| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,944 |
| تعداد مقالات | 33,229 |
| تعداد مشاهده مقاله | 43,866,241 |
| تعداد دریافت فایل اصل مقاله | 20,372,528 |
پهنه‌بندی پوشش گیاهی شهرستان صومعه‌سرا با استفاده از سری زمانی تصاویر ماهواره‌ای | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 6، دوره 30، شماره 4 - شماره پیاپی 90، اسفند 1401، صفحه 365-381 اصل مقاله (880.72 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2023.361480.2088 | ||
| نویسندگان | ||
| سیده محدثه حاتمی شاه خالی1؛ شهرام شریفی هشجین* 2؛ فرزانه نصیری جان آقا1؛ سیده فاطمه امامی3 | ||
| 1کارشناس پژوهشی گروه مطالعات ناحیه‌ای، پژوهشکده محیط‌زیست جهاد دانشگاهی، رشت، ایران | ||
| 2استادیار، گروه مطالعات ناحیه‌ای، پژوهشکده محیط‌زیست جهاد دانشگاهی، رشت، ایران | ||
| 3پژوهشگر پسا دکتری جغرافیا و برنامه ریزی روستایی، دانشکده علوم انسانی، دانشگاه گیلان، رشت، ایران | ||
| چکیده | ||
| تهیه نقشه‌های پهنه‌بندی در مناطق دارای پوشش‌های مشابه از نظر طیفی، به‌عنوان یک چالش در حوزه سنجش از دور شناخته می‌شود. هدف از پژوهش پیش‌رو، افزایش صحت طبقه‌بندی پوشش گیاهی با تمرکز بر شناسایی صنوبرکاری‌های شهرستان صومعه‌سرا در استان گیلان بود. اطلاعات مستخرج از تصاویر ماهواره‌ای به‌همراه نمونه‌های زمینی در روش طبقه‌بندی جنگل تصادفی برای پهنه‌بندی باغ‌های صنوبرکاری‌شده استفاده شد. این اطلاعات از تلفیق باندها و شاخص‌های طیفی تصاویر نوری ماهواره سنتینل 2 و باندهای راداری ماهواره سنتینل 1 به‌صورت سری زمانی و مدل رقومی ارتفاعی که توسط ماهواره ALOSpalsar تهیه شده است، به‌دست آمد. پس از تجزیه‌وتحلیل‌های سری زمانی، بهترین تصاویر از نظر قدرت تفکیک مناطق صنوبرکاری از مناطق دیگر برای ورود به الگوریتم طبقه‌بندی انتخاب شدند. نتایج نشان داد که تصاویر نوری نسبت‌به راداری توانایی بهتری برای پهنه‌بندی داشتند. همچنین، استفاده از سری زمانی به‌جای تک‌تصویر و استفاده از شاخص‌ها به‌طور میانگین تا سه درصد صحت کلی طبقه‌بندی را افزایش داد. به‌طور کلی، تصاویر نوری و راداری ماهواره‌های سنتینل 1 و سنتینل 2 و ایلوس با استفاده از روش ارائه‌شده از قابلیت زیادی در طبقه‌بندی درختان صنوبر در مناطق وسیع دارند. مساحت این مناطق در شهرستان صومعه‌سرا 7778 هکتار برآورد شد. | ||
| کلیدواژه‌ها | ||
| تصویر ماهواره سنتینل؛ شاخص‌های طیفی؛ صنوبرکاری | ||
| عنوان مقاله [English] | ||
| Land cover mapping of the Sowmeh Sara city using time series of satellite imagery | ||
| نویسندگان [English] | ||
| S.M. Hatami Shah Khali1؛ Sh. Sharifi Hashjin2؛ F. Nasiri Aghajan1؛ S.F. Emami3 | ||
| 1Research Expert, Department of Regional Studies, Jihad University Environmental Research Institute, Rasht, Iran | ||
| 2Assistant Prof, in the Department of Regional Studies, Jihad University Environmental Research Institute, Rasht, Iran | ||
| 3Postdoctoral Researcher in Geography and Rural Planning, Faculty of Humanities, Guilan University, Rasht, Iran | ||
| چکیده [English] | ||
| The remote sensing field faces a significant challenge in preparing land use maps in regions with similar spectral coverages. This research aimed to enhance crop classification accuracy by prioritizing poplar in Guilan province's Soomesara county. To classify poplar plantations, the researchers used data extracted from satellite imagery and field samples employing a random forest classification method. They utilized Sentinel-2 optical images, Sentinel-1 radar polarization data, and the ALOSpalsar digital elevation model produced in a time series to obtain this information. The team selected optimal images with high separability power for distinguishing poplar farms from other classes after analyzing the time series. Results demonstrated that optical images had better capabilities for land use classification than radar images. Additionally, implementing time series data instead of single images and using indices increased overall classification accuracy up to three percent. In conclusion, the researchers found that the proposed method utilizing Sentinel-1, Sentinel-2, and ALOS optical and radar satellite images has a high potential for poplar mapping in large areas. The estimated area of these regions in Soomesara County was 7,778 hectares. | ||
| کلیدواژه‌ها [English] | ||
| Poplar, sentinel satellite image, spectral indices | ||
| مراجع | ||
|
- Ahmadloo, F., Rezaei, A., Farahpour, M., Calagari, M. and Mehrabi, A., 2021. Investigating the area and production of poplar plantations in Sowmeeh Sara city using field data and GIS. Ecology of Iranian Forest, 9(18): 159-6 (In Persian with English summary). - Bayatkashkoli, A., Azizi, M. and Faezipour, M., 2021. Quantitative analysis of poplar plantations in four Iranian Provinces (Case of the study: East Azerbaijan, Zanjan, Ardabil and Kermanshah). Iranian Journal of Wood and Paper Industries, 12(3), 375-389 (In Persian with English summary). - Breiman, L., 2001. Random forests. Machine learning, 45(1): 5-32. - Chandrasekar, K., Sesha Sai, M., Roy, P. and Dwevedi, R., 2010. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product. International Journal of Remote Sensing, 31(15): 3987-4005. - Chauhan, S., Darvishzadeh, R., Lu, Y., Boschetti, M. and Nelson, A., 2020. Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, 243: 111804. - Clerici, N., Valbuena Calderón, C. A. and Posada, J. M., 2017. Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia. Journal of Maps, 13(2): 718-726. - D’Amico, G., Francini, S., Giannetti, F., Vangi, E., Travaglini, D., Chianucci, F. and Corona, P., 2021. A deep learning approach for automatic mapping of poplar plantations using Sentinel-2 imagery. GIScience and Remote Sensing, 58(8): 1352-1368. - Dobrinić, D., Gašparović, M. and Medak, D., 2021. Sentinel-1 and 2 time-series for vegetation mapping using random forest classification: A case study of Northern Croatia. Remote Sensing, 13(12): 2321. - Dobrinić, D., Medak, D. and Gašparović, M., 2020. Integration of multitemporal Sentinel-1 and Sentinel-2 imagery for land-cover classification using machine learning methods. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43: 91-98. - Fang, H., Baret, F., Plummer, S. and Schaepman‐Strub, G., 2019. An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics, 57(3): 739-799. - Feizolahpour, F., Besharat, S., Feizizadeh, B., Rezaverdinejad, V. and Hessari, B., 2021. The Efficiency of Vegetation Spectral Indices Using Remote Sensing Drone Images. Iranian Journal of Soil and Water Research, 2(5): 969-979 (In Persian with English summary). - Frampton, W. J., Dash, J., Watmough, G. and Milton, E. J., 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82: 83-92. - Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S. and Hostert, P., 2021. National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252: 112128. - Freeman, A., Villasenor, J., Klein, J., Hoogeboom, P. and Groot, J., 1994. On the use of multi-frequency and polarimetric radar backscatter features for classification of agricultural crops. International Journal of Remote Sensing, 15(9): 1799-812. - Gascon, F., Bouzinac, C., Thépaut, O., Jung, M., Francesconi, B., Louis, J. and Gaudel-Vacaresse, A., 2017. Copernicus Sentinel-2A calibration and products validation status. Remote Sensing, 9(6): 584. - Gašparović, M. and Dobrinić, D., 2020. Comparative assessment of machine learning methods for urban vegetation mapping using multitemporal sentinel-1 imagery. Remote Sensing, 12(12): 1952. - Gašparović, M. and Klobučar, D., 2021. Mapping floods in lowland forest using Sentinel-1 and Sentinel-2 data and an object-based approach. Forests, 12(5): 553. - Ghafarian Malamiri, H. R. and Zare Khormizie, H., 2017. Reconstruction of cloud-free time series satellite observations of land surface temperature (LST) using harmonic analysis of time series algorithm (HANTS). Journal of RS and GIS for Natural Resources, 8(3): 37-55 (In Persian with English summary). - Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L. and Liu, S., 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7): 2607-2654. - Gonzalez-Piqueras, J., Calera, A., Gilabert, M. A., Cuesta, A. and De la Cruz Tercero, F., 2004. Estimation of crop coefficients by means of optimized vegetation indices for corn. Paper presented at the Remote Sensing for Agriculture, Ecosystems, and Hydrology V(52321): 110-118. - Hansen, M. C. and Loveland, T. R., 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122: 66-74. - Huete, A. R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309. - Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. and Ferreira, L. G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213. - Ienco, D., Interdonato, R., Gaetano, R. and Minh, D. H. T., 2019. Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture. ISPRS Journal of Photogrammetry and Remote Sensing, 158: 11-22. - Kordi, F., Hamzeh, S., Atarchi, S. and Alavipanah, S. K., 2018. Agricultural Product Classification for Optimal Water Resource Management Using the Data Time Series of Landsat8. Iranian Journal of Ecohydrology, 5(4): 1267-1283. - Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Bezner Kerr, R., Lupafya, E. and Dakishoni, L., 2021. Crop type and land cover mapping in northern Malawi using the integration of sentinel-1, sentinel-2, and planetscope satellite data. Remote Sensing, 13(4): 700. - Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y. and Pei, F., 2017. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168: 94-116. - Luo, C., Qi, B., Liu, H., Guo, D., Lu, L., Fu, Q. and Shao, Y., 2021. Using time series Sentinel-1 images for object-oriented crop classification in Google earth engine. Remote Sensing, 13(4): 561. - Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U. and Gascon, F., 2017. Sen2Cor for sentinel-2. Paper presented at the Image and Signal Processing for Remote Sensing, 10427: 37-48. - Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L. and Marchamalo, M., 2019. Evaluation of Sentinel-1 and 2 time series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes. Remote Sensing, 11(8): 979. - Molaei, S., 2008. Wood agriculture from view of a man who planted poplar. Paper presented at the Proceeding of Second National Congress on Poplar and Potential Use in Poplar Plantation, (2): 73-74. - Morlin Carneiro, F., Angeli Furlani, C. E., Zerbato, C., Candida de Menezes, P., da Silva Gírio, L. A. and Freire de Oliveira, M., 2020. Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors. Precision Agriculture, 21(5): 979-1007. - Ozturk, M. Y. and Colkesen, I., 2020. Mapping of poplar tree growing fields with machine learning algorithms using multi-temporal Sentinel-2A imagery. Paper presented at the 41th Asian Conference on Remote Sensing (ACRS), Deqing, China, Nov. 2020:9-11. - Pérez-Hoyos, A., Udías, A. and Rembold, F., 2020. Integrating multiple land cover maps through a multi-criteria analysis to improve agricultural monitoring in Africa. International Journal of Applied Earth Observation and Geoinformation,1(88): 102064. - Razaghmanesh, A., Allahyari Bek, S. and Safdarinezhad, A., 2020. A sparse representation method to detect saffron agricultural lands using sentinel-ii satellite images time. Journal of Geospatial Information Technology, 8(1): 101-123 (In Persian with English summary). - Schucknecht, A., Erasmi, S., Niemeyer, I. and Matschullat, J., 2013. Assessing vegetation variability and trends in north-eastern Brazil using AVHRR and MODIS NDVI time series. European Journal of Remote Sensing, 46(1): 40-59. - Senf, C., Leitão, P. J., Pflugmacher, D., van der Linden, S. and Hostert, P., 2015. Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sensing of Environment, 156: 527-536. - Shakeri, I., Safdarinezhad, A. and Jafari, M., 2020. Herbal plants zoning using target detection algorithms on time-series of Sentinel-2 multispectral images (Amygdalus Scoparia). Journal of Geospatial Information Technology, 7(4): 193-214 (In Persian with English summary). - Tonbul, H., Colkesen, I. and Kavzoglu, T., 2020. Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery. Journal of Geodetic Science, 10(1): 14-22. - Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127-150. - Villamuelas, M., Fernández, N., Albanell, E., Gálvez-Cerón, A., Bartolomé, J., Mentaberre, G. and López-Martín, J. M., 2016. The Enhanced Vegetation Index (EVI) as a proxy for diet quality and composition in a mountain ungulate. Ecological Indicators, 61: 658-666. - Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C. and Ng, W.-T., 2018. How much does multi-temporal Sentinel-2 data improve crop type classification? International Journal of Applied Earth Observation and Geoinformation, 72: 122-130. - Wistuba, M., Grabocka, J. and Schmidt-Thieme, L., 2015. Ultra-fast shapelets for time series classification. arXiv preprint arXiv, 1503-05018. - Xue, Z., Du, P. and Feng, L., 2014. Phenology-driven land cover classification and trend analysis based on long-term remote sensing image series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1142-1156. - Zhang, L., Zhang, Z., Luo, Y., Cao, J., Xie, R. and Li, S., 2021. Integrating satellite-derived climatic and vegetation indices to predict smallholder maize yield using deep learning. Agricultural and Forest Meteorology, 311: 108666. - Zhang, W., Brandt, M., Wang, Q., Prishchepov, A. V., Tucker, C. J., Li, Y. and Fensholt, R., 2019. From woody cover to woody canopies: How Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas. Remote Sensing of Environment, 234: 111465. -Darvishsefat, A. A ., Dafchahi . F. G . and Bonyad, A .E, 2014. Feasibility of satellite imagery for poplar plantation mapping(Case study: Sowe’eh sara). Iranian Journal of Forest and Poplar Research, 22(3) (In Persian with English summary). -Eslami,A. and Zahedi,S.S., 2011. Providing poplar plantation map by Indian remote sensing (IRS) satellite imagery in Northern Iran. African Journal of Agricultural Research, 6(20), 4769-4774. -Mosayeb Neghad, I., Rostami Shahraji, T., Kahneh, E. and Porbabaii, H., 2007. Evaluation of native broadleaved forest plantations in east of Guilan province. Iranian Journal of Forest and Poplar Reaserch, 15(4), 319-311 (In Persian with English summary). | ||
|
آمار تعداد مشاهده مقاله: 371 تعداد دریافت فایل اصل مقاله: 290 |
||