Assessment of Spatial and temporal changes in land use using remote sensing (case study: Jayransoo rangeland, North Khorasan)
Subject Areas : Remote sensing and biogeography
Mohabat Nadaf
1
,
Reza Omidipour
2
,
Hossein Sobhani
3
1 - Department of Biology, University of Payame Noor, P.O. BOX 19395-3697 Tehran, Iran
2 - Department of Rageland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran
3 - M.Sc. of Natural Resources, Ashkhane county, North Khorasan, Iran
Keywords: Landsat satellite, Destruction of natural resources, Machine learning, Object-oriented classification, Jayransoo rangeland,
Abstract :
Awareness of changes process, as well as the proper management of land use in natural ecosystems, is of great importance in conservation natural resources. In this regard, the use of remote sensing has become a common approach due to the provision an extent spatial and temporal information. In this research, in order to land use mapping, first, the accuracy of three common methods of pixel-based (maximum likelihood), machine learning (support vector machine) and object-oriented methods were compared. Then, the spatial and temporal changes of land use in a period of 26 years (1997-2023) assessed using six Landsat satellite imagery. The accuracy of image classification methods was evaluated using Kappa coefficient and overall accuracy indices and the change trend was evaluated using crosstab and spatial evaluation methods. Based on the results, the support vector machine method had the highest kappa coefficient (0.71 to 0.98) and overall accuracy (86 to 99%) for all studied courses. According to the results, poor rangeland had a decreasing trend, and the land uses of very poor rangeland, bare soil, and rainfed agriculture had increasing trends. The area of poor rangeland decreased from 962 hectares (44.36%) in 1997 to 489 hectares (22.57%) in 2023, while very poor rangeland increased from 1138 hectares (52.48%) to 1606 hectares (74.05 percent) in the same period. The results of this research indicated that the trend of land use changes in Jayransoo rangeland is towards the destruction of rangelands and with the passage of time this trend is intensifying. Also, based on the results obtained from this research, it is suggested to use machine learning based classification method to prepare land use mapping in future research.
امیدی¬پور، ر.، مرادی، ح. ر.، آرخی، ص. (1392). مقایسه روش های طبقه¬بندی پیسکل پایه و شیءگرا در تهیه نقشه کاربری اراضی با استفاده از تصاویر ماهواره¬ای. مجله سنجش از دور و GIS ایران، 5(3): 110-99.
رضایی، مجتبی، امیدی پور، رضا، رضایی، اشکان، نداف، محبت. (1401). مقایسه تاثیر تغییرات روند کاربری اراضی و بارش بر دبی سالانه (مطالعه موردی حوزه آبخیز کیار) .مدیریت جامع حوزه های آبخیز. 2(2): 74-62.
زارع گاریزی، آ، بردی شیخ، و، سعدالدینی، آ، سلمان ماهینی، ع، (1391). شبیه‌سازی مکانی-زمانی تغییرات گستره جنگل در آبخیز چهل چای استان گلستان با استفاده از مدل تلفیقی سلو‌‌ل‌های خودکار و زنجیره مارکوف، فصلنامه علمی-پژوهشی تحقیقات جنگل و صنوبر ایران، 20(2)، 273-285.
فتاحي، م، (1373). بررسي جنگل‌هاي بلوط زاگرس و مهمترين عوامل تخريب آن. موسسه تحقيقات جنگل‌ها و مراتع، چاپ اول، 63 صفحه.
محمودزاده، ح.، مسعودي،ح. (1398). تحليلي بر تغييرات ساختاري سيماي سرزمين کلانشهر تبريز با استفاده از مباني اکولوژي سيماي سرزمين و با تأکيد بر مفهوم پيوستگي،آمايش سرزمين، 21: 204-179.
Asad, M. H., & Bais, A. (2020). Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture, 7(4), 535-545.
Cavender-Bares, J., Gamon, J. A., & Townsend, P. A. (2020). Remote sensing of plant biodiversity. Springer Nature. 581 P.
Choate, M., Rengarajan, R., Micijevic, E., & Lubke, M. (2021, August). Comparing geometric differences between Landsat Collection 1 to Collection 2 level-1 products. In Earth Observing Systems XXVI. (11829), 114-124.
Dhingra, S., & Kumar, D. (2019). A review of remotely sensed satellite image classification. International Journal of Electrical and Computer Engineering, 9(3), 1720.
Ge, W., Deng, L., Wang, F., & Han, J. (2021). Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Science of the Total Environment, 773, 145648.
Ha, N. T., Manley-Harris, M., Pham, T. D., & Hawes, I. (2020). A comparative assessment of ensemble-based machine learning and maximum likelihood methods for mapping seagrass using sentinel-2 imagery in Tauranga Harbor, New Zealand. Remote Sensing, 12(3), 355.
Jin, B., Ye, P., Zhang, X., Song, W., & Li, S. (2019). Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images. Journal of the Indian Society of Remote Sensing, 47, 951-965.
Jin, B., Ye, P., Zhang, X., Song, W., & Li, S. (2019). Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images. Journal of the Indian Society of Remote Sensing, 47, 951-965.
Jozdani, S. E., Johnson, B. A., & Chen, D. (2019). Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification. Remote Sensing, 11(14), 1713.
Kaur, S., Bansal, R. K., Mittal, M., Goyal, L. M., Kaur, I., Verma, A., & Son, L. H. (2019). Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. Journal of the Indian Society of Remote Sensing, 47(3), 427-437.
Kumar, N., Yamaç, S. S., & Velmurugan, A. (2015). Applications of remote sensing and GIS in natural resource management. Journal of the Andaman Science Association, 20(1), 1-6.
Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., & Zhang, C. (2020). A review of remote sensing for environmental monitoring in China. Remote Sensing, 12(7), 1130.
Liu, H., Xu, H., Wu, Y., Ai, Z., Zhang, J., Liu, G., & Xue, S. (2021). Effects of natural vegetation restoration on dissolved organic matter (DOM) biodegradability and its temperature sensitivity. Water Research, 191, 116792.
Lohse, J., Doulgeris, A. P., & Dierking, W. (2019). An optimal decision-tree design strategy and its application to sea ice classification from SAR imagery. Remote Sensing, 11(13), 1574.
Long, H., & Qu, Y. (2018). Land use transitions and land management: A mutual feedback perspective. Land use policy, 74, 111-120.
Luo, L., Ma, W., Zhuang, Y., Zhang, Y., Yi, S., Xu, J., Long, Y., Ma, D. and Zhang, Z. (2018). The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor. Ecological Indicators, 93, 24-35.
Marsett, R.C., Qi, J., Heilman, P., Biedenbender, S.H., Watson, M.C., Amer, S., Weltz, M., Goodrich, D. & Marsett, R. (2006). Remote sensing for grassland management in the arid southwest. Rangeland Ecology & Management, 59(5), 530-540.
Martins, V. S., Kaleita, A. L., Gelder, B. K., da Silveira, H. L., & Abe, C. A. (2020). Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 56-73.
Myers‐Smith, I.H., Grabowski, M.M., Thomas, H.J., Angers‐Blondin, S., Daskalova, G.N., Bjorkman, A.D., Cunliffe, A.M., Assmann, J.J., Boyle, J.S., McLeod, E. & McLeod, S. (2019). Eighteen years of ecological monitoring reveals multiple lines of evidence for tundra vegetation change. Ecological Monographs, 89(2), e01351.
Ramezan, C. A., Warner, T. A., Maxwell, A. E., & Price, B. S. (2021). Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data. Remote Sensing, 13(3), 368.
Richards, J. A., & Richards, J. A. (2022). Supervised classification techniques. Remote sensing digital image analysis, 263-367.
Rimal, B., Rijal, S., & Kunwar, R. (2020). Comparing support vector machines and maximum likelihood classifiers for mapping of urbanization. Journal of the Indian Society of Remote Sensing, 48, 71-79.
Schnebele, E., Tanyu, B. F., Cervone, G., & Waters, N. J. E. T. R. R. (2015). Review of remote sensing methodologies for pavement management and assessment. European Transport Research Review, 7(2), 1-19.
Senekane, M. (2019). Differentially private image classification using support vector machine and differential privacy. Machine Learning and Knowledge Extraction, 1(1), 483-491.
Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308-6325.
Sisodia, P. S., Tiwari, V., & Kumar, A. (2014). Analysis of supervised maximum likelihood classification for remote sensing image. In International conference on recent advances and innovations in engineering (ICRAIE-2014) (1-4). IEEE.
Smith, P., House, J.I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., West, P.C., Clark, J.M., Adhya, T., Rumpel, C. & Paustian, K. (2016). Global change pressures on soils from land use and management. Global change biology, 22(3), 1008-1028.
Sun, L., Li, H., Wang, J., Chen, Y., Xiong, N., Wang, Z., Wang, J. & Xu, J. (2023). Impacts of Climate Change and Human Activities on NDVI in the Qinghai-Tibet Plateau. Remote Sensing, 15(3), 587.
Sun, X., Liu, L., Li, C., Yin, J., Zhao, J., & Si, W. (2019). Classification for remote sensing data with improved CNN-SVM method. Ieee Access, 7, 164507-164516.
Verbyla, D. L. (2022). Satellite remote sensing of natural resources. CRC Press.
Weiskopf, S.R., Rubenstein, M.A., Crozier, L.G., Gaichas, S., Griffis, R., Halofsky, J.E., Hyde, K.J., Morelli, T.L., Morisette, J.T., Muñoz, R.C. & Pershing, A.J. (2020). Climate change effects on biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Science of the Total Environment, 733, 137782.
Wood, C.M., Smart, S.M., Bunce, R.G., Norton, L.R., Maskell, L.C., Howard, D.C., Scott, W.A. & Henrys, P.A. (2017). Long-term vegetation monitoring in Great Britain–the Countryside Survey 1978–2007 and beyond. Earth System Science Data, 9(2), 445-459.
Yang, J., Weisberg, P. J., & Bristow, N. A. (2012). Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis. Remote sensing of environment, 119, 62-71.
Zhang, C., Yue, P., Tapete, D., Shangguan, B., Wang, M., & Wu, Z. (2020). A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high-resolution remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 88, 102086.
Zhou, L., Tian, Y., Myneni, R.B., Ciais, P., Saatchi, S., Liu, Y.Y., Piao, S., Chen, H., Vermote, E.F., Song, C. & Hwang, T. (2014). Widespread decline of Congo rainforest greenness in the past decade. Nature, 509(7498), pp.86-90.
Zhu, L., Huang, L., Fan, L., Huang, J., Huang, F., Chen, J., Zhang, Z. and Wang, Y., 2020. Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network. Sensors, 20(6), 1576.
