scholarly journals Measurement of 30-Year Urban Expansion Using Spatial Entropy in Changwon and Gimhae, Korea

2021 ◽  
Vol 13 (2) ◽  
pp. 632
Author(s):  
Ki Hwan Cho ◽  
Do-Hun Lee ◽  
Tae-Su Kim ◽  
Gab-Sue Jang
Keyword(s):  
Land Cover ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Urban Expansion ◽  
Spatial Context ◽  
Concentric Ring ◽  
Common Boundary ◽  
Spatial Entropy ◽  
Two Cities ◽  
Land Cover Map

Entropy is widely used for measuring the degree of urban sprawl. However, despite the intense use of the entropy concept in urban sprawl, entropy’s spatial context has been largely ignored. In this study, we analyzed urban sprawl in Changwon and Gimhae cities, as they shared a common boundary but differed in their population growth and urban expansion. The land cover type, “urban and dry area,” was used to identify urban areas in the two cities, and a land cover map showed the areas of expansion in the 1980s, 1990s, 2000s, and 2010s. Different zoning schemes, namely concentric rings and regular partitioning, were applied. Shannon’s and Batty’s spatial entropy indices were used to measure urban sprawl. The results showed that concentric ring zoning was not suitable for measuring urban sprawl in a decentralized and polycentric city. Batty’s spatial entropy was less affected by the zoning scheme used and reflected the pattern of urban expansion more accurately. Urban sprawl, a phenomenon occurring within a spatial context, can be better understood by measuring spatial entropy with appropriate zoning schemes.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

Historical and Future Land-Cover Change in a Municipality of Ghana

2011 ◽  
Vol 15 (9) ◽  
pp. 1-26 ◽  
Author(s):  
Emmanuel M. Attua ◽  
Joshua B. Fisher
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Land Tenure ◽  
Urban Areas ◽  
Poverty Reduction ◽  
Urban Expansion ◽  
Political Stability ◽  
Land Cover Changes ◽  
Natural Ecosystems ◽  
Land Administration

Abstract Urban land-cover change is increasing dramatically in most developing nations. In Africa and in the New Juaben municipality of Ghana in particular, political stability and active socioeconomic progress has pushed the urban frontier into the countryside at the expense of the natural ecosystems at ever-increasing rates. Using Landsat satellite imagery from 1985 to 2003, the study found that the urban core expanded by 10% and the peri-urban areas expanded by 25% over the period. Projecting forward to 2015, it is expected that urban infrastructure will constitute 70% of the total land area in the municipality. Giving way to urban expansion were losses in open woodlands (19%), tree fallow (9%), croplands (4%), and grass fallow (3%), with further declines expected for 2015. Major drivers of land-cover changes are attributed to demographic changes and past microeconomic policies, particularly the Structural Adjustment Programme (SAP); the Economic Recovery Programme (ERP); and, more recently, the Ghana Poverty Reduction Strategy (GPRS). Pluralistic land administration, complications in the land tenure systems, institutional inefficiencies, and lack of capacity in land administration were also key drivers of land-cover changes in the New Juaben municipality. Policy recommendations are presented to address the associated challenges.

scholarly journals Using machine learning to produce a very high-resolution land-cover map for Ireland and beyond

2021 ◽  
Author(s):  
Geoffrey Bessardon ◽  
Emily Gleeson ◽  
Eoin Walsh
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Weather Forecasting ◽  
Land Cover Classification ◽  
Surface Processes ◽  
Land Cover Map ◽  
Very High

<p>An accurate representation of surface processes is essential for weather forecasting as it is where most of the thermal, turbulent and humidity exchanges occur. The Numerical Weather Prediction (NWP) system, to represent these exchanges, requires a land-cover classification map to calculate the surface parameters used in the turbulent, radiative, heat, and moisture fluxes estimations.</p><p>The land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM NWP system for operational weather forecasting is ECOCLIMAP. ECOCLIMAP-SG (ECO-SG), the latest version of ECOCLIMAP, was evaluated over Ireland to prepare ECO-SG implementation in HARMONIE-AROME. This evaluation suggested that sparse urban areas are underestimated and instead appear as vegetation areas in ECO-SG [1], with an over-classification of grassland in place of sparse urban areas and other vegetation covers (Met Éireann internal communication). Some limitations in the performance of the current HARMONIE-AROME configuration attributed to surface processes and physiography issues are well-known [2]. This motivated work at Met Éireann to evaluate solutions to improve the land-cover map in HARMONIE-AROME.</p><p>In terms of accuracy, resolution, and the future production of time-varying land-cover map, the use of a convolutional neural network (CNN) to create a land-cover map using Sentinel-2 satellite imagery [3] over Estonia [4] presented better potential outcomes than the use of local datasets [5]. Consequently, this method was tested over Ireland and proven to be more accurate than ECO-SG for representing CORINE Primary and Secondary labels and at a higher resolution [5]. This work is a continuity of [5] focusing on 1. increasing the number of labels, 2. optimising the training procedure, 3. expanding the method for application to other HIRLAM countries and 4. implementation of the new land-cover map in HARMONIE-AROME.</p><p> </p><p>[1] Bessardon, G., Gleeson, E., (2019) Using the best available physiography to improve weather forecasts for Ireland. In EMS Annual Meeting.Retrieved fromhttps://presentations.copernicus.org/EMS2019-702_presentation.pdf</p><p>[2] Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,. . . Køltzow, M. Ø. (2017). The HARMONIE–AROME Model Configurationin the ALADIN–HIRLAM NWP System. Monthly Weather Review, 145(5),1919–1935.https://doi.org/10.1175/mwr-d-16-0417.1</p><p>[3] Bertini, F., Brand, O., Carlier, S., Del Bello, U., Drusch, M., Duca, R., Fernandez, V., Ferrario, C., Ferreira, M., Isola, C., Kirschner, V.,Laberinti, P., Lambert, M., Mandorlo, G., Marcos, P., Martimort, P., Moon, S., Oldeman,P., Palomba, M., and Pineiro, J.: Sentinel-2ESA’s Optical High-ResolutionMission for GMES Operational Services, ESA bulletin. Bulletin ASE. Euro-pean Space Agency, SP-1322,2012</p><p>[4] Ulmas, P. and Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification, pp. 1–11,http://arxiv.org/abs/2003.02899, 2020</p><p>[5] Walsh, E., Bessardon, G., Gleeson, E., and Ulmas, P. (2021). Using machine learning to produce a very high-resolution land-cover map for Ireland. Advances in Science and Research, (accepted for publication)</p>

Spatiotemporal Pattern Analysis of Rapid Urban Expansion Using GIS and Remote Sensing

2010 ◽  
Vol 1 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Hyun Joong Kim
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Urban Expansion ◽  
Local Scale ◽  
Spatiotemporal Pattern ◽  
Land Use Land Cover ◽  
Modeling Studies

Rapidly growing urban areas tend to reveal distinctive spatial and temporal variations of land use/land cover in a locally urbanized environment. In this article, the author analyzes urban growth phenomena at a local scale by employing Geographic Information Systems, remotely sensed image data from 1984, 1994, and 2004, and landscape shape index. Since spatial patterns of land use/land cover changes in small urban areas are not fully examined by the current GIS-based modeling studies or simulation applications, the major objective of this research is to identify and examine the spatial and temporal dynamics of land use changes of urban growth at a local scale. Analytical results demonstrate that sizes, locations, and shapes of new developments are spatio-temporally associated with their landscape variations and major transportation arteries. The key findings from this study contribute to GIS-based urban growth modeling studies and urban planning practices for local communities.

Land Cover Change Simulations in Yangon Under Several Scenarios of Flood and Earthquake Vulnerabilities with Master Plan

2018 ◽  
Vol 13 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Tanakorn Sritarapipat ◽  
◽  
Wataru Takeuchi
Keyword(s):  
High Risk ◽  
Land Cover ◽  
Land Cover Change ◽  
Urban Areas ◽  
Urban Expansion ◽  
Land Cover Changes ◽  
Master Plan ◽  
Economic Area ◽  
Urban Planning And Management ◽  
Multiple Scenarios

Yangon is the largest city and major economic area in Myanmar. However, it is considered to have a high risk of floods and earthquakes. In order to mitigate future flood and earthquake damage in Yangon, land cover change simulations considering flood and earthquake vulnerabilities are needed to support urban planning and management. This paper proposes land cover change simulations in Yangon from 2020 to 2040 under various scenarios of flood and earthquake vulnerabilities with a master plan. In our methodology, we used a dynamic statistical model to predict urban expansion in Yangon from 2020 to 2040. We employed a master plan as the future dataset to enhance the prediction of urban expansion. We applied flood and earthquake vulnerabilities based on multi-criteria analysis as the areas vulnerable to disaster. We simulated land cover changes from 2020 to 2040 considering the vulnerable areas with a master plan for multiple scenarios. The experiments indicated that by using a master plan, some of the predicted urban areas are still located in areas highly vulnerable to floods and earthquakes. By integrating the prediction of urban expansion with flood and earthquake vulnerabilities, the predicted urban areas can effectively avoid areas highly vulnerable to floods and earthquakes.

A method characterizing urban expansion based on land cover map at 30 m resolution

2016 ◽  
Vol 59 (9) ◽  
pp. 1738-1744 ◽  
Author(s):  
Xi Yu ◽  
BingQi Zhang ◽  
Qiang Li ◽  
Jin Chen
Keyword(s):  
Land Cover ◽  
Urban Expansion ◽  
Land Cover Map

Monitoring of Rapid Urban Sprawl in Beijing with Time Series Remote Sensing Data and Analysis of Driving Forces

2013 ◽  
Vol 726-731 ◽  
pp. 4591-4595 ◽  
Author(s):  
Jin Ling Zhao ◽  
Dong Yan Zhang ◽  
Hao Yang ◽  
Lin Sheng Huang
Keyword(s):  
Economic Development ◽  
Urban Area ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Driving Forces ◽  
Urban Expansion ◽  
Sustainable Urban Development ◽  
Significant Information ◽  
South Central ◽  
Urban Construction

Beijing has experienced a rapid urban sprawl over the past three decades, along with accelerated socio-economic development. This study investigated the change patterns and figured out the driving forces of urban expansion in the study area. To obtain urban class, decision tree classification techniques were used to identify the land cover types using four scenes of Landsat images from four periods of 1978-era, 1992-era, 2000-era and 2010-era. Then, the urban areas were identified by excluding water, agriculture, forest, grassland and bare land. The analysis results showed that: 1) urban construction land had been expanded very quickly and the urban area is mainly in the south-central part of the municipality; 2) the urban area increased by 96284.97 ha and the ratio was 5.88%; and 3) population growth, economic development, urban construction and industrial structure adjustment could explain the expansion. These analysis results can provide significant information on the monitoring and management of sustainable urban development.

scholarly journals Urban Sprawl Assessment and Its Effect on Land Use Transformation in Varanasi City using Remote Sensing and GIS

2019 ◽  
Vol 9 (2) ◽  
pp. 1056-1061
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Gis And Remote Sensing ◽  
Land Use Transformation ◽  
Vegetation Water ◽  
Almost All

This paper seeks to examine the effect of urbanization on changes in land use in the peri-urban areas of Varanasi city in India. The area of study is divided into six different classes of land use: built-up area, agriculture, vegetation, water bodies, sand and other land use. Using the maximum likelihood technique, Landsat 5 TM satellite data were used to identify land use and land cover changes from 1996 to 2017. The findings indicate a substantial increase in the built-up area, associated with reduced water and other land use cover. The urban sprawl is observed in almost all directions from the city boundaries, and along highways. Shannon’s entropy analysis reveals dispersed distribution of built-up area. The approach based on GIS and remote sensing data, together with statistical analysis, has proved instrumental in the analysis of urban expansion. It also helps to identify priority areas that require adequate planning for sustainable development.

scholarly journals SPATIO-TEMPORAL ANALYSIS AND MODELING OF URBAN GROWTH OF BIRATNAGAR CITY, NEPAL

2019 ◽  
Vol IV-5/W2 ◽  
pp. 97-102
Author(s):  
S. Shrestha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Expansion ◽  
Urban Change ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Spatial Metrics ◽  
Land Cover Map ◽  
Spatio Temporal

Abstract. Increasing land use land cover changes, especially urban growth has put a negative impact on biodiversity and ecological process. As a consequences, they are creating a major impact on the global climate change. There is a recent concern on the necessity of exploring the cause of urban growth with its prediction in future and consequences caused by this for sustainable development. This can be achieved by using multitemporal remote sensing imagery analysis, spatial metrics, and modeling. In this study, spatio-temporal urban change analysis and modeling were performed for Biratnagar City and its surrounding area in Nepal. Land use land cover map of 2004, 2010, and 2016 were prepared using Landsat TM imagery using supervised classification based on support vector machine classifier. Urban change dynamics, in term of quantity, and pattern was measured and analyzed using selected spatial metrics and using Shannon’s entropy index. The result showed that there is increasing trend of urban sprawl and showed infill characteristics of urban expansion. Projected land use land cover map of 2020 was modeled using cellular automata-based approach. The predictive power of the model was validated using kappa statistics. Spatial distribution of urban expansion in projected land use land cover map showed that there is increasing threat of urban expansion on agricultural land.

Using Combination Technique for Land Cover Classification of Optical Multispectral Images

2021 ◽  
Vol 12 (4) ◽  
pp. 22-39
Author(s):  
Keerti Kulkarni ◽  
Vijaya P. A.
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Geographical Area ◽  
Land Cover Classification ◽  
Combination Method ◽  
Support Vector ◽  
Combination Technique ◽  
Land Cover Map

The need for efficient planning of the land is exponentially increasing because of the unplanned human activities, especially in the urban areas. A land cover map gives a detailed report on temporal dynamics of a given geographical area. The land cover map can be obtained by using machine learning classifiers on the raw satellite images. In this work, the authors propose a combination method for the land cover classification. This method combines the outputs of two classifiers, namely, random forests (RF) and support vector machines (SVM), using Dempster-Shafer combination theory (DSCT), also called the theory of evidence. This combination is possible because of the inherent uncertainties associated with the output of each classifier. The experimental results indicate an improved accuracy (89.6%, kappa = 0.86 as versus accuracy of RF [87.31%, kappa = 0.83] and SVM [82.144%, kappa = 0.76]). The results are validated using the normalized difference vegetation index (NDVI), and the overall accuracy (OA) has been used as a comparison basis.

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