Abstract
Monitoring the changes in forest cover has become an important tool for forest management due to its impact on climate change, desertification, soil erosion, and flooding. The Zagros Mountains forest-steppe is a temperate broadleaf and mixed forest ecoregion in Western Asia, and the present study analyzes the changes in Zagros forests from 1986 to 2019 using remote sensing techniques. Three Landsat images, acquired in 1986, 2002, and 2019, were used to map land cover classification in the study area. Classification kappa coefficients were 85.2, 94.1, and 94.7 for three images, respectively. Focusing on the class level, the results show that between 1986 and 2002, the primary cause of forest loss in the study area was the conversion of 7.8 thousand ha of oak forests to agricultural land. Since 2002, 0.8 thousand ha of oak forest cover have been changed to barrenlands. The decrease of Zagros forests is a main threat to the rich biodiversity found in the West of Iran. To avoid worsening soil erosion, restoration programs should prioritize planting patterns on rainfall and hill slopes, texturally dissimilar soils, and regeneration in forest gaps, particularly at high elevations and steep slopes. It is recommended that future research should use higher resolution datasets and encompass more expansive landscape units, such as the whole Zagros Mountain forests.
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References
Azadi, H., Samari, D., Zarafshani, K., Hosseininia, G., & Witlox, F. (2013). Sustainable forest management in Iran: A factor analysis. Sustainability Science. https://doi.org/10.1007/s11625-012-0190-4
Azren, P. D., Lee, S. Y., Emang, D., & Mohamed, R. (2019). History and perspectives of induction technology for agarwood production from cultivated Aquilaria in Asia: a review. Journal of Forestry Research, 30(1), 1–11. https://doi.org/10.1007/s11676-018-0627-4
Cheng, G., Yang, C., Yao, X., Guo, L., & Han, J. (2018). When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2017.2783902
Fortin, J. A., Cardille, J. A., & Perez, E. (2020). Multi-sensor detection of forest-cover change across 45 years in Mato Grosso Brazil. Remote Sensing of Environment, 238, 111266. https://doi.org/10.1016/j.rse.2019.111266
Fricker, G. A., Ventura, J. D., Wolf, J. A., North, M. P., Davis, F. W., & Franklin, J. (2019). A convolutional neural network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery. Remote Sensing. https://doi.org/10.3390/rs11192326
Fu, T., Ma, L., Li, M., & Johnson, B. A. (2018). Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery. Journal of Applied Remote Sensing. https://doi.org/10.1117/1.jrs.12.025010
Gao, Y., Cheng, J., Meng, H., & Liu, Y. (2019). Measuring spatio-temporal autocorrelation in time series data of collective human mobility. Geo-Spatial Information Science, 22(3), 166–173. https://doi.org/10.1080/10095020.2019.1643609
Hafemann, L. G., Oliveira, L. S., Cavalin, P. R., & Sabourin, R. (2015). Transfer learning between texture classification tasks using convolutional neural networks. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2015.7280558
Kim, D. H., Sexton, J. O., Noojipady, P., Huang, C., Anand, A., Channan, S., Feng, M., & Townshend, J. R. (2014). Global, Landsat-based forest-cover change from 1990 to 2000. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2014.08.017
Kontschieder, P., Fiterau, M., Criminisi, A., & Bulò, S. R. (2016). Deep neural decision forests. IJCAI International Joint Conference on Artificial Intelligence.
Kussul, N. N., Lavreniuk, N. S., Shelestov, A. Y., Yailymov, B. Y., & Butko, I. N. (2016). Land cover changes analysis based on deep machine learning technique. Journal of Automation and Information Sciences. https://doi.org/10.1615/jautomatinfscien.v48.i5.40
Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2017.2681128
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2019.04.015
Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2016). Fully convolutional neural networks for remote sensing image classification. International Geoscience and Remote Sensing Symposium (IGARSS), 2016-Novem, pp 5071–5074. https://doi.org/10.1109/IGARSS.2016.7730322
Mardoyan, A., & Braun, P. (2015). Analysis of czech subsidies for solid biofuels. International Journal of Green Energy. https://doi.org/10.1080/15435075.2013.841163
Maroušek, J., Strunecký, O., & Stehel, V. (2019). Biochar farming: defining economically perspective applications. Clean Technologies and Environmental Policy. https://doi.org/10.1007/s10098-019-01728-7
Mihai, B., Săvulescu, I., Rujoiu-Mare, M., & Nistor, C. (2017). Recent forest cover changes (2002–2015) in the Southern Carpathians: A case study of the Iezer Mountains Romania. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.04.226
Miyamoto, M. (2020). Poverty reduction saves forests sustainably: Lessons for deforestation policies. World Development. https://doi.org/10.1016/j.worlddev.2019.104746
Musa, S. I., Hashim, M., & Reba, M. N. M. (2017). Urban growth assessment and its impact on deforestation in Bauchi metropolis, Nigeria using remote sensing and GIS techniques. ARPN Journal of Engineering and Applied Sciences, 12(6), 1907–1914.
Nouri, H., Faramarzi, M., Sadeghi, S. H., & Nasseri, S. (2019). Effects of regional vegetation cover degradation and climate change on dusty weather types. Environmental Earth Sciences. https://doi.org/10.1007/s12665-019-8763-5
Püttker, T., Crouzeilles, R., Almeida-Gomes, M., Schmoeller, M., Maurenza, D., Alves-Pinto, H., Pardini, R., Vieira, M. V., Banks-Leite, C., Fonseca, C. R., Metzger, J. P., Accacio, G. M., Alexandrino, E. R., Barros, C. S., Bogoni, J. A., Boscolo, D., Brancalion, P. H. S., Bueno, A. A., Cambui, E. C. B., … Prevedello, J. A. (2020). Indirect effects of habitat loss via habitat fragmentation: A cross-taxa analysis of forest-dependent species. Biological Conservation. https://doi.org/10.1016/j.biocon.2019.108368
Sen, S., & Sen, R. L. (2019). Green Manufacturing. Waste Management. https://doi.org/10.4018/978-1-7998-1210-4.ch020
Sharifi, A. (2020). Development of a method for flood detection based on Sentinel-1 images and classifier algorithms. Water and Environment Journal, 35(3), 924–929. https://doi.org/10.1111/wej.12681
Sharifi, A., Amini, J., Sri Sumantyo, J. T., & Tateishi, R. (2015). Speckle Reduction of PolSAR Images in Forest Regions Using Fast ICA Algorithm. Journal of the Indian Society of Remote Sensing, 43(2), 339–346. https://doi.org/10.1007/s12524-014-0423-3
Tian, H., Chaoqun, L., Yang, J., Banger, K., Huntzinger, D. N., Schwalm, C. R., Michalak, A. M., Cook, R., Ciais, P., Hayes, D., Huang, M., Ito, A., Jain, A. K., Lei, H., Mao, J., Pan, S., Post, W. M., Peng, S., Poulter, B., … Zeng, N. (2015). Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions. Global Biogeochemical Cycles, 29(6), 775–792. https://doi.org/10.1002/2014GB005021
Wang, Y., Ziv, G., Adami, M., de Almeida, C. A., Antunes, J. F. G., Coutinho, A. C., Esquerdo, J. C. D. M., Gomes, A. R., & Galbraith, D. (2020). Upturn in secondary forest clearing buffers primary forest loss in the Brazilian Amazon. Nature Sustainability. https://doi.org/10.1038/s41893-019-0470-4
Yu, Y., Kalashnikova, O. V., Garay, M. J., Lee, H., & Notaro, M. (2018). Identification and characterization of dust source regions across North Africa and the Middle East using MISR satellite observations. Geophysical Research Letters, 45(13), 6690–6701. https://doi.org/10.1029/2018GL078324
Yue, J., Zhao, W., Mao, S., & Liu, H. (2015). Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters. https://doi.org/10.1080/2150704X.2015.1047045
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2018). An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.06.034
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Moradi, E., Sharifi, A. Assessment of forest cover changes using multi-temporal Landsat observation. Environ Dev Sustain 25, 1351–1360 (2023). https://doi.org/10.1007/s10668-021-02097-2
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DOI: https://doi.org/10.1007/s10668-021-02097-2