scholarly journals Assessment of Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case of Osogbo and Its Peripheral Areas in Nigeria

2021 ◽  
Vol 25 (4) ◽  
pp. 543-548
Author(s):  
O.S. Afolabi ◽  
O.J. Aigbokhan ◽  
J.O. Mephors ◽  
A.J. Oloketuyi
Keyword(s):  
Land Cover ◽  
Water Body ◽  
Satellite Image ◽  
Urban Land ◽  
Data Sets ◽  
Erdas Imagine ◽  
Vegetation Water ◽  
Peripheral Areas ◽  
Landsat Satellite ◽  
Over Time

The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing city areas. Landsat satellite imageries of three different time periods, i.e., Landsat Thematic Mapper (TM) of 1982, 2000 and 2018 were acquired by Global Land Cover Facility Site (GLCF) and earth explorer site, quantify the changes in the Osogbo and its peripheral areas from 1982 to 2018 over a period of 36 years. These data sets were imported in ArcGIS 10.3, ERDAS Imagine and IDRIS Selva, satellite image processing softwares to create a false colour composite (FCC), supervised classification methodology was employed using maximum likelihood technique. The images of the study area were categorized into four different classes namely Core-urban, Peri-urban, Vegetation, water body. The results indicate that during the last thirty-six (36) years, Core-Urban land and water body have been increased by 2.74% (38.20 km2) and 0.98% (13.69 km2) while Peri-Urban land, and vegetation cover have decreased by 0.35% (5.00 km2), and 3.36 % (46.87 km2), respectively. The results quantify the land cover change patterns in the city and its peripheral area and demonstrate the potential of multitemporal Landsat data to provide an accurate, economical means to map and analyse changes in land cover over time that can be used as inputs to land management and policy decisions.

scholarly journals Change Detection in Landuse/ Landcover of Abeokuta Metropolitan Area, Nigeria Using Multi-Temporal Landsat Remote Sensing

2019 ◽  
Vol 51 (2) ◽  
pp. 217
Author(s):  
Adebayo Oluwasegun Hezekiah ◽  
Otun. W. O ◽  
Daniel, I. Samuel
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Planning ◽  
Water Body ◽  
Land Use Land Cover ◽  
Land Utilization ◽  
Erdas Imagine ◽  
Multi Temporal ◽  
Landsat Satellite

This research paper examined the changes in land use/ land cover of Abeokuta, Nigeria between 1984 and 2015 using Multi-Temporal Landsat Remote Sensing paired with Geographic Information System (GIS) techniques. The evaluation of the trend, rate and magnitude changes was the objectives of this study.  Five Landsat satellite images of different dates,  i.e., Landsat Thematic Mapper (TM) of 1984, 2001, 2006, 2011 and 2015 with spatial resolution ranging from 15, 30 and 60metres were obtained from National Aeronautics Space Administration(NASA),United State Geological Survey Website and  GIS facility of Sioux Falls Website  and quantify the changes  over a period of thirty-one (31) years. Supervised classification methodology was applied to the acquired multi-band raster imageries using maximum livelihood technique in ERDAS Imagine 9.3. The images of the study area were classified into three (3) classes namely; vegetation, water body and built-up area and were overlay with vector maps of the study area generated in ArcGIS 10. The results show that for the period of 31years (1984-2015), vegetation which covered 76.20% of the total area has decreased to 39.29%, water body decreases from 6.63% to 1.89% while the built –up area which initially was 17.14% as at 1984 increased to 58.82%. The study, however, recommended that there is a need for a timely Land use/ Land cover mapping of the entire Abeokuta and its environs in order to reduce the effects of undiscrimate land utilization in the area. This will also facilitate necessary Land use planning and forestall the rising sprawl not only in Abeokuta but also in other urban centres.

scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

scholarly journals Remote Sensing Application for Exploring Land Use and Land Cover Dynamics in and Around Chatra Wetland, English Bazar, West Bengal

2021 ◽  
Vol 9 (1) ◽  
pp. 15-27
Author(s):  
Saleha Jamal ◽  
Md Ashif Ali
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Changes ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Gis And Remote Sensing ◽  
Erdas Imagine ◽  
Landsat 7 ◽  
Vegetation Water

Wetlands are often called as biological “supermarket” and “kidneys of the landscape” due to their multiple functions, including water purification, water storage, processing of carbon and other nutrients, stabilization of shorelines and support of aquatic lives. Unfortunately, although being dynamic and productive ecosystem, these wetlands have been affected by human induced land use changes. India is losing wetlands at the rate of 2 to 3 per cent each year due to over-population, direct deforestation, urban encroachment, over fishing, irrigation and agriculture etc (Prasher, 2018). The present study tries to investigate the nature and degree of land use/land cover transformation, their causes and resultant effects on Chatra Wetland. To fulfil the purpose of the study, GIS and remote sensing techniques have been employed. Satellite imageries have been used from United States Geological Survey (USGS) Landsat 7 Enhanced Thematic Mapper plus and Landsat 8 Operational Land Imager for the year 2003 and 2018. Cloud free imageries of 2003 and 2018 have been downloaded from USGS (https://glovis.usgs.gov/) for the month of March and April respectively. Image processing, supervised classificationhas been done in ArcGis 10.5 and ERDAS IMAGINE 14. The study reveals that the settlement hasincreased by about 90.43 per cent in the last 15 years around the Chatra wetland within the bufferzone of 2 Sq km. Similarly agriculture, vegetation, water body, swamp and wasteland witnessed asignificant decrease by 5.94 per cent, 57.69 per cent, 26.64 per cent 4.52 per cent and 55.27 per centrespectively from 2003 to 2018.

scholarly journals Assessment and simulation of land use and land cover change impacts on the land surface temperature of Chaoyang District in Beijing, China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9115 ◽  
Author(s):  
Muhammad Amir Siddique ◽  
Liu Dongyun ◽  
Pengli Li ◽  
Umair Rasool ◽  
Tauheed Ullah Khan ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Cover Change ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Rapid Urbanization ◽  
K Value ◽  
Vegetation Water ◽  
Landsat Satellite

Rapid urbanization is changing the existing patterns of land use land cover (LULC) globally, which is consequently increasing the land surface temperature (LST) in many regions. The present study is focused on estimating current and simulating future LULC and LST trends in the urban environment of Chaoyang District, Beijing. Past patterns of LULC and LST were identified through the maximum likelihood classification (MLC) method and multispectral Landsat satellite images during the 1990–2018 data period. The cellular automata (CA) and stochastic transition matrix of the Markov model were applied to simulate future (2025) LULC and LST changes, respectively, using their past patterns. The CA model was validated for the simulated and estimated LULC for 1990–2018, with an overall Kappa (K) value of 0.83, using validation modules in IDRISI software. Our results indicated that the cumulative changes in built-up to vegetation area were 74.61 km2 (16.08%) and 113.13 km2 (24.38%) from 1990 to 2018. The correlation coefficient of land use and land cover change (LULCC), including vegetation, water bodies and built-up area, had values of r =  − 0.155 (p > 0.005), −0.809 (p = 0.000), and 0.519 (p > 0.005), respectively. The results of future analysis revealed that there will be an estimated 164.92 km2 (−12%) decrease in vegetation area, while an expansion of approximately 283.04 km2 (6% change) will occur in built-up areas from 1990 to 2025. This decrease in vegetation cover and expansion of settlements would likely cause a rise of approximately ∼10.74 °C and ∼12.66 °C in future temperature, which would cause a rise in temperature (2025). The analyses could open an avenue regarding how to manage urban land cover patterns to enhance the resilience of cities to climate warming. This study provides scientific insights for environmental development and sustainability through efficient and effective urban planning and management in Beijing and will also help strengthen other research related to the UHI phenomenon in other parts of the world.

Quantification of Urban Heat Intensity with Land Use/Land Cover Changes Using Landsat Satellite Data Over Urban Landscapes

2021 ◽  
Author(s):  
Ruchi Bala ◽  
Rajendra Prasad ◽  
Vijay Pratap Yadav
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Urban Land ◽  
Land Cover Changes ◽  
Urban Landscapes ◽  
Positive Contribution ◽  
Negative Contribution ◽  
Urban Land Cover ◽  
Urban Heat ◽  
Landsat Satellite

Abstract Urban heat island (UHI) is a phenomenon which may have adverse effects on our environment and is stimulated as a result of urbanisation or land cover changes. Thermal remote sensing has been found beneficial to study the effect of urbanisation on UHI intensity. This paper analyses the variation in Land surface temperature (LST) with land cover changes in Varanasi city of India from 1989 to 2018 using Landsat satellite images. A new index named Urban Heat Intensity Ratio Index (UHIRI) was proposed to quantify the urban heat intensity from 1989 to 2018 which was found to increase from 0.36 in year 1989 to 0.87 in year 2018. Further, contribution of each land cover towards UHI was determined using Land cover contribution index (LCCI). The negative value of LCCI for water and vegetation indicates its negative contribution towards UHI whereas positive value of LCCI for bare soil and built-ups depicted its positive contribution towards UHI. The LCCI value for urban land cover shows significant increase in 29 years i.e. 0.49, 1.43, 3.40, 4.37 for years 1989, 1997, 2008 and 2018 respectively. The change in normalized LST from years 1989 to 2018 for the conversion of bare land to built-ups and vegetation to built-ups were found to be as -0.11 and 0.42 respectively. This led to conclusion that the replacement of vegetation with urban land cover has severe impact on increasing UHI intensity.

scholarly journals Classification of Land Use/Land Cover Changes Using GIS and Remote Sensing Technique in Lake Kenyir Basin, Terengganu, Malaysia

2018 ◽  
Vol 7 (3.14) ◽  
pp. 12 ◽  
Author(s):  
Mohd Khairul Amri Kamarudin ◽  
Kabir Abdulkadir Gidado ◽  
Mohd Ekhwan Toriman ◽  
Hafizan Juahir ◽  
Roslan Umar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Water Body ◽  
Geographical Information ◽  
Landsat Images ◽  
Gis And Remote Sensing ◽  
Remote Sensing Technique ◽  
Multi Temporal ◽  
Vegetation Water

Geographical information system (GIS) techniques and Remote Sensing (RS) data are fundamental in the study of land use (LU) and land cover (LC) changes and classification. The aim of this study is to map and classify the LU and LC change of Lake Kenyir Basin within 40 years’ period (1976 to 2016). Multi-temporal Landsat images used are MSS 1976, 1989, ETM+ 2001 and OLI 8 2016. Supervised Classification on Maximum Likelihood Algorithm method was used in ArcGIS 10.3. The result shows three classes of LU and LC via vegetation, water body and built up area. Vegetation, which is the dominant LC found to be 100%, 88.83%, 86.15%, 81.91% in 1976, 1989, 2001 and 2016 respectively. While water body accounts for 0%, 11.17%, 12.36% and 13.62% in the years 1976, 1989, 2001 and 2016 respectively and built-up area 1.49% and 4.47 in 2001 and 2016 respectively. The predominant LC changes in the study are the water body and vegetation, the earlier increasing rapidly at the expense of the later. Therefore, proper monitoring, policies that integrate conservation of the environment are strongly recommended. 

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