Article -Stump Detection, Segmentation, Classification, and Measurement Using Unmanned Aerial Vehicle (UAV) Imagery

Stefano Puliti * ID , Bruce Talbot and Rasmus Astrup

Division of and Forest Resources, Norwegian Institute for Bioeconomy Research (NIBIO), Høgskoleveien 8, 1433 Ås, Norway; [email protected] (B.T.); [email protected] (R.A.) * Correspondence: [email protected]; Tel.: +47-936-78-891

Received: 25 January 2018; Accepted: 26 February 2018; Published: 27 February 2018

Abstract: Unmanned aerial vehicles (UAVs) are increasingly used as tools to perform a detailed assessment of post-harvest sites. One of the potential use of UAV photogrammetric data is to obtain tree-stump information that can then be used to support more precise decisions. This study developed and tested a methodology to automatically detect, segment, classify, and measure tree-stumps. Among the potential applications for single stump data, this study assessed the possibility (1) to detect and map root- and butt-rot on the stumps using a machine learning approach, and (2) directly measure or model tree stump diameter from the UAV data. The results revealed that the tree-stumps were detected with an overall accuracy of 68–80%, and once the stump was detected, the presence of root- and butt-rot was detected with an accuracy of 82.1%. Furthermore, the root mean square error of the UAV-derived measurements or model predictions for the stump diameter was 7.5 cm and 6.4 cm, respectively, and with the former systematically under predicting the diameter by 3.3 cm. The results of this study are promising and can lead to the development of more cost-effective and comprehensive UAV post-harvest surveys.

Keywords: tree-stumps; UAV; automatic detection; machine learning; root- and butt-rot

1. Introduction

1.1. Background The increasing need of precise information in has rapidly led to an increment in the application of Unmanned Aerial Vehicles (UAVs) in data acquisition for forest managers [1,2]. UAVs, in combination with photogrammetric methods, have been successfully used for assessing forested areas for pre- [2] and post-harvest assessment [3–7]. In post-harvest assessment, [3,5,8] explored the possibilities of using UAV-borne photogrammetric three-dimensional (3D) data for quantifying soil disturbance from forest machinery. When compared to ground-based solutions [9], UAVs that are equipped with imaging sensors represent a more efficient tool to acquire highly detailed and spatially continuous 3D data. Thus, UAVs represent an important tool for post-harvest assessment, especially when increasing the requirements for environmental monitoring and the reporting of post-harvest sites [3]. Furthermore, because of the small dimensions, scattered distribution, and difficult access of harvest sites, UAVs may be particularly attractive for the assessment of these sites [3]. Until now, studies using UAVs for post-harvest assessment have used UAV data for a single purpose, e.g., assessing soil displacement, quantifying timber theft [7], or estimating forest transpiration [6]. In the future, it would be desirable to derive multiple levels of information from single UAVs acquisitions. The development of multipurpose UAV post-harvest applications could provide a more comprehensive site assessment, while increasing the cost-effectiveness of using UAVs.

Forests 2018, 9, 102; doi:10.3390/f9030102 www.mdpi.com/journal/forests Forests 2018, 9, 102 2 of 14

Some of the most evident features on a post-harvest site are tree stumps, as they are relatively uniform, their surface is generally brighter than the surrounding area, and they typically protrude (10–40 cm) above ground level. These distinct characteristics suggest that UAVs may be used to retrieve information on the distribution and characteristics of tree stumps. Jaskierniak, et al. [6] first suggested the use of UAV orthomosaics to assess stumps’ characteristics. In their specific case, UAV imagery was used to manually detect tree stumps and measure sapwood area as a proxy variable to estimate annual transpiration in eucalyptus forests. The first attempt to automate the stump detection was made by Samiappan, et al. [7], who adopted image pattern recognition methods for this purpose with the objective of detecting and measuring tree stumps for the quantification of illegal . In addition to the abovementioned applications, improved information on tree-stumps may be useful for a number of others applications, such as: (1) detecting, quantifying, and mapping the presence of root- and butt-rot that is caused by pathogens. Root- and butt-rot is frequently transmitted from the stumps and root network to the next tree generation [10–12]. Information on the spatial extent of root- and butt-rot can be valuable in designing regeneration strategies that ensure that more resistant species are planted in areas with a significant presence of such pathogens [13,14]; (2) estimation of tree-stump height and fibre losses [15], and in modelling below-ground left on site [16]; (3) better understanding of the spatial variability in stand productivity and transpiration for use in development of forest growth models, such as shown by Jaskierniak, et al. [6]; and, (4) supporting better georeferencing of individual tree positional data from forest harvesters. The spatial matching of variables such as UAV-derived and harvester-measured tree-stump diameter could in fact enable the assignment of an accurate position to the harvester head, providing high precision single-tree data [17,18]. The limited literature on the use of UAVs for post-harvest assessment is not commensurate with the perceived opportunities, indicating that there is potential in the development of methods to use these data to extract additional types of information.

1.2. Aim The primary aim of this study was to develop and test the accuracy of automatic tree-stump detection and segmentation algorithm using UAV imagery and derived 3D products. The secondary objectives were to assess the possibility to (i) detect and map the presence of root- and butt-rot presence in the stumps, and (ii) directly measure and/or model tree-stump diameter.

2. Materials

2.1. Study Area The study area consisted of one forest stand of 4.5 ha located in south-eastern Norway. The stand had been harvested in February 2017, using a cut-to-length (CTL) system for the harvest and extraction of timber. Logging residues had been left on site, or piled on the machine trails to reduce soil disturbance where necessary. No special consideration related to the study was made during the harvesting operations.

2.2. Field Data A field campaign was conducted in spring-summer 2017 to acquire single tree-stump field data. A systematic clustered sampling design was adopted. A total of 11 field plots were systematically selected at the vertices of a 57 m × 57 m grid that was generated based on the extent of the stand (Figure1). The systematic design was adopted to ensure a uniform coverage of the stand. The field plots were circular and with a radius of 8.92 m (area = 250 m2), and covered a total area of 0.275 ha, or 6.1% of the stand area. Within each plot, every tree-stump was measured. The total sample size (i.e., single tree-stumps) was 265 tree-stumps. Forests 2018, 9, 102 3 of 14 Forests 2018, 9, x FOR PEER REVIEW 3 of 14

Forests 2018, 9, x FOR PEER REVIEW 3 of 14

FigureFigure 1. 1.Overview Overview of of the the sampling sampling design design for for field field data data acquisition acquisition with with a a detail detail on on a a single single field field plot. Theplot. orthomosaic The orthomosaic displayed displayed in the backgroundin the background was the was one the derived one deri fromved thefrom Unmanned the Unmanned Aerial Aerial Vehicles (UAV)Vehicles photogrammetric (UAV) photogrammetric processing processing (i.e., ground (i.e., samplingground sampling distance distance = 1.7 cm). = 1.7 cm).

TheTheFigure position position 1. Overview of the the centre centre of the of sampling of the the field field design plot plot wafors fieldwas measured data measured acquisition using usinga Topconwith aa Topcondetail GR5 on GNSS a GR5 single receiver GNSS field fitted receiver fittedwith withcellularplot. cellular The communication orthomosaic communication displayed for Real in for theTime background Real Kinematic Time was Kinematic (RTK) the one correction deri (RTK)ved from correctionlive the via Unmanned the liveGSM Aerial via network. the GSM network.Furthermore,Vehicles Furthermore, for (UAV) each tree-stumpphotogrammetric for each the tree-stump measurements processing (i.e., the inclground measurementsuded: sampling (a) GNSS distance included: positioning = 1.7 cm). (a) of the GNSS centre positioning of each tree-stump; (b) two diameters in the north-south (N-S) and east-west (E-W) directions, respectively; (c) the of the centreThe position of each of tree-stump; the centre of the (b) field two plot diameters was measured in the using north-south a Topcon GR5 (N-S) GNSS and receiver east-west fitted (E-W) presence or absence of root- and butt-rot; (d) tree-stump occlusion classification (non-obscured tree- directions,with cellular respectively; communication (c) the for presence Real Time or absenceKinematic of (RTK) root- correction and butt-rot; live via (d) the tree-stump GSM network. occlusion stumps or tree-stumps that were partially or fully obscured by logging residuals); and, (e) tree-stump classificationFurthermore, (non-obscured for each tree-stump tree-stumps the measurements or tree-stumps included: that were(a) GNSS partially positioning or fully of the obscured centre of by each logging damage classification (undamaged tree-stumps or tree-stumps damaged by machinery). residuals);tree-stump; and, (b) (e) two tree-stump diameters damage in the north-south classification (N-S) (undamagedand east-west (E-W) tree-stumps directions, or respectively; tree-stumps (c) damaged the Requirements on the northing and easting GNSS accuracy of the estimate of the position of the centre by machinery).presence or absence of root- and butt-rot; (d) tree-stump occlusion classification (non-obscured tree- of the tree-stumps dimensions was at single centimetre level when obtaining a fix. The two cross-sectional Requirementsstumps or tree-stumps on the that northing were pa andrtially easting or fully GNSS obscured accuracy by loggin of theg residuals); estimate and, of the(e) tree-stump position of the diametersdamage were classification measured (undamaged using a me tree-stuasuringmps tape or and tree-stumps were later damaged averaged by to machinery). obtain the tree-stump mean centre of the tree-stumps dimensions was at single centimetre level when obtaining a fix. The two diameterRequirements (; cm). Figure on the2 provides northing an and overview easting GNSS of the accura distributioncy of the estimate for the measuredof the position plots. of the centre cross-sectional diameters were measured using a measuring tape and were later averaged to obtain of the tree-stumps dimensions was at single centimetre level when obtaining a fix. The two cross-sectional the tree-stump mean diameter (D; cm). Figure2 provides an overview of the D distribution for the diameters were measured using a measuring tape and were later averaged to obtain the tree-stump mean measureddiameter plots. (; cm). Figure 2 provides an overview of the distribution for the measured plots.

Figure 2. Mean tree-stump diameter distribution for the tree-stumps measured in the field.

The presence or absence of root- and butt-rot, tree-stump occlusion, and tree-stump damage were Figure 2. Mean tree-stump diameter distribution for the tree-stumps measured in the field. visually Figureassessed. 2. MeanThe presence tree-stump of root- diameter and distributionbutt-rot in the for thestump tree-stumps was assessed measured based in on the discoloration field. and presenceThe presence of or decay. absence The of pr root-oportion and butt-rot, of tree-stumps tree-stump among occlusion, the different and tree-stump classes are damage presented were in TableThevisually 1. presence assessed. or The absence presence of root- of root- and and butt-rot, butt-rot tree-stump in the stump occlusion, was assessed and based tree-stump on discoloration damage were visuallyand assessed.presence of The wood presence decay. The of root-proportion and butt-rotof tree-stumps in the among stump the was different assessed classes based are onpresented discoloration in and presenceTable 1. of wood decay. The proportion of tree-stumps among the different classes are presented in Table1.

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Forests 2018, 9, x FOR PEER REVIEW 4 of 14 Table 1. Summary of the field-defined tree-stump classes in terms of number of tree-stumps (n) and Table 1. Summary of the field-defined tree-stump classes in terms of number of tree-stumps (n) and percentage of the total number (%). percentage of the total number (%).

RotRot Presence Presence Stump Stump Occlusion Occlusion Damage Damage VariableVariable AbsentAbsent PresentPresent Not Not Occluded Occluded Occluded Intact Intact Damaged Damaged n n 231231 3434 237 28 28 227 227 38 38 %% 87%87% 13%13% 89% 11% 11% 86% 86% 14% 14%

FigureFigure3 offers3 offers a a visual visual comparisoncomparison of of the the differ differentent classes classes adopte adoptedd for the for classification the classification of the of thetree-stumps. tree-stumps.

FigureFigure 3. 3.Different Differentclasses classes usedused forfor thethe field-basedfield-based classification classification of of the the tree-stumps. tree-stumps. Note Note that that thethe coveredcovered tree-stump tree-stump was was visible visible onlyonly afterafter the manu manualal removal removal of of the the logging logging residuals residuals over over it. it.

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Forests 2018, 9, x FOR PEER REVIEW 5 of 14 2.3. Remotely Sensed Data 2.3. Remotely Sensed Data UAV imagery was acquired wall-to-wall for the stand of interest on 23 April 2017 within a single flight lastingUAV imagery about eightwas acquired minutes wall-to-wall that conducted for the in stan open-skyd of interest conditions on 23 April in the 2017 middle within of a thesingle data (12:00flight a.m.). lasting By about performing eight minutes the UAV that imagery conducted acquisition in open-sky shortly conditions after the in the harvest, middle the of effectsthe data of (12:00 natural discolorationa.m.). By performing of the tree-stumps the UAV overimagery time acquisition were reduced, shortly hence after ensuring the harvest, large the contrast effects betweenof natural the tree-stumpsdiscoloration and of the tree-stumps background over and time between were reduced, healthy hence wood ensuring and root- large and contrast butt-rot between (see the Figure tree-4). UAVstumps imagery and wasthe acquiredbackground using and the between integrated healthy camera wood on and a DJI root- Phantom and butt-rot 4 quadcopter (see Figure (DJI, 4). Shenzhen, UAV imagery was acquired using the integrated camera on a DJI Phantom 4 quadcopter (DJI, Shenzhen, China), which is fitted with a 1/2.3-inch complementary metal-oxide semiconductor (CMOS) sensor China), which is fitted with a 1/2.3-inch complementary metal-oxide semiconductor (CMOS) sensor with with 12.4 mega pixels [19]. The UAV flights were conducted at an average altitude of 40 m above 12.4 mega pixels [19]. The UAV flights were conducted at an average altitude of 40 m above ground, ground, resulting in a ground sampling distance of 1.7 cm. The flight altitude was determined by the resulting in a ground sampling distance of 1.7 cm. The flight altitude was determined by the need of high need of high resolution data to detect tree-stumps, while clearing retention and ensuring the resolution data to detect tree-stumps, while clearing retention trees and ensuring the coverage of the entire coveragestand within of the a entiresingle flight. stand The within effective a single overlap flight. betw Theeen effectivethe images overlap was 3.9, between meaning thethat images each point was on 3.9, meaningthe ground that was each visible point on on average the ground in approximately was visible four onaverage images. in approximately four images.

Figure 4. Overview of the UAV data used for the purpose of this study: on the left the orthophoto Figure 4. Overview of the UAV data used for the purpose of this study: on the left the orthophoto with ground sampling distance (GSD) of 1.7 cm, and on the right the slope raster of GSD = 3.5 cm that with ground sampling distance (GSD) of 1.7 cm, and on the right the slope raster of GSD = 3.5 cm that was derived from the digital surface model. The slope raster is included exclusively for visualization was derived from the digital surface model. The slope raster is included exclusively for visualization purposes as it clearly shows the shape of the tree-stumps. purposes as it clearly shows the shape of the tree-stumps. Photogrammetric processing of the UAV imagery was performed using Agisoft Photoscan (version 1.3.3,Photogrammetric Agisoft, Saint Petersburg, processing Russia) of [20]. the UAVFive ground imagery control was points performed were measured using Agisoft using the Photoscan Topcon (versionRTK GNSS 1.3.3, (Topcon, Agisoft, Tokyo, Saint Japan), Petersburg, and were Russia) marked [20 in]. the Five field. ground These were control then points used in were Photoscan measured to usingensure the an Topcon accurate RTK geolocation GNSS (Topcon, and avoid Tokyo, distortion Japan), in the and resulting were marked models. in The the dense field. point These cloud were was then usedgenerated in Photoscan using high to ensurequality settings an accurate with aggressive geolocation filtering and avoidin order distortion to obtain a in high the resolution resulting digital models. Thesurface dense model point (DSM) cloud with was minimal generated noise. using The highphotogrammetric quality settings processing with aggressiveresulted in the filtering export of in two order to obtainraster products, a high resolution namely the digital DSM and surface a three-band model (DSM)(i.e., red, with green, minimal and blue) noise. orthophoto The photogrammetric at a resolution processingof 3.5 cm resultedand 1.7 cm, in therespectively. export of Figure two raster 4 provides products, some namely visual insight the DSM in the and high a three-band spatial resolution (i.e., red, characterizing these data. green, and blue) orthophoto at a resolution of 3.5 cm and 1.7 cm, respectively. Figure4 provides some visual insight in the high spatial resolution characterizing these data.

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3. Method The following sub-sections describe in detail the steps adopted to detect, segment, classify, and measure tree-stumps. The parametrization of the proposed method was mostly performed according to a trial and error approach. Thus, some of the used values may be unique to the study area, and therefore require some tuning when applying the algorithm to new data. The entire methodology was developed using R software [21], using commonly used geospatial packages, such as ‘sp’ [22], ‘rgdal’ [23], and ‘raster’ [24].

3.1. Detection and Segmentation Raster pre-processing: the input data consisted of UAV-derived orthophoto and DSM. A digital terrain model (DTM) was initially generated from the DSM based on a local minima filter of size 22.5 cm × 25.5 cm. The DTM was then subtracted from the DSM, thus obtaining the stump height model (SHM), which is a digital elevation model representing the height of any object above the ground, including objects tree-stumps. Pixels with a height above ground of less than 2 cm or larger than 1 m were discarded from any further analysis. The red band of the orthophoto and the SHM were then combined through a multiplication, thus generating a raster representing simultaneously the spectral and 3D information. Throughout this study, this raster will be referred as red-SHM. Finally, this raster was tessellated into 100 m2 square tiles and further processing was then performed at tile level. Seed search: the tree-stump detection was first initialized by searching for seeds through a local maxima search using the focal function implemented in the R package ‘raster’ [24] on the red-SHM and with a moving window of size 93.5 cm × 93.5 cm. The choice of this window size was determined as the one allowing for the detection of most stumps in preliminary tests. Even though it generally resulted in an over-detection, the seeds that were not stump were automatically removed in the following step. Iterative region growing: once the tree-stump seeds were located, an iterative region growing procedure was performed for of all of the seeds simultaneously within each single 100 m2 tile. At each iteration, a buffer of size equal to the raster resolution (i.e., 1.7 cm) was computed for all of the seeds, and within this, all of the pixels with red-SHM values >15th percentile of all the tree-stump segments within the tile were considered valid for defining the tree-stump in the next iteration. While this region growing resulted in the production of stump polygons, it also resulted in the removal of part of the original seeds that were not representing stumps. This was the case for those seed buffers that had red-SHM values < 15th percentile of the whole set of polygons within the tile. The remaining pixels were discarded from further growth. The low percentile was defined by a trial-and-error approach as the most suitable since larger values led to excessively conservative growth. Furthermore, within each growth iteration, the occurrence of small isolated polygons was reduced by removing any polygon that was not intersecting the original tree-stump polygon as these were generally representing scattered woody debris. The total number of iterations was set to 15 to correspond to a maximum growth in radius to 25.5 cm, which corresponds well with the maximum radius that was measured in the field (i.e., 23.2 cm), while limiting the possibility to grow in regions outside the tree-stumps. Refinement: after the simultaneous segmentation of all tree-stumps within the tile, some of the tree-stumps still presented anomalies in their shape, with portions of the tree-stump not being correctly segmented. Thus, a tree-stump level refinement step was performed by thresholding pixels within a varying buffer size proportional to the polygon area (buffer radius = radius of a circumference with same area of the tree-stump segment). Within each buffer, all of the pixels that had red-band values smaller than the 90th percentile, and slope larger than the 90th percentile of the respective variables were discarded from the raster. This thresholding aimed at removing those pixels that were not representing stumps because they were not meeting the stump definition as objects with flat surface (slope threshold) and brighter (red-band threshold) than the immediate surroundings. The choice of a conservative threshold (90th percentile) was driven by the need of discarding only those pixels that were surely not part of a stump. The remaining pixels were then aggregated into refined polygons. Forests 2018, 9, 102 7 of 14

Clean up: The resulting tree-stump segmentation resulted in most of the tree-stumps being segmented, however the described procedure produced a number of polygons that were clearly not tree-stumps.Forests 2018, 9, x FOR These PEER REVIEW consisted mostly of logging residues and rocks. Thus, in the7 final of 14 step, the tree-stump segments were classified to tree-stump or non-tree-stump objects at stand level. This was performedsegments by were using classified the random to tree-stump forest or algorithm non-tree-stump implemented objects at stand in the level. R This software, was performed ‘randomForest’ by using the random forest algorithm implemented in the R software, ‘randomForest’ package [25]. A total package [25]. A total of 31 explanatory variables were extracted for each segment, including: of 31 explanatory variables were extracted for each segment, including: spectral, dimensional, and spectral, dimensional, and geometrical variables. Out of all the segmented tree-stumps, a sample of geometrical variables. Out of all the segmented tree-stumps, a sample of 500 randomly selected and 500 randomlymanually classified selected tree-stumps and manually was us classifieded as training tree-stumps data for a random was used forest as classifier. training The data classifier for a was random forestthen classifier. applied The to classify classifier the waswhole then set appliedof segments to classify and those the segments whole set that of segmentswere not classified and those as segmentstree- that werestumps not were classified discarded as from tree-stumps any further were analysis. discarded from any further analysis. FigureFigure5 provides 5 provides a schematic a schematic representation representation ofof thethe described steps steps adopted adopted for forthe thedetection detection and and segmentationsegmentation of theof the tree tree tree-stumps. tree-stumps.

FigureFigure 5. Schematic 5. Schematic representation representation ofof thethe stepstep adop adoptedted in in the the proposed proposed algorithm algorithm for tree-stump for tree-stump detectiondetection and and segmentation. segmentation. The The example example is provided for for one one of ofthe the 100 100 m2 mtiles2 tiles that thatwere were initially initially clipped.clipped. Throughout Throughout steps steps 1 to1 to 4, 4, the the green green polygonspolygons represent represent potential potential tree-stumps tree-stumps while while in step in 5 step 5 theythey represent represent the finalthe final detected detected tree-stumps, tree-stumps, while while the redthe polygonsred polygons represent represent non-tree-stump non-tree-stump objects. objects. A detailed description of the different steps is provided in Section 3.1. A detailed description of the different steps is provided in Section 3.1.

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The tree-stump detection was then validated by comparison with field measured tree-stumps. A correct match was defined if a segmented polygon was within 1 m from a field measured tree-stump. The overall classification accuracy, omission error, and commission error were assessed for four different strata in the stand: (1) All of the stumps in the stand independent of condition; (2) Only clearly visible stumps, obtained by removing occluded stumps from the dataset; (3) Only undamaged stumps where damaged tree-stump class was removed from the dataset observations; and, (4) only visible and non-damaged stumps that were defined by removing the occluded and damaged tree-stump classes from the dataset. Furthermore, for stratum 1, the detection accuracy was also assessed for six 10 cm wide tree-stump D classes (i.e., 0 cm–10 cm, . . . , 50 cm–60 cm).

3.2. Machine Learning Random Forest Classification A machine learning random forest classification was adopted for classifying the tree-stumps, according to the presence or not of root- and butt-rot. For this purpose, only the first strata was analysed. Due to the small number of tree-stumps with root- and butt-rot in the field data (34 or 13% of the total number), the field data was integrated with 137 manually classified tree-stumps with root- and butt-rot presence. This resulted in a more balanced dataset composed of a total of 309 tree-stumps out of which 156 had presence of root- and butt-rot. A total of 17 explanatory variables were extracted for each single tree-stump segment. These included spectral variables obtained from the orthophoto, including: band (red, green, and blue) mean values and standard deviation. Textural variables (mean and standard deviation of mean, variance, homogeneity, contrast, dissimilarity, and entropy) were also computed from the red band using the ‘glcm’ package in R [26]. The out-of-bag data (oob) from the random forest was used for validation

3.3. Diameter Measurement and Modelling Additional variables were extracted from the segmented stumps. Among these, the dimensional variables included: area (cm2), perimeter (cm), length (polygon length in the y axis; cm), width (polygon length in the x axis; cm), and ellipse diameter (diameter of the circumference with area equal to that of an ellipse fitted to the vertexes of the polygon; cm). Furthermore, the geometrical variables included: asymmetry index (length to width ratio), roundness index (area to perimeter ratio), and compactness index (ratio between the area and the area of circle with same perimeter). The ellipse diameter was then used as the direct measurement of D, while the model predicted value of D was obtained by fitting a univariate linear model linking field measured D and the ellipse diameter. The direct measurement and the model predictions were then assessed by independent validation and leave one out cross validation (loocv), respectively. The presence of random and systematic errors were assessed by the root mean square error (RMSE), the mean difference (MD), and their respective values as percentage of the mean (RMSE% and MD%).

4. Results

4.1. Tree-Stump Detection The assessment of the accuracy of tree-stump detection revealed that, depending on the stratum considered, the tree-stumps were detected with an overall accuracy ranging from 67.9% (stratum 1) to 79.9% (stratum 4). The omission errors decreased from 32.1% in stratum 1 to 25.7%, 28.2%, and 20.1% when tree-stumps that were either covered, damaged, or covered and damaged were removed from the sample in strata 2, 3, and 4, respectively. An opposite trend was observed for the commission errors which increased from 26.0% to 34.7% going from stratum 1 to 4. Figure6 summarizes the overall accuracy, omission, and commission errors for the different strata that were adopted in this study. Forests 2018, 9, 102 9 of 14 Forests 2018, 9, x FOR PEER REVIEW 9 of 14

FigureFigure 6. 6.Summary Summary of of the the tree-stump tree-stump detectiondetection validation,validation, including the the overa overallll accuracy, accuracy, omission, omission, andand commission commission errors errors for for the the four four different different strata.strata.

When looking at the detection accuracy for different tree-stump diameter () classes (see Table 2) in When looking at the detection accuracy for different tree-stump diameter (D) classes (see Table2) stratum 1, the overall accuracy increased steadily when increasing the tree-stump diameter from a in stratum 1, the overall accuracy increased steadily when increasing the tree-stump diameter from minimum of 28.6% to a maximum of 100% for the classes in the ranges 0 cm–10 cm and 50 cm–60 cm, a minimum of 28.6% to a maximum of 100% for the D classes in the ranges 0 cm–10 cm and 50 cm– respectively. 60 cm, respectively. Table 2. Accuracy assessment for the tree-stump detection according to six different 10 cm wide Table 2. Accuracy assessment for the tree-stump detection according to six different 10 cm wide D classes for stratum 1. classes for stratum 1. Class. (cm) Measured (n) Detected (n) Omitted (n) Overall Accuracy (%) Omission Error (%) D Class.0–10 (cm) Measured7 (n) Detected 2 (n) Omitted 5 (n) Overall28.6 Accuracy (%) Omission 71.4 Error (%) 10–200–10 749 225 24 5 51.0 28.6 49.0 71.4 10–2020–30 4987 2554 33 2462.1 51.037.9 49.0 20–30 87 54 33 62.1 37.9 30–4030–40 7979 63 63 16 16 79.7 79.7 20.3 20.3 40–5040–50 4141 34 34 7 7 82.9 82.9 17.1 17.1 50–6050–60 22 2 2 0 0 100.0 100.0 0.0

4.2.4.2. Root- Root- and and Butt-Rot Butt-Rot Machine Machine Learning Learning ClassificationClassification TheThe five five most most importantimportant variables variables according according to tothe the random random forest forest variable variable importance importance ranking ranking were werespectral spectral and andtextural textural variables. variables. More specifically, More specifically, in order in of order importance, of importance, they were: they the were: mean theof the mean blue of theband, blue the band, mean the variance, mean variance, the standard the standarddeviation deviationof the variance, of the the variance, standard the deviation standard of deviation the blue band, of the blueand band, the average and the of averagethe mean. of The the mean.results Theregarding results the regarding detection the of tree-stumps detection of with tree-stumps root- and with butt-rot root- andrevealed butt-rot that revealed for the thatstratum for the1, a stratumtotal of 19 1, atree-st totalumps of 19 tree-stumpsor 56% of the or number 56% of of the measured number tree-stumps of measured tree-stumpswith root- and with butt-rot root- and were butt-rot correctly were detected. correctly For detected. the tree-stumps For the without tree-stumps root- withoutand butt-rot, root- the and butt-rot,detection the rate detection increased rate to increased70% of the tototal 70% number of the of total field number measured of field tree-stumps measured with tree-stumps no root- and with butt- no root-rot. and butt-rot.

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Forests 2018, 9, x FOR PEER REVIEW 10 of 14 The confusion matrix obtained through loocv for the classification of the presence or absence of root- andThe butt-rot confusion on matrix the tree-stumps obtained through (Table 3loocv) revealed for the anclassification overall accuracy of the presence and kappa or absence value of of root- 78.3% andand 0.6%, butt-rot respectively. on the tree-stumps The class-specific (Table 3) revealed classification an overall accuracy accuracy for and tree-stumps kappa value without of 78.3% and and with 0.6%, root- andrespectively. butt-rot was The 74.5% class-specific (114 out classification of 153) and accuracy 82.1% (128 for tree-stumps out of 156), without respectively. and with root- and butt-rot was 74.5% (114 out of 153) and 82.1% (128 out of 156), respectively. Table 3. Confusion matrix for the classification of presence or absence of root- and butt-rot on theTable tree-stumps. 3. Confusion matrix for the classification of presence or absence of root- and butt-rot on the tree-stumps. No. Rot Root- and Butt-Rot Classification Accuracy No. Rot Root- and Butt-Rot Classification Accuracy No rot 114 39 74.5% Root- andNo butt-rot rot 11428 39 128 74.5% 82.1% OverallRoot- Accuracy and butt-rot 28 128 82.1% 78.3% Overall Accuracy 78.3%

AA heatmap heatmap ofof thethe stumps stumps classified classified with with root- root- and andbutt-rot butt-rot was obtained was obtained by applying by applyingthe trained the trainedrandom random forest forestclassifier classifier to all of to the all ofstumps the stumps that were that segmented were segmented within withinthe stand the (Figure stand (Figure7). This7 ). Thisrepresents represents an example an example of a offurther a further output output from fromthe proposed the proposed analysis, analysis, which could which be could used bein better used in betterunderstanding understanding the cause the causeand distribution and distribution patterns patternsof root- and of root-butt-rot. and butt-rot.

Figure 7. Heat map of the stumps with presence of root- and butt-rot calculated using kernel density. Figure 7. Heat map of the stumps with presence of root- and butt-rot calculated using kernel density. The warmer colours represent areas with largest density of stumps with root- and butt-rot, while the The warmer colours represent areas with largest density of stumps with root- and butt-rot, while the colder colours represent areas where it is less prevalent. colder colours represent areas where it is less prevalent. 4.3. Diameter Direct Measurement and Modelling 4.3. Diameter Direct Measurement and Modelling The results concerning the direct measurement of the tree-stump diameter showed that the most correlatedThe results variable concerning was the theellipse direct diameter measurement (Pearson’s of correlation the tree-stump coefficient diameter = 0.74). showed Thus, this that variable the most correlatedwas used variable for the wasdirect the measurement, ellipse diameter as well (Pearson’s as for the correlation modelling coefficientapproach. As = 0.74). shown Thus, in Table this variable4, the wasdirect used measurement for the direct approach measurement, had a as wellof 7.5as cm for (23.6% the modellingof the mean), approach. while this diminished As shown to in 5.4 Table cm 4, thein direct the modelling measurement approach. approach The haddifference a RMSE betweeof 7.5n cmthe (23.6%two methods of the mean),was more while pronounced this diminished when to 5.4comparing cm in the the modelling . The approach.direct measurement The difference method betweensystematically the two under-estimated methods was the more tree-stump pronounced by whenapproximately comparing 3.3 the cmMD and. that The difference direct measurement was significant method (p-value systematically < 0.000), whereas under-estimated the modelling the tree-stumpapproach Dwasby not approximately affected by significant 3.3 cm and systematic that difference error (p-value was significant= 0.99). Such ( pa-value difference < 0.000), between whereas the two methods was also clearly visible in the scatterplots shown in Figure 8.

Forests 2018, 9, 102 11 of 14 the modelling approach was not affected by significant systematic error (p-value = 0.99). Such a difference between the two methods was also clearly visible in the scatterplots shown in Figure8. Forests 2018, 9, x FOR PEER REVIEW 11 of 14

Table 4. Summary of the accuracy assessment of the measurement of the mean diameter using either Table 4. Summary of the accuracy assessment of the measurement of the mean diameter using either the direct measurement approach or the modelling approach. The results are reported for RMSE, MD, the direct measurement approach or the modelling approach. The results are reported for , , and their respective values as percentage of the mean observed mean diameter (RMSE%, MD%). and their respective values as percentage of the mean observed mean diameter (%, %).

MeasurementMeasurement Method MethodRMSE(cm) (cm)RMSE% % MD(cm)(cm) MD% % DirectDirect measurement measurement 7.57.5 23.6%23.6% 3.33.3 10.7% 10.7% ModelModel 6.46.4 20.2% 20.2% −−0.004 −0.013%−0.013%

FigureFigure 8. 8. ScatterplotsScatterplots of of the the mean mean between between two two fi fieldeld measured measured cross-sectional cross-sectional diameters diameters ( (D)) against against thethe diameters diameters obtained obtained either either through through direct direct meas measurementurement (left (left panel) panel) or or through through a a diameter diameter model model (right(right panel). panel). For For the the direct direct measurement measurement method, method, the the xx-axis-axis represented represented the the diameter diameter of of the the circumferencecircumference with with area area equal equal to to an an ellipse ellipse fitted fitted to to the the vertices vertices of of the the segmented segmented polygons polygons (Diameter (Diameter ellipse). For the modelling method, the x-axis represented the loocv predicted diameter. ellipse). For the modelling method, the x-axis represented the loocv predicted diameter.

5.5. Discussion Discussion ThisThis study study introduced introduced a amethodology methodology for for the the use use of ofUAV UAV imagery imagery in inpost-harvest post-harvest site site assessment assessment to obtainto obtain tree-stump tree-stump information. information. This represents This represents a case awhere case whereUAVs UAVsare possibly are possibly currently currently the only the remote only sensingremote data sensing acquisition data acquisition platform platformallowing for allowing the retrieval for the of retrieval such detailed of such information detailed information with acceptable with levelsacceptable of accuracy levels at of specific accuracy poin at specificts in time points following in time the following harvest. Un thetil harvest. now, only Until very now, few only studies very used few tree-stumpstudies used information tree-stump obtained information from obtainedUAV data, from however UAV data,the possibility however to the retrieve possibility it cost-effectively to retrieve it couldcost-effectively lead to the coulddevelopment lead to of the applications development where of applications its use couldwhere be beneficial its use on could a wider be beneficial scale. Thus, on thisa wider paper scale. also proposed Thus, this some paper of alsothese proposed applications some, providing of these insights applications, on the providingpotential of insights UAV-derived on the tree-stumppotential of information. UAV-derived tree-stump information. ThisThis paper paper deals deals with with a a relatively new new research area, area, hence the detection accuracy and and diameter measurementmeasurement results results can can only only be be compared compared with with those those of of the the one one known known study study by by Samiappan, Samiappan, et et al. al. [7]. [7]. TheThe overall overall accuracy accuracy of of the the proposed proposed detection detection me methodthod for for stratum stratum 1 1was was of of 67.9%, 67.9%, where where all all of of the the harvestingharvesting residues residues are are retained retained on site. on site.When When lookin lookingg at the other at the strata, other the strata, detection the detectionaccuracy increased accuracy upincreased to 79.9% up in tostratum 79.9% 4 in (visible stratum and 4 non-damage (visible andd non-damaged stumps) where stumps) the stumps where are thenot stumpsdriven on. are The not remainingdriven on. strata The remainingrepresented strata intermediate represented conditions intermediate between conditions strata 1 and between 4 both in strata terms 1 of and disturbance 4 both in toterms the stumps of disturbance (i.e., damage to the and stumps occlusion), (i.e., damageas well as and for occlusion),the detection as rates, well aswhich for thedecreased detection with rates, an increasewhich decreased in disturbance. with anIt can increase be expected in disturbance. that the proposed It can be methods expected may that not the work proposed well in methods cases where may substantialnot work wellparts inof casesthe trees where are left substantial standing (i.e., parts sele ofctive the harvest trees are or leftthinning), standing as some (i.e., of selective the tree stumps harvest wouldor thinning), be occluded as some by ofthe the canopy. tree stumps The values would that be occludedwere obtained by the for canopy. the overall The values accuracy that were were comparable to those that were reported by Samiappan, et al. [7] who found values in the range 41.0–77.3%. Nevertheless, in their study, the omission (16.3–20.4%) and commission errors (12.8–79.5%) were slightly lower than those that were found in this study (20.1–32.1% and 26.0–34.7% for omission and commission errors, respectively). The omission errors were mainly caused by severe discoloration or non-visibility of the tree-stump surface, while the commission errors were mainly caused by the detection of a number of

Forests 2018, 9, 102 12 of 14 obtained for the overall accuracy were comparable to those that were reported by Samiappan, et al. [7] who found values in the range 41.0–77.3%. Nevertheless, in their study, the omission (16.3–20.4%) and commission errors (12.8–79.5%) were slightly lower than those that were found in this study (20.1–32.1% and 26.0–34.7% for omission and commission errors, respectively). The omission errors were mainly caused by severe discoloration or non-visibility of the tree-stump surface, while the commission errors were mainly caused by the detection of a number of non-tree-stump objects, such as logging residuals and rocks. Omission error increased with decreasing stump diameter, indicating that higher resolution cameras than the one that was used here (12.4 megapixels) might improve detection. Both omission and commission errors were possibly attributable to the large variability present in the spectral information. The intermediate random forest classification step was a drawback of the proposed methodology as it introduces a manual step in an otherwise automated routine. Nevertheless, in an operational application, the input training data for the random forest could be derived from a tree-stump machine-learning library built on previously acquired datasets, thus removing the need for this manual step. It is also important to acknowledge that this study presented the results that were obtained in a single stand covered within a single UAV flight, thus under uniform light and atmospheric conditions. Because of the large variations in light, azimuth, and atmospheric conditions that can characterize different UAV image based data acquisition, the methods that were proposed in this study may require some tuning when applied to new datasets. Some of these negative effects could be alleviated by image processing and better understanding of the most suitable conditions for different UAV applications. In an attempt to provide some indications on the potential applications for which UAV-derived tree-stump information could be used, this study evaluated the possibility to classify root- and butt-rot and measure tree-stump diameter. Despite a relatively low detection rate of tree-stumps with root- and butt-rot (56%), this study proved that once the tree-stump was detected it could be classified with root- and butt-rot rather accurately (82.1%). These results are encouraging for spatially characterizing the presence of rot (see Figure7), thus providing improved information of the spatial extent of root- and butt-rot within a stand. Such information can potentially be used in designing regeneration strategies that where species are planted in different areas of the stand, and consequently reducing the spread of the root from the stumps and roots to the newly established trees. The results that were obtained for tree-stump D measurement revealed that despite a rather similar RMSE between the direct measurement (7.50 cm; 23.6% of the mean) and modelling methods (6.4 cm; 20.2% of the mean), the former was affected by a significant systematic error (MD) of 3.3 cm (10.7% of the mean). It is important to note that because of the irregular shape of the tree-stumps, the field reference diameter may have been affected by errors since it was calculated as the mean of the two cross-sectional diameters in the N-S and E-W directions. These two measures may not be enough to describe the diameter variations at tree-stump level. Thus, future field campaigns should rather focus on measuring the actual basal area through the use of automatically scaled and segmented tree-stump images. Overall, the smaller RMSE and the lack of systematic errors in the modelling method, suggested this to be a more suitable approach. Thus, further effort should be dedicated to obtaining more precise diameter measurements by using circle-fitting methods that allow for outlier removal, such as the random sample consensus (RANSAC) algorithm [27]. A more accurate diameter estimate could potentially be matched with the harvester recorded cut diameter, potentially complementing or solving the challenge of single-tree location [17,18]. The contribution of this study lies in the introduction of novel levels of information obtainable from post-harvest UAV imagery. If on one hand, the results were encouraging, it is important to confirm and improve these results on a variety of stands and using different detection and segmentation algorithms. In particular, it would be beneficial to compare the performances of the proposed method against image pattern recognition algorithms [7]. Among the outputs from the proposed methodology, the possibility of mapping the presence and distribution of root- and butt-rot within a stand merits further investigation. [7] The retrieval of such information has been economically unfeasible up-to-date. Forests 2018, 9, 102 13 of 14

However, the possibility to generate root- and butt-rot distribution maps with relatively inexpensive methods may prove to be valuable in supporting a more precise decision making in forestry. Further research should also shed new light on the relationships between the presence of root- and butt-rot and micro-climatic and micro-topographic conditions. Such relationships could then enable the estimation and mapping of root- and butt-rot at a landscape level.

6. Conclusions Overall, this study showed that UAVs may provide accurate information on tree-stump location, categorical properties, and dimensions. Despite the need of further improvement and the validation of the proposed algorithm, the results were encouraging for further application. The results were reasonably accurate and when seen in combination with other uses of the same UAV data (e.g., soil disturbance or harvest residue availability), the proposed method could provide a more comprehensive assessment of post-harvest sites while increasing the cost-effectiveness of using UAVs.

Acknowledgments: This research was funded by the Research Council of Norway, PRECISION project (project number ES604162). Author Contributions: S.P., B.T., and R.A. conceived the idea of this study, S.P. and B.T. performed the field and UAV data acquisition, S.P. performed the experiment and wrote the paper, B.T. and R.A. provided comments on the paper. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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