Wubishet Tadesse

Fractionation of residual cadmium, copper, nickel, lead, and zinc in previously sludge‐amended soil

Abstract The fractionation of heavy metals in previously sludge‐amended soil is important to evaluate their behavior in the environment in terms of mobility and availability to crop plants. A surface soil that received two types of sludges at two different rates, plus fertilizer only and no treatment (control), having been fallow for nine years, was used in this study. The contents of cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) fractions in previously sludge‐amended soils were governed by the total content of these metals in the sludges applied and by the rate of sludge application. The contents of these metals were higher for soils that received the Chicago sludge as compared to that receiving the Huntsville sludge. Furthermore, soils that received 20 Mg/ha/yr of sludge for five years generally had higher levels of these metals than those receiving a single dose at the 100 Mg/ha application rate. The percentage of the total content in the water soluble and exchangeable forms was very...

Comparison of extractants for available sludge-borne metals: A residual study

To study the availability of sludge-borne Zn, Cu, Cd, Ni and Pb over time, a field study was conducted. Anaerobically digested sewage sludge (dried on sandbeds) from Huntsville and Chicago were applied to a Decatur silty clay loam soil (clayey, kaolinitic, thermic, Rhodic Paleudult), pH 6.2, for 5 consecutive yr. The sludges were applied at rates of 0, 20 (annual application for 5 yr) and 100 mt ha−1 (single application). Corn (Zea mays L.) and sudangrass (Sorghum sudanenses) were grown on the sludge-treated plots as test crops in 1987. Plant tissue samples were collected at different growth stages. Soil samples collected from the sludge-treated plots were extracted for Zn, Cu, Cd, Ni and Pb by 0.1 M HCI, DTPA, Mehlich 1 and Mehlich 3 extractants. Statistically, Mehlich 1, Mehlich 3, DTPA and 0.1M HCI extractants all gave highly significant correlations with the plant accumulation of Zn, Cu, Ni and Cd, with DTPA giving the highest at any growth stage for both corn and sudangrass, but they gave poor correlations for Pb. Zinc removed by four extractants was more highly correlated with Zn accumulation by corn (r=0.72** to 0.93** p=0.01) and sudangrass (r=0.50** to 0.96**, p=0.01) than other metals. Based on higher significant linear correlation coefficients (r), DTPA would be the extractant of choice for both crops; however the advantage to using the Mehlich 3 extractant is that, with a shorter shaking time of 5 min (compared to 2 hr for DTPA), it may be better suited for routine analysis of large numbers of soil samples.

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...

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.

The application of remote sensing, geographic information systems, and Global Positioning System technology to improve water quality in northern Alabama

Recently, the water quality status in northern Alabama has been declining due to urban and agricultural growth. Throughout the years, the application of remote sensing and geographic information system technology has undergone numerous modifications and revisions to enhance their ability to control, reduce, and estimate the origin of non-point source pollution. Yet, there is still a considerable amount of uncertainty surrounding the use of this technology as well as its modifications. This research demonstrates how the application of remote sensing, geographic information system, and global positioning system technologies can be used to assess water quality in the Wheeler Lake watershed. In an effort to construct a GIS based water quality database of the study area for future use, a land use cover of the study area will be derived from LANDSAT Thermatic Mapper (TM) imagery using ERDAS IMAGINE image processing software. A Digital Elevation Model of the Wheeler Lake watershed was also from an Environmental Protection Agency Basins database. Physical and chemical properties of water samples including pH, Total Suspended Solids (TSS), Total Fecal Coliform (TC), Total Nitrogen (TN), Total Phosphorus (TP), Biological Oxygen Demand (BOD), Dissolved Oxygen (DO), and selected metal concentrations were measured.

Land Use/Cover change detection of the city of Addis Ababa, Ethiopia using remote sensing and geographic information system technology

Information about changes in land use/cover provides valuable insights while devising future natural resource management strategies. Remotely sensed data, serve as an effective tool for deriving this kind of information. Landsat Thematic Mapper images of 1987 and 1999 were used to extract land use/cover change of the city of Addis Ababa and the surrounding area. Analysis of the multitemporal Landsat images has clearly revealed the loss of forest to urban and residential sprawl within the city limit and the surrounding area.

The use of temporal multispectral data to assess change in rural ecosystems

Ecological land classification systems that are supported by digital satellite and terrain data are being used by an increasing number of natural resource managers and urban developers. This increased use of digital data may be attributed to the increased spectral and spatial resolution of remotely sensed imagery, accuracy of digital terrain models, more robust geographic information systems (GIS), and the availability of sophisticated algorithms to analyze these data. Historical satellite data sensed over the Madison County, Alabama, metropolitan area were used to assess the impact of urban expansion on rural ecosystems over the past twenty years. Ecological land classification thematic maps were generated using Landsat satellite multispectral data acquired in June 1977 and August 1997. The results show that the expansion of urban communities has resulted in a significant decline in agricultural and forest areas and an increase in industrial, commercial, and recreational areas.

Effect of vegetation density and vegetation conditions on the spectral backscattering in the visible and the near infrared

The work presented in this paper investigates the sensitivity of the hyperspectral remotely sensed data to the vegetation density under different soil moisture conditions. The research testbed comprised four corn plots with 4 different densities, one grass plot, and one bare soil plot. For this purpose, the hyperspectral data were recorded simultaneously as the field measurements, which included soil moisture and temperature, soil characterization (gravimetric soil moisture, bulk density, surface roughness), and vegetation measurements (biomass; plant height; leaf orientations, length, thickness; dielectric constant of stalks and leaves; stalk diameter and height). The findings of this study showed that physical and physiological aspects, as well as the structure of the vegetation, have noticeable effects on its spectral response. The results showed distinct spectral response among the different vegetation densities, thus biomass. They also showed that hyperspectral data are effective in detecting soil moisture variability and discriminating among vegetation densities and conditions. The hyperspectral data were in agreement with the ground data and discriminated among small variations in soil moisture and vegetation densities and conditions. This study also showed that the variation in the spectral variability from different vegetation densities becomes negligible when the vegetation leaves cover completely the ground surface.

Precision agriculture with hyperspectral remotely sensed data, GIS, and GPS technology: a step toward environmentally responsible farming

Traditional farming usually resorts to the use of large amounts of fertilizers and other chemicals to meet the needs for food production. These practices ultimately cause dreadful harm to human lives. Precision agriculture has emerged as a valuable and a promising aid to mitigate these adverse effects by helping farmers increase crop yield while sustaining a clean environment. As an outgrowth of the efforts exerted in this field, we conducted a study, using hyperspectral remotely-sensed data, GIS, and GPS to adequately manage the nitrogen (N) fertilizer applications to optimize the crop yield while protecting the environment. Three nitrogen rates (i.e., 40, 80, and 120 kg/ha) from different sources were applied to four replications, with 20 plots (i.e., treatments) in each replication and data were collected at different growth stages of cotton. Statistical analyses indicated significant correlation between the hyperspectral data and the leaf chlorophyll content and crop yield. The results also indicated no significant difference in yield between 80 and 120 kg/ha N application rates, which suggests that high rates of N does not benefit the farmer more than it hurts the environment. GIS analysis visually revealed site specific (using GPS) relationship among these elements. Through this study, our ultimate goal of predicting the N content from non- destructive remotely-sensed observations in order to adequately and rapidly manage the utilization of N fertilizer was achieved, which will benefit farmers and help protect the environment.

Evaluation of remote-sensing methods used to differentiate forested wetlands

Accurate assessment of forested wetlands is essential for forest managers in the development of management plans because these areas are considered unsuitable for timber production and therefore affect the allowable sale quantity of the forest. Three methods of quantifying wetland habitats using Thematic Mapper (TM) imagery were evaluated to determine the most effective method of assessing this forest resource. The methods of evaluating the TM imagery were the Kauth-Thomas transformation, principal component analysis (PCA), and a maximum likelihood supervised classification algorithm using TM bands 2, 3, 4, and 5. A summer and winter TM scene was used to allow for those areas that are seasonal and may be dry for periods of the year. The results of this study revealed that the maximum likelihood supervised classification using TM bands 2, 3, 4, and 5 was the most effective method of quantifying wetland habitats. However, this method was the most time consuming and required the user to have good ancillary data and skills in site selection and assessment of those signatures used as input into the algorithm.