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Languages : en
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Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM).
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Languages : en
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Book Description
Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM).
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM).
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Languages : en
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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 x 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). 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.
Author: Jeannine Cavender-Bares
Publisher: Springer Nature
ISBN: 3030331571
Category : Science
Languages : en
Pages : 595
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Book Description
This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale.
Author: Prasad S. Thenkabail
Publisher: CRC Press
ISBN: 0429775210
Category : Science
Languages : en
Pages : 484
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Book Description
Hyperspectral remote sensing has been increasingly used in studding and assessing biophysical and biochemical properties of agricultural crops. This volume demonstrates the experience and the methods used in studying terrestrial vegetation using hyperspectral data. It focuses on specific applications, reviews existing “state-of-art” knowledge, highlights the advances made, and provides guidance for appropriate use of hyperspectral data in applications such as crop yield modeling, crop biophysical and biochemical property characterization, and crop moisture assessment. Includes extensive discussions on data processing and how to implement data processing mechanisms.
Author: R. Knapp
Publisher: Springer Science & Business Media
ISBN: 9401023441
Category : Science
Languages : en
Pages : 333
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Book Description
During the International Botanical Congress in Edinburgh, 1964, Mrs. 1. M. WEISBACH-J UNK of The Hague discussed a plan for preparation by her publishing company (Dr. W. Junk b.v.) of an international Handbook of Vegetation Science. She proposed a series that should give a comprehensive survey of the varied directions within this science, and their achievements to date as well as their objectives for the future. The challenge of such an enterprise, and its evident value for the further development of vegetation research, induced the undersigned after some consideration to accept the offer of the honorable but also burdensome task of General Editor. The decision was encouraged by a well formulated and detailed outline for the Handbook worked out by the Dutch phytosociolo gists J. J. BARKMAN and V. WESTHOFF. A circle of scholars from numerous countries was invited by the Dr. Junk Publishing Com pany to The Hague in January 1966 to draw up a list of editors and contributors for the parts of the Handbook. The outline and list have served since for the organization of the Handbook, with no need for major change. The different burdens of editors and authors have compelled quite different timings for completion of the individual sections.
Author: John Monteith
Publisher: Butterworth-Heinemann
ISBN: 9780713129311
Category : Nature
Languages : en
Pages : 308
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Book Description
Thoroughly revised and up-dated edition of a highly successful textbook.
Author: Cristina Secades
Publisher:
ISBN: 9789292255183
Category : Biodiversity
Languages : en
Pages : 186
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Book Description
Author: Victor H. Rivera-Monroy
Publisher: Springer
ISBN: 3319622064
Category : Science
Languages : en
Pages : 407
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Book Description
This book presents a comprehensive overview and analysis of mangrove ecological processes, structure, and function at the local, biogeographic, and global scales and how these properties interact to provide key ecosystem services to society. The analysis is based on an international collaborative effort that focuses on regions and countries holding the largest mangrove resources and encompasses the major biogeographic and socio-economic settings of mangrove distribution. Given the economic and ecological importance of mangrove wetlands at the global scale, the chapters aim to integrate ecological and socio-economic perspectives on mangrove function and management using a system-level hierarchical analysis framework. The book explores the nexus between mangrove ecology and the capacity for ecosystem services, with an emphasis on thresholds, multiple stressors, and local conditions that determine this capacity. The interdisciplinary approach and illustrative study cases included in the book will provide valuable resources in data, information, and knowledge about the current status of one of the most productive coastal ecosystem in the world.
Author: Caryl L. Elzinga
Publisher: DIANE Publishing
ISBN: 9780788148378
Category :
Languages : en
Pages : 190
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Book Description
This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.
