DOI: 10.5061/dryad.9s4mw6mgh
title: Data from: Early detection of encroaching woody Juniperus virginiana and its classification in multi-species forest using UAS imagery and semantic segmentation algorithms

description: 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.
description: <strong>Dataset collection:</strong>
 RGB images were collected at a University of Nebraska-Lincoln
 rangeland property (41°05'05.2"N, 100°45'53.7"W)
 located in North Platte, Nebraska. A rotary-wing UAS (Matrice 600 Pro,
 DJI, Shenzhen, China) with a RGB camera (Zenmuse X5R, DJI, Shenzhen,
 China) was flown over the study area at an altitude of 30.5 meters above
 the ground level on May 18<sup>th</sup>, 2019 with the front
 and side overlaps of 90% and 85%.
 <strong>Orthomosaic:</strong> The
 images were stitched into an orthomosaic in an original spatial resolution
 of 0.694 cm using Pix4Dmapper software (PIX4D, Lausanne, Switzerland).
 Geometric correction was performed during the processing by correcting the
 geolocation of the orthomosaic with those from surveyed by the RTK GNSS
 system. Four segments were cropped from the orthomosaic with the same size
 of raw images. <strong>Labelling class
 name:</strong> The boundary of each tree was
 manually delineated as polygon shapefile in ArcGIS (ESRI, Redlands, USA).
 The shapefiles were converted to the raster type with the targeted spatial
 resolution.
description: The following data is included in the dataset:
 (1) <strong>Raw_Images</strong>: 68 raw images
 (DJI_0xxx.png) and 4 cropped segments from the orthomosaic (DJI_990x.png)
 in a size of 4608 by 3456 pixels. (2)
 <strong>Label_Images</strong>: labelled images with four
 classes, redcedar (pixel value as 1), defoliation (pixel value as 2), pine
 (pixel value as 3), and others (pixel value as 0) (3)
 <strong>Stitch_Image</strong>: stitched orthomosaic.