Digital Repository for University of Tripoli

With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches
Osama A. Alkishriwo(9-2020)

Soil textural information is an important component underlying other soil health indicators. Soil texture analysis is a common procedure, but it can be labor intensive and expensive. Soil texture data typically are available from the Soil Survey Geographic (SSURGO) database, which may be an option for determining soil health texture groups (SHTG). The SSURGO database provides soil texture information in the soil map unit (SMU) name, taxonomic class category (family), and detailed values (≤ 2 mm soil fraction) of percent sand, silt and clay by soil horizon. The objective of this study was to examine the possibility of using SSURGO data for SHTG at the 147-ha Cornell University Willsboro Research Farm in New York state as an alternative for soil texture data determined manually on collected soil core samples. Comparative results revealed that representative values for soil texture from the SSURGO database generally matched measured mean values for all SMUs. arabic 11 English 65
Elena Mikhailova, Christopher Post, Mark Schlautman, John Galbraith, Hamdi Zurqani(9-2019)

Soil databases are very important for assessing ecosystem services at different administrative levels (e.g., state, region etc.). Soil databases provide information about numerous soil properties, including soil inorganic carbon (SIC), which is a naturally occurring liming material that regulates soil pH and performs other key functions related to all four recognized ecosystem services (e.g., provisioning, regulating, cultural and supporting services). However, the ecosystem services value, or “true value,” of SIC is not recognized in the current land market. In this case, a negative externality arises because SIC with a positive value has zero market price, resulting in the market failure and the inefficient use of land. One potential method to assess the value of SIC is by determining its replacement cost based on the price of commercial limestone that would be required to amend soil. The objective of this study is to assess SIC replacement cost value in the contiguous United States (U.S.) by depth (0–20, 20–100, 100–200 cm) and considering different spatial aggregation levels (i.e., state, region, land resource region (LRR) using the State Soil Geographic (STATSGO) soil database. A replacement cost value of SIC was determined based on an average price of limestone in 2014 ($10.42 per U.S. ton). Within the contiguous U.S., the total replacement cost value of SIC in the upper two meters of soil is between $2.16T (i.e., 2.16 trillion U.S. dollars, where T = trillion = 1012) and $8.97T. States with the highest midpoint total value of SIC were: (1) Texas ($1.84T), (2) New Mexico ($355B, that is, 355 billion U.S. dollars, where B = billion = 109) and (3) Montana ($325B). When normalized by area, the states with the highest midpoint SIC values were: (1) Texas ($2.78 m−2), (2) Utah ($1.72 m−2) and (3) Minnesota ($1.35 m−2). The highest ranked regions for total SIC value were: (1) South Central ($1.95T), (2) West ($1.23T) and (3) Northern Plains ($1.01T), while the highest ranked regions based on area-normalized SIC value were: (1) South Central ($1.80 m−2), (2) Midwest ($0.82 m−2) and (3) West ($0.63 m−2). For land resource regions (LRR), the rankings were: (1) Western Range and Irrigated Region ($1.10T), (2) Central Great Plains Winter Wheat and Range Region ($926B) and (3) Central Feed Grains and Livestock Region ($635B) based on total SIC value, while the LRR rankings based on area-normalized SIC value were: (1) Southwest Plateaus and Plains Range and Cotton Region ($3.33 m−2), (2) Southwestern Prairies Cotton and Forage Region ($2.83 m−2) and (3) Central Great Plains Winter Wheat and Range Region ($1.59 m−2). Most of the SIC is located within the 100–200 cm depth interval with a midpoint replacement cost value of $2.49T and an area-normalized value of $0.34 m−2. Results from this study provide a link between science-based estimates (e.g., soil order) of SIC replacement costs within the administrative boundaries (e.g., state, region etc.). arabic 19 English 114
Garth Groshans, Elena Mikhailova, Christopher Post, Mark Schlautman, Hamdi Zurqani, Lisha Zhang(12-2018)

A “soil carbon hotspot” (SCH) is a geographic area having an abundance of soil carbon, and therefore higher ecosystem services value based on avoided social costs of CO2 emissions. Soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TSC) are critical data to help identify SCH at the farm scale, but monetary methods of hotspot evaluation are not well defined. This study provides a first of its kind quantitative example of farm-scale monetary value of soil carbon (C), and mapping of SCH based on avoided social cost of CO2 emissions using both Soil Survey Geographic (SSURGO) database and field measurements. The total calculated monetary value for TSC storage at the Willsboro Farm based on the SSURGO database was about 7.3 million U.S. dollars ($7.3 M), compared to $2.8 M based on field data from averaged soil core results. This difference is attributed to variation in soil sampling methodology, laboratory methods of soil C analyses, and depth of reported soil C results. Despite differences in total monetary valuation, observed trends by soil order were often similar for SSURGO versus field methods, with Alfisols typically having the highest total and area-normalized monetary values for SOC, SIC, and TSC. Farm-scale C accounting provides a more detailed spatial resolution of monetary values and SCH, compared to estimates based on country-level reports in soil survey databases. Delineation and mapping of SCH at the farm scale can be useful tools to define land management zones, to achieve social profit for farmers, and to realize United Nations (UN) Sustainable Development Goals (SDGs) based on avoided social cost of CO2 emissions. arabic 24 English 105
Elena Mikhailova, Christopher Post, Mark Schlautman, Gregory Post, Hamdi Zurqani(1-2020)

Lithospheric-derived resources such as soil texture and coarse fragments are key soil physical properties that contribute to ecosystem services (ES), which can be valued based on "soil" or "mineral" stocks. Soil survey data provides an inexpensive alternative to detailed field measurements which are often labor-intensive, time-consuming, and costly to obtain. However, both field and soil survey data contain heterogeneous information with a certain level of variability and uncertainty in data. This study compares the potential of using field measurements and information from the Soil Survey Geographic database (SSURGO) for coarse fragments (CF), sand (S), silt (Si), clay (C), and texture class (TC) in the surface soil (Ap horizon) for the 147-hectare Cornell University Willsboro Research Farm, NY. Maps were created based on following methods: (a) utilizing data from the SSURGO database for individual soil map unit (SMU) at the field site and using representative or reported values across individual SMU; (b) averaging the field data within a specific SMU boundary and using the averaged value across the SMU; and (c) interpolating field data within the farm boundaries based on the individual soil cores. This study demonstrates the important distinction between mapping using the "crisp" boundaries of SSURGO databases compared to the actual spatial heterogeneity of field interpolated data. Maps of CF, S, Si, C, and TC values derived from interpolated field core samples were dissimilar to maps derived by using averaged core results or SSURGO values over the SMUs. Dissimilarities in the maps of CF, S, Si, C, and TC can be attributed to several factors (e.g., official soil series data being collected from "type locations" outside of the study areas). Correlation plot of clay estimates for each SMU showed statistically significant correlations between SSURGO and field-averaged (r = 0.823, p = 0.003) and field-interpolated clay (r = 0.584, p = 0.028) estimates, but no correlation was found for CF, S, and Si. Ecosystem services provided by quantitative data such as CF, S, Si, and C may not be independent from each other and other soil properties. Key soil properties should also include categorical data, such as texture class, which is used for another key soil property-available soil water ratings. Current valuation of soil texture is often linked to specific mineral commodities, which does not always address the issue of soil based valuation including indirect use value. arabic 19 English 133
Elena Mikhailova, Christopher Post, Patrick Gerard, Mark Schlautman, Michael Cope, Garth Groshans, Roxanne Stiglitz, Hamdi Zurqani, John Galbraith(9-2019)

Current applications of the Ecosystems Services (ES) framework to soils are narrowly defined (e.g., soil-based, pedosphere-based, etc.), and focus on soil properties while treating soil as a closed system. Because soil is an open system, it receives and loses matter across its boundaries within Earth’s spheres (atmosphere, biosphere, hydrosphere, lithosphere, ecosphere, and anthroposphere), which also need to be accounted for in economic analysis. In market economies, the market transforms resources from the Earth’s pedosphere and related spheres into goods and services for societal welfare with non-market institutions mediating human and environmental interactions. These transformations and mediations can result not only in welfare but damages as well. The concept of soil ES and ecosystem disservices (ED) is a human-centered framework, which can be a useful tool in business decision-making. Soil ES (e.g., provisioning, regulation/ maintenance, and cultural) are used to produce goods and services, but the value of these ES and ED are not always accounted for as a part of business decision-making. The objective of this review is to illustrate the monetary valuation of ecosystem services of soil systems (SS) with examples based on the organizational hierarchy of soil systems. The organizational hierarchy of soil systems can be used in economic valuations of soil ES by scale (e.g., world, continent), time (e.g., soil, geologic), qualitative and quantitative degrees of computation (e.g., mental, verbal, descriptive, mathematical, deterministic, stochastic), and degree of complexity (e.g., mechanistic, empirical). Soil survey databases, soil analyses, Soil Data Systems (SDS), and Soil Business Systems (SBS) provide tools and a wide range of quantitative/qualitative data and information to evaluate goods and services for various business applications, but these sources of soil data may be limited in scope due to their static nature. Valuation of soil resources based on soil and non-soil science databases (e.g., National Atmospheric Deposition Program (NADP) databases, etc.) is critically needed to account for these ES/ED as part of business decision-making to provide more sustainable use of soil resources. Since most ecosystems on Earth have been modified by human activity, “soil systems goods and services” (SSGS) may be a more applicable term to describe soil contributions (benefits/damages) to economic activity, compared to a term such as “soil ecosystem goods and services.” arabic 8 English 47
Elena Mikhailova, Christopher Post, Mark Schlautman, Gregory Post, Hamdi Zurqani(7-2020)

This book presents the soil pedodiversity in Libya. Soils are the source of all life; there can be no life without them. Further, each soil has its own history and its present conditions, which have been shaped by many different factors (e.g. climate, biota, parent material, and relief or topography). The book, divided into eight chapters, provides extensive information on Libyan soils. Chapter one provides an introduction and a broad perspective of the subject, while Chapter two covers the history of soil mapping and research in Libya. Chapter three focuses on local factors of soil formation and describes the geology and climate of the region to explain the diversity of its soils. Chapter four discusses soil classification systems and those most commonly used in the country. The fifth chapter illustrates the constraints and limiting factors that negatively affect agricultural activities across the country. The land cover/land use and the vegetation of the country are described in Chapter six. In turn, Chapter seven presents the status quo of soil biology, the corresponding related research activities, and the other biological properties of Libyan soils. The final chapter (Chapter eight) focus on land degradation and desertification in Libya, emphasizing the main causes, impacts of the phenomena, and efforts to combat it. This book demonstrates the problems that the country is currently facing as a result of climate change, soil erosion, salinization, and pollution, and outlines potential remedies to improve local food security. Bringing together the perspectives and expertise of many distinguished scientists from various universities and institutions in and outside of Libya, the book represents a unique and highly valuable resource. arabic 3 English 15
Hamdi Zurqani, Khaled Ben Mohamed, Azzeddin Elhawej, Mukhtar Elaalem, Bashir Nwer, Az Ali, Eman Ferjani, Merfat Ben Mahmoud, Asma Alnajjar(12-2020)

Sodicity and salinity can adversely affect soil structure and are common constraints to plant growth in arid regions. Current remote sensing techniques cannot distinguish between the various classes of salt-affected soils. Field and laboratory measurements of salt-affected soils are time-consuming and expensive. Mapping of the salt-affected soils can be used in soil conservation planning to identify regions with different degrees of limitations. There is a need to use existing field and laboratory measurements to create maps of classes of salt-affected soils. The objectives of this study are to classify salt-affected soils, use existing field data to interpolate and validate geospatial predictions of the classes of salt-affected soils using Geographic Information Systems (GIS), and create maps showing the different classes and distribution of salt-affected soils. The classification framework for salt-affected soils is based on electrical conductivity (ECe), soil pH and the sodium adsorption ratio (SAR), and provides four degrees of limitations to salt-affected soils: slight (normal soils), moderate (saline soils), severe (sodic soils), and extreme (saline-sodic soils). Spatial interpolation of the field data from northwestern Libya was verified by cross-validation, and maps of the salt-affected soils in the region were created. The majority of soils in this region of Libya are normal (slight degree of limitation). Twenty percent of the topsoil is saline-sodic (extreme degree of limitation). Land use recommendations and rehabilitation strategies can be developed from such maps of salt-affected soil classes. The methodology followed in this study can be applied to other arid regions around the world, particularly in developing countries where budgetary constraints limit detailed field and laboratory measurements of sodicity and salinity. arabic 11 English 70
Hamdi Zurqani, Elena Mikhailova, Christopher Post, Mark Schlautman, Julia Sharp(2-2018)