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Protection and RegeneratiOn of MOuNTains

Supporting a greener and climate resilient Adriatic-Ionian region

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  • Drones in Conservation: Enhancing Monitoring and Resource Management (Montenegro)

    forest managementmonitoring & digital tools

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  • Digital Tree Tags: Revolutionizing Forest Monitoring and Data Collection (Montenegro)

    forest managementmonitoring & digital tools

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  • Example of a flight planning mission for Twin Creek eld site—observe that the heading for the flight passes was designed to be perpendicular to the dominant slope. (https://doi.org/10.1016/j.rama.2024.06.016?utm_source=chatgpt.com )

    Mapping Floral Resources in Montane Landscapes Using Unmanned Aerial Systems and Two-Step Random Forest Classifications

    monitoring & digital tools

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  • Cutouts of the drone images from the test dataset of the random dataset split overlaid with the predicted segmentation masks of the detection model and the ground truth bounding boxes. On the grassland, most predictions are correct, even in mixed lighting. However, objects like a marker cross (b), tree branches (c) or fences (d) can lead to false positives

    Detection of Colchicum autumnale in Drone Images Using a Machine-Learning Approach

    monitoring & digital tools

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  • Typical invasion of Hakea on a grassy slope with other scattered shrubs

    Detecting Plant Species in the Field With Deep Learning and Drone Technology

    monitoring & digital toolsspecies conservation

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  • (a) The location of Mountain Tianmu, Zhejiang province; (b) the flight routes of UAV RGB image data acquisition; the small yellow planes represent take-off and landing points; (c) the UAV RGB orthoimage of the study area after pre-processing.

    Seeing Trees from Drones: The Role of Leaf Phenology Transition in Mapping Species Distribution in Species-Rich Montane Forests

    forest managementmonitoring & digital tools

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  • Selection of typical mispredictions. All thin bounding boxes are correct predictions. The bold red bounding boxes denote false positive and the bold violet bounding boxes denote false negative predictions. There are various explanations for the mispredictions: Overlapping flowers (A), partially withered flowers (B,E), collections of flowers (C), missing ground truth annotations (D) and flowers that are missing in the training data (F).

    Flower Mapping in Grasslands with Drones and Deep Learning

    monitoring & digital tools

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  • Getis-Ord G∗i scores of habitat quality

    Monitoring the Spatiotemporal Dynamics of Habitat Quality and Its Driving Factors Based on the Coupled NDVI-InVEST Model: A Case Study from the Tianshan Mountains in Xinjiang, China

    climate resiliencemonitoring & digital tools

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  • (a) Overview of the Lehmkuhlen reservoir; (b) location of the study area in Germany; (c) photograph illustrating the abundance of Dactylorhiza majalis during drone flight; (d) close-up picture of a Dactylorhiza majalis inflorescence.

    Using Drones to Monitor Broad-Leaved Orchids in High-Nature-Value Grassland

    monitoring & digital toolsspecies conservation

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  • An example of tree crown detection (tree peaks visualized by green dots): (a) watersheds surrounding treetops (white line) split nearby trees—the borders are highlighted in red; DSM on the background; (b) the height mask (yellow line) cuts one side of the tree crowns in a place with a high altitude difference (red highlight); (c) the shadow mask (orange line) reduces the dark parts of tree crowns and completes the tree crown borders; multispectral imagery from the Parrot Sequoia sensor on the background (false-colour composition: Green, Red, Red edge); and, (d) final result of tree crown detection. Unmanned Aerial Vehicle (UAV) equipment used in this study: (a) three-dimensional (3D) models of the mounts designed for the Parrot Sequoia multispectral camera; (b) DJI Phantom 4 quadcopter with attached Parrot Sequoia camera; and, (c) planned flight path in the flylitchi.com web tool.

    Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

    climate resilienceforest managementmonitoring & digital tools

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  • Point cloud data of the study area, Top view Satellite image of the area (40° 11' 05.8", -8° 24' 54.9")

    Tree geometrical attributes measurement using UAV-born laser scanning

    forest managementmonitoring & digital tools

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