tantely Maminiana Rakotoarijaona, Jean-Roger Nourtier, Marie Bernoux, Martial Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
description |
Land degradation and regeneration are complex processes that greatly impact climate regulation, ecosystem service provision, and population well‐being and require an urgent and appropriate response through land use planning and interventions. Spatially explicit land change models can greatly help decision makers, but traditional regression approaches fail to capture the nonlinearity and complex interactions of the underlying drivers. Our objective was to use a machine learning algorithm combined with high‐resolution data sets to provide simultaneous and spatial forecasts of deforestation, land degradation, and regeneration for the next two decades. A 17,000‐km2 region in the south of Madagascar was taken as the study area. First, an empirical analysis of drivers of change was conducted, and then, an ensemble model was calibrated to predict and map potential changes based on 12 potential explanatory variables. These potential change maps were used to draw three scenarios of land change while considering past trends in intensity of change and expert knowledge. Historical observations displayed clear patterns of land degradation and relatively low regeneration. Amongst the 12 potential explanatory variables, distance to forest edge and elevation were the most important for the three land transitions studied. Random forest showed slightly better prediction ability compared with maximum entropy and generalized linear model. Business‐as‐usual scenarios highlighted the large areas under deforestation and degradation threat, and an alternative scenario enabled the location of suitable areas for regeneration. The approach developed herein and the spatial outputs provided can help stakeholders target their interventions or develop large‐scale sustainable land management strategies. |
format |
article |
topic_facet |
U10 - Informatique, mathématiques et statistiques P01 - Conservation de la nature et ressources foncières K70 - Dégâts causés aux forêts et leur protection modélisation environnementale modélisation des cultures apprentissage machine déboisement dégradation des terres régénération conservation du paysage changement dans l'usage des terres analyse spatiale http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_9000024 http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_15590 http://aims.fao.org/aos/agrovoc/c_34823 http://aims.fao.org/aos/agrovoc/c_6486 http://aims.fao.org/aos/agrovoc/c_35166 http://aims.fao.org/aos/agrovoc/c_fac4b794 http://aims.fao.org/aos/agrovoc/c_40da9d3b http://aims.fao.org/aos/agrovoc/c_4510 |
author |
Grinand, Clovis Vieilledent, Ghislain Razafimbelo, Tantely Maminiana Rakotoarijaona, Jean-Roger Nourtier, Marie Bernoux, Martial |
author_facet |
Grinand, Clovis Vieilledent, Ghislain Razafimbelo, Tantely Maminiana Rakotoarijaona, Jean-Roger Nourtier, Marie Bernoux, Martial |
author_sort |
Grinand, Clovis |
title |
Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
title_short |
Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
title_full |
Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
title_fullStr |
Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
title_full_unstemmed |
Landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
title_sort |
landscape-scale spatial modelling of deforestation, land degradation, and regeneration using machine learning tools |
url |
http://agritrop.cirad.fr/595514/ http://agritrop.cirad.fr/595514/7/595514.pdf |
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