Department of Soil and Sand

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Who works at the Department of Soil and Sand

Department of Soil and Sand has more than 26 academic staff members

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Dr. Hamdi Abdalkhaliq Ali zurqani

Dr. Hamdi A. Zurqani is one of the faculty members at the Department of Soil and Water Sciences, Faculty of Agriculture, University of Tripoli, Tripoli, Libya. Dr. Zurqani is a recognized expert as a result of his internationally acclaimed work in the areas of Environmental Information Science, Remote Sensing, Land Evaluation, Sustainability, Pedology, and Soil Science Education. He has conducted research across the world, including the United States of America, and Africa. Dr. Zurqani is a distinguished soil scientist with a wide range of scientific and working experiences in Libya and abroad. He received his M.Sc. (2010) from the University of Tripoli, Tripoli, Tripoli, Libya, and Ph.D. (2019) from Clemson University, Clemson, SC, USA. His major research and teaching activities at the University of Tripoli have focused mainly on Soil Genesis and Classification and the Environmental Information Sciences (Remote Sensing and Geographic Information System). He has published broadly in many journals (e.g., Nature “Scientific Reports”, Geoderma; International Journal of Applied Earth Observation and Geoinformation; Journal of Geological Sciences; Land; Frontiers in Environmental Science; Communications in Soil Science and Plant Analysis; and others). Dr. Zurqani is a member of the Editorial Board for Remote Sensing (MDPI) Journal, counseling outcome and research evaluation. He also was appointed to serve as a Guest Editor for the Special Issue "Applications of Remote Sensing in Earth Observation and Geo-Information Science". In addition, Dr. Zurqani conducted peer-review for many journals including Journal of Environmental Informatics, Applied Sciences, SN Applied Sciences, Remote Sensing, Heliyon, Geosciences, Land, Water, Agronomy, Agriculture, Sustainability, Arid Land Research and Management, International Journal of Environmental Research and Public Health, Natural Hazards, and Conference of the Arabian Journal of Geosciences. He is also one of the authors of the lab manual entitled “GIS Exercises for Natural Resource Management”. Dr. Zurqani has been the recipient of numerous awards and honors: Recipient of Douglas R. Phillips Award for Graduate Students, Department of Forestry and Environmental Conservation, Clemson University, April 12, 2019; the First Place Best Judged Poster (CAFLS) at the GRADS 2019: Clemson Student Research Forum on April 4, 2019; the Second Place Poster at the 11th Clemson Biological Sciences Annual Student Symposium, April 6, 2019; the Second Place Best Judged Poster at the Clemson Student Research Forum on April 4, 2018; and the Third Place Poster at the 9th Clemson Biological Sciences Annual Student Symposium, February 25, 2017. Dr. Zurqani conducts cutting-edge research in the field of environmental information science, remote sensing, land use management/planning, change detection of landscape degradation, and geographic information system (GIS) models. He has focused on his research efforts on the development of new technologies in the field of environmental information sciences, geo-intelligence (advanced geo-information science and earth observation, machine and deep learning, and big data analytics), remote sensing, land evaluation, pedology, land use management/ planning, monitoring and evaluating sustainable land management, change detection of landscape degradation, and geographic information system models.

Publications

Some of publications in Department of Soil and Sand

تتبع التغير في الغطاء الآرض ي لمنطقة الخمس للسنوات 0422 و 2110 و 2100 م باستخدام تقنية الاستشعار عن بعد.

يهدف هذا البحث إلى تتبع التغير الحاصل في الغطاء الأرضي للسنوات 1987 و 2001 و 2015 لمنطقة الخمس. تم الاعتماد على عمليات التجزئة والاستقطاع والتحسين والتصنيف غير الموجه وطريقة الاحتمالية القصوى في عملية التصنيف الموجه للبيانات والتي غطت مساحة 89768 هكتار. كما تم أخذ 95 نقطة تدريب ممثلة للبصمات الطيفية الموجودة بالمرئية الفضائية لسنة 2015. أوضحت النتائج أن منطقة الدراسة صنفت إلى ستة أغطية أراضية، وهي أراضي غابات وشجيرات، وأراضي زراعات مروية، وأراضي حضرية، وأراضي زراعات بعلية، وأراضي مراعي، وأراضي جرداء لكافة سنوات الدراسة. كما تبين أن أراضي الغابات أنخفضت بنسبة - 19.21 % في سنة 2001 عن سنة 1987 وبنسبة - 42.70 % سنة 2015، بينما الأراضي المروية ازدادت بنسبة 58.56 % سنة 2001 عن سنة 1987 وبنسبة 126.73 % سنة 2015. أما الأراضي الحضرية فلقد ازدادت بنسبة 136.65 % في سنة 2001 عن سنة 1987 وبنسبة 280.90 % في سنة 2015. أما الأراضي الزراعات البعلية فلقد أنخفضت بنسبة - 4.79 % من سنة 1987 إلى سنة 2001 وبنسبة - 32.50 % في سنة 2015. فيما أظهرت أراضي المراعي انخفاضاً بنسبة - 10.81 % سنة 2001 عن سنة 1987 وبنسبة - 14.62 % سنة 2015. تبين أيضاً أن الأراضي الجرداء ازدادت بنسبة 192.78 % في سنة 2001 ونسبة 353.15 % في سنة 2015. أوضحت النتائج أن الأنحسار الذي شهدته مساحات المراعي والغابات في المنطقة قد يؤدي إلى تدهور الغطاء النباتي واستفحال ظاهرة التصحر. كما تكشف الدراسة أهمية استخدام تقنيات الاستشعار عن بعد في مراقبة التغيرات التي قد تحدث على الغطاء الأرضي وتفسير تلك التغيرات.
مختار محمود مختار العالم, مصطفي شاكر دريبيكة, محمد مؤيد بن عمارة (11-2017)
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Usability of soil survey soil texture data for soil health indicator scoring

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)
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Prediction of Evapotranspiration using Artificial Neural Networks Model

Evapotranspiration is an important component in many hydrological, ecological and agricultural studies. There are many available direct and indirect methods to determine the evapotranspiration rate. In this study, alternative model based on multilayer Artificial Neural Network (ANN) using the backpropagation algorithm was proposed to estimate evapotranspiration as referred to pan evaporation. The meteorological data used in this study were obtained from Al-Zahra and Al-Zawia stations which located on the coastal area of western Libya lie. The input data were consisted of mean temperature, mean relative humidity and mean of actual sunshine hours of consecutive years (1995, 1996, 1997 and 1999). The performance of the ANN model was evaluated against a set of data that never seen by the model during the training phase. The evaluation of ANN model was also performed against Blaney and Criddle, Radiation and modified Penman methods. The results showed that ANN forecasts were superior to the ones obtained by Blaney and Criddle and Radiation methods. Due to its little input data, ANN is considered to be more efficient as compared with the modified Penman method. However, this application of ANN as a fitting tool should be useful in evapotranspiration modeling. Keywords: Evapotranspiration Pan evaporation, Artificial neural networks, Backpropagation algorithm.
Ahmed Ibrahim Ekhmaj(1-2012)
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