scholarly journals A Spatio-temporal Study of Land Use Land Cover Change Detection Using GIS and Remote Sensing Techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 026-031
Author(s):  
Snehalata Chaware ◽  
◽  
Nitin Patil ◽  
Gajanan Satpute ◽  
M. R. Meshram ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Use Planning ◽  
Water Body ◽  
Temporal Dynamics ◽  
Kappa Coefficient ◽  
Population Pressure ◽  
Land Use Land Cover ◽  
Spatio Temporal

Land resources in India are under severe pressure and it is widely believed that marginal lands are being brought under cultivation. The extent of such changes needs to be known for better land use planning decisions. The present study illustrates the spatio-temporal dynamics of land use land cover of Nagjhari watershed in Bhatkuli block of Amravati, Maharashtra. Multi-temporal high resolution of Sentinel and Landsat satellite data were used to identify the significant positive and negative Land use land cover changes over a decade of 2007 to 2017. From 2007 to 2017, the ‘habitation’ class increased by 34% due to increasing population pressure. There was a decrease in ‘wasteland’ by 10.3%, while the area under ‘agriculture’ decreased by approximately 4.7% because of the increased area under ‘habitation’ and ‘water body’ at Nagjhari watershed. The biggest change occurred in land use class ‘water body’ increased sharply from 2013-17 by 62.7 per cent due to consequence of state policy of watershed development that was implemented after 2014. The forest class recorded maximum loss (18.3%) due to increasing population maximum land converted into habitation. The study shows overall classification accuracy as 85.46% and kappa coefficient (K) of 0.85. Kappa coefficient indicated that land use land cover assessment from remote sensing data show the best accuracy. These finding will help in deciding land use policy for future and its impact on land management of the watershed.

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 Land Use/Land Cover Changes of Ado-Ekiti LGA, Ekiti State, Nigeria Using Remote Sensing Techniques

2021 ◽  
pp. 32-42
Author(s):  
Ajagbe, Abeeb Babajide ◽  
Oguntade, Sodiq Solagbade ◽  
Abiade, Idunnu Temitope
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Local Government ◽  
Land Cover ◽  
Water Body ◽  
Local Government Area ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Bare Surface

Land use assessment and land cover transition need remote sensing (RS) and geographic information systems (GIS). Land use/land cover changes of Ado-Ekiti Local Government Area, Ekiti State, Nigeria, were examined in this research. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1985, 2000, and 2015 respectively. Image scene with path 190 and row 055 was used for the three Landsat Images. A supervised digital image classification approach was used in the study, which was carried out using the ArcMap 10.4 Software. Five land use/land cover categories were recognised and recorded as polygons, including Built-up Areas, Bare surface, water body, Dense Vegetation and Sparse Vegetation. The variations in the area covered by the various polygons were measured in hectares. This study revealed that between 1985 and 2015, there was a significant change in Built-up areas from 1694 hectares to 5656 hectares. However, there was a reduction in water body from 25 hectares in 1985 to 19 hectares in 2015; there was a severe reduction in the bare surface from 4641 hectares in 1985 to 2237 hectares in 2015. Generally, the findings show that the number of people building houses in the study area has grown over time, as many people reside in the outskirts of the Local Government Area, resulting in a decrease in the vegetation and bare surfaces. The maps created in this research will be useful to the Ekiti State Ministry of Land, Housing, Physical Planning, and Urban Development to develop strategies and government policies to benefit people living in the Ado-Ekiti Local Government Area of the State.

scholarly journals Contribution à l’étude de la vulnérabilité des terres de cultures de la région de Thiès (Sénégal) à l’aide de la télédétection et des systèmes d’information géographique (SIG)

2019 ◽  
pp. 6731-6746 ◽  
Author(s):  
Amadou SALL ◽  
Assize TOURE ◽  
Alioune KANE ◽  
Awa Niang Fall
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Temporal Dynamics ◽  
Remote Sensing And Gis ◽  
Cultivated Land ◽  
Landsat Images ◽  
Systèmes D’Information Géographique ◽  
Spatio Temporal ◽  
Systèmes D’Information

L’objectif de cette étude est d’établir à partir de la télédétection et des SIG, la dynamique spatio-temporelle des terres de cultures et d’explorer les futurs possibles de l’occupation du sol dans trois communes rurales de la région de Thiès (Fandène, Notto Diobass et Taiba Ndiaye). Une classification multidate des images landsat (1988, 2002 et 2014) a permis de quantifier les changements d’occupation des terres. Les résultats montrent que les zones de culture de Fandène sont passées entre 1988 et 2014 de 62% à 52% de la superficie totale de la commune. A l’opposée la commune de Taiba Ndiaye connait une expansion des zones de culture entre ces deux dates. Les changements enregistrés à Notto sont négligeables. Les simulations, faites sur la base des probabilités pour que la valeur d’une cellule i reste inchangée ou prenne la valeur d’une autre cellule j à l’horizon 2035, révèlent que les terres de culture de Fandène ont 69% de probabilité d’évoluer vers d’autres classes d’occupation du sol. ABSTRACT The objective of this study is to quantify from remote sensing and GIS the spatio temporal dynamics of cultivated land and explore possible futures of land use in three rural municipalities of Thies (Fandene, Notto Diobass, and Taiba Ndiaye). A multidate classification Landsat images (1988, 2002 et 2014) was used to quantify change in land cover. The results show that between 1988 and 2014 Fandene cropping areas have passed from 62% to 52% of the total area. At the opposite the commune of Taiba Ndiaye has known an expansion of cropping areas between these two dates. Minor changes are noted in Notto district. Simulations carried out on the basis of probabilities for a unit i to stay in the same cell or to be converted to another unit j in 2035, reveals that the probability for a cultivated land unit to be transformed into a another land cover category is high in Fandene (69 %).

scholarly journals Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis

2021 ◽  
Author(s):  
Hayder Dibs ◽  
Hashim Ali Hasab ◽  
Ammar Shaker Mahmoud ◽  
Nadhir Al-Ansari
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Spatial Resolution ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Multispectral Imagery ◽  
Spectral Angle Mapper ◽  
Spectral Angle

AbstractAdopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions.

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 Monitoring Land Use/Land Cover Changes in a River Basin due to Urbanization using Remote Sensing and GIS Approach

2014 ◽  
Vol XL-8 ◽  
pp. 949-953
Author(s):  
S. Shukla ◽  
M. V. Khire ◽  
S. S. Gedam
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
River Basin ◽  
Remote Sensing And Gis ◽  
Land Use Land Cover ◽  
Agricultural Lands ◽  
Sensing Technology ◽  
Liss Iii ◽  
Spatio Temporal

Faster pace of urbanization, industrialization, unplanned infrastructure developments and extensive agriculture result in the rapid changes in the Land Use/Land Cover (LU/LC) of the sub-tropical river basins. Study of LU/LC transformations in a river basin is crucial for vulnerability assessment and proper management of the natural resources of a river basin. Remote sensing technology is very promising in mapping the LU/LC distribution of a large region on different spatio-temporal scales. The present study is intended to understand the LU/LC changes in the Upper Bhima river basin due to urbanization using modern geospatial techniques such as remote sensing and GIS. In this study, the Upper Bhima river basin is divided into three adjacent sub-basins: Mula-Mutha sub-basin (ubanized), Bhima sub-basin (semi-urbanized) and Ghod sub-basin (unurbanized). Time series LU/LC maps were prepared for the study area for a period of 1980, 2002 and 2009 using satellite datasets viz. Landsat MSS (October, 1980), Landsat ETM+ (October, 2002) and IRS LISS III (October 2008 and November 2009). All the satellite images were classified into five LU/LC classes viz. built-up lands, agricultural lands, waterbodies, forests and wastelands using supervised classification approach. Post classification change detection method was used to understand the LU/LC changes in the study area. Results reveal that built up lands, waterbodies and agricultural lands are increasing in all the three sub-basins of the study area at the cost of decreasing forests and wastelands. But the change is more drastic in urbanized Mula-Mutha sub-basin compared to the other two sub-basins.

scholarly journals Analysis of Long-Term Spatio-Temporal Trends in Land Use/Land Cover in Devikulam Taluk, Kerala Using Geospatial Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 34-52
Author(s):  
Srishti Solanki ◽  
Chindu Chandran ◽  
J.K. Garg ◽  
Prodyut Bhattacharya
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Planning ◽  
Temporal Trends ◽  
Thematic Mapper ◽  
Anthropogenic Pressure ◽  
Land Use Land Cover ◽  
The Sustainable Development ◽  
Drastic Increase ◽  
Spatio Temporal

This study was undertaken to evaluate the spatial as well as the temporal changes in land use/ land cover in Devikulam Taluk, Idukki District, Kerala, and to assess the effects of increasing anthropogenic pressure on the fragile ecosystem of this area. For analysis, land use/ land cover maps of four different years, i.e., 1988, 1999, 2008 and 2017, were generated using LANDSAT TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI/TIRS (Operational Land Imager/Thermal InfraRed Sensor) satellite imagery. The results of the study suggested that there has been a drastic increase in the built-up area and a continuous decline in the forest area in Devikulam from 35.31 km2 built-up in 1988 to 73.92 km2 in 2017, and 1374.52 km2 forest in 1988 to 1247.24 km2 in 2017, respectively. Over this period of approximately 40 years, around 47.85 km2 area of the forest got converted to built-up. This could be due to the increasing anthropogenic pressure in terms of migration or booming tourism contributing to the increased demand for infrastructures. Therefore, appropriate land use planning is a fundamental step towards the sustainable development of this biogeographically rich and unique area of Devikulam Taluk.

scholarly journals Land use/Land cover Changes in Sangamner City by using Remote Sensing and GIS

2019 ◽  
Vol 8 (2) ◽  
pp. 4614-4621
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Water Body ◽  
Urban Expansion ◽  
Geographical Information ◽  
Space Distribution ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
The Town

This paper examines that, with the help of Remotes Sensing (RS) and Geographical Information system (GIS) Land use/Land cover of the town area from period 1975 to 2017 are classified into different classes. The town information is extracted from Toposheet and Remote Sensing Landsat-7 ETM+ images of 1975 to 2017. There are five expansion types are considered during 42 years, including water body, built-up area, forest, Agriculture and exposed Rock. By analyzing the data from the year 1975 to 2017 we found that the natural feature area such as water body, the forest is decreasing continuously and the area of town that is built-up area increase partially etc. Shannon’s Entropy approach identifies the degree of special concentration and dispersion growth, its value is close to 1 which indicates that space distribution is evenly dispersed. According to get the value of statistical Kappa Coefficient which lies in between 0.75 to 0.89 we say that there is accuracy in the requirement of research. Also, in addition to that population for the next three-decade help to define the built-up area of the city, the method used to forecast the population are Arithmetic increase method, Geometric increase method, Incremental increase method, Decreasing rate of growth method and Simple graphical method, this method gives a forecast of urban expansion from the year 2021 to 2041. The Land use/ Land cover changes classification is useful for proper planning, utilization and management of resources. Land use/Land cover changes are contributed to creating community spirit and a properly balanced population structure.

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