Journal articles: 'Forest automata'

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Holik, Lukas. "Shape Analysis Based on Forest Automata." Electronic Proceedings in Theoretical Computer Science 73 (November 11, 2011): 18. http://dx.doi.org/10.4204/eptcs.73.3.

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Zeng, Hongcheng, Timo Pukkala, Heli Peltola, and Seppo Kellomäki. "Optimization of irregular-grid cellular automata and application in risk management of wind damage in forest planning." Canadian Journal of Forest Research 40, no. 6 (June 2010): 1064–75. http://dx.doi.org/10.1139/x10-052.

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This study demonstrated how cellular automata, using irregular grids, can be used to minimize the risk of wind damage in forest management planning. The development of a forest in central Finland was simulated for a 30-year period with three subplanning periods. A forest growth and yield model in association with a mechanistic wind damage model was applied to simulate forest growth and to calculate the length of stand edges at risk. Irregular cellular automata were utilized to optimize the harvest schedules for reducing the risk and maintaining a sustainable harvest level. The cellular automata produced rational results, i.e., new clearcuts were often placed next to open gaps, thereby, reducing the amount of vulnerable stand edges. The algorithms of the cellular automata rapidly converged and optimized the harvest schedules in an efficient way, especially when risk minimization was the only objective. In a planning problem that included even-flow timber harvesting objectives (harvest level equal to the total timber growth), the targets were almost achieved. Although the cellular automaton had slightly larger deviations of harvesting from the targets compared with other tested heuristic approaches (simulated annealing, tabu search, and genetic algorithms), it had the best performance when minimizing the expected wind damage.

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Malhi, Ramandeep Kaur M., Akash Anand, Prashant K. Srivastava, G. Sandhya Kiran, George P. Petropoulos, and Christos Chalkias. "An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas." ISPRS International Journal of Geo-Information 9, no. 9 (September 2, 2020): 530. http://dx.doi.org/10.3390/ijgi9090530.

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Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

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Mathey, Anne-Hélène, Emina Krcmar, David Tait, Ilan Vertinsky, and John Innes. "Forest planning using co-evolutionary cellular automata." Forest Ecology and Management 239, no. 1-3 (February 2007): 45–56. http://dx.doi.org/10.1016/j.foreco.2006.11.007.

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Habermehl, Peter, Lukáš Holík, Adam Rogalewicz, Jiří Šimáček, and Tomáš Vojnar. "Forest automata for verification of heap manipulation." Formal Methods in System Design 41, no. 1 (April 11, 2012): 83–106. http://dx.doi.org/10.1007/s10703-012-0150-8.

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Hernández Encinas, A., L. Hernández Encinas, S. Hoya White, A. Martín del Rey, and G. Rodríguez Sánchez. "Simulation of forest fire fronts using cellular automata." Advances in Engineering Software 38, no. 6 (June 2007): 372–78. http://dx.doi.org/10.1016/j.advengsoft.2006.09.002.

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Hernández Encinas, L., S. Hoya White, A. Martín del Rey, and G. Rodríguez Sánchez. "Modelling forest fire spread using hexagonal cellular automata." Applied Mathematical Modelling 31, no. 6 (June 2007): 1213–27. http://dx.doi.org/10.1016/j.apm.2006.04.001.

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Savargiv, Mohammad, Behrooz Masoumi, and Mohammad Reza Keyvanpour. "A New Random Forest Algorithm Based on Learning Automata." Computational Intelligence and Neuroscience 2021 (March 27, 2021): 1–19. http://dx.doi.org/10.1155/2021/5572781.

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Bhakti, H. D., H. Ibrahim, F. Fristella, and M. Faisal. "Fire spread simulation using cellular automata in forest fire." IOP Conference Series: Materials Science and Engineering 821 (May 29, 2020): 012037. http://dx.doi.org/10.1088/1757-899x/821/1/012037.

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Cannas, Sergio A., Sergio A. Páez, and Diana E. Marco. "Modeling plant spread in forest ecology using cellular automata." Computer Physics Communications 121-122 (September 1999): 131–35. http://dx.doi.org/10.1016/s0010-4655(99)00297-0.

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Rui, Xiaoping, Shan Hui, Xuetao Yu, Guangyuan Zhang, and Bin Wu. "Forest fire spread simulation algorithm based on cellular automata." Natural Hazards 91, no. 1 (November 22, 2017): 309–19. http://dx.doi.org/10.1007/s11069-017-3127-5.

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Kovyazin, Vasiliy F., Dang Thi Lan Anh, and Dang Viet Hung. "PREDICTING FOREST LAND COVER CHANGES OF DONG NAI RESERVE, VIETNAM." Vestnik SSUGT (Siberian State University of Geosystems and Technologies) 25, no. 3 (2020): 214–28. http://dx.doi.org/10.33764/2411-1759-2020-25-3-214-228.

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The study was conducted in Dong Nai Reserve, specially protected natural area (SPNA), Vietnam. It aims to analyze and forecast forest land cover in the Reserve. For these purposes were studied satellite images (Landsat 5, Landsat 7 and Landsat 8) taken in 2003, 2011 and 2019. The Normalized Difference Vegetation Index (NDVI) was used to identify vegetation quality. Forest land cover was divided into 5 categories using maximum likelihood classifier algorithm. In order to detect and evaluate forest land cover changes, supervised classification and image differencing method are applied. Then, Cellular Automata and Markov Chain model was employed for making forecast of forest land cover in this area. The results of the study indicate that forest land cover change is being transformed in Dong Nai Reserve. According to our estimation, from 2003 to 2019, the area covered by woody vegetation increased by 7.0 %. By 2035, the area of broad-leaved forests will increase by 1.6 %, due to a decrease in areas of meadows and shrubs. The dynamics of increasing forest land is explained by the measures taken by the Vietnamese government to expand the area of forests in SPNA.

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Karafyllidis, Ioannis, and Adonios Thanailakis. "A model for predicting forest fire spreading using cellular automata." Ecological Modelling 99, no. 1 (June 1997): 87–97. http://dx.doi.org/10.1016/s0304-3800(96)01942-4.

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Mutthulakshmi, K., Megan Rui En Wee, Yew Chong Kester Wong, Joel Weijia Lai, Jin Ming Koh, U. Rajendra Acharya, and Kang Hao Cheong. "Simulating forest fire spread and fire-fighting using cellular automata." Chinese Journal of Physics 65 (June 2020): 642–50. http://dx.doi.org/10.1016/j.cjph.2020.04.001.

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Mathey, Anne-Hélène, Emina Krcmar, Suzana Dragicevic, and Ilan Vertinsky. "An object-oriented cellular automata model for forest planning problems." Ecological Modelling 212, no. 3-4 (April 2008): 359–71. http://dx.doi.org/10.1016/j.ecolmodel.2007.11.003.

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Bone, Christopher, Suzana Dragicevic, and Arthur Roberts. "A fuzzy-constrained cellular automata model of forest insect infestations." Ecological Modelling 192, no. 1-2 (February 2006): 107–25. http://dx.doi.org/10.1016/j.ecolmodel.2005.09.013.

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Sarkar, Sahotra, Kelley A. Crews-Meyer, Kenneth R. Young, Christopher D. Kelley, and Alexander Moffett. "A Dynamic Graph Automata Approach to Modeling Landscape Change in the Andes and the Amazon." Environment and Planning B: Planning and Design 36, no. 2 (January 1, 2009): 300–318. http://dx.doi.org/10.1068/b33146.

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A generalization of cellular automata was developed that allows flexible, dynamic updating of variable neighborhood relationships, which in turn allows the integration of interactions at widely disparate spatial and temporal scales. Cells in the landscapes were modeled as vertices of dynamic graph automata that allow temporally variable causal connectivity between spatially nonadjacent cells. A trial was carried out to represent changes in an Amazonian and a tropical Andean landscape modeled as dynamic graph automata with input from a Landsat TM-derived Level 1 classification with the following classes: for the Amazon—forest, nonforest vegetation, water, and urban or bare (soil); for the Andes—forest, scrub (shrub or grassland), agriculture, and bare or exposed ground. Explicit automata transition rules were used to simulate temporal land-cover change. These rules were derived independently from fieldwork in each area, including vegetation plots or transects and informal interviews. Such a generalization of cellular automata was useful for modeling land-use–land-cover change (LULCC), although it potentially increases the computational complexity of an already data intensive process (involving 5–8 million cells, in 1000 stochastic simulations, with each simulation encompassing 15 annual time steps). The interannual predicted LULCC, while more nuanced in the Andean site, poses a serious threat to compositional and configurational stability in both the Andes and the Amazon, with implications for landscape heterogeneity and habitat fragmentation.

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Yassemi, S., and S. Dragićević. "Web Cellular Automata: A Forest Fire Modeling Approach and Prototype Tool." Cartography and Geographic Information Science 35, no. 2 (January 2008): 103–15. http://dx.doi.org/10.1559/152304008784090595.

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Melati, Dian Nuraini. "Multi Temporal Remotely Sensed Image Modelling For Deforestation Monitoring." Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana 3, no. 1 (May 31, 2019): 43. http://dx.doi.org/10.29122/alami.v3i1.3368.

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Tropical rainforest in Indonesia faces critical issue related to deforestation. Human activities which convert forest cover into non-forest cover has been a major issue. In order to sustain the forest resources, monitoring on deforestation and forest cover prediction is necessary to be done. Remotely sensed data, Landsat images, with acquisition in 1996, 2000, and 2005 are used in this study. In this study area, forest cover decreased around 6 % in the period of 1996 - 2005. For the purpose of forest cover modelling, three model (i.e. Stochastic Markov Model, Cellullar Automata Markov (CA_Markov) Model, dan GEOMOD) were tested. Based upon the Kappa index, GEOMOD performed better with the highest Kappa index. Therefore, GEOMOD is recommended to forecast forest cover.

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LICHTENEGGER, KLAUS, and WILHELM SCHAPPACHER. "A CARBON-CYCLE-BASED STOCHASTIC CELLULAR AUTOMATA CLIMATE MODEL." International Journal of Modern Physics C 22, no. 06 (June 2011): 607–21. http://dx.doi.org/10.1142/s0129183111016488.

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In this paper a stochastic cellular automata model is examined, which has been developed to study a "small" world, where local changes may noticeably alter global characteristics. This is applied to a climate model, where global temperature is determined by an interplay between atmospheric carbon dioxide and carbon stored by plant life. The latter can be released by forest fires, giving rise to significant changes of global conditions within short time.

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Kamusoko, Courage, and Jonah Gamba. "Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model." ISPRS International Journal of Geo-Information 4, no. 2 (April 1, 2015): 447–70. http://dx.doi.org/10.3390/ijgi4020447.

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Lett, C., C. Silber, P. Dubé, J. M. Walter, and M. Raffy. "Forest Dynamics: A Spatial Gap Model Simulated on a Cellular Automata Network." Canadian Journal of Remote Sensing 25, no. 4 (October 1999): 403–11. http://dx.doi.org/10.1080/07038992.1999.10874739.

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Abdulla, Parosh Aziz, Lukáš Holík, Bengt Jonsson, Ondřej Lengál, Cong Quy Trinh, and Tomáš Vojnar. "Verification of heap manipulating programs with ordered data by extended forest automata." Acta Informatica 53, no. 4 (May 7, 2015): 357–85. http://dx.doi.org/10.1007/s00236-015-0235-0.

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D'Andrea, M., P. Fiorucci, and T. P. Holmes. "A stochastic Forest Fire Model for future land cover scenarios assessment." Natural Hazards and Earth System Sciences 10, no. 10 (October 13, 2010): 2161–67. http://dx.doi.org/10.5194/nhess-10-2161-2010.

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Abstract. Land cover is affected by many factors including economic development, climate and natural disturbances such as wildfires. The ability to evaluate how fire regimes may alter future vegetation, and how future vegetation may alter fire regimes, would assist forest managers in planning management actions to be carried out in the face of anticipated socio-economic and climatic change. In this paper, we present a method for calibrating a cellular automata wildfire regime simulation model with actual data on land cover and wildfire size-frequency. The method is based on the observation that many forest fire regimes, in different forest types and regions, exhibit power law frequency-area distributions. The standard Drossel-Schwabl cellular automata Forest Fire Model (DS-FFM) produces simulations which reproduce this observed pattern. However, the standard model is simplistic in that it considers land cover to be binary – each cell either contains a tree or it is empty – and the model overestimates the frequency of large fires relative to actual landscapes. Our new model, the Modified Forest Fire Model (MFFM), addresses this limitation by incorporating information on actual land use and differentiating among various types of flammable vegetation. The MFFM simulation model was tested on forest types with Mediterranean and sub-tropical fire regimes. The results showed that the MFFM was able to reproduce structural fire regime parameters for these two regions. Further, the model was used to forecast future land cover. Future research will extend this model to refine the forecasts of future land cover and fire regime scenarios under climate, land use and socio-economic change.

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Hasan, Mohammad Emran, Biswajit Nath, A. H. M. Raihan Sarker, Zhihua Wang, Li Zhang, Xiaomei Yang, Mohammad Nur Nobi, Eivin Røskaft, David J. Chivers, and Ma Suza. "Applying Multi-Temporal Landsat Satellite Data and Markov-Cellular Automata to Predict Forest Cover Change and Forest Degradation of Sundarban Reserve Forest, Bangladesh." Forests 11, no. 9 (September 21, 2020): 1016. http://dx.doi.org/10.3390/f11091016.

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Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.

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Koranteng, Addo, and Tomasz Zawila-Niedzwiecki. "Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana." Folia Forestalia Polonica 57, no. 2 (June 1, 2015): 96–111. http://dx.doi.org/10.1515/ffp-2015-0010.

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Abstract Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automata-Markov) are utilized to predict for land use land cover (LULC) change for 2020 and 2030. The processes used include: (i) a data pre-processing (geometric corrections, radiometric corrections, subset creation and image enhancement) of epoch Landsat images acquired in 1990, 2000, and Disaster Monitoring Constellation (DMC) 2010; (ii) classification of multispectral imagery (iii) Change detection mapping (iv) using Cellular Automata-Markov to generate land use change in the next 20 years. The results illustrate that in years 2020 to 2030 in the foreseeable future, there will an upsurge in built up areas, while a decline in agricultural land use is envisaged. Agricultural land use would still be the dominant land use type. Forests would be drastically reduced from close to 50% in 1990 to just fewer than 10% in 2030. Land use decision making must be very circumspect, especially in an era where Ghana has opted to take advantage of REDD+. Studies such as this provide vital pieces of information which may be used to monitor, direct and influence land use change to a more beneficial and sustainable manner

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Septiono, Dony Setiawan, and Mussadun Mussadun. "Model Perubahan Penggunaan Lahan Untuk Mendukung Rencana Pengelolaan Kesatuan Pengelolaan Hutan (Studi Kasus KPH Yogyakarta)." JURNAL PEMBANGUNAN WILAYAH & KOTA 12, no. 3 (December 29, 2016): 277. http://dx.doi.org/10.14710/pwk.v12i3.12903.

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Special Region of Yogyakarta (DIY) experience the dynamics of changes in land use so that the decline in the forest area of the country. The government set the FMU Forest Management Unit as part of efforts to protect the forests remain sustainable so we need a study that could support optimal implementation of the Management Plan Forest Management Unit (FMU RP). One method to support the optimization is to do a land change prediction models. The purpose of this study include: (1) analyze the land use change from 1990 to 2013 period and (2) predicting the year 2023. Changes in land use land studied is 1990 and 2013, which would then be used as a base projection in 2013-2023. Methods to be used are: 1) Analysis of input output, 2) the integration of Markov chain Celullar automata (CA-MC) with logistic regression. The prediction model will use two scenarios, namely: 1) the existing condition of the existing and 2) the assumption of government intervention with the basic rules. The results showed in the period of 1990-2013 there is a change of land use is of 23%, or around 3,703 ha. From the results predicted changes in land use in 2023, with scenario 1 change-forest land dry land agriculture as an area of 1,337 ha and a change of scenario 2 of forest land area of 1264.36 ha.

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Yudarwati, Rani, Santun R. P. Sitorus, and Khursatul Munibah. "ARAHAN PENGENDALIAN PERUBAHAN PENGGUNAAN LAHAN MENGGUNAKAN MARKOV - CELLULAR AUTOMATA DI KABUPATEN CIANJUR." TATALOKA 18, no. 4 (November 19, 2017): 211. http://dx.doi.org/10.14710/tataloka.18.4.211-221.

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Controlling the rate of land use change is necessary due to maintaining environment sustainability. One of the efforts is studying the changes that occur in the past few years. These changes can be studied by Markov - Cellular Automata model.Cianjur is one of the regency that has a high risk of landslide hazard, so it is necessary to control land use change in order to realize environmental sustainability in accordance with the spatial plan of Cianjur regency (RTRW). The purpose of this study was to see land use changes that occurred and evaluated with the spatial plan (RTRW) and also to conduct controlling scenarios of land use changes. The analysis showed that Cianjur regency has drastically decreased in forest area up to 10,3% and landuse inconsistencyof 10,4%. The prediction results showed that landuse change without intervention would dramatically increase inconsistency up to 20,5%. Land use scenario of restoring forest could reduce inconsistency up to 16,6%.

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Jellouli, O., A. Bernoussi, M. Mâatouk, and M. Amharref. "Forest fire modelling using cellular automata: application to the watershed Oued Laou (Morocco)." Mathematical and Computer Modelling of Dynamical Systems 22, no. 5 (July 4, 2016): 493–507. http://dx.doi.org/10.1080/13873954.2016.1204321.

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Rienow, Andreas, Ahmed Mustafa, Leonie Krelaus, and Claudia Lindner. "Modeling urban regions: Comparing random forest and support vector machines for cellular automata." Transactions in GIS 25, no. 3 (May 5, 2021): 1625–45. http://dx.doi.org/10.1111/tgis.12756.

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Darmawan, Soni, Dewi Kania Sari, Ketut Wikantika, Anggun Tridawati, Rika Hernawati, and Maria Kurniawati Sedu. "Identification before-after Forest Fire and Prediction of Mangrove Forest Based on Markov-Cellular Automata in Part of Sembilang National Park, Banyuasin, South Sumatra, Indonesia." Remote Sensing 12, no. 22 (November 11, 2020): 3700. http://dx.doi.org/10.3390/rs12223700.

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In 1997, the worst forest fire in Indonesia occurred and hit mangrove forest areas including in Sembilang National Park Banyuasin Regency, South Sumatra. Therefore, the Indonesian government keeps in trying to rehabilitate the mangrove forest in Sembilang National Park. This study aimed to identify the mangrove forest changing and to predict on the future year. The situations before and after forest fire were analyzed. This study applied an integrated Markov Chain and Cellular Automata model to identify mangrove forest change in the interval years of 1989–2015 and predict it in 2028. Remote sensing technology is used based on Landsat satellite imagery (1989, 1998, 2002, and 2015). The results showed mangrove forest has decreased around 9.6% from 1989 to 1998 due to forest fire, and has increased by 8.4% between 1998 and 2002, and 2.3% in 2002–2015. Other results show that mangroves area has continued to increase from 2015 to 2028 by 27.4% to 31% (7974.8 ha). It shows that the mangrove ecosystem is periodically changing due to good management by the Indonesian government.

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Yudichandra, Fahrizal Kreshna, Widiatmaka Widiatmaka, and Syaiful Anwar. "Perubahan dan Prediksi Penggunaan Lahan Menggunakan Markov – Cellular Automata di Kota Batu." TATALOKA 22, no. 2 (May 29, 2020): 202–11. http://dx.doi.org/10.14710/tataloka.22.2.202-211.

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Along with the development of Batu City as a tourist city, it is feared that there will be an increase in land use conversion from apple orchards and other agricultural land into residential and tourism land. The rate of land use change must be controlled to maintain environment sustainability. One of the effort is studying the change that occurred in the past few years. The purpose of this study were to observe land use change pattern that occured in 2006, 2012, and 2018, and to predict the land use at 2030 in Batu City. Land use prediction was evaluated with Markov – Cellular Automata models. The analysis showed that forest area decreased up to 5% and the built area increased up to 5.2% from 2006 to 2018. Prediction of land use in 2030 showed that there will be a decrease in forest, agriculture, and bareland areas, and an increase in shrubs and built areas. Agricultural land needs to be directed to be protected or conserved, while shrubs and open land need to be directed into potential land for apple orchards development in Batu City.

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Prieto-Amparán, Jesús A., Federico Villarreal-Guerrero, Martin Martínez-Salvador, Carlos Manjarrez-Domínguez, Griselda Vázquez-Quintero, and Alfredo Pinedo-Alvarez. "Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico." PeerJ 7 (March 21, 2019): e6617. http://dx.doi.org/10.7717/peerj.6617.

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The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

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Lin, Jiayang, Qiang Fan, Jie Lu, Yuhui Peng, and Wencheng Sun. "A Hybrid Fire Warning Model Based on Cellular Automata." E3S Web of Conferences 299 (2021): 02018. http://dx.doi.org/10.1051/e3sconf/202129902018.

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The rapid development of cities and the increasing complexity of its internal structure have led to pressing fire security problems, which calls for an effective and accurate comprehensive fire warning model. Most existing fire warning models predict only for a single fire scenario and can hardly balance the speed and accuracy in their predictions, which are not suitable for large-scale scenarios with complex structures. This paper proposes a fire warning model that includes both forest and building area based on Cellular Automata. Experimental areas were established to simulate fire warning according to the proposed hybrid model. The experimental results have shown that the model can quickly and accurately simulate the fire spread process and provide effective support for emergency decision-making in complex scenarios.

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Mathey, Anne-Hélène, Emina Krcmar, and Ilan Vertinsky. "Re-evaluating our approach to forest management planning: A complex journey." Forestry Chronicle 81, no. 3 (June 1, 2005): 359–64. http://dx.doi.org/10.5558/tfc81359-3.

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The evolution of forest values from timber supply to ecological and social values has been leading to the redefinition of the Sustainable Forest Management (SFM) paradigm. In parallel, scientific knowledge is expanding and uncovering the interconnectedness of the various processes that support these values. We thus have many wishes and much knowledge but we have to ensure that we have the decision support tools that will pull them together to promote SFM. After a broad review of the evolution of decision support tools in forest management, this paper presents a case for more holistic numerical planning tools. To illustrate that such tools can be designed, we propose a simple decentralized approach. In this approach, a landscape management strategy evolves based on local decisions, integrating spatial and aspatial, multi-period and period-specific goals. Such tools could become a useful platform for sustainable forest management planning. Key words: decision support tools, sustainable forest management, evolution, holistic planning, complexity, cellular automata

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Ausonio, Elena, Patrizia Bagnerini, and Marco Ghio. "Drone Swarms in Fire Suppression Activities: A Conceptual Framework." Drones 5, no. 1 (March 7, 2021): 17. http://dx.doi.org/10.3390/drones5010017.

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The recent huge technological development of unmanned aerial vehicles (UAVs) can provide breakthrough means of fighting wildland fires. We propose an innovative forest firefighting system based on the use of a swarm of hundreds of UAVs able to generate a continuous flow of extinguishing liquid on the fire front, simulating the effect of rain. Automatic battery replacement and extinguishing liquid refill ensure the continuity of the action. We illustrate the validity of the approach in Mediterranean scrub first computing the critical water flow rate according to the main factors involved in the evolution of a fire, then estimating the number of linear meters of active fire front that can be extinguished depending on the number of drones available and the amount of extinguishing fluid carried. A fire propagation cellular automata model is also employed to study the evolution of the fire. Simulation results suggest that the proposed system can provide the flow of water required to fight low-intensity and limited extent fires or to support current forest firefighting techniques.

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Pinheiro, E. A. L., N. A. Camini, M. R. S. Soares, and S. S. Sumida. "CELLULAR AUTOMATA MODEL – LANDSCAPE DYNAMICS SIMULATION TOOL IN THE PROCESS OF CHANGE IN LAND USE AND COVER IN THE CITY OF GAÚCHA DO NORTE – MT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 447–51. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-447-2020.

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Abstract. The factors that contribute to land use change in the municipality of Gaúcha do Norte - MT, are entirely linked to the economic process and agricultural production. This process has left Brazil in a state of alert due to the process of deforestation and loss of tropical forests. From 2000 to 2010, the forest areas converted into agriculture accounted for 13.3%, the main factor that directly potentiated with deforestation was the cultivation of soybeans, which in turn was occupying places previously occupied by livestock and pushing the livestock forest inside. The phenomena of land use change and land cover start from multidimensional issues in the environmental and economic context. The use of environmental modeling through cellular automata to analyze land use change phenomena and reproduce the trajectory through future land use simulations and evolution establishes an integration associated by mathematical models and flow integration systems. That predict the trajectory of land use change, thus generating a dynamic model capable of predicting future land use changes by replicating possible patterns of landscape evolution and enabling assessments of future ecological implications for the environment.

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Sharma, Bhavna, and Kiranmay Sarma. "Status Identification and Prediction of Kaziranga-Karbi Anglong Wildlife Corridor of Assam, India, Using Geospatial Technology." Journal of Landscape Ecology 7, no. 2 (November 20, 2014): 45–58. http://dx.doi.org/10.2478/jlecol-2014-0015.

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Abstract In the present study, an attempt has been made to discover the impacts of various developmental activities on the Kaziranga-Karbi Anglong wildlife corridor of Assam, India, using geospatial technology; as well as to predict the future status of the wildlife corridor by using the Cellular Automata Markov Model. Due to various anthropogenic activities the condition of the natural corridor has deteriorated, and in recent years many wild animals have been killed by road traffic accidents; in particular, greater one-horned (Indian) rhinoceros (Rhinoceros unicornis) are killed indiscriminately by the poachers, having been deviated from their regular routes. Changes were evident during the two decades between 1990 and 2010, when a large number of dense forest areas were converted to open forest, combined with losses of areas of scrub and marshy land. The area under agriculture and plantation crop increased along with the grassland during the decades. It has been found that the forests in Kaziranga-Karbi Anglong corridor are fragmented, and the area within the corridor is shrinking. There is considerable increase in patchiness, proportion of edge, and a perforated reduction of core areas within the corridor. The predicted land use/cover map of Kaziranga-Karbi Anglong corridor shows expansion of agricultural land, as well as plantation areas. It is estimated that only 25.66 percent of the present dense forest and 20.72 percent of open forest will remain by 2030, while areas under agriculture and plantation will increase by 33.91 and 5.33 percent, respectively.

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Liu, Hang, Riken Homma, Qiang Liu, and Congying Fang. "Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model." ISPRS International Journal of Geo-Information 10, no. 8 (July 26, 2021): 503. http://dx.doi.org/10.3390/ijgi10080503.

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The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

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Zouiten, Mohammed, Jamal Chaaouan, and Ibtissam Naoui. "Predicting Land Use Changes within the Tazekka Park and Its Borders via a Cellular Automata-Markov Modeling of Satellite Images." Journal of Southwest Jiaotong University 56, no. 2 (April 30, 2021): 534–41. http://dx.doi.org/10.35741/issn.0258-2724.56.2.43.

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This article describes a new approach of land cover study to predicting and combatting deforestation based on satellite imagery as environmental statistics. Specifically, a stochastic mathematical cellular automata-Markov model was used to predict land-use changes in the Tazekka Park and its borders in TAZA province in Morocco. The model was used mainly to create thematic forecast maps. Through the proposed approach, we derived data and statistics covering the period 2000 to 2020 and then constructed a predictive map for the year 2040 using ArcGIS 10.4. The evaluation of our model’s effectiveness was confirmed by calculating the Markov transition matrix in the derivation of the final map. These results can improve the management of forest areas and serve as a reference in addressing the direct effects of forests on the environment.

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Trucchia, Andrea, Mirko D’Andrea, Francesco Baghino, Paolo Fiorucci, Luca Ferraris, Dario Negro, Andrea Gollini, and Massimiliano Severino. "PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator." Fire 3, no. 3 (July 6, 2020): 26. http://dx.doi.org/10.3390/fire3030026.

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PROPAGATOR is a stochastic cellular automaton model for forest fire spread simulation, conceived as a rapid method for fire risk assessment. The model uses high-resolution information such as topography and vegetation cover considering different types of vegetation. Input parameters are wind speed and direction and the ignition point. Dead fine fuel moisture content and firebreaks—fire fighting strategies can also be considered. The fire spread probability depends on vegetation type, slope, wind direction and speed, and fuel moisture content. The fire-propagation speed is determined through the adoption of a Rate of Spread model. PROPAGATOR simulates independent realizations of one stochastic fire propagation process, and at each time-step gives as output a map representing the probability of each cell of the domain to be affected by the fire. These probabilities are obtained computing the relative frequency of ignition of each cell. The model capabilities are assessed by reproducing a set of past Mediterranean fires occurred in different countries (Italy and Spain), using when available the real fire fighting patterns. PROPAGATOR simulated such scenarios with affordable computational resources and with short CPU-times. The outputs show a good agreement with the real burned areas, demonstrating that the PROPAGATOR can be useful for supporting decisions in Civil Protection and fire management activities.

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Šiugždaite, R., and S. Norvaišas. "CELLULAR AUTOMATA AND ENERGETICS SYSTEM FORMATION." Mathematical Modelling and Analysis 7, no. 2 (December 15, 2002): 319–26. http://dx.doi.org/10.3846/13926292.2002.9637203.

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Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agentoriented approach, where local interactions lead to a global behavior. This approach helps to understand how non‐deterministic behavior that is near self‐organized criticality (SOC) is used to explain natural and social phenomena can emerge from local interactions between agents. The basis of our decision to develop cellular automata (CA) as a model for energetics system formation and development in restricted region is its hypothetical dependence on and origin from the “urban slice” which is basis for “energetics slice”. In the urban CA model there we introduce five types of cells representing empty area, roads, houses, water and forest. Some types of cells are introduced only for the model better correspondence to the real system and don't have essentially influence to the modeling results. We assign all cell types certain weight, which affects the probability of new “houses” cells appearance replacing “empty area” cells. Usually all cells except the empty ones have much bigger weight, therefore the dynamic of houses distribution in restricted area is organized in clusters. A first step in model ‘reliability’ is an understanding how these systems behave over time. CA's are an alternative to differential equations on an attempt to model these systems. One of the most important features of CA models is its desirable capacity to capture quantitative micro‐level dynamics and relate them to qualitative macro‐level behavior. Energetics system formation is dynamic process that directly depends on houses conglomerate formation, energy production and transferring prices, energy consumption factor etc. Dynamical CA model can be used to realize certain purposes of energetic policy and to make decisions about volume of production as well as prices of generation and transmission. These decisions, in one's turn, influence successive urban and energetics system dynamics. There is analyzed how various policies influence urban system development as well as its stability after the new capacity is installed and prices in generation and transmission as well as system administration are changed, etc [3]. Also there is explored how energetics system dynamics obtained with help of dynamic model corresponds with CA obtained dynamics.

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Vázquez-Quintero, Griselda, Raúl Solís-Moreno, Marín Pompa-García, Federico Villarreal-Guerrero, Carmelo Pinedo-Alvarez, and Alfredo Pinedo-Alvarez. "Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata." Sustainability 8, no. 3 (March 2, 2016): 236. http://dx.doi.org/10.3390/su8030236.

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Dou, Chunxia, Shengqi Teng, Tengfei Zhang, Bo Zhang, and Kai Ma. "Layered management and hybrid control strategy based on hybrid automata and random forest for microgrid." IET Renewable Power Generation 13, no. 16 (November 21, 2019): 3113–23. http://dx.doi.org/10.1049/iet-rpg.2019.0664.

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Bone, Christopher, Suzana Dragićević, and Arthur Roberts. "Evaluating forest management practices using a GIS-based cellular automata modeling approach with multispectral imagery." Environmental Modeling & Assessment 12, no. 2 (January 10, 2007): 105–18. http://dx.doi.org/10.1007/s10666-006-9055-5.

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Abeyrathna, Kuruge Darshana, Ole-Christoffer Granmo, and Morten Goodwin. "Adaptive Sparse Representation of Continuous Input for Tsetlin Machines Based on Stochastic Searching on the Line." Electronics 10, no. 17 (August 30, 2021): 2107. http://dx.doi.org/10.3390/electronics10172107.

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This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially hundreds of them). We evaluate the performance of the new scheme empirically using five datasets, along with a study of interpretability. On average, TMs with SSL feature representation use 4.3 times fewer literals than the TM with static threshold-based features. Furthermore, in terms of average memory usage and F1-Score, our approach outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees (XGBoost), and Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs. Our approach further outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in terms of the Area Under Curve while performing competitively on COMPAS.

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Malamud, B. D., G. Morein, and D. L. Turcotte. "Log-periodic behavior in a forest-fire model." Nonlinear Processes in Geophysics 12, no. 5 (June 9, 2005): 575–85. http://dx.doi.org/10.5194/npg-12-575-2005.

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Abstract. This paper explores log-periodicity in a forest-fire cellular-automata model. At each time step of this model a tree is dropped on a randomly chosen site; if the site is unoccupied, the tree is planted. Then, for a given sparking frequency, matches are dropped on a randomly chosen site; if the site is occupied by a tree, the tree ignites and an "instantaneous" model fire consumes that tree and all adjacent trees. The resultant frequency-area distribution for the small and medium model fires is a power-law. However, if we consider very small sparking frequencies, the large model fires that span the square grid are dominant, and we find that the peaks in the frequency-area distribution of these large fires satisfy log-periodic scaling to a good approximation. This behavior can be examined using a simple mean-field model, where in time, the density of trees on the grid exponentially approaches unity. This exponential behavior coupled with a periodic or near-periodic sparking frequency also generates a sequence of peaks in the frequency-area distribution of large fires that satisfy log-periodic scaling. We conclude that the forest-fire model might provide a relatively simple explanation for the log-periodic behavior often seen in nature.

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Surabuddin Mondal, M., N. Sharma, M. Kappas, and P. K. Garg. "CA MARKOV MODELING OF LAND USE LAND COVER DYNAMICS AND SENSITIVITY ANALYSIS TO IDENTIFY SENSITIVE PARAMETER(S)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 723–29. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-723-2019.

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<p><strong>Abstract.</strong> An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. Sensitivity analysis has been carried out to identify the land use land cover parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928.</p>

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Hasani, Mohammad, Abdolrassoul Salmanmahiny, and Alireza Mikaeili Tabrizi. "An Integrative Modelling Approach to Analyse Landscape Dynamics Through Intensity Analysis and Cellular Automata-Markov Chain Model." European Spatial Research and Policy 27, no. 1 (June 30, 2020): 243–61. http://dx.doi.org/10.18778/1231-1952.27.1.11.

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The goal of this study is offer a deep understanding of the landscape dynamics in the Gorgan Township, the Golestan Province, Iran. Landsat satellite imagery of two different time thresholds, i.e. the years 1992 and 2011, was acquired from the US Geological Survey database and the changes were quantified for the Gorgan area covering a 19-year time span. Furthermore, an integrated Cellular Automata-Markov Chain (CA-MC) model was applied to predict future changes up to the year 2030. We used the intensity analysis method to compare the historical dynamics of different land categories at multiple levels. The results indicated that during the 19 years, the built-up and forest areas increased by 2.33% and 0.27%, respectively, while agriculture and remnant vegetation decreased by 2.43% and 0.24%, respectively. The CA-MC model illustrated that in the following 19 years, the built-up areas could increase by 2.45%. An intensity analysis revealed that forest gains and losses were dormant while remnant vegetation gains and losses were active. The built-up area’s gains and water bodies’ losses were active and stationary during both time intervals. The transitions from water bodies and remnant vegetation to agriculture were regularly targeting and stationary, while the transition from forest to agriculture was regularly avoiding and stationary. Our findings also indicated a heavy systematic transition from agriculture to built-up areas. Regarding the increasing population growth and urbanisation in the region, the outcomes of this study can help make informed decisions for the management and protection of natural resources in the study area.

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Ulloa-Espíndola, René, and Susana Martín-Fernández. "Simulation and Analysis of Land Use Changes Applying Cellular Automata in the South of Quito and the Machachi Valley, Province of Pichincha, Ecuador." Sustainability 13, no. 17 (August 24, 2021): 9525. http://dx.doi.org/10.3390/su13179525.

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Rapid urban growth has historically led to changes in land use patterns and the degradation of natural resources and the urban environment. Uncontrolled growth of urban areas in the city of Quito has continued to the present day since 1960s, aggravated by illegal or irregular new settlements. The main objective of this paper is to generate spatial predictions of these types of urban settlements and land use changes in 2023, 2028 and 2038, applying the Dinamica EGO cellular automata and multivariable software. The study area was the Machachi Valley between the south of the city of Quito and the rural localities of Alóag and Machachi. The results demonstrate the accuracy of the model and its applicability, thanks to the use of 15 social, physical and climate predictors and the validation process. The analysis of the land use changes throughout the study area shows that urban land use will undergo the greatest net increase. Growth in the south of Quito is predicted to increase by as much as 35% between 2018 and 2038 where new highly vulnerable urban settlements can appear. Native forests in the Andes and forest plantations are expected to decline in the study area due to their substitution by shrub vegetation or agriculture and livestock land use. The implementation of policies to control the land market and protect natural areas could help to mitigate the continuous deterioration of urban and forest areas.

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Journal articles on the topic 'Forest automata'

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