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Mediterranean central Chile is globally recognized as a hotspot for terrestrial biodiversity due to its high endemism and massive habitat loss. However, within the rural landscape of central Chile, significant extents of natural areas remain, especially on less productive, steep slopes, and vegetation strips extending from the surrounding hills to agricultural areas. Accordingly, vegetation strips or corridors, within lowland farms, constitute key elements to support the conservation of biodiversity in rural landscapes. To assess the ecological performance of corridors in 22 commercials vineyards in central Chile, we characterized them in terms of width-, length-, area-, and perimeter-to-area ratios, as well as the number of connections with natural areas. Based on a set of previously defined ecological indicators (species, functional groups, and structural components), we compared their occurrence in corridors within vineyards and in the surrounding natural areas. We evaluated the effects of corridor attributes on the occurrence of the selected ecological indicators, using a generalized linear mixed model with each vineyard as a random factor. The area, width, and length of vegetation corridors varied widely (1.2–86.3 ha, 10.5–95 m, and 380–5000 m, respectively). We found significant differences in the occurrence of indicators between corridors and natural areas. All sampled ecological indicators in corridors showed a negative relationship with the distance to the nearest natural area. Vegetation strips within vineyards represent important opportunities for biodiversity conservation that significantly enhance habitat quality in the agricultural landscape for biodiversity and habitat connectivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]