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Quantifying The Urban Forest Environment Using Dense Discrete Return Lidar And Aerial Color Imagery For Segmentation And Object Level Biomass Assessment
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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 Mapping Forest Structure, Species Gradients and Growth in an Urban Area Using Lidar and Hyperspectral Imagery by :
Download or read book Mapping Forest Structure, Species Gradients and Growth in an Urban Area Using Lidar and Hyperspectral Imagery written by and published by . This book was released on 2015 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Urban forests play an important role in the urban ecosystem by providing a range of ecosystem services. Characterization of forest structure, species variation and growth in urban forests is critical for understanding the status, function and process of urban ecosystems, and helping maximize the benefits of urban ecosystems through management. The development of methods and applications to quantify urban forests using remote sensing data has lagged the study of natural forests due to the heterogeneity and complexity of urban ecosystems. In this dissertation, I quantify and map forest structure, species gradients and forest growth in an urban area using discrete-return lidar, airborne imaging spectroscopy and thermal infrared data. Specific objectives are: (1) to demonstrate the utility of leaf-off lidar originally collected for topographic mapping to characterize and map forest structure and associated uncertainties, including aboveground biomass, basal area, diameter, height and crown size; (2) to map species gradients using forest structural variables estimated from lidar and foliar functional traits, vegetation indices derived from AVIRIS hyperspectral imagery in conjunction with field-measured species data; and (3) to identify factors related to relative growth rates in aboveground biomass in the urban forests, and assess forest growth patterns across areas with varying degree of human interactions. The findings from this dissertation are: (1) leaf-off lidar originally acquired for topographic mapping provides a robust, potentially low-cost approach to quantify spatial patterns of forest structure and carbon stock in urban areas; (2) foliar functional traits and vegetation indices from hyperspectral data capture gradients of species distributions in the heterogeneous urban landscape; (3) species gradients, stand structure, foliar functional traits and temperature are strongly related to forest growth in the urban forests; and (4) high uncertainties in our ability to map forest structure, species gradient and growth rate occur in residential neighborhoods and along forest edges. Maps generated from this dissertation provide estimates of broad-scale spatial variations in forest structure, species distributions and growth to the city forest managers. The associated maps of uncertainty help managers understand the limitations of the maps and identify locations where the maps are more reliable and where more data are needed.
Book Synopsis Examination of Airborne Discrete-return Lidar in Prediction and Identification of Unique Forest Attributes by : Brian M. Wing
Download or read book Examination of Airborne Discrete-return Lidar in Prediction and Identification of Unique Forest Attributes written by Brian M. Wing and published by . This book was released on 2012 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R2) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications.
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 Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada by : Qin Ma
Download or read book Quantifying Forest Structure Parameters and Their Changes from LiDAR Data and Satellite Imagery in the Sierra Nevada written by Qin Ma and published by . This book was released on 2018 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sierra Nevada forests have provided many economic benefits and ecological services to people in California, and the rest of the world. Dramatic changes are occurring in the forests due to climate warming and long-term fire suppression. Accurate mapping and monitoring are increasingly important to understand and manage the forests. Light Detection and Range (LiDAR), an active remote sensing technique, can penetrate the canopy and provide three-dimensional estimates of forest structures. LiDAR-based forest structural estimation has been demonstrated to be more efficient than field measurements and more accurate than those from passive remote sensing, like satellite imagery. Research in this dissertation aims at mapping and monitoring structural changes in Sierra Nevada forests by taking the advantages of LiDAR. We first evaluated LiDAR and fine resolution imagery-derived canopy cover estimates using different algorithms and data acquisition parameters. We suggested that LiDAR data obtained at 1 point/m2 with a scan angle smaller than 12°were sufficient for accurate canopy cover estimation in the Sierra Nevada mix-conifer forests. Fine resolution imagery is suitable for canopy cover estimation in forests with median density but may over or underestimate canopy cover in extremely coarse or dense forests. Then, a new LiDAR-based strategy was proposed to quantify tree growth and competition at individual tree and forest stand levels. Using this strategy, we illustrated how tree growth in two Sierra Nevada forests responded to tree competition, original tree sizes, forest density, and topography conditions; and identified that the tree volume growth was determined by the original tree sizes and competitions, but tree height and crown area growth were mostly influenced by water and space availability. Then, we calculated the forest biomass disturbance in a Sierra Nevada forest induced by fuel treatments using bi-temporal LiDAR data and field measurements. Using these results as references, we found that Landsat imagery-derived vegetation indices were suitable for quantifying canopy cover changes and biomass disturbances in forests with median density. Large uncertainties existed in applying the vegetation indices to quantify disturbance in extremely dense forests or forests only disturbed in the understory. Last, we assessed vegetation losses caused by the American Fire in 2013 using a new LiDAR point based method. This method was able to quantify fire-induced forest structure changes in basal area and leaf area index with lower uncertainties, compared with traditional LiDAR metrics and satellite imagery-derived vegetation indices. The studies presented in this dissertation can provide guidance for forest management in the Sierra Nevada, and potentially serve as useful tools for forest structural change monitoring in the rest of the world.
Book Synopsis Measuring Forest Biomass Using AIMS Lidar and Aerial High-resolution Imagery by : Danelle Laflower
Download or read book Measuring Forest Biomass Using AIMS Lidar and Aerial High-resolution Imagery written by Danelle Laflower and published by . This book was released on 2012 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: I hypothesized that I could estimate stand-level biomass using the Airborne Imaging Multispectral Sensor's (AIMS) high-resolution imagery and lidar height measurements. To test this notion, I selected a study area on Mount Holyoke College property, in South Hadley, Massachusetts and systematically sampled 366 trees for species, height, DBH, and canopy data. I obtained lidar-derived canopy height and high resolution imagery with the AIMS system. For the ground validation of biomass, I created ten 900m2 subplots, where I identified species, measured DBH for all live stems >12.4cm, and recorded place in the canopy. I calculated biomass using the corresponding biomass equations, summed the results, and scaled to hectare. I also calculated biomass using only dominant and co-dominant trees. I averaged the lidar values and the ground-sampled trees' heights within each plot to obtain plot average height for each method. By dividing the area into 20 plots, a linear regression indicated that the lidar average height was a significant predictor of dominant ground-sampled tree average height (p
Book Synopsis Biomass and Stem Volume Equations for Tree Species in Europe by : Dimitris Zianis
Download or read book Biomass and Stem Volume Equations for Tree Species in Europe written by Dimitris Zianis and published by . This book was released on 2005 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: A review of stem volume and biomass equations for tree species growing in Europe is presented. The mathematical forms of the empirical models, the associated statistical parameters and information about the size of the trees and the country of origin were collated from scientific articles and from technical reports. The collected information provides a basic tool for estimation of carbon stocks and nutrient balance of forest ecosystems across Europe as well as for validation of theoretical models of biomass allocation.
Book Synopsis Evaluating High-resolution Imagery and LiDar for Mapping Structures in the Wildland-urban Interface by :
Download or read book Evaluating High-resolution Imagery and LiDar for Mapping Structures in the Wildland-urban Interface written by and published by . This book was released on 2008 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project tested remote sensing tools for locating man-made structures in the wildland-urban interface (WUI). Creating fire-suppression plans and responding to wildland fires become much easier when information on the location of critical structures is available. High-resolution digital imagery and lidar data were tested in two study areas. Vegetation ranged from sagebrush and scrub in Oregon to dense forest canopy in Montana. The tools were semi-automated, meaning that users had to interact with the data sets to interpret the structures. The digital imagery used in the study was 1-meter natural-color photography from the National Aerial Imagery Program (NAIP). The multireturn lidar data had 4 returns and a density of 1.7 points per square meter. The tests were completed in the following manner: 1) In the Oregon study area, Feature Analyst, a semi-automated feature-extraction software package developed by Visual Learning Systems (VLS) mapped structures using 1-meter natural-color imagery. The area is sagebrush-dominated urban wildland containing both dense subdivisions and dispersed buildings. 2) In the Montana study area, a lidar feature-extraction software package, VLSs LIDAR Analyst, mapped structures in a dense forest containing dispersed cabins. An independent accuracy assessment was completed for both study sites by comparing the results with manual image interpretation. Although the results for feature extraction using NAIP imagery were promising, roughly 50 percent of the structures were missed (omission error) and an additional 50 percent were wrongly delineated (commission error) in the Oregon study. In the heavily forested area in Montana, LIDAR Analysts analysis of the multireturn lidar data was mediocre because structures were confused with understory forest canopy. Based on these case studies, it appears that feature extraction using high-resolution imagery or multireturn lidar data is only partially effective. We recommend using heads-up image interpretation and manual digitizing for more accurate and timely results.
Book Synopsis Remote Sensing of Forests Using Discrete Return Airborne LiDAR by : Hamid Hamraz
Download or read book Remote Sensing of Forests Using Discrete Return Airborne LiDAR written by Hamid Hamraz and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Airborne discrete return light detection and ranging (LiDAR) point clouds covering forested areas can be processed to segment individual trees and retrieve their morphological attributes. Segmenting individual trees in natural deciduous forests, however, remained a challenge because of the complex and multi-layered canopy. In this chapter, we present (i) a robust segmentation method that avoids a priori assumptions about the canopy structure, (ii) a vertical canopy stratification procedure that improves segmentation of understory trees, (iii) an occlusion model for estimating the point density of each canopy stratum, and (iv) a distributed computing approach for efficient processing at the forest level. When applied to the University of Kentucky Robinson Forest, the segmentation method detected about 90% of overstory and 47% of understory trees with over-segmentation rates of 14 and 2%. Stratifying the canopy improved the detection rate of understory trees to 68% at the cost of increasing their over-segmentations to 16%. According to our occlusion model, a point density of ~170 pt/m2 is needed to segment understory trees as accurately as overstory trees. Lastly, using the distributed approach, we segmented about two million trees in the 7440-ha forest in 2.5 hours using 192 processors, which is 167 times faster than using a single processor.
Book Synopsis Lidar and Machine Learning Estimation of Hardwood Forest Biomass in Mountainous and Bottomland Environments by : Bowei Xue
Download or read book Lidar and Machine Learning Estimation of Hardwood Forest Biomass in Mountainous and Bottomland Environments written by Bowei Xue and published by . This book was released on 2015 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Light detection and ranging (lidar) has been applied in various forest applications, such as to retrieve forest structural information, to build statistical models for identification of tree species, and to monitor forest growth. However, despite significant progress in these areas, the choice of regression approach and parameter tuning remains an ongoing critical question. This study focused on choosing the right spatial generalization level to transform lidar point clouds to 2D images which can be further processed by mature image processing and pattern recognition approaches. It also compared the prediction ability of popular machine learning algorithms applied to aboveground forest biomass estimation. A neighborhood technique was employed to calculate lidar-derived height metrics which were used as predictors to estimate forest total biomass at the image object (or segment) level. Three machine learning algorithms were tested to explore the relationship between the lidar-derived height metrics and biomass observed in situ. The height metrics were calculated as percentile heights and canopy coverage based on the lidar points falling within certain spatial extents (neighborhoods). The effect of neighborhood size was examined by developing regression models using Support Vector Machine (SVM), Cubist, and Random Forest on images created by applying 0.5, 2.5, 5, 10, and 15-meter neighborhood. Experiments were conducted in two study sites, the Ozark Mountains of Arkansas and the Trinity River Basin of Texas, with significantly different landscapes, hardwood tree species, and lidar point distributions. Regression models were constructed and evaluated with 10-fold cross validation. Results showed that optimal neighborhood configurations depend on the lidar data and regression techniques that are applied. The optimal model among all neighborhoods and algorithms achieved training accuracies of 0.988 and 0.990, and validation accuracies of 0.902 and 0.853 (adjusted R2) at the two study sites respectively.
Book Synopsis Urban Forest Inventory Using Airborne LiDAR Data and Hyperspectral Imagery by : Caiyun Zhang
Download or read book Urban Forest Inventory Using Airborne LiDAR Data and Hyperspectral Imagery written by Caiyun Zhang and published by . This book was released on 2010 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this research was to develop new algorithms to automate urban forest inventory at the individual tree level using two emerging remote sensing technologies, LiDAR and hyperspectral sensors. LiDAR data contain 3-Dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter, while hyperspectral data contain rich spectral contents that can be used to discriminate tree species. The synergy of two data sources would allow precision urban forest inventory down to individual trees. Unlike most of the published algorithms that isolate individual trees from a raster surface built from LiDAR data to estimate tree metrics, this study worked directly from the vector LiDAR point cloud data for separating individual trees and estimating tree metrics, in order to generate a better accuracy by preserving the original height values.
Book Synopsis Using LiDAR to Measure the Urban Forest in DeKalb, Il by : Dustin Bergman
Download or read book Using LiDAR to Measure the Urban Forest in DeKalb, Il written by Dustin Bergman and published by . This book was released on 2016 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Urban and metropolitan areas have grown significantly during the 21st century. With more than 50% of the global population living in cities, they are uniquely susceptible to high temperatures, poor air quality, and increases in peak storm water runoff during inclement weather; however, urban and metropolitan areas often have significant forest resources that can greatly ameliorate these factors. To maintain urban forests and maximize their benefits, tree surveys are often performed requiring extensive fieldwork. However, automatable techniques using LiDAR data and aerial orthoimagery have the potential to provide similar metrics over larger areas, more rapidly, and at lower cost. This study sought to develop a method to accurately and efficiently estimate tree height and stem diameters of roadside trees using tools readily available to geographic information system (GIS) operators. Incorporating two prior parkway tree surveys for the City of DeKalb as a starting point, I repaired and updated an urban tree database using orthoimagery, utilized LiDAR to estimate heights of new and existing trees, and estimated diameters using allometric equations. Results suggest that LiDAR can reasonably estimate tree height in an urban environment (R2 = 0.80; RMSE = 3.36 m) and further utilize those estimates to predict diameter at breast height (dbh) using a simple regression (R2 = 0.85; RMSE = 0.13 m) derived from a sample of approximately 1,000 trees.
Download or read book Crown-condition Classification written by and published by . This book was released on 2007 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Forest Inventory and Analysis (FIA) Program of the Forest Service, U.S. Department of Agriculture, conducts a national inventory of forests across the United States. A systematic subset of permanent inventory plots in 38 States is currently sampled every year for numerous forest health indicators. One of these indicators, crown-condition classification, is designed to estimate tree crown dimensions and assess the impact of crown stressors. The indicator features eight tree-level field measurements in addition to variables traditionally measured in conjunction with FIA inventories: vigor class, uncompacted live crown ratio, crown light exposure, crown position, crown density, crown dieback, foliage transparency, and crown diameter. Indicators of crown health derived from the crown data are intended for analyses at the State, regional, and national levels, and contribute to the core tabular output in standard FIA reports. Crown-condition measurements were originally implemented as part of the Forest Health Monitoring (FHM) Program in 1990. Except for crown diameter, these measurements were continued when the FIA Program assumed responsibility for FHM plot-based detection monitoring in 2000. This report describes in detail the data collection and analytical techniques recommended for crown-condition classification.
Book Synopsis Measuring the Urban Forest by : Dara O'Beirne
Download or read book Measuring the Urban Forest written by Dara O'Beirne and published by . This book was released on 2012 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the Use of Rapid-scan, Low Point Density Terrestrial Laser Scanning (TLS) for Structural Assessment of Complex Forest Environments by : Ali Rouzbeh Kargar
Download or read book On the Use of Rapid-scan, Low Point Density Terrestrial Laser Scanning (TLS) for Structural Assessment of Complex Forest Environments written by Ali Rouzbeh Kargar and published by . This book was released on 2020 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS."--Abstract.
Book Synopsis Using LiDAR Data and Geographical Information System (GIS) Technology to Assess Municipal Street Tree Inventories by : John Wesley Jones
Download or read book Using LiDAR Data and Geographical Information System (GIS) Technology to Assess Municipal Street Tree Inventories written by John Wesley Jones and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Market and nonmarket urban forest resource values can be achieved through many cost reductions (e.g., improved air quality, fossil fuels for heating and cooling, stormwater runoff) and increases in tax bases for communities from improved property values. These benefits need to be measured quantitatively so decision makers can understand economic gains or losses provided by street trees. Resource inventories are often undertaken as part of the planning phase in a tree management program. It is a comprehensive assessment that requires an inventory of a community's tree resources and it acts as a fundamental starting point for most urban and community forestry programs. Whether an inventory is an estimate or a complete count, quantitative benefits and costs for urban forestry programs cannot accurately be represented without one. This study provides a new approach to understanding a city's street tree structure using data from a Light Detection And Ranging (LiDAR) sensor and other publicly available data (e.g., roads, city boundaries, aerial imagery). This was accomplished through feature (e.g., trees, buildings) extraction from LiDAR data to identify individual trees. Feature extraction procedures were used with basic geographic information system (GIS) techniques and LiDAR Analyst to create street tree inventory maps to be used in determining a community's benefit/cost ratio (BCR) for its urban forest. Only by explaining an urban forest's structure can dollar values be assigned to street trees. Research was performed with LiDAR data and a sample of ground control trees in Pass Christian, and Hattiesburg, Mississippi, located in the lower U.S. South where many communities have publicly available geospatial data warehouses (e.g., MARIS in Mississippi, ATLAS in Louisiana). Results from each city's estimated street trees revealed a BCR 3.23:1 and 6.91:1 for Pass Christian and Hattiesburg, respectively. This study validated a regression model for predicting street tree occurrence in cities using LiDAR Analyst and a street sample. Results demonstrated that using LiDAR Analyst as a street tree inventory tool with publicly available LiDAR data and a sample adequately described 88% of a community's street trees which was used to calculate both market and nonmarket resource values.
Book Synopsis Quantifying Vertical and Horizontal Stand Structure Using Terrestrial LiDAR in Pacific Northwest Forests by : Alexandra N. Kazakova
Download or read book Quantifying Vertical and Horizontal Stand Structure Using Terrestrial LiDAR in Pacific Northwest Forests written by Alexandra N. Kazakova and published by . This book was released on 2013 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stand level spatial distribution is a fundamental part of forest structure that influences many ecological processes and ecosystem functions. Vertical and horizontal spatial structure provides key information for forest management. Although horizontal stand complexity can be measured through stem mapping and spatial analysis, vertical complexity within the stand remains a mostly visual and highly subjective process. Tools and techniques in remote sensing, specifically LiDAR, provide three dimensional datasets that can help get at three dimensional forest stand structure. Although aerial LiDAR (ALS) is the most widespread form of remote sensing for measuring forest structure, it has a high omission rate in dense and structurally complex forests. In this study we used terrestrial LiDAR (TLS) to obtain high resolution three dimensional point clouds of plots from stands that vary by density and composition in the second-growth Pacific Northwest forest ecosystem. We used point cloud slicing techniques and object-based image analysis (OBIA) to produce canopy profiles at multiple points of vertical gradient. At each height point we produced segments that represented canopies or parts of canopies for each tree within the dataset. The resulting canopy segments were further analyzed using landscape metrics to quantify vertical canopy complexity within a single stand. Based on the developed method, we have successfully created a tool that utilizes three dimensional spatial information to accurately quantify the vertical structure of forest stands. Results show significant differences in the number and the total area of the canopy segments and gap fraction between each vertical slice within and between individual forest management plots. We found a significant relationship between the stand density and composition and the vertical canopy complexity. The methods described in this research make it possible to create horizontal stand profiles at any point along the vertical gradient of forest stands with high frequency, therefore providing ecologists with measures of horizontal and vertical stand structure. Key Words: Terrestrial laser scanning, canopy structure, landscape metrics, aerial laser scanning, lidar, calibration, Pacific Northwest
