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بررسی الگوریتم طبقهبندی درختی با استفاده از تصاویر ماهوارهای لندست 8 و سنتینل 2 و تفسیر بصری تصاویر گوگل ارث در تفکیک و طبقهبندی واحدهای اکولوژیک گیاهی | ||
| تحقیقات مرتع و بیابان ایران | ||
| مقاله 10، دوره 29، شماره 4 - شماره پیاپی 89، دی 1401، صفحه 608-626 اصل مقاله (1.4 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijrdr.2022.128072 | ||
| نویسندگان | ||
| سمانه سادات محزونی کچپی1؛ عطاالله ابراهیمی2؛ پژمان طهماسبی* 2؛ محمد حسن جوری3 | ||
| 1دانشجوی دکتری گروه علوم مرتع، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، ایران | ||
| 2دانشیار گروه علوم مرتع، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، ایران | ||
| 3دانشیار گروه منابع طبیعی، دانشگاه آزاد اسلامی واحد نور، ایران | ||
| چکیده | ||
| از مهمترین روشهای استخراج اطلاعات از دادههای ماهوارهای، تکنیکهای مختلف طبقهبندی تصاویر میباشد. مطالعه حاضر با هدف تفکیک و طبقهبندی واحدهای اکولوژیک گیاهی توسط الگوریتم طبقهبندی درختی بر روی تصاویر ماهوارهای و همچنین تفسیر بصری تصاویر گوگل ارث در یکی از مراتع نیمهاستپی استان چهارمحال و بختیاری صورت گرفته است. پس از اعمال الگوریتم طبقهبندی در نرمافزار Idrisi TerrSet، ماتریس خطا توسط نرمافزار تولید و بر اساس مقادیر داخل این ماتریس ارزیابی آمارههای استخراج شده انجام شد. نتایج حاصل از تفسیر بصری نشان داد که در نهایت 7 نوع واحد اکولوژیک گیاهی که از نظر ویژگیهای ساختاری متفاوت بودند شناسایی و به‌صورت آمار توصیفی بیان گردیدند که شامل Astragalus verus، Bromus tomentellus، Scariola orientalis، Astragalus verus-Bromus tomentellus، Astragalus verus-Stipa hohenikeriana، Bromus tomentellus-Stipa hohenikeriana و Stipa hohenikeriana میباشند. نتایج همچنین نشان داد که دقت کلی طبقهبندی و ضریب کاپا برای تصاویر لندست 8 به ترتیب برابر 92/0 درصد و 89/0 و برای تصاویر سنتینل 2 برابر 94/0 درصد و 92/0 میباشد. بر اساس نتایج بهدست آمده مشخص شد که تصاویر ماهوارهای و تصاویر هوایی دارای قابلیت تفکیک مناسبی جهت تهیه نقشه واحدهای اکولوژیک گیاهی هستند. از آنجاییکه تفسیر بصری تصاویر گوگل ارث، روشی زمانبر بوده و هزینه بالایی در بر دارد بنابراین میتوان نتیجهگیری نمود که تصاویر ماهوارهای بهویژه سنتیل 2 به دلیل رزولوشن بالا و ارائه تصاویری با قدرت تفکیک زیاد، میتوانند بهعنوان ابزاری کاربردی و قابل استفاده، اطلاعات و جزئیات دقیقی را از پدیدههای سطح زمین فراهم آورند و بهعنوان منبع مناسبی جهت تهیه نقشه واحدهای اکولوژیک گیاهی مورد استفاده قرار گیرند. | ||
| کلیدواژه‌ها | ||
| طبقهبندی درختی؛ تفسیر بصری؛ لندست 8؛ سنتینل 2؛ واحدهای اکولوژیک | ||
| عنوان مقاله [English] | ||
| Investigation of Classification Tree Analysis Algorithm Using Landsat 8 and Sentinel 2 Satellite Images and Visual Interpretation of Google Earth Images in Separation and Classification of Plant Ecological Units | ||
| نویسندگان [English] | ||
| Samaneh Sadat Mahzooni-Kachapi1؛ Ataollah Ebrahimi2؛ Pejman Tahmasebi2؛ Mohammad Hassan Jouri3 | ||
| 1Ph.D. Student, Department of Rangeland Management, Faculty of Natural Resources and Earth Science, Shahrekord University. Iran | ||
| 2Associate Professor, Department of Rangeland Management, Faculty of Natural Resources and Earth Science, Shahrekord University, Iran | ||
| 3Associate Professor, Department of Natural Resources, Islamic Azad University, Noor Branch, Iran | ||
| چکیده [English] | ||
| One of the most important methods of extracting information from satellite data is various image classification techniques. The current research was conducted to separate and classify plant ecological units by classification tree analysis algorithm on satellite images and also visual interpretation of Google Earth images in one of the semi-steppe rangelands of Chaharmahal and Bakhtiari province. After applying the classification algorithm in Idrisi TerrSet software, the software generated the error matrix, and then based on the values inside this matrix, the extracted statistics were evaluated. The results of visual interpretation showed that finally, seven types of plant ecological units that were different in terms of structural features were identified and expressed as descriptive statistics, including Astragalus verus, Bromus tomentellus, Scariola orientalis, Astragalus verus-Bromus tomentellus, Astragalus verus -Stipa hohenikeriana, Bromus tomentellus-Stipa hohenikeriana, and Stipa hohenikeriana. The results also showed that the overall accuracy and kappa coefficients of 0.92% and 0.89 for Landsat 8 images and 0.94% and 0.92 for sentinel 2 images were achieved. Based on the obtained results, it was found that satellite images and aerial images have a suitable separation capability to prepare a map of plant ecological units. Since the visual interpretation of Google Earth images is a time-consuming and expensive method, it can be concluded that satellite images, especially Centile 2, can be used as a tool due to their high resolution and high-resolution images, Practical and usable, provide accurate information and details of the earth's surface phenomena and be used as a suitable source for preparing a map of plant ecological units. | ||
| کلیدواژه‌ها [English] | ||
| Classification Tree Analysis, Visual Interpretation, Landsat 8, Sentinel 2, Ecological Units | ||
| مراجع | ||
|
- Adam, E., Abd Elbasit, M.A.M., Adelabu, S.A. and Bande, P., 2018. Comparing landsat 8 and sentinel 2 in mapping water quality at VAAL DAM. Conference: International Geoscience and Remote Sensing Symposium At: Valencia, Spain, 9781-5386-7150-4.
- Afrasinei, G., Melis, M., Buttau, C., Bradd, J. and Arras, C., 2017. Assessment of remote sensing-based classification methods for change detection of salt-affected areas (Biskra area, Algeria). Journal of Applied Remote Sensing, 11 (1): 016025.
- Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J., 1984. Classification and regression trees, Wadsworth and Brooks/Cole, 358 p.
- Culman, S.W., Gauch, H.G., Blackwood, C.B. and Thies, J.E., 2008. Analysis of T-RFLP data using analysis of variance and ordination methods: a comparative study. Journal of Microbiological Methods, 75 (1): 55-63.
- Dogan, O.K., Akyurek, Z. and Beklioglu, M., 2009. Identification and mapping of submerged plants in a shallow lake using quickbird satellite data. Journal of environmental management, 90 (7): 2138-2143.
- Duffy, J.P., Pratt, L., Anderson, K., Land, P.E. and Shutler, J.D., 2018. Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone. Estuarine, Coastal and Shelf Science, 200: 169-180.
- Eugenio, F., Martin, J., Marcello, J. and Fraile-Nuez, E., 2013. Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 29: 53-66.
- Feng, D., Yu, L., Zhao, Y., Cheng, Y., Xu, Y., Li, C. and Gong, P., 2018. A multiple dataset approach for 30-m resolution land cover mapping: A case study of continental Africa. International Journal of Remote Sensing, 39 (12): 3926–3938.
- Ge, G., Shi, Z., Zhu, Y., Yang, X. and Hao, Y., 2020. Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22: e00971.
- Huete, A., 2004. Remote Sensing for Natural Resources Management and Enviromental Monitoring: Manual of remote sensing, Univercity of California, Davis, 3 edition, 4.
- Hurskainen, P., Adhikari, H., Siljander, M., Pellikka, P.K.E. and Hemp, A., 2019. Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes. Remote Sensing of Environment, 233: 111354.
- Jafari, Sh., Rahimi, Kh. and Arazzadeh, Y., 2012. Land use mapping using Google Earth data (Case study: Karaj). Sixth National Conference and Specialized Exhibition of Environmental Engineering, Tehran, https: //civilica.com/doc/170175. (In Persian).
- Jia, K., Wei, X., Gu, X., Yao, Y., Xie, X. and Li, B., 2014. Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China. Geocarto International, 29 (8): 941- 951.
- Jiang, X.B., Zhou, Q.G. and Li, A.N., 2004. Landscape pattern of Diqing, Yunnan. Journal of Mountain Research, 22: 164-168.
- Kashi Zenouzi, L., Saadat, H. and Namdar, M., 2016. Comparison between the accuracy of geomorphological map using traditional and analytical photogrammetry methods (Case study: Harzand chai waters). Geographical data, 97 (25): 57-66. (In Persian).
- Koomen, E., Stillwell, J., Bakema, A. and Scholten, H.J., 2007. Modelling land-use change. Progress and Applications, Springer Dordrecht, 90: 1-22.
- Kusbach, A., Long, J.N., Van Miegroet, H. and Shultz, L.M., 2012. Fidelity and diagnostic species concepts in vegetation classification in the Rocky Mountains, Northern Utah, USA. Botany, 90 (8): 678-693.
- Lillesand, T.M. and Kiefer, R.W., 1994. Remote sensing and image interpretation. 3rd edition, John Wiley and Sons, New York, 750 p.
- Lillesand, T.M., Kiefer, R.W. and Chipman, J.W., 2004. Remote sensing and image interpretation. 5th edition, John Wiley and Sons, New York.
- Lima, T.A., Beuchle, R., Langner, A., Grecchi, R.C., Griess, V.C. and Achard, F., 2019. Comparing Sentinel 2 MSI and Landsat 8 OLI Imagery for Monitoring Selective Logging in the Brazilian Amazon. Remote sensing, 961 (11): 1- 21.
- Ludwig, A., Meyer, H. and Nauss, T., 2016. Automatic classification of Google Earth images for a larger scale monitoring of bush encroachment in South Africa. International Journal of Applied Earth Observation and Geoinformation, 50: 89-94.
- Marangoz, A.M., Sekertekin, A. and Akcin, H., 2017. Analysis of land use land cover classification results derived from Sentinel-2 image. 17th International Multidisciplinary Scientific GeoConference SGEM, Photogrammetry and Remote Sensing, 3-8.
- Maynard, J.J. and Karl, J.W., 2017. A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites. PloS ONE 12 (4):e0175201.
- MohammadHassanpour, M., 2013. Preparation of land use map of Ghoshchi Pass area of Urmia using Google Earth images and GIS. Third Conference on Environmental Planning and Management, Tehran, https://civilica.com/doc/240157. (In Persian).
- Mountrakis, G., Im, J. and Ogole, C., 2011. Support vector machines in remote sensing: A review. Photogrammetry and Remote Sensing, 66: 247–259.
- Mueller-Dombois, D. and Ellenberg, H., 1974. Aims and methods of vegetation ecology. John Wiley and Sons, New York, 2 (2): 158-159.
- Navarro, G., Caballero, I., Silva, G., Parra, P.C., Vázquez, Á. and Caldeira, R., 2017. Evaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery. International Journal of Applied Earth Observation and Geoinformation, 58 (2): 97-106.
- Niazi, Y., Ekhtesasi, M., Malekinezhad, H. and Hosseini, S.Z., 2011. Comparison Between two Classification Methods of Maximum likelihood and Artificial Neural Network for Providing Land use Maps Case Study: Ilam Dam Area, Geography and Development, 20: 119-132.
- Ouzemou, J.E., El Harti, A., Lhissou, R., El Moujahid, A., Bouch, N., El Ouazzani, R., Bachaoui, E.M. and El Ghmari, A., 2018. Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system. Remote Sensing Application: Society Environment, 11: 1–28.
- Pourbagherkordi, M., 2018. Comparison of visual and automated methods based on object in identifying landforms in Yazd-Ardakan basin. Remote Sensing and GIS Iran, 1: 73-90. (In Persian).
- Sepehri, A., 2002. Investigating the capability of mineart method in removing the effect of topography in satellite images. Natural Resources of Iran, 55: 107-122. (In Persian).
- Smith, P.C., Dellepiane, S.G. and Schowengerdt, R.A., 2010. Quality Assessment of Image Classification Algorithms for Land Cover Mapping: A review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20 (8): 1461-1486.
- Tahmasebi, P., Moradi, M. and Omidipour, R., 2017. Plant Functional Identity as the Predictor of Carbon Storage in Semi-Arid Ecosystems. Plant Ecology & Diversity, 2-3 (10): 139-151.
- Thenkabail, P.S. and Prasad, S., 2015. Remotely Sensed Data Characterization, Classification and Accuracies. Object-Based Image Analysis: Evolution, History, State of the Art, and Future Vision, 276-293.
- Thenkabail, P.S. and Lyon, J.G., 2016. Hyperspectral remote sensing of vegetation, Second Edition, Four Volume Set: CRC Press. 1632 p.
- Topaloglu, R.H., Sertel, E. and Musaoglu, N., 2016. Assessment of classification accuracies of Sentinel 2 and Landsat-8 data for land cover/use mapping. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XXIII ISPRS Congress, 1055-1059.
- Unger, D.R., Kulhavy, D.L. and Hung, I.K., 2013. Validating the Geometric Accuracy of High Spatial Resolution Multispectral Satellite Data. GIScience and Remote Sensing, 50 (3): 271–280.
- Weng, Q., 2018. Remote Sensing Time Series Image Processing. Taylor & Francis Series in Imaging Science International Standard Book, 13, 264 p.
- Xie, Z., Chen, Y., Lu, D., Li, G. and Chen, E., 2019. Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data. Remote. Sensing, 11 (2): 1-27.
- Xu, M., Watanachaturaporn, P., Varshney, P.K. and Arora, M.K., 2005. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97 (3): 322-336.
- Yoneyama, Y., Suzuki, S., Sawa, R., Yoneyama, K., Power, G.G. and Araki, T., 2002. Increased plasma adenosine concentrations and the severity of preeclampsia. Obstetrics & Gynecology, 100 (6): 1266-1270.
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