scholarly journals UHI SPATIO-TEMPORAL ANALYSIS WITH GEOSPATIAL TECHNIQUES: A CASE OF AHMEDABAD CITY.

2021 ◽  
pp. 194-200
Author(s):  
Darshana Rawal ◽  
Vishal Gupta
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Google Earth ◽  
Landsat Data ◽  
Land Use Land Cover ◽  
Barren Land ◽  
Thermal Data ◽  
Spatio Temporal

Spatio-temporal changes in land use land cover (LULC) have been relevant factors in causing the changes in Urban Heat Island (UHI) pattern across rural and urban areas all over the world. Studies conducted have shown that the relation between LULC on scale of the UHI can be an important factor assessing the condition not only for a country but for environment of a city also. Over the years it is reflected in health of vegetation and urbanization pattern of cities. As the thermal remote sensing has been evolved, the measurement of the temperature through satellite products has become possible. Thermal data derived through remote sensing gives us birds-eye-view to see how the thermal data varies in the entire city. In this study such relations are shown over Ahmedabad city of India for the period of 2007 to 2020 using Landsat series satellite data. Land Surface Temperature (LST) is calculated using Google Earth Engine Platform Surface Brightness Temperature for Landsat data and using Radiative Transfer Equation for Landsat data. LST is correlated with land use land cover mainly Built-up, Vegetation, Barren land, Water & Other and corresponding Land Use and Land Cover respectively, and it is found that LST is positively related with all indices except for Normalize Difference Vegetation Index (NDVI) with strong negative correlation and R 2 of 0.51.

scholarly journals Land Use/Land Cover Mapping of Sambar Watershed by Using Remote Sensing and GIS

2021 ◽  
Vol 10 (9) ◽  
pp. 578-585
Author(s):  
B. Varpe Shriniwas D. Payal Sandip
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Remote Sensing And Gis ◽  
Rapid Expansion ◽  
Water Bodies ◽  
Land Cover Mapping ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Barren Land

In the present study, an effort has been made to study in detail of Land Use/Land Cover Mapping for Sambar watershed by using Remote Sensing and GIS technique was carried out during the year of 2020-2021 in Parbhani district. In this research the Remote Sensing and Geographical Information system technique was used for identifying the land use/land cover classes with the help of ArcGIS 10.8 software. The Sambar watershed is located in 19º35ʹ78.78˝ N and 76º87ʹ88.44˝ E in the Parbhani district of Marathwada region in Maharashtra. It is covered a total area 97.01 km2. The land use/land cover map and its classes were identified by the Supervised Classification Method in ArcGIS 10.8 software by using the Landsat 8 satellite image. Total six classes are identified namely as Agricultural area, Forest area, Urban area, Barren land, Water bodies and Fallow land. The Agricultural lands are well distributed throughout the watershed area and it covers 4135 ha. (43 per cent). Forest occupies 502 ha area and sharing about 5 per cent of the total land use land cover of the study area. The Urban land occupies 390 ha. area (4 per cent) and there was a rapid expansion of settlement area. Barren land occupies 3392 ha. area (35 per cent). A water bodies occupy 630 ha. area (6 per cent) and the Fallow land occupies 650 ha (7 per cent) but well-developed dendritic drainage pattern and good water availability is in the Sambar watershed.

scholarly journals SATELLITE IMAGERY DRIVEN ASSESSMENT OF LAND USE LAND COVER, URBANIZATION AND SURFACE TEMPERATURE PATTERN DYNAMICS OVER TROPICAL MEGACITIES

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 313-320
Author(s):  
S. Verma ◽  
S. Agrawal ◽  
K. Dutta
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Temperature Pattern ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Pattern Dynamics ◽  
Spatio Temporal ◽  
Supervised Image Classification

Abstract. In most of the developing nations, fast paced urbanisation is going on. This has changed the spatial patterns of Land Use Land Cover (LULC) and Land Surface Temperature (LST) over time. Continual studies are required in this context to know these phenomena more clearly. This study is carried out to analyse the spatio-temporal changes in LULC, urbanisation and LST in the metropolitan cities of India namely Delhi and Bengaluru. Landsat images of TM and OLI sensors are taken for the years 2001, 2010 and 2020. The LULC layer is obtained through supervised image classification. Concentric circles at the interval of 2 km are drawn from the centroid of the study areas which are used to compute Shannon's entropy through zonal analysis of the reclassified LULC layer. The thermal band of the Landsat is used for the computation of LST. The results of both the study areas revealed 1) decline in the open land, vegetation and water body; 2) rampant growth of built-up and urban area which become more compact over the years; and 3) spread of the higher LST zones.

scholarly journals Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

2020 ◽  
Vol 12 (24) ◽  
pp. 10452
Author(s):  
Auwalu Faisal Koko ◽  
Wu Yue ◽  
Ghali Abdullahi Abubakar ◽  
Roknisadeh Hamed ◽  
Akram Ahmed Noman Alabsi
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Cover ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Barren Land ◽  
Spatio Temporal

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Land Use/Land Cover Dynamic Monitoring and Analysis Using Remote Sensing and GIS Techniques: Case Study of Qingpu District, Shanghai

2012 ◽  
Vol 518-523 ◽  
pp. 5704-5709
Author(s):  
Yi Lin ◽  
Bing Liu ◽  
Feng Xie ◽  
Wen Wei Ren
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Dynamic Monitoring ◽  
Land Use Land Cover ◽  
Ecological Value ◽  
Barren Land

This paper illustrates almost twenty years (1986～2007) of Land use/land cover change (LULCC) in Qingpu-one district of Shanghai. Qingpu District is an area of Upper Huangpu Catchment for fresh water supply with considerable ecological value, but it is also experiencing urban sprawl from development. To reveal the trends underlie LULCC, we propose a novel procedure to quantify different land use/land covers and implement it in the case study. In this procedure, we first collect historical remote-sensing data and co-registered or corrected them to the same spatial resolution and radioactive level. Based upon preliminary interpretation or investigation, land use/land cover types in study area can be included in 5 categories, i.e. Water, Agricultural Land, Urban or Built-up Land, Forest Land, and Barren Land or others. Moreover, data is clipped via boundary of study area for reducing computation load, followed by FPCR-ISODATA classification to divide the data into k groups (k>the number of land types). After postprocessing, e.g., merge the same connoted subgroups and correct misclassified units accompany with validation and verification, the detailed land use/land cover results can be achieved accurately. The quantitative and regression analysis indicate that during the past twenty years the area of agricultural land of Qingpu decreased coupled with urban or built-up area increased linearly. The water area had the minimum change during the decades. Forests had the smallest average proportion (9.6%) of the total area. It occupied so small proportion of land that we can only find points of it in the maps. Barren land can be an indicator for monitoring uncompleted redevelopment or transition of land.

scholarly journals Land-Use/Land Cover Changes Contribute to Land Surface Temperature: A Case Study of the Upper Indus Basin of Pakistan

2022 ◽  
Vol 14 (2) ◽  
pp. 934
Author(s):  
Akhtar Rehman ◽  
Jun Qin ◽  
Amjad Pervez ◽  
Muhammad Sadiq Khan ◽  
Siddique Ullah ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Sustainable Land Use ◽  
Indus Basin ◽  
Land Use Land Cover ◽  
Rapid Urbanization ◽  
Barren Land

Land-use/land cover (LULC) changes have an impact on land surface temperature (LST) at the local, regional, and global scales. To simulate the LULC and LST changes of the environmentally important area of northern Pakistan, this research focused on spatio-temporal LULC and associated LST changes since 1987 and made predictions to 2047. We classified LULC from Landsat TM and ETM data, using the maximum probability supervised categorization approach. LST was retrieved using the Radiative Transfer Equation (RTE) methodology. Furthermore, we simulated LULC using the integrated approaches of Cellular Automata (CA) and Weighted Evidence (WE) and used a regression model to predict LST. The built-up areas and vegetation have increased by 2.1% and 11% due to a decline in the barren land by −8.5% during the last 30 years. The LULC is expected to increase, particularly the built-up and vegetation classes by 2.74% and 13.66%, respectively, and the barren land would decline by −4.2% by 2047. Consequently, the higher LST classes (i.e., 27 °C to <30 °C and ≥30 °C) soared up by about 25.18% and 34.26%, respectively, during the study period, which would further expand to 30.19% and 14.97% by 2047. The lower LST class (i.e., 12 °C to <21 °C) indicated a downtrend of about −41.29% and would further decrease to −3.13% in the next 30 years. The study findings are useful for planning and management, especially for climatologists, land-use planners, and researchers in sustainable land use with rapid urbanization.

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