Plantation Loblolly Pine Seedling Counts with Unmanned Aerial Vehicle Imagery: A Case Study

Abstract

An unmanned aircraft system was evaluated for its potential to capture imagery for use in plantation loblolly pine (Pinus taeda L.) regeneration surveys. Five stands located in the Virginia Piedmont were evaluated. Imagery was collected using a recreational grade unmanned aerial vehicle at three flight heights above ground with a camera capable of capturing red–green–blue imagery. Two computer vision approaches were evaluated for their potential to automatically detect seedlings. The results of the study indicated that the proposed methods were limited in capability of generating reliable counts of seedlings in the locations evaluated. In conditions with low numbers of natural seedlings and sufficiently large planted seedlings, the detection methods performed with higher levels of accuracy. Challenges including global positioning system errors and image distortion made comparisons between ground samples and imagery difficult. In summary, unmanned aircraft systems have potential for use in plantation pine regeneration surveys if the challenges encountered can be addressed. Study Implications: Following the establishment of a pine plantation, it is important to estimate survival and possible recruitment of natural conifers. As the popularity of unmanned aircraft systems (UAS) has increased, forest managers have begun to explore their use for resource assessment. This study investigated using imagery captured with a recreational grade UAS, in conjunction with automated computer vision counting techniques, for use in regeneration surveys. The results of this research indicate that significant challenges must be addressed before UAS can become an integral component of survival assessments. Aircraft constraints, legal restrictions, low image quality, and high levels of natural pine regeneration limited the success of the proposed methods. In selected cases, however, favorable conditions led to accurate detection. Additionally, UAS imagery has the potential for assessing other stand characteristics such as competing vegetation and drainage patterns. Going forward, UAS imagery and automated counting approaches have the potential to supplement, but not fully replace, ground regeneration surveys if the challenges encountered in this study can be addressed.

Using Leaf-Off and Leaf-On Multispectral Airborne Laser Scanning Data to Characterize Seedling Stands

“…For example, UAV data have been processed with individual tree detection (ITD) methods and used for tree density estimation [7,8]. Other methods, such as area-based approach (ABA [9]), computer visioning [10], convolutional neural networks (CNNs [11]), manual interpretation [12], and object-based image analysis (OBIA [13][14][15]), have also been used for tree density estimation. Previously, aerial imagery was used to investigate stocking and regeneration density using manual image interpretation [16,17], competition assessment [18], and to develop an ITD method [19].…”

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

“…Moreover, young and advanced regenerating forests were studied using UAV-based hyperspectral imagery and photogrammetric point clouds (Imangholiloo et al 2019) and multispectral ALS data (Imangholiloo et al 2020), both under leaf-off and leaf-on conditions in boreal forests. Similarly, UAV data have been applied for regeneration assessments in seedling stands (Puliti et al 2019;Castilla et al 2020;Green and Burkhart, 2020).…”

Adding single tree features and correcting edge tree effects enhance the characterization of seedling stands with single-photon airborne laser scanning

“…For RS-based seedling stand inventories, unmanned aerial vehicles (UAV) have been used as the carrier platform for sensors providing three-dimensional information to be used in characterizing the structure of seedling stands. The UAV photogrammetry has been identified as a feasible technique to provide this information (Vepakomma et al, 2015;White et al, 2018;Feduck et al, 2018;Goodbody et al, 2018;Puliti et al, 2019;Fromm et al, 2019;Imangholiloo et al, 2019;Green and Burkhart, 2020;Hao et al, 2021).…”