Automated segmentation of vegetation structure units in a Mediterranean landscape

Abstract

In Mediterranean regions, the combination of disturbances, life histories, plant regeneration traits, and microhabitat variability form highly heterogeneous vegetation mosaics which shift in space and time. Consequently, structure-based forest management is emerging as a superior alternative to management of vegetation formations in such areas. Delineation of management units in these areas is often based on manual interpretation of aerial imagery coupled with field surveys. Here, we propose an alternative approach that is based on segmentation of remotely sensed height and cover maps derived from light detection and ranging (LiDAR) imagery. A large suite of alternative segmentation maps was generated using multiresolution segmentation (MS) with different parameters, and an area-fit approach used to select the map that most successfully captured a reference set of structural units delineated manually. We assessed the feasibility of this approach in a nature reserve in northern Israel, compared the resulting map with a traditional vegetation formations map, and explored the performance of the segmentation algorithm under various parameter combinations. Pronounced differences between the structure and formation maps highlight the suitability of this approach as an alternative to the existing methods of delineating vegetation units in Mediterranean systems, and possibly in other systems as well.

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

“…However, previous studies indicate that the East Mediterranean woody species are not necessarily an easy target for classification by remote sensing. Most local vegetation formations are characterized by densely-growing evergreen species, often with similar spectral properties [58][59][60], small intra-annual differences [61] and morphological similarities [62][63][64][65]. To the best of our knowledge, there are currently no studies that use high spatial resolution imagery with a limited number of bands for the classification of East Mediterranean common species.…”

“…We did not choose to focus on ideal target species (e.g., large homogeneous canopies with visually distinct phenophases), rather, we examined the local species assembly as-is, including species with challenging spectral and morphological properties with regard to classification by remote sensing [59,60,62,65]; furthermore, after examining the preliminary classification results, we did not see fit to merge or omit species in order to increase overall accuracy.…”

Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography

“…Recently, object-based image analysis (OBIA; [39][40][41]) has been used to process LiDAR data and particularly LiDAR-derived canopy height models (CHM), to delineate individual trees [42][43][44] and forest stands [31,45,46] and to discriminate between different land cover types [47]. Several studies have integrated LiDAR data with other sources to increase the accuracy of vegetation mapping at various scales [48][49][50] and find the best case-specific solutions. Overall, many studies have demonstrated the outstanding capacity of LiDAR data to monitor various stages of forest succession.…”

Cattle grazing effects on mountain gazelles in Mediterranean natural landscapes

“…Bar Massada et al () classified a vegetation structure polygon layer based on segmentation of remotely sensed height and cover maps derived from LiDAR imagery. We used this layer to calculate the minimum distance to vegetation structure edges and to model animal orientation within a given patch.…”