Data from: Early detection of encroaching woody Juniperus virginiana and its classification in multi-species forest using UAS imagery and semantic segmentation algorithms

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 an 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.