Document Type : Original Article

Authors

1 Ph.D. Student, Dep. of Forestry, Faculty of Natural Resources, Tarbiat Modarres University

2 Associate Prof., Dep. of Forestry, Faculty of Natural Resources, Tarbiat Modarres University

3 Prof. of Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU)

4 Assistant Prof., Institute of Geomatics, University of Natural Resources and Life Sciences, Vienna (BOKU)

Abstract

Despite the low area coverage, riparian vegetation presents several ecosystem services. But there is no precise spatial information on these ecosystems in Iran. Considering the lack of such information, mapping and providing a spatial database is essential. Due to the mixture of these vegetation types and other land covers, the detection of these types of vegetation is challenging, and choosing an appropriate classification method is of great significance. This study examines the performance of pixel-based and object-based classification approaches for the detection of these vegetation types using freely available Sentinel-2 imagery. Five different riparian areas in Chaharmahal-va-Bkhtiari province were selected and used for training the classification models. We used random forest algorithm and multi-temporal Sentinel-2 data to perform the classification models. The validation of classification models was based on independent validation points spread across the province. Our results showed that multi-temporal Sentinel-2 imagery has a high capability to map riparian vegetation in the Zagros Mountains. Moreover, the pixel-based classification approach (overall accuracy of 83.9%) represents more accurate results compared to the object-based classification approach (overall accuracy of 76.7%). Overall, this study recommends a pixel-based classification approach to map these vegetation types using freely available multi-temporal Sentinel-2 imagery. Note that it is important to use pure pixels for training the classification models.

Keywords

امیدی‌پور، ر.، مرادی.، ح.ر.، آرخی، ص.، 1392، مقایسة روش‌های طبقه‌بندی پیکسل‌‌پایه و شیء‌گرا در تهیة نقشة کاربری اراضی با استفاده از داده‌های ماهواره‌ای، سنجش از دور و GIS ایران، سال پنجم، شمارة 3، صص. 110-99.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
رفیعیان، ا.، درویش‌صفت، ع.، بابایی، س.، متاجی، ا.، 1390، ارزیابی طبقه‌بندی‌های پیکسل‌پایه و شیء‌پایة تصاویر هوایی برای تشخیص گونه‌های درختی (مطالعة موردی: جنگل‌کاری چمستان نور)، مجلة جنگل ایران، سال سوم، شمارة 1، صص. 47-35.
روستایی، ش.، مختاری، د.، ولیزاده، خ.، خدائی، ل.، 1398، مقایسة روش پیکسل‌پایه (بیشترین شباهت) و شی‌گراء (ماشین بردار پشتیبان) در طبقه‌بندی کاربری اراضی (منطقة اهرـ ورزقان)، پژوهش‌های ژئومورفولوژی کمّی، سال بیست‌ونهم، شمارة 1، صص. 129-118.
شتایی، ش.، درویش‌صفت، ع.، سبحانی، ه.، 1386، مقایسة روش‌های طبقه‌بندی شیءپایه و پیکسل‌پایة تصاویر ماهواره‌ای در طبقه‌بندی تیپ‌های جنگل، منابع طبیعی ایران، سال شصتم، شمارة 3، صص. 881-869.
کشاورز، ا.، ابراهیمی، ع.، نقی‌پور، ع.، 1399، مقایسة دقت روش‌های طبقه‌بندی پیکسل‌پایه و شیءگرا در تهیة نقشة تیپ‌های گیاهی (مطالعة موردی: مرجن بروجن)، نشریة علمی‌ـ پژوهشی مرتع، سال چهاردهم، شمارة 2، صص. 285-272.
Adam, H.E., Csaplovics, E. & Elhaja, M.E., 2016, A Comparison of Pixel-Based and Object-Based Approaches for Land Use Land Cover Classification in Semi-Arid Areas, Sudan, In IOP Conference Series: Earth and Environmental Science, 37, P. 012061.
Aksoy, S., Akçay, H.G. & Wassenaar, T., 2010, Automatic Mapping of Linear Woody Vegetation Features in Agricultural Landscapes Using Very High Resolution Imagery, IEEE Trans. Geosci. Remote Sens., 48, PP. 511-522.
Atzberger, C., 2013, Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs, Remote Sens., 5, PP. 949-981.
Belgiu, M. & Drăguţ, L., 2016, Random Forest in Remote Sensing: A Review of Applications and Future Directions, ISPRS J. Photogramm, Remote Sens., 114, PP. 24-31.
Berhane, TM., Lane, CR., Wu, Q., Anenkhonov, OA., Chepinoga, VV., Autrey, BC. & Liu, H., 2017, Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes, Remote Sens., 10, P. 46.
Blaschke, T., 2010, Object Based Image Analysis for Remote Sensing, ISPRS J. Photogramm. Remote Sens., 65, PP. 2-16.
Daryaei, A., Sohrabi, H., Atzberger, C. & Immitzer, M., 2020, Fine-Scale Detection of Vegetation in Semi-Arid Mountainous Areas with Focus on Riparian Landscapes Using Sentinel-2 and UAV Data, Comput Electron Agr, 177, P. 105686.
Diaz-Uriarte, R. & Alvarez De Andres, S., 2006, Gene Selection and Classification of Microarray Data Using Random Forest, BMC Bioinformatics, 7, P. 3.
Duro, DC., Franklin, SE. & Dubé, M.G., 2012, A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery, Remote Sens. Environ., 118, PP. 259-272.
FAO., 2001, Global Forest Resources Assessment 2000, Main Report, Food and Agriculture Organization, Rome, Italy.
Furuya, D.E.G., Aguiar, J.A.F., Estrabis, N.V, Pinheiro, M.M.F., Furuya, M.T.G., Pereira, D.R., Gonçalves, W.N., Liesenberg, V., Li, J., Marcato Junior, J. & …, 2020, A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imager, Remote Sens., 12, P. 4086.
Immitzer, M., Atzberger, C. & Koukal, T., 2012, Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band Worldview-2 Satellite Data, Remote Sens., 4, PP. 2661-2693.
Immitzer, M., Vuolo, F. & Atzberger, C., 2016, First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe, Remote Sens., 8(3), P. 166.
Immitzer, M., Böck, S., Einzmann, K., Vuolo, F., Pinnel, N., Wallner, A. & Atzberger, C., 2018, Fractional Cover Mapping of Spruce and Pine at 1 ha Resolution Combining Very High and Medium Spatial Resolution Satellite Imagery, Remote Sens. Environ., 204, PP. 690-703.
Immitzer, M., Neuwirth, M., Böck, S., Brenner, H., Vuolo, F. & Atzberger, C., 2019, Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data, Remote Sens., 11(22), P. 2599.