Research Papers

Identification of grassland management practices from leaf area index time series

[+] Author Affiliations
Pauline Dusseux

University Rennes 2, LETG Rennes COSTEL, UMR CNRS 6554 OSU, Place du recteur Henri Le Moal, Rennes Cedex 35 043, France

Xing Gong

University Rennes 2, LETG Rennes COSTEL, UMR CNRS 6554 OSU, Place du recteur Henri Le Moal, Rennes Cedex 35 043, France

CASIA—Chinese Academy of Sciences, Institute of Automation, ZhongGuanCun Dong Lu, Beijing 100190, China

Laurence Hubert-Moy

University Rennes 2, LETG Rennes COSTEL, UMR CNRS 6554 OSU, Place du recteur Henri Le Moal, Rennes Cedex 35 043, France

Thomas Corpetti

University Rennes 2, LETG Rennes COSTEL, UMR CNRS 6554 OSU, Place du recteur Henri Le Moal, Rennes Cedex 35 043, France

CASIA—Chinese Academy of Sciences, Institute of Automation, ZhongGuanCun Dong Lu, Beijing 100190, China

J. Appl. Remote Sens. 8(1), 083559 (Sep 02, 2014). doi:10.1117/1.JRS.8.083559
History: Received March 25, 2014; Revised August 1, 2014; Accepted August 11, 2014
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Abstract.  The main objective of this study is to identify grassland management practices using time series of remote sensing images. The accelerating agricultural intensification has strongly reduced grassland surfaces in some areas, generating important changes in their management and affecting environmental and socio-economic systems. Therefore, the identification of grassland management practices in farming systems is a key issue for sustainable agriculture. To this end, the leaf area index (LAI) estimated from remote sensing images was used since its temporal evolution is informative about farming practices. We evaluate the performances of two common classification algorithms using time profiles of LAI derived from simulated data and high spatial resolution satellite images. We show that they exhibit limited performances, mainly because they rely on criteria that are not suited for the comparison of time series. We then suggest the use of more advanced classification tools that work in a transformed space designed by a kernel function. We show that a kernel based on time warping measurements which are suited for the comparison of time series, perform better than classical ones based on Gaussian functions. This is a promising result for the analyzing of the future SENTINEL data that will be embedded in many time series.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Pauline Dusseux ; Xing Gong ; Laurence Hubert-Moy and Thomas Corpetti
"Identification of grassland management practices from leaf area index time series", J. Appl. Remote Sens. 8(1), 083559 (Sep 02, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083559


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