Agrarian landscapes linear features detection from LiDAR: application to artificial drainage networks

Linear features of agrarian landscapes are the anthropogenic elements such as hedgerows, ditches, and bench terraces that strongly impact agrarian areas' environmental behaviour, especially the ecological and hydrological areas. The need to map these linear elements for environmental impact assessments of agrarian areas is thus increasing since these maps limit the developments of spatial indicators and spatially distributed modelling. Until now, no generic remote sensing methodology has been proposed for such mapping purposes. This research was designed to assess the use of airborne LiDAR data for agrarian landscape linear features mapping. We proposed a methodology that uses LiDAR data in three steps. We first estimated elevation profiles from LiDAR points on a set of pre‐located sites. We secondly performed profile shape description with wavelet transform or a watershed algorithm. Finally, we classified the profiles using classification trees with predictors coming from shape analysis. Methodology accuracies were calculated for a ditch network detection problem in a Mediterranean vineyard landscape. LiDAR Toposys data and field survey data for ditch location were collected in June 2002. As ditches are always located on field boundary lattices, elevation profiles were only computed on field boundary sites. Methodologies, using wavelets or the watershed algorithm, gave similar accuracies. Overall accuracy is about 70% with a high ditch omission rate (50%) but low commission rate (15%). The omissions conform to those obtained when performing visual classification of profiles. This high omission rate in ditch detection is therefore due to LiDAR data, not methods. Dense vegetation over ditches during the summertime and the specific LiDAR points spatial sampling design explain these omissions. However, the proposed methodology, especially using wavelets transform, looks transposable for the automatic detection or characterization of other agrarian linear features.

[1]  F. Burel,et al.  Landscape Ecology : Concepts, Methods, and Applications , 2003 .

[2]  A. Haar Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[3]  M. Voltz,et al.  Herbicide transport to surface waters at field and watershed scales in a Mediterranean vineyard area. , 2001, Journal of environmental quality.

[4]  Noémie Varado Contribution au développement d'une modélisation hydrologique distribuée : application au bassin versant de la Donga, au Bénin , 2004 .

[5]  Zbigniew R. Struzik,et al.  Wavelet methods in (financial) time-series processing , 2000 .

[6]  M. Voltz,et al.  Effects of the spatial organization of agricultural management on the hydrological behaviour of a farmed catchment during flood events , 2001 .

[7]  Josiane Zerubia,et al.  An application of marked point processes to the extration of linear networks from images , 2002 .

[8]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[9]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[10]  Marc Pierrot Deseilligny,et al.  A region-based method for graph to image registration with an application to cadastre data , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[11]  S. Olliera,et al.  Comparing and classifying one-dimensional spatial patterns : an application to laser altimeter profiles , 2003 .

[12]  J. Baudry,et al.  Why and how we should study field boundary biodiversity in an agrarian landscape context , 2002 .

[13]  Jerry C. Ritchie,et al.  Measuring channel and gully cross-sections with an airborne laser altimeter , 1994 .

[14]  Nitin K. Tripathi,et al.  Directional morphological image transforms for lineament extraction from remotely sensed images , 2000 .

[15]  Nadia Carluer,et al.  Assessment and modelling of the influence of man-made networks on the hydrology of a small watershed: implications for fast flow components, water quality and landscape management , 2004 .

[16]  F. J. Cortijo,et al.  A comparative study of some non-parametric spectral classifiers. Applications to problems with high-overlapping training sets , 1997 .

[17]  Philippe Lagacherie,et al.  An indicator approach for describing the spatial variability of artificial stream networks with regard to herbicide pollution in cultivated watersheds , 2006 .

[18]  P. Bates,et al.  Integration of high-resolution topographic data with floodplain flow models. , 2000 .

[19]  David C. Mason,et al.  Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation , 2003 .

[20]  G. Teschke,et al.  Extraction and Analysis of Structural Features in Cloud Radar and Lidar Data Using Wavelet Based Methods , 2002 .

[21]  Emmanuelle Bournay Bouchereau,et al.  Analyse d'images par transformées en ondelettes. Application aux images sismiques , 1997 .

[22]  Peter M. Atkinson,et al.  Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping , 2006 .

[23]  M. Maudsley A review of the ecology and conservation of hedgerow invertebrates in Britain , 2000 .

[24]  Pedro Beja,et al.  Conserving the Cabrera vole, Microtus cabrerae, in intensively used Mediterranean landscapes , 2006 .

[25]  D. Tarboton A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .

[26]  T. Sparks,et al.  Linear hotspots? The floral and butterfly diversity of green lanes , 2005 .

[27]  Jean-Pierre Antoine,et al.  Shape characterization with the wavelet transform , 1997, Signal Process..

[28]  Günter Blöschl,et al.  Advances in the use of observed spatial patterns of catchment hydrological response , 2002 .

[29]  Thomas Blaschke,et al.  Spatial indicators for nature conservation from European to local scale , 2005 .

[30]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[32]  G. A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[33]  J. Baudry,et al.  Variation of farm spatial land use pattern according to the structure of the hedgerow network (bocage) landscape: a case study in northeast Brittany , 2004 .

[34]  L. M Gomes Pereira,et al.  Suitability of laser data for deriving geographical information: A case study in the context of management of fluvial zones , 1999 .

[35]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[36]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[37]  J. Baudry,et al.  Habitat quality and connectivity in agricultural landscapes: The role of land use systems at various scales in time , 2005 .