Tommy L. Coleman

Polarimetric scanning radiometer C and X band microwave observations during SMEX03

Soil Moisture Experiments 2003 (SMEX03) was the second in a series of field campaigns using the NOAA Polarimetric Scanning Radiometer (PSR/CX) designed to validate brightness temperature data and soil moisture retrieval algorithms for the Advanced Microwave Scanning Radiometer on the Aqua satellite. Data from the TRMM Microwave Imager were also used for X-band comparisons. The study was conducted in different climate/vegetation regions of the US (Alabama, Georgia, Oklahoma). In the current investigation, more than one hundred flightlines of PSR/CX data were extensively processed to produce gridded brightness temperature products for the four study regions. Variations associated with soil moisture were not as large as hoped for due to the lack of significant rainfall in Oklahoma. Observations obtained over Alabama include a wide range of soil moisture and vegetation conditions. Comparisons were made between the PSR and AMSR for all sites

Calibration and modification of impedance probe for near surface soil moisture measurements

A reliable and low cost sensor that can measure soil moisture at or near the soil surface is currently not available. The objectives of this study were: (i) to evaluate the possibility of modifying an impedance probe (IP) to measure soil moisture content at a very shallow depth (2–5 cm); and (ii) to compare the soil moisture values obtained using the IP to the values obtained using the traditional gravimetric method. The research was conducted at the Winfred A. Thomas Agricultural Research Station (WTARS) Hazel Green, Alabama. The standard IP that is capable of measuring soil moisture content at 6-cm soil depth was modified to measure soil moisture at 2-, 3-, and 5-cm depths. Using a site and depth-specific calibration technique it provided results that were comparable to the values that were obtained following the traditional gravimetric water content determination protocol. We found that the instrument was very sensitive to changes in soil moisture content and has great potential as a replacement for th...

Application of remote sensing and GIS technologies to assess the impact of surface mining at Tarkwa, Ghana

The industrial and small-scale mining industries have provoke serious environmental degradation in the form of deforestation, soil erosion and soil and water contamination by Hg and other heavy metals. Remote sensing and GIS technologies were used to investigate the temporal and spatial extent of environmental degradation from 1986 to 2000 in the Tarkwa mining area. While most of the study area was a healthy ecosystem in 1986, by 2001, over 60% of the land was degraded to the point where it could not be used for any commercial activity. An additional 35,000 ha of land has been polluted and remediation would be very expensive. This state of environmental degradation through mining activities was further aggravated by extreme human population encroachment of the region. This study puts into perspective the need for the government to institute strict environmental regulations to protect mining areas.

Image enhancement using the modified cosine function and semi-histogram equalization for gray-scale and color images

The paper outlines two image enhancement operators for gray scale and color images. A modified cosine function was developed for image enhancement, in which some enhanced images may appear a little darker if the average pixel value in the image generally falls below the intermediate value (which is 128 in most images used in the experiments (256 gray-level)). A Semi-Histogram-Equalization (SHE) method is then proposed for enhancement so that regardless of how dark or bright the image is, it would give a good contrast enhancement. Experimental results show that the proposed operators can enhance gray scale and color images effectively.

Image Texture Classification Using Texture Spectrum and Local Binary Pattern

Spatial image classifier which incorporates contextual information to classify each pixel in the raw images has been used widely in texture analysis. The spatial classifier strives to capture the spatial relationships encoded in aerial photographs, textural and natural images. In this paper, we aimed to analyze and compare some of the simple but powerful spatial image classifiers to explore their strengths and weaknesses in remote sensing applications. Specifically, Texture Spectrum (TS) and Local Binary Pattern (LBP) will be compared. These features are easy to compute and yet useful in discriminating different patterns of textures. Co-occurrence probabilities (GLCPs) are used as the benchmark for the evaluation. There are some reviews and discussions about these methods in the literature; however, no experimental comparisons are made so far. Experimental results will be provided in this report.

Laboratory and field measurements with a fiber optic soil moisture sensor

A novel design of a fiber-optic soil moisture sensor is described together with its performance under laboratory and field conditions. The sensor utilizes total internal reflection of light in a right-angled glass prism. The sensor-head can be buried at any depth below the soil surface and is linked to a remotely operated light source. The sensor is tested for several days of continuous operation using different soil types and drying conditions.

A comparative study of remotely sensed data classification using principal components analysis and divergence

This paper investigates the principal components analysis (PCA) and divergence for transforming and selecting data bands for multispectral image classification. As the principal components are independent of one another, a color combination of the first three components can be useful in providing maximum visual separability of image features. Therefore, principal components analysis is used to generate a new set of data. Divergence, a measurement of statistical separability, is employed as a method of feature selection to choose the optimal m-band subset from the n-band data for use in the automated classification process. Classification accuracy assessment is carried out using large scale aerial photographs. Classification results on the Landsat Thematic Mapper (TM) data show that PCA is a more effective approach than divergence.

Relationships between Climate Parameters and Forest Vegetation At and Near Digya National Park, Ghana

This paper evaluates the effect of three climate parameters on forest cover in Ghana and the normalized difference vegetation index (NDVI) at Digya National Park derived from Landsat image data. Climate data (temperature, humidity, dewpoint, rainfall) are assembled from statistics provided by Ghanas Meteorological Agency. The study introduces a weighted averaging method by computing weather information from neighbouring stations. Also, this research introduces a model of dewpoints, enabling the direct calculation of dewpoints from temperature and humidity data. The major finding is that while temperature significantly affects forest cover and Park vegetation, dew-points and rainfall do not. The paper suggests where future research may be more fruitful in analyzing the effects of climate on vegetation.

Investigation of the impact of urban sprawl in three Sahelian cities using remotely-sensed information

Rapid urban growth and the environmental, social and political consequences are major issues in Africa, especially in the major Sahelian cities. Using remote sensing technologies, it has been established that between 1980-2000, the rate of urbanization more than doubled in the capital city of Burkina Faso (118% increase) and nearly doubled in capitals of Mali (62%) and Niger (63% increase). There has been a drastic conversion of vegetated and open lands to urban and residential use. Population in these cities also grew significantly, clearly outpacing the overall population growth of the countries. Data obtained is useful in policy issues related to sustainable urban planning and also serves as input into urban growth to predict patterns of future urban expansion.

Integration of the soil and water assessment tool with the information system in watersheds modeling

Geographic Information Systems have become indispensable tools for watershed-scale hydrologic analysis and modeling. The integrative capabilities of GIS can emulate real-world complexity, facilitating interdisciplinary research and communication. Landsat Thermal Mapper raster images represent topography, land use, land cover, as well as spatial data of surface and ground water hydrology, weather, and soils types are all integrated in GIS themes such as views, tables, charts, and layouts. GIS is being used in data visualization, processing, and management. This paper presents a case study on the integration between the Geographical Resources and Assessment Support System (GRASS) and the distributed parameter and physical process watershed model, Soil and Water Assessment Tool (SWAT) for the management of land practices. This integration proved to be effective and efficient for input data extraction and management for simulating the baseline conditions as well as other scenarios. SWAT is capable of continuous time simulation and flexible domain delineation. The spatial distribution of SWAT output results was successfully presented by using Geomedia-Intergraph software. By using SWAT-Geomedia integration, areas under sever water quality problems could be identified. Sediment and nutrients loads were studied in order to determine a better management of surface water resources in large agricultural watershed.