scholarly journals Implication of Human Induced Activities on Ecotourism in Ikogosi Warm Spring Centre, Ekiti State, Southern western, Nigeria

2018 ◽  
Vol 17 (1) ◽  
pp. 33-44
Author(s):  
Olusola Olufayo Adetoro
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Supervised Classification ◽  
Satellite Images ◽  
Diversity Index ◽  
Anthropogenic Activities ◽  
Land Use Land Cover ◽  
Maximum Likelihood Classification ◽  
Dense Vegetation ◽  
Developmental Phases

<p>The magnitude effect of human activities on the environment is of great concern. In this study investigation on the pattern of land use land cover change in the Ikogosi Ekiti, South Western and Nigeria was carried out. A 30 meter Landsat image of TM 1991, ETM+ 2002 and OLI 2015 were used for the study. The satellite images were digitally processed using Arcgis10.3 and Idrisi Selva 17.0 while Markov Chain Modeler was employed for prediction. Supervised Classification was performed through Maximum Likelihood Classification resulting into identification of five LULC classes which were built-up, rock outcrop, dense vegetation, light vegetation and water body; fragmentation analysis was done using Fragstat 4.0.</p><p>The results showed that anthropogenic activities resulted in 25.93% increase in built-up between the periods of 1991 and 2015 with a substantial loss (29.97%) of dense vegetation within the study area were detected. Fragmentation metric showed that the Number of Patches (NP) increased by 257, 268 and 281 while Shannon Diversity Index (SHIDI) correspondingly showed a decreased of 0.54, 0.47 and 0.21 for species diversity in year 1991, 2002 and 2015 respectively; indigenous respondents (70.1%) affirmed the extinction of biodiversity. Furthermore, the Markov Chain Modeler revealed that built-up was expected to increase by 36.7, 39.1 and 69.6% while dense vegetation will correspondingly decrease by 25.4, 22.9 and 18.7% in year 2030, 2050 and 2065 respectively. The result revealed that anthropogenic activities in the study area had contributed to massive removal of vegetation and this pattern had negatively affected the biomass condition of the study area indicting the region to experience an ecosystem imbalance and incidence of global warming. The changing spatial pattern was attributed to the tourism developmental phases in-around Ikogosi community which had increased deforestation, exotic plants and poaching by Ikogosi inhabitants and tourist visits to the study area.</p><p>The study concluded that various tourism development activities had adversely affected the nature of biodiversity, threatened land-use management and vegetation in the study area.</p>

scholarly journals Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018 ◽  
Vol 10 (10) ◽  
pp. 3421 ◽  
Author(s):  
Rahel Hamad ◽  
Heiko Balzter ◽  
Kamal Kolo
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Cover ◽  
Land Use Changes ◽  
Landsat 8 ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Kurdistan Region ◽  
The Past ◽  
Past Trend

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

scholarly journals Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia

2019 ◽  
Vol 41 (1) ◽  
pp. 146-153 ◽  
Author(s):  
Megersa Olumana Dinka ◽  
Degefa Dhuga Chaka
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Landsat Imagery ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Post Processing ◽  
Ancillary Data ◽  
The Stability ◽  
Land Cover Maps

Abstract Land use/land cover changes (LULCC) at Adei watershed (Ethiopia) over a period of 23 years (1986–2009) has been analysed from LANDSAT imagery and ancillary data. The patterns (magnitude and direction) of LULCC were quantified and the final land use/land cover maps were produced after a supervised classification with appropriate post-processing. Image analysis results revealed that the study area has undergone substantial LULCC, primarily a shift from natural cover into managed agro-systems, which is apparently attributed to the increasing both human and livestock pressure. Over the 23 years, the aerial coverage of forest and grass lands declined by 8.5% and 4.3%, respectively. On the other hand, agricultural and shrub lands expanded by 9.1% and 3.7%, respectively. This shows that most of the previously covered by forest and grass lands are mostly shifted to the rapidly expanding farm land use classes. The findings of this study suggested that the rate of LULCC over the study period, particularly deforestation due to the expansion of farmland need to be given due attention to maintain the stability and sustainability of the ecosystem.

Applying Supervised Clustering to Landsat MSS Images into GIS-Application

2013 ◽  
Vol 4 (3) ◽  
pp. 114-122
Author(s):  
Miguel Torres ◽  
Marco Moreno-Ibarra ◽  
Rolando Quintero ◽  
Giovanni Guzmán
Keyword(s):  
Supervised Classification ◽  
Satellite Images ◽  
Training Data ◽  
Clustering Method ◽  
Maximum Likelihood Classification ◽  
Gis Application ◽  
Supervised Clustering ◽  
Gis Applications ◽  
Geographic Environment ◽  
Decision Making Model

In this paper, the authors describe and implement an algorithm to perform a supervised classification into Landsat MSS satellite images. The Maximum Likelihood Classification method is used to generate raster digital thematic maps by means of a supervised clustering. The clustering method has been proved in Landsat MSS images of different regions of Mexico to detect several training data related to the geographic environment. The algorithm has been integrated into Spatial Analyzer Module to improve the decision making model and the spatial analysis into GIS-applications.

scholarly journals An Assessment of Changes the Land Use Land Cover and Impact of the It Park at Talwade Pune On the Nearby Villages

2020 ◽  
pp. 172-179
Author(s):  
Ujjwala Khare ◽  
Prajakta Thakur
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Rapid Growth ◽  
Urban Areas ◽  
Satellite Images ◽  
Ground Truth ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Tertiary Sector ◽  
Land Cover Maps

<p>The expansion of urban areas is common in metropolitan cities in India. Pune also has experienced rapid growth in the fringe areas of the city. This is mainly on account of the development of the Information Technology (IT) Parks. These IT Parks have been established in different parts of Pune city. They include Hinjewadi, Kharadi, Talwade and others like the IT parks in Magarpatta area. The IT part at Talwade is located to close to Pune Nashik Highway has had an impact on the villages located around it. The surrounding area includes the villages of Talwade, Chikhli, Nighoje, Mahalunge, Khalumbre and Sudumbre.</p> <p>The changes in the land use that have occurred in areas surrounding Talwade IT parks during the last three decades have been studied by analyzing the LANDSAT images of different time periods. The satellite images of the 1992, 2001 and 2011 were analyzed to detect the temporal changes in the land use and land cover.</p> <p>This paper attempts to study the changes in land use / land cover which has taken place in these villages in the last two decades. Such a study can be done effectively with the help of remote sensing and GIS techniques. The tertiary sector has experienced a rapid growth especially during the last decade near the IT Park. The occupation structure of these villages is also related to the changes due to the development of the IT Park.</p> <p>The land use of study area has been analysed using the ground truth applied to the satellite images at decadal interval. Using the digital image processing techniques, the satellite images were then classified and land use / land cover maps were derived. The results show that the area under built-up land has increased by around 14 per cent in the last 20 years. On the contrary, the land under agriculture, barren, pasture has decreased significantly.</p>

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 Land Use/Land Cover Change Detection Study Using Remote Sensing and GIS Technique in Puthimari River Basin-A Transboundary Basin Between Bhutan and India

2021 ◽  
pp. 26-34
Author(s):  
Swapnali Barman ◽  
Jaivir Tyagi ◽  
Waikhom Rahul Singh
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
River Basin ◽  
Remote Sensing And Gis ◽  
Rural Settlement ◽  
Land Use Land Cover ◽  
Dense Vegetation ◽  
Gis Technique

Using remote sensing and GIS technique, we analyse the change detection of different land use/land cover (LULC) types that has taken place in Puthimari river basin during a two-decade period from 1999 to 2019. Supervised classification method with maximum likelihood algorithm have been applied to prepare the LULC maps. The LULC change detection has been performed employing a post-classification detection method. Puthimari is a north bank sub-catchment of River Brahmaputra, the northern part of which falls in Bhutan and the rest falls in the Assam state of India. The primary LULC types of the basin are, dense vegetation which is predominant in the upper catchment, crop land and rural settlement. Thus, five different classes have been considered for the analysis, viz., dense vegetation, water bodies, silted water, cropland and rural settlement. The results showed that the rural settlement and water bodies in the basin increased by 42.70% and 30.31% from 1999 to 2019. However, dense vegetation, silted water and cropland decreased by 9.24%, 27.47% and 28.10% during these two decades.

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