پایش تغییرات کاربری اراضی با استفاده از تصاویر ماهواره‌ای و مدل طبقه بندی شیء محور (نمونۀ موردی: دشت مهران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد در حفاظت آب و خاک، گروه مرتع و آبخیزداری، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران

2 دانشیار، گروه مرتع و آبخیزداری، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران

3 دکتری بیابان‌زدایی، گروه مدیریت مناطق خشک و بیابانی، دانشکده منابع طبیعی و کویرشناسی، دانشگاه یزد، یزد، ایران

4 استادیار، گروه مرتع و آبخیزداری، دانشکده کشاورزی، دانشگاه ایلام، ایلام، ایران

چکیده

تغییرات کاربری و پوشش اراضی، از مهم­ترین فرآیندهای محیط­زیستی–اجتماعی است که بر اکوسیستم­ها، تنوع­زیستی و تأمین خدمات اکوسیستمی تأثیرمی­گذارد. افزایش شهرنشینی، گسترش کشاورزی و تبدیل مراتع به اراضی زراعی، از مهم­ترین محرک­های تغییر کاربری هستند که می­توانند به تخریب اراضی و کاهش پایداری منابع منجرشوند؛ از این­رو، پایش مستمر و دقیق تغییرات کاربری پوشش اراضی، برای برنامه­ریزی و مدیریت منابع ضروری است. هدف از پژوهش حاضر، بررسی تغییرات کاربری اراضی دشت مهران در استان ایلام، با استفاده از تصاویر ماهواره‌ای لندست شامل: لندست 5 برای سال 1375، لندست 7 برای سال­های 1381 و 1389 و لندست 8 برای سال 1401 با استفاده از روش طبقه­بندی شیء­محور می­باشد. برای نشان­دادن نتایج طبقه­بندی، از برخی شاخص­ها مانند تجزیه­وتحلیل مؤلفة اصلی، شاخص نرمال­شدة تفاوت پوشش گیاهی و پایش تغییرات استفاده شد. نتایج نشان­داد اراضی ­بدون پوشش با دقت تولیدکننده و استفاده­کننده بالای 99 درصد مربوط به سال 1381 به­دست آمده‌اند که نشان­دهندة قدرت تفکیک طیفی بالایی برای داده­های سنجش­ازدور برای این کاربری می­باشد. ثانیاً، با توجه به نتایج، مشاهده شد که کمترین دقت تولیدکننده و استفاده­کننده مربوط به کاربری اراضی ­بدون ­پوشش بوده است که به­ترتیب 79 و 77 درصد برای سال 1389 می‌باشد. همچنین سال 1401 با دقت کل 28/90 و ضریب کاپای 89/86 درصد، بیشترین دقت را در طبقه‌بندی کاربری اراضی منطقة مورد مطالعه داشته است. بیشترین تغییرات مساحت کاربری اراضی در این منطقه، مربوط به کاربری­های کشاورزی و مرتع متوسط و فقیر می­باشد. نتایج به­دست­آمدة شاخص نرمال­شدة تفاوت پوشش گیاهی از سال 1375 تا 1401، نشان­دهندة افزایش پوشش گیاهی در سال 1401 به­دلیل گسترش سطح اراضی کشاورزی در این سال می­باشد. پایش ­تغییرات نشان­داد که بیشترین تغییرات مربوط به ­تبدیل مرتع­ متوسط به اراضی­کشاورزی و مرتع فقیر است؛ از این­رو نتایج حاصله می­تواند در ارزیابی اراضی، مطالعات زیست­محیطی، برنامه­ریزی و مدیریت یکپارچه به­منظور بهره‌برداری صحیح از منابع طبیعی و کاهش تخریب این منابع با ارزش، مورد استفاده قرارگیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Monitoring Land Use Changes Using Satellite Images and an Object-Based Classification Model (Case Study: Mehran Plain)

نویسندگان [English]

  • Amin Mohammadi 1
  • Marzban Faramarzi 2
  • Hasan Fathizad 3
  • Reza Omidipour 4
1 MSc in Soil and Water Conservation, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.
2 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.
3 PhD in Combating Desertification, Department of management arid and desert regions, College of Natural Resources and Desert, Yazd University, Yazd, Iran.
4 Assistant Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.
چکیده [English]

Land use and land cover (LULC) changes are among the most significant environmental–socioeconomic processes affecting ecosystems, biodiversity, and the provision of ecosystem services. Urban expansion, agricultural development, and the conversion of rangelands into croplands are major drivers of land use change that may lead to land degradation and reduced resource sustainability. Therefore, continuous and accurate monitoring of LULC changes is essential for resource planning and management. The present study aims to investigate land use changes in the Mehran Plain, Ilam Province, using Landsat satellite imagery including Landsat 5 (1996), Landsat 7 (2002 and 2010), and Landsat 8 (2022) through an object-based classification approach. To present and analyze the classification results, indices such as Principal Component Analysis (PCA), the Normalized Difference Vegetation Index (NDVI), and change detection techniques were employed. The results indicated that barren lands achieved producer’s and user’s accuracies exceeding 99% in 2002, demonstrating high spectral separability of remote sensing data for this class. Conversely, the lowest producer’s and user’s accuracies were observed for barren lands in 2010, with values of 79% and 77%, respectively. The highest overall classification accuracy was obtained for 2022, with an overall accuracy of 90.28% and a Kappa coefficient of 86.89%. The greatest land use area changes in the study area were related to agricultural lands and fair and poor rangelands. NDVI results from 1996 to 2022 indicated an increase in vegetation cover in 2022, mainly due to the expansion of agricultural lands. Change detection analysis revealed that the most significant transitions involved the conversion of fair rangelands into agricultural lands and poor rangelands. The findings can contribute to land evaluation, environmental studies, and integrated planning and management for the sustainable utilization of natural resources and the reduction of land degradation in the region

کلیدواژه‌ها [English]

  • Object-Based Classification
  • Land Use Change
  • NDVI
  • Landsat
  • Mehran Plain

مراجع

Al-Gaadi, K. A., Samdani, M. S., & Patil, V. C. (2011). Assessment of temporal land cover changes in Saudi Arabia using remotely sensed data. Middle-East J Sci Res, 9 (6), 711-717.
Aquilué, N., De Cáceres, M., Fortin, M. J., Fall, A., & Brotons, L. (2017). A spatial allocation procedure to model land-use/land-cover changes: Accounting for occurrence and spread processes. Ecological Modelling, 344, 73-86.
DOI: https://doi.org/10.1016/j.ecolmodel.2016.11.005
Baatz, M. (1999). Object-oriented and multi-scale image analysis in semantic networks. In Proc. 2nd Intl. Symp. Operationalization of Remote Sensing. August 16th-20th 1999. Enschede. ITC.
Baker, B. A., Warner, T. A., Conley, J. F., & McNeil, B. E. (2013). Does spatial resolution matter? A multi-scale comparison of object-based and pixel-based methods for detecting change associated with gas well drilling operations. International journal of remote sensing, 34 (5), 1633-1651.
DOI: https://doi.org/10.1080/01431161.2012.724540
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), 2-16.
DOI: https://doi.org/10.1016/j.isprsjprs.2009.06.004
Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., ... & Tiede, D. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS journal of photogrammetry and remote sensing, 87, 180-191.
DOI: https://doi.org/10.1016/j.isprsjprs.2013.09.014
Cai, Y. P., Huang, G. H., Yang, Z. F., & Tan, Q. (2009). Identification of optimal strategies for energy management systems planning under multiple uncertainties. Applied Energy, 86 (4), 480-495.
DOI: https://doi.org/10.1016/j.apenergy.2008.09.025
Chen, J., Mao, Z., Philpot, B., Li, J., & Pan, D. (2013). Detecting changes in high-resolution satellite coastal imagery using an image object detection approach. International journal of remote sensing, 34 (7), 2454-2469.
DOI: https://doi.org/10.1080/01431161.2012.743691
Chen, Z., Wang, G., & Liu, J. (2012). A modified object-oriented classification algorithm and its application in high-resolution remote-sensing imagery. International Journal of Remote Sensing, 33 (10), 3048-3062.
DOI: https://doi.org/10.1080/01431161.2011.625055
Chavez, P. S., & MacKinnon, D. J. (1994). Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogrammetric engineering and remote sensing, 60(5).
Chirici, G., Mura, M., McInerney, D., Py, N., Tomppo, E. O., Waser, L. T., ... & McRoberts, R. E. (2016). A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data. Remote Sensing of Environment, 176, 282-294.
DOI: https://doi.org/10.1016/j.rse.2016.02.001
Cockx, K., Van De Voorde, T., Canters, F., Poelmans, L., Uljee, I., Engelen, G., ... & Van Der Kwast, J. (2013). Incorporating land-use mapping uncertainty in remote sensing based calibration of land-use change models. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 7-12.
DOI: https://doi.org/10.5194/isprsarchives-XL-2-W1-7-2013
Definiens, A. G. (2006). Definiens professional 5 user guide. Definiens AG, Munich.
desert cities of Makkah and Al-Taif using satellite data. Advances in Remote Sensing, 3(03), 106.
Dingle Robertson, L., & King, D. J. (2011). Comparison of pixel-and object-based classification in land cover change mapping. International Journal of Remote Sensing, 32 (6), 1505-1529.
DOI: https://doi.org/10.1080/01431160903571791
Friedl, M. A., & Brodley, C. E. (1997). Decision tree classification of land cover from remotely sensed data. Remote sensing of environment, 61 (3), 399-409.
DOI: https://doi.org/10.1016/S0034-4257(97)00049-7
Giri, C. P. (Ed.). (2012). Remote sensing of land use and land cover: principles and applications. CRC press.
Hofmann, T., Puzicha, J., & Buhmann, J. M. (1998). Unsupervised texture segmentation in a deterministic annealing framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (8), 803-818.
DOI: 10.1109/34.709593
Hong, F., He, G., Wang, G., Zhang, Z., & Peng, Y. (2023). Monitoring of Land Cover and Vegetation Changes in Juhugeng Coal Mining Area Based on Multi-Source Remote Sensing Data. Remote Sensing, 15 (13), 3439.
DOI: https://doi.org/10.3390/rs15133439
Huang, L., & Ni, L. (2008, June). Object-oriented classification of high resolution satellite image for better accuracy. In Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences (pp. 211-218).
Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of photogrammetry and remote sensing, 80, 91-106.
DOI: https://doi.org/10.1016/j.isprsjprs.2013.03.006
Ioannou, K. (2023). On the Identification of Agroforestry Application Areas Using Object-Oriented Programming. Agriculture, 13 (1), 164.
DOI: https://doi.org/10.3390/agriculture13010164
Jensen, J. R. (1983). Urban/suburban land use analysis. Manual of Remote Sensing, second edition, 1571-1666
Kindu, M., Schneider, T., Teketay, D., & Knoke, T. (2013). Land use/land cover change analysis using object-based classification approach in Munessa-Shashemene landscape of the Ethiopian highlands. Remote sensing, 5(5), 2411-2435.
DOI: https://doi.org/10.3390/rs5052411.
Lang, S., & Blaschke, T. (2006, July). Bridging remote sensing and GIS–What are the main supportive pillars. In Proceedings of the 1st international conference on object-based image analysis (pp. 4-5).
Lin, C. Y. (1997). A study on the width and placement of vegetated buffer strips in a mudstone-distributed watershed. J China Soil water conserv, 29 (3), 250-266.
Lin, Q., Guo, J., Yan, J., & Heng, W. (2018). Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images. European Journal of Remote Sensing, 51(1), 1036-1048.
DOI: https://doi.org/10.1080/22797254.2018.1534532.
Liu, J., Zhan, J., & Deng, X. (2005). Spatio-temporal patterns and driving forces of urban land expansion in China during the economic reform era. AMBIO: a journal of the human environment, 34 (6), 450-455.
DOI: https://doi.org/10.1579/0044-7447-34.6.450.
Liu, Y., Gong, W., Hu, X., & Gong, J. (2018). Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data. Remote Sensing, 10 (6), 946.
DOI: https://doi.org/10.3390/rs10060946.
Liu, Y., Huang, C., & Zhang, L. (2023). The Spatio-Temporal Patterns and Driving Forces of Land Use in the Context of Urbanization in China: Evidence from Nanchang City. International Journal of Environmental Research and Public Health, 20 (3), 2330.
DOI: https://doi.org/10.3390/ijerph20032330.
Luo, K., Li, B., & Moiwo, J. P. (2018). Monitoring Land-Use/Land-Cover changes at a provincial large scale using an object-oriented technique and medium-resolution remote-sensing images. Remote Sensing, 10 (12), 2012.
DOI: https://doi.org/10.3390/rs10122012.
MacLean, M. G., Campbell, M. J., Maynard, D. S., Ducey, M. J., & Congalton, R. G. (2013). Requirements for labelling forest polygons in an object-based image analysis classification. International journal of remote sensing, 34 (7), 2531-2547.
DOI: https://doi.org/10.1080/01431161.2012.747017.
Mao, J., & Jain, A. K. (1992). Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern recognition, 25 (2), 173-188.
DOI: https://doi.org/10.1016/0031-3203(92)90099-5.
Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J., & Kumar, K. V. (2010). Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology, 116 (1-2), 24-36.
DOI: https://doi.org/10.1016/j.geomorph.2009.10.004.
Mather, P. M., & Koch, M. (2011). Computer processing of remotely-sensed images: an introduction. John Wiley & Sons.
Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote sensing of environment, 115(5), 1145-1161.
DOI: https://doi.org/10.1016/j.rse.2010.12.017.
Myint, S. W., Yuan, M., Cerveny, R. S., & Giri, C. P. (2008). Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data. Sensors, 8 (2), 1128-1156.
DOI: https://doi.org/10.3390/s8021128.
Oruc, M., Marangoz, A. M., & Buyuksalih, G. (2004). Comparison of pixel-based and object-oriented classification approaches using Landsat-7 ETM spectral bands.
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern recognition, 26 (9), 1277-1294.
DOI: https://doi.org/10.1016/0031-3203(93)90135-J.
Panjwani, D. K., & Healey, G. (1995). Markov random field models for unsupervised segmentation of textured color images. IEEE Transactions on pattern analysis and machine intelligence, 17 (10), 939-954.
DOI: 10.1109/34.464559.
Paul, S. S., Li, J., Wheate, R., & Li, Y. (2018). Application of object oriented image classification and Markov chain modeling for land use and land cover change analysis. Journal of Environmental Informatics, 31(1), 30-40.
DOI: 10.3808/jei.201700368.
Petropoulos, G. P., Kalaitzidis, C., & Vadrevu, K. P. (2012). Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences, 41, 99-107.
DOI: https://doi.org/10.1016/j.cageo.2011.08.019.
Purvaja, R., Ramesh, R., Glavovic, B., Ittekkot, V., & Samseth, J. (2015). Regional initiatives for interlinking global coastal scientific research projects. Environmental Development, 14, 66-68.
DOI: https://doi.org/10.1016/j.envdev.2015.03.003.
Ramachandra, T. V., & Kumar, U. (2004). Geographic Resources Decision Support System for land use, land cover dynamics analysis. In Proceedings of the FOSS/GRASS users conference (Vol. 15).
Rodrigo-Ilarri, J., Romero, C. P., & Rodrigo-Clavero, M. E. (2020). Land Use/Land Cover Assessment over Time Using a New Weighted Environmental Index (WEI) Based on an Object-Oriented Model and GIS Data. Sustainability, 12 (24), 10234.
DOI: https://doi.org/10.3390/su122410234.
Schmid, T., Rico, C., Rodríguez-Rastrero, M., Sierra, M. J., Díaz-Puente, F. J., Pelayo, M., & Millan, R. (2013). Monitoring of the mercury mining site Almadén implementing remote sensing technologies. Environmental research, 125, 92-102.
DOI: https://doi.org/10.1016/j.envres.2012.12.014.
Shahini, Z., Faramarzi, M., Garaee, P., & Alimoradi, S. (2021). Evaluating desertification intensity with emphasis on groundwater criteria using IMDPA model (Case study: Mehran plain in Ilam province). Integrated Watershed Management, 1(1), 17-28.
Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International journal of remote sensing, 10 (6), 989-1003.
DOI: https://doi.org/10.1080/01431168908903939.
Sinha, P., & Kumar, L. (2013). Binary images in seasonal land-cover change identification: a comparative study in parts of New South Wales, Australia. International Journal of Remote Sensing, 34 (6), 2162-2186.
DOI: https://doi.org/10.1080/01431161.2012.742214.
Shi, G. L., Li, X., Feng, Y. C., Wang, Y. Q., Wu, J. H., Li, J., & Zhu, T. (2009). Combined source apportionment, using positive matrix factorization–chemical mass balance and principal component analysis/multiple linear regression–chemical mass balance models. Atmospheric Environment, 43(18), 2929-2937.
DOI: https://doi.org/10.1016/j.atmosenv.2009.02.054.
Tariq, A., Shu, H., Siddiqui, S., Imran, M., & Farhan, M. (2021). Monitoring land use and land cover changes using geospatial techniques, a case study of Fateh Jang, Attock, Pakistan. Geography, Environment, Sustainability, 14 (1), 41-52.
DOI: https://doi.org/10.24057/2071-9388-2020-117.
Toure, S., Stow, D., Shih, H. C., Coulter, L., Weeks, J., Engstrom, R., & Sandborn, A. (2016). An object-based temporal inversion approach to urban land use change analysis. Remote Sensing Letters, 7 (5), 503-512.
DOI: https://doi.org/10.1080/2150704X.2016.1157640.
VLAD ȘANDRU, M. I., IAŢU, C., ȘANDRU, D. C., & Cimbru, D. G. (2017). Approaching Land Cover-Land Use Changes Using Statistical Data Validation for Urban Policies Improvement. Journal of Settlements & Spatial Planning, 8 (2).
Walker, R. (2001). Urban sprawl and natural areas encroachment: linking land cover change and economic development in the Florida Everglades. Ecological Economics, 37 (3), 357-369.
DOI: https://doi.org/10.1016/S0921-8009(01)00178-1.
Walker, R. T. (2022). Geography, Von Thünen, and Tobler’s first law: Tracing the evolution of a concept. Geographical Review, 112(4), 591-607.
https://doi.org/10.1080/00167428.2021.1906670
Wang, Q., Wang, H., Chang, R., Zeng, H., & Bai, X. (2022). Dynamic simulation patterns and spatiotemporal analysis of land-use/land-cover changes in the Wuhan metropolitan area. China. Ecological Modelling, 464, 109850.
DOI: https://doi.org/10.1016/j.ecolmodel.2021.109850.
Wang, S. H., Huang, S. L., & Budd, W. W. (2012). Integrated ecosystem model for simulating land use allocation. Ecological Modelling, 227, 46-55.
DOI: https://doi.org/10.1016/j.ecolmodel.2011.12.009.
Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. In Proceedings of XIX ISPRS congress (Vol. 33, pp. 35-42). Amsterdam: IAPRS.
 
Xie, Y., & Weng, Q. (2016). Updating urban extents with nighttime light imagery by using an object-based thresholding method. Remote Sensing of Environment, 187, 1-13. DOI:
https://doi.org/10.1016/j.rse.2016.10.002.
Yan, G. (2003). Pixel based and object oriented image for coal fire research (Doctoral dissertation, Thesis (MSc) International institute for geo-information science and earth and observation Enschede. ITC, Netherlands). 93p.
Zhang, Z., Wang, X., Zhao, X., Liu, B., Yi, L., Zuo, L., ... & Hu, S. (2014). A 2010 update of National Land Use/Cover Database of China at 1: 100000 scale using medium spatial resolution satellite images. Remote sensing of environment, 149, 142-154.
DOI: https://doi.org/10.1016/j.rse.2014.04.004.
Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144, 152-171.
https://doi.org/10.1016/j.rse.2014.01.011
جغرافیا و آمایش شهری منطقه‌ای
دوره 16، شماره 59
تیر 1405
صفحه 217-248

فایل ها

سابقه مقاله

  • تاریخ دریافت: 01 تیر 1403
  • تاریخ بازنگری: 20 فروردین 1405
  • تاریخ پذیرش: 24 فروردین 1405

هم رسانی

ارجاع به این مقاله

آمار