Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC.

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Integrating Artificial Neural Network and Taguchi Method for Land Use and Land Cover Classification of Multispectral Images

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt45670.2019.8944445 ◽

2019 ◽

Author(s):

Puja Nagarkar ◽

Priti P. Rege

Keyword(s):

Neural Network ◽

Land Use ◽

Artificial Neural Network ◽

Land Cover ◽

Taguchi Method ◽

Land Cover Classification ◽

Multispectral Images ◽

Artificial Neural

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An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

10.20944/preprints202103.0247.v1 ◽

2021 ◽

Author(s):

Fatwa Ramdani ◽

Budi Setiawan ◽

Alfi Rusydi ◽

Muhammad Furqon

Keyword(s):

Neural Network ◽

Land Use ◽

Artificial Neural Network ◽

Land Cover ◽

Negative Impact ◽

Population Increase ◽

Land Use Land Cover ◽

Neural Network Approach ◽

The Future ◽

Artificial Neural

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

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MODEL PERUBAHAN PENUTUPAN/PENGGUNAAN LAHAN UNTUK IDENTIFIKASI LAHAN KRITIS DI KABUPATEN BOGOR, KABUPATEN CIANJUR, DAN KABUPATEN SUKABUMI

MAJALAH ILMIAH GLOBE ◽

10.24895/mig.2016.18-1.391 ◽

2016 ◽

Vol 18 (1) ◽

pp. 21 ◽

Cited By ~ 1

Author(s):

Siti Hadjar Kubangun ◽

Oteng Haridjaja ◽

Komarsa Gandasasmita

Keyword(s):

Neural Network ◽

Land Use ◽

Artificial Neural Network ◽

Spatial Models ◽

Change Models ◽

The Road ◽

Sustainable Environment ◽

Main Factors ◽

Artificial Neural ◽

Artificial Neural Network Ann

<div class="WordSection1">Pemanfaatan lahan yang melampaui kemampuan lahannya, dapat mengakibatkan degradasi lahan. Degradasi lahan jika dibiarkan akan menimbulkan lahan kritis. Dampak yang terjadi akibat lahan kritis mengakibatkan lahan mengalami penurunan kualitas sifat-sifat tanah, penurunan fungsi konservasi, fungsi produksi, hingga berpengaruh pada kehidupan sosial dan ekonomi masyarakat yang memanfaatkan lahan tersebut. Penelitian ini bertujuan untuk mengidentifikasi lahan kritis, berdasarkan pemodelan perubahan penutupan/penggunaan lahan dengan metode Artificial Neural Network (ANN). Hasil penelitian ini menunjukkan bahwa lahan-lahan yang tergolong kritis mencakup lahan berlereng dengan penutupan/penggunaan lahan yang telah terkonversi. Faktor utama penyebab konversi lahan adalah tingginya kebutuhan hidup terhadap pangan, sandang, dan papan, akibat meningkatnya kepadatan penduduk. Selain hal tersebut, kemiringan lereng, jarak dari jalan, dan jarak dari permukiman juga menjadi faktor penyebab perubahan lahan. Upaya pemanfaatan lahan sebaiknya didukung oleh peningkatan kualitas sumber daya manusia, yang tidak hanya berorientasi pada kebutuhan sosial dan ekonomi, namun juga berorientasi pada lingkungan yang berkelanjutan.Kata kunci: jaringan saraf tiruan, perubahan penutupan/penggunaan lahan, model spasial, lahan kritis.ABSTRACTOver used of land can caused the degradation, it can be lead to the critical of the land. The impacts of this issue such as the decreasing of the soil characteristics quality, conservation function, production, affecting social and economic of the society which used the land. This research aims to identify the critical land based on the land use cover change models with Artificial Neural Network (ANN) method. This research shows the critical lands including land with the slope which has been converted with the land use cover change models. The main factors caused land converse are the high of need of food, clothing, and shelter, cause of the increasing population density. Besides those factors, the shape of slope, distance from the road and settlements are also the result of the land changing. The efforts in using the land should be supported by the increasing of the human resources, which are not only be oriented on the need of social and economic, but also on the sustainable environment.Keywords: artificial neural network (ANN), land use cover change (LUCC), spatial models, critical land.</div>

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Forest Land Cover Classification Using Statistical and Artificial Neural Network Approaches Applied to IRS LISS ‐ III Sensor

Geocarto International ◽

10.1080/10106040108542192 ◽

2001 ◽

Vol 16 (2) ◽

pp. 39-44 ◽

Cited By ~ 6

Author(s):

Ramanathan Sugumaran

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Land Cover ◽

Land Cover Classification ◽

Forest Land ◽

Liss Iii ◽

Artificial Neural ◽

Forest Land Cover ◽

Network Approaches

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Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

Ecological Chemistry and Engineering S ◽

10.2478/eces-2021-0029 ◽

2021 ◽

Vol 28 (3) ◽

pp. 429-447

Author(s):

Mir Mehrdad Mirsanjari ◽

Jurate Suziedelyte Visockiene ◽

Fatemeh Mohammadyari ◽

Ardavan Zarandian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Land Cover ◽

Urban Expansion ◽

Landscape Patterns ◽

Forest Land ◽

Dense Forest ◽

Land Cover Dynamics ◽

Artificial Neural ◽

Surrounding Areas

Abstract The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.

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Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India

GeoJournal ◽

10.1007/s10708-020-10274-5 ◽

2020 ◽

Cited By ~ 1

Author(s):

Saleem Ahmad Yatoo ◽

Paulami Sahu ◽

Manik H. Kalubarme ◽

Bhagirath B. Kansara

Keyword(s):

Neural Network ◽

Land Use ◽

Artificial Neural Network ◽

Cellular Automata ◽

Land Use Changes ◽

Future Prospects ◽

Artificial Neural

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Application of Neural Network in Urban Land Use Suitability Evaluation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.474-476.681 ◽

2011 ◽

Vol 474-476 ◽

pp. 681-686

Author(s):

Xiao Rui Zhang ◽

Gang Chen

Keyword(s):

Neural Network ◽

Land Use ◽

Artificial Neural Network ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Urban Land ◽

Urban Land Use ◽

Suitability Evaluation ◽

Land Use Suitability ◽

Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

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