Author:

G.P. Patil

Center for Statistical Ecology and Environmental Statistics
Department of Statistics
The Pennsylvania State University
University Park, PA 16802
http://www.stat.psu.edu/~gpp

Content:

1. Multiple and Single Raster Map Analysis

1.1 Introduction

1.2 Setting the Geospatial Raster Stage

1.3 Digital FrontierMovement in Federal Government

1.4 Digital ResearchMovement in Academic Community

1.5 Prospectus

1. Inhouse Prototype Studies and Interactive Case Studies
2. Ecosystem Health Assessment of Landscapes and Watersheds with Remote
   Sensing Data
3. Landscape Patterns and Their Comparison
4. Classified Raster Map Modeling and Simulation with Hierarchical Markov
   Transition Matrix Models
5. Classified Raster Map Analysis for Assessment of Accuracy and Change Detection
   of Landcover and Landuse Maps
6. Analyzing Spatial Variation in Quantitative Data and Determining Contexts
   of Temporal Changes and Class Errors Using Echelon Analysis
7. Integrated Regional Assessment, Model Prediction and Regional Scale Comparison
   Involving Classified Raster Maps
8. Information Visualization, Understanding and Communication
9. Pattern-Based Compression of Remotely Sensed Multi-band Image Data
10. Synergistic Workplan
11. Knowledge Sharing and Technology Transfer
12. References

Publisher:

stat.psu.edu

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Author:

Nelley Kovalevskaya and Vladimir Pavlov

Abstract:

Environmental maps show the probable environmental states of different types of land use or development of landscape in a geographic context. Remotely sensed data are particularly efficient for environmental mapping in order to outline major environmental types. Multiple schemes of image classification used in environmental mapping are either traditionally statistical or heuristic. While the former methods do not take account of spatial variability in space and aerial data, the latter ones does not lend themselves to optimal solutions we present.  Novel probabilistic models of piecewise-homogeneous images are used in environmental mapping to segment real images. The models consider both an image and a land cover map. Such a pair constitutes an example of a Markov random field specified by a joint Gibbs probability distribution of images and maps. Parameters of the model are estimated by using a stochastic approximation technique. Its convergence to the desired values is studied experimentally. Addition of spatial attributes appears to be necessary in most areas where the differences in spatial data between regions in the image occur. Experiments in generating the pairs of images and environmental maps and in segmenting the simulated as well as real images are discussed.

Publisher:

Journal of Environmental Quality 31:1462-1470 (2002)
© 2002

Article Credit:

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By : Chaichoke Vaiphasa

Content :

• Remote sensing for mangrove studies
• Hyperspectral remote sensing for mangrove discrimination
• Burdens of hyperspectral data
• Dimensionality problems
• Noise levels
• mangrove-environment relationships
• Hyperspectral Data for Mangrove Discrimination
• Acquisition of hyperspectral data
• Spectral Smoothing
• Smoothing techniques (Moving average, Savitzky-Golay)
• Hyperspectral data collection
• Experimental use of smoothing filters (Statistical comparisons , Spectral separability analysis)
• Ecological Data Integration (Ecological data collection, Mangrove sampling)
• Input data for the post-classifier (Soil pH interpolation, Plant-environment relationships, The classified image)
• The post-classifier
• Hyperspectral data for mangrove discrimination
• Utilizing mangrove-environment relationships

Abstract :

Mangroves, important components of the world’s coastal ecosystems, are threatened by the expansion of human settlements, the boom in commercial aquaculture, the impact of tidal waves and storm surges, etc.  Such threats are leading to the increasing demand for detailed mangrove maps for the purpose of measuring the extent of the decline of mangrove ecosystems. Detailed mangrove maps at the community or species level are, however, not easy to produce, mainly because mangrove forests are very difficult to access. Without doubt, remote sensing is a serious alternative to traditional field-based methods for mangrove mapping, as it allows information to be gathered from the forbidding environment of mangrove forests, which otherwise, logistically and practically speaking, would be extremely difficult to survey. Remote sensing applications for mangrove mapping at the fundamental level are already well established but, surprisingly, a number of advanced remote sensing applications have remained unexplored for the purpose of mangrove mapping at a finer level. Consequently, the aim of this thesis is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies.  Specifically, this thesis focuses on improving class separability between mangrove species or community types. It is based on two important ingredients:
(i) the use of narrow-band hyperspectral data, and
(ii) the integration of ecological knowledge of mangroveenvironment relationships into the mapping process.
Overall, the results of this study reveal the potential of both ingredients. They show that delicate spectral details of hyperspectral data and the spatial relationships between mangroves and their surrounding environment help to improve mangrove class separability at the species level. Despite the optimism generated by the overall results, it was found that appropriate data treatments and analysis techniques such as spectral band selection and noise reduction were still required to harness essential information from both hyperspectral and ecological data. Thus, some aspects of these data treatments and analysis techniques are also presented in this thesis. Finally, it is hoped that the methodology presented in this thesis will prove useful and will be followed for producing mangrove maps at a finer level.

ISBN: 90-8504-353-0
ITC Dissertation Number: 129
International Institute for Geo-information Science & Earth Observation,
Enschede, The Netherlands
© 2006 Chaichoke Vaiphasa

Free Download Link: http://library.wur.nl/wda/dissertations/dis3897.pdf (1.55 MB)