Veranstaltungsprogramm

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Sitzungsübersicht
Session
B18: Joint GI_Forum and EARSeL UAS Summit I
Zeit:
Donnerstag, 04.07.2019:
11:30 - 13:00

Chair der Sitzung: Alexander Almer
Ort: B - Blauer Hörsaal
HS402

Zusammenfassung der Sitzung

additional presentations:

On the Potentiality of UAV Multispectral Imagery to Detect Oak Wilt Disease Using Convolutional Neural Networks

Hwa-Seon Lee1, Won-Woo Seo2, Kyu-Sung Lee3

Organisations: 1: Inha University, Korea, Republic of (South Korea); 2: Inha University, Korea, Republic of (South Korea); 3: Inha University, Korea, Republic of (South Korea)

Presenting author: Hwa-Seon Lee

Topics: Machine learning, deep learning, EO applications, societal benefits, SDGs

Keywords: UAV, multi-spectral, oak wilt disease, CNN


Mapping of Rumex obtusifolius in Native Grassland using Unmanned Aerial Vehicle: From Object-based Image Analysis to Deep Learning

Olee Hoi Ying Lam1, Bethany Melville1, Marcel Dogotari1, Moritz Prüm1, Hemang Narendra Vithlani1, Corinna Roers2, Rolf Becker1, Frank Zimmer1

Organisations: 1: Hochschule Rhein-Waal, Faculty of Communication and Environment, Germany; 2: Naturschutzzentrum im Kreis Kleve e.V., Germany

Presnting author: Olee Hoi Ying Lam

Topics: Machine learning, deep learning, Computer vision, knowledge organising systems

Keywords: weed mapping, image segmentation, neural network, UAV, RGB-imagery


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Präsentationen

Extracting Tree Heights of Dense Rainforests Based on UAV Imagery- A Best Practice Approach

Stefan Reder, Lilli Waßermann

HNE Eberswalde, Deutschland

For this study in the Ecuadorian lowland rainforest, a consumer UAV was used to develop an applicable and cost-efficient workflow for height measurements in remote areas. Structure from Motion (SfM) was combined with a digital terrain model (DTM) from the Shuttle Radar Topography Mission (SRTM). A vertical shift was applied to match the elevation data from the different sources. Afterwards the DSM was normalized to a canopy height model (CHM). The derived height values indicate that the proposed workflow is abdicable and emphasizes the importance of UAVs in rainforests research.



Aerial and Terrestrial Photogrammetric Point Cloud Fusion for Intensive Forest Monitoring

Stuart Krause1,2,3

1Thünen Institute of Forest Ecosystems; 2Faculty of Forest and Environment, Eberswalde University of Sustainable Development; 3Department of Geography, University of Bonn

Remote sensing methods for forest monitoring are rapidly evolving due to recent advancements in UAVs and photogrammetry. Photogrammetric point clouds provide the possibility to achieve the non-destructive derivation of individual tree parameters at a low cost. The fusion of aerial and terrestrial photogrammetry for creating full tree point clouds is of utility for forest research, as tree volume can be assessed more economically and efficiently than traditional methods. This however is a challenge to implement due to difficulties with co-registration and issues of occlusion. This study explores the possibility to use spherical targets typically used for Terrestrial Laser Scanning in order to accomplish the co-registration of UAV-based and terrestrial photogrammetric datasets. Results show a full tree point cloud derived from UAV oblique imagery with terrestrial imagery. Despite issues of noise produced from the sky in terrestrial imagery, the study shows a promising methodology for aerial and terrestrial point cloud fusion.



 
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