Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning
Abstract
Spatial data classification is famous over recent years in order to extract knowledge and insights into the data. It occurs because vast experimentation was used with various classifiers, and significant improvement was examined in accuracy and performance. This study aimed to analyze forest cover change detection using machine learning. Supervised and unsupervised learning methods were used to analyze spatial data. A Vector machine was used to support the supervised learning, and a neural network method was used to support unsupervised learning. The Normalized Difference Vegetation Index (NDVI) was used to identify the bands and extract pixel information relevant to the vegetation. The supervised method shows better results because of its robust performance and better analysis of spatial data classification using vegetation index. The proposed system experimentation was implemented by analyzing the results obtained from Support Vector Machine (SVM) and NN (Neural Network) methods. It is demonstrated in the results that the use of NDVI mainly enhances the performance and increases the classifier's accuracy to a greater extent.
Keywords: Spatial data; Normalized Difference Vegetation Index; NDVI;Vegetation index, Support Vector Machine; Neural Network; Forest Cover Change
Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
References
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Liu, B., Gao, L., Li, B., Marcos-Martinez, R., & Bryan, B. A. (2020). Nonparametric machine learning for mapping forest cover and exploring influential factors. Landscape Ecology, 35(7), 1683-1699. doi:10.1007/s10980-020-01046-0.
Lu, K. C., & Yang, D. L. (2009). Image processing and image mining using decision trees. Journal of Information Science and Engineering, 25(4), 989–1003. https://doi.org/10.6688/JISE.2009.25.4.2.
Maity, A. (2016). Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features. arXiv preprint arXiv:1608.00501.
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Murad, C. A., & Pearse, J. (2018). Landsat study of deforestation in the amazon region of colombia: Departments of caquetá and putumayo. Remote Sensing Applications: Society and Environment, 11, 161-171. doi:10.1016/j.rsase.2018.07.003.
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Yu, J., Li, F., Wang, Y., Lin, Y., Peng, Z., & Cheng, K. (2020). Spatiotemporal evolution of tropical forest degradation and its impact on ecological sensitivity: A case study in jinghong, xishuangbanna, china. Science of the Total Environment, 727 doi:10.1016/j.scitotenv.2020.138678.
Zurqani, H. A., Post, C. J., Mikhailova, E. A., Cope, M. P., Allen, J. S., & Lytle, B. A. (2020). Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and google earth engine. Scientific Reports, 10(1) doi:10.1038/s41598-020-69743-z.
Addink, E. A., De Jong, S. M., & Pebesma, E. J. (2007). The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogrammetric Engineering and Remote Sensing, 73(8), 905–912. https://doi.org/10.14358/PERS.73.8.905.
Ahmad, I., Singh, A., Fahad, M., & Waqas, M. M. (2020). Remote sensing-based framework to predict and assess the interannual variability of maize yields in pakistan using landsat imagery. Computers and Electronics in Agriculture, 178 doi:10.1016/j.compag.2020.105732.
Al-Obeidat, F., Al-Taani, A. T., Belacel, N., Feltrin, L., & Banerjee, N. (2015). A fuzzy decision tree for processing satellite images and landsat data. Procedia Computer Science, 52(C), 1192–1197. https://doi.org/10.1016/j.procs.2015.05.157.
An, K., Zhang, J., & Xiao, Y. (2007). Object-oriented urban dynamic monitoring - A case study of Haidian District of Beijing. Chinese Geographical Science, 17(3), 236–242. https://doi.org/10.1007/s11769-007-0236-1.
Aubrecht, C., Steinnocher, K., Hollaus, M., & Wagner, W. (2009). Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land-use. Computers, Environment and Urban Systems, 33(1), 15–25. https://doi.org/10.1016/j.compenvurbsys.2008.09.007.
Chi, M., Feng, R., & Bruzzone, L. (2008). Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Advances in Space Research, 41(11), 1793-1799. doi:10.1016/j.asr.2008.02.012.
Das, P., & Pandey, V. (2019). Use of logistic regression in land-cover classification with moderate-resolution multispectral data. Journal of the Indian Society of Remote Sensing, 47(8), 1443-1454. doi:10.1007/s12524-019-00986-8.
Devi, M.S., Dewangan, S., Ambashta, S. K., Jaiswal, A., & Kondapalli, S. (2019). Recognition of forest fire spruce type tagging using machine learning classification. International Journal of Recent Technology and Engineering, 8(3), 4309-4313. doi:10.35940/ijrte.C5176.098319.
Foody, G. M., & Mathur, A. (2004). A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1335–1343. https://doi.org/10.1109/TGRS.2004.827257.
Gangappa, M., Mai, D., & Sammulal, D. (2016). ESDAS: Explorative Spatial Data Analysis Scale to predict spatial structure of landscape. International Journal of Scientific and Engineering Research, 7(12), 466–472. https://doi.org/10.14299/ijser.2016.12.001.
Gangappa, M., Mai, D. C. K., & Sammulal, D. P. (2017). Analysis of Advanced Data Mining prototypes in Spatial data Analysis'. Journal of Engineering and Applied Sciences, 12(12), 3213-3219.
Gumma, M. K., Thenkabail, P. S., Teluguntla, P. G., Oliphant, A., Xiong, J., Giri, C., . . . Whitbread, A. M. (2020). Agricultural cropland extent and areas of south asia derived using landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the google earth engine cloud. GIScience and Remote Sensing, 57(3), 302-322. doi:10.1080/15481603.2019.1690780.
Ham, J. S., Chen, Y., Crawford, M. M., & Ghosh, J. (2005). Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 492–501. https://doi.org/10.1109/TGRS.2004.842481.
Oon, A., Mohd Shafri, H. Z., Lechner, A. M., & Azhar, B. (2019). Discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands using vegetation indices and supervised classification of Landsat-8. International Journal of Remote Sensing, 40(19), 7312-7328. doi:10.1080/01431161.2019.1579944.
Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26 (1), 217–222. https://doi.org/10.1080/01431160412331269698.
Pozdnoukhov, A., & Kanevski, M. (2006). Monitoring network optimisation for spatial data classification using support vector machines. International Journal of Environment and Pollution, 28(3–4), 465–484. https://doi.org/10.1504/IJEP.2006.011223.
Rajani, A., & Varadarajan, S. (2020). LU/LC change detection using NDVI & MLC through remote sensing and GIS for kadapa region. Cognitive Informatics and Soft Computing (pp. 215-223). Springer, Singapore. doi:10.1007/978-981-15-1451-7_24.
Ramos, A. P. M., Osco, L. P., Furuya, D.E.G., Gonçalves, W.N., Santana, D.C., Teodoro, L.P.R, . . . Pistori, H. (2020). A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices. Computers and Electronics in Agriculture, 178 doi:10.1016/j.compag.2020.105791.
Rawat, J. S., & Kumar, M. (2015). Monitoring land-use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002.
Shanthini, D., Shanthi, M., & Bhuvaneswari, M. (2017). A comparative study of SVM kernel functions based on polynomial. Int. J. Eng. Comput. Sci., 6(3).
Sharma, R. C., Tateishi, R., & Hara, K. (2016). A biophysical image compositing technique for the global-scale extraction and mapping of barren lands. ISPRS International Journal of Geo-Information, 5(12) doi:10.3390/ijgi5120225.
Singh, R. P., Singh, N., Singh, S., & Mukherjee, S. (2016). Normalized Difference Vegetation Index (NDVI) Based Classification to Assess the Change in Land-use/Land Cover (LULC) in Lower Assam, India. International Journal of Advanced Remote Sensing and GIS, 5(1), 1963–1970. doi:10.23953/cloud.ijarsg.74.
Somching, N., Wongsai, S., Wongsai, N., & Koedsin, W. (2020). Using machine learning algorithm and landsat time series to identify establishment year of para rubber plantations: A case study in thalang district, phuket island, thailand. International Journal of Remote Sensing, 41(23), 9075-9100. doi:10.1080/01431161.2020.1799450.
Khalil, R.Z., & Haque, S. (2018). InSAR coherence-based land cover classification of okara, pakistan. Egyptian Journal of Remote Sensing and Space Science, 21, S23-S28. doi:10.1016/j.ejrs.2017.08.005.
Liu, B., Gao, L., Li, B., Marcos-Martinez, R., & Bryan, B. A. (2020). Nonparametric machine learning for mapping forest cover and exploring influential factors. Landscape Ecology, 35(7), 1683-1699. doi:10.1007/s10980-020-01046-0.
Lu, K. C., & Yang, D. L. (2009). Image processing and image mining using decision trees. Journal of Information Science and Engineering, 25(4), 989–1003. https://doi.org/10.6688/JISE.2009.25.4.2.
Maity, A. (2016). Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features. arXiv preprint arXiv:1608.00501.
Mahmon, N. A., & Ya'acob, N. (2014). A review on classification of satellite image using Artificial Neural Network (ANN). In 2014 IEEE 5th Control and System Graduate Research Colloquium (pp. 153-157). IEEE.
Mirici, M. E., Satir, O., & Berberoglu, S. (2020). Monitoring the mediterranean type forests and land-use/cover changes using appropriate landscape metrics and hybrid classification approach in eastern mediterranean of turkey. Environmental Earth Sciences, 79(21) doi:10.1007/s12665-020-09239-1.
Murad, C. A., & Pearse, J. (2018). Landsat study of deforestation in the amazon region of colombia: Departments of caquetá and putumayo. Remote Sensing Applications: Society and Environment, 11, 161-171. doi:10.1016/j.rsase.2018.07.003.
Nachappa, T. G., Ghorbanzadeh, O., Gholamnia, K., & Blaschke, T. (2020). Multi-hazard exposure mapping using machine learning for the state of salzburg, austria. Remote Sensing, 12(17) doi:10.3390/RS12172757.
Yu, J., Li, F., Wang, Y., Lin, Y., Peng, Z., & Cheng, K. (2020). Spatiotemporal evolution of tropical forest degradation and its impact on ecological sensitivity: A case study in jinghong, xishuangbanna, china. Science of the Total Environment, 727 doi:10.1016/j.scitotenv.2020.138678.
Zurqani, H. A., Post, C. J., Mikhailova, E. A., Cope, M. P., Allen, J. S., & Lytle, B. A. (2020). Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and google earth engine. Scientific Reports, 10(1) doi:10.1038/s41598-020-69743-z.
Published
2020-12-30
How to Cite
REDDY, R. Sanjeeva; BABU, G. Anjan; REDDY, A. Rama Mohan.
Geospatial Approach for the Analysis of Forest Cover Change Detection using Machine Learning.
Geosfera Indonesia, [S.l.], v. 5, n. 3, p. 335-351, dec. 2020.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/20157>. Date accessed: 22 dec. 2024.
doi: https://doi.org/10.19184/geosi.v5i3.20157.
Section
Original Research Articles
