Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

Integrated crop-livestock (ICL) systems combine livestock and crop production in the same area, increasing the efficiency of land use and machinery, while mitigating greenhouse gas emissions, and reducing production risks, plant diseases and pests. ICL systems are primarily divided into annual (ICLa...

Full description

Bibliographic Details
Main Authors: Víctor Danilo Manabe, Marcio R. S. Melo, Jansle Vieira Rocha
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/9/1322
id doaj-2be5237159874e45b523627a7fc5c8e8
record_format Article
spelling doaj-2be5237159874e45b523627a7fc5c8e82020-11-25T00:13:17ZengMDPI AGRemote Sensing2072-42922018-08-01109132210.3390/rs10091322rs10091322Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, BrazilVíctor Danilo Manabe0Marcio R. S. Melo1Jansle Vieira Rocha2School of Agricultural Engineering, University of Campinas, Campinas, SP 13083-875, BrazilCampus of Paragominas, Federal Rural University of Amazonia, Paragominas, PA 68625-970, BrazilSchool of Agricultural Engineering, University of Campinas, Campinas, SP 13083-875, BrazilIntegrated crop-livestock (ICL) systems combine livestock and crop production in the same area, increasing the efficiency of land use and machinery, while mitigating greenhouse gas emissions, and reducing production risks, plant diseases and pests. ICL systems are primarily divided into annual (ICLa) and multi-annual (ICLm) systems. Projects such as the “Integrated crop-livestock-forest Network” and the “Livestock Rally” have estimated the ICL areas for Brazil on a state or regional basis. However, it remains necessary to create methods for spatial identification of ICL areas. Thus, we developed a framework for mapping ICL areas in Mato Grosso, Brazil using the Enhanced Vegetation Index time-series of Moderate Resolution Imaging Spectroradiometer and a Time-Weighted Dynamic Time Warping (TWDTW) classification method. The classification of ICL areas occurred in three phases. Phase 1 corresponded to the classification of land use from 2008 to 2016. In Phase 2, the ICLa areas were identified. Finally, Phase 3 corresponded to the ICLm identification. The framework showed overall accuracies of 86% and 92% for ICL areas. ICLm accounted for 87% of the ICL areas. Considering only agricultural areas or only pasture areas, ICL systems represented 5% and 15%, respectively.http://www.mdpi.com/2072-4292/10/9/1322time seriesenhanced vegetation indexland-use intensificationTime-Weighted Dynamic Time Warping (TWDTW)temporal pattern
collection DOAJ
language English
format Article
sources DOAJ
author Víctor Danilo Manabe
Marcio R. S. Melo
Jansle Vieira Rocha
spellingShingle Víctor Danilo Manabe
Marcio R. S. Melo
Jansle Vieira Rocha
Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
Remote Sensing
time series
enhanced vegetation index
land-use intensification
Time-Weighted Dynamic Time Warping (TWDTW)
temporal pattern
author_facet Víctor Danilo Manabe
Marcio R. S. Melo
Jansle Vieira Rocha
author_sort Víctor Danilo Manabe
title Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
title_short Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
title_full Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
title_fullStr Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
title_full_unstemmed Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil
title_sort framework for mapping integrated crop-livestock systems in mato grosso, brazil
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-08-01
description Integrated crop-livestock (ICL) systems combine livestock and crop production in the same area, increasing the efficiency of land use and machinery, while mitigating greenhouse gas emissions, and reducing production risks, plant diseases and pests. ICL systems are primarily divided into annual (ICLa) and multi-annual (ICLm) systems. Projects such as the “Integrated crop-livestock-forest Network” and the “Livestock Rally” have estimated the ICL areas for Brazil on a state or regional basis. However, it remains necessary to create methods for spatial identification of ICL areas. Thus, we developed a framework for mapping ICL areas in Mato Grosso, Brazil using the Enhanced Vegetation Index time-series of Moderate Resolution Imaging Spectroradiometer and a Time-Weighted Dynamic Time Warping (TWDTW) classification method. The classification of ICL areas occurred in three phases. Phase 1 corresponded to the classification of land use from 2008 to 2016. In Phase 2, the ICLa areas were identified. Finally, Phase 3 corresponded to the ICLm identification. The framework showed overall accuracies of 86% and 92% for ICL areas. ICLm accounted for 87% of the ICL areas. Considering only agricultural areas or only pasture areas, ICL systems represented 5% and 15%, respectively.
topic time series
enhanced vegetation index
land-use intensification
Time-Weighted Dynamic Time Warping (TWDTW)
temporal pattern
url http://www.mdpi.com/2072-4292/10/9/1322
work_keys_str_mv AT victordanilomanabe frameworkformappingintegratedcroplivestocksystemsinmatogrossobrazil
AT marciorsmelo frameworkformappingintegratedcroplivestocksystemsinmatogrossobrazil
AT janslevieirarocha frameworkformappingintegratedcroplivestocksystemsinmatogrossobrazil
_version_ 1725395275794612224