Evaluation of area and canopy density of forests in the Guilan Province using satellite data | ||
| تحقیقات جنگل و صنوبر ایران | ||
| Article 10, Volume 27, Issue 1 - Serial Number 75, March 2019, Pages 100-111 PDF (761.15 K) | ||
| Document Type: Research article | ||
| DOI: 10.22092/ijfpr.2019.119185 | ||
| Authors | ||
| Masoud Amin Amlashi* 1; Khosro Kh. Mirakhorlou2 | ||
| 1Assistant Prof., Forests Rangelands, and Watershed Management Research Department, Guilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, AREEO, Rasht, Iran | ||
| 2Senior Research Expert, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran | ||
| Abstract | ||
| Knowledge on the area and amount of forest coverage at landscape scale can be one of the most important indicators in forest sustainable development. In this study, we used Landsat-8 full coverage imagery across the Guilan Province and the supervised classification method for forest canopy cover mapping in the summer of 2014. Field data were collected by a two-stage sampling method and 316 number of 0.5-ha plots. Subsequently, information on the types of land use and the canopy density (the ratio of the level of forest floor lightness per unit area) were recorded. With an overall accuracy of 91.8% and kappa coefficient of 0.80, results showed that 498804 ha of the total land area of Guilan Province is covered by forests, from which dense, semi-dense, and scattered forests account for about 42.1, 41.5, and 16.4% of the forested areas, respectively. This study demonstrated the negative effect of spectral similarity between farmlands with scatter and semi-dense forests in the accuracy of forest classification. This study demonstrated the proper performance of Landsat 8 data in providing thematic maps such as density and forest cover. Therefore, these data and information can be recommended for use in forest management decision-making, conservation, and restoration. | ||
| Keywords | ||
| Hyrcanian forests; land use; Landsat 8 | ||
| References | ||
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- Abdollahi, H., Shataee Jooybari, Sh., Sepehri, A. and Zanganeh, H., 2010. Comparing investigation on Landsat-ETM+ and IRS-P6-LISS IV data for canopy cover mapping of Zagros forests (case study, Javanroud forests). Journal of Wood and Forest Science and Technology, 17(3): 1-18 (In Persian). - Akbari, E., Zangane Asadi, M.A. and Taghavi Moghaddam, E., 2016. Change detectionn land use and land cover regional neyshabour using Different methods of statistical training theory. Geospatial Planning of Space Quarterly Journal, 6(20): 35-49 (In Persian). - Akike, S. and Samanta, S., 2016. Land use/land cover and forest canopy density monitoring of Wafi-Golpu project area, Papua New Guinea. Journal of Geoscience and Environment Protection, 4: 1-14. - Alizadeh, M., Mirzaei, R. and Kia, S.H., 2016. The potential of land cover/use detection using Landsat 8 satelite imagery (Case Study: Jajroud Basin). Third Conference on New Findings in the Environment and Agricultural Ecosystems. Tehran University, Tehran. 21-22 Sep. 2016: 9p (In Persian). - Amiri, M., Rahamani, R., Sagheb Talebi, Kh. and Habashi, H., 2015. Structural characteristics of dead wood in a natural untouched of Fagus orientalis Lipsky mixed stand forest (Case Study: Shastklateh Forest, Gorgan, Iran). Journal of Wood and Forest Science and Technology, 22(1): 185-205 (In Persian). - Arkhi, S., 2015. Detecting land cover/land use changesby object-oriented processing of satellite images using IdrisiSelva software (Case study: Abdanan region). Scientific - Research Quarterly of Geographical Data (SEPEHR), 24(95): 51-62 (In Persian). - Aslami, F., Ghorbani, A., Sobhani, B. and Panahandeh, M., 2015. Comparing artificial neural network, support vector machine and object-based methods in preparation land use/cover maps using landSat-8 images. Journal of RS and GIS for Natural Resources, 6(3): 1-14 (In Persian). - Hasanimehr, S.S. 2013. Recognizing the usage of Guilan forests potential based on development attitude approach. Human Geography Research, 45(1): 185-198 (In Persian). - Hashemi, S.A., Fatemi Talab, S.R., Kavousi Kalashmi, H. and Madanipour Kermanshahi, M., 2016. Change detection in the forest cover of Siyahmezgi watershed of Guilan using LandSat images. Journal of RS and GIS for Natural Resources, 7(3): 78-88 (In Persian). - Himayah, S., Hartono and Danoedoro, P., 2016. The utilization of landsat 8 multitemporal imagery and forest canopy density (FCD) model for forest reclamation priority of natural disaster areas at Kelud mountain, East Java. 2nd International Conference of Indonesian Society for Remote Sensing (ICOIRS). Yogyakarta, Indonesia, 17-20 Oct. 2016: 10p. - Hu, T., Yang, J., Li, X. and Gong, P., 2016. Mapping urban land use by using Landsat images and open social data. Remote Sensing, 8(2): 151. - Javan, F. and Hasani Moghaddam, H., 2017. Deforestation detection of Hyrcanian forest using satellite imagery and support vector machine (Case study: Rezvanshahr county). Forest Strategical Approchment Journal, 2(5): 1-11 (In Persian). - Kooch, Y. and Bayranvand, M., 2017. Effect of canopy gaps area on soil biological activities and organic matter fractions in a beech forest stand. Iranian Journal of Forest, 8(4): 533-546 (In Persian). - Makhdoum, M.F., Darvishsefat, A.A., Jafarzadeh, H. and Makhdoum, A.F. 2001. Environmental Evaluation and Planning by Geographic Information Systems. University of Tehran Press, Tehran, 304p (In Persian). - Mirakhorlou, Kh. and Akhavan, R., 2008. Investigation on boundary changes of northern forests of Iran using remotely sensed data. Iranian Journal of Forest and Poplar Research, 16(1): 139-148 (In Persian). - Mirakhorlou, Kh. and Akhavan, R., 2017. Forest density and orchard classification in Hyrcanian forests of Iran using Landsat 8 data. Journal of Forest Science, 63(8): 355-362. - Mirzaei Zadeh, V., Niknejad, M. and Hojjati, S.M., 2015. Estimation of forest canopy density using FCD. Ecology of Iranian Forests, 3(5): 63-75 (In Persian). - Mohammadyari, F., Pourkhabaz, H., Tavakoli, M. and Aghdar, H., 2014. Mapping vegetation and monitoring its changes using remote sensing and GIS techniques (Case study: Behbahan city). Scientific - Research Quarterly of Geographical Data (SEPEHR), 23(92): 24-34 (In Persian). - Mokhtari, M. and Najafi, A., 2015. Comparison of support vector machine and neural network classification methods in land use information extraction through Landsat TM data. Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), 19(72): 35-44 (In Persian). - Pakkhesal, E. and Bonyad, A.E., 2013. Classification and delineating natural forest canopy density using FCD model (Case study: Shafarud area of Guilan). Iranian Journal of Forest and Poplar Research, 21(1): 99-114 (In Persian). - Parma, R. and Shataee, Sh., 2010. Preparation of land use map using Landsat 7 ETM + sensor (Case study of Qaljaj forests of Kermanshah province). Geomatics Conference. Iran National Cartographic Center, Tehran, 9 May 2010: 10p (In Persian). - Richards, J.A., 2013. Remote Sensing Digital Image Analysis. Fifth Edition, Springer-Verlag Berlin Heidelberg, New York, 494p. - Salman Mahini, A., Nadali, A., Feghhi, J. and Riazi, B., 2012. Tree cover detection through Maxlike classification of Land sat ETM + images of the year 2001 in Golestan province. Journal of Environmental Science and Technology, 14(3): 47-56 (In Persian). - Shahvali Kouhshour, A., Pir Bavaghar, M. and Fatehi, P., 2012. Forest cover density mapping in sparse and semi dense forests using forest canopy density model (Case study: Marivan forests). Journal of RS and GIS for Natural Resources, 3(3):73-83 (In Persian). - Tazeh, M., Ghezelseflu, N. and Sadeghi Asl, M., 2014. Estimation of Landsat satellite imagery to map forest area (case study: Golestan province). National Conference of Geography, Urban Planning and Sustainable Development. Tehran, 27 Feb. 2014: 12p (In Persian). - Wentz, E.A., Stefanov, W.L., Gries, C. and Hope, D., 2006. Land use and land cover mapping from diverse data sources for an arid urban environments. Computers, Environment and Urban Systems, 30(3): 320-346. - Wulder, M. and Boudewyn, P., 2000. Remote estimation of forest density using empirical methods on image spectral and textural data. Remote Sensing and Spatial Data Integration: Measuring, Monitoring and Modeling, 22nd Symposium of the Canadian Remote Sensing Society. Victoria, British Columbia, 20-25 Aug. 2000: 6p. - Yousefi, S., Tazeh, M., Mirzaee, S., Moradi, H.R. and Tavangar, Sh., 2014. Comparison of different classification algorithms in satellite imagery to produce land use maps (Case study: Noor city). Journal of RS and GIS for Natural Resources, 5(3): 67-76 (In Persian). - Zaidi, S.M., Akbari, A., Abu Samah, A., Kong, N.S., Isabella J. and Gisen, A., 2017. Landsat-5 time series analysis for land use/land cover change detection using NDVI and semi-supervised classification techniques. Polish Journal of Environmental Studies, 26(6): 2833-2840. - Zobeiri, M., 2002. Forest Biometry. University of Tehran Press, Tehran, 411p (In Persian).
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