drones

Article Estimating Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy

Shohei Kameyama 1 and Katsuaki Sugiura 2,*

1 Graduate School of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa 252-0880, Japan; [email protected] 2 College of Bioresource Sciences, Nihon University, 1866 Kameino, Fujisawa, Kanagawa 252-0880, Japan * Correspondence: [email protected]; Tel.: +81-466-84-3670

 Received: 18 March 2020; Accepted: 9 May 2020; Published: 11 May 2020 

Abstract: This study aimed to investigate the effects of differences in shooting and flight conditions for an unmanned aerial vehicle (UAV) on the processing method and estimated results of aerial images. images were acquired under 80 different conditions, combining various aerial photography methods and flight conditions. We verified errors in values measured by the UAV and the measurement accuracy with respect to tree height and volume. Our results showed that aerial images could be processed under all the studied flight conditions. However, although tree height and crown were decipherable in the created 3D model in 64 conditions, they were undecipherable in 16. The standard deviation (SD) in crown area values for each target tree was 0.08 to 0.68 m2. UAV measurements of tree height tended to be lower than the actual values, and the RMSE (root mean square error) was high (5.2 to 7.1 m) through all the 64 modeled conditions. With the estimated volume being lower than the actual volume, the RMSE volume measurements for each flight condition were from 0.31 to 0.4 m3. Therefore, irrespective of flight conditions for UAV measurements, accuracy was low with respect to the actual values.

Keywords: unmanned aerial vehicle; structure from motion; estimated value accuracy; flight altitude; degree of overlap in photography

1. Introduction An unmanned aerial vehicle (UAV), also called a drone, is the name for any aircraft that can be operated remotely. Presently, there are various small to large models of drones, and these are being utilized for various applications, including pesticide application, disaster damage surveying, logistics, and media. In Japan, the Ministry of Agriculture, and Fisheries has prepared the “UAV Stand Inventory Manual” [1] for use in forest surveys, which summarizes specific procedures and key points for stand inventory methods using UAV, and provides a formula to estimate the diameter at breast height (DBH) of . Examples of previous studies that use UAV in the field of and forestry include surveys of forest information, such as the number of standing trees, tree height, and single tree volume obtained using structure from motion (SfM) technology. SfM calculates the position of the camera and the shape and position of objects based on multiple aerial photography images captured with a UAV [2–15]. Chen et al. developed and evaluated a method of estimating vegetation height on linear disturbances in the Boreal Forest using point clouds derived from UAV photogrammetry and assessed the accuracy and cost of various application scenarios [16]. Moreover, oblique images were used to prepare a digital surface model (DSM) [17] and estimate the aboveground tree [18]. Huang et al. demonstrated

Drones 2020, 4, 19; doi:10.3390/drones4020019 www.mdpi.com/journal/drones Drones 2020, 4, 19 2 of 21 that tree canopy structure factors, especially foliar amount or volume, can have a substantial effect on tree height estimation using UAV images [19]. Teng et al. examined an effective method to reconstruct color images measured using a UAV-borne camera with different focal length lenses [20]. These previous surveys were conducted under differing flight conditions, such as using different models of UAV, flight altitude, and the degree of overlap in aerial photographs. When capturing images using a UAV, flight conditions such as flight altitude and degree of overlap in aerial images (overlap and side overlap) cause notable differences in the number and interval of images. Therefore, the present study focused on the effect of using images of the same site, taken under different flight conditions, on image processing and measurement accuracy. Several other studies have examined the measured values using UAV aerial images taken under different flight conditions. For example, Torres-Sánchez et al. [21] studied the processing time required for the different overlaps that were recorded, and the accuracy was assessed by analyzing tree detection, reconstructed tree volume, and tree height. Domingo et al. [22] analyzed the effects of camera type, image resolution, side overlap, and some practical considerations that often have to be made in operational surveys on the accuracy of biomass model predictions in a tropical woodland using an unmanned aerial system (UAS). Dandois et al. [23] performed, at a single site, a replicated set of image acquisitions that were carried out under cross treatments of lighting, flight altitude, and image overlap. Seifert et al. [24] provide scientific evidence of the influence of altitude, image overlap, and image resolution on a multiview geometry (MVG) reconstruction of a forest from video-based drone imagery. Ni et al. [25] systematically investigated the impact of forward overlaps and image resolutions on the measurements of forest 3D structures using UAV-based stereo imagery over broader ranges of image resolutions (8.6, 17.2, 34.4, 68.8, and 137.6 cm) and forward overlaps (90%, 80%, 70%, and 60%). The determination of flight conditions varies based on the UAV model, flight application, and the size of the imaging area; thus, preparing a manual for flight conditions is extremely difficult. There are many issues to consider for UAV utilization, such as various flight conditions and differences in tree species. Therefore, in order to utilize UAV in , it is important to accumulate basic data related to estimates of tree height and volume using UAV, for verification of accuracy. The objective of the present study was to examine the effect of different flight conditions of a UAV on estimations of tree height and volume using aerial image processing and estimates of tree height and volume at a small survey site. A short-term investigation over a small area was conducted in order to reduce the impact of the investigation period. We collected aerial images with a UAV under multiple flight conditions that combined several flight altitude and degrees of overlap in the aerial images. From the analysis of these aerial images and the estimates of tree height and volume, we verified the errors in such estimations made with the use of a UAV and compared the accuracy of these estimations to the results of a tree survey. In addition to this, based on the results of this study, we discuss issues concerning the use of UAV in this paper. In this study, more flight conditions were set than in previous studies [21–25]. Additionally, although this study uses only one case in Japan, the UAV and flight applications used were made by DJI. DJI is a large company that makes a variety of drone and aerial photography products. Therefore, the use of one of their products is important as it expands knowledge of UAV utilization.

2. Materials and Methods The present study was conducted in the following sequence: (1) aerial photography was conducted with a UAV; (2) aerial images, measurements, and estimates were processed; and (3) the accuracy of measured and estimated values was verified. Details of the conditions for each step and the analytical methods used are provided below.

2.1. Survey Site The study was set up in a 0.16 ha test site within 144 compartments and 23 subcompartments of the Yakumo Practice Forests of Nihon University located in Yakumo-cho, Futami-gun, Hokkaido Drones 2020, 4, x FOR PEER REVIEW 3 of 21 Drones 2020, 4, 19 3 of 21

(Figure 1). On the four corners of the survey site, we installed aerial targets to make confirmation of (Figurethe surveyDrones1). 2020 Onarea, 4 the, easierx FOR four PEER in corners the REVIEW aeri of al images the survey captured site, we by installedthe UAV aerial(Figure targets 1). Aerial to make photography confirmation3 of 21 of the ofsite the was survey performed area easier during in the November aerial images and capturedDecember by 2018. the UAVThe (Figureterrain 1of). Aerialthe survey photography site was (Figure 1). On the four corners of the survey site, we installed aerial targets to make confirmation of generally flat, and the stand was a 46-year-old Sakhalin fir . We also conducted a tree of thethe site survey was performedarea easier in during the aeri Novemberal images captured and December by the UAV 2018. (Figure The 1). terrain Aerial photography of the survey of sitethe was generallysurveysite in was flat,the performed andsurvey the site stand during during was November a the 46-year-old same andtime SakhalinDecemb perioder firas 2018. plantation.the aerialThe terrain photography. We also of the conducted survey Parameters site a tree wassurvey of the intree the surveygenerally survey were site flat, tree during and height, the the stand same DBH was time (1.2 a m46-year-old period height), as the andSakhalin aerial position photography.fir plantation. of the trees We Parameters within also conducted the of survey the treea site.tree survey Tree wereheightsurvey tree was height, measuredin the survey DBH using (1.2 site mduringVertex height), the III same(Haglof), and positiontime periodand ofDBH as the the was trees aerial measured within photography. the with survey aParameters caliper. site. Height Tree of the height was wasmeasured measuredtree survey three using were times tree Vertex for height, each III (Haglof), DBHtree, (1.2 and andm the height), DBH mean and was was position measured calculated of the with trees and a caliper. withinused asthe Height the survey measurement was site. measured Tree of threetree height.height times was forThe eachmeasured tree tree,survey using and showed theVertex mean that III (Haglof), was there calculated were and 72DBH target and was used treesmeasured as with the with a measurement mean a caliper. height Height of tree25.2 was height.m/tree Theand treemeanmeasured survey volume three showed of times 1.09 thatfor m 3each/tree. there tree, We were and ensured 72 the target mean the trees wasweather withcalculated was a mean sunnyand heightused with as of thea 25.2 wind measurement m /speedtree and of of mean0 to 3 volumem/s andtree of thatheight. 1.09 the mThe UAV3/tree. tree flights survey We ensured and showed aerial the that weatherphotography there were was 72 sunnyoccurred target withtrees between with a wind a mean 9 speed am height and of 011 of to am.25.2 3 m m/tree/s and that and mean volume of 1.09 m3/tree. We ensured the weather was sunny with a wind speed of 0 to 3 the UAV flights and aerial photography occurred between 9 am and 11 am. m/s and that the UAV flights and aerial photography occurred between 9 am and 11 am.

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

FigureFigure 1. (a 1.) Location (a) Location of ofsurvey survey site, site, andand ((b) the the general general landscape landscape of ofthe the survey survey site. site.site.

2.2. Flight 2.2. Flight Conditions Conditions of the of the UAV UAV The UAV used was a Phantom3 Advanced (Figure 2) produced by DJI in China. The camera that The UAV usedused waswas aa Phantom3 AdvancedAdvanced (Figure(Figure2 2)) produced produced by by DJI DJI in in China. China. TheThe cameracamera thatthat is a standard feature of the Phantom3 Advanced was used for aerial photography. Specifications of is aa standardstandard featurefeature of of the the Phantom3 Phantom3 Advanced Advanced was wa useds used for for aerial aerial photography. photography. Specifications Specifications of the of Phantom3the Phantom3 Advanced Advanced and the and camera the camera are provided are provided in Table in Table1[ 26 1]. [26]. The The DJI DJI drone drone used used in in this this study the Phantom3study was aAdvanced consumer gradeand the equipment. camera are provided in Table 1 [26]. The DJI drone used in this wasstudy a consumerwas a consumer grade equipment.grade equipment.

Figure 2. Phantom3 Advanced unmanned aerial vehicle (UAV, or drone).

Figure 2. Phantom3Phantom3 Advanced Advanced unmanned unmanned aeri aerialal vehicle (UAV, oror drone).drone).

Drones 2020, 4, 19 4 of 21

Table 1. Specifications of the Phantom3 Advanced and the camera.

Model Name Phantom3 Advanced Weight 1280 g Dimensions 350 mm (excluding the propellers) Maximum flight speed 16 m/s Maximum flight time Approximately 23 min Sensor SONY EXMOR 1/23” Lens FOV94◦20 mm (35 mm conversion) Maximum still image size 4000 3000 ×

The flight altitude of the UAV was to be 150 m above ground level or lower, according to the guideline of the Ministry of Land, Infrastructure, Transport, and Tourism [27]. The survey site provided generally flat areas for takeoff, flight, and landing; thus, as long as the flight altitude of the UAV was 150 m or lower, there were no concerns. However, if the flight altitude was too low, the UAV could come into contact with trees and crash. Therefore, flight altitudes were set at 60, 80, 100, 120, and 140 m (Table2). Furthermore, the ground sample distances (GSD, i.e., image resolution) were 2.6 cm (when flight altitude = 60 m), 3.5 cm (flight altitude = 80 m), 4.3 cm (flight altitude = 100 m), 5.2 cm (flight altitude = 120 m), and 6.1 cm (flight altitude = 140 m).

Table 2. Flight conditions and aerial imaging conditions for the UAV (unmanned aerial vehicle).

Item Value(s) Condition Flight altitude (m) 60, 80, 100, 120, 140 5 Overlap (%) 80, 85, 90, 95 4 Side overlap (%) 80, 85, 90, 95 4 Flight speed 15.0 m/s 1 Photography method Hovering 1 Total 80

In terms of the degree of overlap in aerial images (overlap: degree of overlap in aerial images along the flight path; side overlap: degree of overlap in aerial images between flight paths), the user manual provided for the Terra Mapper Desktop version from TerraDrone [28] used for the SfM processing recommends that the overlap should be 90% or higher and that the side overlap should be 60% or higher. The Manual for Public Survey Using UAV [29] recommends an overlap of 80% or higher and a side overlap of 60% or higher. Therefore, we set the overlap to be 80%, 85%, 90%, and 95% (Table2). Although a 60% or higher side overlap is recommended, as the flight area of the survey area was small, aerial photography could not be taken between multiple flight paths when the side overlap ratio was low and flight altitude was high. Thus, we set the side overlap to be 80%, 85%, 90%, and 95% (Table2), where aerial photography between multiple flight paths was possible at the flight altitude of 140 m. We also set the flight speed and photography method. The flight speed was set at 15.0 m/s (Table2), which was the maximum speed for the DJI GS PRO flight application [30], in order to shorten the flight time. Photography methods using DJI GS PRO are hovering, equal-time-interval, and equal-distances. However, even-equal-time-interval and equal-distances are performed while the UAV is moving, which may cause blurring of the photos and inaccuracies in the degree of overlap in the photography. Therefore, the photography method selected was hovering to minimize the effect of distortions and blurs on measurement accuracy (Table2). All aerial images were taken using vertical orientation. In other words, the flight speed and photography method were consistent for all flight conditions. We have not examined oblique images since there are many previous studies by vertical images. The use of an oblique image in a part may improve the processing accuracy of SfM [17]. However, since the vertical shooting is automatic, partial oblique images are not shot. Drones 2020, 4, x FOR PEER REVIEW 5 of 21 of base to height ratio (B/H) between flight altitude and overlap (photography interval) (Table 3). The bigger the value of B/H, the more exaggerated the SfM processing. Images acquired on multiple flight paths are required for SfM processing. Therefore, the flight path was set oblique to the rectangular target area in order to increase the number of flight paths of the target area with a small area (Figure 3). In addition, the arrangement of trees in the survey site planted at the time of shooting was irregular. The flight altitude and side overlap determine the flight path. Therefore, the 20 flight paths in thisDrones study2020 are, 4, 19 shown in Figure 3. However, it is possible to set multiple flight paths under5 of 21 one flight condition (for example, right angle or oblique to the survey site). The flight paths setting affects the number of shots. However, this study is to examine the effects of flight altitude, overlap, and side A total of 80 flight conditions were used, wherein five flight altitudes, four different degrees of overlap. Therefore, we do not consider changing the flight paths. In addition, the flight paths are overlap, and four different degrees of side overlap were combined (Table2). There is a relationship of carried out with the default value of the application. base to height ratio (B/H) between flight altitude and overlap (photography interval) (Table3). The bigger the value of B/H, the more exaggerated the SfM processing. Images acquired on multiple flight paths are required forTable SfM 3. processing. Base to height Therefore, ratio of the flight flight altitude path was and set overlap oblique. to the rectangular targetFlight area altitude in order to increase theOverlap number of flight pathsOverlap of the target areaOverlap with a small area (FigureOverlap3). In addition, the arrangement of trees in the survey site planted at the time of shooting was irregular. (m) 80% 85% 90% 95% The flight altitude and side overlap determine the flight path. Therefore, the 20 flight paths in this study are60 shown in Figure3. However,0.347 it is possible0.260 to set multiple flight0.173 paths under one0.087 flight condition80 (for example, right angle0.346 or oblique to the survey0.260 site). The flight0.173 paths setting affects0.086 the number100 of shots. However, this0.346 study is to examine0.260 the eff ects of flight altitude,0.173 overlap, and0.087 side overlap.120 Therefore, we do not consider0.346 changing the0.260 flight paths. In addition,0.173 the flight paths0.087 are carried out140 with the default value0.346 of the application. 0.260 0.173 0.086

FigureFigure 3. UAV 3. UAV (unmanned (unmanned aerial aerial vehicle) vehicle) flight flight path.path. Note:Note: the the green green line line represents represents the flight the flight path. path. Drones 2020, 4, 19 6 of 21

Table 3. Base to height ratio of flight altitude and overlap.

Flight Altitude Overlap Overlap Overlap Overlap (m) 80% 85% 90% 95% 60 0.347 0.260 0.173 0.087 80 0.346 0.260 0.173 0.086 100 0.346 0.260 0.173 0.087 120 0.346 0.260 0.173 0.087 140 0.346 0.260 0.173 0.086

We used the DJI GS PRO to set these flight conditions and operate the UAV. Aerial photography was performed via the automatic flight setting with DJI GS PRO. However, as takeoff and landing were performed from a road, there was a possibility of collision with trees and crashing during ascent and descent. Therefore, operation from takeoff to the start of automatic flight and from the end of automatic flight to landing were performed manually by an operator.

2.3. Processing of Aerial Images and Measurements/Estimate Methods Terra Mapper was used for SfM analysis. The specifications of the computer used are as follows: Windows 10 Home, 64 bytes with 32 GB memory, and an Intel Core i7-7700HQ processor. Processing of aerial images was performed according to the manual after uploading aerial images to Terra Mapper. The procedure was as follows: Step (1) adjustment of the camera position (flight altitude was entered, the “high speed” mode was selected, and if this could not be processed, then the “low speed” mode was selected); Step (2) generation of the point cloud (the point cloud level was set as “high” and the minimum number of overlaps was set as “3”; if this could not be processed, the minimum number of overlaps was changed to “2”); and Step (3) 3D modeling (Figure4). In Step 1, the most important aspect is the processing speed. The Terra Mapper manual recommends that a “high speed” mode is used when the data volume is large and that a “low speed” mode is used when the shooting site is long and the side overlap is small [28]. The most significant factor for Step 2 is the number of laps required for point cloud generation. When the minimum number of overlaps is “3”, no point cloud is created for the point with two laps. The manual recommends that a minimum number of overlaps “3” is used [28]. However, if the minimum number of overlaps is “3” and it cannot be performed, it will be performed with “2”. Furthermore, the process NG (incomplete) in Steps 1 and 2 is when an error is displayed in the software. Under flight conditions in which Step 3 (3D modeling) was able to render the images properly and provide complete and satisfactory outputs, we prepared the DSM and orthographic images. For flight conditions where 3D modeling of the majority or part of the survey site was insufficient, we did not take any further measurements or perform verification for accuracy. Measurements and estimates with the UAV were performed based on the prepared 3D model, DSM images, and orthographic images. Measurements of tree height and crown area were performed using Terra Mapper. For tree height, we used the Z value of point cloud data from Terra Mapper. Furthermore, we used the area calculation function of Terra Mapper to determine the measured value for the area of the crown. To estimate the volume, we obtained a correlation formula from the crown area measured with the UAV and the actual measurements of diameters at breast height according to the method described by the Ministry of Agriculture, Forestry and Fisheries [31]. We then estimated DBH and volume from the tree height measured with the UAV. Drones 2020, 4, x FOR PEER REVIEW 6 of 21

We used the DJI GS PRO to set these flight conditions and operate the UAV. Aerial photography was performed via the automatic flight setting with DJI GS PRO. However, as takeoff and landing were performed from a logging road, there was a possibility of collision with trees and crashing during ascent and descent. Therefore, operation from takeoff to the start of automatic flight and from the end of automatic flight to landing were performed manually by an operator.

2.3. Processing of Aerial Images and Measurements/Estimate Methods Terra Mapper was used for SfM analysis. The specifications of the computer used are as follows: Windows 10 Home, 64 bytes with 32 GB memory, and an Intel Core i7-7700HQ processor. Processing of aerial images was performed according to the manual after uploading aerial images to Terra Mapper. The procedure was as follows: Step (1) adjustment of the camera position (flight altitude was entered, the “high speed” mode was selected, and if this could not be processed, then the “low speed” mode was selected); Step (2) generation of the point cloud (the point cloud level was set as “high” and the minimum number of overlaps was set as “3”; if this could not be processed, the minimum number of overlaps was changed to “2”); and Step (3) 3D modeling (Figure 4). In Step 1, the most important aspect is the processing speed. The Terra Mapper manual recommends that a “high speed” mode is used when the data volume is large and that a “low speed” mode is used when the shooting site is long and the side overlap is small [28]. The most significant factor for Step 2 is the number of laps required for point cloud generation. When the minimum number of overlaps is “3”, no point cloud is created for the point with two laps. The manual recommends that a minimum number of overlaps “3” is used [28]. However, if the minimum number of overlaps is “3” and it cannotDrones 2020 be ,performed,4, 19 it will be performed with “2”. Furthermore, the process NG (incomplete)7 of 21 in Steps 1 and 2 is when an error is displayed in the software.

Step 1: Adjustment of the camera position

NG NG High speed Low speed Cannot be processed

OK OK

Step 2: Point cloud generation (analysis level: high)

NG NG Minimum overlap 3 Minimum overlap 2 Cannot be processed

OK OK NG Step 3: 3D modeling Cannot be prepared

OK NG

Tree height and crown area clear Tree height and crown area not clear (analysis not possible)

FigureFigure 4. 4. FlowchartFlowchart of UAV of UAV (unmanned (unmanned aerial aerial vehicle) vehicle) aerial aerialimage image processing processing using Terra using Mapper Terra software.Mapper software.

2.4.Under Verification flight of conditions Accuracy of in Measured which andStep Estimated 3 (3D modeling) Values was able to render the images properly and provideWe used complete the standard and deviationsatisfactory (SD) outputs, to examine we prepared variations the in theDSM values and orthographic measured and images. estimated For flightwith theconditions UAV. In where case true 3D valuesmodeling (actual of the values) majority were or obtained part of fromthe survey the tree site survey, was insufficient, the difference, we didi.e., not root take mean any squarefurther errormeasurements (RMSE) and or perform relative rootverification mean square for accuracy. error (RMSE%), between the valuesMeasurements measured and and estimated estimates with with the UAVthe UAV and thewere actual performed values was based used on [ 7the,12 ].prepared The equations 3D model, are DSM images, and orthographic images. Measurements of tree height and crown area were performed as follows: s Pn 2 = (yi y´i) RMSE = i 1 − (1) n RMSE RMSE[%] = 100 (2) y ∗

where n is the total number or samples, yi is the predicted values, y´i is the observed values, and y is the mean of n observed values. A Kolmogorov–Smirnov goodness of fit test was performed to determine whether data were normally distributed. The statistical difference was determined by a two-sided paired t test. A difference with p < 0.05 was considered significant. These statistical analyses were performed using Reviewer by the Data Science Institute, Japan.

3. Results

3.1. Aerial Images and Their Clarity In Step 1 (i.e., the adjustment of the camera position for the 80 flight conditions), we processed images with the “high speed” mode for 36 conditions and the “low speed” mode for 44. In Step 2 (point cloud generation), we processed 48 flight conditions with the minimum number of overlaps being “3” and 32 flight conditions with “2”. In Steps 1 and 2, there were no flight conditions that made processing impossible. Step 3 (3D modeling) was possible under all flight conditions and aerial images could be processed under all conditions. Under 64 flight conditions, we were able to read the tree height Drones 2020, 4, 19 8 of 21 and crown area from the 3D model (Figure5a). Under the remaining 16 flight conditions (Table4), 3D modeling was incomplete, and tree height and crown area could not be read for any of the trees (Figure5b).

Table 4. Combinations of flight conditions where tree height and crown area were not clear.

60/80/80 60/85/80 60/85/85 80/80/80 80/80/85 80/85/80 80/85/85 100/80/80 100/85/80 120/80/80 120/85/80 140/80/80 140/80/85 140/85/80 140/85/85 140/90/85 Note: Data are given in the order of flight altitude (m)/overlap (%)/side overlap (%). Drones 2020, 4, x FOR PEER REVIEW 8 of 21

：Survey site ：Survey site

(a) (b)

FigureFigure 5.5. (a) 3D model where tree tree height height and and crown crown area area were were clear, clear, and and (b (b) )3D 3D model model where where tree tree heightheight andand crowncrown areaarea werewere not clear.

ForFor flightflight conditionsconditions where processed aerial aerial images images could could not not be be read, read, many many flight flight conditions conditions hadhad degreesdegrees of of overlap overlap and and side side overlap overlap of of 80% 80% and an 85%,d 85%, respectively. respectively. When When the the camera camera position position was setwas to set “low to “low speed” speed” and theand minimumthe minimum number number of overlaps of overlaps for for the the point point cloud cloud generation generation was was “2”, “2”, the readingthe reading of tree of tree height height and and crown crown area area was was often often impossible. impossible. In contrast, In contrast, when when the the camera camera position position was setwas at set “high at “high speed” speed” and the and minimum the minimum number number of overlaps of overlaps for the for point the point cloud cloud generation generation wasset was at set “3”, theat “3”, tree the height tree andheight area and of area crown of couldcrown be could read be under read allunder flight all conditions. flight conditions. TheThe majoritymajority of the survey area was incomplete because because of of an an insufficient insufficient degree degree of of overlap overlap in in thethe photographs,photographs, thereby thereby making making analysis analysis insufficient. insufficient. In Inthe the preparation preparation of the of theDSM, DSM, points points maymay be bemissing missing owing owing to towind wind and and shadow shadow [5]. [5 ].Light Light conditions conditions are are also also an an important important factor factor that that may may have have causedcaused aa certain certain amount amount of errorof error [32]. Imagery[32]. Imagery quality quality is strongly is strongly impacted impacted by variation by invariation illumination in dueillumination to season, due weather, to season, and shading weather, [13 and]. Therefore, shading [1 there3]. Therefore, might have there been might an eff ecthave from been wind-shaken an effect treetopsfrom wind-shaken and sunlight treetops causing and insu sunlightfficient causing SfM processing insufficient for SfM part processing of the survey for part site. of Additionally, the survey SfMsite. requiresAdditionally, the overlapping SfM requires of ninethe overlapping or more images, of nine and or missing more images, parts may and increase missing due parts to amay lack ofincrease images due [20 ].to In a thislack study,of images the study[20]. In area this was study, small, the and study the area number was ofsmall, pictures and takenthe number were less,of resultingpictures taken in insu werefficient less, 3D resulting modeling in insufficient with gaps. 3D modeling with gaps. FlightFlight conditionsconditions with normal 3D modeling included included conditions conditions that that had had an an overlap overlap and and side side overlapoverlap ofof 80%80% and and 85%, 85%, respectively. respectively. However, However, considering considering that that processing processing could could be be insu insufficient,fficient, and and UAV aerial imaging need to be performed again in the field, overlap and side overlap should have a high degree of overlap in photography, i.e., 90% or higher.

3.2. Crown Area Measurement Figure 6 shows the measurement results of crown area for each tree, and the SD from 64 flight conditions for which tree height and crown measurements were clear. The SD for each tree ranged from 0.08 to 0.68 m2. Forty-eight trees had an SD of either 0.08–0.2 m2 or 0.2–0.32 m2 accounting for more than half of the sampled trees. Trees with higher SD were found near the edge of the forest, and the SD was lower in areas where the tree stand was dense or away from the edge. The actual value of the crown area was not measured. The comparison of UAV measurement values shows that even if measurements were taken under different flight conditions, when image processing and measurements were possible, the error in the measured values among trees was small. Drones 2020, 4, 19 9 of 21

UAV aerial imaging need to be performed again in the field, overlap and side overlap should have a high degree of overlap in photography, i.e., 90% or higher.

3.2. Crown Area Measurement Figure6 shows the measurement results of crown area for each tree, and the SD from 64 flight conditions for which tree height and crown measurements were clear. The SD for each tree ranged from 0.08 to 0.68 m2. Forty-eight trees had an SD of either 0.08–0.2 m2 or 0.2–0.32 m2 accounting for more than half of the sampled trees. Trees with higher SD were found near the edge of the forest, and the SD was lower in areas where the tree stand was dense or away from the edge. The actual value of the crown area was not measured. The comparison of UAV measurement values shows that even if measurements were taken under different flight conditions, when image processing and measurements were possible, Drones 2020the, error4, x FOR in the PEER measured REVIEW values among trees was small. 9 of 21

Figure 6. SD (standard deviation) of measurement for the crown area of each target tree. Note: For the Figure 6. SD (standard deviation) of measurement for the crown area of each target tree. Note: For horizontal axis, ‘[’ means , ‘]’ means , and ‘(’ means <. the horizontal axis, ‘[‘ means ≤≤, ‘]’ means≥ ≥, and ‘(‘ means <. Based on the density of trees from the survey, this was a relatively sparse stand. As the actual area Basedof the on crown the wasdensity not obtained, of trees we from are unable the survey, to make this a comparison; was a relatively however, sparse measurement stand. values As the for actual area ofthe the crown crown area was in the not present obtained, survey we may are be overestimated.unable to make Orthographic a comparison; images afterhowever, SfM processing measurement values indicatedfor the crown that the area edges in of the the forestpresent experienced survey amay larger be overestimation. overestimated. Overestimation Orthographic around images the after edge of the forest may be a common problem and not limited to the present survey site. The present SfM processing indicated that the edges of the forest experienced a larger overestimation. survey site utilized the area calculation function of Terra Mapper and identified the area in order Overestimationto measure around the crown the area; edge however, of the such forest a calculation may be is a di commonfficult in dense problem stands and and measurementnot limited to the presentresults survey may site. be underestimated.The present survey site utilized the area calculation function of Terra Mapper and identified the area in order to measure the crown area; however, such a calculation is difficult in 3.3. Tree Height dense stands and measurement results may be underestimated. The normality of the measured and measured values of tree height was calculated using a 3.3. TreeKolmogorov–Smirnov Height goodness of fit test (P > 0.05). Normality was confirmed under all flight conditions (Table5). Statistical di fferences were confirmed by two-sided paired t tests. As a result, a Thesignificant normality difference of the was measured observed inand all casesmeasured (P < 0.05) va (Tablelues 6of). tree height was calculated using a Kolmogorov–Smirnov goodness of fit test (P > 0.05). Normality was confirmed under all flight conditions (Table 5). Statistical differences were confirmed by two-sided paired t tests. As a result, a significant difference was observed in all cases (P < 0.05) (table 6).

Table 5. P-value by A Kolmogorov–Smirnov goodness of fit test of tree height.

Flight Altitude Overlap (%) / Side overlap (%) (m) 80 / 80 80 / 85 80 / 90 80 / 95 85 / 80 85 / 85 85 / 90 85 / 95 60 ― 0.48 0.56 0.50 ― ― 0.66 0.45 80 ― ― 0.27 0.45 ― ― 0.44 0.43 100 ― 0.24 0.49 0.34 ― 0.41 0.26 0.87 120 ― 0.34 0.31 0.21 ― 0.30 0.76 0.27 140 ― ― 0.23 0.60 ― ― 0.83 0.50 Flight Altitude Overlap (%) / Side overlap (%) (m) 90 / 80 90 / 85 90 / 90 90 / 95 95 / 80 95 / 85 95 / 90 95 / 95 60 0.65 0.61 0.43 0.31 0.42 0.53 0.35 0.40 80 0.18 0.19 0.58 0.37 0.32 0.31 0.31 0.31 100 0.78 0.63 0.46 0.28 0.33 0.59 0.49 0.42 120 0.09 0.15 0.66 0.50 0.91 0.38 0.31 0.48 140 0.71 ― 0.58 0.71 0.53 0.60 0.27 0.53

Drones 2020, 4, 19 10 of 21

Table 5. P-value by A Kolmogorov–Smirnov goodness of fit test of tree height.

Flight Altitude Overlap (%)/Side Overlap (%) (m) 80/80 80/85 80/90 80/95 85/80 85/85 85/90 85/95 60 — 0.48 0.56 0.50 — — 0.66 0.45 80 — — 0.27 0.45 — — 0.44 0.43 100 — 0.24 0.49 0.34 — 0.41 0.26 0.87 120 — 0.34 0.31 0.21 — 0.30 0.76 0.27 140 — — 0.23 0.60 — — 0.83 0.50 Flight Altitude Overlap (%)/Side Overlap (%) (m) 90/80 90/85 90/90 90/95 95/80 95/85 95/90 95/95 60 0.65 0.61 0.43 0.31 0.42 0.53 0.35 0.40 80 0.18 0.19 0.58 0.37 0.32 0.31 0.31 0.31 100 0.78 0.63 0.46 0.28 0.33 0.59 0.49 0.42 120 0.09 0.15 0.66 0.50 0.91 0.38 0.31 0.48 140 0.71 — 0.58 0.71 0.53 0.60 0.27 0.53

Table 6. P-value by a two-sided paired t test of tree height.

Flight Altitude Overlap (%)/Side Overlap (%) (m) 80/80 80/85 80/90 80/95 85/80 85/85 85/90 85/95 60 — <0.001 *** <0.001 *** <0.001 *** — — <0.001 *** <0.001 *** 80 — — 0.014 * <0.001 *** — — <0.001 *** <0.001 *** 100 — 0.004 ** <0.001 *** <0.001 *** — <0.001 *** 0.001 ** <0.001 *** 120 — <0.001 *** <0.001 *** <0.001 *** — 0.002 ** <0.001 *** <0.001 *** 140 — — <0.001 *** <0.001 *** — — <0.001 *** <0.001 *** Flight Altitude Overlap (%)/Side Overlap (%) (m) 90/80 90/85 90/90 90/95 95/80 95/85 95/90 95/95 60 0.012 * 0.004 ** <0.001 *** <0.001 *** <0.001 *** <0.001 *** 0.001 ** <0.001 *** 80 0.005 ** <0.001 *** 0.012 * 0.004 ** <0.001 *** <0.001 *** 0.001 ** 0.002 ** 100 <0.001 *** <0.001 *** 0.003 ** <0.001 *** <0.001 *** <0.001 *** <0.001 *** <0.001 *** 120 <0.001 *** <0.001 *** <0.001 *** <0.001 *** <0.001 *** <0.001 *** <0.001 *** <0.001 *** 140 <0.001 *** — 0.04 * <0.001 *** <0.001 *** <0.001 *** <0.001 *** <0.001 *** Note: * = P < 0.05, ** = P < 0.01, *** = P < 0.001.

Figure7 shows the comparison of mean tree height calculated using the UAV for each tree under different flight conditions, as well as the actual tree heights. Figure8 shows the comparison of the RMSE of target trees calculated using the UAV for each tree under different flight conditions and the actual tree heights. The SD of tree height measured with the UAV for each tree (Figure7) ranged between 0.5 and 1.2 m. Therefore, the effect of different flight conditions on errors in for each tree was negligible. In addition, tree height measured with the UAV tended to be lower than the actual values. Measured tree heights in the present study were underestimated; however, analysis with the SfM analytical software, PhotoScan Professional (Agisoft), tended to overestimate tree height [2,4]. Thus, our results were inconsistent with previous studies. The RMSE for each tree (Figure8) ranged between 0.54 (2%) and 16.22 m (77%). For 47 trees, the RMSE% was 5% or lower. The relationship between the RMSE and the actual values is that most of the target trees have a RMSE of 4 m or less, when the actual value is about 26 m or less. Therefore, the accuracy of the target tree up to around 26 m was relatively good. However, when the actual value exceeds 26 m, the RMSE value tends to increase (representing decreased accuracy). The higher the tree height, the more remarkable the decrease in accuracy. Measurement error is likely because the UAV-measured values under all flight conditions were underestimated compared to the actual values. Drones 2020, 4, x FOR PEER REVIEW 10 of 21

Table 6. P-value by a two-sided paired t test of tree height.

Flight Overlap (%) / Side overlap (%) altitude (m) 80 / 80 80 / 85 80 / 90 80 / 95 85 / 80 85 / 85 85 / 90 85 / 95 60 ― <0.001*** <0.001*** <0.001*** ― ― <0.001*** <0.001*** 80 ― ― 0.014* <0.001*** ― ― <0.001*** <0.001*** 100 ― 0.004** <0.001*** <0.001*** ― <0.001*** 0.001** <0.001*** 120 ― <0.001*** <0.001*** <0.001*** ― 0.002** <0.001*** <0.001*** 140 ― ― <0.001*** <0.001*** ― ― <0.001*** <0.001*** Flight Overlap (%) / Side overlap (%) altitude (m) 90 / 80 90 / 85 90 / 90 90 / 95 95 / 80 95 / 85 95 / 90 95 / 95 60 0.012* 0.004** <0.001*** <0.001*** <0.001*** <0.001*** 0.001** <0.001*** 80 0.005** <0.001*** 0.012* 0.004** <0.001*** <0.001*** 0.001** 0.002** 100 <0.001*** <0.001*** 0.003** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** 120 <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** 140 <0.001*** ― 0.04* <0.001*** <0.001*** <0.001*** <0.001*** <0.001*** Note: * = P < 0.05, ** = P < 0.01, *** = P < 0.001.

Figure 7 shows the comparison of mean tree height calculated using the UAV for each tree under different flight conditions, as well as the actual tree heights. Figure 8 shows the comparison of the RMSE of target trees calculated using the UAV for each tree under different flight conditions and the actual tree heights. The SD of tree height measured with the UAV for each tree (Figure 7) ranged between 0.5 and 1.2 m. Therefore, the effect of different flight conditions on errors in tree height measurement for each tree was negligible. In addition, tree height measured with the UAV tended to be lower than the actual values. Measured tree heights in the present study were underestimated; however, analysis with the SfM analytical software, PhotoScan Professional (Agisoft), tended to overestimate tree height [2,4]. Thus, our results were inconsistent with previous studies. The RMSE for each tree (Figure 8) ranged between 0.54 (2%) and 16.22 m (77%). For 47 trees, the RMSE% was 5% or lower. The relationship between the RMSE and the actual values is that most of the target trees have a RMSE of 4 m or less, when the actual value is about 26 m or less. Therefore, the accuracy of the target tree up to around 26 m was relatively good. However, when the actual value exceeds 26 m, the RMSE value tends to increase (representing decreased accuracy). The higher the tree height, the more remarkable the decrease in accuracy. Measurement error is likely because the UAV-measured Drones 2020, 4, 19 11 of 21 values under all flight conditions were underestimated compared to the actual values.

30

25

20 Measured values (m) values Measured

15 15 20 25 30 35 40 45 Actual values (m)

Drones 2020FigureFigure, 4, x 7. FOR Comparison 7. Comparison PEER REVIEW between between mean mean tree tree height height from from UAV UAV (unmanned aerialaerial vehicle) vehicle) measurement measurement 11 of 21 andand actual actual tree tree height. height. Note Note:: error error bars bars indi indicatecate the the SD SD (standard (standard deviation).deviation). 20.0

16.0

12.0

8.0 RMSE (m) RMSE

4.0

0.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 Actual values (m) FigureFigure 8. Relationship 8. Relationship between between tree tree height height andandRMSE RMSE (root (root mean mean square square error) error) of each of targeteach target tree. tree. Figure9 shows the RMSE for the measured results of tree height under di fferent flight conditions (flightFigure altitude); 9 shows Figures the RMSE 10 and for 11 the show measured the RMSE results for the of measurement tree height resultsunder ofdiffe treerent height flight for eachconditions (flightflight altitude); condition Figures (overlap 10 and and 11 side show overlap, the RMSE respectively). for the RMSEmeasurement ranged from results 5.2 of (21.9%) tree height to 7.1 mfor each flight(34.8%). condition The RMSE(overlap at a flightand side altitude overlap, of 60 m respectively). was between 5.20 RMSE (21.9%) ranged and 5.76 from m (25.7%); 5.2 (21.9%) at a flight to 7.1 m (34.8%).altitude The ofRMSE 80 m itat wasa flight between altitude 5.26 (22.3%)of 60 mand was 5.7 between m (25.3%); 5.20 at (21.9%) 100 m it and was 5.76 from m 5.28 (25.7%); (22.5%) at to a flight altitude5.67 of m (25.9%);80 m it atwas 120 between m it was from5.26 5.42(22.3%) (23.3%) and to 6.365.7 m m (30.0%);(25.3%); and at 100 the RMSEm it was at a flightfrom altitude 5.28 (22.5%) of to 5.67 m140 (25.9%); m was between at 120 5.21m it (21.8%) was from and 7.15.42 m (23.3%) (34.8%). Accordingto 6.36 m to(30.0%); Figures 10and and the 11 ,RMSE there is at no a tendency flight altitude of 140to m improve was between the measurement 5.21 (21.8%) accuracy, and even 7.1 if m the (34.8%). degree of According overlap in photographyto Figures 10 (overlap and 11, and there side is no overlap) of the photographs is increased. However, increasing both the overlap and the side overlap tendency to improve the measurement accuracy, even if the degree of overlap in photography tends to reduce the possibility that measurement due to loss of the 3D model becomes impossible. (overlapThe presentand side result overlap) shows thatof the flight photographs conditions with is increased. good RMSE However, and accurate increasing tree height both tended the tooverlap and thehave side a low overlap flight altitude. tends to Under reduce all flightthe possibility conditions, that measurement measurement accuracy due was to 20% loss or of more, the 3D but model becomessince impossible. variation in RMSE The present was small result in low shows flight altitudethat flight conditions, conditions the di withfference good in flight RMSE conditions and accurate tree heighthad little tended effect onto errorshave a in low the measurementsflight altitude. of Under tree height. all flight Therefore, conditions, it is shown measurement from the tree accuracy was height20% or measurement more, but resultssince thatvariation flight altitudein RMSE has awas large small effect in on low measurement flight altitude accuracy, conditions, and the the difference in flight conditions had little effect on errors in the measurements of tree height. Therefore, it is shown from the tree height measurement results that flight altitude has a large effect on measurement accuracy, and the degree of overlap in photography has a large effect on SfM processing. It is necessary for the flight altitude to be low and the degree of overlap in photography to be high.

Drones 2020, 4, 19 12 of 21

degree of overlap in photography has a large effect on SfM processing. It is necessary for the flight altitude to be low and the degree of overlap in photography to be high. In this study, the analytical software methodology used resulted in small variations in the measured values of tree height calculated for each tree under different flight conditions; however, compared to actual values, accuracy was low and the errors were large. It can be suggested that accuracy was low Drones because2020, 4, xthere FOR PEER was also REVIEW a significant difference in the results of the two-sided paired t tests. The results12 of 21 show that the accuracy for tree height for any of the flight conditions was low.

7.5 7.5

7.0 7.0

6.5 6.5

6.0 25.7% 6.0 RMSE (m) RMSE RMSE (m) RMSE 25.1% 25.3% 24.4% 24.7% 24.4% 23.7% 23.8% 23.5% 23.4% 5.5 23.0% 22.9% 5.5 23.0% 24.4% 21.9% 24.9% 22.2% 23.9% 24.1% 22.6% 23.0% 22.3% 22.7% 22.4% 23.0% 5.0 5.0 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap)

(a) (b) 7.5 7.5

7.0 7.0

6.5 6.5 30.0% 27.6% 26.4% 6.0 25.9% 6.0 25.9% 26.1% 25.9%

25.6% (m) RMSE RMSE (m) RMSE 25.5% 25.7% 25.3% 25.3% 25.5% 25.1% 25.1% 24.6% 26.9% 23.7% 23.9% 23.3% 26.2% 5.5 22.5% 23.0% 25.4% 22.7% 25.6% 5.5 24.5% 23.9%

5.0 5.0 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap)

(c) (d)

7.5 34.8% 7.0 31.3% 6.5 28.5% 28.5% 28.6%

6.0

RMSE (m) RMSE 25.8% 24.6% 27.8% 25.2% 23.8% 5.5 21.8%

5.0 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap)

(e)

FigureFigure 9. RMSE 9. RMSE (root mean (root mean square square error) error) and andRMSE% RMSE% (relative (relative root root mean mean square square error) error) of oftree tree height measurementheight measurement results for results each forflight each condition flight condition (flight (flight altitude); altitude); (a (a)) flight flight altitudealtitude= 60= m;60 (m;b) flight(b) flight altitudealtitude = 80 =m;80 (c m;) flight (c) flight altitude altitude = 100= 100 m; m; (d ()d )flight flight altitude altitude == 120120 m; m; and and (e (e)) flight flight altitude altitude= 140 = 140 m. m.

Drones 2020, 4, x FOR PEER REVIEW 13 of 21 Drones 2020, 4, 19 13 of 21 Drones 2020, 4, x FOR PEER REVIEW 13 of 21

7.5 7.5 7.5 7.5 7.0 7.0 7.0 7.0 6.5 60 m 6.5 60 m 60 m 60 m 6.5 80 m 6.5 80 m 6.0 80 m 6.0 80 m

RMSE (m) RMSE 100 m (m) RMSE 100 m 6.0 6.0

RMSE (m) RMSE 100 m (m) RMSE 100 m 5.5 120 m 5.5 120 m 5.5 120 m 5.5 120 m 140 m 140 m 140 m 140 m 5.0 5.0 5.05.0 80-8080-80 80-85 80-85 80-90 80-90 80-95 80-95 85-8085-80 85-85 85-85 85-90 85-90 85-95 85-95 FlightFlight conditions conditions (overlap—side (overlap—side overlap) overlap) FlightFlight conditions conditions (overlap—side (overlap—side overlap) overlap)

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

7.5 7.5 7.5 7.5

7.0 7.0 7.0 7.0 6.5 60 m 6.5 60 m 60 m 60 m 6.5 80 m 6.5 80 m 6.0 80 m 6.0 80 m RMSE (m) RMSE RMSE (m) RMSE 100 m 100 m 6.0 6.0 RMSE (m) RMSE RMSE (m) RMSE 100 m 100 m 5.5 120 m 5.5 120 m 5.5 120140 m m 5.5 140 m120 m 5.0 5.0 140 m 140 m 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 5.0 5.0 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) (c) (d)

Figure 10. RMSE (root( cmean) square error) of tree height measurement (resultsd) for each flight condition

Figure(overlap);Figure 10. RMSE 10.(aRMSE) overlap(root (root mean = mean80%; square ( squareb) overlap error) error) =of 85%; of tree tree ( cheight) height overlap measurement measurement = 90%; and results( dresults) overlap for for each = each 95%. flight flight condition condition (overlap);(overlap); (a) overlap (a) overlap = 80%;= 80%; (b) (overlapb) overlap = =85%;85%; (c ()c )overlap overlap = 90%;90%; andand( d(d)) overlap overlap= 95%.= 95%.

7.5 7.5

7.0 7.0 7.5 7.5 6.5 60 m 6.5 60 m 7.0 80 m 7.0 80 m 6.0 6.0 RMSE (m) RMSE 100 m (m) RMSE 100 m 6.5 60 m 6.5 60 m 120 m 120 m 5.5 80 m 5.5 80 m 140 m 140 m 6.0 6.0 RMSE (m) RMSE 5.0 100 m (m) RMSE 5.0 100 m 80-80 85-80 90-80 95-80 120 m 80-85 85-85 90-85 95-85 120 m 5.5 5.5 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) 140 m 140 m 5.0 5.0 80-80 85-80(a) 90-80 95-80 80-85( 85-85b) 90-85 95-85 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap)

7.5 7.5 (a) (b) 7.0 7.0

6.5 60 m 6.5 60 m 7.5 7.5 80 m 80 m 6.0 6.0 RMSE (m) RMSE RMSE (m) RMSE 100 m 100 m 7.0 7.0 120 m 120 m 5.5 5.5 6.5 60140 m m 6.5 140 m 60 m 5.0 80 m 5.0 80 m 6.0 80-90 85-90 90-90 95-90 6.0 80-95 85-95 90-95 95-95 RMSE (m) RMSE RMSE (m) RMSE 100 m 100 m Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) 120 m 120 m 5.5 5.5 140 m 140 m 5.0 (c) 5.0 (d) 80-90 85-90 90-90 95-90 80-95 85-95 90-95 95-95 FigureFigure 11. 11.Flight RMSERMSE conditions (root (root (overlap—side mean mean square square overlap) error) error) of of tree tree height height measurement measurementFlight conditions results (overlap—side for each overlap) flight flight condition (side(side overlap) overlap) ( (aa)) side side overlap overlap = 80%;80%; ( (bb)) side side overlap = 85%; ( c) side overlap = 90%; and (d) sideside overlapoverlap == 95%.95%. (c) (d)

FigureIn 11.this RMSE study, (root the mean analytical square software error) of treemethodology height measurement used resulted results in forsmall each variations flight condition in the measured(side overlap) values (a) ofside tree overlap height = calculated 80%; (b) sidefor eachoverlap tree = under 85%; (differentc) side overlap flight conditions;= 90%; and however, (d) side overlap = 95%.

In this study, the analytical software methodology used resulted in small variations in the measured values of tree height calculated for each tree under different flight conditions; however, Drones 2020, 4, 19 14 of 21

3.4. Tree Volume The relationship coefficient between canopy area and DBH was between R2 = 0.21 and 0.63. A positive correlation was observed in many flight conditions. The normality of the estimated and measured values of the volume was confirmed by a Kolmogorov–Smirnov goodness of fit test (P > 0.05). Normality was confirmed under all flight conditions (Table7). Statistical di fferences were confirmed by two-sided paired t tests. Significant differences were found under many conditions (P < 0.05) (Table8).

Table 7. P-value by a Kolmogorov–Smirnov goodness of fit test of tree volume.

Flight Altitude Overlap (%)/Side Overlap (%) (m) 80/80 80/85 80/90 80/95 85/80 85/85 85/90 85/95 60 — 0.52 0.69 0.56 — — 0.45 0.89 80 — — 0.73 0.29 — — 0.74 0.75 100 — 0.34 0.73 0.68 — 0.32 0.43 0.49 120 — 0.20 0.62 0.31 — 0.69 0.44 0.37 140 — — 0.46 0.19 — — 0.32 0.35 Flight Altitude Overlap (%)/Side overlap (%) (m) 90/80 90/85 90/90 90/95 95/80 95/85 95/90 95/95 60 0.63 0.73 0.65 0.31 0.56 0.51 0.37 0.72 80 0.63 0.63 0.74 0.66 0.34 0.76 0.93 0.73 100 0.41 0.74 0.56 0.53 0.87 0.26 0.12 0.85 120 0.44 0.40 0.37 0.85 0.73 0.73 0.58 0.87 140 0.86 — 0.20 0.21 0.50 0.73 0.37 0.22

Table 8. P-value by a two-sided paired t test of tree volume.

Flight Altitude Overlap (%)/Side Overlap (%) (m) 80/80 80/85 80/90 80/95 85/80 85/85 85/90 85/95 60 — 0.02 * 0.012 * 0.04 * — — 0.012 * 0.012 * 80 — — 0.13 0.019 * — — 0.008 ** 0.02 * 100 — 0.10 0.005 ** 0.007 ** — 0.026 * 0.04 * 0.001 ** 120 — 0.004 ** <0.001 *** <0.001 *** — 0.04 * 0.003 ** 0.002 ** 140 — — 0.025 * <0.001 *** — — <0.001 *** 0.002 ** Flight Altitude Overlap (%)/Side Overlap (%) (m) 90/80 90/85 90/90 90/95 95/80 95/85 95/90 95/95 60 0.14 0.08 0.03 * 0.04 * 0.004 ** 0.007 ** 0.04 * 0.014 * 80 0.08 0.007 ** 0.13 0.07 0.004 ** 0.008 ** 0.04 * 0.04 * 100 0.005 ** 0.004 ** 0.05 0.007 ** 0.015 * 0.001 ** 0.003 ** 0.006 ** 120 0.006 ** <0.001 *** 0.02 * 0.0014 ** 0.007 ** <0.001*** 0.002 ** 0.003 ** 140 <0.001 *** — 0.2 0.019 * <0.001 *** <0.001 *** <0.001 *** 0.006 ** Note: * = P < 0.05, ** = P < 0.01, *** = P < 0.001.

Figure 12 provides a comparison between the mean volume estimated from UAV measurements for each tree under different flight conditions and the actual values. Figure 13 shows the relationship between volume and RMSE for each target tree. The SD for each tree ranged between 0.04 and 0.21 m3. Volumes estimated from UAV measurements were underestimated compared to the actual volumes. The RMSE for each tree volume ranged between 0.04 (5%) and 1.94 m3 (99%). Only 32 trees had a RMSE of 0.2% or less (RMSE% = 20%). Ten trees with RMSE% = 50% or more were found. In addition, most of the target trees with an actual measurement value of 1.5 m3 or less had a RMSE = 0.2 m3 (RMSE% = 20%) or less. However, the RMSE value was extremely degraded when the measured tree was 1.5 mm or more. The reason for the loss of accuracy is related to the underestimation of tree height, as discussed in Section 3.3. Drones 2020, 4, x FOR PEER REVIEW 15 of 21

Drones 2020, 4, x FOR PEER REVIEW 15 of 21 volumes. The RMSE for each tree volume ranged between 0.04 (5%) and 1.94 m3 (99%). Only 32 trees volumes.had a RMSE The RMSE of 0.2% for eachor less tree (RMSE% volume ranged = 20%). between Ten trees 0.04 with(5%) andRMSE% 1.94 m =3 (99%).50% or Only more 32 weretrees found. In hadaddition, a RMSE most of 0.2% of theor less target (RMSE% trees =with 20%). an Ten actual trees measurement with RMSE% = value 50% or of more 1.5 m were3 or found.less had In a RMSE = addition,0.2 m3 most(RMSE% of the = target 20%) trees or less.with anHowever, actual measurement the RMSE valuevalue of was1.5 m extremely3 or less had degraded a RMSE = when the 0.2 m3 (RMSE% = 20%) or less. However, the RMSE value was extremely degraded when the Dronesmeasured2020, 4, 19tree was 1.5 mm or more. The reason for the loss of accuracy is 15related of 21 to the measuredunderestimation tree was of 1.5 tree mm height, or more. as discussed The reason in Section for the 3.3. loss of accuracy is related to the underestimation of tree height, as discussed in Section 3.3.

Figure 12. Comparison of mean estimated volume and actual volume of each target tree. Note: Error Figure 12. Comparison of mean estimated volume and actual volume of each target tree. Note: Error barsFigure indicate 12. theComparison SD (standard of deviation).mean estimated volume and actual volume of each target tree. Note: Error barsbars indicate indicate the the SD SD (standard (standard deviation). deviation). 1.4 1.4 1.2

1 1.2 ) 3 0.8 1 ) 3 0.6 0.8 RMSE (m RMSE 0.4 0.6 RMSE (m RMSE 0.2 0.4 0 0.2 00.511.522.53 3 0 Actual value (m )

Figure 13. Relationship00.511.522.53 between volume and RMSE (root mean square error) of each target tree. Figure 13. Relationship between volume and RMSEActual (root meanvalue square (m3) error) of each target tree. Figure 14 shows the RMSE values of tree volumes estimated under different flight conditions (flightFigure altitude); 14 shows Figures the 15 RMSE and 16 values show of the tree RMSE volume of trees estimated volume measurement under different results flight for conditions each flight (flightcondition altitude);Figure (overlap Figures13. Relationship and 15 side and overlap, 16 between show respectively). the volume RMSE andof Thetree RMSE RMSEvolume (root ranged measurement mean between square resultserror) 0.31 and of for each0.4 each mtarget 3flight. The tree. conditionlowest RMSE (overlap was 0.31and mside3 and overlap, RMSE% respecti was 30.1%.vely). The The RMSE most common ranged between values for 0.31 RMSE and were0.4 m within3. The lowest0.34 and FigureRMSE 0.376 was14 m 3shows .0.31 In Figure m 3the and 14RMSE RMSE%, there values is was no remarkable30.1%. of tree The volume most difference commons estimated between values theunder for estimated RMSE different were results flightwithin due conditions 0.34to(flight the and change altitude);0.376 inm3 flight. In Figures Figure altitude 1514, and andthere 16 the is show resultsno remarkable the of RMSE the volume di offference tree at eachvolume between flight measurement the altitude. estimated Therefore, resultsresults while duefor each flight toitcondition isthe necessary change (overlap in to flight improve altitudeand accuracy,side and overlap, the di ffresultserences respecti of inth flightevely). volume altitude The at RMSEeach have flight ranged little altitude. eff ectbetween on Therefore, the accuracy0.31 andwhile of0.4 m3. The itvolume lowestis necessary estimations.RMSE to wasimprove As0.31 shown accuracy,m3 and in Figures RMSE% differences 15 wasand in 16 30.1%.fl,ight the influencealtitude The most have of thecommon little overlap effect values and on the for sideaccuracy RMSE overlap ofwere within volumewas0.34 not and estimations. so 0.376 significant m3 .As In that shown Figure a higher in 14, Figures degreethere 15 is of and no overlap 16,remarkable the in influence photography difference of the of overlap thebetween image and resulted thethe sideestimated inoverlap better results due wasaccuracy.to thenot changeso Therefore,significant in flight therethat altitudea ishigher no eff anddegreeect ofthe altitudeof results overlap when of in th photography measuringe volume at volume of each the imageflight from theresultedaltitude. measurement in Therefore, better while accuracy. Therefore, there is no effect of altitude when measuring volume from the measurement resultit is necessary of the volume. to improve However, accuracy, it is necessary differences to set thein fl flightight altitude lowhave when little the effect measurement on the accuracy of resultresults of of the tree volume. height areHowever, relatively it is good. necessary In addition, to set the the flight degree altitude of overlap low inwhen photography the measurement has little volume estimations. As shown in Figures 15 and 16, the influence of the overlap and the side overlap effect on measurement accuracy in terms of the measurement results of the tree height. However, was not so significant that a higher degree of overlap in photography of the image resulted in better considering that SfM processing is insufficient, a high degree of overlap in photography is required. Accuracyaccuracy. of Therefore, volume for there each flightis no conditioneffect of wasaltitude mostly when 30% measuring or more, but volume variation from in the the RMSE measurement result of the volume. However, it is necessary to set the flight altitude low when the measurement Drones 2020, 4, 19 16 of 21

Drones was2020, small. 4, x FOR Similar PEER REVIEW to the result obtained for each tree height, estimates for volume might have been16 of 21 underestimated because the accuracy of tree height measurement was low. results of treeWhen height estimating are relatively tree volume good. under In di addifferenttion, flight the conditions, degree of variations overlap inin the photography estimated values has little effect foron eachmeasurement tree were small; accuracy thus, estimatesin terms withof the relatively measurement small variation results wereof the possible. tree height. In addition, However, the results of the two-sided paired t-tests also showed significant differences under many conditions, considering that SfM processing is insufficient, a high degree of overlap in photography is required. indicating that accuracy was low. However, when compared to the actual values, the error became Accuracy of volume for each flight condition was mostly 30% or more, but variation in the RMSE was larger. Variations in estimated values were also small for each flight condition, but since measured small.values Similar did to not the seem result reasonable, obtained it is necessaryfor each totree improve height, measurement estimates accuracyfor volume for tree might height have and been underestimatedthe accuracy because of the estimated the accuracy values. of tree height measurement was low.

0.40 39.3% 0.4 39.3% 0.38 36.1% 37.1% 0.38 38.0% 38.2% 37.1% 36.2% 36.0% ) 38.5% ) 37.1% 3 36.1% 3 37.4% 36.2% 0.36 0.36 35.0% 36.2% 33.9% 34.2% 34.1% 36.4% 34.1% 34.0% 33.7% 0.34 0.34 RMSE (m RMSE RMSE (m RMSE 32.3%

0.32 0.32 30.1%

0.30 0.3 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap)

(a) (b) 0.40 40.6% 0.40

39.0% 39.4% 39.0% 38.9% 0.38 0.38 36.2% 38.2% 37.1% 37.2% 37.6% ) ) 39.1% 35.8% 3 3 36.4% 35.5% 39.4% 0.36 36.6% 34.8% 0.36 36.0% 36.5% 37.4% 35.6% 34.8% 35.4% 34.5% 35.0% 36.6% 0.34 0.34 RMSE (m RMSE RMSE (m RMSE 34.7%

0.32 0.32

0.30 0.30 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap)

(c) (d) 0.4 42.9% 41.3% 37.9% 0.38 39.6% 38.1% )

3 35.8% 0.36 33.7% 35.7% 36.4% 0.34 RMSE (m RMSE

0.32

0.3 80-80 80-85 80-90 80-95 85-80 85-85 85-90 85-95 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap)

(e)

FigureFigure 14. RMSE 14. RMSE (root (root mean mean square square error) error) and and RMSE% RMSE% (relative(relative root root mean mean square square error) error) of tree of tree volumevolume measurement measurement results results for for each each flightflight condition condition (flight (flight altitude) altitude) (a) flight (a) altitude flight =altitude60 m; (b =) flight60 m; (b) flight altitudealtitude= =80 80 m; m; (c ()c flight) flight altitude altitude= 100 = 100 m; ( dm;) flight (d) flight altitude altitude= 120 m;= 120 and m; (e) and flight (e altitude) flight =altitude140 m. = 140 m.

Drones 2020, 4, x FOR PEER REVIEW 17 of 21

0.40 0.40 Drones 2020, 4, 19 17 of 21 Drones 20200.38 , 4, x FOR PEER REVIEW 0.38 17 of 21 ) ) 3 0.36 60 m 3 0.36 60 m 0.40 0.40 80 m 80 m

0.34 0.38 100 m 0.340.38 100 m RMSE (m RMSE (m RMSE ) )

3 120 m 3 120 m 0.32 0.36 60 m 0.320.36 60 m 140 m80 m 14080 mm 0.30 0.30 0.34 100 m 0.34 100 m RMSE (m RMSE 80-80 80-85 80-90 80-95 (m RMSE 85-80 85-85 85-90 85-95 120 m 120 m Flight0.32 conditions (overlap—side overlap) 0.32Flight conditions (overlap—side overlap) 140 m 140 m 0.30 0.30 80-80(a 80-85) 80-90 80-95 85-80 85-85(b) 85-90 85-95 0.40 Flight conditions (overlap—side overlap) 0.40 Flight conditions (overlap—side overlap)

0.38 (a) 0.38 (b) ) )

3 0.40 3 0.40 0.36 60 m 0.36 60 m 80 m 80 m 0.38 0.38 0.34 100 m 0.34 100 m RMSE (m RMSE (m RMSE ) ) 3 60 m 3 60 m 0.36 120 m 0.36 120 m 0.32 80 m 0.32 80 m 140 m 140 m 0.34 100 m 0.34 100 m 0.30 (m RMSE 0.30 (m RMSE 120 m 120 m 0.3290-80 90-85 90-90 90-95 0.32 95-80 95-85 95-90 95-95 Flight conditions (overlap—side overlap) 140 m Flight conditions (overlap—side overlap) 140 m 0.30 0.30 90-80 90-85 90-90 90-95 95-80 95-85 95-90 95-95 Flight conditions(c) (overlap—side overlap) Flight conditions(d (overlap—side) overlap)

Figure 15. RMSE (root mean(c) square error) of volume measurement results(d) for each flight condition (overlap) (a) overlap = 80%; (b) overlap = 85%; (c) overlap = 90%; and (d) overlap = 95%. FigureFigure 15. 15. RMSERMSE (root meanmean squaresquare error) error) of of volume volume measurement measurement results results for eachfor each flight flight condition condition (overlap)(overlap) ( (a) overlap == 80%;80%; ((bb)) overlap overlap= =85%; 85%; (c ()c overlap) overlap= 90%;= 90%; and and (d) ( overlapd) overlap= 95%. = 95%.

0.40 0.40

0.38 0.40 0.380.40 ) ) 3 60 m 3 60 m 0.36 0.38 0.360.38 80 m 80 m ) ) 3 60 m 3 60 m 0.34 0.36 100 m 0.340.36 100 m RMSE (m RMSE (m RMSE 80 m 80 m 120 m 120 m 0.32 0.34 100 m 0.320.34 100 m RMSE (m RMSE 140 m (m RMSE 140 m 120 m 120 m 0.30 0.32 0.300.32 80-80 85-80 90-80 95-80 140 m 80-85 85-85 90-85 95-85 140 m Flight0.30 conditions (overlap—side overlap) 0.30Flight conditions (overlap—side overlap) 80-80 85-80 90-80 95-80 80-85 85-85 90-85 95-85 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) (a) (b) (a) (b) 0.40 0.40

0.40 0.40 0.38 0.38

) 0.38 ) 0.38 3 0.36 60 m 3 0.36 60 m ) ) 3 0.36 80 m60 m 3 0.36 8060 m m 0.34 100 m80 m 0.34 10080 mm RMSE (m RMSE (m RMSE 0.34 100 m 0.34 100 m RMSE (m RMSE 120 m (m RMSE 120 m 0.32 0.32 120 m 120 m 0.32 140 m 0.32 140 m 0.30 140 m 0.30 140 m 0.3080-90 85-90 90-90 95-90 0.30 80-95 85-95 90-95 95-95 80-90 85-90 90-90 95-90 80-95 85-95 90-95 95-95 Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap) Flight conditions (overlap—side overlap)

(c) (c) (d()d )

FigureFigureFigure 16. RMSE 16. 16. RMSERMSE (root (root mean meanmean square squaresquare error) error) error) of of volumeof volume volume measurementme measurementasurement results results results for for eachfor each each flight flight flight condition conditioncondition (side overlap) (a) side overlap = 80%; (b) side overlap = 85%; (c) side overlap = 90%; and (d) side (side (sideoverlap) overlap) (a) side (a) sideoverlap overlap = 80%; = 80%; (b) (sideb) side overlap overlap = 85%;= 85%; (c )( cside) side overlap overlap = = 90%; 90%; andand ((dd)) side overlap = 95%. overlapoverlap = 95%. = 95%.

WhenWhen estimating estimating tree tree volume volume under under different different fl ightflight conditions, conditions, variations variations inin thethe estimatedestimated valuesvalues for eachfor each tree treewere were small; small; thus, thus, estimates estimates wi with threlatively relatively small small variation variation werewere possible.possible. InIn addition,addition, the results the results of the of thetwo-sided two-sided paired paired t-tests t-tests also also showed showed significant significant differences differences underunder many conditions,conditions, indicating indicating that that accuracy accuracy was was low. low. Ho However,wever, when when compared compared to to thethe actualactual values,values, thethe Drones 2020, 4, 19 18 of 21

3.5. Analyzing Comprehensive Results The present study verified the accuracy of the measured values and those estimated through different aerial photography methods and flight conditions. The results show that, based on the ability to process images, the overlap and side overlap of photographs need to be 90% or higher when taking measurements in forests. Though there is little effect from flight altitude on image processing when taking measurements in forests, the terrain of the takeoff site, landing site, and flight area need to be taken into consideration when setting the flight altitude. The verification of accuracy showed that when image processing, 3D modeling, DSM imaging, and orthographic imaging provided complete and satisfactory outputs, irrespective of flight conditions for UAV measurements, the accuracy was low with respect to the actual values. However, the measured values were stable at low flight altitudes. In processing and measuring the aerial images, analysis with SfM processing becomes more unstable as the degree of overlap in photography decreases. Reduced accuracy could cause errors in measured values. However, when the degree of overlap in photography (overlap and side overlap) was changed and the measured and estimated values were compared, there were errors, though the variation was small. Similar to the findings reported by Sugai et al. [33], there was a noticeable difference in accuracy as a result of changing the degree of overlap in the photography. For this reason, we consider the flight altitude to have a considerable influence on the accuracy of measurement, and that the degree of overlap in photography (overlap and side overlap) has a substantial effect on SfM processing. Moreover, previous studies have recommended that the flight altitude should be low [24] and that the required degree of overlap for photography should be high [23–25], indicating similar tendencies to those found in this study.

3.6. Points to Note Regarding UAV Use The following points describe factors and issues that contributed to the poor accuracy found in this study: (1) the survey and flight was performed in a small area, (2) measurements were made using SfM processing software, and (3) there were no GCPs (ground control points). Each of these is described in detail below. In the present study, we entered the numerical values for flight conditions (flight altitude, overlap, and side overlap) into DJI GS PRO for automatic UAV flight and aerial photography. Values for flight altitude input were the flight altitudes measured from the takeoff site of the UAV. However, flight altitude in the aerial photography areas is measured as the height above ground level. Therefore, there could be discrepancies in height depending on the differences between flight altitude and height above ground level. As input values and actual flight altitudes were different, the degree of overlap in photography (overlap and side overlap) might be insufficient. We believe this is possible for any UAV or flight application. However, in the case of a small area, since the number of images is small, the influence of image loss leads to a large error. When the number of images is large, it is considered that other images can be used to compensate for some loss. Therefore, a lot of care must be taken while flying, especially in small areas. In the case of manual operation and photography, it is difficult to determine overlap and side overlap, which increases the difficulty in photography and flight. The advantages of using UAV include low cost, high resolution, high-density data, as well as versatility, safety, and flexibility in shooting [34–37]. For example, if simple photographs are to be taken over a wide area to understand damage from landslides, a manual flight with more freedom might be more effective than an automated flight. However, when conducting a detailed survey in which a 3D model is created using SfM technology, it would be essential to use automated flight and photography by identifying the condition of the survey site, considering the advantages of a UAV flight. Additionally, there is a risk of crashing in bad weather [1,38]. Therefore, even in an automatic flight or a manual flight with SfM processing, it is recommended to stop or change the flight plan, depending on the weather. Users need to understand the characteristics of the survey site, the UAV used, and the flight application in order to operate the equipment properly. Drones 2020, 4, 19 19 of 21

In this study, the measurements of tree height and crown area were performed using analysis software. No other case of measurements made using analysis software, as was done in this study, are known. Regarding the processing of aerial images, when using a multicopter-style UAV, the trend differed with each software program [39,40], indicating that the algorithm of the SfM software has not been disclosed [33]. Moreover, the UAV Stand Inventory Manual points out that there is little knowledge regarding analysis technology [1]. When performing a forest survey, such as for tree height using a UAV, the use of SfM software becomes essential, and there are several choices ranging from free to expensive software. It is difficult to understand the measurement algorithms of all the software; however, it is important to recognize that SfM software tends to be highly versatile with relatively high reliability. In this study, the influence of SfM software is also a factor in the low accuracy generated. Therefore, analysis using other SfM software and verification is also necessary, and is a subject for future research. Many UAV-based surveys and research projects have GCPs set up. However, this study did not use this method. Iizuka et al. [9] also did not use GCPs, but the RMSE of the tree height was 1.712 m and the accuracy was high. As a result, a roughly accurate measurement was performed, indicating a different tendency. The installation of GCPs requires precise surveying and the addition of highly accurate location information [1]. Additionally, even if GCPs are installed, there is a possibility that the antiaircraft sign may not be recognized owing to the influence of the upper tree [1,9]. For this reason, installation requires labor, and a wide gap is required in the sky. Users need to determine whether GCPs are installed and how many GCPs should be used. This will depend on the purpose of use and whether it is better to perform accurate measurements or to collect simple information, such as forest monitoring and 3D model creation.

4. Conclusions This study aimed to investigate the effects of differences in shooting and flight conditions for UAV on the processing method and estimated results of aerial images. Forest images were acquired under 80 different conditions, combining various aerial photography methods and flight conditions. We verified errors in values measured by the UAV and the measurement accuracy with respect to tree height and volume. However, irrespective of flight conditions for UAV measurements, accuracy was low with respect to the actual values. In the present study, we selected a flat survey site to minimize the effect of the terrain. Therefore, the data we collected are insufficient for an examination of the effects of microtopography and slopes. Moreover, the UAV Stand Inventory Manual prepared by the Ministry of Agriculture, Forestry and Fisheries [1] is currently limited to forests with a uniform forest type. As there are still many challenges and limitations in forest measurement using UAV, set investigation of various flight conditions is required. In the future, we will proceed with studies to address the factors and solve the issues that caused the deterioration in accuracy encountered in this research. The accumulation of data on the settings of flight conditions for aerial photography is necessary to improve accuracy when measurements are taken on a slope.

Author Contributions: Conceptualization, S.K.; formal analysis, S.K.; investigation, S.K.; methodology, S.K.; writing—original draft, S.K. and K.S.; writing—review and editing, S.K. and K.S. All authors have read and agreed to the published version of the manuscript. Funding: Part of this research was supported by a large research grant from the College of Bioresource Sciences, Nihon University from fiscal year 2016 to fiscal year 2018. Conflicts of Interest: The authors declare no conflict of interest.

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