scholarly journals Land use and land cover change-induced landscape dynamics: a geospatial study of Durgapur Sub-Division, West Bengal (India).

2021 ◽  
Author(s):  
Sribas Patra ◽  
Kapil Kumar Gavsker
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Supervised Classification ◽  
West Bengal ◽  
Google Earth ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Process Of Change ◽  
Detection Analysis

Abstract This article examines the factors and process of change in the land use and land cover change-induced landscape dynamics in the Durgapur Sub-Division region of West Bengal in 1989, 2003, and 2018 by employing the satellite imageries of Landsat 5 (1989 and 2003) and Landsat 8 (2018). The images of the study area were categorized into seven specific land use classes with the help of Google Earth Pro. Based on the supervised classification methodology, the change detection analysis identified a significant increase in built-up land from 11% to 23% between 1989 and 2003 and from 23% to 29% in 2003 and 2018. The areas under fallow land and vegetation cover have mainly decreased, while the areas of industrial activities and urbanization expanded during the study period.

scholarly journals APLIKASI CITRA SATELIT PENGINDERAAN JAUH UNTUK PERCEPATAN IDENTIFIKASI TANAH TERLANTAR

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Westi Utami ◽  
I Gede Kusuma Artika ◽  
Aziz Arisanto
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Planning ◽  
Supervised Classification ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Gis Analysis ◽  
Maximum Likelihood Algorithm ◽  
Abandoned Land ◽  
And Control

Abstract: Identification and regulation of abandoned land needs to be intensified, to contribute identification of Objects of Agrarian Reform (TORA). Mapping of potential abandoned land carried out by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) was considered not optimally implemented if compared between the setting targets with the achievements each year. Utilization of google earth imagery and Geographic Information System (GE and GIS) is expected accelerate mapping of potentialabandoned land. Google earth image was used to interpret land cover as the basis to identify land use. Land cover classification was done using supervised classification with maximum likelihood algorithm. The results showed that google earth image and GIS were able to present existing land use, and able to identifyland that has not been used as the permit rights granted. The result of interpretation and GIS analysis was expected to be used as tool to identify potential abandoned land, as the basis to regulate, accelerate and control abandoned land in Indonesia.Intisari: Identifikasi dan penertiban tanah terlantar perlu dilakukan secara intensif, salah satunya untuk memberikan sumbangan bagi Tanah Obyek Reforma Agraria (TORA). Pemetaan potensi tanah terlantar yang dilakukan Kementerian Agraria dan Tata Ruang/Badan Pertanahan Nasional (ATR/BPN) selama ini dirasa belum optimal apabila dibandingkan antara target yang ditetapkan dengan capaian setiap tahunnya. Pemanfaatan citra google earth dan Sistem Informasi Geografi diharapkan dapat membantu pekerjaanpemetaan potensi dan identifikasi tanah terlantar. Data yang digunakan adalah citra google earth untuk interpretasi tutupan tanah sebagai dasar untuk menentukan penggunaan tanah. Klasifikasi tutupan tanah pada penelitian ini menggunakan klasifikasi terselia (supervised) dengan algoritma maxsimum likelihood. Hasil penelitian menunjukkan bahwa pemanfaatan citra google earth dan SIG mampu menyajikan data penggunaan tanah eksisting terbaru, dan mampu mengidentifikasi tanah-tanah yang tidak dimanfaatkan sesuai arahan dalam izin hak yang diberikan. Hasil interpretasi dan analisis dengan SIG ini diharapkan dapat digunakan sebagai identifikasi obyek potensi tanah terlantar untuk kemudian dijadikan sebagai dasar dalam kegiatan penertiban tanah terlantar sehingga dapat membantu percepatan penertiban tanah terlantar di Indonesia.  

scholarly journals Remote Sensing and GIS based Land Use Land Cover Analysis in Chandel District, Manipur, India

2021 ◽  
Vol 889 (1) ◽  
pp. 012046
Author(s):  
Ashangbam Inaoba Singh ◽  
Kanwarpreet Singh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Geographical Area ◽  
Temporal Trends ◽  
Water Bodies ◽  
Landsat 8 ◽  
Rapid Urbanization ◽  
Barren Area ◽  
Landsat 7

Abstract Rapid urbanization has dramatically altered land use and land cover (LULC). The focus of this research is on the examination of the last two decades. The research was conducted in the Chandel district of Manipur, India. The LULC of Chandel (encompassing a 3313 km2 geographical area) was mapped using remotely sensed images from LANDSAT4-5, LANDSAT 7 ETM+, and LANDSAT 8 (OLI) to focus on spatial and temporal trends between years 2000 and 2021. The LULC maps with six major classifications viz., Thickly Vegetated Area (TVA), Sparsely Vegetated Area (SVA), Agriculture Area (AA), Population Area (PA), Water Bodies (WB), and Barren Area (BA) of the were generated using supervised classification approach. For the image classification procedure, interactive supervised classification is adopted to calculate the area percentage. The results interpreted that the TVA covers approximately 65% of the total mapped area in year 2002, which has been decreased up to 60% in 2007, 56% in 2011, 55 % in 2017, and 52% in 2021. The populated area also increases significantly in these two decades. The change and increase in the PA has been observed from year 2000 (8%) to 2021 (11%). Water Bodies remain same throughout the study period. Deforestation occurs as a result of the rapid rise of the population and the extension of the territory.

scholarly journals Monitoring Land Use and Land Cover Change of Forest Ecosystems of Shendurney Wildlife Sanctuary, Western Ghats, India

2021 ◽  
pp. 20-27
Author(s):  
H. Bilyaminu ◽  
P. Radhakrishnan ◽  
K. Vidyasagaran ◽  
K. Srinivasan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Forest Ecosystem ◽  
Forest Ecosystems ◽  
Evergreen Forest ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Wildlife Sanctuary

Understanding forest degradation due to human and natural phenomena is crucial to conserving and managing remnant forest resources. However, forest ecosystem assessment over a large and remote area is usually complex and arduous. The present study on land use and land cover change detection of the Shendurney Wildlife Sanctuary forest ecosystems was carried out to utilize the potential application of remote sensing (RS) and geographic information system (GIS). Moreover, to understand the trend in the forest ecosystem changes. The supervised classification with Maximum Likelihood Algorithm and change detection comparison approach was employed to study the land use and land cover changes, using the Landsat Enhanced Thematic Mapper (ETM±) and Landsat 8 OLI-TIRS using data captured on July 01, 2001, and January 14, 2018. The study indicated the rigorous land cover changes. It showed a significant increase in the proportion of degraded forest with negligible gain in the proportion of evergreen forest from 21.31% in 2001 to 22.97% in 2018.  A substantial loss was also observed in moist deciduous from 27.11 % in 2001 to 17.23 % in 2018. The result of the current study indicated the degree of impacts on forests from the various activities of their surroundings. This study provides baseline information for planning and sustainable management decisions.

scholarly journals Analysis of Spatiotemporal Land Use and Land Cover Changes using Remote Sensing and GIS: A Review

2018 ◽  
Vol 7 (4.34) ◽  
pp. 159
Author(s):  
Kabir Abdulkadir Gidado ◽  
Mohd Khairul Amri Kamarudin ◽  
Nik Ahmad Firdausaq ◽  
Aliyu Muhammad Nalado ◽  
Ahmad Shakir Mohd Saudi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Lulc Changes ◽  
Detection Analysis ◽  
Socio Economic Factors

The land-use and land-cover (LULC) pattern of an area is an outcome of natural and socio-economic factors and their use spatially by man; this LULC varies from the forest, water body, agricultural land and so on. Remote Sensing (RS) and Geographical Information System (GIS) studies have predominantly focused on providing the technical knowledge of, where, and the type of LULC change that has occurred and its impacts on man and the environment. Knowledge about LULC changes is essential for understanding the relationships and interfaces between humans and the natural environment. The purpose of this article is to review the previous studies of the spatiotemporal LULC changes. However, thirty (30) articles were reviewed from 2011 to 2017. However, these articles studied the LULC, classification, changes and change detection analysis, using different methods and software of RS and G.I.S. The finding shows that these articles have overall accuracy assessment ranges from 75% to 95% validations. Also, supervised classification in Maximum Likelihood Algorithm method was mostly employed for the LULC classification. Moreover, these reviewed articles confirmed that LULC changes are imminent as a result of both natural and human factors which lead to increase and decrease of one LULC cover to another. Therefore proper monitoring of LULC changes when applied help the relevant government bodies, agencies and environmental managers utilise the environment to the fullest.  

Sign in / Sign up

Export Citation Format

Share Document