Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

Abstract

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.

Systematic review and best practices for drone remote sensing of invasive plants

“…Ground surveys should coincide with the timing of the drone flight to ensure the growth stage and environmental conditions are consistent, with dates and times of data collection reported. In the studies we reviewed, field survey information was sometimes provided (Brooks et al., 2021; Marzialetti et al., 2021; Wang et al., 2021), but details were often omitted or not described in sufficient detail to allow replication. Most field studies focused on capturing the presence/absence or percent cover of the focal species, either in plots or as points.…”

“…Overlapping images provide multiple perspectives of the ground features, which permit image matching for generating 3D reconstructions. Image overlap was reported in 52% of studies, but there was high variability in the amount of overlap, ranging from 20% to 90% (Bolch et al., 2021; Lopatin et al., 2019; Wang et al., 2021). The most common overlap was 80% for both forward and sidelap (Casas et al., 2021; Lam et al., 2021; Weisberg et al., 2021), which adheres to recommendations for at least 70% forward and 40% sidelaps (Singh & Frazier, 2018; Su et al., 2016; Figure S2).…”

“…Overlapping images provide multiple perspectives of the ground features, which permit image matching for generating 3D reconstructions. Image overlap was reported in 52% of studies, but there was high variability in the amount of overlap, ranging from 20% to 90% (Bolch et al, 2021;Lopatin et al, 2019;Wang et al, 2021).…”

Preliminary evidence of upland plant encroachment in high‐elevation wetlands of Volcanoes National Park, Rwanda

“…Although our study is preliminary, we propose that these rankings will provide initial guidance for future studies (e.g. remote sensing studies of shrub encroachment; Wang et al, 2021) or management actions (e.g. which species to actively remove from wetlands; Kamocki et al, 2017).…”

Herbicide Ballistic Technology for Unmanned Aircraft Systems

“…Unmanned Aerial Systems (UAS) provide a platform for remote sensing and have been used for mapping the distributions of plants (e.g., invasive and endangered) and pests for forest management [8][9][10][11][12][13][14][15][16][17][18]. Advancements have been made in automated detection of miconia from georeferenced UAS imagery which can replace and/or expand search operations at reduced costs [13,15,19].…”