پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,112,837 |
| تعداد دریافت فایل اصل مقاله | 97,216,703 |
ارزیابی صحت روش‌های سنجش از دور در استخراج و پایش تغییرات پهنۀ آبی دریاچۀ زریبار | ||
| اکوهیدرولوژی | ||
| دوره 9، شماره 3، مهر 1401، صفحه 505-516 اصل مقاله (1.1 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2023.342056.1632 | ||
| نویسندگان | ||
| کریم سلیمانی* 1؛ شادمان درویشی2؛ فاطمه شکریان3 | ||
| 1استاد، مرکز سنجشازدور، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| 2کارشناس ارشد، دانشکدۀ علوم محیطی، مؤسسۀ آموزش عالی آبان هراز آمل | ||
| 3استادیار، گروه مهندسی آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| چکیده | ||
| منابع آب نقش بسیار مهمی در زندگی انسان، گیاهان و جانوران ایفا میکند. استخراج و تعیین میزان تغییرات نواحی آبی میتواند در پیشبینی بسیاری از مشکلات کارساز باشد. امروزه، روشها و الگوریتمهای زیادی با استفاده از دادههای سنجشازدور برای پایش تغییرات آب معرفی شده است. بنابراین، بررسی صحت این روشها بسیار لازم و ضروری است. در همین راستا، هدف مطالعۀ حاضر ارزیابی صحت روش‌های سنجشازدور برای پایش آب دریاچۀ زریبار طی یک دورۀ 33 ساله (1984 تا 2017) است. از اینرو، بعد از تصحیحات تصاویر پهنۀ دریاچۀ زریبار با استفاده از الگوریتمهای حداکثر احتمال، حداقل فاصله، فاصلۀ ماهالانویی، ماشین بردار پشتیبان و شاخصهای NDWI، MNDWI و AWEI در محیط نرمافزارهای ENVI5.3 و ArcGIS10.4 استخراج شد و سپس، اعتبارسنجی روشها با استفاده از نقاط کنترل زمینی انجام گرفت. مطابق نتایج بهدستآمده تمامی روشها از صحت کلی80 درصد برخوردار هستند، اما الگوریتمهای ماشین بردار پشتیبان و حداکثر احتمال با صحت کلی بیش از 90 درصد صحت بیشتری دار‌ند. همچنین، نتایج بررسی تغییرات آب دریاچه نشان میدهد طی دورۀ 1984 تا 2017 مساحت آب دریاچه بر اساس الگوریتمهای ماشین بردار پشتیبان و حداکثر احتمال به‌ترتیب 46/738 هکتار (83/46 درصد) و 06/613 هکتار (45/42 درصد) کاهش یافته است. همچنین، در همین دوره بر اساس الگوریتمهای ماشین بردار پشتیبان و الگوریتم حداکثر احتمال به‌ترتیب 35/47 درصد و 22/36 درصد به مساحت نیزارهای سطح دریاچه افزوده شده است. با توجه به روند کاهشی آب دریاچه و به‌خصوص روند افزایشی نیزارهای سطح دریاچه لزوم برنامهریزی صحیح برای جلوگیری از هدررفت آب و از بین بردن نیزارها در این ناحیه بیش از پیش احساس میشود. | ||
| کلیدواژه‌ها | ||
| تغییرات آب؛ شاخصهای طیفی؛ الگوریتمهای نظارتشده؛ لندست؛ دریاچۀ زریبار | ||
| عنوان مقاله [English] | ||
| Accuracy assessment of remote sensing methods for extraction and monitoring of Zrebar Lake, Iran | ||
| نویسندگان [English] | ||
| Karim Solaimani1؛ Shadman Darvishi2؛ Fatemeh Shokrian3 | ||
| 1Professor, Remote Sensing Centre, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 2M.Sc. Faculty of Environmental Sciences, Aban Haraz Institute of Higher Education, Amol, Iran | ||
| 3Assistant Professor, Dept. of Watershed Management, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| چکیده [English] | ||
| Water resources play a very important role in the life of humans, plants and animals. Extracting and monitoring the extent of changes in water areas can be effective in predicting many problems. Today, many methods and algorithms have been introduced using remote sensing data to monitor water changes, so it is very necessary to study the accuracy of these methods. In this regard, the aim of the present study is to evaluate the accuracy of remote sensing methods for monitoring the water of Zarebar Lake over a period of 33 years (1984 to 2017). Therefore, after images preprocessing, water body of Zaribar Lake was extracted using maximum likelihood algorithms, minimum distance, Mahalanobis distance, support vector machine and NDWI, MNDWI and AWEI indices in ENVI5.3 and ArcGIS10.4 softwares, then these method was validated using ground control points. According to the results, all methods have an accuracy of 80%, but the support vector machine and maximum likelihood algorithms have a higher accuracy with a kappa coefficient above 85%. Also, the results of the study of lake water changes show that during the period 1984 to 2017, the water body based on support vector machine and maximum likelihood algorithms has decreased by 738.46/ha (46.83 percent) and 613.06/ha (42.45 percent), respectively. Also, in the same period, based on support vector machine algorithms and maximum likelihood algorithm 47.35% and 36.22% have been added to the reed bed on the lake. Due to the decreasing trend of lake and invasion of vegetation, it is necessary to consider a plane to prevent the lake water body for future. | ||
| کلیدواژه‌ها [English] | ||
| Water changes, spectral indices, supervised algorithms, Landsat, Zaribar Lake | ||
| مراجع | ||
|
[1]. Moradi M, Sahebi M, Shokri M. Modified optimization water index (MOWI) for landsat 8 OLI/TIRS. the International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Tehran's Joint ISPRS Conferences of GI Research. 2017; 185-190.
[2]. Brezonik P, Menken KD, Bauer M. Landsat based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM). Lake and Reservoir Management. 2005; 21(4):373-382
[3]. Prasad P R, Rajan K, Bhole V, Dutt C. Is rapid urbanization leading to loss of water bodies. Journal of Spatial Science. 2009; 2: 43-52.
[4]. Giardino C, Bresciani M, Villa P, Martinelli, A. Application of remote sensing in water resource management: the case study of Lake Trasimeno, Italy. Water Resources Management. 2010; 24: 3885-3899.
[5]. Ridd MK, Liu J. A comparison of four algorithms for change detection in an urban environment. Remote Sensing of Environment. 1998; 63: 95-100
[6]. Rokni R, Ahmad A, Selamat A, Hazini Sh. Water Feature Extraction and Change detection using Multi temporal Landsat Imagery. journal of Remote Sensing. 2014; 6: 4173-4189.
[7]. Sharma RC, Tateishi R, Hara K, Viet Nguyen L. Developing Superfine Water Index (SWI) for global water cover mapping using MODIS data, journal of Remote Sensing. 2015; 7: 13807-13841.
[8]. Shen L, Li C. Water Body Extraction from Landsat ETM+ Imagery Using Adaboost Algorithm. In Proceedings of 18th International Conference on Geo informatics Beijing China. 2010; 1:1-18.
[9]. Tomar P, Singh SK, Kanga S, Pattanaik, A. Water Bodies mapping and monitoring using high-resolution satellite images, Sustainability Agri Food Environmental Research. 2021; 11(1): 1-18.
[10]. Wang Y, Ruan R, She Y, Yan M. Extraction of water information based on RADARSAT SAR and Landsat ETM+. Procedia Environmental Sciences. 2011; 10: 2301-2306.
[11]. Xu H. Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing. 2006; 27: 3025-3033.
[12]. Gautam V K, Gaurav K P, Murugan P, Annadurai M. Assessment of Surface Water Dynamicsin Bangalore using WRI, NDWI, MNDWI, Supervised Classification and K-T Transformation. Aquatic Procedia. 2014; 4:739-746
[13]. Zhang FF, Li J, Shen Q, Zhang B, Ye H, Wang Sh, et al.. Dynamic Threshold Selection for the Classification of Large Water Bodies within Landsat-8 OLI Water Index Images. Preprints. 2016; 1: 1- 18
[14]. Elsahabi M, Negm A, EI Tahan MH. Performances Evaluation of surface water Area Extraction Technique Using Landsat ETM+ Data: Case Study Aswan High Dam Lake (AHDL). procedia Technology. 2016; 22:1205-1212.
[15]. Manjula TR, Samyuktha SS, Navya G, Priyanka S, Reddy MP, Garudachar, R. Mapping and Monitoring of Water Bodies Using Sentinel 1A Images. Advances in Intelligent Systems and Computing. 2021; 173:193-201.
[16]. Șerban C, Maftei C, Dobrică G. Surface Water Change Detection via Water Indices and Predictive Modeling Using Remote Sensing Imagery: A Case Study of Nuntasi-Tuzla Lake, Romania. Water. 2022; 14(4), 556.
[17]. Riyaz Khan NH, Study of Fluctuations in Surface Area of Lake Haramaya using NDWI and MNDWI Methods. Journal of Geospatial Information Science and Engineering (JGISE). 2022; 5(1): 36-41.
[18]. Li W, Zhang W, Li Z, Wang Y, Chen H, Gao H, Zhou Z, Hao J, Li Ch, Wu X. A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model. European Journal of Remote Sensing, 2022; 55(1): 303-312
[19]. Rasouli A A, Abbasian S, Jahanbakhsh S. Monitoring of Urmia Lake Water Surface Fluctuations by Processing of Multi- Sensors and Multi-Temporal Imageries. The Journal of Spatial Planning. 2008; 12 (2): 53-71. [Persian]
[20]. Abedini M, Sotoudehpour A. Detection of lakes changes trends with using geography information system (GIS) and Remote sensing (Rs).case study: tectonically Zarivar Lake. Journal of natural of Geography. 2017; 10(35): 45-60. [Persian]
[21]. Khosravian M, Entezari A, Rahmani A, Baaghide M. Monitoring the Disturbance of Lake District Water Level Changes Using Remote Sensing Indices. Journal of Hydrogeomorphology. 2018; 4(13): 99-120. [Persian]
[22]. Dastranj H, Tavakoli F, Soltanpour. Investigating the water level and volume variations of Lake Urmia using satellite images and satellite altimetry. The Journal of "Geographical Data (SEPEHR). 2018; 27(107):149-163. [Persian]
[23]. Hajarian MH, Atarchi S, Hamzeh H. Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing data. Physical Geography Research. 2021; 53(3): 365-380. [Persian]
[24]. Barari MH, BagherI A, Hashemi SM. Analysis of the issues of Lake Zrêbar in a context of Integrated Water Resources Management using a stakeholders' participatory approach in a basin scale. Iran-Water Resources Research. 2016; 12(2): 1-12. [Persian]
[25]. Sarp G, Ozcelik M. Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey. Journal of Taibah University for Science. 2017;11(3): 381-391
[26]. Li J, Ma R, Cao Z, Xue K, Xiong J, Hu M, Feng X. Satellite Detection of Surface Water Extent: A Review of Methodology. Water. 2022; 14(7), 1148
[27]. Feyisa GL, Meilby H, Fensholt R, Proud SR. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment. 2014; 114: 23-35
[28]. Danyu Q, Jinhui Z, Han L, Lei D. Application of Water Extraction Methods from Landsat Imagery for Different Environmental Background. Journal of Geo-information Science. 2021; 23(4): 710-722.
[29]. Pal S, Ziaul S. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science. 2017; 20: 125-145
[30]. Roy A, Inamdar AB. Multi-temporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy. Heliyon. 2019; 5(4): e01478.
[31]. Jensen JR. Introductory Digital Image Processing A Remote Sensing Perspective. 4th ed. London: Pearson publisher; 2015.
[32]. Kandrika S, Roy PS. Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach. International Journal of Applied Earth Observation and Geo information. 2008; 10: 186-193
[33]. Mather P M, Koch, M. Computer Processing of Remotely Sensed Images An Introduction. 4th Ed. New York: John Willley & Sons press; 2010.
[34]. Vapink VN. The nature of statistical learning theory. 2nd ed. New York: springer; 2000.
[35]. Ceccato P, Flasse S, Tarantola S, Jacquemond S, Gregoire JM. Detecting vegetation water content using reflectance in the optical domain. Remote Sensing of Environment. 2001; 77: 22-33.
[36]. Zhai K, Wu X, Yuanwei Q, Du P. Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Journal of Geo-spatial Information Science. 2015; 18: 32-42.
[37]. Fang-fang Z, Bing Z, Jun-sheng L, Qian S, Yuan-feng W, Yang S. Comparative analysis of automatic water identification method based on multispectral Remote Sensing. Procedia Environmental Sciences. 2011; 11: 1482-1487.
[38]. Feyisa G L, Meilby H, Fensholt R, Proud S R. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment. 2014; 140: 23-35.
[39]. Darvishi Sh, Solaimani K, Rashidpour M. Impact of vegetation indices and urban surface characteristics on land surface temperature changes (Case study: Sanandaj city). RS & GIS for Natural Resources. 2019; 10(1): 17-35. [Persian]
[40]. Tilahun A, Islam Z. Google Earth for land use land cover change detection in the case of gish abbay sekela, west Gojjam, Amhara State, Ethiopia. International Journal of Advancement in Remote Sensing GIS and Geography. 2015; 3: 80-87.
[41]. Mather P, Tso B. Classification methods for remotely sensed Data. 2nd ed. New York: CRC Press Taylor & Francis; 2009.
[42]. Smits PC, Dellepiane SG, Schowengerdt RA. 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. 1999; 20: 1461-1486.
[43]. Ji L, Geng X, Sun K, Zhao Y, Gong P. Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery. Water. 2015; 7: 794-817. | ||
|
آمار تعداد مشاهده مقاله: 317 تعداد دریافت فایل اصل مقاله: 243 |
||