Estimating Compositional Characteristics of Vegetation on the North Slope of Alaska Using Remotely Sensed Spectral Reflectance Data PDF Download
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Author: John Seele Kimball
Publisher:
ISBN:
Category : Remote sensing
Languages : en
Pages : 324
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Author: John Seele Kimball
Publisher:
ISBN:
Category : Remote sensing
Languages : en
Pages : 324
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Book Description
Author: James F. Reynolds
Publisher: Springer Science & Business Media
ISBN: 366201145X
Category : Science
Languages : en
Pages : 447
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Book Description
Following the discovery of large petroleum reserves in northern Alaska, the US Department of Energy implemented an integrated field and modeling study to help define potential impacts of energy-related disturbances on tundra ecosystems. This volume presents the major findings from this study, ranging from ecosystem physiology and biogeochemistry to landscape models that quantify the impact of road-building. An important resource for researchers and students interested in arctic ecology, as well as for environmental managers concerned with practical issues of disturbances.
Author: Debasish Chaudhuri
Publisher:
ISBN:
Category : Ecological mapping
Languages : en
Pages : 182
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Book Description
"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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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).
Author:
Publisher:
ISBN:
Category : Astronautics in earth sciences
Languages : en
Pages : 758
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Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 968
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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).
Author: L. A. Morrissey
Publisher:
ISBN:
Category : Vegetation mapping
Languages : en
Pages : 66
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Author:
Publisher:
ISBN:
Category : Ecology
Languages : en
Pages : 982
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Author: Willem W. S. Van Hees
Publisher:
ISBN:
Category : Forests and forestry
Languages : en
Pages : 60
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Book Description
