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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Manabe, Victor Danilo [1] ; Melo, Marcio R. S. [2] ; Rocha, Jansle Vieira [1]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Sch Agr Engn, BR-13083875 Campinas, SP - Brazil
[2] Fed Rural Univ Amazonia, Campus Paragominas, BR-68625970 Paragominas, PA - Brazil
Total Affiliations: 2
Document type: Journal article
Source: REMOTE SENSING; v. 10, n. 9 SEP 2018.
Web of Science Citations: 2
Abstract

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. (AU)

FAPESP's process: 14/26928-2 - Characterization and detection of intensified pasture areas using MODIS time series
Grantee:Jansle Vieira Rocha
Support type: Regular Research Grants