Article
Green & Sustainable Science & Technology
Wafaa Majeed Mutashar Al-Hameedi, Jie Chen, Cheechouyang Faichia, Biswajit Nath, Bazel Al-Shaibah, Ali Al-Aizari
Summary: Understanding future landscape risk pattern change scenarios can help manage and utilize natural resources effectively. This study examined various landscape and anthropogenic factors and found that land use/cover change and land surface temperature are significant drivers of environmental changes. The analysis of past and future changes in these factors revealed an increase in risk levels in Baghdad City over the coming decades.
Article
Biodiversity Conservation
Marzieh Mokarram, Tam Minh Pham
Summary: This study uses a novel meteorological drought-based approach to predict the yield of pomegranate and palm trees in southern Iran, and identifies the most effective drought indices through remote-sensing indices and principal component analysis. The results predict that approximately 50-60% of the region will have low yields for these crops in 2040. This approach provides a framework for predicting the decreasing crop yield due to drought effects and supporting decision-making in sustainable horticultural management.
ECOLOGICAL INDICATORS
(2022)
Article
Environmental Sciences
Sajjad Hussain, Muhammad Mubeen, Wajid Nasim, Faisal Mumtaz, Hazem Ghassan Abdo, Raoof Mostafazadeh, Shah Fahad
Summary: This research utilized remote sensing technology to analyze historical trends and predict future changes in land use, land cover, and climate. The findings reveal an increasing built-up area, decreasing vegetation, and rising land surface temperature in the Multan region. This study underscores the importance of remote sensing technology in understanding past and predicting future land transformations and temperature variations, providing valuable insights for decision makers in sustainable land use and climate adaptation.
Article
Engineering, Environmental
Islam Atef, Wael Ahmed, Ramadan H. Abdel-Maguid
Summary: This study utilizes satellite images and algorithm to monitor the land use land cover (LULC) changes in El-Fayoum governorate. It predicts future scenarios and validates the simulation using imagery data. The findings provide crucial information for planning and sustainable land use management.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Environmental Studies
Qingyang Zhang, Xinyan Cai, Xiaoliang Liu, Xiaomei Yang, Zhihua Wang
Summary: Urbanization has negative effects on the ecological environment of urban agglomerations, including the reduction of forest area and fragmentation of landscape indices. The development of urbanization in the Greater Bay Area is strongly correlated with changes in forest and grassland indicators, indicating the significant impact of urbanization on the peripheral environment.
Article
Environmental Sciences
Zhenyu Zhang, Georg Hoermann, Jinliang Huang, Nicola Fohrer
Summary: Understanding land use/cover change (LUCC) in watersheds is crucial for sustainable development. The machine learning-based CA-Markov model comprehensively evaluates the factors influencing LUCC, identifies patterns under different scenarios, and can serve as a helpful tool for watershed management.
Article
Environmental Studies
Yecheng He, Weicheng Wu, Xinyuan Xie, Xinxin Ke, Yifei Song, Cuimin Zhou, Wenjing Li, Yuan Li, Rong Jing, Peixia Song, Linqian Fu, Chunlian Mao, Meng Xie, Sicheng Li, Aohui Li, Xiaoping Song, Aiqing Chen
Summary: Land use/cover change detection and modeling are crucial for global environmental change research and policy-making. In this study, a new hybrid model (LMCM) was proposed to better predict future land use patterns using logistic regression coefficients as impact weights. Experimental results showed that the LMCM model outperformed other models in terms of simulation accuracy. The results of this study can provide scientific support for spatial planning in Hefei, China, and the LMCM model can be applied to similar purposes in other areas.
Article
Environmental Studies
Aboubakar Gasirabo, Chen Xi, Baligira R. Hamad, Umwali Dufatanye Edovia
Summary: The population growth and economic development in the Nile Nyabarongo River basin have significantly impacted land use and cover (LULC) patterns. This study evaluates past and predicts future changes in LULC, highlighting an increase in cropland and grassland and a decrease in forest and water areas over the next thirty years.
Article
Environmental Sciences
Yang Yi, Chen Zhang, Jinqi Zhu, Yugang Zhang, Hao Sun, Hongzhang Kang
Summary: With the rapid advancement of urbanization and industrialization, the contradiction between the social economy and resources and the environment has become increasingly prominent. The aim of this study is to obtain land-use-change data in the study area using remote-sensing data inversion and multiple-model simulation, and to predict and optimize the land use structure in 2030 based on land suitability evaluation. The study also evaluates and compares ecosystem services, and emphasizes the importance of reasonable and scientific land use planning for regional ecosystem service function improvement.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Computer Science, Interdisciplinary Applications
Nabaz R. Khwarahm, Sarchil Qader, Korsh Ararat, Ayad M. Fadhil Al-Quraishi
Summary: This study utilized the synergy Cellular Automata (CA)-Markov model to model and predict future changes of land use land cover (LULC) in the Erbil governorate of the Kurdistan region in Iraq. The results indicate an increase in built-up land, agricultural land, dense vegetation, and water bodies, while a decrease in sparse vegetation and barren land is projected.
EARTH SCIENCE INFORMATICS
(2021)
Article
Environmental Sciences
Salman A. H. Selmy, Dmitry E. Kucher, Gintautas Mozgeris, Ali R. A. Moursy, Raimundo Jimenez-Ballesta, Olga D. Kucher, Mohamed E. Fadl, Abdel-rahman A. Mustafa
Summary: Understanding the dynamics of land use and land cover change is crucial for ecological management and land use planning. This study used remote sensing data and modeling to identify, simulate, and predict the historical and future changes in land use and land cover in the Sohag Governorate, Egypt. The results showed that urban areas expanded, desert lands decreased, and cultivated lands increased. The validation of the model indicated that it was accurate in predicting land use and land cover changes.
Article
Geosciences, Multidisciplinary
Aqil Tariq, Jianguo Yan, Faisal Mumtaz
Summary: Urbanization is a global phenomenon that leads to dramatic changes in land use and land cover in many regions, particularly due to urban sprawl and its consequences. This study assesses and predicts the urban growth and land use and land cover changes in Peshawar city using the Cellular-Automata-Markov-Chain model. The results show significant expansion in built-up areas and vegetation, replacing bare lands. The model predicts further growth in built-up areas and vegetation in the future.
PHYSICS AND CHEMISTRY OF THE EARTH
(2022)
Article
Environmental Sciences
Jingyi Liu, Yong Zhou, Li Wang, Qian Zuo, Qing Li, Nan He
Summary: Land use/cover change (LUCC) accompanied by climate change and human activities will have unpredictable impacts on watershed ecosystems. However, the extent to which these land use changes affect the spatial and temporal distribution of ecosystem services (ESs) in different regions remains unclear. This study assessed the impact of LUCC on water yield, soil conservation, carbon storage, and habitat quality in the Qingjiang Watershed, and predicted the effects of different land use scenarios on these ecosystem services in 2034. The results suggest that the EEC scenario can be chosen as a future development plan for promoting sustainable regional development in mountainous watershed areas.
Article
Forestry
Deqing Liu, Xiaoli Zhang
Summary: Using the Cellular Automata (CA)-Markov model, this study predicts the occurrence area of Pine Wilt Disease (PWD) in Anhui Province in 2030 and analyzes its spatial patterns and trends. The research provides scientific support for the prevention and control of PWD in the region and reveals the potential future spread of the disease.
Article
Biodiversity Conservation
Chenglong Xu, Qibin Zhang, Qiang Yu, Jiping Wang, Fei Wang, Shi Qiu, Mingsi Ai, Jikai Zhao
Summary: Land use/cover change (LUCC) is the main factor contributing to changes in carbon storage in ecosystems. However, there are limited studies on the impact and driving mechanisms of LUCC on carbon storage at spatial-temporal scales. This study focuses on the Yellow River Basin (YRB) and analyzes LUCC from 2000 to 2020, predicts land use patterns in 2040 under different scenarios, and quantifies carbon storage using integrated models. The findings show significant LUCC in the YRB, an upward trend in carbon storage with woodland playing a key role, and the importance of converting built-up land for enhancing carbon sequestration efficiency. It emphasizes the need for ecological protection and low-carbon development in the YRB to ensure sustainable carbon storage.
ECOLOGICAL INDICATORS
(2023)
Article
Multidisciplinary Sciences
Kashif Ullah, Jiquan Zhang
Article
Environmental Sciences
Bazel Al-Shaibah, Xingpeng Liu, Jiquan Zhang, Zhijun Tong, Mingxi Zhang, Ahmed El-Zeiny, Cheechouyang Faichia, Muhammad Hussain, Muhammad Tayyab
Summary: Erlong Lake in Jilin, China, was assessed for water quality using Landsat images, showing strong correlations between estimated and measured water quality parameters, with high accuracy after calibration. The study highlights the importance of remote sensing in monitoring and assessing water quality in freshwater bodies.
Article
Green & Sustainable Science & Technology
Muhammad Hussain, Muhammad Tayyab, Jiquan Zhang, Ashfaq Ahmad Shah, Kashif Ullah, Ummer Mehmood, Bazel Al-Shaibah
Summary: This study used a Geographic information system (GIS)-based multi-criteria approach to assess detailed flood vulnerability in the District Shangla. The results showed that the western to northern parts of the study area have high vulnerability. Even after integrating coping capacity, the western-central and northern parts still have high vulnerability, while the coping capacities in the central and eastern areas are higher.
Article
Environmental Sciences
Wafaa Majeed Mutashar Al-Hameedi, Jie Chen, Cheechouyang Faichia, Bazel Al-Shaibah, Biswajit Nath, Abdulla-Al Kafy, Gao Hu, Ali Al-Aizari
Summary: This study analyzes the land use/cover changes in Baghdad City over the past 35 years using remote sensing data, revealing rapid urban construction land expansion and significant loss of agricultural land and natural vegetation. Future simulations indicate a massive decreasing trend in agricultural land compared to other categories by 2050.
Article
Environmental Sciences
Saira Munawar, Ghani Rahman, Muhammad Farhan Ul Moazzam, Muhammad Miandad, Kashif Ullah, Nadhir Al-Ansari, Nguyen Thi Thuy Linh
Summary: Climate change directly impacts the cryosphere and hydrosphere, making it one of the leading issues affecting river basins. General circulation models (GCMs) are commonly used tools to assess climate change, but their coarse spatial resolution limits their direct application for local studies. This study evaluated the performance of five CMIP5 GCMs for downscaling climatic indicators and found that statistical downscaling methods (SDSM) were more efficient than the Long Ashton Research Station Weather Generator (LARS-WG). The results also showed that winter and pre-monsoon seasons would be most affected by warming and increased precipitation in the future.
Article
Green & Sustainable Science & Technology
Wafaa Majeed Mutashar Al-Hameedi, Jie Chen, Cheechouyang Faichia, Biswajit Nath, Bazel Al-Shaibah, Ali Al-Aizari
Summary: Understanding future landscape risk pattern change scenarios can help manage and utilize natural resources effectively. This study examined various landscape and anthropogenic factors and found that land use/cover change and land surface temperature are significant drivers of environmental changes. The analysis of past and future changes in these factors revealed an increase in risk levels in Baghdad City over the coming decades.
Article
Environmental Sciences
Ali R. Al-Aizari, Yousef A. Al-Masnay, Ali Aydda, Jiquan Zhang, Kashif Ullah, Abu Reza Md Towfiqul Islam, Tayyiba Habib, Dawuda Usman Kaku, Jean Claude Nizeyimana, Bazel Al-Shaibah, Yasser M. Khalil, Wafaa M. M. AL-Hameedi, Xingpeng Liu
Summary: This study assesses flood susceptibility in the desert environment of Yemen using remote sensing devices and machine learning algorithms. The results show that all models have a high capacity to predict floods, with the tree-based ensemble algorithms performing the best. This research is important for assessing disaster susceptibility and reducing the risk of natural disasters.
Article
Geosciences, Multidisciplinary
Kashif Ullah, Yi Wang, Zhice Fang, Lizhe Wang, Mahfuzur Rahman
Summary: Multi-hazard susceptibility prediction is crucial for disaster risk management. This study proposes a mapping framework based on Convolutional Neural Networks (CNN) to predict the probability of flash floods, debris flows, and landslides. The results show that the CNN method outperforms conventional machine learning algorithms, and the susceptibility maps of the hazards can be combined to create a multi-hazard susceptibility map. This research has practical implications for engineers, disaster managers, and government officials in land management and risk mitigation.
GEOSCIENCE FRONTIERS
(2022)
Article
Multidisciplinary Sciences
Yousef A. Al-Masnay, Nabil M. Al-Areeq, Kashif Ullah, Ali R. Al-Aizari, Mahfuzur Rahman, Changcheng Wang, Jiquan Zhang, Xingpeng Liu
Summary: In this study, four advanced machine learning algorithms were used to assess the hazard susceptibility of earth fissures, with random forest algorithm showing the best performance. The findings can assist decision-makers in land management planning and protecting society and the ecosystem.
SCIENTIFIC REPORTS
(2022)
Article
Geography, Physical
Penglei Li, Yi Wang, Tongzhen Si, Kashif Ullah, Wei Han, Lizhe Wang
Summary: In this study, a novel Domain Style and Feature Adaptation (DSFA) method is proposed for cross-scene landslide detection from high spatial resolution images, which leverages labeled source domain images and unlabeled target domain images to mine robust landslide representations. Experimental results demonstrate that DSFA has superior detection capability and outperforms other methods in unsupervised landslide domain detection.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2023)
Article
Engineering, Civil
Mahfuzur Rahman, Md Sakib Hasan Tumon, Md Monirul Islam, Ningsheng Chen, Quoc Bao Pham, Kashif Ullah, Sumaiya Jarin Ahammed, Sharmina Naznin Liza, Md Abdul Aziz, Salit Chakma, Muhammad Esmat Enan, Md. Alomgir Hossain, Tian Shufeng, Ashraf Dewan
Summary: This study successfully predicts current and future drought susceptibility in Bangladesh using historical climate data and model prediction, providing valuable information for reducing future drought impacts and assisting policy responses.
JOURNAL OF HYDROLOGY
(2023)
Article
Environmental Sciences
Muhammad Hussain, Muhammad Tayyab, Kashif Ullah, Safi Ullah, Zahid Ur Rahman, Jiquan Zhang, Bazel Al-Shaibah
Summary: Flood resilience assessment is crucial for understanding a community's ability to withstand and recover from flood disasters. However, quantifying and operationalizing resilience remains challenging. This study proposes a novel flood resilience model, CapFlooR-M, which integrates machine learning, GIS, RS, and AHP. The model incorporates different components such as flood hazard susceptibility, coping capacity, adaptive capacity, and transformative capacity. By using RF and SVM models, a susceptibility map is created, and AHP is utilized to compute the relative scores of core capacities. The integration of these maps with overlay analysis in GIS produces a flood resilience map. The findings of this study provide valuable insights for policymakers and planners in building resilience against flood hazards.
