Assessment of Canopy Health with Drone-Based Orthoimagery in a Southern Appalachian Red Spruce Forest

Article Geography, Physical

Mapping microhabitat structure and connectivity on a tropical inselberg using UAV remote sensing

Angela Aristizabal-Botero et al.

Summary: In the past decade, drones have become powerful tools for ecologists, providing valuable information on the 3D structure and spatial variation of ecosystems, including proxies for habitat quality and connectivity. This technology has the potential to revolutionize ecological research by capturing detailed information that is difficult to obtain using ground-based methods.

PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT (2021)

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Article Forestry

Spatial and temporal fuels changes in whitebark pine (Pinus albicaulis) from mountain pine beetle (Dendroctonus ponderosae)

Curtis A. Gray et al.

Summary: Mountain pine beetle outbreaks cause significant changes in fine surface fuels and foliar fuel moisture in whitebark pine trees, leading to differences in fuel depth and moisture content among trees with different crown conditions. The hazard of fuel availability varies across the landscape following beetle attacks.

FOREST ECOLOGY AND MANAGEMENT (2021)

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Article Environmental Sciences

Using High-Spatial Resolution UAV-Derived Data to Evaluate Vegetation and Geomorphological Changes on a Dune Field Involved in a Restoration Endeavour

Stefano Fabbri et al.

Summary: The study aims to explore the potential of low-cost UAV surveys in monitoring coastal sand dunes' geomorphic and vegetation state using high-resolution RGB orthophotos and DSMs. By calculating spectral information and identifying key morphological features, the research reveals the effectiveness of planting operations and restoration projects.

REMOTE SENSING (2021)

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Article Green & Sustainable Science & Technology

Unmanned aerial vehicle and artificial intelligence revolutionizing efficient and precision sustainable forest management

Tiedong Liu et al.

Summary: By utilizing UAV-SfM technology for 3D point cloud reconstruction and combining it with the Convolutional Neural Network method for forest type classification, this study accurately evaluated the forest biomass distribution and biodiversity in the water conservation district. The results demonstrated the superior effectiveness of UAV technology in tropical forest monitoring, with a forest type classification accuracy of 0.61 based on the CNN method.

JOURNAL OF CLEANER PRODUCTION (2021)

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Article Forestry

sUAS for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam

Cuizhen Wang et al.

Summary: This study tested a sUAS procedure for 3D tree surveying in a closed-canopy woodland, using three different DJI drones. Results showed that different drone models had different performances in tree height measurement, with the Phantom 4 Pro being recommended as the optimal choice for operational tree surveying. The study reveals the good potential of sUAS for assessing tree overgrowth and monitoring the environment.

FORESTS (2021)

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Article Environmental Sciences

Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

Elizabeth M. Prior et al.

Summary: Vegetation heights derived from drone laser scanning and structure from motion photogrammetry were used to determine hydraulic roughness at different spatial scales. Results showed that the pixel method had the lowest water surface elevation root mean square errors, and that the reach method tended to overestimate while the pixel method tended to underestimate flood extents. Models utilizing DLS data had lower WSE RMSEs compared to models using SfM.

REMOTE SENSING (2021)

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Article Environmental Sciences

Estimating Primary Forest Attributes and Rare Community Characteristics Using Unmanned Aerial Systems (UAS): An Enrichment of Conventional Forest Inventories

Benjamin T. Fraser et al.

Summary: The research utilizes Unmanned Aerial Systems (UAS) and advanced image processing techniques to estimate tree diameters and various stand-level parameters in forests, with an average error reported. Stand-level parameters were either overestimated or underestimated, with a lesser overestimation for stands larger than 9 hectares. Random forest supervised classification achieved a promising overall accuracy of 85% in identifying large trees, offering local land managers opportunities for better understanding forested ecosystems. Future research on individual tree crown detection, especially for co-dominant or suppressed trees, will further enhance these efforts.

REMOTE SENSING (2021)

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Article Remote Sensing

Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images

Anastasiia Safonova et al.

Summary: This study introduces an approach to monitor tree structure parameters and damage using remote-sensing data collected by UAV for forest management. The proposed method utilizes ITCD algorithm for tree species classification and assessment of forest stands, demonstrating high accuracy in crown contouring and pixel-by-pixel classification. The advantage lies in combined processing of multispectral and RGB photo images.

DRONES (2021)

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Review Forestry