Skip to main content

Advertisement

SpringerLink
Log in

Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and material

Raw data were generated at the Gorgan University of Agricultural Sciences and Natural Resources. We confirm that the data, models, and methodology used in the research are proprietary, and derived data supporting the findings of this study are available from the first author on request.

Computer code availability

Not applicable.

References

  • Akbari, A., Ibrahimi, M., & Amir Ahmadi, A. (2013). Providing the land use of Sabzevar using maximum likelihood method and MLP artificial neural network method. J Environ Plan, 6, 148–127.

    Google Scholar 

  • Aksoy, H., & Kaptan, S. (2021). Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: A study in Northern Turkey. Environmental Monitoring and Assessment, 193(8), 1–21.

    Article  Google Scholar 

  • Alawamy, J. S., Balasundram, S. K., & Mohd. Hanif, A. H., & Boon Sung, C. T. (2020). Detecting and analyzing land use and land cover changes in the region of Al-Jabal Al-Akhdar, Libya using time-series landsat data from 1985 to 2017. Sustainability, 12(11), 4490.

    Article  Google Scholar 

  • Ali, M. Z., Qazi, W., & Aslam, N. (2018). A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier. Egypt J Remote Sens Sp Sci, 21, S29–S35.

    Google Scholar 

  • AlizadehRabie, H. (1993). Remote sensing: Principles and applications. The Organization for Researching and Composing university text books in the Humanities.

  • Arumugam, T., Yadav, R. L., & Kinattinkara, S. (2021). Assessment and predicting of LULC by kappa analysis and CA Markov model using RS and GIS techniques in Udham Singh Nagar District, India.

  • Betru, T., Tolera, M., Sahle, K., & Kassa, H. (2019). Trends and drivers of land use/land cover change in Western Ethiopia. Applied Geography, 104, 83–93.

    Article  Google Scholar 

  • Buchanan, G. M., Fishpool, L. D., Evans, M. I., & Butchart, S. (2013). Comparing field-based monitoring and remote-sensing, using deforestation from logging at Important Bird Areas as a case study. Biological Conservation, 167, 334–338.

    Article  Google Scholar 

  • Chang, C. I. (2004). New hyperspectral discrimination measure for spectral characterization. Optical Engineering, 43, 1777.

    Article  Google Scholar 

  • Chiang, L. C., Wang, Y. C., Chen, Y. K., & Liao, C. J. (2021). Quantification of land use/land cover impacts on stream water quality across Taiwan. Journal of Cleaner Production, 318, 128443.

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.

    Article  Google Scholar 

  • Cracknell, M. J., & Reading, A. M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers & Geosciences, 63, 22–33.

    Article  Google Scholar 

  • Deval, K., & Joshi, P. K. (2022). Vegetation type and land cover mapping in a semi-arid heterogeneous forested wetland of India: Comparing image classification algorithms. Environment, Development and Sustainability, 24(3), 3947–3966.

    Article  Google Scholar 

  • Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: One or the other, or both? International Journal of Remote Sensing, 29, 1185–1206.

    Article  Google Scholar 

  • Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272.

    Article  Google Scholar 

  • Egidi, G., Cividino, S., Paris, E., Palma, A., Salvati, L., & Cudlin, P. (2021). Assessing the impact of multiple drivers of land sensitivity to desertification in a Mediterranean country. Environmental Impact Assessment Review, 89, 106594.

  • Ge, G., Shi, Z., Zhu, Y., Yang, X., & Hao, Y. (2020). Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation, 22, e00971.

    Article  Google Scholar 

  • Gergel, S.E., & Turner, M. (2002). Learning landscape ecology: A practical guide to concepts and techniques, (M. G. Turner, Ed.). Learning Landscape Ecology. (p. 347). Springer New York. New York, NY.

  • Ghosh, A., & Joshi, P. K. (2014). A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. International Journal of Applied Earth Observation and Geoinformation, 26, 298–311.

    Article  Google Scholar 

  • Hagner, O., & Reese, H. (2007). A method for calibrated maximum likelihood classification of forest types. Remote Sensing of Environment, 110, 438–444.

    Article  Google Scholar 

  • Haut, J. M., Paoletti, M. E., Plaza, J., Plaza, A., & Li, J. (2019). Hyperspectral image classification using random occlusion data augmentation. IEEE Geoscience and Remote Sensing Letters, 16, 1751–1755.

    Article  Google Scholar 

  • Kii, M., Moeckel, R., & Thill, J. C. (2019). Land use, transport, and environment interactions: WCTR 2016 contributions and future research directions. Comput Environ and Urban Systems, 77, 101335.

    Article  Google Scholar 

  • Krishnaswamy, J., Bawa, K. S., Ganeshaiah, K. N., & Kiran, M. C. (2009). Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate. Remote Sensing of Environment, 113, 857–867.

    Article  Google Scholar 

  • Le Louarn, M., Clergeau, P., Briche, E., & Deschamps-Cottin, M. (2017). “Kill two birds with one stone”: Urban tree species classification using bi-temporal Pléiades images to study nesting preferences of an invasive bird. Remote Sensing, 9(9), 916.

  • Leyequien, E., Verrelst, J., Slot, M., Schaepman, G., Heitkonig, I., & Skidmore, A. (2007). Capturing the fugitive: Applying remote sensing to terrestrial animal distribution and diversity. International Journal of Applied Earth Observation and Geoinformation, 9, 1–20.

    Article  Google Scholar 

  • Li, D., Ke, Y., Gong, H., & Li, X. (2015). Object-based urban tree species classification using bi-temporal WorldView-2 and WorldView-3 images. Remote Sensing, 7(12), 16917–16937.

  • Li, D., Wang, Y., Wang, J., Wang, C., & Duan, Y. (2020). Recent advances in sensor fault diagnosis: A review. Sensors and Actuators a: Physical, 309, 111990.

    Article  CAS  Google Scholar 

  • Lim, H.S., MatJafri, M.Z., Abdullah, K., & Jeng, W. (2009). Regional land use/cover classification in Malaysia Based on conventional digital camera imageries. In 2009 IEEE Aerospace conference. (pp. 1–7). IEEE

  • Liu, X., He, J., Yao, Y., Zhang, J., Liang, H., Wang, H., & Hong, Y. (2017). Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31, 1675–1696.

    Article  Google Scholar 

  • Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25, 2365–2401.

    Article  Google Scholar 

  • Mahdavian Cheshmeh Gol, A., & Niazi, S. (2014). Comparison of classification methods of maximum likelihood (similarity), the nearest neighbor and neural networks for satellite images. The first National Conference on application of advanced models of spatial analysis (remote sensing and GIS) in land us.

  • Makwinja, R., Kaunda, E., Mengistou, S., & Alamirew, T. (2021). Impact of land use/land cover dynamics on ecosystem service value—A case from Lake Malombe. Southern Malawi. Environ Monit and Assess, 193(8), 1–23.

    Google Scholar 

  • Mao, D., He, X., Wang, Z., Tian, Y., Xiang, H., Yu, H., & Zheng, H. (2019). Diverse policies leading to contrasting impacts on land cover and ecosystem services in Northeast China. Journal of Cleaner Production, 240, 117961.

  • Marian, T., Mokryn, O., & Shavitt, Y. (2013). Sensing clouds: A distributed cooperative target tracking with tiny binary noisy sensors. Ad Hoc Networks, 11, 2356–2366.

    Article  Google Scholar 

  • Martínez-Lopez, J., Carreno, M. F., Palazon-Ferrando, J. A., & Martínez-Fernández, J. (2014). Free advanced modeling and remote-sensing techniques for wetland watershed delineation and monitoring. International Journal of Geographical Information Science, 28, 1610–1625.

    Article  Google Scholar 

  • Mas, J. F. (2003) An artificial neural networks approach to map land use/cover using landsat imagery and ancillary data. In Proceedings of the International Geosciences and Remote Sensing Symposium IEEE IGARSS (Vol. 6, pp. 3498–3500).

  • Mazer, A. S., Martin, M., Lee, M., & Solomon, J. (1988). Image processing software for imaging spectrometry analysis. Remote Sensing of Environment, 24, 201–210.

    Article  Google Scholar 

  • Nasiri, A. (1997). Spectral and spatial classification methods in preparing the land use and land cover map, Ministry of Agriculture Tehran.

  • Oommen, T., Misra, D., Twarakavi, N. K. C., Prakash, A., Sahoo, B., & Bandopadhyay, S. (2008). An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences, 40, 409–424.

    Article  Google Scholar 

  • Osaliya, R. (2021). Impact of land use/cover and climate change on surface water resources in semi-arid lokok and Lokere Catchments, Uganda (Doctoral dissertation, University of Nairobi).

  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27–S31.

    Article  Google Scholar 

  • Priede, I. G., & Miller, P. I. (2009). A basking shark (Cetorhinus maximus) tracked by satellite together with simultaneous remote sensing II: New analysis reveals orientation to a thermal front. Fisheries Research, 95, 370–372.

    Article  Google Scholar 

  • Reyes-Acosta, J. L., & Lubczynski, M. W. (2013). Mapping dry-season tree transpiration of an oak woodland at the catchment scale, using object-attributes derived from satellite imagery and sap flow measurements. Agricultural and Forest Meteorology, 174–175, 184–201.

    Article  Google Scholar 

  • Reyes, A., Solla, M., & Lorenzo, H. (2017). Comparison of different object-based classifications in LandsatTM images for the analysis of heterogeneous landscapes. Measurement, 97, 29–37.

    Article  Google Scholar 

  • Richards, J. A., & Jia, X. (1999). Remote sensing digital image analysis. Berlin Heidelberg, Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  • Sanhouse-García, A. J., Rangel-Peraza, J. G., Bustos-Terrones, Y., Garcia Ferrer, A., & Mesas Carrascosa, F. J. (2016). Land use mapping from CBERS-2 images with open source tools by applying different classification algorithms. Physics and Chemistry of the Earth Parts, 91, 27–37.

    Article  Google Scholar 

  • Shaharum, N. S. N., Shafri, H. Z. M., Gambo, J., & Abidin, F. A. Z. (2018). Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sensing Applications: Society and Environment, 10, 24–35.

    Article  Google Scholar 

  • Singh, U. S., & Singh, R. K. (2020). Application of maximum-likelihood classification for segregation between Arctic multi-year ice and first-year ice using SCATSAT-1 data. Remote Sensing Applications: Society and Environment, 18.

  • Soffianian, A., Mohammadi-Tawfiq, A., Khodakarami, L., & Amiri, F. (2011). Providing the land use map using Artificial Neural Network method (Case Study: Kabudarahang watershed, Razan and Khonjin-Talkhab in Hamedan Province. Journal Remote Sensing Applications GIS Natural Resources and Science, 2, 1–11.

    Google Scholar 

  • Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19, e00662.

  • Tso, B., & Mather, P. (2009). Classification methods for remotely sensed data. Chapter 2–3. 2nd ed., Taylor and Francis Pub., America

  • Valdez, M., Chen, C. F., Chiang, S. H., Chang, K. T., Lin, Y. W., Chen, Y. F., & Chou, Y. C. (2019). Illegal land use change assessment using GIS and remote sensing to support sustainable land management strategies in Taiwan. Geocarto International, 34, 133–148.

    Article  Google Scholar 

  • Velli, A., Pirola, A., & Ferrari, C. (2019). Evaluating landscape changes using vegetation and land-use maps: An integrated approach. Landscape Research, 44, 768–781.

    Article  Google Scholar 

  • Wahidin, N., Siregar, V. P., Nababan, B., Jaya, I., & Wouthuyzen, S. (2015). Object-based image analysis for coral reef benthic habitat mapping with several classification algorithms. Procedia Environmental Sciences, 24, 222–227.

    Article  Google Scholar 

  • Wang, X., Wu, D., Kuang, M., & Li, Z. (2022). Meticulous land cover classification of high-resolution images based on interval type-2 fuzzy neural network with Gaussian regression model. Remote Sensing, 14(15), 3704.

    Article  Google Scholar 

  • Wijaya, A. (1995). Application of multi-stage classification to detect illegal logging with the use of multi-source data. Master Thesis, ITC, the Netherlands. pp. 732

  • Wilkinson, G.G. (1997). Open questions in neurocomputing for earth observation. In Neuro-Computation in Remote Sensing Data Analysis, edited by I. Kanellopoulos GG.

  • Wolaver, B. D., Pierre, J. P., Labay, B. J., LaDuc, T., Duran, C. M., Ryberg, A., & Hibbitts, T. (2018). An approach for evaluating changes in land-use from energy sprawl and other anthropogenic activities with implications for biotic resource management. Environment and Earth Science, 77, 171.

    Article  Google Scholar 

  • Xu, Y., Du, Q., Li, W., & Younan, N. H. (2019). Efficient probabilistic collaborative representation-based classifier for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 16, 1746–1750.

    Article  Google Scholar 

  • Yaghobi, S., Heidarizadi, Z., & Mirzapour, H. (2019). Comparing NDVI and RVI for forest density estimation and their relationships with rainfall (Case study: Malekshahi, Ilam Province). Environ Resour Res, 7, 117–128.

    Google Scholar 

  • Yang, X., Liu, Y., Wu, Z., Yu, Y., Li, F., & Fan, W. (2020). Forest age mapping based on multiple-resource remote sensing data. Environmental Monitoring and Assessment, 192(11), 1–15.

    Article  CAS  Google Scholar 

  • Yao, X., Tham, L. G., & Dai, F. C. (2008). Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China. Geomorphology, 101, 572–582.

    Article  Google Scholar 

  • Yao, Y., Suonan, D., & Zhang, J. (2020). Compilation of 1:50,000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the North-South transitional zone of China. Journal of Geographical Sciences, 30, 267–280.

    Article  Google Scholar 

  • Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98, 317–328.

    Article  Google Scholar 

  • Zeshan, M. T., Mustafa, M. R. U., & Baig, M. F. (2021). Monitoring land use changes and their future prospects using GIS and ANN-CA for Perak River Basin. Malaysia. Water, 13(16), 2286.

    Google Scholar 

  • Zhang, C., Di, L., Yang, Z., Lin, L., & Hao, P. (2020). AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine. Environmental Modelling and Software, 129, 104694.

    Article  Google Scholar 

  • Zhang, C., Wei, S., Ji, S., & Lu, M. (2019). Detecting large-scale urban land cover changes from very high resolution remote sensing images using CNN-based classification. ISPRS International Journal of Geo-Information, 8(4), 189.

    Article  Google Scholar 

  • Zhang, X., & Li, P. (2014). Lithological mapping from hyperspectral data by improved use of spectral angle mapper. International Journal of Applied Earth Observation and Geoinformation, 31, 95–109.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Watershed & Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

    Soraya Yaghobi

  2. Department of Watershed Management Sciences and Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

    Alireza Daneshi & Sajad Khoshnood

  3. Department of Economics and Rural Development, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium

    Hossein Azadi

Authors
  1. Soraya Yaghobi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alireza Daneshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sajad Khoshnood
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hossein Azadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S. A. and A. D.: Conceptualization, methodology, software, Writing—original draft, and Visualization. S. Kh. and H. A.: Reviewing, editing, and validation.

Corresponding author

Correspondence to Hossein Azadi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yaghobi, S., Daneshi, A., Khoshnood, S. et al. Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran. Environ Monit Assess 195, 486 (2023). https://doi.org/10.1007/s10661-023-10985-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-023-10985-5

Keywords

Search

Navigation