Format du dépôt |
Notice |
Type de dépôt |
Article dans une revue |
Résumé |
en
The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the "raw NDVI + raw EVI" data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the "raw NDVI" and the "raw NDVI + phenometrics" datasets. The "NDVIderived phenometrics" alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems.
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Titre |
en
Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso, Brazil
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Auteur(s)
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Patrick Calvano Kuchler
1, 2
, Agnes Begue
1, 3
, Margareth Simoes
4, 2
, Raffaele Gaetano
1, 3
, Damien Arvor
5
, Rodrigo P D Ferraz
4
1
UMR TETIS -
Territoires, Environnement, Télédétection et Information Spatiale
( 1002492 )
- Maison de la télédétection - 500 rue Jean-François Breton - 34093 Montpellier Cedex 5
- France
-
Centre de Coopération Internationale en Recherche Agronomique pour le Développement UMR91 ( 11574 )
;
-
AgroParisTech ( 148117 )
;
-
Centre National de la Recherche Scientifique UMR9000 ( 441569 )
;
-
Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement UMR1470 ( 577435 )
2
UERJ -
Universidade do Estado do Rio de Janeiro [Brasil] = Rio de Janeiro State University [Brazil] = Université d'État de Rio de Janeiro [Brésil]
( 4627 )
- UERJ, Av. Sao Francisco Xavier, 524, Maracana, 20550-900, Rio de Janeiro
- Brésil
3
Cirad-ES -
Département Environnements et Sociétés
( 420902 )
- Campus international de Baillarguet TA C-DIR / B 34398 Montpellier Cedex 5 France
- France
-
Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( 11574 )
4
Embrapa Solos
( 132972 )
- Rua Jardim Botânico, 1.024 - Jardim Botânico Rio de Janeiro, RJ - Brasil - CEP 22460-000
- Brésil
-
Ministério da Agricultura ( 301760 )
5
LETG - Rennes -
Littoral, Environnement, Télédétection, Géomatique
( 3177 )
- Maison de la Recherche Place du Recteur Henri Le Moal 35043 RENNES CEDEX
- France
-
Littoral, Environnement, Télédétection, Géomatique UMR 6554 ( 14266 )
;
-
Université de Caen Normandie ( 7127 )
;
-
Normandie Université ( 455934 )
;
-
Université d'Angers ( 74911 )
;
-
École Pratique des Hautes Études ( 110691 )
;
-
Université Paris Sciences et Lettres ( 564132 )
;
-
Université de Brest ( 300314 )
;
-
Université de Rennes 2 ( 406201 )
;
-
Centre National de la Recherche Scientifique UMR 6554 ( 441569 )
;
-
Institut de Géographie et d'Aménagement Régional de l'Université de Nantes ( 530572 )
;
-
Université de Nantes 93263 ( 93263 )
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Volume |
92
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Page/Identifiant |
102150
|
Licence |
Paternité
|
Titre de la collection |
International Journal of Applied Earth Observation and Geoinformation
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Audience |
Internationale
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Comité de lecture |
Oui
|
Vulgarisation |
Non
|
Nom de la revue |
|
Langue du document |
Anglais
|
Date de publication |
2020-10
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Domaine(s) |
-
Sciences de l'Homme et Société/Géographie
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Sciences de l'environnement/Environnement et Société
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Mots-clés |
en
Mato Grosso, Integrated systems, Classification, Phenometrics, Smoothing, Agricultural intensification, TIMESAT
|
DOI |
10.1016/j.jag.2020.102150 |
UT key WOS |
000550572100002 |