Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method
Mathematics and Computer Science
Volume 4, Issue 6, November 2019, Pages: 112-129
Received: Jun. 26, 2019;
Accepted: Jul. 30, 2019;
Published: Dec. 24, 2019
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Paulo Marcelo Tasinaffo, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Afonso Henriques Moreira Santos, Electrical Engineering Institute, Federal University of Itajuba (UNIFEI), Itajuba, Brazil
Elias Cavalcante Junior, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Carlos Henrique Quartucci Forster, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Rafael Augusto Lopes Shigemura, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Rafael Jacomel, IBM Brazil, São Paulo, Brazil
Victor Ulisses Pugliese, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Bruno Koshin Vazquez Iha, Mathematics and Statistics Institute, Sao Paulo University (USP), Sao Paulo, Brazil
Adilson Marques da Cunha, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Gildarcio Sousa Goncalves, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Luiz Alberto Vieira Dias, Computer Science Department, Brazilian Aeronautics Institute of Technology (ITA), São Jose dos Campos, Brazil
Land cover classification analysis from satellite imagery methods are important because they are the basis for characterizing surface conditions and evolution, supporting the management and optimization of land resources, evaluating global climate and environmental changes, and facilitating sustainable regional economic and social development. In order to address these necessities, artificial neural networks have been used extensively. In addition, other methods based on computer vision are very useful to solve this task. In this paper, the authors propose an approach based on Monte Carlo method and artificial neural networks in order to classify regions of small forest reserves from drones’ images and calculate their respective areas. Next to the small forest reserve will be extended a standard rectangular tarpaulin of 250 square meters and based on this reference it will be possible to calculate the area of the forest reserve if the ground is relatively flat. The proposed approach will be compared with a method based on watershed algorithm. The automatic calculation of the forest area through images generated by drones has much practical application for environmental engineers, for example, for the calculation of environmental impact and determination of carbon loss if such forests are consequently deforested.
Paulo Marcelo Tasinaffo,
Afonso Henriques Moreira Santos,
Elias Cavalcante Junior,
Carlos Henrique Quartucci Forster,
Rafael Augusto Lopes Shigemura,
Victor Ulisses Pugliese,
Bruno Koshin Vazquez Iha,
Adilson Marques da Cunha,
Gildarcio Sousa Goncalves,
Luiz Alberto Vieira Dias,
Determination of Forest Reserves Area Using Images Processed by Drones, Neural Networks and Monte Carlo Method, Mathematics and Computer Science.
Vol. 4, No. 6,
2019, pp. 112-129.
Q. Weng, D. Lu, A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States, International Journal of Applied Earth Observation and Geoinformation, 2008, 10 (1), pp. 68–83.
J. Cihlar, Land cover mapping of large areas from satellites: Status and research priorities, International, Journal of Remote Sensing, 2000, 21 (6-7), pp. 1093–1114.
Y. Hu, B. Nacun, An analysis of land-use changes and grassland degradation from a policy perspective in inner Mongolia, Sustainability, 2018, 10 (11), pp. 4048.
B. Claas, N. Yunfeng, H. Tobia, Land-use change and land degradation on the mongolian plateau from 1975 to 2015—A case study from Xilingol, China, Land Degradation & Development, 2018, 29 (6), pp 1595–1606.
J. Patino, J. Duque, A review of regional science applications of satellite remote sensing in urban settings, Computer Environments and Urban Systems, 2013, 37, pp. 1–17.
J. Liu, W. Kuang, Z. Zhang, X. Xu, Y. Qin, Y, J. Ning, W. Zhou, S. Zhang, R. Li, C. Yan, Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s, Journal of Geographical Sciences, 24 (10), pp. 195–210.
B. Quesada, A. Arneth, N. Noblet-Ducoudré, Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate Picture, JGR Atmospheres, 2017, 122 (10), pp. 5113–5131.
K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks, 1989, 2 (5), pp. 359–366.
N. Cotter, The Stone-Weierstrass and its application to neurais networks, IEEE Transactions on Neural Networks, 1990, 1 (4), pp. 290–295.
Y. Hu, Q. Zhang, Y. Zhang, H. Yan, A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China, remote sensing, 2018, 10 (12), pp. 322–337.
C. Cleve, M. Kelly, F. Kearns, M. Moritz, Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography, Computers Environment and Urban Systems, 2008, 32, pp. 317–326.
Y. Qin, M. Chi, X. Liu, Y. Zhang, Y. Zeng and X. Zhao, Classification of high resolution urban remote sensing images using deep networks by integration of social media photos, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Netherlands, 2018, pp. 7243–7246.
M. Omati, M. Sahebi, Change Detection of Polarimetric SAR Images Based on the Integration of Improved Watershed and MRF Segmentation Approaches, IEEE Journal of selected topics in applied earth observation and remote sensing, 2018, 11 (11), pp. 4170–4179.
G. Matasci, N. Coops, D. Willians, N. Page, Mapping tree canopies in urban environments using airborne laser scanning (ALS): a Vancouver case study, Forest Ecosystems, 2018, 5 (31), pp. 1–9.
Artificial neural networks (accessed 26.05.2019).
Neural Network and appllications in information retrieval system (accessed 26.05.2019).
S, Haykin, Neural Networks, Bookman Publisher, 2007.
Training of Artificial Neural Networks Based on Variable Structure Systems with Adaptative Learning Rate. < https://www.ppgee.ufmg.br/documentos/Defesas/669/Tese_Ademir_Nied.pdf> (accessed 26.05.2019).
L. Fleck et al., Neural Network Basic Principle, Magazine of Eletronic Science Innovation and Technology, 2016, 7 (13) pp. 47–57.
J, More, The Levenberg-Marquardt algorithm: implementation and theory, Numerical analysis, 1978, pp. 105–116.
M. Loukrakis, A brief description of the Levenberg-Marquardt algorithm implemented by levmar, Foundation of Research and Technology, 2005, 4 (1), pp. 1–6.
P. Tasinaffo, G. Gonçalves, A. Cunha, L. Dias, Using Monte Carlo method to estimate the behaviour of neural training between balanced and unbalanced data in classification of patterns, Artificial Intelligence Reasearch, 2018, 7 (2), pp. 1-25.
N. Metropolis, S. Ulam, The Monte Carlo method, Journal of the American statistical association, 1949, 44 (247), pp. 335–341.
C. Graham C., D. Talay, Strong Law of Large Numbers and Monte Carlo Methods, Stochastic Simulation and Monte Carlo Methods, Stochastic Modelling and Applied Probability, 2013, 68, pp. 13–35.
J. M. Hammersley, Monte Carlo methods, Monographs on Applied Statistcs and Probability, 1964, pp. 178.
Y. A. Shreider, The Monte Carlo method, Pergamon Press, 1966.
P. Glasserman, Monte Carlo methods in financial engineering: stochastic modeling and applied probability, Srping Publlisher, 2003, 53, pp. 53.
D. P. Kroese, T. Taimre and Z. I. Botev, Handbook of Monte Carlo methods. Wiley Series in probability and statistics”, 2011.
A. Papoulis, Probability, random variables, and stochastic processes, McGraw-Hill Europe Publisher, 2002, 4, pp. 55–57.
A. Jazwinski, Stochastic processes and filtering theory, Dover Publisher, 1970, 12, pp. 104–106.
J. Vuolo, Fundamentos da teoria de erros, Blucher Publisher, 1996, 2, pp. 32–34.
Monte Carlo Methods - a special topics course. (acessed 26.05.2019).
S. Beucher, C. Lantuejoul, Use of Watersheds in Contour Detection, International Workshop on image processing: Real-time Edge and motion detection/estimation, 1979.
A. Schiavetti and A. Camargo, Basin Concepts: Theories and Applications, UFSC Publisher, 2002, pp. 17.
V. Kalpana, Analysis of rain fall and the temperature of Coimbatore District using land use and land cover change detection by image segmentation, Multimedia Tools and Applications, 2018, 77 (23), pp. 30487–30504.
S. Madhumitha, M. Manikandan, Quantitative analysis of marker-based watershed image segmentation, Current Science, 2018, 114 (5), pp. 1007–31013.
A. Goudie and D. Cuff, Encyclopedia of Global Change. Environmental Change and Human Society, Oxford University Press, 2001.
Decree n°23.793 (accessed 01.06.2019).
Decree-law nº 4.471 (accessed 01.06.2019).
1988 Brazilian Constitution, Art. 23 VI (accessed 01.06.2019).