Land-cover change, a major driver of the distribution and functioning of ecosystems, is characterized by a high diversity of patterns of change across space and time. Thus, a large amount of information is necessary to analyse change and develop plans for proper management of natural resources. In this work we tested MaxEnt algorithm in a completely remote land-cover classification and change analysis. In order to provide an empirical example, we selected south-eastern Italian Alps, manly Trentino-South Tyrol, as test region. We classified two Landsat images (1976 and 2001) in order to forecast probability of occurrence for unsampled locations and to determine the best subset of predictors (spectral bands). A difference map for each land cover class, representing the difference between 1976 and 2001 probability of occurrence values, was built. In order to better address the patterns of change analysis, we put together difference maps and topographic variables. The latter are considered, at least in the study area, as the main environmental drivers of land-use change, in connection with climate change. Our results indicate that the selected algorithm, applied to land cover classes, can provide reliable data, especially when referring to classes with homogeneous texture properties and surface reflectance. The performed models had satisfactory predictive performance, showing relatively clear patterns of difference between the two considered time steps. The development of a methodology that, in the absence of field data, allow to obtain data on land use change dynamics, is of extreme importance for land planning and management.

Amici, V.; Marcantonio, M.; La Porta, N.; Rocchini, D. (2017). A multi-temporal approach in MaxEnt modelling: a new frontier for land use/land cover change detection. ECOLOGICAL INFORMATICS, 40 (1): 40-49. doi: 10.1016/j.ecoinf.2017.04.005 handle: http://hdl.handle.net/10449/44507

A multi-temporal approach in MaxEnt modelling: a new frontier for land use/land cover change detection

La Porta, N.;Rocchini, D.
Ultimo
2017-01-01

Abstract

Land-cover change, a major driver of the distribution and functioning of ecosystems, is characterized by a high diversity of patterns of change across space and time. Thus, a large amount of information is necessary to analyse change and develop plans for proper management of natural resources. In this work we tested MaxEnt algorithm in a completely remote land-cover classification and change analysis. In order to provide an empirical example, we selected south-eastern Italian Alps, manly Trentino-South Tyrol, as test region. We classified two Landsat images (1976 and 2001) in order to forecast probability of occurrence for unsampled locations and to determine the best subset of predictors (spectral bands). A difference map for each land cover class, representing the difference between 1976 and 2001 probability of occurrence values, was built. In order to better address the patterns of change analysis, we put together difference maps and topographic variables. The latter are considered, at least in the study area, as the main environmental drivers of land-use change, in connection with climate change. Our results indicate that the selected algorithm, applied to land cover classes, can provide reliable data, especially when referring to classes with homogeneous texture properties and surface reflectance. The performed models had satisfactory predictive performance, showing relatively clear patterns of difference between the two considered time steps. The development of a methodology that, in the absence of field data, allow to obtain data on land use change dynamics, is of extreme importance for land planning and management.
GIS
Land-cover change
Machine learning
MaxEnt
Probability distribution
Remote sensing
Settore BIO/03 - BOTANICA AMBIENTALE E APPLICATA
Amici, V.; Marcantonio, M.; La Porta, N.; Rocchini, D. (2017). A multi-temporal approach in MaxEnt modelling: a new frontier for land use/land cover change detection. ECOLOGICAL INFORMATICS, 40 (1): 40-49. doi: 10.1016/j.ecoinf.2017.04.005 handle: http://hdl.handle.net/10449/44507
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/44507
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