Robust Single-Image Tree Diameter Estimation with Mobile Phones

Abstract

Ground-based forest inventories are reliable methods for forest carbon monitoring, reporting, and verification schemes and the cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else require manual intervention or offline processing to identify and measure tree trunks. In this paper, we designed an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate the trunk diameter automatically from a single image in complex and realistic field conditions. We implemented our design and built it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational costs to run on this commodity platform in near real-time. We evaluated our app in 3 different forests across 3 seasons and found that in a corpus of 97 sample tree images, our app estimated the trunk diameter with a RMSE of 3.7 cm ($R^2$ = 0.97; 8.0% mean absolute error) compared to manual DBH measurement. It achieved a 100% tree detection rate while reducing the surveyor time by up to a factor of 4.6. Our work contributes to the search for a low-cost, low-expertise alternative to terrestrial laser scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating the trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurements outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions.

1. Introduction

Ground-based forest inventories are key components in the study and restoration of forest carbon. Reforestation and anti-deforestation incentive programs at the national and international levels often specify that project monitoring, reporting, and verification must be performed in forest plots in situ to measure the actual degree of carbon sequestration achieved [1,2,3]. Newer remote sensing technology, such as aerial laser scanning and satellite imagery, allows data collection for large areas, but it fundamentally relies on calibration from ground-based forest inventory surveys [4,5].

The standard ground-based forest inventory technique is the manual inventory. This process typically involves mapping out sample plots and measuring inventory variables such as height, species, and trunk diameter at breast height (DBH) by hand [6]. The most mature ground-based alternative to the manual forest inventory is terrestrial laser scanning (TLS), which uses high-end surveying LiDAR to scan forest environments. These instruments cost USD 50,000–125,000 [7,8,9] and require a high degree of technical expertise to process the resulting point cloud data. More recently, low-cost (USD < 1000), short-range (3–5 m) LiDAR on mobile phones and tablets, originally intended for augmented reality applications, has been found suitable for measuring DBH and tree locations in certain forest environments [10,11,12,13,14,15]. Other researchers have used structure from motion and stereography to create point clouds or depth maps with only handheld color cameras, though they find that these systems require longer processing and data collection times and do not match the depth map accuracy of mobile LiDAR [12,16]. This recent research on mobile devices has been aimed at reducing barriers—in time, money, and expertise—to perform forest inventories relative to TLS or the manual process.

A continuing challenge in deploying mobile camera and phone technology for forest inventories lies in improving the performance and usability of these systems in real-world scenarios. These technologies are needed in diverse forest environments, including those with occlusion from branches, leaves, and low-lying vegetation. Recent work does not focus on these environments [10,13,17], requires manual intervention [11], or uses processing pipelines that are run offline for several hours on a powerful desktop computer [15,16] to identify the tree trunks to be measured within each image scan. There are also ease-of-use limitations: almost all existing mobile systems require the user to walk in a prescribed path around each tree to scan it from every angle, though as Cakir et al. [11] note, in the case of “thorns, bushes, tall grasses, etc., it becomes physically difficult to walk around and between individual trees, making the scanning challenging in some forest conditions”. For mobile systems to be viable alternatives to TLS or the manual process, they need to be more robust, usable, and efficient (in surveying and computation time) in complex forest environments.

In this work, focusing on estimating trunk diameter, we consider the occlusions of a forest understory as the primary use case, resulting in a substantially different design than has previously been attempted. We built an Android app for a commodity-mobile phone equipped with a LiDAR sensor that requires users to capture only a single depth image per tree. The images are automatically processed on the phone with an algorithm that we designed that first segments the images, i.e., separates the trunk from surrounding leaves, branches, and low-lying vegetation, and then automatically computes and saves an estimate of the trunk diameter. The processing takes place in near real-time, allowing user feedback without disrupting the surveying process. We compare the app’s diameter estimates to DBH measurements obtained manually through the traditional forest inventory method and find that our system is around four times faster, while incurring a mean absolute error of 8% ($R^2=0.97$; RMSE = 3.7 cm).

2. Materials and Methods

2.1. Assumptions

While we believe that our method improves over past approaches in estimating tree trunks in complex forest environments, it relies on some important assumptions. The principal one is that trunks consist of single (roughly cylindrical) stems. Additional assumptions, as well as proposed steps to remove them, are discussed in detail in Appendix A.1.

It is important to note that our algorithm does not explicitly estimate DBH—that is, it does not identify the cross-section of the trunk 1.3 m above the ground level and estimate the diameter of that cross-section. Rather, to maintain robustness to occlusions, burls, and low-growing branches that may occur at breast height, the algorithm computes an estimate of this diameter based on the entire range of the trunk visible in the captured depth image. We discuss this choice further in Section 5, and in the evaluation (Section 3.3), we report the error of this estimator against DBH measurements made using traditional methods.

2.2. App Design and User Experience

In this work, we design an app for an Android phone with a depth sensor. The app allows users to walk around a forest, taking pictures of each tree as they pass it. The main app screen is a continuously updated color camera preview, similar to one that users may be accustomed to in a standard camera app (Figure 1 left). The preview screen has a “Capture“ button and two lines overlaid on the image dividing it into thirds, guiding users to center the tree in the image. When the user points the phone at a tree trunk and selects “Capture,” the app saves the color (RGB) and depth frames from the camera sensor and initiates the processing algorithm. This does not require an internet or cell connection; the algorithm is run locally on the phone.

We designed our system for speed, both in terms of user input time and computational time. Thus, the user only needs to take one picture of the tree from 1–2 m away. They do not need to capture the tree from multiple angles and can stand in whichever spot near the tree is most accessible, according to understory conditions. The algorithm itself completes in under a second, allowing two-way communication between the user and the app. When the algorithm completes, the estimated trunk boundaries are reprojected onto the RGB image and immediately displayed back to the user (Figure 1 right). The user can then assist the app with a one-click confirmation, selecting “Save” or “Redo” based on whether the algorithm appears to have successfully identified the trunk. This confirmation step is intended to be a quick check to eliminate cases where the algorithm is widely off or incorrectly identifies another object in the frame as the tree; the user is not expected to carefully judge the accuracy of the diameter estimate. The fact that the image processing takes place locally in near-real time also allows the app to assist the user, displaying algorithm errors and directing the user to adjust their position if necessary. We further discuss the effects of this confirmation step on the tree detection rate in Section 3.1.

2.3. Image Processing Algorithm

In the following sections, we present our core image processing algorithm for identifying trunks and estimating their diameters, which is automatically invoked when the user taps the app’s “Capture“ button. In Figure 2, we demonstrate each step of the algorithm on a sample tree image with considerable occlusion (the original RGB and depth images are shown in Figure 2a).

2.3.1. Step 1: Approximate Trunk Depth

To begin, we make a rough estimate of the trunk depth, which guides the subsequent processing. We expect the trunk to contain a large set of points of similar depth (unlike vegetation or branches, which will either be of relatively inconsistent depth or small size). We make the natural assumption that the user will attempt to center the tree trunk in the image, and we also provide guiding lines in the app to help the user do so. To estimate trunk depth, we slice the image vertically into thirds, bucket the depth values in the center third of the image into 3 cm ranges, and take the mode bucket as the approximate trunk depth ${\delta }_m$. We then filter the image for pixels whose depth value is within $\pm 10\%$ of ${\delta }_m$. This forms a rough segmentation, or labeling of the image pixels, $I_s$.

2.3.2. Step 2: Filter & Orient Trunk Pixels

‘$I_s$’ may include some leaves and branches that happen to match the trunk depth while omitting portions of the trunk that are obscured by closer leaves and branches, as seen in image (b) of Figure 2. Conceptually, we now want to find a tight boundary for the trunk that will exclude these outliers while still containing (“filling in”) the obscured portions. The distance between the left and right sides of this boundary will then correspond to the diameter of the trunk. We also require the orientation of the trunk, because the diameter must be estimated perpendicular to this orientation. Finding the orientation first improves the efficiency of boundary detection: we use the orientation angle to rotate the image so that the trunk is vertical and search only along vertical lines for appropriate trunk boundaries.

Overall, the pixels in $I_s$ are typically dominated by trunk pixels, which form a rough oblong cluster. We follow the approach described by Rehman et al. [18] for automatically aligning such images, finding the principal axis of the trunk using principal component analysis (PCA). PCA is sensitive to outliers but is able to identify the principal axis relatively well even with a small subset of the trunk pixels missing. As a result, we prefer to over-filter the image, ensuring that few non-trunk pixels remain, even at the possible cost of losing some true trunk pixels. We call this highly filtered input to PCA $I_f$. (Notably, $I_f$ will not be suitable for identifying the boundaries of the trunk directly. The over-filtering at this stage would lead to underestimating the trunk diameter.)

To obtain $I_f$, we remove two main sets of outlier pixels: small clusters of pixels corresponding to individual leaves or small elements in the environment and substantial objects (e.g., shrubs, large branches, additional trunks) that happen to share the trunk depth. We begin by parsing $I_s$ into its connected components [19], an example of which can be seen in image (c) of Figure 2. To filter out small clusters, we remove all connected components below a threshold number of pixels, $\alpha =300$ pixels. This thresholding is analogous to the pre-PCA filtering performed by Rehman et al. [18].

Depending on the level of occlusion, the trunk may be split across multiple connected components, as is the case in image (c) of Figure 2. We need to identify which subset of components comprise the trunk and which correspond to other objects in the image. Since the underlying trunk is a large, oblong shape, the set of connected components that represent the trunk should, intuitively, form a dense cluster in the image. Here, we define the density of a set of connected components as the ratio of the area of the components to the area of the convex hull around those components. Large components that represent leaves and branches will tend to extend beyond the convex hull around the trunk components, because they are far from the main trunk or point in a different direction, so including them in the trunk subset will result in a low-density measurement. Our algorithm, therefore, searches for a dense subset of components in the image (Appendix A.2); this subset is $I_f$.

$I_f$ is a highly filtered version of the image; we show a sample $I_f$ in image (d) of Figure 2, with the fitted convex hull outlined in black. To perform PCA, we use the set of pixel coordinates where $I_f$ is non-zero. PCA computes the eigenvectors of the covariance matrix of these data points, which indicate the direction of maximum data spread. Based on these eigenvectors, we can identify the principal axis of the trunk and orient the image so that the trunk is vertical, as shown in image (e) of Figure 2. Rotating the image using PCA was robust even with highly tilted trunks (see, e.g., Figure A1 in Appendix A.3).

2.3.3. Step 3: Identify Trunk Boundaries

With the principal axis identified, we return to $I_s$ (Figure 2b), the minimally filtered image, to search for the trunk boundaries. We use the direction of the principal axis found in Step 2 to rotate $I_s$ to orient the trunk vertically. We then use a two-pass algorithm to iterate through vertical scan lines of a binary version of $I_s$, which is 0 where $I_s=0$, and 1 otherwise. We first iterate inward until reaching a line at which the ratio of nonzero pixels to zero pixels exceeds a high threshold, $T_{high}=0.6$, then back outwards until the ratio of nonzero pixels to zero pixels falls below a low threshold $T_{low}=0.5$. The final segmentation consists of all the pixels in $I_s$ that lie within this boundary, as shown in image (f) of Figure 2. To select $T_{high}$ and $T_{low}$, we vary these parameters over a small test data set of trees collected from a Carolinian forest in leaf-on conditions (the Laurel Creek location described in Section 2.4.2) and choose the thresholds that result in the lowest bias (mean error) metric. It is possible that accuracy could be improved by setting these parameters based on a test sample specific to each study area, but we use the same parameters in all evaluation environments.

2.3.4. Step 4: Estimate Diameter

Finally, we translate the trunk boundaries and depth pixel values into a diameter estimate. In the equations below, we will use the subscript p to denote a quantity in pixels, and m to denote a quantity in meters.

In general, distances in an image are related to real-world distances by the sensor’s calibration constant, ${\gamma }_p$, which is defined as the width, in pixels, of a 1 m object at a depth of 1 m. The length of an object k meters away that appears to be d pixels wide in an image is   

$$\begin{array}{c}\hfill d_m=\frac{d_p·k_m}{{\gamma }_p}\end{array}$$

(1)

We can, thus, obtain a first approximation of the diameter of the tree as follows: the left and right boundary lines found in Step 3 (Section 2.3.3) can be defined as the lines $x=l_p$ and $x=r_p$, respectively. We set $d_p=l_p-r_p$ and $k_m={\delta }_m$ in the above formula, where ${\delta }_m$ is the modal depth in the center third of the image.

However, the simple approximation given in Equation (1) tends to consistently underestimate the trunk diameter, especially for large trees and when the user stands close to the trunk. This is because this equation does not account for the geometry of the trunk, which forms a rough cylinder that extends closer to the sensor than the true depths of the left and right boundary lines. It also does not account for parallax effects: the sensor will not capture pixels at the widest part of the trunk (on the true diameter, if the trunk were a perfect cylinder). Rather, the boundary pixels will occur on lines tangent to the trunk perimeter that intersects with the depth sensor location, and so will define a smaller chord than the diameter.

The derivation shown in Appendix A.4 accounts for the effects of parallax and the tree geometry to arrive instead at the following diameter estimate, $D_m$:

$$\begin{array}{cc}\hfill D_m& =\frac{d_p·{\delta }_m}{{\gamma }_p-\frac{d_p}{4}}\hfill \end{array}$$

(2)

2.4. App Evaluation

2.4.1. Mobile Phone Hardware

We evaluated our app on a Huawei P30 Pro phone, which, at the time of purchase, retailed for around USD 1100, but has since fallen to under USD 600. The P30 has three rear-facing cameras (40, 20, and 8 MP), one rear-facing time-of-flight (LiDAR) sensor, and a front-facing camera (32 MP). It is also equipped with 128 GB of SSD storage, 6 GB of RAM, an 8-core CPU, and a separate GPU. Huawei does not publicly disclose the full specifications of its LiDAR sensor, though investigative teardowns of the phone reveal that the sensor uses the Lumentum flood illuminator to emit infrared light, and Sony’s integrated circuit image sensor [20,21]. The sensor has a resolution of 180 × 240 pixels.

2.4.2. Measurement Environment and Procedure

We evaluated our work in three different forest areas, which are summarized in

Table 1. Summary of evaluation data sets.

Name	Location	Season	Leaf-on?	No. Samples	Diameter Range
Laurel Creek	Waterloo, ON, Canada	Summer	Y	28	8–33 cm
Beechwoods	Cambridge, UK	Autumn	Y	42	6–75 cm
Van Cortlandt	New York, NY, USA	Winter	N	29	6–105 cm

. Sample images from each evaluation plot can be found in Appendix A.6 and all RGB and depth images used in the evaluation are available at http://dx.doi.org/10.5061/dryad.vdncjsxxj (accessed on 27 January 2023). The Laurel Creek forest is a naturally managed Carolinian forest [22] with a mixture of broadleaf and conifer species. Midsummer leaf-on conditions resulted in significant trunk occlusion from leaves and branches across the samples. The topography of the preserve is relatively flat, with elevation gains of up to 30 m [23]. The Beechwoods Nature Reserve is dominated by beech trees, the oldest of which were planted in the 1840s. It also contains moderate understory growth of English yew, hemlock, and holly [24], but it is possible to walk through largely unobstructed. It has little to no elevation change. The Van Cortlandt Park Preserve includes old-growth forests and some wetlands, with a diverse array of black oak, sweetgum, red maple, and other, mostly deciduous, species native to the northeastern United States. At the time of our evaluation, it was under active ecological restoration to remove non-native invasive, such as oriental bittersweet (Celastrus orbiculatus), a vine that strangles native trees, and Rosa multiflora, a shrub whose thickets smother competing plant growth [25]. Even in winter leaf-off conditions, the climbing vines and underbrush led to significant trunk occlusion and difficult walking conditions in many areas. It contained up to 50 m of elevation gain and rocky terrain [26].

At the time of the Laurel Creek evaluation, users could not retry samples and were not able to view the captured images or see the results of the algorithm until they had left the forest. The Laurel Creek data, thus, included 2 samples each of 14 trees, with 1 set taken at 1 m and the other at 2 m away. In the Beechwoods and Van Cortlandt sites, the app provided displayed the results back to the user, as shown in the bottom panel of Figure 1. The Beechwoods data included 87 images of 42 trees, and the Van Cortlandt sample included 53 images of 29 trees. There were more images per tree when the first images were rejected by the user based on the on-screen presentation of results and errors. The user was instructed to stand roughly 1.5 m from the tree, at a comfortable distance according to site conditions. The resulting images included trunks that were 1 to 2 m away. In all samples, we established ground truth by measuring the circumference of each tree with a tape measure and computing the reference DBH to the nearest tenth of a centimeter. Abnormally shaped trunks with burls or other irregularities at breast height were measured according to the standards outlined by Schlegel et al. [6].

3. Results

3.1. Trunk Detection

In the Laurel Creek data set, collected when the app had a limited user interface that did not offer any user assistance (users could not even view the image they had just captured), the tree detection rate was 93%. In one of the images, the camera was unable to obtain any depth points. A later version of the app would have identified this as an error and relayed it to the user. In another sample, some of the depth points were captured, but they did not correspond to the tree trunk. This would have been identified as a warning in the later version of the app.

With the version of the app interface used in the Beechwoods and Van Cortlandt evaluations, which included warnings and an on-screen presentation of results, the system achieved a 100% detection rate. By examining these two data sets further, we can compare the effect of user assistance between multiple image captures of the same tree. Note that we require the user to save the first image they capture, whether or not warnings were displayed or they believed the app failed to capture the trunk. In Figure 3, we compare the results for each tree measurement between the first captured image and the last, “best attempt“ image, which the user believed based on app feedback had successfully captured the trunk. In 29 of 71 trees (41%), the first image was considered a satisfactory measurement—in these cases, the first and last captured images are the same. 79% of trees were captured satisfactorily in at most two images, and 96% in at most three. In cases when more than one image was required, it was usually a quick adjustment to avoid a leaf or branch in the way of the trunk. In the unassisted set of first images, 5 images had well over 20 cm in errors because no trunk was found or an incorrect object was identified as the trunk, with a 93% detection rate. In the assisted set of last images, the tree trunk was found in all images. Incorporating user assistance in the app interface, thus, allowed us to improve trunk detection overall.

3.2. Accuracy

For the following section, we report the accuracy of results only for images in which the tree was detected. For the Beechwoods and Van Cortlandt data sets that incorporated user assistance, this means that we consider only the last, “best attempt” image for each tree. Our results are therefore reported on 26 images in the Laurel creek data set and 42 and 29 images for the Beechwoods and Van Cortlandt data sets, respectively.

We find that the app’s diameter estimates are in good agreement with measured DBH ($R^2$ = 0.97), as shown in Figure 4. Overall, the RMSE was 3.7 cm, with a bias value (mean error) of 0.6 cm. The mean absolute percent error was 8.0%. The RMSE was affected by an outlier in the Van Cortlandt data set: a 1.04 m diameter tree with −24 cm of error (23%). We discuss the cause of this outlier and ways to correct it in Section 5. If we omit this sample in the combined data set (including only trees with diameters under 1.0 m), the overall RMSE drops to 2.7 cm, with a bias of 0.9 cm and a mean absolute percent error of 7.8%. We show more detailed numerical results in

Table 2. Error distribution.

Data Set	No. Samples	RMSE (cm)	Mean Absolute	Bias (cm)	Mean abs. % Error
			Error (cm)
Laurel Creek	26	2.2	1.5	0.2	8.2
Beechwoods	42	3.0	2.1	1.1	8.1
Van Cortlandt	29	5.3	2.8	0.2	7.5
Combined	97	3.7	2.2	0.6	8.0
Van Cortlandt ${}^{*}$	28	2.7	2.0	1.1	6.9
Combined ${}^{*}$	96	2.7	1.9	0.9	7.8

* With the outlier discussed in Section 5 removed.

. These errors are higher than TLS, which consistently achieves 1–3 cm RMSE even in complex forest plots [27]. They are more in line with both the 1.2–5.1 cm error range reported in prior work with mobile devices [10,11,12,13,16,28] and with estimates of a 3–7% coefficient of variation in manual DBH measurements or up to $12.8\%$ with untrained surveyors [29].

Table A1. Comparison with prior work.

Reference	Technology	Single Image per Tree	On-Device Processing	Handles Occlusions	Manual Intervention	Evaluated DBH Range	Reported DBH RMSE
This study	Huawei P30 Pro	Yes	Yes	Yes	Optional (retake image)	6–104 cm ${}^{*}$	3.7 cm; 2.7 cm for trunks up to 100 cm.
Tatsumi et al. [13]	iPhone 13 Pro/iPad Pro	No	Yes	No	Yes (measure 1.3 m height)	5–70 cm	2.27 cm (iPhone)/ 2.32 cm (iPad)
Çakir et al. [11]	iPad Pro	No	No	Yes	Yes (remove occlusions)	31.5–59.7 cm	2.9 cm (Urban forest)/2.5 cm (Managed forest)
Gollob et al. [15]	iPad Pro	No	No	Yes	No	5–59.9 cm	3.64 (3D Scanner)/4.51 (Polycam)/3.13 (SiteScape)
Hyyppä et al. [32]	Google Tango/ Microsoft Kinect	No	No	Unknown	Yes (image segmentation)	6.8–50.8 cm	0.73 cm (Tango)/1.9 cm (Kinect)
Mokroš et al. [12]	iPad Pro/ MultiCam Photogrammetry	No	No	Unknown	No	3.1–74.3 cm	2.6–3.4 cm (iPad)/6.98 cm (MultiCam)
Fan et al. [10]	Google Tango	Yes	Yes	No	No	6.1–34.5 cm	1.26 cm
Piermattei et al. [16]	Nikon camera	No	No	Yes	No	6.4–63.9 cm	1.21–5.07 cm
KATAM [17]	Most mobile phones supported	Continuous video	On-device but not real-time	No	No	N/A	Unknown

* Limited evaluation of trees with DBH over 50 cm (13% of the sample).

in Appendix A.5 provides a systematic comparison with prior work.

3.3. Data Collection Time and Ease of Use

One key strength of our designed system is the speedup in measuring trees in the field. In a survey in Van Cortlandt Forest, we timed the manual and app-based measurement of trees in sets of two to three nearby trees at once, and found that the app reduced surveyor time by up to a factor of 4.6, with a mean speedup of 3.6×. Some of the time saved was by avoiding walking through the underbrush from one tree to the next since the user could traverse less distance and stand in more convenient locations when using the app.

One particular sample highlighted the ease and efficiency of our system: measuring a small stand of northern white cedars (Thuja occidentalis) in the Van Cortlandt Forest. The cedars are depicted in Figure 5. The trunks are impossible to see or reach directly from the exterior of their canopies, meaning that in order to measure their circumference manually, the surveyor had to find an appropriate gap in the branches and crawl underneath. By contrast, with the designed app, they could simply hold the phone just inside the outer canopy and measure the trunk within. The measurement of the two cedars took around 2.5 min manually and less than 30 s with the app.

4. Discussion

Our algorithm does not attempt to explicitly estimate DBH by, for example, identifying the ground plane, determining the cross-section of the trunk 1.3 m above this ground level, and fitting a circle to the depth points at this location. Instead, we develop a diameter estimator, which allows us to produce a robust estimation from a single mobile LiDAR depth image with minimal manual intervention. With the low-end LiDAR sensors offered on mobile devices, the common approach of fitting a circle to the depth points at a cross-section of the trunk [10,13] is not reliable for images with high occlusion. For example, in Figure 6 we show the depth and RGB images captured by our app for the tree shown in Figure 5. The images were taken with the phone held just under the tree canopy. Based on these images, our app was able to estimate the diameter of the tree with <1 cm of error. However, the cross-section of depth points on the trunk, shown on the right in Figure 6, does not look circular, because of the branch on the left side sloping away from the camera and the branch and needles on the right sloping toward it. Obtaining an accurate diameter estimate by fitting a circle to the points in this cross-section would not be straightforward. It would also have been challenging to walk around this tree while maintaining line-of-sight contact between the phone LiDAR sensor and the trunk, given the tree’s thick needle canopy and dense branching.

In our work, we take a “whole bole” approach to estimate the trunk diameter, similar to cylinder-fitting methods used for TLS data [30,31], by following the assumptions of DBH measurement in a way that is more natural for image data. For example, DBH is not always measured at 1.3 m above ground level: if a branch or large burl occurs at this height, surveyors must measure at another height, where the branch or burl no longer affects the results [6]. DBH measurements also implicitly model the tree as a cylinder by measuring the trunk’s circumference and dividing by $\pi$. Our technique is similarly robust to branches and burls, modeling the trunk of the tree without these irregularities. To achieve this robustness, we use as input the entire range of the trunk visible in the image and estimate the edges of the trunk based on a threshold majority of pixels throughout that range. Fitting linear boundaries and estimating the trunk according to Equation (2) use the same modeling assumptions of the cylindrical shape of the trunk as manual DBH measurement. Finally, we evaluate our diameter estimates against the DBH measured manually using traditional forest inventory methods and find that it has small errors and biases relative to these measurements.

However, our approach is not without its limitations. We evaluated our system on a diverse range of tree sizes, from 6 to 104 cm in diameter, a wider range than any previous work we are aware of using mobile phone depth sensors. In its current iteration, though, our algorithm may give poor results on trees outside of the evaluated range. The largest tree measured in our data set was a 1.04 m diameter tree in Van Cortlandt forest, and it was a significant outlier in terms of estimation error, with a final diameter estimate that was off by 24 cm (23%). A close analysis of the algorithm’s performance, in this case, reveals that the fault lay in the first step of the algorithm. Part of the tree was omitted when filtering for depths within $\pm 10\%$ of the mode trunk depth due to the shape and size of the trunk, which affected both the rotation of the image and the estimation of trunk boundaries Figure A6 in Appendix A.7). There were other trees in our evaluation data set of similar size (the second-largest tree had a 99 cm DBH) that did not have similar problems. However, we note that only 13% of our evaluation data set has a DBH over 50 cm, and further evaluation is required to assess the algorithm’s accuracy on large trees. Moreover, to consistently handle large and irregularly shaped trunks, such as the one discussed here, a more flexible first step of the algorithm may be required. For example, we might consider incorporating the RGB image into the initial rough segmentation, which we did not otherwise find necessary. Alternatively, we could search for edges (pixels dissimilar to their neighbors) in the depth image, rather than depths within a particular range. In addition to adjustments for large trees, there are other forested areas, particularly in the tropics, which present further challenges for mobile phone LiDAR systems such as ours. For example, while our app handles the occlusion from leaves, branches, and low-lying vegetation found in our tested environments, it does not handle lianas or buttressed trees, and has not been tested with the diversity of tree sizes and forms found in the tropics. Handling an even broader range of trees and forest conditions is a highly appropriate direction for future work.

In the existing literature on using short-range, low-end sensors for estimating a diameter, algorithm robustness, especially regarding the occlusions that naturally occur in diverse forest environments, is under-studied. The literature that performs evaluations in such environments uses manual segmentation of depth points [11] or computationally intensive algorithms that must be run offline on a desktop computer [15,16]. We contribute to the research in this area by highlighting the challenges that low-end mobile depth scanners face in occluded conditions and offering a lightweight, fully automatic approach for segmenting depth images and estimating trunk diameter despite these challenges.

5. Conclusions

We demonstrate the use of smartphones equipped with depth sensors to estimate the trunk diameters of trees in forest plots. In many forest environments, trunk images can be occluded, lighting conditions can be challenging, and it may not be easy to walk around each tree. Unlike previous research into using mobile phones, our work considers the presence of undergrowth and occlusion as a primary use case. We design an algorithm that requires only a single image to estimate diameter and is computationally efficient enough to run directly on a mobile phone in near real-time. We incorporate our algorithm into an interactive mobile phone app with user feedback and evaluate our system in partly-managed forest settings. We find that in a corpus of 97 sample tree images, it estimates trunk diameters with a RMSE of 3.7 cm ($R^2$ = 0.97; 8.0% mean absolute error). This is comparable to the results achieved by prior approaches but, unlike prior work, our solution is capable of obtaining results in a dense, leafy understory. We believe that our proposed system is a promising direction for research in the use of sophisticated smartphone technologies for performing robust, efficient, and inexpensive in situ forest carbon estimates.