نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی

پایش سریع تغییرات پوشش مانگرو با استفاده از الگوریتم ماشین بردار پشتیبان در پلتفرم محاسباتی گوگل ارث انجین (مطالعه موردی: جنگل‌های حرا قشم)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز

2 دانشجوی کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده جغرافیا؛ دانشگاه تهران

3 گروه سنجش از دورو GIS دانشگاه تبریز

4 گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامه ریزی و علوم محیطی، دانشگاه تبریز

چکیده

جنگل‌های مانگرو از مهم­ترین جنگل‌های مناطق حاره‌ای و نیمه حاره‌ای هستند که در امتداد سواحل جزر و مدی یافت می‌شوند. این جنگل‌ها یکی از آسیب ­پذیرترین اکوسیستم‌های دریایی به‌شمار می‌آیند که طی چندین سال تحت تأثیر فعالیت‌های انسانی دستخوش تغییرات می‌شوند. بنابراین پایش سریع و به موقع آن‌ها بایست در اهداف برنامه‌ریزان زیست محیطی قرار گیرد. امروزه سنجش از دور به‌عنوان ابزاری قدرتمند و به‌روز جهت پایش سریع جنگل‌ها شناخته شده است. هدف از این مطالعه بررسی تغییرات 30 ساله جنگل‌های مانگرو دماغه شمالی جزیره قشم طی سال‌های 1986، 2000 و 2020 است. براساس اهداف پژوهشی نقشه پوششی مانگرو با اعمال الگوریتم ماشین بردار پشتیبان بروی تصاویر لندست 5 و 8 در محیط آنی گوگل ارث انجین استخراج گردید. نتایج یررسی تغییرات 30 ساله حاکی از آن بود که جنگل‌های مانگرو طی ادوار پژوهشی (1986 تا 2020) روندی افزایشی داشته است بدین صورت‌که مساحت جنگل‌های مانگرو در سال 1986 از 78/5130 هکتار به 87/5471 هکتار در سال 2000 رسیده است که در حقیقت به میزان 23/6% مساحت جنگل‌ها افزایش داشته است. اما مساحت این جنگل‌ها در سال 2020 به 13/5967 هکتار رسیده است که این میزان نسبت به مساحت یاد شده در سال 1986 به 02/14%  افزایش یافته است. بطور کلی نتایج این پژوهش نشان می‌دهد که افزایش جنگل‌های مانگرو طی ادوار مطالعاتی نتیجه‌ی جنگل‌کاری مصنوعی توسط بومیان منطقه به منظور حفظ این اکوسیستم دریایی در منطقه بوده است که به تبع آن باعث رونق صنعت گردشگری در استان هرمزگان و حتی منطقه قشم شده است. آنچه که در پژوهش کنونی باعث تسریع فرایند پردازشی گردید، استفاده از الگوریتم ماشین بردار پشتیبان و سامانه آنلاین گوگل ارث انجین است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Rapid monitoring of mangrove cover changes using support vector machine algorithm in Google Earth Engine computing platform (Case study: Qeshm mangrove forests)

نویسندگان [English]

  • Mostafa Mahdavifard 1
  • Shahin Jafari 2
  • Khalil Valizadeh_Kamran 3
  • Sadra Karimzadeh 4

1 Master graduate from tabriz U, Department of RS & GIS

2 MSc. student of Remote Sensing and Geographic Information System, Faculty of Geography; University of Tehran

3 Associate Prof Department of Remote Sensing and GIS. Faculty of Planning and Environmental Sciences. University of Tabriz

4 Assistant Professor of Remote Sensing and Geographic Information System, Faculty of Planning and Environmental Sciences, University of Tabriz

چکیده [English]

Mangrove forests are one of the most important tropical forests found along the tidal coast. These forests are one of the most vulnerable marine ecosystems that are changing over the years due to human activities. Therefore, their prompt and timely monitoring should be the goal of environmental planners. The aim of this study was to investigate the 30-year changes of mangrove forests in the northern part of Qeshm Island during 1986, 2000 and 2020. Based on the research objectives, the mangrove cover map was extracted by applying SVM algorithm to Landsat 5 and 8 images in the instantaneous environment of Google Earth Engine. The results of 30 years of change showed that mangrove forests during the research periods (1986 to 2020) have an increasing trend, so that the area of mangrove forests in 1986 increased from 5130.78 hectares to 5471.87 hectares in 2000, which in fact amounted to 6.23% The area of forests has increased. However, the area of these forests reached 5967.13 hectares in 2020, which is an increase of 14.02% compared to the mentioned area in 1986. the results of this study show that the increase of mangrove forests during the study periods has been the result of artificial afforestation by the natives in order to preserve this marine ecosystem in the region, which has led to the prosperity of tourism industry in Qeshm region. What has accelerated the processing process in the current study is the use of the SVM algorithm and the Google Earth Engine system.

کلیدواژه‌ها [English]

  • Mangrove Forest
  • Change Detection
  • Google Earth Engine
  • SVM Algorithm
  • Landsat
  • Qeshm Island
Adi, W., & Sari, S. P. (2016). Detection of mangrove distribution in Pongok Island. Procedia Environmental Sciences, 33, 253-257.
Alatorre, L. C., Sánchez-Andrés, R., Cirujano, S., Beguería, S., & Sánchez-Carrillo, S. (2011). Identification of mangrove areas by remote sensing: The ROC curve technique applied to the northwestern Mexico coastal zone using Landsat imagery. Remote Sensing, 3(8), 1568-1583.
Bihamta, N., Soffianian, A. R., Fakheran, S., & Pourmanafi, S. (2019). Incorporating CART algorithm and i for mapping Mangrove using Landsat 8 imagery. Journal of Forest Research and Development, 5(4), 557-569.
Bihamta Toosi, N., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & T Waser, L. (2020). Land Cover Classification in Mangrove Ecosystems Based on VHR Satellite Data and Machine Learning—An Upscaling Approach. Remote Sensing, 12(17), 2684.
Brown, M.I., Pearce, T., Leon, J., Sidle, R., & Wilson, R. (2018). Using remote sensing and traditional ecological knowledge (TEK) to understand mangrove change on the Maroochy River, Queensland, Australia. Applied Geography, 94, 71-83.
Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., . . . Wu, Z. (2017). A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 104-120.
Chen, P., Liew, S. C., Lim, R., & Kwoh, L. K. (2011). Mapping coastal ecosystems of an offshore landfill island using WorldView-2 high resolution satellite imagery. Paper presented at the Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, Australia.
Dan, T., Chen, C., Chiang, S., & Ogawa, S. (2016). MAPPING AND CHANGE ANALYSIS IN MANGROVE FOREST BY USING LANDSAT IMAGERY. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 3(8).
Jafarnia, S., Oladi, J., Hoojati, S., & Mir Akhor Loo, K. (2016). Status and change detection of Mangrove forest in Qeshm Island using satellite imagery from 1988 to 2008. Journal of Environmental Science and Technology, 18(1), 177-191. (In Persian)
Jensen, J. R. (1986). Introductory digital image processing: a remote sensing perspective. Retrieved from
Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215-221.
Jia, M., Zhang, Y., Wang, Z., Song, K., & Ren, C. (2014). Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data. International Journal of Applied Earth Observation and Geoinformation, 33, 226-231.
Jones, T.G., Glass, L., Gandhi, S., Ravaoarinorotsihoarana, L., Carro, A., Benson, L., . . . Cripps, G. (2016). Madagascar’s mangroves: Quantifying nation-wide and ecosystem specific dynamics, and detailed contemporary mapping of distinct ecosystems. Remote Sensing, 8(2), 106.
Hoa, N. H. (2016). Using Landsat imagery and vegetation indices differencing to detect mangrove change: A case in Thai Thuy district, Thai Binh province. J. For. Sci. Technol, 5, 59-66.
Gilman, E. L., Ellison, J., Duke, N. C., & Field, C. (2008). Threats to mangroves from climate change and adaptation options: a review. Aquatic botany, 89(2), 237-250.
Giri, C., Long, J., Abbas, S., Murali, R. M., Qamer, F. M., Pengra, B., & Thau, D. (2015). Distribution and dynamics of mangrove forests of South Asia. Journal of environmental management, 148, 101-111.
Giri, C., Pengra, B., Zhu, Z., Singh, A., & Tieszen, L. L. (2007). Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine, coastal and shelf science, 73(1-2), 91-100.
Kanniah, K. D., Sheikhi, A., Cracknell, A. P., Goh, H. C., Tan, K. P., Ho, C. S., & Rasli, F. N. (2015). Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia. Remote Sensing, 7(11), 14360-14385.
Khoorani, A., Biniaz, M., & Amiri, H. R. (2015). Investigating the changes of mangrove forests between Khamir port and Gheshmiseland and its correlation with climatic elements. Journal of Aquatic Ecology, 5(2), 100-111. (In Persian)
Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V., & Dech, S. (2011). Remote sensing of mangrove ecosystems: A review. Remote Sensing, 3(5), 878-928.
Mafi Gholami, D., Baharlouii, M., & Mahmoudi, B. (2018). Erosion and accretion monitoring in mangrove forests using remote sensing and Digital Shoreline Analysis System (DSAS)(Case study: Hara Biosphere reserve). Journal of Environmental Studies, 43(4), 633-646. doi:10.22059/jes.2018.225288.1007381 (In Persian)
Mahendra, W., Jamaluddin , I., & Kamal , M. (2019). Mangroves Change Detection using Support Vector Machine Algorithm on Google Earth Engine (A Case Study in Part of Gulf of Bone, South Sulawesi, Indonesia). The 40th Asian Conference on Remote Sensing, 1-8.
Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: an introduction: John Wiley & Sons.
Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. T. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19, e00662.
Mehrabian, A., Naqinezhad, A., Mahiny, A. S., Mostafavi, H., Liaghati, H., & Kouchekzadeh, M. (2009). Vegetation mapping of the Mond protected area of Bushehr province (south‐west Iran). Journal of integrative plant biology, 51(3), 251-260.
Midekisa, A., Holl, F., Savory, D. J., Andrade-Pacheco, R., Gething, P. W., Bennett, A., & Sturrock, H. J. (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PloS one, 12(9), e0184926.
Olagoke, A., Proisy, C., Féret, J.-B., Blanchard, E., Fromard, F., Mehlig, U., . . . Berger, U. (2016). Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data. Trees, 30(3), 935-947.
Pham, L. T., & Brabyn, L. (2017). Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS Journal of Photogrammetry and Remote Sensing, 128, 86-97.
Pimple, U., Simonetti, D., Sitthi, A., Pungkul, S., Leadprathom, K., Skupek, H., . . . Towprayoon, S. (2018). Google earth engine based three decadal landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand.
Shi, T., Liu, J., Hu, Z., Liu, H., Wang, J., & Wu, G. (2016). New spectral metrics for mangrove forest identification. Remote Sensing Letters, 7(9), 885-894.
Son, N.-T., Chen, C.-F., Chang, N.-B., Chen, C.-R., Chang, L.-Y., & Thanh, B.-X. (2014). Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), 503-510.
Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. T. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19, e00662.
Viana, C. M., Oliveira, S., Oliveira, S. C., & Rocha, J. (2019). Land use/land cover change detection and urban sprawl analysis Spatial modeling in GIS and R for earth and environmental sciences (pp. 621-651): Elsevier.
Wang, L., Silván-Cárdenas, J. L., & Sousa, W. P. (2008). Neural network classification of mangrove species from multi-seasonal Ikonos imagery. Photogrammetric Engineering & Remote Sensing, 74(7), 921-927.
Wang, T., Zhang, H., Lin, H., & Fang, C. (2016). Textural–spectral feature-based species classification of mangroves in Mai Po Nature Reserve from Worldview-3 imagery. Remote Sensing, 8(1), 24.
Zhang, C., Kovacs, J. M., Liu, Y., Flores-Verdugo, F., & Flores-de-Santiago, F. (2014). Separating mangrove species and conditions using laboratory hyperspectral data: A case study of a degraded mangrove forest of the Mexican Pacific. Remote Sensing, 6(12), 11673-11688.
Zhang, H., Wang, T., Liu, M., Jia, M., Lin, H., Chu, L., & Devlin, A. T. (2018). Potential of combining optical and dual polarimetric SAR data for improving mangrove species discrimination using rotation forest. Remote Sensing, 10(3), 467.
نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی
دوره 2، شماره 2 - شماره پیاپی 2
خرداد 1401
صفحه 36-23