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Multi Scale Lidar Based Approaches To Characterizing Stream Networks Surface Roughness And Landforms Of Forest Watersheds
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Book Synopsis Multi-scale Lidar-based Approaches to Characterizing Stream Networks, Surface Roughness and Landforms of Forest Watersheds by : Kristen M. Brubaker
Download or read book Multi-scale Lidar-based Approaches to Characterizing Stream Networks, Surface Roughness and Landforms of Forest Watersheds written by Kristen M. Brubaker and published by . This book was released on 2011 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Forest-Water Interactions by : Delphis F. Levia
Download or read book Forest-Water Interactions written by Delphis F. Levia and published by Springer Nature. This book was released on 2020-02-05 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: The United Nations has declared 2018-2028 as the International Decade for Action on Water for Sustainable Development. This is a timely designation. In an increasingly thirsty world, the subject of forest-water interactions is of critical importance to the achievement of sustainability goals. The central underlying tenet of this book is that the hydrologic community can conduct better science and make a more meaningful impact to the world’s water crisis if scientists are: (1) better equipped to utilize new methods and harness big data from either or both high-frequency sensors and long-term research watersheds; and (2) aware of new developments in our process-based understanding of the hydrological cycle in both natural and urban settings. Accordingly, this forward-looking book delves into forest-water interactions from multiple methodological, statistical, and process-based perspectives (with some chapters featuring data sets and open-source R code), concluding with a chapter on future forest hydrology under global change. Thus, this book describes the opportunities of convergence in high-frequency sensing, big data, and open source software to catalyze more comprehensive understanding of forest-water interactions. The book will be of interest to researchers, graduate students, and advanced undergraduates in an array of disciplines, including hydrology, forestry, ecology, botany, and environmental engineering.
Book Synopsis The Use of LiDAR in Multi-scale Forestry Applications by :
Download or read book The Use of LiDAR in Multi-scale Forestry Applications written by and published by . This book was released on 2017 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest ecosystems are a significant faction of the Earth's landscape, and accurate estimates of forest structures are important for understanding and predicting how forest ecosystems respond to climate change and human activities. Light detection and ranging (LiDAR) technology, an active remote sensing technology, can penetrate the forest canopy and greatly improve the efficiency and accuracy of mapping forest structures, compared to traditional passive optical remote sensing and radar technologies. However, currently, LiDAR has two major weaknesses, the lack of spectral information and the limited spatial coverage. These weaknesses have limited its accuracy in certain forestry applications (e.g., vegetation mapping) and its application in large-scale forest structure mapping. The aim of research described in this dissertation is to develop data fusion algorithms to address these limitations. In this dissertation, the effectiveness of LiDAR in estimating forest structures and therefore monitoring forest dynamics is first compared with aerial imagery by detecting forest fuel treatment activities at the local scale. Then, a vegetation mapping algorithm is developed based on the fusion of LiDAR data and aerial imagery. This algorithm can automatically determine the optimized number of vegetation units in a forest and take both the vegetation species and vegetation structure characteristics into account in classifying the vegetation types. To extend the use of LiDAR in mapping forest structures in areas without LiDAR coverage, a data fusion algorithm is proposed to map fine-resolution tree height from airborne LiDAR, spaceborne LiDAR, optical imagery and radar data in regional scale. Finally, this dissertation further investigates the methodology to integrate spaceborne LiDAR, optical imagery, radar data and climate surfaces for the purpose of mapping national- to global-scale forest aboveground biomass. The proposed data fusion algorithms and the generated regional to global forest structure parameters will have important applications in ecological and hydrologic studies and forest management.
Book Synopsis LiDAR Principles, Processing and Applications in Forest Ecology by : Qinghua Guo
Download or read book LiDAR Principles, Processing and Applications in Forest Ecology written by Qinghua Guo and published by Academic Press. This book was released on 2023-03-10 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
Book Synopsis Quantifying the Urban Forest Environment Using Dense Discrete Return LiDAR and Aerial Color Imagery for Segmentation and Object-level Biomass Assessment by : Madhurima Bandyopadhyay
Download or read book Quantifying the Urban Forest Environment Using Dense Discrete Return LiDAR and Aerial Color Imagery for Segmentation and Object-level Biomass Assessment written by Madhurima Bandyopadhyay and published by . This book was released on 2015 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management."--Abstract.
Book Synopsis A Lidar-based Approach to Measure Channel Incision in Headwater Streams in an Urbanizing Landscape by : Marina Jean Metes
Download or read book A Lidar-based Approach to Measure Channel Incision in Headwater Streams in an Urbanizing Landscape written by Marina Jean Metes and published by . This book was released on 2018 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stream channel incision can occur following landscape disturbances commonly related to urbanization. A method was developed to map reach-scale incision from lidar-derived digital elevation models using topographic openness, a landscape metric measuring the enclosure of an area (i.e. channel bottoms) relative to the surrounding landscape (i.e. stream banks). The method was validated with field surveys and local photogrammetric models of stream banks. The method was then applied to watersheds undergoing urban development with lidar coverage for six time steps spanning an 11 year period. Channel incision was detected near the outlet of newly developed stormwater management facilities, but temporal analysis also identified areas already severely incised prior to urbanization, highlighting influence from previous agricultural land use, as well as areas that have resisted incision following urbanization. Although incision patterns varied across each watershed, there appeared to be no direct connection to the placement of SWM facilities beyond outlets.
Book Synopsis Multi-Source Remote Sensing Data for Automated Extraction of Fine-scale Attributes in a Northern Hardwood Forest by : Jian Yang
Download or read book Multi-Source Remote Sensing Data for Automated Extraction of Fine-scale Attributes in a Northern Hardwood Forest written by Jian Yang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forest resources require careful management and planning as they are under increasing pressure to support wood industry and conservation needs. The management of structurally complex, uneven-aged, deciduous-dominated forests requires detailed and accurate data on fine-scale forest attributes (e.g., gap dynamics, crown sizes, species distributions). New advances in remote sensing techniques will transform traditional forest inventory practices that have relied on expensive ground-based measurements or less accurate interpretation of aerial photography. Recently, multiple sources of high spatial resolution remote sensing data have demonstrated great potential for automated extraction of fine-scale forest attributes. In this context, my PhD research aims to utilize multi-source high spatial resolution remote sensing data to develop methods for automated extraction of fine-scale forest attributes in deciduous-dominated forests, including canopy gap identification, crown delineation, and species classification. This study was carried out in Haliburton Forest and Wildlife Reserve, an uneven-aged, deciduous-dominated forest located in the Great Lakes-St. Lawrence region of Central Ontario, Canada. Specifically, the thesis first quantified the accuracy of canopy gap segmentation and classification by integrating optical and LiDAR data. Thereafter, the thesis proposed a novel method for individual tree crown (ITC) delineation, involving multispectral watershed segmentation and multi-scale fitting. Finally, the thesis explored the feasibility of using multi-seasonal WorldView-3 images to map tree species using the delineated ITCs. Results indicated that: (1) the independent use of LiDAR data performed the best segmentation of canopy gaps while the synergistic use of optical and LiDAR data provided higher classification accuracy for non-forest and forest gap identification; (2) the proposed multispectral watershed segmentation and multi-scale fitting method was able to produce ITC maps of higher quality; (3) the combined use of late-spring, mid-summer, and early-spring images substantially improved the accuracy of individual tree-based species classification. The goal of this study was to develop automated methods for extracting fine-scale forest attributes for operational purposes. Although the proposed methods were mainly designed for temperate deciduous-dominated forests, they could be implemented in other types of temperate or boreal forests, such as coniferous-dominated forests.
Book Synopsis Characterization of Intertidal Geomorphology Based on Multi-scale Analysis of Airborne LiDAR Data by : Peter Andrew Horne
Download or read book Characterization of Intertidal Geomorphology Based on Multi-scale Analysis of Airborne LiDAR Data written by Peter Andrew Horne and published by . This book was released on 2013 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Fine-scale Inventory of Forest Biomass with Ground-based LiDAR. by : Zhouxin Xi
Download or read book Fine-scale Inventory of Forest Biomass with Ground-based LiDAR. written by Zhouxin Xi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomass measurement provides a baseline for ecosystem valuation required by modern forest management. The advent of ground-based LiDAR technology, renowned for 3D sampling resolution, has been altering the routines of biomass inventory. The thesis develops a set of innovative approaches in support of fine-scale biomass inventory, including automatic extraction of stem statistics, robust delineation of plot biomass components, accurate classification of individual tree species, and repeatable scanning of plot trees using a lightweight scanning system. Main achievements in terms of accuracy are a relative root mean square error of 11% for stem volume extraction, a mean classification accuracy of 0.72 for plot wood components, and a classification accuracy of 92% among seven tree species. The results indicate the technical feasibility of biomass delineation and monitoring from plot-level and multi-species point cloud datasets, whereas point occlusion and lack of fine-scale validation dataset are current challenges for biomass 3D analysis from ground.
Book Synopsis Advanced Methods for 3-D Forest Characterization and Mapping from Lidar Remote Sensing Data by : Carlos Alberto Silva
Download or read book Advanced Methods for 3-D Forest Characterization and Mapping from Lidar Remote Sensing Data written by Carlos Alberto Silva and published by . This book was released on 2018 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate and spatially explicit measurements of forest attributes are critical for sustainable forest management and for ecological and environmental protection. Airborne Light Detection and Ranging (lidar) systems have become the dominant remote sensing technique for forest inventory, mainly because this technology can quickly provide highly accurate and spatially detailed information about forest attributes across entire landscapes. This dissertation is focused on developing and assessing novel and advanced methods for three dimensional (3-D) forest characterization. Specifically, I map canopy structural attributes of individual trees, as well as forests at the plot and landscape levels in both natural and industrial plantation forests using lidar remote sensing data. Chapter 1 develops a novel framework to automatically detect individual trees and evaluates the efficacy of k-nearest neighbor (k-NN) imputation models for estimating tree attributes in longleaf pine (Pinus palustris Mill.) forests. Although basal area estimation accuracy was poor because of the longleaf pine growth habit, individual tree locations, height and volume were estimated with high accuracy, especially in low-canopy-cover conditions. The root mean square distance (RMSD) for tree-level height, basal area, and volume were 2.96%, 58.62%, and 8.19%, respectively. Chapter 2 presents a methodology for predicting stem total and assortment volumes in industrial loblolly pine (Pinus taeda L.) forest plantations using lidar data as inputs to random forest models. When compared to reference forest inventory data, the accuracy of plot-level forest total and assortment volumes was high; the root mean square error (RMSE) of total, commercial and pulp volume estimates were 7.83%, 7.71% and 8.63%, respectively. Chapter 3 evaluates the impacts of airborne lidar pulse density on estimating aboveground biomass (AGB) stocks and changes in a selectively logged tropical forest. Estimates of AGB change at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg ̇ha−1 when pulse density decreased from 12 to 0.2 pulses ̇m−2. The effects of pulse density were more pronounced in areas of steep slope, but when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and subsequent AGB stocks and change estimates did not exceed 20 Mg ̇ha−1. Chapter 4 presents a comparison of airborne small-footprint (SF) and large-footprint (LF) lidar retrievals of ground elevation, vegetation height and biomass across a successional tropical forest gradient in central Gabon. The comparison of the two sensors shows that LF lidar waveforms are equivalent to simulated waveforms from SF lidar for retrieving ground elevation (RMSE=0.5 m, bias=0.29 m) and maximum forest height (RMSE=2.99 m; bias=0.24 m). Comparison of gridded LF lidar height with ground plots showed that an unbiased estimate of aboveground biomass at 1-ha can be achieved with a sufficient number of large footprints (> 3). Lastly, Appendix A presents an open source R package for airborne lidar visualization and processing for forestry applications.
Book Synopsis Characterizing Vertical Forest Structure Using Small-footprint Multi-return Airborne LiDAR by : Daniel A. Zimble
Download or read book Characterizing Vertical Forest Structure Using Small-footprint Multi-return Airborne LiDAR written by Daniel A. Zimble and published by . This book was released on 2002 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Utilising Airborne Scanning Laser (LiDAR) to Improve the Assessment of Australian Native Forest Structure by : Alex C. Lee
Download or read book Utilising Airborne Scanning Laser (LiDAR) to Improve the Assessment of Australian Native Forest Structure written by Alex C. Lee and published by . This book was released on 2008 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Enhanced understanding of forest stocks and dynamics can be gained through improved forest measurement, which is required to assist with sustainable forest management decisions, meet Australian and international reporting needs, and improve research efforts to better respond to a changing climate. Integrated sampling schemes that utilise a multi-scale approach, with a range of data sourced from both field and remote sensing, have been identified as a way to generate the required forest information. Given the multi-scale approach proposed by these schemes, it is important to understand how scale potentially affects the interpretation and reporting of forest from a range of data. To provide improved forest assessment at a range of scales, this research has developed a strategy for facilitating tree and stand level retrieval of structural attributes within an integrated multi-scale analysis framework. ..."
Book Synopsis Improving Forest Inventory and Assessment with LiDAR Data by : Michael J. Falkowski
Download or read book Improving Forest Inventory and Assessment with LiDAR Data written by Michael J. Falkowski and published by . This book was released on 2008 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chapter 4 evaluates the use of LiDAR data for characterizing forest successional stages, across a structurally diverse, mixed-species forest in northern Idaho. A variety of LiDAR-derived metrics were used in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy greater than 95%. The algorithmic model developed ecologically meaningful decision rules based upon LiDAR metrics quantifying mean vegetation height and canopy cover, among others.
Download or read book River Dynamics written by Bruce L. Rhoads and published by Cambridge University Press. This book was released on 2020-04-29 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rivers are important agents of change that shape the Earth's surface and evolve through time in response to fluctuations in climate and other environmental conditions. They are fundamental in landscape development, and essential for water supply, irrigation, and transportation. This book provides a comprehensive overview of the geomorphological processes that shape rivers and that produce change in the form of rivers. It explores how the dynamics of rivers are being affected by anthropogenic change, including climate change, dam construction, and modification of rivers for flood control and land drainage. It discusses how concern about environmental degradation of rivers has led to the emergence of management strategies to restore and naturalize these systems, and how river management techniques work best when coordinated with the natural dynamics of rivers. This textbook provides an excellent resource for students, researchers, and professionals in fluvial geomorphology, hydrology, river science, and environmental policy.
Book Synopsis QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A Report on Field Monitoring, Remote Sensing MMV, GIS Integration, and Modeling Results for Forestry Field Validation Test to Quantify Aboveground Tree Biomass and Carbon by :
Download or read book QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A Report on Field Monitoring, Remote Sensing MMV, GIS Integration, and Modeling Results for Forestry Field Validation Test to Quantify Aboveground Tree Biomass and Carbon written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO2) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 across H"0,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of>70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density between the 2003 and 2009 did not affect the biomass estimates. Overall, LiDAR data coupled with field reference data offer a powerful method for calculating pools and changes in aboveground carbon in forested systems. The results of our study suggest that multitemporal LiDAR-based approaches are likely to be useful for high quality estimates of aboveground carbon change in conifer forest systems.
Book Synopsis The Influence of Accuracy, Grid Size, and Interpolation Method on the Hydrological Analysis of LiDAR Derived Dems by : Brian W. Clarkson
Download or read book The Influence of Accuracy, Grid Size, and Interpolation Method on the Hydrological Analysis of LiDAR Derived Dems written by Brian W. Clarkson and published by . This book was released on 2013 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) provide accurate, high resolution digital surfaces for precise topographic analysis. The following study investigates the accuracy of LiDAR derived DEMs by calculating the Root Mean Square Error (RMSE) of multiple interpolation methods with grid cells ranging from 0. 5 to 10-meters. A raster cell with smaller dimensions will drastically increase the amount of detail represented in the DEM by increasing the number of elevation values across the study area. Increased horizontal resolutions have raised the accuracy of the interpolated surfaces and the contours generated from the digitized landscapes. As the raster grid cells decrease in size, the level of detail of hydrological processes will significantly improve compared to coarser resolutions including the publicly available National Elevation Datasets (NEDs). Utilizing a LiDAR derived DEM with the lowest RMSE as the `ground truth', watershed boundaries were delineated for a sub-basin of the Clear Creek Watershed within the territory of the Seneca Nation of Indians located in Southern Erie County, NY. An investigation of the watershed area and boundary location revealed considerable differences comparing the results of applying different interpretation methods on DEM datasets of different horizontal resolutions. Stream networks coupled with watersheds were used to calculate peak flow values for the 10-meter NEDs and LiDAR derived DEMs.
Book Synopsis A Voxel-based Method for Individual Tree Detection Using Airborne Lidar in Eastern U.S. Hardwood Forests by : Jeff Hershey
Download or read book A Voxel-based Method for Individual Tree Detection Using Airborne Lidar in Eastern U.S. Hardwood Forests written by Jeff Hershey and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For more than a century, forest inventories have been used to support forest management and timber valuation activities. Today's inventories still rely primarily on manual measurements combined with sampling and modeling techniques. In recent years, new opportunities in carbon sequestration and an increasingly sophisticated timber market have prompted a need for more scalable and efficient inventory methods. To meet this demand, the industry has turned to remote sensing--predominantly light detection and ranging (LiDAR), which utilizes lasers to scan and measure features in 3D space. While much progress has been made, data resolution and cost challenges for both airborne and terrestrial LiDAR still exist. Airborne Laser Scanning (ALS) is more efficient for measuring large forest areas but faces challenges with respect to resolution and occlusion, leading to omission of understory trees. Terrestrial Laser Scanning (TLS) performs better in those respects but relies on expensive and typically unwieldy hardware. Area-based LiDAR approaches have been successful for large-scale applications but are not ideal for smaller parcels. As such, a need persists for a LiDAR-based solution that enables efficient generation of large-area forest inventory data yet is scalable to smaller forest plots and a range of forest types. The objective of this study was the development of a scalable individual tree detection method that leveraged airborne LiDAR data and performed well in mixed-species hardwood forests found in the northeastern United States. Existing research in individual tree detection has focused on methods that work well in conifer-dominated forests and homogenous settings such as plantations. These approaches, many of them based on top-down canopy height models, perform less favorably in deciduous stands due to the canopy complexity and crown characteristics inherent to these forest types. The voxel-based method proposed here uses detailed ground-measured tree survey data and leaf-off LiDAR collected in 2019-2020 over the Shavers Creek Watershed in Pennsylvania, United States. The method detected 68% of all reference trees greater than 10cm diameter at breast height (DBH) and 87% of sawtimber-sized trees greater than 28cm DBH, and it performed consistently across 48 subplots in the three-hectare test area. A new tree matching method leveraging linear integer programming was used for training and evaluation of the method. This tool enabled true one-to-one matching of predicted and reference trees and the validation of tree detections. Mean positional accuracy for predicted trees was within one meter of ground-measured reference trees. The results indicate the method has potential to be operationalized for both traditional forest management activities and in meeting the demand for more frequent and scalable inventories spurred by a growing forest carbon sequestration industry.
