Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio

The creation of an accurate simulation of future urban growth is considered one of the most important challenges in urban studies that involve spatial modeling. The purpose of this study is to improve the simulation capability of an integrated CA-Markov Chain (CA-MC) model using CA-MC based on the Analytical Hierarchy Process (AHP) and CA-MC based on Frequency Ratio (FR), both applied in Seremban, Malaysia, as well as to compare the performance and accuracy between the traditional and hybrid models. Various physical, socio-economic, utilities, and environmental criteria were used as predictors, including elevation, slope, soil texture, population density, distance to commercial area, distance to educational area, distance to residential area, distance to industrial area, distance to roads, distance to highway, distance to railway, distance to power line, distance to stream, and land cover. For calibration, three models were applied to simulate urban growth trends in 2010; the actual data of 2010 were used for model validation utilizing the Relative Operating Characteristic (ROC) and Kappa coefficient methods Consequently, future urban growth maps of 2020 and 2030 were created. The validation findings confirm that the integration of the CA-MC model with the FR model and employing the significant driving force of urban growth in the simulation process have resulted in the improved simulation capability of the CA-MC model. This study has provided a novel approach for improving the CA-MC model based on FR, which will provide powerful support to planners and decision-makers in the development of future sustainable urban planning

Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model

Urban growth, a dynamic and demographic phenomenon, refers to the increased spatial value of urban areas, such as cities and towns, due to social and economic forces. Nowadays, urban lands are rapidly increasing, replacing non-urban lands such as agricultural, forest, water, rural, and open lands. In this study, a CA-Markov model was utilized to predict the growth of urban lands and their spatial trends in Seremban, Malaysia. The performance of the CA-Markov model was also assessed. The Markov chain model was applied to produce the quantitative values of transition probabilities for urban and non-urban lands. Subsequently, the CA model was used to predict the dynamic spatial trends of land changes. The change in urban and non-urban land use from 1984 to 2010 was modeled using the CA-Markov model for calibration purposes and to compute optimal CA transition rules, as well as to predict future urban growth. For accuracy assessment, the CA-Markov model was validated using a kappa coefficient. An 83% overall accuracy was observed for the kappa index statistics, which indicates the excellent performance of the proposed model. Finally, based on the CA transition rules and the transition area matrix produced from the Markov chain model-based calibration process, the future urban growth in Seremban for 2020 and 2030 was simulated

Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and land cover (LULC) changes in the Cholistan and Thal deserts in Punjab, Pakistan. We plotted the distribution of the LULC throughout the desert terrain for the years 1990, 2006 and 2022. The Random Forest methodology was utilized to classify the data obtained from Landsat 5 (TM), Landsat 7 (ETM+) and Landsat 8 (OLI/TIRS), as well as ancillary data. The LULC maps generated using this method have an overall accuracy of more than 87%. CA-Markov was utilized to forecast changes in land usage in 2022, and changes were projected for 2038 by extending the patterns seen in 2022. A CA-Markov-Chain was developed for simulating long-term landscape changes at 16-year time steps from 2022 to 2038. Analysis of urban sprawl was carried out by using the Random Forest (RF). Through the CA-Markov Chain analysis, we can expect that high density and low-density residential areas will grow from 8.12 to 12.26 km2 and from 18.10 to 28.45 km2 in 2022 and 2038, as inferred from the changes occurred from 1990 to 2022. The LULC projected for 2038 showed that there would be increased urbanization of the terrain, with probable development in the croplands westward and northward, as well as growth in residential centers. The findings can potentially assist management operations geared towards the conservation of wildlife and the eco-system in the region. This study can also be a reference for other studies that try to project changes in arid are as undergoing land-use changes comparable to those in this study

PENGARUH DINAMIKA LAHAN URBAN TERHADAP SEBARAN KEKRITISAN DAERAH RESAPAN PADA DAERAH ALIRAN SUNGAI YANG BERMUARA DI TELUK JAKARTA

Jakarta sebagai pusat pemerintahan dan perekonomian Indonesia, telah menjadi tujuan utama urbanisasi selama bertahun-tahun. Kondisi tersebut mendorong terjadinya perubahan tutupan lahan yang masif, terutama pertumbuhan lahan urban pada wilayah Jakarta dan peri urban Jakarta. Pertumbuhan lahan urban yang tidak terkendali memberikan dampak negatif bagi kota Jakarta, mengingat secara geografis kota Jakarta merupakan wilayah hilir dari delapan Daerah Aliran Sungai (DAS). Penelitian ini memiliki lima tujuan, yang pertama menganalisis tren pertumbuhan lahan urban pada seluruh DAS yang bermuara di Teluk Jakarta. Tahap analisis ini dilakukan dengan melihat tipe pertumbuhan lahan urban (infilling, edge expansion dan outlying), pola pertumbuhan lahan urban melalui analisis spasial metrik dan arah pertumbuhan lahan urban melalui pembagian delapan zona sesuai arah mata angin. Hasilnya menunjukkan bahwa tipe pertumbuhan lahan urban pada areal studi didominasi tipe Edge Expansion. Pola pertumbuhan lahan urban cenderung semakin kompak dengan tingkat sprawl yang cenderung menurun. Selain itu pertumbuhan lahan urban di wilayah Barat Laut (NW) cenderung lebih teratur dibanding wilayah lain, sedangkan wilayah Timur (E) cenderung sebaliknya. Tujuan kedua adalah menganalisis faktor pendorong dan penghambat pertumbuhan lahan urban melalui studi referensi dan wawancara mendalam. Hasilnya diperoleh tiga belas faktor pendorong dan enam faktor penghambat pertumbuhan lahan urban. Lahan urban eksisting menjadi faktor pendorong utama pertumbuhan lahan urban pada areal studi. Tujuan ketiga adalah memprediksi pertumbuhan lahan urban tahun 2029 dengan model cellular automata melalui dua skenario yaitu skenario tren pertumbuhan lahan urban (skenario 1) dan skenario rencana pengembanan wilayah (skenario 2). Hasilnya, prediksi pertumbuhan lahan urban tahun 2029 untuk skenario 1 dan skenario 2 tidak menunjukkan perbedaan yang signifikan. DAS Bekasi, DAS Angke Pesanggrahan dan DAS Cisadane diprediksi menjadi DAS dengan pertumbuhan lahan urban terbesar tahun 2029. Tujuan keempat adalah menganalisis kekritisan daerah resapan mengacu pada Penyusunan Rencana Teknik Rehabilitasi Hutan dan Lahan Daerah Aliran Sungai. Hasilnya menunjukkan bahwa pertumbuhan lahan urban tahun 2001 - 2017 maupun tahun 2017 - prediksi tahun 2029 (skenario 1 dan 2) telah menambah secara signifikan luas daerah resapan dengan tingkatan agak kritis dan kritis. Tujuan yang kelima adalah menyusun strategi pengendalian pertumbuhan lahan urban pada areal studi melalui analisis SWOT. Hasil analisis merekomendasikan upaya pengendalian pertumbuhan lahan urban dengan strategi konservatif. Srategi yang dapat diterapkan berupa pembuatan sistem informasi berbasis internet (komputer) yang mampu menjadi jembatan koordinasi sekaligus media pengawasan serta penguatan peran BKSP Jabodetabekpunjur dalam pengendalian pertumbuhan lahan urban. Kata Kunci : Dinamika Lahan Urban, Kekritisan Daerah Resapan, Daerah Aliran Sungai, cellular automata, Teluk Jakarta, analisis SWOT Jakarta as the center of Indonesia's economy and govenment, has become the main destination for urbanization for many years. This condition encourages massive land cover changes, especially urban growth in Jakarta and peri-urban areas of Jakarta. Uncontrolled urban growth has negative impacts on Jakarta, considering that geographically Jakarta is a downstream area of eight watersheds. This study has five objectives, the first to analyze urban growth trends in all watersheds that flow into the Jakarta Bay. This analysis is carried out through studies of urban growth types (infilling, edge expansion and outlying), urban growth patterns through spatial metric analysis and urban growth direction by dividing the study area into eight zones. The results show that the type of urban growth in the study area is dominated by the Edge Expansion type. Urban growth patterns tend to be more compact with sprawl rates that tend to decline. In addition, urban growth in the Northwest region (NW) tends to be more regular than other regions, while the Eastern region (E) tends to be the opposite. The second objective is to analyze the driving factors and inhibiting factors of urban growth through reference studies and in-depth interviews. The results obtained thirteen driving factors and six inhibiting factors of urban growth. Existing urban areas were the main driving factor for urban growth in the study area. The third objective is to predict urban growth in 2029 with cellular automata models through two scenarios, namely scenario of urban growth trends (scenario 1) and scenario of regional development plans (scenario 2). The result show that predictions of urban growth in 2029 for scenario 1 and scenario 2 did not show a significant difference. The Bekasi watershed, Angke Pesanggrahan watershed and the Cisadane watershed are predicted to experience the largest urban growth in 2029. The fourth objective is to analyze the criticality level of the catchment area referring to Planning Procedures for Forest and Land Rehabilitation Engineering Watershed. The results show that urban growth from 2001 to 2017 and 2017 to the prediction of 2029 have significantly increased catchment areas with rather critical and critical levels. The fifth objective is to develop a strategy for controlling urban growth in the study area through a SWOT analysis. The results of the analysis recommend controlling the urban growth with a conservative strategy. The strategy that can be applied is the creation of an internet-based information system (computer) that is able to become a bridge of coordination as well as a media of supervision. In addition, it is necessary to strengthen the role of the BKSP Jabodetabekpunjur in controlling urban growth. Keywords: Urban dynamics, criticality level of the cactment area, watershed, cellular automata, Jakarta Bay, SWOT analysi

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050

A cost-effective and efficient framework to determine water quality monitoring network locations

A crucial part in designing a robustwater qualitymonitoring network is the selection of appropriatewater quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficientmanner. Themain contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoywatershed in northwest Iran,where financial resources andwater quality data are limited. To achieve this purpose, the rivermixing lengthmethod (RML)was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata–Markov chainmodel (CA–Markov)was applied to simulate future change in non-point sources during the period 2026–2036. Finally, by considering anthropogenic activities through land-usemapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling pointswere identified for the present and the future. It is not expected, however, that the present location of the proposed sampling pointswill change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations

Assessing the impact of land use changes on hydropower production and erosion in the Coca River basin. A contribution towards Integrated Water Resources Management in Ecuador

Die Region mit der weltweit höchsten Rate an Landnutzungsänderungen sind die feuchten Tropen. Es ist ein weit verbreiteter Prozess in diesen Regionen durch Entwaldung Raum für landwirtschaftliche Flächen und Weiden zu schaffen. Darüber hinaus ist diese Region für ihre große Wasserverfügbarkeit und ihr Potenzial zur Erzeugung von Wasserkraft bekannt. Daher sind in den letzten Jahrzehnten Bemühungen zur Erhaltung und zum Schutz der natürlichen Waldbedeckung der tropischen Wassereinzugsgebiete zu einer Priorität innerhalb der Prozesse des integrierten Wasserressourcenmanagements (IWRM) geworden. Landnutzungsänderungen (LUC) beeinflussen den Wasserhaushalt eines Einzugsgebiets, indem sie das verfügbare Wasser zusammen mit der Veränderung der anderen Wasserhaushaltskomponenten beeinflussen. Das Verständnis der LUC und ihrer Auswirkungen auf die Hydrologie eines Einzugsgebiets ist für das Management und die Nutzung der Wasserressourcen in einem Einzugsgebiet von entscheidender Bedeutung. Daher ist es wichtig, die Auswirkungen von Landnutzungsänderungen in den letzten Jahrzenten auf die Abflussmenge eines Wassereinzugsgebiets zu verstehen, um in Zukunft - innerhalb eines IWRM-Rahmens - ein ordnungsgemäßes Wassermanagement und eine Wasserressourcenplanung durchführen zu können. Diese Studie bewertet die historischen Trends von Niederschlag und Stromfluss und analysiert die Reaktionen des Stromflusses auf Landnutzungsänderungen unter verschiedenen Szenarien und Zukunftsprojektionen im oberen Coca-Einzugsgebiet. Dieses befindet sich am Osthang der ecuadorianischen Anden und ist Teil der oberen ecuadorianischen Amazonasregion. Die Ergebnisse des Mann-Kendall-Tests (MK) zeigen, dass kein statistisch signifikanter Trend in den täglichen Niederschlags- und monatlichen Flussabflussmessungen im Wassereinzugsgebiet existiert. Der Pettitt-Test kann keinen Sprungpunkt in den einzugsgebietsweiten Niederschlagsreihen feststellen. Die Landnutzungskarten von 1990, 2000, 2008 und 2016 werden für die LUC-Erkennungsanalyse verwendet, sowie das CA_Markov-Modell, um die zukünftigen LUC-Projektionen unter drei verschiedenen Szenarien vorherzusagen: Trendszenario, “Best-Case-Szenario”, “Worst-Case-Szenario”. Die Vorhersagen für die Jahre 2026 und 2036 werden unter Berücksichtigung der physischen und sozioökonomischen Treiber der LUC-Dynamik im Einzugsgebiet berechnet. Das Trendszenario behält die für die Jahre 2026 und 2036 prognostizierten Wahrscheinlichkeiten für Landnutzungsänderungen bei. Das Best-Case-Szenario befasst sich mit den Wahrscheinlichkeiten für Änderungen der LUC in Richtung eines ausgewogenen Szenarios, zwischen der Erhaltung natürlicher Ökosysteme und produktiven Aktivitäten innerhalb des Einzugsgebiets. Das “Worst-Case-Szenario” befasst sich mit den Wahrscheinlichkeiten einer Änderung der LUC in Richtung eines Szenarios, in dem Rohstoffaktivitäten vorherrschen und die Produktionsbereiche in der Wasserscheide zunehmen. Die LUC-Erkennungsergebnisse zeigen eine Zunahme der landwirtschaftlichen Flächen und eine Abnahme der Waldbedeckung zwischen 1990 und 2016. Statistisch gesehen, verringerte sich die natürliche Waldbedeckung von 61,2% im Jahr 1990 auf 57,12% im Jahr 2016, während der Anteil der landwirtschaftlichen Flächen von 2,9% auf 7,23% zwischen die Jahren 1990 und 2016 zunahm. Die Ergebnisse der LUC- Projektion für die Jahre 2026 und 2036 in Bezug auf das Jahr 2016 deuten darauf hin, dass die landwirtschaftlichen Flächen im Jahr 2026 voraussichtlich um 9,3% und im Jahr 2036 um 19,2% im Trendszenario zunehmen werden. Für das “Best-Case-Szenario” wird eine Zunahme der landwirtschaftlichen Flächen um 1,1% bzw. 3% im Jahr 2026 bzw. 2036 prognostiziert. Die Ergebnisse des “Worst-Case–Szenarios” für die Jahre 2026 und 2036 prognostizieren eine Zunahme der landwirtschaftlichen Flächen um 26,1% bzw. 54,3%. Darüber hinaus wird für das Trendszenario im Vergleich zu 2016 ein Rückgang der natürlichen Waldbedeckung im Einzugsgebiet um 0,6% (2026) und um 1,5% (2036) prognostiziert. Für das “Best-Case-Szenario” wird prognostiziert, dass die Waldbedeckung um 0,2% (2026) und um 0,4% (2036) abnehmen wird. Das “Worst-Case-Szenario” prognostiziert für die Jahre 2026 und 2036 einen Rückgang der natürlichen Waldbedeckung um 2,6% bzw. 5,8% gegenüber 2016. Die Ergebnisse der hydrologischen Modellierung zeigen, dass aufgrund der Auswirkungen von LUC der durchschnittliche tägliche Stromfluss für das Trendszenario im Vergleich zu 2016 um 1,04% (2026) und 1,45% (2036) anstieg. Für das “Best-Case– Szenario” verringerte sich der durchschnittliche tägliche Stromfluss in den Jahren 2026 und 2036 gegenüber 2016 um 4,91% (-24,8 m³/s) bzw. 6,10% (-30,8 m³/s). Für das Szenario “Worst-Case” wird in Bezug auf das Jahr 2016 ein Anstieg des durchschnittlichen täglichen Stromflusses um 2,08% (2026) und um 2,37% (2036) prognostiziert. Die Ergebnisse zu den Auswirkungen von LUC auf den Stromfluss unter den verschiedenen vorgeschlagenen Szenarien zeigen, dass die Änderungen des Stromflusses kein Faktor sind, der die Wasserkrafterzeugung im Einzugsgebiet beeinflussen könnte. Die Ergebnisse zeigen jedoch, dass die Wasserhaushaltskomponenten durch die räumliche und zeitliche Verteilung von LUC im Untersuchungsgebiet beeinflusst werden, was für ein einzugsgebietsweites integriertes Wasserressourcenmanagement nützlich ist. Das Ausmaß dieser Effekte kann jedoch durch Unsicherheiten verdeckt werden, die sich aus den hydrologischen und LUC-Modellierungsprozessen ergeben. Daher sind weitere Studien zur Optimierung von Landnutzungsänderungen und Untersuchungen zur Bewertung von Niederschlag-Abfluss-Prozessen im Untersuchungsgebiet unerlässlich. Nichtsdestotrotz sollten Nachhaltigkeitsaspekte, die mit dem Vorhandensein der Wasserkraftanlage im Untersuchungsgebiet verbunden sind, nicht vernachlässigt werden. Um eine nachhaltige Entwicklung im Einzugsgebiet gewährleisten zu können (die die langfristige Wasserkraftproduktion, die Erhaltung der Ökosysteme und das sozioökonomische Wohlergehen der Bevölkerung im Einzugsgebiet umfasst), müssen in weiteren Arbeiten innerhalb eines IWRM-Rahmens weitere Variablen und Prozesse analysiert werden, die in dieser Studie nicht behandelt wurden

Spatiotemporal model for landscape ecological assessment in landscape planning

Ecological landscape area is rich with biodiversity and ecosystem are two important factors that balance the serenity of the environment through its ecological function and services. However, landscape change especially rapid urbanization has led to extensive land use and land cover (LULC) transformation that degrades the ecological landscape area and ecosystem services. The limitation of integration analysis in LULC change with ecological interaction has caused detrimental impact on natural landscape area and environmental quality. Analysing the spatiotemporal characteristics of landscape changes and ecological response in a multidisciplinary research is necessary to extend the understanding of spatial change behaviour and ecological consequences. Thus, the aim of this research is to study the integration of spatiotemporal dimension of landscape change with ecological landscape sensitivity consideration in Iskandar Malaysia region (Johor Bahru). The spatiotemporal dimension of historical and future LULC change is analysed to identify the direction and characteristics of the landscape structure and function change. Logistic regression model, analytical hierarchical process, markov chain model and cellular automata were used to identify the spatiotemporal LULC change in the study area. A series of landscape matrices in landscape index at class and landscape levels were used to analyse the spatiotemporal dimension of the landscape change pattern. It includes measurement of the ecological integrity and function responses towards spatiotemporal landscape change by using Core Area Model. Satellite images of 1994, 2000, 2007 and 2013 were used to understand the historical landscape changes and as a basis for future projection. Geographic Information System and Remote Sensing were utilized to evaluate the temporal landscape characteristics and spatial pattern changes. The results indicate that rapid urbanization of Iskandar Malaysia region from 2007 to 2013 has substantially changed the structure and function of the ecological area. The urban area significantly increased from 8,031.6 hectares (3.84%) in 1994 to 42,972.94 (20.1%) in 2013, and expected to increase to 112,224.6 hectares (53.59%) in 2030. As a consequence, the natural ecological areas reduced from 55,201.77 hectares (26.37%) in 1994 to 19,011.5 hectares (9.08%) in 2013. Due to the landscape mosaic change, the core ecological areas are affected from 21,465.9 hectares (38%) reduced to 9,317.61 hectares (49%) and expected to further reduce at 8,416.71 hectares (41%) in 1994, 2013 and 2030, respectively. It shows the response of ecological condition in natural landscape areas towards the landscape changes which subsequently disturb the ecological values and services. As a conclusion, the findings of this research could provide decision makers with better understanding on the environmental consequences of the landscape changes. In addition, it contributes to enhancement of methods in multidisciplinary research and finally increases the capability of the process in adaptive management for the spatiotemporal landscape change

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Data-driven Analysis of Potential Impacts of Land-use/cover Change on Water Resources in Coastal Watersheds: Perspectives from Non-stationarity and Nonlinearity

Water resource conditions are highly influenced by human activities. As one of the most important indicators that reflects the intensity of human activities, LUCC has drawn much attention in recent decades. Thus, it is necessary to understand the LUCC patterns in watersheds and identify their impacts on the local water resources. We also analyzed the impacts of the human activities on the streamflow regime as well as the regional climate changes. Furthermore, the nonlinear relationship between land use and water quality was identified in this study. The major findings of this study are as follows: (1) Spatial variation in land use was highly related to the driving factors, and population and local economic development may be the major factors influencing urbanization processes in the coastal watersheds. (2) Streamflow extremes are highly impacted by the human activities and climate variability, and the human activities may be the major factor controlling streamflow extremes at short time scales. (3) The coupled effects of climate variability and human activities were identified by analyzing the relationship between urbanization and climate patterns in the studied watersheds, and the patterns of precipitation and temperature may be modified in highly urbanized areas. (4) A nonlinear relationship between land use and water quality has been widely observed, especially in highly polluted watersheds