Suas for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam

Article sUAS for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam

Cuizhen Wang * , Grayson Morgan and Michael E. Hodgson

Department of Geography, University of South Carolina, Columbia, SC 29208, USA; [email protected] (G.M.); [email protected] (M.E.H.) * Correspondence: [email protected]

Abstract: Defined as “personal remote sensing”, small unmanned aircraft systems (sUAS) have been increasingly utilized for landscape mapping. This study tests a sUAS procedure of 3D tree surveying of a closed-canopy woodland on an earthen dam. Three DJI drones—Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M assembly—were used to collect imagery in six missions in 2019–2020. A canopy height model was built from the sUAS-extracted point cloud and LiDAR bare earth surface. Treetops were delineated in a variable-sized local maxima filter, and tree crowns were outlined via inverted watershed segmentation. The outputs include a tree inventory that contains 238 to 284 trees (location, tree height, crown polygon), varying among missions. The comparative analysis revealed that the M100/RedEdge-M at a higher flight altitude achieved the best performance in tree height measurement (RMSE = 1 m). However, despite lower accuracy, the Phantom 4 Pro is recommended as an optimal drone for operational tree surveying because of its low cost and easy deployment. This study reveals that sUAS have good potential for operational deployment to assess tree overgrowth toward dam remediation solutions. With 3D imaging, sUAS remote sensing can be counted as a reliable, consumer-oriented tool for monitoring our ever-changing environment.

Citation: Wang, C.; Morgan, G.; Keywords: sUAS; personal remote sensing; point cloud; watershed segmentation; 3D tree inventory Hodgson, M.E. sUAS for 3D Tree Surveying: Comparative Experiments on a Closed-Canopy Earthen Dam. Forests 2021, 12, 659. https://doi.org/ 1. Introduction 10.3390/f12060659 Dams provide beneficial functions such as flood control and reliable water supplies in Academic Editor: John Couture our living environments. Around 65% of U.S. dams are privately owned earthen dams that are aging and lacking maintenance [1]. As hurricanes and tropical storms on the Atlantic Received: 20 April 2021 coast are reportedly occurring more frequently in a warmer climate [2,3], earthen dams raise Accepted: 17 May 2021 high concerns about their hydraulic stability against severe wind and heavy precipitation Published: 22 May 2021 during extreme weather events. Dam failures may have monumental repercussions, with dramatic consequences such as loss of life and severe damage to property. The failure Publisher’s Note: MDPI stays neutral of over 70 regulated dams in South Carolina (SC) as a result of back-to-back hurricanes— with regard to jurisdictional claims in Joaquin in October 2015 and Matthew in October 2016—is an illustrative example of the published maps and institutional affil- human–environment consequences of dam breaches [4,5]. iations. Trees growing on dam slopes are one of the more controversial factors contributing to dam failures. In 2000, the Association of State Dam Safety Officials (ASDSO) reported that about 50% of state-regulated dams in 48 U.S. states had excessive tree growth [6]. South Carolina, for example, has 2249 regulated dams, and 60–80% of these dams have Copyright: © 2021 by the authors. trees. Green cover helps to stabilize soil mass on dam slopes and to reduce erosion from Licensee MDPI, Basel, Switzerland. overtopping when the water level rises. However, old-growth tree roots loosen the soil This article is an open access article mass and create root cavities, allowing more water into the dam, which can lead to seepage distributed under the terms and failure [6]. An earthen dam can be characterized in different zones: upstream slope, crest, conditions of the Creative Commons and downstream slope. The impact of trees growing on dam slopes varies with their size, Attribution (CC BY) license (https:// health, and location. For example, large trees on the lower downstream toe area below the creativecommons.org/licenses/by/ seepage line are of greater concern than similarly sized trees on the upper downstream area. 4.0/).

Forests 2021, 12, 659. https://doi.org/10.3390/f12060659 https://www.mdpi.com/journal/forests Forests 2021, 12, 659 2 of 18

Dam owners are required to remove trees with high impact on dam safety, and quantitative tree survey records on dams are necessary for decision making. Walkthroughs (i.e., in situ surveys of the dam) are currently the primary means of tree inspection. However, walkthroughs of thousands of dams in a state are time consuming, costly, and difficult to access, especially in cases of tree overgrowth on earthen dams. Very-high-resolution (VHR) remote sensing provides a promising way of collecting and documenting vegetation metrics for field engineers. Earthen dams are generally small in size. For example, the SC state-regulated dams could be as small as 25 feet high from the natural bed of the stream, with 50 acre-feet impoundment capacity [5]. Dam crests are often a few feet wide and 100 to 200 feet long. The meter/sub-meter-level VHR optical imagery is typically not adequate and considerably costly for systematic tree surveys on dams. LiDAR emits laser pulses to collect a 3D point cloud, and is considered to be superior for measuring forest environments [7,8]. Today, LiDAR data products have been collected in almost every county across the United States. However, airborne LiDAR instruments are costly and operationally difficult for repetitive data collection. In SC, a state-wide aerial LiDAR data collection program for all 46 counties was initiated in 2007, and completed 10 years later in 2017 [9]. The roughly 1.4-meter observation spacing is also inadequate for characterizing and monitoring trees on small earthen dams. Defined as “personal remote sensing” [10], small unmanned aircraft systems (sUAS), or drones, have been increasingly utilized for timely surveillance at centimeter-level spatial resolutions. Rapid development of sUAS technology has equipped drones with increased capabilities of payload, sensors, and flight time, allowing them to accomplish a variety of missions in the field [11]. Recent advancements in computer vision and photogrammetry demonstrate their capabilities of quantitative information extraction, and automated photogrammetric algorithms have been made available in sUAS data analysis packages. The most commonly utilized processing approaches are the structure from motion (SfM) and multi-view stereo (MVS) techniques, which provide the 3D reconstruction of the landscape from a flight mission. The SfM technique builds an orthomosaic from the highly overlapping 2D images in a redundant bundle adjustment procedure [12,13], while the MVS approach develops a complete 3D topographic model from images taken at known camera viewpoints [14]. Relying on these techniques, both orthoimage and point cloud are extracted. The 3D landscape contains advanced spatial details for quantitative tree surveying and cost-efficient inventory. In this respect, intensive studies have been conducted utilizing airborne LiDAR data. Hyyppä et al. [15] provided a comprehensive review of earlier efforts at LiDAR-based forest inventory extraction in boreal forests. Amiri et al. [8] examined a clustering-based 3D segmentation of the canopy height model (CHM) from full waveform airborne laser scanning (ALS) point clouds, in order to extract the regeneration coverage of a multilayer temperate forest. Dalponte et al. [16] reported that ALS outperforms hyperspectral imagery in delineating individual tree crowns. Aside from surveying tree crowns, ALS is superior in extracting tree height for a tree inventory. Ferraz et al. [17] applied a mean-shift-based statistical approach to segmenting ALS point clouds into overstory and understory, and extracted tree height and canopy base height from the extracted CHM in each layer. Later, this approach was updated, and applied to estimating the aboveground biomass in a tropical forest [18]. These studies reveal that LiDAR-based tree surveying, if the point density is high, could achieve similar accuracy to ground- based inventories. sUAS point clouds for tree surveying have emerged as a possible alternative to, or augmentation of, ALS in recent years. Similar to the CHM-oriented LiDAR studies (e.g., [19]), a local maxima algorithm is commonly used to detect individual treetops, and a watershed segmentation is used to extract tree crown area [20,21]. Dong et al. [22] extracted individual tree crown areas in an orchard from a point cloud extracted from DJI Mavic 2 Pro imagery, which reached an overall accuracy of 0.26–0.39 m in tree height and 0.48–0.72 m2 in crown area. These studies were conducted in open-canopy forests, where individual Forests 2021, 12, x FOR PEER REVIEW 3 of 17

Forests 2021, 12, 659 Mavic 2 Pro imagery, which reached an overall accuracy of 0.26–0.39m in tree height3 of and 18 0.48–0.72m2 in crown area. These studies were conducted in open-canopy forests, where individual crowns visually stood out. Coniferous trees were also easier to detect in sUAS crownspoint cloud visuallys due stood to their out. pointy Coniferous shapes trees [20 were]. In areas also easier of tree to overgrowth detect in sUAS into point closed clouds cano- duepies to, image their pointycalibration shapes and [20 individual]. In areas tree of tree detection overgrowth may be into problematic closed canopies, in sUAS image mis- calibrationsions. and individual tree detection may be problematic in sUAS missions. ThisThis study study aims aims to to test test thethe feasibilityfeasibility and and procedure procedure of of sUAS-based sUAS-based tree tree surveying surveying in in closed-canopyclosed-canopy woodlands.woodlands. AnAn earthenearthen damdam experiencingexperiencing treetreeovergrowth overgrowth in in Lexington Lexington County,County, SC SC was was selected selected as as the the study study site. site. ThreeThree DJIDJI drones—Mavicdrones—Mavic Pro,Pro, PhantomPhantom 44 Pro,Pro, andand Matrice100 Matrice100 (M100) (M100) assembled assembled with with the the RedEdge-MRedEdge-M multispectral multispectral sensor—were sensor—were tested tested atat various various flight flight configurations. configurations. Orthoimages Orthoimages and and point point clouds clouds were were extracted extracted in in order order to to mapmap treestrees and and to to delineate delineate their their heights heights and and crown crown areas. areas. While While the the performance performance varied varied amongamong these these drones, drones, the the study study demonstrated demonstrat thated that sUAS sUAS remote remote sensing sensing may offer may improved offer im- accuracyproved accuracy and efficiency and efficiency over traditional over traditional walkthroughs walkthroughs for assessing for assessingtree overgrowth tree over- in supportgrowth ofin damsupport remediation. of dam remediation.

2.2. MaterialsMaterials andand MethodsMethods 2.1.2.1. StudyStudy SiteSite TheThe studystudy sitesite was was the the Sweet Sweet Bay Bay Pond Pond Dam, Dam, an an earthen earthen dam dam located located 11 11 miles miles from from downtowndowntown Columbia, Columbia, South South Carolina. Carolina. This This is a is state-regulated a state-regulated C1 dam, C1 dam, meaning meaning it has it high has hazardhigh hazard potential—likely potential—likely loss of loss life of and/or life and/or serious serious damage damage to infrastructure to infrastructure [6]. A [6] low-. A obliquelow-oblique photo photo (Figure (Figure1) taken 1) bytaken a Mavic by a ProMavic on Pro 26 October on 26 October 2019 reveals 2019 thereveals overgrowth the over- ofgrowth trees into of trees a closed into canopya closed oncanopy the dam’s on the downslope. dam’s downslope. It is difﬁcult It is difficult for ﬁeld for engineers field engi- to record all trees and inspect their impacts on the dam during a walkthrough. neers to record all trees and inspect their impacts on the dam during a walkthrough.

FigureFigure 1. 1.An An obliqueoblique viewview ofof SweetSweet BayBay PondPond DamDam inin LexingtonLexington County,County, SC. SC. This This fall-season fall-season photo photo was taken with a Mavic Pro on 25 October 2019. was taken with a Mavic Pro on 25 October 2019.

TheThe damdam crestcrest is 180 meters meters long long from from the the northwest northwest entrance entrance to tothe the spillway spillway at the at thesouthwest southwest side side.. A large A large number number of trees of trees of different of different sizes sizesgrow grow on its on downstr its downstreameam slope. slope.Root cavities, Root cavities, especially especially from older from trees, older may trees, cause may erosion cause of erosion soil mass of soiland massimpact and the impactdam’s thestability. dam’s The stability. most Thecommon most tree common species tree is speciesblack gum is black (Nyssa gum sylvatica (Nyssa) sylvatica, which ),is whichsenescent is senescent and leaf-off and in leaf-off Figure in1. Other Figure common1. Other trees common include trees tulip include poplar tulip (Liriodendron poplar (Liriodendrontulipifera) and tulipifera loblolly) pine and loblolly(Pinus taeda pine), which (Pinus remain taeda), whichgreen. remainOther trees green. species Other are trees lim- speciesited. are limited.

2.2.2.2. DroneDrone FlightsFlights and and Data Data Sets Sets InIn 2019–2020, 2019–2020, we we launched launched multiple multiple sUAS sUAS flights flights over o thever damthe dam and collectedand collected centimeter- centi- levelmeter true-level color true and color color and infrared color infrared images usingimages the using three the DJI three drones. DJI Thedrones. Mavic The Pro Mavic has aPro built-in has a FC220 built-in camera FC220 (true camera color (true 4000 color× 3000), 4000 while× 3000) the, while Phantom the Phantom 4 Pro has 4 aPro built-in has a FC6310built-in (true FC6310 color (true 4864 × color3648). 4864 The × M100 3648). is assembled The M100 with is assembled a MicaSense with RedEdge-M a MicaSense multispectral sensor (5-band 1280 x 960): blue, green, red, red edge, and near-infrared (NIR). Six flights were launched in four days over a two-year period (Table1). For the autonomous flight missions, we used the DroneDeploy APP with the Mavic Pro and Phantom 4 Pro and the Atlas Flight APP for the M100/RedEdge-M. All flights were made Forests 2021, 12, 659 4 of 18

between 11:00 a.m. and 1:00 p.m. on sunny days. Each flight was completed in 5–13 min without the need of changing batteries. The same launch site in the open space at the dam entrance was used, and the remote pilot was located at the launch site and satisfied the visual line of sight (VLOS) rule under the U.S. Federal Aviation Administration sUAS Part 107 regulation [23]. sUAS images were taken with 85% endlap (along the path) and 80% sidelap (cross the path). Five flights were set at sixty meters above ground level (AGL). The last flight, M100, on 22 September 2020, was at 90 m AGL, with all other flight parameters the same as M100 on 26 August 2020. Different flight path plans were also tested. As shown in Table1, we tried all options (cross-grid, long-grid, and short-grid paths) for the Mavic Pro, the double-grid path for the Phantom 4 Pro, and the default short-grid path for the M100 missions. The Pix4DMapper package was used to process all sUAS images in this study. Two Mavic Pro flights, on 6 November 2019 and 26 August 2020, could not be satisfactorily calibrated. Their calibration rates, or the percentage of images calibrated in the mission, were 71% and 58%, respectively. These two missions, one in long-grid and the other in short-grid plans, were not considered further in this study. As shown in Table1, the flight areas of these missions were slightly different. For the purpose of comparison, we decided to survey all trees within 60 meters downward from the dam crest. A study polygon of 1.606 hectares (Ha) was clipped from all missions.

Table 1. Six ﬂight missions at the study site.

Flight Flight Area GSD 1 GCP RMSE Calib. Date Drone Images Alt. (m) Path (ha) (cm) 2 (cm) Rate 25/10/2019 Mavic Pro 60 Cross-grid 141 2.62 1.96 10 2.8 95% Mavic Pro 60 Long-grid 195 2.33 1.92 8 / 71% 06/11/2019 Phantom 4 Double- 60 217 5.01 1.87 8 2.9 93% Pro grid Mavic Pro 60 Short-grid 72 1.51 1.83 9 / 58% 26/08/2020 M100 60 Short-grid 1280 * 3.00 4.19 13 1.5 91% 22/09/2020 M100 90 Short-grid 1390 * 4.08 5.41 9 3.1 92% 1 GSD: ground sampling distance, or average pixel size of the orthoimage. 2 GCP: ground control point. * Note: The M100/RedEdge-M images are recorded as single-band TIFF ﬁles.

A set of 12” × 12” black and white checker boards (vinyl tiles) were randomly set up across the study site to serve as our ground control points (GCPs). During each mission, 8–13 GCPs were collected using a survey-grade GNSS (Emlid Reach RS2) in real-time kinematic RTK mode. With one RS2 unit serving as the base (at least 2 hours recording in the field) and the other as a rover, the GCP recordings could reach about 2-cm-level accuracy after precise point positioning (PPP) correction services, such as the Online Positioning User Service (OPUS) at the NOAA National Geodetic Survey [24,25]. GCPs within the closed-canopy woodland were not available. The GCPs were applied in the orthomosaic and point cloud process in the Pix4DMapper package, and the resulting horizontal root- mean-square errors (RMSEs) were within 1.5 to 3 cm, roughly 1 to 1.5 pixels of the extracted orthoimages (as shown in Table1). These RMSEs reasonably depict the (x,y) locational accuracy of the orthoimages and point clouds of each flight. The vertical (z) accuracy was evaluated using selected points on the flat dam crest. Trees on the dam slope grow into a closed-canopy, and are covered with dense understory shrubs, making it difficult to walk through for field survey. The lack of open space also restricts the measurement of tree height with the commonly used laser rangefinders. For validation purposes, we selected a strip of trees along the road nearby to serve as our validation site. On 27 September 2020, all of the 62 trees at this site were measured using a handheld Nikon Forestry Pro. These field measurements served as ground truth to evaluate sUAS performance in estimating tree height. Only the M100/RedEdge-M mission was conducted at this validation site. The LiDAR point cloud was downloaded from the U.S. Geological Survey (USGS) National Geospatial Program [26] in order to build a bare-earth model of the study site. Forests 2021, 12, 659 5 of 18

The LiDAR data for Lexington County, SC were acquired in 2010 at a 1.4 m nominal point spacing and 18 cm vertical accuracy (RMSE) [27]. In accordance with LiDAR Base Specification V2.0 [28], the released USGS LiDAR products have been classified as ground, low/medium/high vegetation, building, road surface, water, etc. The LiDAR dataset at the study site contained 38,695 points (Table2). Visually referencing with the sUAS orthoimages, we combined the LiDAR points classified as ground, model key, and road to represent the bare-earth surface. This ”bare earth” set contained 26.40% of the original LiDAR points at the study site. The majority of the dataset (73.42%) was coded as unclassified, mostly consisting of tall and short vegetation in the woodland.

Table 2. Statistics of the USGS point cloud at the study site. The z value is the AGL height.

Interpreted Land Code Class Points Percent z_min z_max Cover 1 Unclassiﬁed 28,411 73.42% 48.61 78.69 Vegetation 2 Ground 9063 23.42% 48.43 52.65 Bare earth 8 Model key 922 2.38% 48.43 52.70 Bare earth Road 11 232 0.60% 48.61 51.90 Bare earth Surface 3,9 Other 67 0.17% 50.45 52.33 Water, low veg Total points 38,695 100%

2.3. Approaches For each flight mission, the sUAS orthoimage and point cloud were created with the Pix4DMapper package using the default settings of image scale: full image size in the initial 1 processing and 2 image size in point cloud densification. Individual trees were identified, and tree heights and crown polygons were extracted. To evaluate the performance of the different missions, the point clouds were compared with LiDAR elevation on the dam crest at the study site, and the extracted tree heights were compared with field measurements at the validation site.

2.3.1. Building sUAS Orthoimages and Point Clouds All sUAS images in each mission were calibrated in the Pix4DMapper package using the collected GCPs (as shown in Table1). Similar to other automated photogrammetric algorithms, Pix4DMapper employs the structure from motion (SfM) technique to build the 3D view of the mission area. Individual images were mosaicked, geocorrected, and orthorectiﬁed with the resolved 3D perception. The created orthoimage holds a nadir view of the landscape. The ground sampling distance (GSD) of each orthoimage is listed in Table1. The cm-level pixel size provides sufﬁcient detail for tree surveying. The point cloud is a collection of 3D mass points (x,y,z) representing the landscape. At cm-level spacing intervals, this is expected to reveal more detailed 3D information than USGS LiDAR data.

2.3.2. Extracting Digital Terrain and Canopy Height The digital surface model (DSM) and digital terrain model (DTM) are needed to extract canopy height [10]. The DSM represents the elevation of land cover above ground, such as canopy tops of trees or rooftops of buildings. The DSM can be easily created from the sUAS point cloud. The DTM represents the elevation of the bare-earth surface. Since sUAS 3D perception relies on photogrammetry, its point cloud only contains a single return (z value) at a (x,y) location. In woodlands, the ground surface under the canopy is often not visible. Therefore, it is difﬁcult to extract the ”ground” points. We used the USGS LiDAR point cloud to create the DTM. While the LiDAR data have a nominal point spacing of 1.4 m, they allow multiple x-y-z returns from a single pulse owning to the laser signal’s strong penetration capacity. As shown in Table2, LiDAR points Forests 2021, 12, 659 6 of 18

coded as ground, model key, and road surface were extracted in a range of 48.43 to 52.70 m ASL. As the earthen dam surface slope was well behaved, these LiDAR ground returns were deemed to be highly representative of the ground elevation. With the LiDAR point cloud (ground returns) and sUAS point cloud (all returns), the DTM and DSM were built in a triangulated irregular network (TIN) process. For the purpose of comparison, both the DTM and the DSM in all missions were resampled to a 20 cm cell size. The canopy height model (CHM) was thus created as [29]:

CHM = DSM − DTM (1)

The CHM is a raster layer of canopy height that represents the height of the topmost components above the ground surface.

2.3.3. Tree Surveying: Tree Height, Treetops, and Crowns Based on plot-based measurements from a U.S. Forest Service research paper [30], most 10-year-old hardwood saplings in the Appalachian upland hardwood forest are 3 to 10 m high, varying with species and clearcut openings. Here we consider trees less than 10 m high to be saplings. All pixels within the CHM of less than 10 m were not considered in the following analysis, because trees in this earlier stage of growth are usually not a concern for dam safety. The topmost point of an individual tree (treetop) in the CHM was identified using a variable-sized window filter [31]. This approach identifies the local maxima with a height-dependent crown searching window. These local maxima represent the treetops of individual trees. Geographically, our study site in the Midlands of SC is located at the southern end of the Piedmont region. It bears the same biophysical similarity of trees as in the work of [31]. For this reason, this study adopts their tree height–crown relationship (units in meters): Crown Width = 2.515 + 0.009 × CHM2 (2) Equation (2) defines the searching window at a given pixel, which varies with tree height at this pixel. A circular searching window was used. Only pixels with a CHM greater than 10 m were counted. Within this filter, the central pixel is compared with all other pixels in the window, and is defined as a local maximum if it presents the highest value. Tree crowns in the CHM were outlined using a generalized marker-controlled watershed segmentation (MCWS) approach [32]. Tree crown is defined as a dominant clump of tree foliage and branches associated with a single treetop. This assumes that a tree crown follows the mathematical morphology of an inverted ”watershed” [33]. Taking an inverted treetop as the bottom of the basin, the model can be simulated via a process of filling water until water overflow. A watershed is thus established that defines an inverted tree crown. Relying on the above extracted treetops, the MCWS approach divides the CHM layer into individual tree crowns at the study site. One crown is associated with one treetop. Therefore, some trees may not be identified where trees grow in clusters, and crowns overlap one another.

2.3.4. Comparison Analysis and Validation The sUAS performances of the four missions were compared in several respects. The ﬁrst comparison was the vertical (z) accuracy. A strip of sand–clay dam crest was outlined and taken as a stable surface during the missions. Counting the LiDAR point clouds as trustful elevation sources, all points on the strip were extracted to test the comparability of the sUAS point clouds against LiDAR. A total of 27 LiDAR points were recorded on the strip. For a given LiDAR point, the nearest sUAS point within 5 cm spacing was extracted in order to build the LiDAR–sUAS sample pair. With the 27 samples, the mean absolute error (MAE) was calculated in order to evaluate the magnitude of the sUAS’s deviation from Forests 2021, 12, 659 7 of 18

LiDAR. The mean error (ME) was also calculated, which takes the directional deviation into consideration: n e − g ∑j=1 Hj Hj MAE = (3) n n e g ∑j=1(Hj − Hj ) ME = (4) n e g where Hj and Hj denote the sUAS-extracted and LiDAR-recorded z values, respectively. The term n represents the sample numbers: n = 27 in this study. A larger MAE but smaller ME value indicates that the sUAS measurements are affected by directional deviation from LiDAR elevation. The second comparison was the (x,y) locational accuracy. This cannot be directly compared among the four missions, because the study area does not have permanent ground control points. Assuming crown shapes do not change dramatically in a one-year period (October 2019–August 2020), it becomes reasonable to compare the (x,y) locations of treetop points in different ﬂight missions. A total of 40 trees with apparent treetops and visually distinguishable crowns on the point cloud were randomly selected. The sUAS performance of different missions was intercompared with respect to locational variations. Note that the CHM has a 20 cm cell size, so larger deviations between the missions are to be expected. Additionally, one must be aware that this comparison is less quantitatively accurate, since treetop locations could be affected by new growth, wind, or other environmental conditions. However, it still provides meaningful information in assessing the capability of delineating individual trees in different missions. Thirdly, at the validation site, the sUAS-extracted tree height was compared with ﬁeld records for accuracy assessment. The RMSE was calculated: v u 2 u n e − g t ∑j=1 Hj Hj RMSE = (5) n

e g where Hj and Hj denote the sUAS-extracted and ﬁeld-measured tree height, respectively. The term n represents the total number of trees in the assessment. The extracted tree crowns cannot be validated without ground reference data. Instead, the sUAS performance of crown extraction in different missions was visually compared against the orthoimages. At cm-level resolution, and with phenological variations in August–November, the orthoimages provide a reasonable level of detail for the visual interpretation of tree crowns.

3. Results 3.1. Orthoimage and Point Cloud The spatial resolution and geocorrection RMSE of the orthoimages from the four missions are listed in Table1 above. For simplicity, all orthoimages are resampled to a pixel size of 5 cm. Hereafter, the Mavic Pro mission on 25 August 2019 is referred to as MP0825, the Phantom 4 Pro mission on 6 November 2019 as P41106, the M100/RedEdge-M mission on 25 October 2020 as RE1025, and the M100/RedEdge-M mission on 22 September 2020 as RE0922. Figure2 displays the orthoimages in an August–November trajectory in 2019–2020. The MP0825 mission exhibits a jagged border at the far end of the dam’s downslope. While the mission reaches a calibration rate of 95% (as shown in Table1), eight individual drone images along the east border are uncalibrated, resulting in data loss, as shown in the orthoimage. Forests 2021, 12, x FOR PEER REVIEW 8 of 17

The orthoimages clearly depict the overgrowth of trees that grow into a closed canopy at the study site. Individual tree crowns, especially those of tall trees, are still discernable. The images visually reveal different spectral and radiometric characteristics of their sensors. The RedEdge-M images are recorded in 16-bit, while the Mavic Pro and Phantom 4 Pro images are in 8-bit. In a true color view, the RedEdge-M images show a greener tone than those of Mavic Pro and Phantom4 Pro. The four-stage orthoimage trajectory also reflects the phenological differences of tree species. For example, black gum has shiny, dark green leaves, and is sensitive to drought stress during the hot summers of SC. It starts to show early fall color in a dark brownish tone in August, which becomes more distinguishable in September. Its leaves are senescent and drop off in October–November. Tulip poplar remains green in summer (August– September), then shows its fall color in a yellowish green tone (October–November). Lob- Forests 2021, 12, x FOR PEER REVIEW 8 of 17 Forests 2021, 12, 659 lolly pine—for example, the one marked in Figure 2—has a large circular crown and8 of re- 18 mains evergreen, although it has a lighter tone in the Phantom4 Pro image. These phenological variations help to delineate tree crowns in data analysis. The orthoimages clearly depict the overgrowth of trees that grow into a closed canopy at the study site. Individual tree crowns, especially those of tall trees, are still discernable. The images visually reveal different spectral and radiometric characteristics of their sensors. The RedEdge-M images are recorded in 16-bit, while the Mavic Pro and Phantom 4 Pro images are in 8-bit. In a true color view, the RedEdge-M images show a greener tone than those of Mavic Pro and Phantom4 Pro. pine The four-stage orthoimage trajectory also reflects the phenological differences of tree species. For example, black gum has shiny, dark green leaves, and is sensitive to drought stress during the hot summers of SC. It starts to show early fall color in a dark brownish tone in August, which becomes more distinguishable in September. Its leaves are senescent and drop off in October–November. Tulip poplar remains green in summer (August– September), then shows its fall color in a yellowish green tone (October–November). Lob- lolly pine—for example, the one marked in Figure 2—has a large circular crown and. re- Figure 2. The mainsorthoimages evergreen extracted , although from four it has flights ﬂights a lighter (RE0826, tone RE0822, in the MP1025, Phantom4 and Pro P41106). image. These phenological variations help to delineate tree crowns in data analysis. TreesThe orthoimages grow on the clearly downslope depict of the the overgrowth dam. The oflowest trees elevation that grow is into in deep a closed woodland canopy at 48. the4 study m ASL. site. In Individual the sUAS point tree crowns, clouds especially(Figure 3), those all points of tall below trees, this are stillthreshold discernable. were countedThe images as low visually noises reveal, and different were removed. spectral The and tallest radiometric point of characteristics canopy height of theiris around sensors. 79 mThe ASL. RedEdge-M The dam imagescrest and are open recorded areas in have 16-bit, low while elevation the Mavic with Proa dark and blue Phantom tone in 4 Prothe images are in 8-bit. In a true color view, the RedEdge-M images show a greener tone than point clouds. Shrubs and short trees surrounding the woodland have higherpine elevation in athose lighter of Mavicblue tone. Pro The and p Phantom4oint cloud Pro.s of the woodland are visually continuous due to their extremeThely four-stage high density orthoimage, with cm trajectory-level spacing also reﬂects between the phenological points. Nevertheless, differences tall of trees tree clearlyspecies. stand For example, out in a blackreddish gum tone has and shiny, circular dark shapes. green leaves, Interestingly, and is sensitive the electric to drought power linestress above during thethe water hot is summers clearly detected of SC. It by starts all sUAS to show point early clouds. fall color in a dark brownish tone in August, which becomes more distinguishable in September. Its leaves are senescent and drop off in October–November. Tulip poplar remains green in summer (August– September), then shows its fall color in a yellowish green tone (October–November). Loblolly pine—for example, the one marked in Figure2—has a large circular crown. and Figure 2. The orthoimagesremains evergreen, extracted from although four flights it has (RE0826, a lighter RE0822, tone MP1025, in the Phantom4 and P41106). Pro image. These phenological variations help to delineate tree crowns in data analysis. TreesTrees grow grow on th thee downslope of the dam. The The lowest lowest elevation elevation is is in in deep deep woodland woodland atat 48. 48.44 m m ASL. ASL. In In the the sUAS sUAS point point clouds clouds ( (FigureFigure 33),), allall pointspoints belowbelow thisthis thresholdthreshold werewere countedcounted as as low low noises noises,, and and were were removed. removed. The The tallest tallest point point of ofcanopy canopy height height is around is around 79 m79 ASL. m ASL. The The dam dam crest crest and and open open areas areas have have low low elevation elevation with with a adark dark blue blue tone tone in in the the pointpoint cloud clouds.s. Shrubs Shrubs and and short short trees trees surrounding surrounding the the woodland woodland have have higher higher elevation elevation in ain lighter a lighter blue blue tone. tone. The Thepoint point cloud cloudss of the of woodland the woodland are visually are visually continuous continuous due to due their to extremetheir extremelyly high high density density,, with with cmcm-levellevel spacing spacing between between points. points. Nevertheless, Nevertheless, tall tall trees trees clearlyclearly stand out inin aa reddishreddish tone tone and and circular circular shapes. shapes. Interestingly, Interestingly, the the electric electric power power line Figure 3. The height-linecoloredabove above thepoint waterthe clouds water is extracted clearly is clearly detected from detected four by flights all by sUAS all (RE0826, sUAS point RE0822,point clouds. clouds. MP1025, and P41106).

FigureFigure 3. 3. TheThe he height-coloredight-colored point point clouds clouds extracted extracted from from four four flights ﬂights (RE0826, (RE0826, RE0822, RE0822, MP1025, MP1025, and P41106).

Forests 2021, 12, 659 9 of 18 Forests 2021, 12, x FOR PEER REVIEW 9 of 17

AmongAmong thethe fourfour flights,flights, the MP1025 mission mission d didid not not perform perform well. well. Data Data loss loss in inits itspoint point cloud cloud was was more more dramatic, dramatic, leaving leaving multiple multiple large largegaps within gaps within the woodland the woodland (Figure (Figure3). The3 other). The three other missions three missions perform performeded better, although better, although data loss datapersistently loss persistently remained, remained,especially especiallythe large gap the at large the southeast gap at the end southeast of the mission end of area. the mission Overall, area. the RE0922 Overall, seems the RE0922superior seems, with superior,the largest with coverage the largest of its coverage point cloud of its in pointthe mission cloud inarea. the Given mission its area.poor Givenperformance its poor in performance terms of both in termsthe calibration of both the (noted calibration earlier), (noted orthoimage earlier), and orthoimage point cloud, and pointthe MP1025 cloud, thewas MP1025 not further was examined not further in examined this study. in this study. WithinWithin thethe samesame flightflight areaarea polygonpolygon (1.606 ha), the USGIS USGIS LiDAR LiDAR po pointint cloud cloud has has a 2 adensity density of of 2.41 2.41 points/m points/m2. Note. Note that that the theLiDAR LiDAR cloud cloud may may contain contain multiple multiple returns returns at a atpoint a point and, and, therefore, therefore, its horizontal its horizontal density density is even is lower. even lower. sUAS point sUAS clouds point cloudsonly contain only containone point one at point each atx- eachy location x-y location.. The P41106 The P41106 point cloud point has cloud the has highest the highest density density of 451.06 of 2 451.06points/m points/m2, providing, providing the most the detail most about detail tree about canopies tree at canopies the study at site. the studyAt a flight site. height At a 2 flightof 90 heightm AGL, of the 90 mRE0922 AGL, point the RE0922 cloud reaches point cloud a density reaches of 52.26 a density points/m of 52.262, slightly points/m lower, 2 slightlythan RE0826 lower (59.44 than points/m RE0826 (59.442) at 60 points/m m AGL. Fi)gure at 60 4 demonstrates m AGL. Figure the4 improveddemonstrates coverage the improvedof sUAS mass coverage points of on sUAS an example mass points pine tree. on an Within example the pinetree crown tree. Within outlined the in tree the crownfigure, outlined in the figure, only 32 LiDAR points are collected in comparison with hundreds of only 32 LiDAR points are collected in comparison with hundreds of RE0922 points. These RE0922 points. These dense sUAS points effectively reveal the tree’s structural details. dense sUAS points effectively reveal the tree’s structural details.

(a) (b) (c)

FigureFigure 4. 4.The The 3D3D pointspoints ofof aa pinepine tree tree extracted extracted from from LiDAR LiDAR ( a(a),), RE0922 RE0922 ( b(b),), and and P41106 P41106 ( c().c).

3.2.3.2. DigitalDigital TerrainTerrain and and CHM CHM LiDARLiDAR bare-earth bare-earth points points have have a a much much lower lower spatial spatial density density than than the the sUAS sUAS point point cloud,cloud, butbut thethe sUASsUAS pointspoints areare generallygenerally ofof the the overlying overlying vegetation, vegetation,and and fewfew represent represent thethe ground.ground. TheThe densitydensity ofof LIDARLIDAR groundground points points under under the the tree tree canopy canopy of of the the woodland woodland (Figure(Figure5 a)5a) was was quite quite adequate adequate for for representing representing the the sloped sloped earthen earthen dam. dam. In In the the created created DTMDTM (Figure(Figure5 b),5b), the the dam dam crest crest has has a a relatively relatively higher higher elevation elevation than than its its slope slope on on both both sides.sides. TheThe spillwayspillway isis anan importantimportant damdam structure,structure, whichwhich isis clearclear atatthe the south south end end of of the the studystudy sitesite (marked(marked inin thethe figure).figure). TheThe damdam crestcrest isis relatively relatively flat, flat, at at an an average average elevation elevation ofof aroundaround 5252 mm ASL.ASL. TheThe lowest lowest elevationelevation inin thethe deepdeep woodland woodland isis 48.448.4 m, m, resulting resulting in in a a maximummaximum dropdrop ofof 3.6 3.6 m m from from dam dam crest crest to to downslope. downslope. WithWith thethe denselydensely distributeddistributed sUASsUAS pointpoint cloud,cloud, thethe extracted extracted DSMDSM ofof each each mission mission revealsreveals aa highhigh levellevel ofof detail detail of of the the 3D 3D tree tree canopies. canopies. Note Note that that all all raster raster layers, layers, including including DTM,DTM, DSM,DSM, and CHM CHM,, are are at at a a20 20 cm cm cell cell size. size. Figure Figure 6 demonstrates6 demonstrates the theCHM CHM layer layer from fromthe RE0922 the RE0922 mission. mission. Similar Similar to the to point the point cloud cloud figure figure (Figure (Figure 3) above,3) above, the thesouthwest southwest end endof the of themission mission suffers suffers from from image image calibration calibration errors errors,, and andtherefore, therefore, results results in a inlarge a large data datagap. gap.A smaller A smaller gap is gap also is observed also observed at the at dam the damentrance entrance in the in northwest. the northwest. The tallest The tallest point pointof the of canopy the canopy is 29.69 is m. 29.69 The m. inset The of inset Figure of 6 Figure is the6 example is the example pine tree pine (marked tree (marked in Figure in2) Figurewith a2 )3D with profile a 3D view profileed from viewed east from to west east to(high west oblique). (high oblique). The tree Thehas treean apparent has an apparentdistinguishable distinguishable circular crown circular in crown the CHM. in the Clumps CHM. Clumpsof shorter of trees shorter nearby trees are nearby also arede- alsotectable detectable but grow but together grow together with a with close a closecanopy, canopy, reflecting reflecting a typic a typicalal pattern pattern of closed of closed--canopy overgrowth. The sUAS point cloud only records the topmost points of the tree canopy

Forests 2021, 12, 659 10 of 18

ForestsForests 2021 2021, ,12 12, ,x x FOR FOR PEER PEER REVIEW REVIEW 1010 of of 17 17

canopy overgrowth. The sUAS point cloud only records the topmost points of the tree canopylayer.layer. As layer.As opposed opposed As opposed to to LiDAR LiDAR to LiDAR returns, returns, returns, ground ground ground points points points from from from the the theopticaloptical optical imagery imagery imagery under under under the the thecanopycanopy canopy are are arenot not notavailable. available. available. Therefore, Therefore, Therefore, the the sUAS sUAS the sUAS-extracted-extracted-extracted CHM CHM CHM is is a a thin, thin, is a hollow thin,hollow hollow 3D 3D surface surface 3D surfaceatopatop the the atop tree tree the canopy. canopy. tree canopy.

SpillwaySpillway

(a(a) ) (b(b) )

FigureFigureFigure 5. 5. 5.Bare-earth Bare Bare-earth-earth returns returns returns from from from the the the LiDAR LiDAR LiDAR point point point cloud cloud cloud ( a( )a(a) and )and and the the the extracted extracted extracted DTM DTM DTM of of of the the the study study study site site site (b ( ).b(b).).

Figure 6. The CHM from the RE0922 mission. The inset shows a 3D view of the example pine tree. FigureFigure 6. 6. The The CHM CHM from from the the RE0922 RE0922 mission. mission. The The inset inset shows shows a a 3D 3D view view of of the the example example pine pine tree. tree.

3.3.3.3.3.3. Treetop Treetop Treetop and and and Crown Crown Crown Delineation Delineation Delineation TheTheThe sUAS sUAS sUAS point point point cloud cloud cloud and and and the the the extracted extracted extracted CHM CHM CHM allow allow allow the the the delineation delineation delineation of of of treetops treetops treetops and and and crownscrownscrowns fromfrom from thethe the continuouscontinuous continuous canopycanopy canopy cover. cover. A AA treetop treetoptreetop is isis a a point point with with a a aloc loc localalal maximum maximum maximum in in inthethe the CHMCHM CHM thatthat that representsrepresents represents thethe the topmosttopmost topmost pointpoint point ofof of aa tree. atree. tree. OneOne One treetree tree is is assumed isassumed assumed toto have tohave have oneone onetreetoptreetop treetop point. point. point. A A threshold threshold A threshold of of CHM CHM of CHM > > 10 10 m >m is 10is applied applied m is applied to to all all CHM toCHM all layers. CHMlayers. layers.Figure Figure 7 Figure7 displays displays7 displays the 3D view of the treetops extracted from the RE0922 mission and its true color thethe 3D 3D view view of of the the treetops treetops extracted extracted from from the the RE0922 RE0922 mission mission and and it itss true true colorcolor or- or- orthoimage. The vertical information (canopy height) is overlaid on the orthoimage to thoimage.thoimage. The The vertical vertical information information (canopy (canopy height) height) is is overlaid overlaid on on the the orthoimage orthoimage to to achieve a 3D perspective, which makes the tree clumps more outstanding. Treetops are achieveachieve a a 3D 3D perspective, perspective, which which makes makes the the tree tree clumps clumps more more outstanding. outstanding. Treetops Treetops are are marked as red columns emerging atop trees. The dam crest is ﬂat in general, but some crest markedmarked as as red red columns columns emerging emerging atop atop trees. trees. The The dam dam crest crest is is flat flat in in gene general,ral, but but some some pixels are affected by canopy pixels above them, and are displayed as erroneously taller, crestcrest pixels pixels are are affected affected by by canopy canopy pixels pixels above above them, them, and and are are displayed displayed as as erroneously erroneously white objects. taller,taller, white white objects. objects. ForFor the the RE0922 RE0922 mission, mission, a a total total of of 284 284 treetops treetops we werere identified identified, ,with with a a height height range range of of 12.312.3 to to 29.69 29.69 m. m. To To reduce reduce uncertainties uncertainties in in the the extracted extracted treetops treetops and and crowns, crowns, trees trees along along thethe woodland woodland borders borders and and data data gaps gaps from from calibration calibration errors errors that that d didid not not contain contain complete complete

Forests 2021, 12, x FOR PEER REVIEW 11 of 17

crowns in the CHM were removed from the analysis. The two insets of Figure 7 display the 3D view of two example tree crowns. The one on the right is a pine tree that has a typical standalone, rounded crown. The one on the left is a green cluster of tulip poplars Forests 2021, 12, 659 that grow together into a big clump. Multiple trees are successfully identified in this11 ofclus- 18 ter, one with a treetop and a crown. It is reasonable, however, that some trees would not be detected if their treetops do not reveal identifiable local maxima.

Figure 7. A 3D view of the RE0922 orthoimage embedded with treetops marked as red columns. Figure 7. A 3D view of the RE0922 orthoimage embedded with treetops marked as red columns. Two insets show the 3D views of a pine tree and a tulip poplar cluster, respectively. Two insets show the 3D views of a pine tree and a tulip poplar cluster, respectively.

ForTree the crowns RE0922 are mission, delineated a total from of the 284 joint treetops marker were-controlled identified, watershed with a height segmentation range of 12.3approach. to 29.69 One m. watershed To reduce uncertaintiessegment is associated in the extracted with a treetop treetops point and located crowns, in trees the alongwater- theshed. woodland For trees borders with and relatively data gaps standalone from calibration crowns, errors crowns that are did extracted not contain with complete regular crownsshapes in(similar the CHM lengths were in removed major fromand minor the analysis. axes). TheIn areas two insetswhere of trees Figure grow7 display into close the 3Dcanop viewies of, their two examplecrowns become tree crowns. irregularly The one shaped. on the As right shown is a pinein the tree tulip that p hasoplar a typicalinset of standalone,Figure 7, the rounded elongated crown. crowns The in one the on cluster the left may is a also green indicate cluster ofmissing tulip poplarstrees. that grow togetherAs intorevealed a big in clump. the point Multiple clouds, trees all aresUAS successfully flights suffer identified from calibration in this cluster,-induced one with data agaps treetop that and vary a according crown. It isto reasonable, systems and however, flight configurations. that some trees Treetops would and not becrowns detected falling if theirwithin treetops or adjacent do not to reveal these identifiable gaps could local not be maxima. extracted. Figure 8 compares the treetops and Treecrown crowns polygons are delineated extracted from the jointthree marker-controlled missions. It is clear watershed that standalone segmentation trees— approach.for example One the watershed example pine segment tree in is previous associated figures with— ac treetopould be pointsuccessfully located extracted in the wa- in tershed.all missions. For treesHowever, with relativelythe effectiveness standalone in closed crowns, canopy crowns areas are varie extractedd among with the regular three shapesmissions. (similar Across lengths the study in major site, and 284 minor trees we axes).re identified In areas where in the trees RE0922 grow mission. into close The canopies,RE0826 mission their crowns produce becomed 238 trees, irregularly suffering shaped. from Ashigher shown calibration in the tulip errors poplar in the inset nor ofth- Figureeast and7, the southeast elongated areas crowns of the in study the cluster site. The may P41106 also indicate mission missing extracted trees. 251 trees, identi- fyingAs more revealed trees inat thethe pointsoutheast clouds, end all of sUAS the study flights area suffer, where from both calibration-induced RedEdge-M missions data gapssuffer thated from vary accordingcalibration to errors. systems In deep and flight woodland configurations. areas where Treetops tree canopies and crowns were fallingclosed, withinhowever, or adjacent it identifie to thesed fewer gaps trees could than not the be other extracted. missions. Figure More8 compares detailed the comparison treetops ands are crowndescribed polygons in the extractednext section. from the three missions. It is clear that standalone trees—for example the example pine tree in previous figures—could be successfully extracted in all missions. However, the effectiveness in closed canopy areas varied among the three missions. Across the study site, 284 trees were identified in the RE0922 mission. The RE0826 mission produced 238 trees, suffering from higher calibration errors in the northeast and southeast areas of the study site. The P41106 mission extracted 251 trees, identifying more trees at the southeast end of the study area, where both RedEdge-M missions suffered from calibration errors. In deep woodland areas where tree canopies were closed, however, it Forests 2021, 12, 659 12 of 18

Forests 2021, 12, x FOR PEER REVIEW 12 of 17

identiﬁed fewer trees than the other missions. More detailed comparisons are described in the next section.

Figure 8. The extracted tree crowns and treetops from three missions (RE0922, RE0826, and P41106). One crown is associ- atedFigure with one 8. The treetop. extracted tree crowns and treetops from three missions (RE0922, RE0826, and P41106). One crown is associated with one treetop.

3.4. 3.4.Comparison Comparison and andAccuracy Accuracy Assessment Assessment 3.4.1.3.4.1. Comparison Comparison of Elevation of Elevation (z) Measurement (z) Measurement against against LiDAR LiDAR TakingTaking the the LiDAR LiDAR point point cloud cloud as as the the reference elevation elevation source, source, the the sUAS sUAS point point clouds cloudswere were compared compared against against LiDAR LiDAR on aon strip a strip of damof dam crest crest that that was wa as considerably a considerably stable, stable,permanent permanent surface. surface. The The strip strip was wa ﬂat,s flat, 15 m15 long m long and and 1.5 m1.5 wide, m wide, containing containing 27 LiDAR 27 LiDARpoints points in an in elevation an elevation range range of 51.85 of 51.85 to 52.04 to 52.04 m, with m, with an average an average of 51.96 of 51.96 m ASL. m ASL. Table 3 Tablesummarizes 3 summarizes the threethe three error error metrics metrics (MAE, (MAE, ME, ME, and and RMSE) RMSE) of the of fourthe four sUAS sUAS missions. missions. Table 3. Locational comparison of the sUAS missions: elevation ASL (z) of the dam crest against LiDAR, and (x,y,CHM) of Table 3. treetopsLocational against comparison RE0922. All of unitsthe sUAS are in missions: cm. elevation ASL (z) of the dam crest against LiDAR ,and (x,y,CHM) of treetops against RE0922. All units are in cm. LiDAR sUAS Mission MP1025 P41106 RE0826 RE0922 sUAS mission MP1025 P41106 RE0826 RE0922 LiDAR (2010)(2010) Dam MAE MAE8.32 8.324.89 4.894.81 4.813.59 3.59 crest (nDam = crestz (n = 27) ME z 7.41ME 7.41−0.05 −0.05−4.81 −4.81 −1.71 −1.71 referencereference 27) RMSE RMSE13.38 13.386.39 6.395.45 5.454.65 4.65 MAE MAE/ /37.0 37.026.25 26.25 x ME x ME/ /37.0 37.0 −3.8 −3.8 RMSE RMSE/ /47.33 47.3340.70 40.70 MAE MAE/ /61.5 61.526.9 26.9 Treetop Treetopy( n = 40) ME y ME/ /−61.5 −61.57.5 7.5 referencereference / / (n = 40) RMSE RMSE/ /68.04 68.0436.74 36.74 MAE MAE/ /36.8 36.814.59 14.59 CHM MECHM ME/ /−22.05 −22.05−0.72 −0.72 RMSE RMSE/ /46.21 46.2119.78 19.78

FromFrom the three the three error error metrics, metrics, RE0922 RE0922 has the has best the match best matchwith LiDAR with LiDAR in terms in of terms ele- of vationelevation measurements measurements with, an with, absolute an absolute deviation deviation (MSE) of (MSE) 3.59 cm of 3.59and cman RMSE and an of RMSE 4.65 of cm. 4.65This cm. metric This slightly metric slightly underestimates underestimates (against (against LiDAR) LiDAR) the elevation the elevation,, with witha negative a negative ME MEof − of1.71− 1.71cm. cm.P41106 P41106 has the has lowest the lowest ME (0.05 ME (0.05 cm), cm),but a but larger a larger MSE MSE of about of about 5 cm 5, cm, indicatindicatinging both both over over-- and andunderprediction underprediction bias biases.es. Table Table 3 also3 also reveals reveals that that sUAS sUAS missions missions at higherat higher flight ﬂight altitude altitudess produce produce better better results. results. For Forall of all the of thethree three error error metrics, metrics, RE0826 RE0826 at a at60 am 60 flight m ﬂight altitude altitude result resulteded in greater in greater error than error RE0922 than RE0922 at a 90 m at altitude. a 90 m altitude. With their With RSMEtheir values RSME in valuesa range in of a 5 range to 6 cm, of 5the to RE0922, 6 cm, the RE0826, RE0922, and RE0826, P41105 and point P41105 clouds point do not clouds need to be adjusted in the z dimension. They can be directly used for DSM and CHM extraction. Again, MP1025 has the largest deviation from LiDAR, confirming that its performance is not comparable with the Phantom 4 and M100/RedEdge-M missions.

Forests 2021, 12, 659 13 of 18

do not need to be adjusted in the z dimension. They can be directly used for DSM and Forests 2021, 12, x FOR PEER REVIEWCHM extraction. 13 of 17

Again, MP1025 has the largest deviation from LiDAR, conﬁrming that its performance is not comparable with the Phantom 4 and M100/RedEdge-M missions.

3.4.2.3.4.2. LocationalLocational andand CHMCHM ComparisonComparison ofof TreetopsTreetops against against RE0922 RE0922 ToTo comparecompare the relative relative positioning positioning accuracies accuracies of ofthe the extracted extracted trees trees in different in different mis- missions,sions, a total a total of of40 40trees trees with with visually visually distinguishable distinguishable crowns crowns were were randomly randomly selected selected in inthe the woodland. woodland. The The RE0922 RE0922 was was taken taken as as a a reference reference because it hadhad aa relativelyrelatively higher higher accuracyaccuracy in in elevation elevation and and better better point point cloud cloud coverage. coverage. Note Note that that the the comparison comparison cannot cannot be countedbe counted as an as accuracy an accuracy assessment assessment because because the treetops,the treetops, deﬁned defined as the as topmost the topmost point point in a localin a local CHM CHM window, window, may slightlymay slightly vary invary different in different missions. missions. TheThe RE0826RE0826 had hadsimilar similar performance performance to to the the RE0922 RE0922 mission, mission, with with low low ME ME deviations deviations ofof (−(−3.8, 7.58)7.58) cmcm inin thethe horizontalhorizontal dimensionsdimensions andand− −0.72 cmcm inin thethe CHMCHM (Table(Table3 ).3). The The MAEMAE and and RMSERMSE were were larger larger due due to to the the impact impact of of directional directional deviation deviation and and outliers. outliers. These These errorserrors werewere within within 1 1 toto 22 cells cells of of the the 20 20cm cm CHM CHM raster raster data, data, which which reveals reveals that that the the M100/RedEdge-MM100/RedEdge-M system has relativelyrelatively stablestable accuracyaccuracy inin locationallocational positions.positions. ForFor thethe PhantomPhantom 4 4 Pro, Pro, the the absolute absolute deviations deviations of theof the extracted extracted treetops treetops were we (37.0,re (37.0,−61.5) −61.5) cm incm the in horizontalthe horizontal dimensions. dimensions. The MAEThe MAE values values were we there same the same as the as ME, therevealing ME, revealing a directional a direc- biastional to thebias east to the and east south. and This south. could This be could as attributed be as attributed to the system to the uncertainties system uncertainties between thebetween two drones. the two The drones. higher The deviations higher deviations in the (x,y) in the dimension (x,y) dimension may also may arise also from arise local from environmentallocal environment effects,al effect suchs, such as wind as wind and and forage forage growth growth over over the the two two years. years. The The CHM CHM measurementsmeasurements of of P41106 P41106 were were about about 22.08 22.08 cm cm lower lower than than RE0922 RE0922 in in terms terms of of ME, ME, although although thethe other other two two errors errors were were higher. higher. The The relative relative CHM CHM deviations deviations between between the the three three missions missions werewere within within1 1 to to 2 2 CHM CHM cells. cells. TheThe statisticsstatistics inin TableTable3 3indicates indicates that that both both Phantom Phantom 4 4 Pro Pro andand M100/RedEdge-MM100/RedEdge-M are capablecapable ofof treetree heightheight extraction.extraction.

3.4.3.3.4.3. VisualVisual ComparisonComparison of of Treetop/Crown Treetop/Crown ExtractionExtraction InIn aa subsetsubset area of of the the woodland woodland (Fig (Figureure 9),9), most most trees trees could could be be extracted extracted from from the thethree three flights ﬂights,, although although their their treetop treetop locations locations and crown and crown boundaries boundaries were slightly were slightly shifted. shifted.The two The RedEdge two RedEdge-M-M missions missions produced produced similar similarresults resultsin both in treetop both treetop and crown and crowndeline- delineation.ation. In the InPhantom the Phantom 4 Pro missi 4 Proon, mission, some sometrees could trees couldnot be not successfully be successfully identified. identiﬁed. Thus, Thus,the tree the crowns tree crowns of the of thePhantom Phantom 4Pro 4Pro mission mission we werere somewhat somewhat larger larger than than those ofof thethe RedEdge-MRedEdge-M missions.missions.

Figure 9. A subset area of the extracted tree crowns and treetops from the three missions. The back- Figure 9. A subset area of the extracted tree crowns and treetops from the three missions. The ground is the RE0922 orthoimage. background is the RE0922 orthoimage. 3.4.4. Accuracy Assessment of sUAS-Extracted Tree Height At the validation site, the sUAS-extracted (M100 only) tree heights were compared with field measurements using a laser rangefinder. The M100 flight was flown 7 days later

Forests 2021, 12, 659 14 of 18

Forests 2021, 12, x FOR PEER REVIEW3.4.4. Accuracy Assessment of sUAS-Extracted Tree Height 14 of 17

At the validation site, the sUAS-extracted (M100 only) tree heights were compared with field measurements using a laser rangefinder. The M100 flight was flown 7 days thanlater RE0922 than RE0922,, using usingthe same the sameflight flightconfiguration configuration;; therefore, therefore, it can itbe can counted be counted as the as vali- the dationvalidation of the of theRE0922 RE0922 mission. mission. Among Among the the 62 62trees trees at atthe the site, site, 59 59 treetops treetops were were extracted. extracted. TheThe scatterplot reveals a linear agreement between field-measuredfield-measured and sUAS-extractedsUAS-extracted treetree heights heights with with,, a a RMSE RMSE of of 1 1 m m ( (FigureFigure 1100).). The The correlation correlation line line (dashed) (dashed) agrees agrees with the 1:1 line. Both Both the the MAE MAE and and ME ME measure measurementsments are within 1 m.

35 r = 0.985 30 RMSE = 1.009 m ME = 0.409 m MAE = 0.718 m 25 (N = 59)

15 RE0922 extracted (m) RE0922extracted

5 5 10 15 20 25 30 35 Field measured (m)

FigureFigure 10. Scatterplot of of the the sUAS sUAS-extracted-extracted and and field ﬁeld-measured-measured tree tree height heightss (RE0922 only) at the validationvalidation site. site.

TheThe RE0826 RE0826 and and P41106 P41106 missions missions were were not not tested tested at at the the validation validation site. site. In In Table Table 33,, theirtheir CHM CHM comparisons comparisons against against RE0922 RE0922 at at the the study study site site have have RMSE RMSE values values of of 0.462 m andand 0.198 0.198 m, m, respectively. respectively. All of these assessments assessments indicate the feasibility feasibility of sUAS remote remote sensingsensing for for tree tree height height extraction. extraction. 4. Discussion 4. Discussion sUAS have been commonly deployed for taking aerial photographs and videos for sUAS have been commonly deployed for taking aerial photographs and videos for near-surface visual inspection. In July 2020, the U.S. Forest Service (USFS) released the near-surface visual inspection. In July 2020, the U.S. Forest Service (USFS) released the procedure for UAS operation standards in order to promote the safe and lawful operation procedure for UAS operation standards in order to promote the safe and lawful operation of sUAS in services including fire and natural resource management, in support of the of sUAS in services including fire and natural resource management, in support of the USFS missions [34]. In respect of quantitative forest inventory, however, the low-cost USFS missions [34]. In respect of quantitative forest inventory, however, the low-cost drones have not been widely utilized for operational applications, due to their noisy image drones have not been widely utilized for operational applications, due to their noisy im- calibration and complicated image analysis procedures. This study tests the feasibility age calibration and complicated image analysis procedures. This study tests the feasibility and comparison of several drones for 3D tree surveying in dense woodlands. Integrating and comparison of several drones for 3D tree surveying in dense woodlands. Integrating the orthoimages and point clouds, the 3D canopy is reconstructed to identify trees and to themeasure orthoimage their heightss and point and cloud crowns.s, the Multiple 3D canopy flight is reconstructed attempts in our to studyidentify show trees that and the to measureMavic Pro their was heights not reliable and forcrowns. tree surveying.Multiple flight Two ofattempts the three in Mavicour study Pro missionsshow that could the Mavicnot achieve Pro was an not acceptable reliable calibrationfor tree surveying. rate (Table Two1), of and the thethree extracted Mavic Pro point missions cloud could from notthe achieve third mission an acceptable contains calibration multiple rate large (Table data gaps1), and (Figure the extracted3). Both point the Phantom cloud from 4 Pro the thirdand the mission M100/RedEdge-M contains multiple performed large data well gaps in building (Figure orthoimages 3). Both the andPhantom point clouds4 Pro and for theacceptable M100/RedEdge tree surveying.-M perform Theed M100/RedEdge-M well in building (USDorthoimages 8000 assembly and point cost) clouds had the for best ac- ceptableagreement tree with surveying. LiDAR in The the M100/RedEdge vertical dimension.-M (USD However, 8000 theassembly lighter cost weight) ha Phantomd the best 4 agreementPro (USD 1500 with all-in-one LiDAR in package) the vertical may dimension. be a better fit However, if budget the and lighter system weight maintenance Phantom are 4considered Pro (USD 1500 for operational all-in-one package) purposes. may The be sensor a better of thefit i Mavicf budget Pro and is considerablysystem maintenance smaller are(6.3 considered× 4.7 mm) thanfor operational that of the Phantompurposes. 4 ProThe (13.2 sensor× 8.8 of mm),the Mavic and thus, Pro theis considerably sharpness of smallerthe resulting (6.3 × 4.7 image mm is) than less. that This of loss the ofPhantom detail may 4 Pro have (13.2 resulted × 8.8 mm) in, its and somewhat thus, the poorersharp- nessresults. of the We resulting expect the image performance is less. This of the loss newer of detail Mavic may 2 Pro have (same resulted sensor in as its the somewhat Phantom poorer4 Pro) toresults. be better, We andexpect comparable the performance to that ofof thethe Phantom newer Mavic 4 drones. 2 Pro (same sensor as the Phantom 4 Pro) to be better, and comparable to that of the Phantom 4 drones. With intensively overlapping images taken from drones flying above the canopy, the 3D landscape can be obtained, which makes the sUAS imaging superior to the 2D view of high-resolution satellites or aerial remote sensing. In the vertical dimension, the low-cost

Forests 2021, 12, 659 15 of 18

With intensively overlapping images taken from drones flying above the canopy, the 3D landscape can be obtained, which makes the sUAS imaging superior to the 2D view of high-resolution satellites or aerial remote sensing. In the vertical dimension, the low-cost drones and cameras may also outcompete LiDAR sensors in terms of affordability, accessibility, and operational efficiency. With drones flying up to 122 m (400 ft) above the ground, the orthoimage reaches cm-level spatial resolution, and the point cloud contains much denser mass points of tree canopies than could be extracted from typical airborne LiDAR data. For the sUAS missions explored in this study, the RedEdge-M point clouds had more than 20 times the arithmetic density of 2.41 points/m2 in the USGS LiDAR point cloud product. Moreover, sUAS offer the flexibility to launch missions quickly and efficiently in order to maximize time-critical data collection. Given the expenses and operational difficulties of the LiDAR mission, it is difficult to repeat and update its 3D data products. In this sense, sUAS have been counted as user-controlled remote sensing, or “personal remote sensing” as defined in current literature [10]. Despite these technical and operational advances, drawbacks of sUAS remote sensing are also obvious, as demonstrated in the study. Image calibration errors are a common issue for sUAS missions flying over dense forests where ground control targets are not visible. In our study, images from all three Phantom 4 Pro and M100/RedEdge-M missions suffered from uncalibrated errors, especially in the southeast part of the mission area farthest from the launch site. Uncalibrated images cause missing data in point clouds and erroneous georeferencing of the orthoimage. Future investigation will be conducted in order to explore the best practices of flight configuration and standardized workflow of sUAS missions. Specifically for 3D tree surveying, one challenge of low-cost sUAS application is the need for a bare-earth surface. As opposed to LiDAR, sUAS cameras only collect a single return from the landscape at any location. In areas with vegetation cover, the sUAS point cloud records the elevation of the topmost points of the canopy, which is directly used to extract the DSM. Since optical sensors are only poorly capable of penetrating the tree canopy, sUAS point clouds cannot reliably collect ground points in woodlands. Even in open areas, the points are heavily deteriorated by short vegetation, such as grasses, forbs, and shrubs. Therefore, this study relied on other sources of ground elevation in order to extract canopy height. Today, LiDAR data products at modest spatial resolutions (1.4 to 1.8 m) have been collected in almost every county across the United States. Although the high cost and operational difficulty of LiDAR missions hinder their repetitive data collection, their approximate one-meter point spacing provides the most accurate digital bare-earth models in areas without rapid terrain change. With these LiDAR data products available, sUAS can be flexibly deployed for quantitative tree surveys. Recent lightweight LiDAR products have been available for sUAS payloads. However, these products often have costs in the range of USD 50-100k, and are therefore too expensive for most operational sUAS deployments. The dense, closed-canopy cover of overgrown forests poses further challenges for 3D tree surveying. This experiment concurs with past studies (Popescu and Wynn 2004; Jaafar et al. 2018) that the local maxima approach to identifying individual treetops works better for trees that are relatively isolated. This study also demonstrates that conifers are easier to delineate, because their crowns are often circular and symmetrical with single treetops. Deciduous trees, such as tulip poplar and black gum, present more complex, irregular crowns that may contain multiple local maxima. In dense stands, deciduous trees tend to overlap one another, forming a continuous canopy cover of multiple trees. This feature affects crown delineation with the inverse watershed segmentation approach. Both the local maxima and watershed segmentation approaches rely solely on the CHM data. As demonstrated in the subset area in Figure9, the extracted tree crowns do not follow regular, rounded crown boundaries as we visually depict. For future research, spectral information from the orthoimages could be examined that may help to refine crown delineation. Nevertheless, this study demonstrates the high potential of sUAS Forests 2021, 12, 659 16 of 18

remote sensing in quantitative field surveying. For the specific applications in this study, the sUAS-assisted dam inspection provides cm-level 3D visualization of trees growing on the dam downslope. Older trees with taller, larger tree crowns are thus inventoried for further risk assessment. Earthen dams are usually small in size and, therefore, the sUAS mission can be fulfilled within 10 to 20 minutes at each dam. This process offers improved time and cost efficiency and personnel safety compared to traditional walkthrough field procedures. The sUAS-based tree survey inventory is also valuable information in other activities such as forest management and wildlife habitat conservation. Owning to its fine spatial details, time efficiency, and flexibility in data acquisition, sUAS remote sensing could fill the gap between traditional remote sensing and intensive field investigation in monitoring our ever-changing natural environment.

5. Conclusions This study explored multiple sUAS missions to test the feasibility and procedure for 3D tree surveying of dense woodland on an earthen dam. For each mission, the orthoimage and point cloud were extracted to reconstruct the 3D canopy, which was further analyzed to measure tree heights and to outline tree crowns. The comparison study confirms that sUAS point clouds can stably build a canopy height model (CHM), with which treetops can be extracted using the local maxima approach and tree crowns can be outlined via watershed segmentation. In closed-canopy conditions, LiDAR is an essential source of bare-earth surfaces in the procedure. Among the drones tested in this study, the Phantom 4 Pro at higher flight altitudes is recommended for operational tree surveying. The M100/RedEdge-M achieves higher accuracy in (x,y,z) positioning and tree height extraction (RMSE = 1 m), but the improvement could be outweighed by its cost and complicated deployment procedure. The Mavic Pro fails to serve as a reliable tool for tree surveying, especially in dense forests. Our experiments indicate that sUAS provide a timely and efficient means of 3D tree surveying for assessment of tree overgrowth to assist in dam safety inspection. The procedure could be adapted for other applications in quantitative landscape mapping. With the rapid development of sUAS technology, sUAS remote sensing could play a more reliable role in monitoring our living environment.

Author Contributions: Conceptualization, C.W. and M.E.H.; methodology, C.W.; data collection and processing, C.W., M.E.H. and G.M.; writing and editing, C.W. and M.E.H. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: This work was supported by the ASPIRE-II Program from the Office of the Vice President for Research at University of South Carolina (UofSC). The authors would like to thank Inthuorn Sasanakul and Herrick Brown at UofSC for their assistance in field experiments. This work could not have been completed without the support of Robert Livingston, who granted us access to his property of Sweet Bay Pond Dam. Conflicts of Interest: The authors declare no conflict of interest.

References 1. National Inventory of Dams (NID). The 2018 National Inventory of Dams. 2018. Available online: http://nid.usace.army.mil (accessed on 4 December 2020). 2. Rahmstorf, S. Rising hazard of storm-surge ﬂooding. Proc. Natl. Acad. Sci. USA 2017, 114, 11806–11808. [CrossRef][PubMed] 3. Vecchi, G.A.; Knutson, T.R. Historical Changes in Atlantic Hurricane and Tropical Storms. 2018. Available online: https: //www.gfdl.noaa.gov/historical-atlantic-hurricane-and-tropical-storm-records/ (accessed on 4 December 2020). 4. Sasanakul, I.; Gassman, S.; Pierce, C.; Ovalle, W.; Starcher, R.; Gheibi, E.; Rahman, M. Dam Failure from the October 2015 Historic Flood Event in South Carolina, Asce Geotech. Spec. Publ. (GSP) 2017, 278, 244–254. 5. SC Department of Health and Environment Control (SDHEC). The State of the Dams: Investment in the Protection of South Carolina’s People, Natural Resources, and Infrastructure through Dam Safety–A Report on the State of Dam Safety in South Carolina. 2020. Available online: https://scdhec.gov/BOW/dams-reservoirs (accessed on 4 December 2020). Forests 2021, 12, 659 17 of 18

6. Federal Emergency Management Agency (FEMA). Technical Manual for Dam Owners: Impacts of Plants on Earthen Dams, FEMA Document 534. 2005. Available online: https://www.fema.gov/media-library-data/20130726-1446-20490-2338/fema-534.pdf (accessed on 30 December 2020). 7. Andersen, H.E.; Mcgaughey, R.S.; Reutebuch, E. Estimating forest canopy fuel parameters using LIDAR data. Remote Sens. Environ. 2005, 94, 441–449. [CrossRef] 8. Amiri, N.; Yao, W.; Heurich, M.; Krzystek, P.; Skidmore, A.K. Estimation of regeneration coverage in a temperate forest by 3D segmentation using airborne laser scanning data. Int. J. Appli. Earth Obs.. Geoinf. 2016, 52, 252–262. [CrossRef] 9. Morgan, G.; Hodgson, M.E.; Wang, C. Using sUAS-derived point cloud to supplement LiDAR returns for improved canopy height model on earthen dams. Pap. Appl. Geogr. 2020, 6, 436–448. [CrossRef] 10. Jensen, J.R. Drone Aerial Photography and Videography: Data collection and image interpretation. 2017 (e-book). Available online: https://www.jensendroneaerialphotographyvideography.com/ (accessed on 25 February 2021). 11. Gundlach, J. Civil and Commercial Unmanned Aircraft Systems; AIAA Education Series; American Institute of Aeronautics and Astronautics: Reston, VA, USA, 2016. [CrossRef] 12. Dandois, J.P.; Ellis, E.C. Remote sensing of vegetation structure using computer vision. Remote Sens. 2010, 2, 1157–1176. [CrossRef] 13. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 2012, 179, 300–314. [CrossRef] 14. Seitz, S.M.; Curless, B.; Diebel, J.; Scharstein, D.; Szeliski, R. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR’06), New York, NY, USA, 17–22 June 2006; Volume 1, pp. 519–526. 15. Hyyppä, J.; Hyyppä, H.D.; Leckie, F.; Gougeon, F.; Yu, X.; Maltamo, M. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int. J. Remote Sens. 2008, 29, 1339–1366. [CrossRef] 16. Dalponte, M.; Reyes, F.; Kandare, K.; Gianelle, D. Delineation of individual tree crowns from ALS and hyperspectral data: A comparison among four methods. Eur. J. Remote Sens. 2015, 48, 365–382. [CrossRef] 17. Ferraz, A.; Bretar, F.; Jacquemoud, S.; Gonçalves, L.; Pereira, L.; Tomé, M.; Soares, P. 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sens. Environ. 2012, 122, 210–223. 18. Ferraz, A.; Saatchi, S.; Mallet, C.; Meyer, V. Lidar detection of individual tree size in tropical forests. Remote Sens. Environ. 2016, 183, 318–333. [CrossRef] 19. Jaafar, W.S.W.M.; Woodhouse, I.H.; Silva, C.A.; Omar, H.; Maulud, K.N.A.; Hudak, A.T.; Klauberg, C.; Cardil, A.; Mohan, M. Improving individual tree crown delineation and attributes estimation of tropical forests using airborne LiDAR data. Forests 2018, 9, 759. [CrossRef] 20. Panagiotidis, D.; Abdollahnejad, A.; Surový, P.; Chiteculo, V. Determining tree height and crown diameter from high-resolution UAV imagery. Int. J. Remote Sens. 2016, 38, 2392–2410. [CrossRef] 21. Mohan, M.; Silva, C.A.; Klauberg, C.; Jat, P.; Catts, G.; Cardil, A.; Hudak, A.T.; Dia, M. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests 2017, 8, 340. [CrossRef] 22. Dong, X.; Zhang, Z.; Yu, R.; Tian, Q.; Zhu, X. Extraction of information about individual trees from high-spatial-resolution UAV-acquired images of an orchard. Remote Sens. 2020, 12, 133. [CrossRef] 23. Federal Aviation Administration (FAA). Fact Sheet - Small Unmanned Aircraft Systems (UAS) Regulations (Part 107). Released on 6 October 2020. Available online: https://www.faa.gov/news/fact_sheets (accessed on 21 January 2021). 24. Emlid Reach RS2 Docs, 2021. Precise Point Positioning (PPP). Available online: https://docs.emlid.com/reachrs2/common/ tutorials/ppp-introduction/ (accessed on 25 February 2021). 25. National Geodetic Survey (NGS). NOAA’s Online Positioning User Service (OPUS). 2021. Available online: https://www.ngs. noaa.gov/INFO/OnePagers/OPUSOnePager.pdf (accessed on 25 February 2021). 26. U.S. Geological Survey (USGS). USGS Lidar Point Cloud (LPC) SC_LexingtonCo_2010_000827 2014-0913 LAS, U.S. Geological Survey. Published on 13 September 2014. 27. SC Department of Natural Resources (SCDNR). LiDAR and Related Data Products. 2015. Available online: http://www.dnr.sc. gov/GIS/lidar.html (accessed on 20 February 2019). 28. Heidemann, H.K. Lidar Base Specification, V1.3. U.S. Geological Survey (USGS) Techniques and Methods. Chapter B4, in Book 11- Collection and Delineation of Spatial Data. 101 p. Revised February 2018. 2018. Available online: https://doi.org/10.3133/tm11b4 (accessed on 21 January 2021). 29. Mielcarek, M.; Stereńczak,K.; Khosravipour, A. Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. Int. J. Appl. Earth Obs. Geoinf. 2018, 71, 132–143. [CrossRef] 30. Smith, H.C. Height of Tallest Saplings in 10-Year Old Appalachian Hardwood Clearcuts. Forest Service Research Paper NE-381, Forest Service, U.S. Department of Agriculture, Northeastern Forest Experiment Station. 1977. Available online: https://www.fs.fed.us/ne/newtown_square/publications/research_papers/pdfs/scanned/OCR/ne_rp381.pdf (accessed on 25 February 2021). 31. Popescu, S.C.; Wynne, R.H. The trees in the forest. Photogramm. Eng. Remote Sens. 2004, 70, 589–604. 32. Vincent, L.; Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13, 583–598. [CrossRef] Forests 2021, 12, 659 18 of 18

33. Meyer, F.; Beucher, S. Morphological segmentation. J. Vis. Commun. Image Represent. 1990, 1, 21–46. [CrossRef] 34. U.S. Forest Service (USFS). Unmanned Aircraft System (UAS) – Forest Service Standards for UAS Operations. U.S. Department of Agriculture. 2020. Available online: https://www.fs.usda.gov/sites/default/ﬁles/2020-07/Forest%20Service%20Standards% 20for%20UAS%20Operations%2007012020.pdf (accessed on 25 February 2021).