Multi-Temporal analysis of remotely sensed information using wavelets
Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification.
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Scientific Research Publishing
2012
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Subjects: | Land Use, Vegetation, Remote Sensing, Moderate Resolution Imaging Spectroradiometer, Land Cover Change, Utilización de la Tierra, Vegetación, Teledetección, Espectrorradiómetro de Imágenes de Resolución Moderada, Alteración de la Cubierta Vegetal, Wavelet Transform, MODIS NDVI Series, |
Online Access: | http://hdl.handle.net/20.500.12123/4478 https://file.scirp.org/Html/11-8401156_22158.htm |
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oai:localhost:20.500.12123-44782019-02-20T18:47:36Z Multi-Temporal analysis of remotely sensed information using wavelets Campos, Alfredo Nicolas Di Bella, Carlos Marcelo Land Use Vegetation Remote Sensing Moderate Resolution Imaging Spectroradiometer Land Cover Change Utilización de la Tierra Vegetación Teledetección Espectrorradiómetro de Imágenes de Resolución Moderada Alteración de la Cubierta Vegetal Wavelet Transform MODIS NDVI Series Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification. Instituto de Clima y Agua Fil: Campos, Alfredo Nicolas. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Departamento de Electrónica; Arentina Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina 2019-02-20T18:43:36Z 2019-02-20T18:43:36Z 2012 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/4478 https://file.scirp.org/Html/11-8401156_22158.htm 2151-1950 2151-1969 (Online) 10.4236/jgis.2012.44044 eng info:eu-repo/semantics/openAccess application/pdf Scientific Research Publishing Journal of geographic information system 4 (4) :ID: 22158. (2012) |
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Land Use Vegetation Remote Sensing Moderate Resolution Imaging Spectroradiometer Land Cover Change Utilización de la Tierra Vegetación Teledetección Espectrorradiómetro de Imágenes de Resolución Moderada Alteración de la Cubierta Vegetal Wavelet Transform MODIS NDVI Series Land Use Vegetation Remote Sensing Moderate Resolution Imaging Spectroradiometer Land Cover Change Utilización de la Tierra Vegetación Teledetección Espectrorradiómetro de Imágenes de Resolución Moderada Alteración de la Cubierta Vegetal Wavelet Transform MODIS NDVI Series |
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Land Use Vegetation Remote Sensing Moderate Resolution Imaging Spectroradiometer Land Cover Change Utilización de la Tierra Vegetación Teledetección Espectrorradiómetro de Imágenes de Resolución Moderada Alteración de la Cubierta Vegetal Wavelet Transform MODIS NDVI Series Land Use Vegetation Remote Sensing Moderate Resolution Imaging Spectroradiometer Land Cover Change Utilización de la Tierra Vegetación Teledetección Espectrorradiómetro de Imágenes de Resolución Moderada Alteración de la Cubierta Vegetal Wavelet Transform MODIS NDVI Series Campos, Alfredo Nicolas Di Bella, Carlos Marcelo Multi-Temporal analysis of remotely sensed information using wavelets |
description |
Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification. |
format |
info:ar-repo/semantics/artículo |
topic_facet |
Land Use Vegetation Remote Sensing Moderate Resolution Imaging Spectroradiometer Land Cover Change Utilización de la Tierra Vegetación Teledetección Espectrorradiómetro de Imágenes de Resolución Moderada Alteración de la Cubierta Vegetal Wavelet Transform MODIS NDVI Series |
author |
Campos, Alfredo Nicolas Di Bella, Carlos Marcelo |
author_facet |
Campos, Alfredo Nicolas Di Bella, Carlos Marcelo |
author_sort |
Campos, Alfredo Nicolas |
title |
Multi-Temporal analysis of remotely sensed information using wavelets |
title_short |
Multi-Temporal analysis of remotely sensed information using wavelets |
title_full |
Multi-Temporal analysis of remotely sensed information using wavelets |
title_fullStr |
Multi-Temporal analysis of remotely sensed information using wavelets |
title_full_unstemmed |
Multi-Temporal analysis of remotely sensed information using wavelets |
title_sort |
multi-temporal analysis of remotely sensed information using wavelets |
publisher |
Scientific Research Publishing |
publishDate |
2012 |
url |
http://hdl.handle.net/20.500.12123/4478 https://file.scirp.org/Html/11-8401156_22158.htm |
work_keys_str_mv |
AT camposalfredonicolas multitemporalanalysisofremotelysensedinformationusingwavelets AT dibellacarlosmarcelo multitemporalanalysisofremotelysensedinformationusingwavelets |
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1756007392928071680 |