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Land Use Change Analysis and Modeling Using Open Source (QGIS) Case Study: Boasher Willayat
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.
... 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]. ...
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 . ...
Forest cover loss has a crucial impact on global climate change. The Nijhum Dwip National Park (NDNP), a deltaic part of the Sundarbans mangrove forest, has changed significantly over the decades. The goal of this study was to detect the decadal changes in forest cover using freely available satellite remote sensing data and GIS tools. A maximum likelihood classification technique was used to classify the Landsat images of 1990, 2001, 2011 and 2020. The potential of NDVI and SAVI-based classification for detection of forest cover changes in comparison to supervised classification was also evaluated. More than 42% of the total forest cover area (1781 ha) has been deforested over the decades between 1990 and 2011. The study also reported that nearly 1235 ha and over 856 ha of forest area disappeared from 4217.5 ha in 1990 to 2982.5 ha and 2126.16 ha in 2001 and 2011 respectively. In contrast, an increase of 310 ha of forest area has been recorded in the last decade, from 2011 to 2020. NDVI performed better in forest cover change detection than SAVI. This research will be invaluable for this type of research continuity and proper monitoring of this protected mangrove forest area, along with providing useful information to decision makers.
... 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. ...
... Para tal fin, se procedió a convertir a formato ráster las capas del Inegi de 1985 (Serie I) y 2015 (Serie VI), definiendo un solo tamaño de pixel y una sola extensión geográfica entre estas dos coberturas, para evitar errores en la geometría. Posteriormente se utilizó el módulo MOLUSCE de QGis 2.18, el cual mide el porcentaje del cambio de área en un periodo dado (30 años en este caso), utilizando métodos de Redes Artificiales Neuronales (Artificial Neural Network) y la regresión logística (Alrubkhi, 2017). Las variables de entrada a MOLUSCE, para explicar las variaciones de cambio de uso del suelo, fueron las capas de carreteras (RNC19) y los datos del terreno (pendiente y aspecto) tomados del MDE del Inegi (tabla 2). ...
Esta obra es el resultado de un esfuerzo conjunto de investigadores especiali-zados, quienes proponen soluciones a una problemática que en México lleva décadas ignorada: el impacto producido por la construcción y modernización de vías de comunicación.
... 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. ...
... Para tal fin, se procedió a convertir a formato ráster las capas del Inegi de 1985 (Serie I) y 2015 (Serie VI), definiendo un solo tamaño de pixel y una sola extensión geográfica entre estas dos coberturas, para evitar errores en la geometría. Posteriormente se utilizó el módulo MOLUSCE de QGis 2.18, el cual mide el porcentaje del cambio de área en un periodo dado (30 años en este caso), utilizando métodos de Redes Artificiales Neuronales (Artificial Neural Network) y la regresión logística (Alrubkhi, 2017). Las variables de entrada a MOLUSCE, para explicar las variaciones de cambio de uso del suelo, fueron las capas de carreteras (RNC19) y los datos del terreno (pendiente y aspecto) tomados del MDE del Inegi (tabla 2). ...
Se presenta una discusión extendida de los resultados de este libro, sobre los impactos que causa la infraestructura vial sobre la fauna silvestre: eliminación de hábitat (EH), efecto de barrera (EB) y deterioro de la calidad del hábitat (DCA). Para tal fin, se realizó un análisis cartográfico-espacial mediante SIG, incorporando datos duros de los estudios de casos. La EH directa es actualmente de poco más de 25,000 ha, causada principalmente por la infraestructura carretera en los últimos 200 años, mientras que la EH inducida ha sido hasta 450 veces mayor. Las vías de comunicación de la península de Yucatán han impactado a 61 especies de mamíferos, 54 de reptiles, 42 de aves y 15 de anfibios. Las áreas de mayor impacto de EB y DCA son los municipios centrales de Tabasco, el área conurbada de Mérida y la Riviera Maya. El Tren Maya exacerbará todos estos impactos, en un periodo de solo 10 años, sobre todo en los corredores biológicos de la región, así como en las ANP de Balam-kú y Calakmul.
... The spatial allocation of land use in 2025 was determined using cellular automata based on an ANN-CA in the Mollusce QGIS 2.18 plug-in [38]. The land use in 2025 was modeled using ANN-CA, which uses the human brain in thinking mechanisms [39], in four steps: (1) determining the input and network architecture, (2) training the network, (3) testing the data network, and (4) predicting future land use based on information from previous steps [40]. ...
The increasing population in Indonesia is challenging rice production to feed more people
while rice fields are being converted to other land-use land cover (LULC). This study analyzes land
use in 2015, 2017, 2019, 2021, and 2025 using an artificial neural network cellular automata (ANN-CA)
and rice data from Statistics Indonesia to predict future rice status in Malang Districts, Indonesia. The
primary LULC change driver was the rapid conversion of rice fields, which had their area reduced by
18% from 2019 to 2021 and 2% from 2021 to 2025. Rice fields are mainly being converted to settlements
and buildings. The Kappa coefficient of simulation achieved 88%, with 91 accuracies. The model
predicted a 2% lower rate of rice production but a 3% higher demand in 2025 compared to 2021.
Lower rice production and higher demand are predicted to reduce the rice surplus by 57% in 2025,
suggesting that the Malang district might lower its supply of rice to other areas by 2025. Our study
provides a food crisis early warning system that decision makers can use to form adequate strategic
plans and solutions to combat food insecurity.
... 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. ...
Monitoring spatial-temporal land use land cover (LULC) patterns and related processes (e.g., land surface temperature—LST) is essential to sustainable development at local, regional, and national levels. In this context, the present study leverages cloud-computing-based Google Earth Engine and geo-information modelling techniques to provide spatial-temporal insights regarding LULC and LST over the past three decades (1990–2020) in Pakistan—a south Asian country with ∼212 million people. Additionally, using Punjab province (the most populous and developed in Pakistan) as the study area, we empirically evaluate the association between several LULC types (i.e., built-up, forests, agriculture, rangeland, barren, and water) and LST. Our results show that due to the transition from rangeland and agriculture LULC to built-up areas (contributing 38 and 37%, respectively), ∼250% increase is observed in the impervious surface in Punjab during 1990–2020. While the rapid urbanization has resulted in ∼8.5 percent annual increase in built-up area during the study period, the highest percent change (∼10.5%) occurred during the most recent decade (i.e., 2010–2020). This increase in built-up areas has led to LST rise with 1.4 °C increase in maximum annual LST in Punjab. In addition, among the evaluated top-20 cities, the most significant rise in LST is observed by Kasur city followed by Chiniot, Sheikhupura, Sahiwal, and Lahore—areas known for industrial development in Pakistan. While the results on LULC provide important references for rational and optimal utilization of land resource via policy implications, the association between LULC and LST ascertains why it is critical to design sustainable LULC planning and management practices for climate change mitigation and adaptation.
... 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. ...
The urban sprawl is one of the potential threat for sustainable development. The present study has been carried out on Ahmedabad Municipal Corporation (AMC) area including Ahmedabad city to monitor the urban sprawl from 1976 to 2017. Normalized Difference Vegetation Index, Normalized Difference Built-Up Index, and Built-Up Index are applied to monitor the urban land use change by using multi-temporal and multi-date remote sensing satellite data from Landsat. The future scenario of urban land use prediction of AMC has been modelled by the use of cellular automata (CA) and artificial neural network (ANN) with GIS techniques. The results of land use monitoring indicate that the built-up area has increased by 156.93 km2 whereas, agricultural land, open spaces, urban vegetation, and water bodies have decreased during the last 4-decades from 1976 to 2017. Moreover, the results of CA simulation reveals that the built-up will continue to increase in the future at the cost of other classes. The decadal built-up growth was 2.58 km2 during the earlier period and it was the highest (5.98 km2) during the decade of 2007–2017. The validation of results related to CA–ANN has been measured by the Kappa coefficient in which the obtained values of Kappa local, Kappa histogram and Kappa overall were 0.92, 0.75, and 0.69 respectively and the measured percentage of correctness was 78.63%. The predicted urban land use growth reveals that the built-up would cover the maximum part of the AMC area by 2027 provided that the present land use trend, demographic growth, and commercial development does not show any major change. Therefore, the study pertaining to LULC change and its future prediction will be highly helpful for urban planners and administrators to prepare a sustainable city planning for the Ahmedabad city and its surroundings.
Land cover changes considered as one of the important global phenomena exerting perhaps one of the most significant effects on the environment than any other factor. It is, therefore, vital that accurate data on land cover changes are made available to facilitate the understanding of the link between land cover changes and environmental changes to allow planners to make effective decisions. In this paper, the post classification approach was used to detect and assess land cover changes of one of the important coastal zones in Egypt, Abu Qir Bay zone, based on the comparative analysis of independently produced classification images of the same area at different dates. In addition to satellite images, socioeconomic data were used with the aid of land use model EGSLR to indicate relation between land cover and land use changes. Results indicated that changes in different land covers reflected the changes in occupation status in specific zones. For example, in the south of Idku Lake zone, it was observed that the occupation of settlers changed from being unskilled workers to fishermen based on the expansion of the area of fish farms. Change rates increased dramatically in the period from 2004 to 2013 as remarkable negative changes were found especially in fruits and palm trees (i.e. loss of about 66km2 of land having fruits and palm trees) due to industrialization in the coastal area. Also, a rapid urbanization was monitored along the coastline of Abu Qir Bay zone due to the political conditions in Egypt (25th of January Revolution) within this period and which resulted to the temporary absence of monitoring systems to regulate urbanization.
The world of GIS is always in development, and this fact is particularly true for Open Source GIS. Among the Geographic Information System (GIS) software, in the last years Geographic Resource Analysis Support System (GRASS) GIS has known wide popularity as it is well documented. GRASS GIS was developed by the United States Army Construction Research Engineering Laboratories (USA CERL) as a tool for military land management and environmental planning. In the early nineties its licenses switched to GPL and by 1998 a GNU/Linux release was freely available. GRASS has evolved into one of the most comprehensive, general purpose open source geoinformation systems since its original design as a land management software tool for military installations. Support for environmental applications has been an integral part of its 20+ years of development. Current stable release includes, among other features, a 2D/3D vectorial engine, SQL based database systems and a rich variety of raster and vector graphic formats, allowing
applications in the areas such as geospatial data management and analysis, image processing, graphics/maps production, spatial modeling and visualization, both in academic and commercial fields as well as by many governmental agencies and environmental consulting companies. Change detection is the measure of the distinct data framework and thematic change information that can guide to more tangible insights into underlying process involving land cover and land use changes than the information obtained from continuous change. Digital change detection is the process that helps in determining the changes associated with land use and land cover properties with reference to geo-registered multitemporal remote sensing data. It helps in identifying change between two or more dates that is uncharacterized of normal variation. Change detection is useful in many applications such as land use changes, habitat fragmentation, rate of deforestation, urban sprawl, and other cumulative changes through spatial and temporal analysis techniques such as GIS and Remote Sensing along with digital image processing techniques. Vegetation indices (VIs) are among the oldest tools in remote sensing studies. Although many variations exist most of them ratio the reflection of light in the red and NIR sections of the spectrum to separate the landscape into water, soil, and vegetation. Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA), Tasseled Cap Transformation (TCT) images were derived from the satellite data analysis. As for land use change detection technique, PCA and unsupervised were used. Land use change detection was based on the difference of multi-date of NDVI, PCA and panchromatic images.
Spatiotemporal changes are very important information to reveal the characteristics of the urbanization process. Sharing the information is beneficial for public awareness which then improves their participation in adaptive management for spatial planning process. Open-source software and web application are freely available tools that can be the best medium used by any individual or agencies to share this important information. The objective of the paper is to discuss on the spatiotemporal land use change in Iskandar Malaysia by using open-source GIS (Quantum GIS) and publish them through web application (Mash-up). Land use in 1994 to 2011 were developed and analysed to show the landscape change of the region. Subsequently, web application was setup to distribute the findings of the study. The result show there is significant changes of land use in the study area especially on the decline of agricultural and natural land which were converted to urban land uses. Residential and industrial areas largely replaced the agriculture and natural areas particularly along the coastal zone of the region. This information is published through interactive GIS web in order to share it with the public and stakeholders. There are some limitations of web application but still not hindering the advantages of using it. The integration of open-source GIS and web application are very helpful in sharing planning information particularly in the study area that experiences rapid land use and land cover change. Basic information from this study is vital for conducting further study such as projecting future land use change and other related studies in the area.
Remotely sensed (RS) imagery is increasingly being adopted in investigations and applications outside of traditional land-use land-cover change (LUCC) studies. This is due to the increased awareness by governments, NGOs and Industry that earth observation data provide important and useful spatial and temporal information that can be used to make better decisions, design policies and address problems that range in scale from local to global. Additionally, citizens are increasingly adopting spatial analysis into their work as they utilize a suite of readily available geospatial tools. This paper examines some of the ways remotely sensed images and derived maps are being extended beyond LUCC to areas such as fire modeling, coastal and marine applications, infrastructure and urbanization, archeology, and to ecological, or infrastructure footprint analysis. Given the interdisciplinary approach of such work, this paper organizes selected studies into broad categories identified above. Findings demonstrate that RS data and technologies are being widely used in many fields, ranging from fishing to war fighting. As technology improves, costs go down, quality increases and data become increasingly available, greater numbers of organizations and local citizens will be using RS in important everyday applications.
Land use/ land cover is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to monitor and detect the changes to maintain a sustainable environment. Land use/ land cover change study is very important aspect of the natural resources data base study. The knowledge of land use and land cover is important for many planning and management activities as it is considered as an essential element for modeling and understanding the earth’s features. The present study aims to find out the land use/land cover features of in and around Rajampet of Kadapa District, Andhra Pradesh, India. The total area of the region is 316.88 sq.km. The study has made use of high resolution IRS LISS III satellite imagery for identifying the land use/land cover classes. ERDAS and ArcGIS software were used to demarcate the land use/land cover features of study area. Remote sensing and GIS provide consistent and accurate base line information than many of the conventional surveys employed for such a task. The land use and land cover analysis in and around Rajampeta area has been attempted based on thematic mapping of the area consisting of built-up land, agriculture land, water bodies, forest and waste land using the satellite image. The research concludes that there is a rapid expansion of built-up area. In the study area the major part is occupied by the cultivated land. Land use and land cover information, when used along with information on other natural resources, like water, soil, hydro-geomorphology, etc. will help in the optimal land use planning at the macro and micro level.
This article develops a spatial analysis applied to examine the main driving forces of land-cover and land-use (LCLU) change in a Mediterranean region. Three different tools have been used in order to differentiate LCLU changes, driving forces and landscape dynamics. LCLU changes have been quantified with remote sensing techniques, driving forces have been analysed with multiple logistic regressions combining biophysical and human variables, whereas landscape dynamics have been quantified using different metrics. Results show the intensification of subsidised herbaceous crops on the coastal agricultural plain, the abandonment of olive trees and vineyards in the transitional area and forest restoration in the mountainous subregion.
Land Use Land Cover Change Analysis of Maverick County Texas along the US Mexico Border
- N Keith
- Muhlestein
Keith N. Muhlestein., 2008. Land Use Land Cover Change Analysis of Maverick County
Texas along the US Mexico Border. University of Texas at San Antonio.
SPATIO-TEMPORAL LAND USE/LAND COVER CHANGES ANALYSIS AND MONITORING IN THE VALENCIA MUNICIPALITY
- Addis Getnet
Addis Getnet Yesserie., 2009. SPATIO-TEMPORAL LAND USE/LAND COVER CHANGES ANALYSIS
AND MONITORING IN THE VALENCIA MUNICIPALITY, SPAIN. Universitat Jaume I.