Detection and Characterization of Hedgerows Using TerraSAR-X Imagery

Remote Sensing

Julie Betbeder³  Jean Nabucet³  Eric Pottier¹  Jacques Baudry²  Samuel Corgne³ 

[1] IETR UMR CNRS 6164, Université de Rennes 1, Campus Beaulieu-bât 11D, 263, av du général Leclerc, CS 74205, Rennes Cedex 35042, France; E-Mail:;INRA SAD-PAYSAGE, 65, rue de St-Brieuc CS 84215, Rennes Cedex 35042, France; E-Mail:;LETG Rennes COSTEL UMR 6554 LETG/OSUR, Université Rennes 2, Place du recteur Henri Le Moal, Rennes Cedex 35043, France; E-Mails:

关键词: linear hedgerow network;    canopy structure;    Radar-SAR imagery;    very high resolution;    Shannon entropy index;    object-oriented classification;   

DOI  :  10.3390/rs6053752

来源: mdpi

【 摘 要 】

Whilst most hedgerow functions depend upon hedgerow structure and hedgerow network patterns, in many ecological studies information on the fragmentation of hedgerows network and canopy structure is often retrieved in the field in small areas using accurate ground surveys and estimated over landscapes in a semi-quantitative manner. This paper explores the use of radar SAR imagery to (i) detect hedgerow networks; and (ii) describe the hedgerow canopy heterogeneity using TerraSAR-X imagery. The extraction of hedgerow networks was achieved using an object-oriented method using two polarimetric parameters: the Single Bounce and the Shannon Entropy derived from one TerraSAR-X image. The hedgerow canopy heterogeneity estimated from field measurements was compared with two backscattering coefficients and three polarimetric parameters derived from the same image. The results show that the hedgerow network and its fragmentation can be identified with a very good accuracy (Kappa index: 0.92). This study also reveals the high correlation between one polarimetric parameter, the Shannon entropy, and the canopy fragmentation measured in the field. Therefore, VHSR radar images can both precisely detect the presence of wooded hedgerow networks and characterize their structure, which cannot be achieved with optical images.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland