scholarly journals Model Spasial Perubahan Penggunaan Lahan dan Pengaruhnya Terhadap Kebijakan Swasembada Padi

2018 ◽  
Vol 32 (1) ◽  
pp. 33 ◽  
Author(s):  
Dede Amrillah ◽  
Eko Kusratmoko ◽  
Supriatna Supriatna
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Spatial Modeling ◽  
Consumption Rate ◽  
Average Rate ◽  
Rice Production ◽  
Multi Layer Perceptron ◽  
Land Change ◽  
Land Change Modeler ◽  
Paddy Land

Perubahan tutupan dan penggunaan lahan di Kecamatan Kalitidu, Kabupaten Bojonegoro cukup signifikan khususnya untuk penggunaan lahan sawah. Suatu wilayah dikatakan berswasembada padi jika produksi berasnya lebih besar dibandingkan dengan angka konsumsi berasnya. Dalam penelitian ini dilakukan pemodelan spasial menggunakan metode jaringan saraf Multi-Layer Perceptron (MLP) dan Markov Chain (MC) yang terdapat dalam metode Land Change Modeler (LCM) pada perangkat lunak Idrisi. Pada pemodelan spasial tersebut digunakan variabel jalan sebagai faktor pendukung perubahan penggunaan lahan di tahun 2025. Hasil yang diperoleh dari pemodelan spasial tersebut yaitu besaran luasan sawah pada tahun 2025 dengan angka 4644.99 hektar dengan nilai akurasi 56.51%. Kemudian nilai tersebut dikalikan dengan angka produktifitas padi tahun 2015 dan angka konversi gabah kering giling (GKG) menghasilkan nilai produksi beras di tahun 2025 sebesar 95705.37 ton. Angka konsumsi beras tahun 2025 sebesar 4648.402 ton didapatkan dengan mengkalikan jumlah penduduk di tahun 2025 yang memiliki angka 52515 jiwa dengan angka rata-rata konsumsi per kapita per tahun yang berada di angka 88.52 kg. Dengan demikian Kecamatan Kalitidu di tahun 2025 mampu berswasembada padi.Changes in land cover and land use in Kalitidu District, Bojonegoro Regency are significant, especially for paddy land use. A region is said to be self-sufficient in rice if its rice production is greater than its rice consumption rate. In this research, spatial modeling using Multi-Layer Perceptron (MLP) and markov chain method is applied in Land Change Modeler (LCM) method in Idrisi software. In spatial modeling used road variables as a driving factor the change of land use in 2025. The results obtained from spatial modeling is the size of paddy field area in 2025 with the number 4644.99 hectares with an accuracy of 56.51%. Then the value is multiplied by the rate of rice productivity in 2015 and the conversion rate of dry milled grain (GKG) produces rice production value in 2025 of 95705.37 tons. The consumption rate of rice in 2025 amounted to 4648,402 tons was obtained by multiplying the number of population in the year 2025 which has the number 52515 people with the average rate per capita consumption per year which is at 88.52 kg. Thus Kalitidu District in 2025 is capable of self-sufficient rice.    

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 MODELAGEM TEMPORAL DINÂMICA DO USO E OCUPAÇÃO DO SOLO BASEADO EM SIG PARA A BACIA DO RIO TAPACURÁ (PE)

2018 ◽  
Vol 37 (1) ◽  
pp. 193-210 ◽  
Author(s):  
Ana Paula Campos XAVIER ◽  
Richarde Marques da SILVA
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Multi Layer Perceptron ◽  
Land Change ◽  
Land Change Modeler

Este estudo teve por objetivo simular cenários de uso e ocupação do solo para t4 (2035), tendo como base as mudanças no uso do solo ocorridas em t1 (1989), t2 (2007) e t3 (2015) para a bacia do Rio Tapacurá, localizada no Estado de Pernambuco. Foi realizada a previsão do uso do solo para t3 (2015), usando três métodos: (a) Rede Neural Multi-Layer Perceptron (RNMLP), (b) Similarity-Weighted Instance-Based Machine Learning Algorithm (SimWeight) e (c) Regressão Logística (RL) e para a metodologia que mostrou melhor desempenho, foi realizada a predição dos cenários futuros para t4 (2035). Os cenários futuros simulados foram: (a) Cenário 1: de continuidade das transições e (b) Cenário 2: de continuidade das transições e intensificação da classe pecuária e expansão da área urbana, usando o módulo Land Change Modeler (LCM) do Idrisi TerrSet e imagens da cobertura do solo. Os resultados da previsão do uso do solo para 2015 mostraram que o melhor desempenho foi obtido usando o método RNMLP com treinamento de 84,22% e 10.000 iterações. A simulação dos cenários futuros para t4 mostrou intensificação das transições observadas nos três anos analisados, com previsão para expansão de cerca de 3% da classe pecuária para os dois cenários simulados.

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 Analyzing and Predicting Land Use and Land Cover Changes in New Jersey Using Multi-Layer Perceptron–Markov Chain Model

Earth ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 845-870
Author(s):  
Kikombo Ilunga Ngoy ◽  
Feng Qi ◽  
Daniela J. Shebitz
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
New Jersey ◽  
Land Cover ◽  
Markov Chain Model ◽  
Mixed Forest ◽  
The United States ◽  
Chain Model ◽  
Multi Layer Perceptron ◽  
Lulc Changes

This study analyzed the changes of land use and land cover (LULC) in New Jersey in the United States from 2007 to 2012. The goal was to identify the driving factors of these changes and to project the five-year trend to 2100. LULC data was obtained from the New Jersey Department of Environmental Protection. The original 86 classes were reclassified to 11 classes. Data analysis and projection were performed using TerrSet 2020. Results from 2007 to 2012 showed that the rate of LULC changes was relatively small. Most changes happened to brush/grasslands, mixed forest lands, farmlands and urban/developed lands. Urban/developed lands and the mixed-forest cover gained while farmlands lost. Using a multi-layer perceptron–Markov chain (MLP–MC) model, we projected the 2015 LULC and validated by actual data to produce a 2100 LULC. Changes from 2012 to 2100 showed that urban/developed lands, as well as brush/grasslands, would continue to gain, while farmlands would lose, although the projected landscape texture would likely be identical to the 2012 landscape. Human and natural factors were discussed. It was concluded that the MLP–MC model could be a useful model to predict short-term LULC change. Unexpected factors are likely to interfere in a long-term projection.

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 Multi-layer perceptron-Markov chain-based artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Md.Abdul Fattah ◽  
Syed Riad Morshed ◽  
Syed Yad Morshed
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Markov Chain ◽  
Surface Temperature ◽  
Land Surface ◽  
Sustainable Urban Development ◽  
Strong Positive Correlation ◽  
Multi Layer Perceptron ◽  
Artificial Neural

AbstractReliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km2(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R2 value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 104tons, and carbon absorptions will decrease by 23. 55 × 104tons and 69.54% of the total study area's LST will be above 390C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

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