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Land Use Change Analysis and Modeling Using Open Source (QGIS) Case Study: Boasher Willayat

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Abstract

Issues of land use/land cover changes and the direct or indirect relationships of these changes have drawn much attention in recent years. This research is an attempt to examine the use of QGIS Open source software integrated with GIS techniques to detect, evaluate, and analyze LULC change between 2000 and 2010, to project the future of LULC. MOLUSCE plugin was used for produced the map of area change between study period, and provide transition matrix the represented the replacement from one to others landuse. Finally to detect the future of LULC for 2025 by used Cellular Automata Simulation. As expected, the results showed an increase in urbanization (residential, public building and transport area) while decreasing in agriculture. In the term of projected LULC, the result also suggests a significant increase in residential and public building area. Meanwhile, predictions suggest the agriculture will be reduced by 1.49% in 2025. In conclusion this technique was to be a powerful tools for monitoring and modeling land use and change in land cover.
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... Arazi kullanımı ve arazi örtüsü bilgisi, yeryüzünün özelliklerini modellemek ve anlamak için temel bir unsur olarak kabul edildiğinden, birçok planlama ve yönetim faaliyeti için önem arz etmektedir (Alrubkhi, 2017). Arazi kullanımı değişikliklerinin etkileri nedeniyle, tüm dünyada bu değişiklikleri izleme, değerlendirme, haritalama ve tahmin etmeye yönelik çalışmalar yoğun olarak yapılmaktadır (El-tantawi, vd., 2019). ...
... Değişim haritaları ve modelleme oluşturabilmek için QGIS 2.18.0 içerisinde bir eklenti olan (plug-in) MOLUSCE (Modules for Land-use Change Evaluation) kullanılmıştır. Modellemenin oluşturulması için gerekli olan değişkenler; daha önce yapılmış çalışmalardan yola çıkarak yükselti, eğim, bakı, akarsuya uzaklık ve kara yoluna uzaklık olarak belirlenmiştir(Alrubkhi, 2017; Rahman vd. 2017; Landry vd. ...
... The CA feature in QGIS is based on the Markov chain algorithm; i.e., it relies on the present state of land use rather than the previous state [15]. This model generates the output data in the form of tables and maps by combining previous and current land use maps with spatial input parameters [61]. Based on that data, MOLUSCE uses algorithms to train the model. ...
... The minimal errors related to the simulation can be attributed to the inaccurate identification of land use patterns and misinterpretation of spatial parameters (i.e., network of roads, elevation, and slope maps). Similar trends have been reported during the validation of simulated maps in previous studies [61,81,82]. ...
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Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.
... As a result, continuous follow-up study does not take place. In order to overcome this problem, a very powerful and robust open source Geographic Information Software, QGIS ( Al-Rubkhi et al., 2017 ;Correia et al., 2018 ;Meyer and Riechert, 2019 ;Usha et al., 2012 ), has been entirely used in this study. The analysis of land cover assessment, particularly focusing on forest land cover of this area, was conducted on the following five categories: Forest, Non-Forest-Vegetation, Barren Land, Sand and Water Body. ...
... All the remote sensing (RS) and Geographic Information Software (GIS) work has been done using QGIS software (version 3.10.1) ( Al-Rubkhi et al., 2017 ;Correia et al., 2018 ;Meyer and Riechert, 2019 ;Usha et al., 2012 ). The work flowchart is given in Fig. 2 . ...
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... Aunado a lo anterior, se realizó un análisis espacial para ponderar el efecto de las vialidades sobre la eliminación de hábitats. Para tal fin, se utilizó el módulo MOLUSCE de QGis 2.18 (Alrubkhi, 2017), usando las capas de carreteras y los datos del terreno (pendiente y aspecto), como variables de entrada para explicar las tendencias de cambio de uso del suelo. ...
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... Aunado a lo anterior, se realizó un análisis espacial para ponderar el efecto de las vialidades sobre la eliminación de hábitats. Para tal fin, se utilizó el módulo MOLUSCE de QGis 2.18 (Alrubkhi, 2017), usando las capas de carreteras y los datos del terreno (pendiente y aspecto), como variables de entrada para explicar las tendencias de cambio de uso del suelo. ...
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... The classified maps, confusion matrix table, and area estimation table are then exported locally from GEE. To analyze multi-year change in the area of each class, change detection is performed using QGIS software (available at https:// qgis.org/en/site/), which is an open-source GIS software (Al-Rubkhi et al., 2017). Furthermore, Python 3 libraries such as NumPy, Pandas, Matplotlib, and Seaborn are used for statistical analyses and visualization. ...
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... For each class, potential transitions are calculated and the simulator develops a raster of the most probable transitions. For every transition belonging to most probable transitions, the simulator searches a fixed number of pixels having the greatest certainty and then changes the class of the pixels (GIS-Lab 2018; Al-Rubkhi et al. 2017). In this study, simulations have been performed with one iteration, meaning that the simulation will run once and will give the simulation of the next time period depending on the gap between the initial and final year. ...
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