UAS Imagery-Based Mapping of Coarse Wood Debris in a Natural Deciduous Forest in Central Germany (Hainich National Park)

Abstract

Dead wood such as coarse dead wood debris (CWD) is an important component in natural forests since it increases the diversity of plants, fungi, and animals. It serves as habitat, provides nutrients and is conducive to forest regeneration, ecosystem stabilization and soil protection. In commercially operated forests, dead wood is often unwanted as it can act as an originator of calamities. Accordingly, efficient CWD monitoring approaches are needed. However, due to the small size of CWD objects satellite data-based approaches cannot be used to gather the needed information and conventional ground-based methods are expensive. Unmanned aerial systems (UAS) are becoming increasingly important in the forestry sector since structural and spectral features of forest stands can be extracted from the high geometric resolution data they produce. As such, they have great potential in supporting regular forest monitoring and inventory. Consequently, the potential of UAS imagery to map CWD is investigated in this study. The study area is located in the center of the Hainich National Park (HNP) in the federal state of Thuringia, Germany. The HNP features natural and unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaign was controlled from the Hainich eddy covariance flux tower located at the Eastern edge of the test site. Red-green-blue (RGB) image data were captured in March 2019 during leaf-off conditions using off-the-shelf hardware. Agisoft Metashape Pro was used for the delineation of a three-dimensional (3D) point cloud, which formed the basis for creating a canopy-free RGB orthomosaic and mapping CWD. As heavily decomposed CWD hardly stands out from the ground due to its low height, it might not be detectable by means of 3D geometric information. For this reason, solely RGB data were used for the classification of CWD. The mapping task was accomplished using a line extraction approach developed within the object-based image analysis (OBIA) software eCognition. The achieved CWD detection accuracy can compete with results of studies utilizing high-density airborne light detection and ranging (LiDAR)-based point clouds. Out of 180 CWD objects, 135 objects were successfully delineated while 76 false alarms occurred. Although the developed OBIA approach only utilizes spectral information, it is important to understand that the 3D information extracted from our UAS data is a key requirement for successful CWD mapping as it provides the foundation for the canopy-free orthomosaic created in an earlier step. We conclude that UAS imagery is an alternative to laser data in particular if rapid update and quick response is required. We conclude that UAS imagery is an alternative to laser data for CWD mapping, especially when a rapid response and quick reaction, e.g., after a storm event, is required.

Using an unmanned aerial system to analyse environmental impacts of charcoal production on tropical savanna ecosystems in northwestern Kenya

“…A total of 4215 images, covering an area of 1.16 km 2 , were collected via parallel flights on five days during January 2019, resulting in a ground resolution of 1.63 cm per pixel. The images were processed with the software Agisoft Metashape Professional 1.5 (Agisoft, 2019a ) to reconstruct 3D-information from 2D-images (Kabiri et al, 2020 ; Kienholz et al, 2020 ; Thiel et al, 2020 ). This resulted in a digital surface model (DSM), a digital terrain model (DTM), and consequently in a normalized digital surface model (nDSM), showing the height of any object on the ground (for more detailed information see SI 1 and Fig.…”

Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors

“…From an application standpoint, the two approaches conceptually most similar to ours use the Hough transform to fit lines representing individual stems in binarized images of target class posterior probabilities obtained on the basis of hand-crafted textural features (Duan et al, 2017) or spectral thresholding (Panagiotidis et al, 2019). Thiel et al (2020) performed generic line detection within RGB orthomosaics derived from very-high resolution unmanned aerial system-acquired imagery to find approximate fallen stem shapes.Lopes Queiroz et al ( 2019) used a generic segmentation procedure on the spectral bands of the aerial image combined with the normalized difference vegetation index (Tucker, 1979), and subsequently classified the resulting clusters based on spectral/textural features augmented with LiDAR derived information (canopy height model). Einzmann et al (2017) applied a similar approach, using large-scale mean shift in the role of the segmentation algorithm and augmenting the set of spectral bands with linear transformations of raw bands, textural features and multiple vegetation indices.…”

Detection of Degraded Acacia Tree Species Using Deep Neural Networks on Uav Drone Imagery

“…Santos et al (2019) made a comparison of three different deep learning frameworks, namely YOLOv3, Faster Region Based Convolutional Neural Networks (Faster R-CNN), and RetinaNet to assess a time series of RGB images in the context of tree crown detection achieving an overall average precision (AP) of 92%. Other studies have used object-based image analysis approach to detect coarse wood debris (CWD) from unmanned aerial systems in conjunction with LiDAR point clouds (Thiel et al, 2020). The authors reported an overall average precision (mAP) of 85% and a recall of 69.2%.…”