Research Team
Kaiwan K. Fatah is working on his PhD project, ‘Using GIS and Remote Sensing to Investigate the Relationship of Climate and LULC with Surface/Ground Water and Their Sustainability and Natural Disasters in Akre District, Kurdistan Region, Iraq’. The major objectives of his research are: (1) mapping and delineating flood-susceptible areas in Akre using remote sensing and GIS-AHP approaches; (2) using remote sensing and GIS-based multi-criteria evaluation and frequency ratio for identifying landslide vulnerability zones and risk valuation in Akre; (3) mapping groundwater potential zones in Akre using several machine learning algorithms through the utilization of remotely sensed data; and (4) exploring the relationship among Land Use/Cover, climate factors, and groundwater level fluctuation using GIS and remote sensing techniques along with the deep learning approaches. Furthermore, our collaboration includes Asst. Prof. Dr. Imaddadin O. Hassan.
Abdulqadeer I. A. Rash started his PhD project, ‘Assessing, Modeling, and Simulating Land Use/Land Cover Dynamics in Selected Districts in the Northeast of Erbil Province’. Abdulqadeer's research is focused on addressing the following inquiries in a professional and academic manner: What is the potential of hybrid modeling for identifying future changes? Which modeling approach is most suitable for land use and land cover (LULC) change analysis? How do changes in LULC impact land surface temperature within the study area? What are the primary driving forces behind LULC change patterns that affect the study area? Which specific types of driving forces predominantly shape the land change process in the study area? To explore these questions, Abdulqadeer employed various machine learning algorithms utilizing both remote sensing and field data. The project's findings will provide a comprehensive overview of the changes that have occurred and forecast future trends. Consequently, policymakers should consider taking appropriate actions based on the outcomes derived from this research. We work together with Asst. Prof. Dr. Rahel Othman Hamad.
Abdullah A. Abdullah conducted his doctoral research on the topic of managing uncertainty in deep learning models through a Bayesian approach. His dissertation centers on the utilization of a non-Bayesian deep learning framework, which is then adapted to incorporate Bayesian deep learning through the application of variational inference as an approximation technique. The primary objective of his study is to evaluate the level of confidence in predicting outcomes generated by deep learning models using BDL. Specifically, his research emphasizes quantifying uncertainty in MLP-mixer architectures rather than solely focusing on attaining optimal results. We work together with Asst. Prof. Dr. Masoud M. Hassan.
Hendaf N. Habeeb's doctoral investigation centers on examining the impact of temporal variations in forest coverage on ecosystem services within socio-ecological systems, while also exploring prospective scenarios for potential alterations in forests and climate. The research employs a combination of remote sensing data, survey techniques, and interviews to comprehensively assess the magnitude and dynamics of the observed fluctuations in forest cover over space and time.
Contact
Yaseen Taha Mustafa, PhD
Professor of Applied Remote Sensing & GIS
University of Zakho,
P.O. Box 12, Zakho, Duhok, Kurdistan Region-Iraq
Mobile: +9647504922553
Email: yaseen.Mustafa@uoz.edu.krd
mustafa@itc.nl
Alumni
Islam Sabah Khurshed successfully concluded his Master of Science project in the year 2024, with a focus on elucidating the pivotal role played by remote sensing technology in the evaluation and comprehension of the spatial and temporal fluctuations in surface soil moisture (SSM) and vegetation cover. Conducted over a twenty-year period from 2001 to 2021 within the Batifa region in the northern part of the Kurdistan Region of Iraq, his research leveraged Landsat satellite imagery to categorize land use and land cover (LULC) by employing a support vector machine (SVM) algorithm, and it systematically extracted soil classifications on an annual basis. The study was distinguished by its exceptional accuracy in LULC mapping, as demonstrated by the high values of overall accuracy and kappa coefficient. Furthermore, Khurshed innovated a random forest (RF) modeling technique for the estimation and mapping of SSM, which showcased considerable predictive precision. His research elucidated the interconnections between SSM and vegetation cover, revealing the influence of human activities on the ecological conditions in Batifa. The project, which benefited from the active collaboration of Assistant Professor Dr. Mohammed A. Fayyahd, highlighted the critical importance of remote sensing technologies in contributing to informed strategies for sustainable environmental management and the conservation of soil resources.
Halmat Sarbast Khalaf accomplished her MSc project in 2023, focusing on the crucial yet understudied domain of Soil Organic Matter (SOM) in Batifa, a district of Zakho in the Kurdistan region of Iraq. Her research utilized advanced machine learning algorithms, notably Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to predict SOM content, leveraging a unique dataset that combined remotely sensed data from Landsat 8 imagery with field observations, including spectral bands and a Digital Elevation Model (DEM). Khalaf's study employed indices such as NDVI and SAVI, among others, to train and test the predictive models. Her findings highlighted the superior accuracy of the XGBoost algorithm in SOM prediction, with significant variables like SAVI and DEM playing a crucial role. This pioneering work, conducted in collaboration with Asst. Prof. Dr. Mohammed A. Fayyadh, offers invaluable insights for agricultural stakeholders in Batifa and similar regions, promoting sustainable practices and informed decision-making for soil conservation and agricultural productivity.
Wassfi H. Sulaiman completed his MSc in 2023, focusing on the critical evaluation of groundwater (GW) availability in the Zakho Basin-Iraqi Kurdistan Region. It is challenged by political complexities, environmental changes, and urban expansion. Utilizing the Analytic Hierarchy Process (AHP) alongside geospatial techniques, Sulaiman's study identified potential GW sites by integrating various criteria such as slope, flow accumulation, and rainfall, among others, into a composite index. The accuracy of his model was validated through significant statistical measures, highlighting areas with varying GW potential. His research culminates in the creation of a Groundwater Potential Index (GWPZ) map, offering invaluable insights for sustainable GW management. This framework not only aids local stakeholders in enhancing water resource utilization but also sets a precedent for addressing GW conservation in other drought-affected regions.
Bushra Shaban Yousif completed her MSc in 2023, exploring soil texture's impact on agriculture and land management in the Batifa, Zakho, Kurdistan Region of Iraq. Addressing the scarcity of digital soil maps, she utilized Landsat 8 imagery and a Digital Elevation Model (DEM) to estimate soil textures using machine learning models: Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Among these, XGBoost showed superior accuracy in predicting soil textures, particularly for clay, silt, and sand. Her analysis, based on 96 soil samples, revealed a predominance of loamy textures within the study area, contributing to a better understanding of soil heterogeneity. This research aids in creating digital soil maps, enhancing agricultural planning, and promoting sustainable soil management in regions with varied soil textures. This project was a collaborative effort with Asst. Prof. Dr. Mohammed A. Fayyadh.
Rebar T. Ali completed his PhD research from 2019 to 2022, focusing on the utilization of geoinformatics techniques for analyzing the environmental impact in Duhok Province, Kurdistan Region, Iraq. The primary objectives of his research included assessing changes in vegetation cover within the four districts of Duhok governorate from 2000 to 2019, using the modified soil-adjusted vegetation index (MSAVI2) in relation to elevation, slope, and aspect. Additionally, he investigated the relationship between spatiotemporal vegetation distribution and climatic factors. Furthermore, he assessed the vulnerability of land degradation (LD) in the Duhok district by integrating remote sensing, GIS, and the Analytic Hierarchy Process (AHP) approach. The research also aimed to determine the extent of LD in the area and identify its spatial distribution within the Duhok district. Moreover, the research aimed to spatially identify the distribution of vegetation and land surface temperature (LST) in the Duhok district and analyze the spatiotemporal relationship between LST and vegetation, as well as the relationship between LST and air temperature. Various data sources were employed in the research project, including satellite images from Landsat and Sentinel-2, meteorological data, geological data, and ground truthing data. As a result of his PhD research, Rebar T. Ali produced three publications. Professor Abdulla A. Omar actively participated in this collaborative project.
Mohammed H. Obeyed completed his PhD between 2016 and 2020, focusing on the establishment of allometric equations for determining the wet weight and dry weight of individual trees and tree stands. Throughout his research, he conducted various field surveys and engaged in laboratory work. Additionally, he utilized high-resolution satellite imagery (WorldView-2) to identify and delineate single trees or groups of trees. The primary outcomes of his study include the estimation of aboveground biomass and carbon sequestration for natural stands of Quercus Aegilops in Duhok Province. Furthermore, he successfully estimated and mapped the aboveground biomass of natural Quercus Aegilops using WorldView-3 imagery. We have been working on this together with Prof. Zaki M. Akrawee.
Shaheen Abdulkareem pursued her PhD research (2015–2019) on the intersection of spatial agent-based modeling (ABM) and artificial intelligence (AI). In her doctoral thesis, she concentrated on investigating the potential of AI-based learning techniques to enhance the decision-making capabilities of agents within ABMs. Shaheen conducted a comprehensive examination of the effects of various learning algorithms employed to augment social, spatial, and institutional intelligence, both at the individual and group levels. She applied these methodologies to two distinct spatial ABMs: a cholera diffusion disease model aimed at analyzing individual protective behavior, and a flood risk housing market model focused on studying expectation formation. Collaboratively, the research was conducted in conjunction with Prof. Tatiana Filatova and Assoc. Prof. Dr. E-W Augustijn.