scholarly journals Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India

2014 ◽  
Vol 64 (1) ◽  
pp. 111-127 ◽  
Author(s):  
Varun Mishra ◽  
Praveen Rai ◽  
Kshitij Mohan
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Changes ◽  
Land Change ◽  
Land Change Modeler

Prediksi Perubahan Lahan Terbangun Dengan OBIA LCM Pada Citra Terfusi di Kota Kendari

2021 ◽  
Vol 5 (2) ◽  
pp. 170
Author(s):  
Adnan Adnan ◽  
Fitra Saleh ◽  
Iradat Salihin
Keyword(s):  
Land Use ◽  
Landsat Imagery ◽  
Land Use Changes ◽  
Landsat 8 ◽  
Land Change ◽  
Land Use Classification ◽  
Landsat 8 Oli ◽  
Land Change Modeler ◽  
Two Factors ◽  
Land Changes

Abstrak: Penggunaan lahan disetiap tahunnya akan mengalami perubahan. Perkembangan tersebut bisa jadi tidak terkendali, sehingga perencanaan prediksi perubahan lahan penting untuk dikaji. Dalam memprediksi dapat dilakukan dengan menggunakan citra, khususnya citra Landsat. Penelitian ini bertujuan untuk: (1) distribusi penggunaan lahan terbangun di Kota Kendari pada tahun 2014 dan 2019 dengan metode OBIA pada citra terfusi; (2) melihat arah perubahan penggunaan lahan terbangun di Kota Kendari pada tahun 2024 dan 2029 dengan metode Land Change Modeler (LCM). Metode yang digunakan dalam penelitian ini  yaitu metode klasifikasi penggunaan lahan berbasis piksel OBIA dan pemodelan prediksi perubahan penggunaan lahan Land Change Modeler (LCM). Hasil penelitian ini antara lain: (1) luas lahan terbangun pada tahun 2014 di Kota Kendari seluas 6.061,85 hektar dan luas penggunaan lahan terbangun di Kota Kendari pada tahun 2019 seluas 6.716,96 hektar dengan perubahan penggunaan lahan terbangun tahun 2014 sampai dengan tahun 2019 dengan pertambahan luas 2,43%; (2) Arah perubahan penggunaan lahan terbangun di Kota Kendari diprediksikan cenderung berkembang ke arah Kecamatan Baruga karena dipengaruhi oleh dua faktor yaitu kemiringan lereng dan jaringan jalan. Kata Kunci : Penggunaan Lahan, Landsat 8 OLI, Penajaman Citra, OBIA, LCM Abstract: Land use will change every year. The development may be uncontrollable, so predictive planning of land changes is important to review. In predicting  can be done using  imagery, especially Landsat imagery. This study aims to:(1)  the distribution of land  use  built  in Kendari City in 2014 and 2019 with OBIA method on diffusion imagery; (2) see the direction of land use changes built in Kendari City in  2024 and 2029 with land change modeler  (LCM) method. The methods used in this study are OBIA pixel-based land  use  classification method and land use change prediction modeling land change modeler (LCM).  The results of this study include: (1) land area  built in 2014 in Kendari City aswide as 6,061.85 hectars and land use area built in Kendari City in 2019 aswide as 6,716.96 hectars with land use changes built in 2014 to 2019 with an increase  of  2.43%; (2) The direction of land use changes built in Kendari City  is predicted   to tend to  develop  towards  Baruga Subdistrict because it is influenced by two factors, namely slope and road network. Keywords: Land Use,  Landsat 8 OLI,  Image Sharpening,  OBIA, LCM

scholarly journals Land use changes in Prague suburban area according to different prediction modelling approaches

Geografie ◽  
2015 ◽  
Vol 120 (3) ◽  
pp. 422-443 ◽  
Author(s):  
Magdalena Indrová ◽  
Lucie Kupková
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Use Changes ◽  
Residential Development ◽  
Software Tools ◽  
Suburban Area ◽  
Soil Protection ◽  
Land Change ◽  
Land Change Modeler ◽  
Prediction Modelling

The main objective of this study was to compare the capabilities of the Dyna- CLUE and Land Change Modeler (LCM) software based on the results of land use/cover development predictions in selected cadastres of the Prague suburban area. Time series of land use data, land use plans of the municipalities, and data on soil protection were used for this analysis. Land use prediction maps for the year 2020 were created using both software tools. The results of the comparison showed that the models respect the restriction of development. In accordance with the local land use plans, new residential development was properly allocated. As for commercial areas, the requirements were not completely fulfilled. It is evident that both models are able to produce correct maps of future land use based on specified requirements at the level of several cadastral units (area approx. 2,000 ha). However, the instability of LCM and the necessity of using other software while working with Dyna-CLUE somewhat complicated the work.

scholarly journals Análise espaço-temporal das mudanças no uso e cobertura do solo no município de São Thomé das Letras / Spatio-temporal land use land and cover changes analysis in the São Thomé das Letras municipality

2015 ◽  
Vol 26 (45) ◽  
pp. 79
Author(s):  
Nayara Lage Silva ◽  
Bráulio Magalhães Fonseca
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Temporal Analysis ◽  
Minas Gerais ◽  
Time Range ◽  
Land Change ◽  
Land Change Modeler ◽  
Rupestrian Fields ◽  
Multi Temporal

<p>O mapeamento do uso e cobertura do solo por meio da utilização de dados de sensoriamento remoto e técnicas de processamento digital de imagens tem se difundindo globalmente por permitir uma análise espacial e dinâmica das tipologias de uso e cobertura. A mineração é uma das atividades transformadoras do meio que mais causa impactos aos ambientes naturais, mesmo que de maneira concentrada, devido ao fator de rigidez locacional da atividade. É uma atividade que demanda controle ambiental em todo processo para reduzir os impactos negativos e garantir o equilíbrio dos processos ambientais. Neste contexto o trabalho objetivou:  1 - realizar uma análise multitemporal da cobertura do solo no município de São Thomé das Letras, no estado de Minas Gerais; 2 - quantificar e espacializar as alterações no período determinado entre 1984 a 2011.  Buscou-se visualizar o comportamento da atividade de mineração desde seu início até os dias atuais, e consequentemente, observar a dinamicidade das mudanças ocorridas na cobertura do solo das outras classes mapeadas. Para o mapeamento do uso e cobertura do solo foi utilizado o programa SPRING/INPE e para a análise temporal/espacial de mudanças utilizou-se o modelo <em>Land Change Modeler</em> acoplado ao programa IDRISI. A partir da análise dos resultados foi possível quantificar e espacializar o avanço da mineração sob o campo rupestre/afloramento rochoso; a perda substancial da vegetação densa no intervalo do período analisado; o crescimento exponencial da ocupação urbana; e o surgimento da atividade reflorestamento.</p><p><strong>Palavras-chave:</strong> Análise multitemporal. Uso e Cobertura do Solo. <span lang="EN-US">Mineração. Sensoriamento Remoto.</span></p><p> </p><p><strong><span lang="EN-US">Abstract</span></strong></p><p><span lang="EN-US">The land use and land cover mapping using remote sensing data and techniques of digital image processing has been widely used by enabling a dynamic spatial analysis of  land use and land cover types. Mining is a human activity that transforms the landscape and is one of the most impactful for natural environments, even in a concentrated way, due to locational rigidity factor of activity. It is an activity that requires environmental control throughout the process to reduce the negative impacts and ensure a balance of environmental processes. In that context the study aimed to: 1 - conduct a multi-temporal analysis of land use and land cover in São Thomé das Letras municipality, in Minas Gerais State, Brazil; 2- quantify and map changes from 1984 to 2011 in the </span><span lang="EN-US">area studied. We attempted to visualize the behavior of mining activity from its inception to the present day, and therefore observe the dynamics of change in land use and land cover of other mapped classes. To map land use and land cover was used SPRING/INPE software and to analyze the changes used the Land Change Modeler model, coupled to the IDRISI software. From the analysis of the results was possible to quantify and spatialize the advancement of mining under the outcrop and Rupestrian Fields; occurred substantial loss of dense vegetation in the analyzed time range; the exponential growth of urban occupation; and the emergence of reforestation activity.</span></p><p> </p><p><strong><span lang="EN-US">Keywords: </span></strong><span lang="EN-US">Multi-temporal analysis. Land Use and Land Cover. Mining. Remote sensing.</span></p>

scholarly journals Assessment and prediction of land use changes in the Anzali wetland Basin, Based on Land Change Modeler (LCM)

2019 ◽  
Vol 11 (2) ◽  
pp. 93-114
Author(s):  
رضا شاکری ◽  
کامران شایسته ◽  
مهدی قربانی
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Land Change ◽  
Anzali Wetland ◽  
Land Change Modeler

scholarly journals Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler

Geographies ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 166-177
Author(s):  
Ibra Lebbe Mohamed Zahir ◽  
Sunethra Thennakoon ◽  
Rev. Pinnawala Sangasumana ◽  
Jayani Herath ◽  
Buddhika Madurapperuma ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Environmental Changes ◽  
Learning Algorithm ◽  
Land Use Changes ◽  
Land Change ◽  
Spatial And Temporal Scales ◽  
Municipal Council ◽  
Land Change Modeler ◽  
Medium Resolution

Land-use change is a predictable and principal driving force of potential environmental changes on all spatial and temporal scales. A land-use change model is a tool that supports the analysis of the sources and consequences of land-use dynamics. This study aims to assess the spatiotemporal land-use changes that occurred during 1990–2020 in the municipal council limits of Batticaloa. A land change modeler has been used as an innovative land planning and decision support system in this study. The main satellite data were retrieved from Landsat in 1990, 2000, 2010, and 2020. For classification, the supervised classification method was employed, particularly with the medium resolution satellite images. Land-use classes were analyzed by the machine learning algorithm in theland change modeler. The Markov chain method was also used to predict future land-use changes. The results of the study reveal that only one land-use type, homestead, has gradually increased, from 12.1% to 34.1%, during the above-mentioned period. Agriculture land use substantially declined from 26.9% to 21.9%. Bare lands decreased from 11.5% to 5.0%, and wetlands declined from 13.9% to 9.6%.

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