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Remote Sens. 2017, 9(6), 600; doi:10.3390/rs9060600

A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series

1
Cirad, UMR TETIS (Land, Environment, Remote Sensing and Spatial Information), Maison de la Télédétection, Rue Jean-François Breton, 34090 Montpellier, France
2
National Institute for Space Research (INPE), Av. dos Astronautas, 1758, São José dos Campos, SP 12227-010, Brazil
3
Embrapa Solos, Rua Jardim Botânico, 1024, Rio de Janeiro, RJ 22460-000, Brazil
4
Department of Computer Engineering, Rio de Janeiro State University (UERJ/FEN/DESC/PPGMA), Rua São Francisco Xavier, 524, 5031 D, Maracanã, Rio de Janeiro, RJ 20550-900, Brazil
*
Author to whom correspondence should be addressed.
Academic Editors: Raul Zurita-Milla, James Campbell and Prasad S. Thenkabail
Received: 2 May 2017 / Revised: 2 June 2017 / Accepted: 10 June 2017 / Published: 13 June 2017
View Full-Text   |   Download PDF [5851 KB, uploaded 13 June 2017]   |  

Abstract

In response to the need for generic remote sensing tools to support large-scale agricultural monitoring, we present a new approach for regional-scale mapping of agricultural land-use systems (ALUS) based on object-based Normalized Difference Vegetation Index (NDVI) time series analysis. The approach consists of two main steps. First, to obtain relatively homogeneous land units in terms of phenological patterns, a principal component analysis (PCA) is applied to an annual MODIS NDVI time series, and an automatic segmentation is performed on the resulting high-order principal component images. Second, the resulting land units are classified into the crop agriculture domain or the livestock domain based on their land-cover characteristics. The crop agriculture domain land units are further classified into different cropping systems based on the correspondence of their NDVI temporal profiles with the phenological patterns associated with the cropping systems of the study area. A map of the main ALUS of the Brazilian state of Tocantins was produced for the 2013–2014 growing season with the new approach, and a significant coherence was observed between the spatial distribution of the cropping systems in the final ALUS map and in a reference map extracted from the official agricultural statistics of the Brazilian Institute of Geography and Statistics (IBGE). This study shows the potential of remote sensing techniques to provide valuable baseline spatial information for supporting agricultural monitoring and for large-scale land-use systems analysis. View Full-Text
Keywords: geographic object-based image analysis (GEOBIA); Moderate Resolution Imaging Spectroradiometer (MODIS); principal components analysis (PCA); cropping systems; Stratification geographic object-based image analysis (GEOBIA); Moderate Resolution Imaging Spectroradiometer (MODIS); principal components analysis (PCA); cropping systems; Stratification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bellón, B.; Bégué, A.; Lo Seen, D.; de Almeida, C.A.; Simões, M. A Remote Sensing Approach for Regional-Scale Mapping of Agricultural Land-Use Systems Based on NDVI Time Series. Remote Sens. 2017, 9, 600.

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