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Remote Sens. 2019, 11(3), 292; https://doi.org/10.3390/rs11030292

Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds

1
Geodan, President Kennedylaan 1, 1079 MB Amsterdam, The Netherlands
2
Spatial Information Laboratory (SPINLab), Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
3
Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94248, 1090 GE Amsterdam, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 21 January 2019 / Accepted: 29 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Remote Sensing for Biodiversity, Ecology and Conservation)
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Abstract

Modernization of agricultural land use across Europe is responsible for a substantial decline of linear vegetation elements such as tree lines, hedgerows, riparian vegetation, and green lanes. These linear objects have an important function for biodiversity, e.g., as ecological corridors and local habitats for many animal and plant species. Knowledge on their spatial distribution is therefore essential to support conservation strategies and regional planning in rural landscapes but detailed inventories of such linear objects are often lacking. Here, we propose a method to detect linear vegetation elements in agricultural landscapes using classification and segmentation of high-resolution Light Detection and Ranging (LiDAR) point data. To quantify the 3D structure of vegetation, we applied point cloud analysis to identify point-based and neighborhood-based features. As a preprocessing step, we removed planar surfaces such as grassland, bare soil, and water bodies from the point cloud using a feature that describes to what extent the points are scattered in the local neighborhood. We then applied a random forest classifier to separate the remaining points into vegetation and other. Subsequently, a rectangularity-based region growing algorithm allowed to segment the vegetation points into 2D rectangular objects, which were then classified into linear objects based on their elongatedness. We evaluated the accuracy of the linear objects against a manually delineated validation set. The results showed high user’s (0.80), producer’s (0.85), and total accuracies (0.90). These findings are a promising step towards testing our method in other regions and for upscaling it to broad spatial extents. This would allow producing detailed inventories of linear vegetation elements at regional and continental scales in support of biodiversity conservation and regional planning in agricultural and other rural landscapes. View Full-Text
Keywords: rural landscapes; classification; LiDAR; linear vegetation; object recognition; point cloud; rectangularity; segmentation rural landscapes; classification; LiDAR; linear vegetation; object recognition; point cloud; rectangularity; segmentation
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Lucas, C.; Bouten, W.; Koma, Z.; Kissling, W.D.; Seijmonsbergen, A.C. Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds. Remote Sens. 2019, 11, 292.

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