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.
Highlights
The European landscape has dramatically changed during the Holocene as a result of human impact and climatic change [1,2]
We present a transparent and accurate method for classifying linear vegetation elements in an agricultural landscape using Light Detection and Ranging (LiDAR) point clouds derived from airborne laser scanning
Our intention was to apply already existing codes in a new context for the extraction of linear vegetation elements in rural landscapes
Summary
The European landscape has dramatically changed during the Holocene as a result of human impact and climatic change [1,2]. Since the industrial revolution, landscapes have been deforested and reshaped into rural and agricultural landscapes These are dominated by a mosaic of grasslands, forests, and urban areas, separated or connected by linear landscape elements such as roads, ditches, tree lines, vegetated lynchets, and hedgerows [3,4,5]. The occurrence of green lanes and hedgerows has strongly diminished in many countries [12,13] This is mostly a consequence of larger agricultural fields, monocultures and a reduction in non-crop features which reduces the complexity and diversity of landscape structure [8]. Detailed knowledge of the spatial occurrence, current status, frequency and ecological functions of linear vegetation elements in a landscape is of key importance for biodiversity conservation and regional planning
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.