فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اخبار و اعلانات

فرم های ضروری نشریه

شرایط و ضوابط ارسال مقاله

راهنمای تنظیم مقاله

ارزیابی و مقایسه دقت منطقه‌ای محصولات جهانی سنجش از دوری در ایران - مطالعه موردی: محصولات پوشش اراضی در استان مازندران

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار گروه جغرافیا و برنامه ریزی شهری دانشکده علوم انسانی و اجتماعی، دانشگاه مازندران، بابلسر، ایران

چکیده

سنجش از دور به‌عنوان یکی از مهم‌ترین منابع داده مکانی در عصر حاضر محسوب می‌شود که روز به روز شاهد توسعه آن در ابعاد مختلف هستیم. انتشار محصولات (پروداکت‌های) جهانی این داده‌ها در سال‌های اخیر با هدف دسترسی و استفاده راحت‌تر متخصصان علوم مکانی یکی از ابعاد این توسعه محسوب می‌شود. محصول پوشش اراضی یکی از این محصولات محسوب می‌شود که سهم بیشتری در استفاده نسبت به سایر محصولات سنجش از دوری دارد. هنگام ارائه این محصولات ویژگی‌های کیفی و کمّی آن‌ها از جمله دقت جهانی آن‌ها نیز منتشر می‌شود. بیان دقت این محصولات به‌صورت جهانی، ارزیابی مجدد دقت آن‌ها را به‌صورت منطقه‌ای برای کاربران این محصولات در مناطق مختلف دنیا لازم و ضروری می‌نماید. در این تحقیق پوشش اراضی سرویس جهانی اراضی برنامه کوپرنیک آژانس فضایی اروپا (CGLS)، محصول پوشش اراضی GlobeLand30 و محصول پوشش اراضی Esri از جمله محصولات جهانی پوشش اراضی هستند که ضمن مقایسه ویژگی‌های اسمی، از لحاظ کمّی برای استفاده به‌صورت منطقه‌ای در کشور (استان مازندران) مورد بررسی و ارزیابی قرار گرفتند. نتایج، دقت منطقه‌ای پوشش اراضی CGLS، GlobeLand30 و Esri را به‌ترتیب برابر با 84، 81 و 75 درصد نسبت به دقت جهانی آن‌ها (80، 83 تا 85 و 86 درصد) نشان می‌دهند. در ارزیابی دقت منطقه‌ای کلاس‌ها، هر سه محصول مورد مطالعه دقتی بالای 90 درصد در کلاس‌های برف و یخ، جنگل، پهنه‌های آبی و ساخت‌وساز انسانی داشته‌اند. برای کلاس اراضی کشاورزی دقتی برابر با 92، 69 و 84 درصد برای پوشش‌های اراضی CGLS، GlobeLand30 و Esri به‌دست آمد. در سه کلاس بوته‌زار، پوشش علفی و تالاب، نتایج دقتی کمتری را نسبت به سایر کلاس‌ها برای هر سه محصول پوشش اراضی نشان می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation and comparison of regional accuracy of global remote sensing products in Iran - Case study of land cover products in Mazandaran Province

نویسنده [English]

  • Qadir Ashournejad

Assistant professor of remote sensing and GIS, Department of geography and urban planning, faculty of humanities and social sciences, University of Mazandaran, Babolsar, Iran

چکیده [English]

Extended Abstract
Introduction
Remote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts in geospatial science is one of the dimensions of this development. The land cover product is one of these products that is used more than other remote sensing products. When presenting these products, their qualitative and quantitative characteristics, including their global accuracy, are also published. Expressing the accuracy of these products globally makes it necessary and necessary to re-evaluate their accuracy regionally for the users of these products in different regions of the world.
Materials & Methods
In this research, the accuracy of the European Space Agency's Copernicus Global Land Service (CGLS), GlobeLand30 and Esri's land cover product were evaluated for regional use in the north of Iran - Mazandaran province. After calculating the area of the classes for each of the land cover products, Pearson's correlation coefficient was used to calculate the correlation between them. For quantitative evaluation, the error matrix was used as one of the most common ways to evaluate the accuracy of land cover products. This method is based on the comparison of classified data and ground reality data. Also, the categorized random sampling method was used to select 1329 evaluation samples in Mazandaran province. For visual evaluation, three areas with dimensions of 6 x 6 km were selected.
Results & Discussion
The regional accuracy evaluation of the studied products shows opposite results compared to the global accuracy of these products. Based on the global accuracy reported for the studied products, the highest accuracy is calculated for the Esri product at 86%, followed by GlobeLand30 and CGLS at 83-85 and 80%. Meanwhile, based on the regional accuracy obtained from the results of this research, the highest regional accuracy for the CGLS product has been calculated at 84% and then for GlobeLand30 and Esri products at 81 and 75%. In evaluating the regional accuracy of the classes, all three studied products (CGLS, GlobeLand30 and Esri) have acceptable accuracy (above 90%) in the classes of snow and ice (100, 100 and 100%), forest (90, 95 and 98 percent), water (96, 94 and 90 percent) and impervious surface (94, 91 and 90 percent). For the agricultural class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers.In the 3 classes of shrubland, Impervious surface and wetland, the accuracy results are less than other classes for all three land cover products and in the amount of (29, 0 and 13 percent), (65, 66 and 42 percent) and (67, 38 and 0 percent).
Conclusion         
By evaluating and comparing the regional accuracy of three CGLS products, GlobeLand30 and Esri, this research answered the question of whether the accuracy stated in global land cover products can be trusted for regional studies and planning. The results show that the regional accuracy of CGLS, GlobeLand30, and Esri are 84, 81, and 75 percent, respectively, compared to their global accuracy (80, 83, 85, and 86 percent). These results show the difference obtained for the Esri product more than the two products CGLS and GlobeLand30. Meanwhile, the remote sensing data used for the Esri product (Sentinel-2 data) and its pixel size (10 meters) are of higher quality and quantity than the other two products. In fact, these results show that only paying attention to the type of data used and the global accuracy is not enough to use products in regional scales and requires evaluations before using them.In addition, by evaluating the classes of each product and comparing them, the need for this evaluation before using these products seems necessary. The results showed that in the evaluation of the regional accuracy of the classes, all three studied products had an accuracy of over 90% in the classes of snow and ice, forest, water areas and human construction. For the agricultural land class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers. In the 3 classes of shrubland, herbaceous cover and wetland, the results show lower accuracy than other classes for all three land cover products. Significant results were also obtained in the visual evaluation, and it seems necessary to pay attention to this evaluation before the applications where it is important to pay attention to a particular class.

کلیدواژه‌ها [English]

  • Remote sensing
  • land cover
  • CGLS
  • GlobeLand30
  • Esri land cover
  • Mazandaran province
  • Iran
مراجع
1- Araujo-Barbosa, C.C.; Atkinson, P.M.; Dearing, J.A., (2015), Remote sensing of ecosystem services: A systematic review. Ecol. Indic.52, 430–443.
2- Ashournejad, Q.; Amiraslani, F.; Kiavarz Moghadam, M.; Toomanian, A., (2019), Assessing the changes of mangrove ecosystem services value in the Pars Special Economic Energy Zone, Ocean & Coastal Management, 179, 104838.
3- Brovelli, Maria Antonia, Monia Elisa Molinari, Eman Hussein, Jun Chen, and Ran Li. (2015). “The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results” Remote Sensing 7, no. 4: 4191-4212.
4- Buchhorn, M., Lesiv, M., Tsendbazar, N. - E., Herold, M., Bertels, L., Smets, B., (2020). Copernicus Global Land Cover Layers—Collection 2. Remote Sensing 2020, 12 (6), 1044. 
5- Chaves, M.E.D.; Picoli, M.C.A.; Sanches, I.D. (2020). Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sens., 12, 3062.
6- Chen J., Ban Y., Li S. (2014), Open access to Earth land-cover map [J]. Nature, 2014, 514(7523): 434-434.
7- Chen J, Cao X, Peng S, Ren H. (2017). Analysis and applications of GlobeLand30: A review. Isprs Int J Geo-Inf, 6: 230.
8- Chen J, Chen J, Liao A, Cao X, Chen L, Chen X, He C, Han G, Peng S, Lu M, Zhang W, Tong X, Mills J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. Isprs-J Photogramm Remote Sens, 103: 7–27.
9- Congalton, R.G., (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35–46.
10- Delgado-Artés, R.; Garófano-Gómez, V.; Oliver-Villanueva, J.; Rojas-Briales, E. (2022). Land use/cover change analysis in the Mediterranean region: A regional case study of forest evolution in Castelló (Spain) over 50 years. Land Use Policy, 114, 105967.
11- Ding, Y.; Yang, X.; Wang, Z.; Fu, D.; Li, H.; Meng, D.; Zeng, X.; Zhang, J. (2022). A Field-Data-Aided Comparison of Three 10 m Land Cover Products in Southeast Asia. Remote Sensing, 14, 5053
12- Gao, Y.; Skutsch, M.; Paneque-Gálvez, J.; Ghilardi, A. (2020). Remote Sensing of Forest Degradation: A Review. Environ. Res. Lett., 15, 103001.
13- Ghorbanian, A.; Kakooei, M.; Amani, M.; Mahdavi, S.; Mohammadzadeh, A.; Hasanlou, M. (2020). Improved Land Cover Map of Iran Using Sentinel Imagery within Google Earth Engine and a Novel Automatic Workflow for Land Cover Classification Using Migrated Training Samples. ISPRS J. Photogramm. Remote Sens., 167, 276–288.
14- Guo, Huadong, Michael F. Goodchild, and Alessandro Annoni. (2020). Manual of Digital Earth. Singapore: Springer.
15- Guo, Huadong, Wenxue Fu, and Guang Liu. (2019). Scientific Satellite and Moon-Based Earth Observation for Global Change. Singapore: Springer.
16- Huan V D, (2022), Accuracy assessment of land use land cover LULC (2020). (ESRI) data in Con Dao island, Ba Ria – Vung Tau province, Vietnam, IOP Conference Series: Earth and Environmental Science.
17- Kang, J.; Sui, L.; Yang, X.; Liu, Y.;Wang, Z.;Wang, J.; Yang, F.; Liu, B.; Ma, Y. (2019). Sea Surface-Visible Aquaculture Spatial-Temporal Distribution Remote Sensing: A Case Study in Liaoning Province, China from 2000 to 2018. Sustainability, 11, 7186.
18- Kang, J.;Wang, Z.; Sui, L.; Yang, X.; Ma, Y.;Wang, J. (2020). Consistency analysis of remote sensing land cover products in the tropical rainforest climate region: A case study of Indonesia. Remote Sens., 12, 1410.
19- Kavvada, A.; Metternicht, G.; Kerblat, F.; Mudau, N.; Haldorson, M.; Laldaparsad, S.; Friedl, L.; Held, A.; Chuvieco, E. (2020). Towards Delivering on the Sustainable Development Goals Using Earth Observations. Remote Sens. Environ., 247, 111930.
20- Liu, L.; Zhang, X.; Gao, Y.; Chen, X.; Shuai, X.; Mi, J. (2021). Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects. J. Remote Sens., 2021, 5289697.
21- Lyons, M. B.; Keith, D. A.; Phinn, S. R.; Mason, T. J.; Elith, J., (2018). A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sens. Environ. 208, 145–153.
22- Nasiri, Vahid, Azade Deljouei, Fardin Moradi, Seyed Mohammad Moein Sadeghi, and Stelian Alexandru Borz. (2022). “Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods” Remote Sensing 14, no. 9: 1977.
23- Phiri, D.; Simwanda, M.; Salekin, S.; Nyirenda, V.R.; Murayama, Y.; Ranagalage, M. (2020). Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sens., 12, 2291.
24- Soliman, M.; Morsy, M.M.; Radwan, H.G. (2022). Assessment of Implementing Land Use/Land Cover LULC 2020-ESRI Global Maps in 2D Flood Modeling Application. Water, 14, 3963.
25- Sun, W.Y.; Ding, X.T.; Su, J.B.; Mu, X.M.; Zhang, Y.Q.; Gao, P.; Zhao, G.J. (2022). Land use and cover changes on the Loess Plateau: A comparison of six global or national land use and cover datasets. Land Use Policy, 119, 106165.
26- Tsendbazar, N.; Herold, M.; Li, L.; Tarko, A.; De Bruin, S.; Masiliunas, D.; Lesiv, M.; Fritz, S.; Buchhorn, M.; Smets, B.; et al. (2021). Towards Operational Validation of Annual Global Land Cover Maps. Remote Sens. Environ., 266, 112686.
27- Venter, Z.S.; Barton, D.N.; Chakraborty, T.; Simensen, T.; Singh, G. (2022). Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sens, 14, 4101
28- Wang, J.; Sui, L.; Yang, X.; Wang, Z.; Liu, Y.; Kang, J.; Lu, C.; Yang, F.; Liu, B. (2019). Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery. Sensors, 19, 1221.
29- Wang, J.; Yang, X.;Wang, Z.; Cheng, H.; Kang, J.; Tang, H.; Li, Y.; Bian, Z.; Bai, Z. (2022). Consistency Analysis and Accuracy Assessment of Three Global Ten-Meter Land Cover Products in Rocky Desertification Region—A Case Study of Southwest China. ISPRS Int. J. Geo-Inf., 11,202.
 30- Wang, Q.; Blackburn, G.A.; Onojeghuo, A.O.; Dash, J.; Zhou, L.; Zhang, Y.; Atkinson, P.M. (2017). Fusion of Landsat 8 OLI and Sentinel-2 MSI Data. IEEE Trans. Geosci. Remote Sens.  55, 3885–3899.
31- Xu, L.; Herold, M.; Tsendbazar, N.-E.; Masiliunas, D.; Li, L.; Lesiv, M.; Fritz, S.; Verbesselt, J. (2022). Time Series Analysis for Global Land ¯ Cover Change Monitoring: A Comparison Across Sensors. Remote Sens. Environ., 271, 112905.
32- Yang, Liping, Joshua Driscol, Sarigai Sarigai, Qiusheng Wu, Haifei Chen, and Christopher D. Lippitt. (2022). “Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review” Remote Sensing 14, no. 14: 3253.
33- You, H.; Tang, X.; Deng, W.; Song, H.; Wang, Y.; Chen, J. (2022). A Study on the Difference of LULC Classification Results Based on Landsat 8 and Landsat 9 Data. Sustainability, 14, 13730.
فصلنامه علمی- پژوهشی اطلاعات جغرافیایی « سپهر»
دوره 32، شماره 127 - شماره پیاپی 127
آذر 1402
صفحه 95-115
فایل ها
هم رسانی
ارجاع به این مقاله
آمار