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Soil Moisture Measurement from Remotely Sensed Images, Multi-dimensional Theory, Watershed modeling, Soil Moisture and more.

Soil Moisture Measurement from Remotely Sensed Images 

Objective: Field collected values of soil moisture from Theta probes are being used to calibrate transformations of Landsat Thematic mapper (TM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), Hyperion and airborne-collected hyperspectral images to create soil moisture values directly from the image radiances.

History: Originally funded by Land Remote Sensing (LRS) as a 3-year project.

Accomplishments: We continue to have a robust collaboration with many distinguished scientists at the U.S. Department of Agriculture (USDA), the Institute for Technology Development (ITD), the University of Georgia, and the University of Missouri - Rolla. We employed a Ph.D. student from the University of Georgia's National Environmentally Sound Production Agriculture Laboratory (NESPAL); this employee completed extensive field work associated with the goals of the project. In addition to satellite remote sensors, we have deployed a leading-edge airborne hyperspectal sensor and collected some unique datasets. Preliminary analyses show good correlations of measured soil moisture with a few specific bands in the infrared.

Results and Outputs: The reported results of our technical approaches from various preliminary stages has resulted in a poster presented at the USGS Land Remote Sensing Science Fair, Reston, VA. In addition, we are preparing a paper for submission as a U. S. Geological Survey Open-File Report and a paper for submission to the journal Geosciences and Remote Sensing.

Current Status (2007): We have an extensive database of images from ASTER, TM, ALI, Hyperion, and airborne hyperspectral (collected under contract in fiscal year 2005 and fiscal year 2006) data over the test site in the Little River, Georgia, watershed. These images have been preprocessed including rectification to ground control, conversion of pixel values to reflectance and radiance, and generation of tasseled-cap transformations. Acquisition of an additional airborne hyperspectral image is now under contract.

Planned Future Work: In collaboration with our partners, the soil moisture values for the point samples from the theta probes are being scaled to pixel size (30 x 30 m) areas with in-field measurements of climate conditions, vegetation, soil and air moisture, and spectral reflectances, during airborne hyperspectral flights. These pixel area measurements will be compared to image reflectances and interpolated to a regional assessment of soil moisture. Through image transformations (including tasseled-cap and modified wetness calculations) and statistical analyses, such as canonical analysis and correlation, we hope to establish a capability to predict soil moisture values from image radiances. In association with USDA, ITD, NESPAL, and UGA, we will complete this project in fiscal year 2007.

Multi-dimensional Theory

Developing high-level constructs for geographic data and feature representation that capture human cognition of geographic space and are efficiently implemented in computer systems is the objective of this project. The project is focused on multidimensional representation, including three-dimensional and temporal object attributes. The initial focus has been on watershed features for modeling and water-quality applications. A current focus is on feature extraction to support The National Map.  Beginning Fiscal Year 2003, the Multidimensional Theory project was consolidated into the Feature Extraction project.

Watershed modeling

This cooperative effort with the USGS Water Resources Discipline is investigating the effects of data resolution on the outputs of watershed and water quality models. Specifically, we are developing raster databases of elevation, land cover, soils, and precipitation data with 3, 30, 60, 120, 210, 240, 480, 960, and 1920 m cells and extracting parameters for water models. The results of these models will be statistically compared to assess the sensitivity of the models to the effects of data resolution.

Soil Moisture

The availability of soil moisture affects plant production potential, rainfall runoff volume, and many other parameters that are of interest to agricultural production, forest management, soil conservation, and watershed management and modeling. Transformations of the spectral reflectance in remotely sensed images may be able to provide significant information on soil water content and, if augmented with existing soil and other geographic information, such as terrain elevation and slope, may provide accurate data on soil water content.

The overarching objectives are to determine the ability to process datasets to generate soil moisture values that match field collected data in a watershed of the Suwannee River in northern Florida and southern Georgia from spaceborne and airborne sensors. We intend, initially, to interrogate the Landsat Enhanced Thematic Mapper Plus (ETM+), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Aircraft-based hyperspectral sensors such as AVIRIS, and The Advanced Microwave Scanning Radiometer (AMSER-E) of the Earth Observing System for this purpose.

We propose to examine the ability to generate accurate soil water content from ETM+, ASTER, AVIRIS, and AMSER-E images in combination with soil, terrain, and other geographic data. The primary objective is to develop a methodology to use remotely sensed data to predict soil moisture accurately. Spectral and spatial transformations can be used to extract soil moisture from remotely sensed images when combined with appropriate geographic data such as terrain and soil types.

For a small watershed in South Georgia, the USDA has 30 instrumented stations continuously collecting soil water content. To these 30 stations, USDA will add 50 more. USDA will collaborate with the USGS to provide accurate soil water content and its distribution among the sampling stations for this watershed for any time. We will then apply standard transformations, such as the Kauth-Thomas or Tassel-Capped transformation, to the image data to generate measures of greenness, brightness, and wetness. The results of these image transformations will be combined with elevation, slope, soil and other geographic data to determine soil water content as a distributed parameter for the watershed.

Feature Extraction

Current cartographic processes, such as generalization and feature extraction, pose fundamental hurdles to The National Map implementation. This proposed research will begin an investigation of the problem of feature extraction from available image and map database sources to help establish a framework for the implementation of The National Map. The research approach is to build a knowledge base of 20 specific features for inclusion in The National Map and develop a table of probabilities for the extraction of those features from current image sources. Expected products from this research include a knowledge base framework and implementation for supporting feature representation and extraction for The National Map, and a specific knowledge base of 20 features with associated tables of extraction probabilities. The knowledge base will be expandable to include other features and image sources.

Web Modeling

This project will demonstrate the application of The National Map to web-enabled modeling by deriving model parameter values from data extracted fromThe National Map, and by recommending modeling tools to be developed for The National Map. The project will use the Agricultural Non-Point Source (AGNPS) pollution model as a case study. AGNPS is a cell-based model that uses information derived from land cover, soils, and elevation data. 

Delaware River Basin Historical Land Use

Delaware Lehigh River Confluence

This long-term U.S. Geological Survey project consists of a number of individual studies applying spatial analysis methods to long-term land-cover change in the Delaware River Basin, particularly in Pennsylvania. The objectives for these projects are to address and quantify predominant affects and outcomes of land-surface change in urbanizing areas. Land-use and land-cover changes affect local residents most directly, but cumulatively, contribute to regional and global changes in natural systems and quality of life. These outcomes have direct impacts on the natural resources that support the health and integrity of human beings, our environment, and our future welfare.

Overview of the Research Projects

Although each project has its own specific objectives, approaches, context, and outcomes, certain concepts are common to them all. A short summary of these concepts is discussed below.

Geospatial Data

The data for the long-term study of the Delaware River Basin primarily relies on the historical topographic maps the USGS published throughout the twentieth century. The primary challenges for building data bases from these sources include data categorization, its integration with other environmental or social data, and problems of resolving data captured at different scales. More specific discussion of these topics is available in a paper on methods for using historical USGS maps for environmental research. Where possible, the data files from these studies can be obtained via the Internet or by contacting the USGS contact person listed below. A graphic index of the quadrangle outlines (by latitude and longitude coordinates) and names together with the map edition timelines is available to assist the identification and selection of data files. Some supplementary data, described under the heading of the specific project, are also available.

Geospatial Technologies for the Study of Temporal Change

The data used in analyses of change with time require the support of logical concepts in space-time continuity in their methods of use. We are examining recent research on temporal change data models for the organization of disparate data sources with time for their effectiveness in representing structure of land-surface change the way we understand it.

Spatial analysis and modeling

Methods of spatial analysis test geographic data for statistically significant patterns, though the added variable of location causes these tests to differ from normal statistics. Besides widely accepted methods such as Regression and Dimensional Analysis, tests are applied to geographical point-pattern, network/path, and regional/shape structures. Some of the most important of these are spatial autocorrelation, accessibility and interaction, matrix analysis, and methods applied to spatially continuous data. Significant findings of spatial analysis lend their mathematical formalizations to statistical/mathematical modeling. The mathematical tests that are applied to organized empirical data are used for either explanatory or predictive (projection) purposes, but some modeling objectives also can be achieved through the topological manipulation of the map-like layers of geographical information systems (GIS).

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