A Method for Tree Detection Based on Similarity with Geometric Shapes of 3D Geospatial Data

This paper presents an approach to detecting patterns in a three-dimensional context, emphasizing the role played by the local geometry of the surface model. The core of the associated algorithm is represented by the cosine similarity computed to sub-matrices of regularly gridded digital surface/canopy models. We developed an accompanying software instrument compatible with a GIS environment which allows, as inputs, locations in the surface/canopy model based on field data, pre-defined geometric shapes, or their combination. We exemplified the approach for a study case dealing with the locations of scattered trees and shrubs previously identified in the field in two study sites. We found that the variation in the pairwise similarities between the trees is better explained by the computation of slopes. Furthermore, we considered a pre-defined shape, the Mexican Hat wavelet. Its geometry is controlled by a single number, for which we found ranges of best fit between the shapes and the actual trees. Finally, a suitable combination of parameters made it possible to determine the potential locations of scattered trees. The accuracy of detection was equal to 77.9% and 89.5% in the two study sites considered. Moreover, a visual check based on orthophotomaps confirmed the reliability of the outcomes

Online Environment as a Tool to Push Forward the Research: An Example for Landscape Disservices

Due to the COVID-19 pandemic, researchers have had to find different resources in order to continue their research and the use of online information can represent a temporary solution. Our research is mainly focusing on a landscape which offers services and disservices. Recently, numerous studies that rely on landscape disservices have appeared. We associate wildlife-human-interactions (WHI) and human-wildlife-interactions (HWI) as part of landscape disservices. More precisely, in the first category (WHI) we have included the interaction of the wild animals with human and in the second category (HWI) we have created a database with animals attacked or/and killed by human. In order to sustain this analysis, we have selected data from local newspapers and Facebook groups, which supports our hypothesis that online resources could provide valuable data. The study area is represented by the Southern and Eastern Carpathians. The most affected mammals for this type of interactions (HWI) are bears, followed by wild boars and red deer, while WHI has intensified in the last five years. Based on the analysed data we can conclude that the animals who generate the most disservices to humans are bears and wild boars. The solutions we have identified, which also include online sources, for both HWI and WHI are relocation, rescue, capturing of the animals in reservations or, as a last resort, euthanasia. In order to reduce these types of interactions it is important to promote ecological education, development and promoting of certain attitudes and behaviour that have a visible impact upon HWI and WHI

Tree's detection & health's assessment from ultra-high resolution UAV imagery and deep learning

Detection and classification of crown trees properties and assessment of their health status have raised much interest for the scientists from the forest and environmental sciences due to their essential role in landscape ecology and forestry management. The present study proposes a method based on consumer-based UAVs and deep learning techniques for detecting individual orchard trees and assessing key properties characterising their health status. In the proposed scheme, the Mask R-CNN model is used for detecting and mapping each individual tree morphometrical property such as the height and the crown width. Tree's health assessment is based on the use of vegetation indices such as the Visual Atmospheric Resistance Index (VARI) and Green Leaf Index (GLI), computed from the visible spectrum camera mounted on the UAV platform. The use of the proposed approach is demonstrated at five different orchard tree species, namely plum, apricot, walnut, olive, and almond, located in Romania and Greece, computing a series of statistical metrics. Results returned outstanding ability of the algorithm's performance to map the individual trees and assess their health for four out of the five tree species (plum, walnut, apricot, almond) and satisfactory results for the fifth (olive trees). Overall, the study findings highlighted the promising potential of the proposed methodological framework and its scalable potential for wider applicability as a low-cost, effective solution in mapping individual trees properties and health conditions in the field