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Applying Lidar And High Resolution Multispectral Imagery For Improved Quantification And Mapping Of Tundra Vegetation Structure And Distribution In The Alaskan Arctic
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Book Synopsis Applying Lidar and High-resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic by : Heather E. Greaves
Download or read book Applying Lidar and High-resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic written by Heather E. Greaves and published by . This book was released on 2017 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Climate change is disproportionately affecting high northern latitudes, and the extreme temperatures, remoteness, and sheer size of the Arctic tundra biome have always posed challenges that make application of remote sensing technology especially appropriate. Advances in high-resolution remote sensing continually improve our ability to measure characteristics of tundra vegetation communities, which have been difficult to characterize previously due to their low stature and their distribution in complex, heterogeneous patches across large landscapes. In this work, I apply terrestrial lidar, airborne lidar, and high-resolution airborne multispectral imagery to estimate tundra vegetation characteristics for a research area near Toolik Lake, Alaska. Initially, I explored methods for estimating shrub biomass from terrestrial lidar point clouds, finding that a canopy-volume based algorithm performed best. Although shrub biomass estimates derived from airborne lidar data were less accurate than those from terrestrial lidar data, algorithm parameters used to derive biomass estimates were similar for both datasets. Additionally, I found that airborne lidar-based shrub biomass estimates were just as accurate whether calibrated against terrestrial lidar data or harvested shrub biomass---suggesting that terrestrial lidar potentially could replace destructive biomass harvest. Along with smoothed Normalized Differenced Vegetation Index (NDVI) derived from airborne imagery, airborne lidar-derived canopy volume was an important predictor in a Random Forest model trained to estimate shrub biomass across the 12.5 km2 covered by our lidar and imagery data. The resulting 0.80 m resolution shrub biomass maps should provide important benchmarks for change detection in the Toolik area, especially as deciduous shrubs continue to expand in tundra regions. Finally, I applied 33 lidar- and imagery-derived predictor layers in a validated Random Forest modeling approach to map vegetation community distribution at 20 cm resolution across the data collection area, creating maps that will enable validation of coarser maps, as well as study of fine-scale ecological processes in the area. These projects have pushed the limits of what can be accomplished for vegetation mapping using airborne remote sensing in a challenging but important region; it is my hope that the methods explored here will illuminate potential paths forward as landscapes and technologies inevitably continue to change.
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 Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets by :
Download or read book Mapping Arctic Plant Functional Type Distributions in the Barrow Environmental Observatory Using WorldView-2 and LiDAR Datasets written by and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-scale modeling of Arctic tundra vegetation requires characterization of the heterogeneous tundra landscape, which includes representation of distinct plant functional types (PFTs). We combined high-resolution multi-spectral remote sensing imagery from the WorldView-2 satellite with light detecting and ranging (LiDAR)-derived digital elevation models (DEM) to characterize the tundra landscape in and around the Barrow Environmental Observatory (BEO), a 3021-hectare research reserve located at the northern edge of the Alaskan Arctic Coastal Plain. Vegetation surveys were conducted during the growing season (June August) of 2012 from 48 1 m 1 m plots in the study region for estimating the percent cover of PFTs (i.e., sedges, grasses, forbs, shrubs, lichens and mosses). Statistical relationships were developed between spectral and topographic remote sensing characteristics and PFT fractions at the vegetation plots from field surveys. These derived relationships were employed to statistically upscale PFT fractions for our study region of 586 hectares at 0.25-m resolution around the sampling areas within the BEO, which was bounded by the LiDAR footprint. We employed an unsupervised clustering for stratification of this polygonal tundra landscape and used the clusters for segregating the field data for our upscaling algorithm over our study region, which was an inverse distance weighted (IDW) interpolation. We describe two versions of PFT distribution maps upscaled by IDW from WorldView-2 imagery and LiDAR: (1) a version computed from a single image in the middle of the growing season; and (2) a version computed from multiple images through the growing season. This approach allowed us to quantify the value of phenology for improving PFT distribution estimates. We also evaluated the representativeness of the field surveys by measuring the Euclidean distance between every pixel. This guided the ground-truthing campaign in late July of 2014 for addressing uncertainty based on representativeness analysis by selecting 24 1 m 1 m plots that were well and poorly represented. Ground-truthing indicated that including phenology had a better accuracy (R2=0.75, RMSE=9.94) than the single image upscaling (R2=0.63, RMSE=12.05) predicted from IDW. We also updated our upscaling approach to include the 24 ground-truthing plots, and a second ground-truthing campaign in late August of 2014 indicated a better accuracy for the phenology model (R2=0.61, RMSE=13.78) than only using the original 48 plots for the phenology model (R2=0.23, RMSE=17.49). After all, we believe that the cluster-based IDW upscaling approach and the representativeness analysis offer new insights for upscaling high-resolution data in fragmented landscapes. This analysis and approach provides PFT maps needed to inform land surface models in Arctic ecosystems.
Book Synopsis Assessment of LiDAR and Spectral Techniques for High-resolution Mapping of Permafrost on the Yukon-Kuskokwim Delta, Alaska by : Matthew Allen Whitley
Download or read book Assessment of LiDAR and Spectral Techniques for High-resolution Mapping of Permafrost on the Yukon-Kuskokwim Delta, Alaska written by Matthew Allen Whitley and published by . This book was released on 2017 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Yukon-Kuskokwim Delta (YKD) is one of the largest and most ecologically productive coastal wetland regions in the pan-Arctic. Formed by the Yukon and Kuskokwim Rivers flowing into the Bering Sea, nearly 130,000 square kilometers of delta support 23,000 Alaskan Natives living subsistence lifestyles. Permafrost on the outer delta commonly occurs on the abandoned floodplain deposits. Ground ice in the soil raises surface elevations on the order of 1-2 meters, creating plateaus on the landscape. Better drainage on the plateaus supports distinct Sphagnum-rich vegetation, which in turn protects the permafrost from rising air temperatures with low thermal conductivity during the summer. This ecosystem-protected permafrost is thus vulnerable to disturbances from rising air temperatures, vegetation mortality, and inland storm surges, which have been known to flood up to 37 km inland. This thesis assesses several novel techniques to map permafrost distribution at high-resolution on the YKD. Accurate baseline maps of permafrost extent are critical for a variety of applications, including long-term monitoring. As air and ground temperatures rise across the Arctic, monitoring landscape change is important for understanding permafrost degradation processes (e.g. thermokarst) and greenhouse gas dynamics from the local to global scales. This thesis separately explored the value of Light Detection And Ranging (LiDAR) and spectral datasets as tools to map permafrost at a high spatial resolution. Furthermore, this thesis sought to automate these processes, with the vision of high-resolution mapping over large spatial extents. Fieldwork was conducted in July 2016 to both parameterize and then validate the mapping efforts. The LiDAR mapping extent assessed a 135 km2 area (~15% permafrost cover), and the spectral mapping extent assessed an 8 km2 area (~20% permafrost cover). For the LiDAR dataset, the use of a simple elevation threshold informed by field ground truth values provided a permafrost map with 94.9% accuracy. This simple approach was possible because of the extremely flat terrain. For the spectral datasets, an ad-hoc masking technique was developed using a combination of texture analysis, principal component analysis, and morphological filtering. Two contrasting workflows were evaluated with fully-automated and semi-automated methods with mixed results. The highest mapping accuracy was 89.4% and the lowest was 79.1%, though the error of omission in mapping the permafrost remained high (7.02 - 59.7%) for most analyses. The spectral mapping algorithms did not replicate well across different high-resolution images, raising questions about the viability of using spectral methods alone to track thermokarst and landscape change over time. However, incorporating the spectral methods explored in this analysis with other datasets (e.g. LiDAR) has the potential to increase mapping accuracies. Both the methods and the results of this thesis enhance permafrost mapping efforts on the YKD, and provide a good first step to monitoring landscape change in the region.
Book Synopsis Vegetation and Production Ecology of an Alaskan Arctic Tundra by : Larry L. Tieszen
Download or read book Vegetation and Production Ecology of an Alaskan Arctic Tundra written by Larry L. Tieszen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume on botanical research in tundra represents the culmination of four years of intensive and integrated field research centered at Barrow, Alaska. The volume summarizes the most significant results and interpretations of the pri mary producer projects conducted in the U.S. IBP Tundra Biome Program (1970-1974). Original data reports are available from the authors and can serve as detailed references for interested tundra researchers. Also, the results of most projects have been published in numerous papers in various journals. The introduction provides a brief overview of other ecosystem components. The main body presents the results in three general sections. The summary chapter is an attempt to integrate ideas and information from the previous papers as well as extant literature. In addition, this chapter focuses attention on pro cesses of primary production which should receive increased emphasis. Although this book will not answer all immediate questions, it hopefully will enhance future understanding of the tundra, particularly as we have studied it in Northern Alaska.
Book Synopsis Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities by : Zhenyu Zhang
Download or read book Adoption of Airborne LiDAR Data and High Spatial Resolution Satellite Imagery for Characterisation and Classification of Forest Communities written by Zhenyu Zhang and published by . This book was released on 2012 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: High resolution spatial data, including airborne LiDAR data and newly available WorldView-2 satellite imagery, offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. Those responsible for monitoring forest changes over time relevant to timber harvesting and native forest conservation see the potential for improved documentation from using such data. However, the transfer of new remote sensing technologies from the research domain into operational forestry applications poses challenges. One of the key challenges is the development of a comprehensive procedure which involves deployment of these new remote sensing data to create forest mapping products that are comparable (or superior) in accuracy to conventional photo-interpreted maps. The last decade has witnessed an increase in interest in the application of airborne LiDAR data and high spatial resolution satellite imagery for tree species identification and classification. The research investigations have focused on open forests, and conifer or deciduous forests which are even-aged and of relatively homogenous structures. The suitability of these new remotely sensed data for delineating the structure of complex forest types, particularly for Australian cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This thesis presents ways of processing airborne LiDAR data and high spatial resolution WorldView-2 satellite imagery for characterisation and classification of forest communities in the Strzelecki Ranges, Victoria, Australia. This is a highly disturbed landscape that consists of forestry plantations and large stands of natural forest, including cool temperate rainforest remnants. The k-means clustering algorithm was applied to nonnalised LiDAR points to stratify the vertical forest structure into three layers. Variables characterising the height distribution and density of forest components were derived from LiDAR data within each of these layers. These layer-specific variables were found to be effective in forest classification. Individual trees, including locations and crown sizes, were identified from a LiDAR-derived canopy height model using the TreeVaW algorithm. Augmentation of infonnation extraction from LiDAR data for tree species identification by inclusion of LiDAR intensity data was then tested using statistical analysis techniques. This study demonstrated the contribution of LiDAR-derived intensity variables to the identification of Myrtle Beech (Nothofagus cunninghamii -the dominant species of the Australian cool temperate rainforest in the study area) and adjacent tree species -notably, Silver Wattle (Acacia dealbata) at the individual tree level. Nonparametric classifiers including support vector machines (SVMs) and decision trees were employed to take full advantage of the rich set of infonnation derived from the LiDAR and WorldView-2 imagery data for further improvement in classification accuracy. It is evident that the SVMs have significant advantages over the traditional classification methods in tenns of classification accuracy. Cool temperate rainforest and adjacent forest species were successfully classified from airborne LiDAR data and WorldView-2 satellite imagery using a decision tree approach to object-based analyses in eCognition software. The improvements in results from the methods developed in this study strongly warrant the operational adoption of airborne LiDAR data and high spatial resolution satellite imagery in the management of Australia's forestry resources.
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.
Author :National Academies of Sciences, Engineering, and Medicine Publisher :National Academies Press ISBN 13 :0309492432 Total Pages :29 pages Book Rating :4.3/5 (94 download)
Book Synopsis Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space by : National Academies of Sciences, Engineering, and Medicine
Download or read book Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2019-06-18 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space (National Academies Press, 2018) provides detailed guidance on how relevant federal agencies can ensure that the United States receives the maximum benefit from its investments in Earth observations from space, while operating within realistic cost constraints. This short booklet, designed to be accessible to the general public, provides a summary of the key ideas and recommendations from the full decadal survey report.
Book Synopsis Hybrid Image Classification Technique for Land-cover Mapping in the Arctic Tundra, North Slope, Alaska by : Debasish Chaudhuri
Download or read book Hybrid Image Classification Technique for Land-cover Mapping in the Arctic Tundra, North Slope, Alaska written by Debasish Chaudhuri and published by . This book was released on 2008 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Remotely sensed image classification techniques are very useful to understand vegetation patterns and species combination in the vast and mostly inaccessible arctic region. Previous researches that were done for mapping of land cover and vegetation in the remote areas of northern Alaska have considerably low accuracies compared to other biomes. The unique arctic tundra environment with short growing season length, cloud cover, low sun angles, snow and ice cover hinders the effectiveness of remote sensing studies. The majority of image classification research done in this area as reported in the literature used traditional unsupervised clustering technique with Landsat MSS data. It was also emphasized by previous researchers that SPOT/HRV-XS data lacked the spectral resolution to identify the small arctic tundra vegetation parcels. Thus, there is a motivation and research need to apply a new classification technique to develop an updated, detailed and accurate vegetation map at a higher spatial resolution i.e. SPOT-5 data. Traditional classification techniques in remotely sensed image interpretation are based on spectral reflectance values with an assumption of the training data being normally distributed. Hence it is difficult to add ancillary data in classification procedures to improve accuracy. The purpose of this dissertation was to develop a hybrid image classification approach that effectively integrates ancillary information into the classification process and combines ISODATA clustering, rule-based classifier and the Multilayer Perceptron (MLP) classifier which uses artificial neural network (ANN). The main goal was to find out the best possible combination or sequence of classifiers for typically classifying tundra type vegetation that yields higher accuracy than the existing classified vegetation map from SPOT data. Unsupervised ISODATA clustering and rule-based classification techniques were combined to produce an intermediate classified map which was used as an input to a Multilayer Perceptron (MLP) classifier. The result from the MLP classifier was compared to the previous classified map and for the pixels where there was a disagreement for the class allocations, the class having a higher kappa value was assigned to the pixel in the final classified map. The results were compared to standard classification techniques: simple unsupervised clustering technique and supervised classification with Feature Analyst. The results indicated higher classification accuracy (75.6%, with kappa value of .6840) for the proposed hybrid classification method than the standard classification techniques: unsupervised clustering technique (68.3%, with kappa value of 0.5904) and supervised classification with Feature Analyst (62.44%, with kappa value of 0.5418). The results were statistically significant at 95% confidence level."--Abstract from author supplied metadata.
Book Synopsis Earth Observation of Ecosystem Services by : Domingo Alcaraz-Segura
Download or read book Earth Observation of Ecosystem Services written by Domingo Alcaraz-Segura and published by CRC Press. This book was released on 2013-11-12 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: A balanced review of differing approaches based on remote sensing tools and methods to assess and monitor biodiversity, carbon and water cycles, and the energy balance of terrestrial ecosystem. Earth Observation of Ecosystem Services highlights the advantages Earth observation technologies offer for quantifying and monitoring multiple ecosystem functions and services. It provides a multidisciplinary reference that expressly covers the use of remote sensing for quantifying and monitoring multiple ecosystem services. Rather than exhaustively cover all possible ecosystem services, this book takes a global look at the most relevant remote sensing approaches to estimate key ecosystem services from satellite data. Structured in four main sections, it covers carbon cycle, biodiversity, water cycle, and energy balance. Each section contains a review of conceptual and empirical methods, techniques, and case studies linking remotely sensed data to the biophysical variables and ecosystem functions associated with key ecosystem services. The book identifies relevant issues and challenges of assessment, presents cutting-edge sensing techniques, uses globally implemented tools to quantify ecosystem functions, and presents examples of successful monitoring programs. Covering recent developments undertaken on the global and national stage from Earth observation satellite data, it includes valuable lessons and recommendations and novel ways to improve current global monitoring systems. The book delineates the use of Earth observation data so that it can be used to quantify, map, value, and manage the valuable goods and services that ecosystems provide to societies around the world.
Book Synopsis International Aerospace Abstracts by :
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1989 with total page 932 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Novel applications of airborne LiDAR and multispectral data for high-resolution geological mapping of vegetated ophiolitic rocks and sedimentary cover, Troodos Range, Cyprus by : S. Grebby
Download or read book Novel applications of airborne LiDAR and multispectral data for high-resolution geological mapping of vegetated ophiolitic rocks and sedimentary cover, Troodos Range, Cyprus written by S. Grebby and published by . This book was released on 2011 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Ground-based Hyperspectral and Spectro-directional Reflectance Characterization of Arctic Tundra Vegetation Communities by : Marcel Buchhorn
Download or read book Ground-based Hyperspectral and Spectro-directional Reflectance Characterization of Arctic Tundra Vegetation Communities written by Marcel Buchhorn and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Arctic tundra, covering approx. 5.5 % of the Earth's land surface, is one of the last ecosystems remaining closest to its untouched condition. Remote sensing is able to provide information at regular time intervals and large spatial scales on the structure and function of Arctic ecosystems. But almost all natural surfaces reveal individual anisotropic reflectance behaviors, which can be described by the bidirectional reflectance distribution function (BRDF). This effect can cause significant changes in the measured surface reflectance depending on solar illumination and sensor viewing geometries. The aim of this thesis is the hyperspectral and spectro-directional reflectance characterization of important Arctic tundra vegetation communities at representative Siberian and Alaskan tundra sites as basis for the extraction of vegetation parameters, and the normalization of BRDF effects in off-nadir and multi-temporal remote sensing data. Moreover, in preparation for the upcoming German EnMAP (Environmental Mapping and Analysis Program ...
Book Synopsis Mapping Wetland Vegetation with LiDAR in Everglades National Park, Florida, USA by : Georgia H. De Stoppelaire
Download or read book Mapping Wetland Vegetation with LiDAR in Everglades National Park, Florida, USA written by Georgia H. De Stoppelaire and published by . This book was released on 2014 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge of the geospatial distribution of vegetation is fundamental for resource management. The objective of this study is to investigate the possible use of airborne LIDAR (light detection and ranging) data to improve classification accuracy of high spatial resolution optical imagery and compare the ability of two classification algorithms to accurately identify and map wetland vegetation communities. In this study, high resolution imagery integrated with LIDAR data was compared jointly and alone; and the nearest neighbor (NN) and machine learning random forest (RF) classifiers were assessed in semi-automated geographic object-based image analysis (GEOBIA) approaches for classification accuracy of heterogeneous vegetation assemblages at Everglades National Park, FL, USA. Within the 145 ha study area, five dominant vegetation communities that establish the vegetation pattern were mapped: Subtropical hardwood forest, Slash pine with hardwoods, Pine savanna, Hardwood scrub, and Sawgrass. A LIDAR-derived canopy height model (CHM) was produced at 1 m spatial resolution and integrated with Digital Orthophoto Quarter-Quadrangle (DOQQ) optical imagery. Object-based segmentation was performed using the multi-resolution segmentation algorithm at optimal scale based on a global score. In a series of 42 experiments using the NN and RF classifiers, two data schemes were tested: fused data and optical imagery. Inclusion of additional first-order statistical features were also tested in the RF experiments. Results showed that the fused data produced significantly higher classification accuracy at the 95% confidence level than optical imagery alone in both sets of experiments. Among the classifiers, data schemes, and feature sets tested, the NN experiment using fused data with features of mean spectral values and mean CHM elevation values produced the highest overall accuracy (OA) of 83.6% and Kappa of 0.782, while the highest accuracy RF experiment produced an OA of 75.73% and Kappa of 0.695. Pairwise comparison of error matrices for the highest accuracy NN and RF experimental results were significantly different at the 95% confidence level with a Z score of 3.26. Findings show that the integration of LIDAR significantly improved classification accuracy of high spatial resolution optical imagery to identify and map wetland vegetation. Results from this study demonstrate fused LIDAR and high resolution imagery can be used to accurately map wetland vegetation assemblages in a repeatable, semi-automated GEOBIA approach.
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 Application of ERTS-B Imagery to the Analysis, Classification and Mapping of Alaskan Vegetation by : James Hugh Anderson
Download or read book Application of ERTS-B Imagery to the Analysis, Classification and Mapping of Alaskan Vegetation written by James Hugh Anderson and published by . This book was released on 1973 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
