Land Use/Land Cover Change Analysis Using Multi-Temporal Remote Sensing Data: A Case Study of Tigris and Euphrates Rivers Basin

Multi-temporal land use/land cover (LULC) change analysis is essential for environmental planning and recourses management. Various global LULC datasets are available now. However, they do not show strong consistency on a regional scale and are mainly time limited. Therefore, high-quality multi-temporal LULC mapping with reasonable consistency on a regional scale is still demanding. In this study, using the Landsat 7, Landsat 8, and the NASA digital elevation model (DEM), LULC mapping of the Tigris and Euphrates rivers basin (TEB) was performed by random forest (RF) classifier in the Google Earth Engine platform during 2000–2022. The spectral bands, spectral indices, morphological, and textural features were applied in the developed procedure. The results indicated that the proposed approach had accurate performance (accuracy = 0.893 and an F score = 0.820) with a good consistency with previous studies. The feature importance evaluation was carried out using Gini index, and spectral indices were identified as the most important features in LULC mapping. Overall, severe LULC change has happened in the TEB during the last two decades. Our results revealed the expansion of water and built-up classes while trees class has experienced a decreasing trend. From a regional perspective, three main areas in the east and south-east of Iraq, north-west of Iraq, and east of Syria were identified where LULC change was intense. These areas are prone to land degradation and dust storms emission problems, and it is necessary to take steps to prevent severe LULC changes in them.

[1]  T. Kavzoglu,et al.  Effects of auxiliary and ancillary data on LULC classification in a heterogeneous environment using optimized random forest algorithm , 2022, Earth Science Informatics.

[2]  H. Yousefi,et al.  Crop classification based on phenology information by using time series of optical and synthetic-aperture radar images , 2022, Remote Sensing Applications: Society and Environment.

[3]  Farbod Farhangi.  Investigating the role of data preprocessing, hyperparameters tuning, and type of machine learning algorithm in the improvement of drowsy EEG signal modeling , 2022, Intell. Syst. Appl..

[4]  Alexander M. Tait,et al.  Dynamic World, Near real-time global 10 m land use land cover mapping , 2022, Scientific Data.

[5]  A. Al-Muhyi.  Impacts of Global Climate Change on Temperature and Precipitation in Basra City, Iraq , 2022, BASRA JOURNAL OF SCIENCE.

[6]  K. Kanniah,et al.  Land use and land cover change and its impact on river morphology in Johor River Basin, Malaysia , 2022, Journal of Hydrology: Regional Studies.

[7]  G. Feyisa,et al.  Detection of Land Use/Land Cover and Land Surface Temperature change in the Suha Watershed, north-western highlands of Ethiopia. , 2022, Environmental Challenges.

[8]  Mohammad Tofayal Ahmed,et al.  Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh , 2022, Remote Sensing Applications: Society and Environment.

[9]  M. A. Delavar,et al.  Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. , 2022, The Science of the total environment.

[10]  V. Sridhar,et al.  Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India , 2021, Sustainability.

[11]  Bishal Roy.  A machine learning approach to monitoring and forecasting spatio-temporal dynamics of land cover in Cox's Bazar district, Bangladesh from 2001 to 2019 , 2021 .

[12]  Martin Drews,et al.  A machine learning-based classification of LANDSAT images to map land use and land cover of India , 2021, Remote Sensing Applications: Society and Environment.

[13]  A. T. Al-Madhhachi,et al.  Statistical analysis of extreme weather events in the Diyala River basin, Iraq , 2021, Journal of Water and Climate Change.

[14]  Abolghasem Sadeghi-Niaraki,et al.  Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale , 2021, Sustainability.

[15]  A. Pandey,et al.  Evaluation of classification algorithms for land use land cover mapping in the snow-fed Alaknanda River Basin of the Northwest Himalayan Region , 2021, Applied Geomatics.

[16]  M. A. Mohamed.  Spatiotemporal Impacts of Urban Land Use/Land Cover Changes on Land Surface Temperature: A Comparative Study of Damascus and Aleppo (Syria) , 2021, Atmosphere.

[17]  Marco Vizzari,et al.  Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park , 2021, Remote. Sens..

[18]  Xiaohong Dang,et al.  Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine , 2021, Geocarto International.

[19]  Aixia Yang,et al.  Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin , 2021, Remote. Sens..

[20]  Jie Dou,et al.  Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms , 2021, Remote. Sens..

[21]  A. A. Kadhim,et al.  Unplanned Urbanization and Agricultural Land Degradation in Baghdad City from 2003 to 2017 , 2021 .

[22]  S. Dhakal,et al.  Time series analysis of land use and land cover changes related to urban heat island intensity: Case of Bangkok Metropolitan Area in Thailand , 2020 .

[23]  C. Schneider,et al.  Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS , 2020, Land.

[24]  Ali Jamali,et al.  Land use land cover mapping using advanced machine learning classifiers: A case study of Shiraz city, Iran , 2020, Earth Science Informatics.

[25]  Antonio J. Plaza,et al.  Deep Learning for Land Cover Classification Using Only a Few Bands , 2020, Remote. Sens..

[26]  Eufemia Tarantino,et al.  Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment , 2020, Remote. Sens..

[27]  Yuei-An Liou,et al.  Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review , 2020, Remote. Sens..

[28]  Saman Nadizadeh Shorabeh,et al.  Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin , 2020 .

[29]  R. Hamad.  A remote sensing and GIS-based analysis of urban sprawl in Soran District, Iraqi Kurdistan , 2019, SN Applied Sciences.

[30]  Sameh W. H. Al-Muqdadi.  Developing Strategy for Water Conflict Management and Transformation at Euphrates–Tigris Basin , 2019, Water.

[31]  Ali P. Yunus,et al.  Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques , 2019, Scientific Reports.

[32]  Abdulhakim M. Abdi,et al.  Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data , 2019, GIScience & Remote Sensing.

[33]  G. Narisma,et al.  The Effect of Urban Expansion in Metro Manila on the Southwest Monsoon Rainfall , 2019, Asia-Pacific Journal of Atmospheric Sciences.

[34]  Ziheng Sun,et al.  Selection of Landsat 8 OLI Band Combinations for Land Use and Land Cover Classification , 2019, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[35]  K. Rahi,et al.  Assessment of Surface Water Resources of Eastern Iraq , 2019, Hydrology.

[36]  N. Lau,et al.  Urban Expansion and Drying Climate in an Urban Agglomeration of East China , 2019, Geophysical Research Letters.

[37]  Boyang Zhou,et al.  Urbanization and Urban Sprawl Issues in City Structure: A Case of the Sulaymaniah Iraqi Kurdistan Region , 2019, Sustainability.

[38]  Chuanhao Wu,et al.  Effect of urban expansion on summer rainfall in the Pearl River Delta, South China , 2019, Journal of Hydrology.

[39]  Michael E. Schaepman,et al.  Barest Pixel Composite for Agricultural Areas Using Landsat Time Series , 2017, Remote. Sens..

[40]  S. Mukherjee,et al.  Normalized Difference Vegetation Index (NDVI) Based Classification to Assess the Change in Land Use/Land Cover (LULC) in Lower Assam, India , 2016 .

[41]  Xingfa Gu,et al.  Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China , 2014 .

[42]  P. K. Sinha,et al.  Pruning of Random Forest classifiers: A survey and future directions , 2012, 2012 International Conference on Data Science & Engineering (ICDSE).

[43]  Nittaya Kerdprasop,et al.  A Heuristic-Based Decision Tree Induction Method for Noisy Data , 2011, FGIT-DTA/BSBT.

[44]  R. Wilby,et al.  A review of the potential impacts of climate change on surface water quality , 2009 .

[45]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[46]  Jindi Wang,et al.  Monitoring the seasonal bare soil areas in Beijing using multitemporal TM images , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[47]  S. Yue,et al.  The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series , 2004 .

[48]  P. Zhai,et al.  Relationship between vegetation coverage and spring dust storms over northern China , 2004 .

[49]  Eleftherios Giovanis,et al.  The Transboundary Effects of Climate Change and Global Adaptation: the Case of the Euphrates-Tigris Water Basin in Turkey and Iraq , 2023, SSRN Electronic Journal.

[50]  M. Khorrami,et al.  Evaluation of tree-based ensemble algorithms for predicting the big five personality traits based on social media photos: Evidence from an Iranian sample , 2022, Personality and Individual Differences.

[51]  Fatih Demir.  Deep autoencoder-based automated brain tumor detection from MRI data , 2022, Artificial Intelligence-Based Brain-Computer Interface.

[52]  Timothy A. Warner,et al.  Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies - Part 2: Recommendations and Best Practices , 2021, Remote. Sens..

[53]  A. Hasan,et al.  MONITORING OF THE TEMPORAL CHANGES IN THE FORESTS OF NORTHERN IRAQ THROUGH THE DIRECTED CLASSIFICATION AND THE INDEX OF NATURAL VEGETATIVE DIFFERENCE , 2020 .

[54]  N. Adamo,et al.  Global Climate Change Impacts on Tigris-Euphrates Rivers Basins , 2020 .

[55]  N. Fawzi,et al.  Culture of common carp (Cyprinus carpio L.) in Basrah Governorate, southern Iraq; Current status and suggestions for development , 2020 .

[56]  Baraa Kamil Abdul Razzaq.  Contrast of the spatial distribution of the problem of random housing in the Baghdad Governorate " Baghdad Al gadeeda district" , 2019 .

[57]  M. D. Ugarte,et al.  An Introduction to the Spatio-Temporal Analysis of Satellite Remote Sensing Data for Geostatisticians , 2018 .

[58]  Biswajeet Pradhan,et al.  Extraction and Modeling of Urban Sprawl Development in Karbala City Using VHR Satellite Imagery , 2017 .

[59]  Biswajeet Pradhan,et al.  Spatial Modeling and Assessment of Urban Form: Analysis of Urban Growth: From Sprawl to Compact Using Geospatial Data , 2017 .