Evaluating the Accuracy of Remote Dendrometers in Tree Diameter Measurements at Breast Height

An accurate tree diameter (DBH) measurement is a significant component of forest inventory. This study assessed the reliability of remote dendrometers to measure tree DBH. We compared direct caliper measurements (reference measurements) to the remote measurements collected from a laser caliper and a smartphone at 0.5 m, 1 m, and 1.5 m distances from each tree within three forest types (pine, oak, and poplar forests). In general, all remote dendrometers underestimated the mean diameter compared to direct caliper measurements, regardless of forest types and distances. We observed that the mean deviation of direct caliper measurement and smartphone measurement at 1.5 m within a pine forest and oak forest were the lowest (0.3 cm and 0.36 cm, respectively). The deviations between direct caliper measurements and smartphone measurements at a 0.5 m distance, across forest types, were noticeably larger compared to others. An ANOVA test was used to determine whether significant deviations existed between caliper measurements and remote measurements at a specific distance, and among three different forest types. We rejected the null hypothesis, which suggested that there were no statistically significant differences (p<0.05) between tree DBH measurements obtained from the direct caliper measurements and indirect measurements (smartphone and laser caliper) captured at a distance. Then, a post-hoc test was performed to examine which set of estimated deviations was different from the reference data. The results suggested that indirect tree DBH measurements using the smartphone app at 1 m and 1.5 m in certain forest types (pine and oak) were not significantly different from direct tree DBH measurements. Also, our test results mostly indicated no significant difference within each forest, except for measurements using the smartphone app at 0.5 m across all forest types when the smartphone measurements were compared to laser caliper measurements..

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Estimating Tree Defect and Log Quality with Terrestrial Structure from Motion Photogrammetry and Handheld LiDAR

Forest inventories are periodic resource assessments that can estimate items such as timber volume and value, tree growth rates, carbon sequestration, forest health status, wildlife habitat, fuel loading, and more. Traditional inventories require a large investment of resources and well-trained workforce. To date, most foresters and timber cruisers inventory forests using specialized tools, manual measurements, and visual estimation. Recent developments in the field of remote sensing are revolutionizing the way forest inventory data are gathered and analyzed. Emerging applications of two remote sensing methods, handheld light detection and ranging (LiDAR) and Structure from Motion photogrammetry (SfM), have achieved promising results in tree detection and measuring tree diameter at breast height (DBH). However, very few studies have utilized handheld LiDAR or SfM to assess tree defect or log grade. Therefore, the main objectives of our study are to assess the ability of handheld LiDAR and SfM to a) detect and quantify tree defects and b) grade and quantify log products in coastal forests of northwest Oregon. We used a Samsung Galaxy S7 smartphone camera to photograph trees and create digital models using SfM, and we used a handheld GeoSLAM Zeb Horizon with a Velodyne VLP-16 sensor to create LiDAR point cloud models of the trees. We compared measurements of damage count, damage length, log grade, log length, log length deduction, and log diameter deduction obtained from handheld LiDAR, SfM photogrammetry, and traditional field methods using linear mixed effects regression models. In assessing tree defect, we found a significantly different mean damage count per tree between the three survey methods, and the field method recorded nearly twice as many damages per tree as the handheld LiDAR and SfM methods. Additionally, for the trees included in our study, there was no evidence that damage length varied between the three survey methods. In log grading for the first log (≤ 40 ft), we found that handheld LiDAR and SfM can categorize logs into quality grades, similar to traditional field methods. In the second log (> 40 ft), the remote sensing methods produced different log grades than the field method. Finally, both remote sensing methods failed to sufficiently model and, therefore, grade, any logs above the second log. In most instances, the remote sensing methods yielded shorter and significantly different log lengths than the field method. Notably, we found height limitations with our remote sensing methods. For example, the average field-measured height for trees included in our study was 31.2 m. The handheld LiDAR models captured an average useable height of 13.6 m, while the SfM models captured an average useable height of 9.3 m. Our study results, combined with previously demonstrated accuracy in diameter and linear measurements for handheld LiDAR and SfM, show promise for these emerging remote sensing technologies. However, more research is needed to replicate and improve upon this work in different regions and forest types. Another natural evolution of our work is using handheld LiDAR and SfM to automatically detect, classify, and measure tree defect and log quality

Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using 'non-professional' instruments and automating estimates of dendrometric parameters

Developing paper-based devices for mapping agricultural pesticides and environmental contaminants

2021 Summer.Includes bibliographical references.The detection of environmental contaminants is important to ensure the health of both humans and the environment. Currently, detection is done by instrumentation like liquid or gas chromatography coupled with mass spectrometry. While sensitive and selective for multiple analytes, these instruments suffer from disadvantages like large size, high sample cost, and the need for a trained analyst to run the samples. As an alternative, microfluidic paper-based analytical devices (µPADs) are becoming more common as inexpensive, fast, easy to use devices to detect and quantify a variety of analytes. My research has been focused on developing µPADs for three different analytes: pesticides, PFAS, and heavy metals. In order to ensure proper crop protection and pest management, it is important to manage and optimize pesticide application. Currently, this is done by water-sensitive papers, which often inaccurately portray the presence of pesticide due to humidity and extraneous water droplets that are not pesticide. In Chapter 2, I have developed a method that uses filter paper to capture a fluorescent tracer dye that has been mixed with the pesticide and then sprayed over the crop. The filter papers are imaged with a lightbox and Raspberry Pi camera system and then analyzed to determine percent coverage. After optimization and validation of the method to WSP, the filter paper method was used to evaluate pesticide distribution in a citrus grove in Florida (Chapter 3). The data from these field studies was used to make recommendations for which application method is best for the different types of pesticides. Paper-based devices are inherently limited by the inability to control fluid properties like mixing. In order to incorporate mixing but also retain a small device that does not require external power to initial flow, a microfluidic device was fabricated out of two glass slides. A staggered herringbone pattern is laser ablated into the slides, and a channel is formed by double-sided adhesive (Chapter 4). Mixing was quantified using blue and yellow dyes. A reaction between horseradish peroxidase and hydrogen peroxide was used as a representative enzymatic reaction and also to determine enzyme kinetics. Since the microfluidic device is made of glass, it is also compatible with non-aqueous solvents. Paper-based devices do not work well with organic solvents because the hydrophobic wax on the paper is dissolved by the solvent. In Chapter 5, the dissertation returns to traditional µPADs for environmental contaminants. Per- and polyfluoroalkyl substances (PFAS) are class of compounds that are highly persistent, toxic, bioaccumulative, and ubiquitous. While multiple instrument-based methods exist for sensitive and selective detection in a variety of matrices, there is a huge need for a fast, inexpensive, and easy-to-use sensor for PFAS detection. This would enable widespread testing of drinking water supplies, ensuring human health. A µPAD was developed for the detection of perfluorooctane sulfonate (PFOS) where the ion-pairing of PFOS and methylene green forms a purple circle. The diameter of the purple circle can be measured by the naked eye with a ruler or with the help of a smartphone to correlate the diameter back to PFOS concentration. At a cost of cents per sample, this µPAD enables fast and inexpensive detection of PFOS to ensure safe drinking water. A common issue with environmental µPADs is the relatively high limits of detection compared to what is needed for regulatory purposes. It can be challenging to lower the limits of detection without incorporating an external pretreatment and/or preconcentration step. As µPADs are small and handle only a small volume of sample (<120 µL), there is the possibility of increasing the sample capacity of the device but without significantly increasing the device size or analysis time. By adding multiple layers of absorbent filter paper underneath radial device for heavy metal detection, the sample volume increased to 1 mL, decreasing the limit of detection for a radial copper detection card from 100 ppb to 5 ppb (Chapter 6). The research presented here achieves the goal of developing µPADs for environmental contaminants. They can be used in different ways to visualize the presence of the contaminant for monitoring and management purposes, ultimately ensuring human and environmental health

A Work Flow and Evaluation of Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions and have added another approach to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board foot volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated segmentation was performed with Trimble eCognition Developer\u27s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and the high resolution imagery was able to provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

D3MOBILE METROLOGY WORLD LEAGUE: TRAINING SECONDARY STUDENTS ON SMARTPHONE-BASED PHOTOGRAMMETRY

The advent of the smartphones brought with them higher processing capabilities and improved camera specifications which boosted the applications of mobile-based imagery in a range of domains. One of them is the 3-D reconstruction of objects by means of photogrammetry, which now enjoys great popularity. This fact brings potential opportunities to develop educational procedures in high schools using smartphone-based 3-D scanning techniques. On this basis, we designed a Project Based e-Learning (PBeL) initiative to introduce secondary students to the disciplines of photogrammetry through the use of their mobile phones in an attractive and challenging way for them. The paper describes the motivation behind the project "D3MOBILE Metrology World League", supported by ISPRS as part of the "Educational and Capacity Building Initiative 2020"programme. With this Science, Technology, Engineering and Mathematics (STEM) initiative, we implement a methodology with the format of an international competition, that can be adapted to daily classwork at the high school level anywhere in the world. Therefore, the championship is essentially structured around a collection of well-thought-out e-learning materials (text guidelines, video tutorials, proposed exercises, etc.), providing a more flexible access to content and instruction at any time and from any place. The methodology allows students to gain spatial skills and to practice other transversal abilities, learn the basics of photogrammetric techniques and workflows, gain experience in the 3-D modelling of simple objects and practice a range of techniques related to the science of measurementS