Fusion Landsat-8 Thermal TIRS and OLI Datasets for Superior Monitoring and Change Detection using Remote Sensing

Hayder Dibs, Alaa Hussein Ali, Nadhir Al-Ansari, Salwan Ali Abed


Currently, updating the change detection (CD) of land use/land cover (LU/LC) geospatial information with high accuracy outcomes is important and very confusing with the different classification methods, datasets, satellite images, and ancillary dataset types available. However, using just the low spatial resolution visible bands of the remotely sensed images will not provide good information with high accuracy. Remotely sensed thermal data contains very valuable information to monitor and investigate the CD of the LU/LC. So, it needs to involve the thermal datasets for better outcomes. Fusion plays a big role to map the CD. Therefore, this study aims to find out a refining method for estimating the accurate CD method of the LU/LC patterns by investigating the integration of the effectiveness of the thermal satellite data with visible datasets by (a) adopting a noise removal model, (b) satellite images resampling, (c) image fusion, combining and integrating between the visible and thermal images using the Grim Schmidt spectral (GS) method, (d) applying image classification using Mahalanobis distances (MH), Maximum likelihood (ML) and artificial neural network (ANN) classifiers on datasets captured from the Landsat-8 TIRS and OLI satellite system, these images were captured from operational land imager (OLI) and the thermal infrared (TIRS) sensors of 2015 and 2020 to generate about of twelve LC maps. (e) The comparison was made among all the twelve classifiers' results. The results reveal that adopting the ANN technique on the integrated images of the combined TIRS and OLI datasets has the highest accuracy compared to the rest of the applied image classification approaches. The obtained overall accuracy was 96.31% and 98.40%, and the kappa coefficients were (0.94) and (0.97) for the years 2015 and 2020, respectively. However, the ML classifier obtains better results compared to the MH approach. The image fusion and integration of the thermal images improve the accuracy results by 5%–6% from the proposed method better than using low spatial-resolution visible datasets alone.


Doi: 10.28991/ESJ-2023-07-02-09

Full Text: PDF


Thermal TIRS Images; Change Detection; Imagery Classification; Maximum Likelihood Classifier; Land Cover; Image Fusion.


Dibs, H. (2018). Comparison of derived Indices and unsupervised classification for AL-Razaza Lake dehydration extent using multi-temporal satellite data and remote sensing analysis. ARPN Jouyrnal of Engineering and Applied Sciences, 13(24), 9495-95038.

Hasab, H. A., Jawad, H. A., Dibs, H., Hussain, H. M., & Al-Ansari, N. (2020). Evaluation of Water Quality Parameters in Marshes Zone Southern of Iraq Based on Remote Sensing and GIS Techniques. Water, Air, and Soil Pollution, 231(4). doi:10.1007/s11270-020-04531-z.

Dibs, H., & Al-Hedny, S. (2019). Detection wetland dehydration extent with multi-temporal remotely sensed data using remote sensing analysis and GIS techniques. International Journal of Civil Engineering and Technology, 10, 143-154.

Hayder Dibs, Shattri Mansor, Noordin Ahmad, Biswajeet Pradhan, & Nadhir Al-Ansari. (2020). Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images. Journal of Civil Engineering and Architecture, 14(6), 339–350. doi:10.17265/1934-7359/2020.06.005.

Abdalkadhum Aljanbi, A. J., Dibs, H., & Alyasery, B. H. (2020). Interpolation and statistical analysis for evaluation of global earth gravity models based on GPS and orthometric heights in the middle of Iraq. Iraqi Journal of Science, 61(7), 1823–1830. doi:10.24996/ijs.2020.61.7.31.

Dibs, H., Mansor, S., Ahmad, N., & Pradhan, B. (2014). Registration model for near-equatorial earth observation satellite images using automatic extraction of control points. International Coneference 2014 - International Systems Group (ISG), Rome, Italy.

Dibs, H., Al-Hedny, S., & Karkoosh, H. A. (2018). Extracting Detailed Buildings 3D Model with Using High Resolution Satellite Imagery by Remote Sensing and GIS Analysis; Al-Qasim Green University a Case Study. International Journal of Civil Engineering and Technology, 9(7), 1097-1108.

Ramos-Bernal, R. N., Vázquez-Jiménez, R., Romero-Calcerrada, R., Arrogante-Funes, P., & Novillo, C. J. (2018). Evaluation of unsupervised change detection methods applied to landslide inventory mapping using ASTER imagery. Remote Sensing, 10(12). doi:10.3390/rs10121987.

Cao, G., Zhou, L., & Li, Y. (2016). A new change-detection method in high-resolution remote sensing images based on a conditional random field model. International Journal of Remote Sensing, 37(5), 1173–1189. doi:10.1080/01431161.2016.1148284.

Asokan, A., & Anitha, J. (2019). Change detection techniques for remote sensing applications: a survey. Earth Science Informatics, 12(2), 143–160. doi:10.1007/s12145-019-00380-5.

Lv, Z., Liu, T., Shi, C., Benediktsson, J. A., & Du, H. (2019). Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images. IEEE Access, 7, 34425–34437. doi:10.1109/ACCESS.2019.2892648.

Dibs, H., Mansor, S., Ahmad, N., & Pradhan, B. (2015). Band-to-band registration model for near-equatorial Earth observation satellite images with the use of automatic control point extraction. International Journal of Remote Sensing, 36(8), 2184–2200. doi:10.1080/01431161.2015.1034891.

Halder, S., Tiwari, Y. K., Valsala, V., Sijikumar, S., Janardanan, R., & Maksyutov, S. (2022). Benefits of satellite XCO2 and newly proposed atmospheric CO2 observation network over India in constraining regional CO2 fluxes. Science of the Total Environment, 812, 151508. doi:10.1016/j.scitotenv.2021.151508.

Xiao, P., Yuan, M., Zhang, X., Feng, X., & Guo, Y. (2017). Cosegmentation for Object-Based Building Change Detection from High-Resolution Remotely Sensed Images. IEEE Transactions on Geoscience and Remote Sensing, 55(3), 1587–1603. doi:10.1109/TGRS.2016.2627638.

Dibs, H., Mansor, S., Ahmadb, N., & Al-Ansari, N. (2020). Simulate New Near Equatorial Satellite System by a Novel Multi-Fields and Purposes Remote Sensing Goniometer. Engineering, 12(06), 325–346. doi:10.4236/eng.2020.126026.

Abdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., & Dewan, A. (2019). Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing, 11(7), 790. doi:10.3390/rs11070790.

Dibs, H., Hasab, H. A., Jaber, H. S., & Al-Ansari, N. (2022). Automatic feature extraction and matching modelling for highly noise near-equatorial satellite images. Innovative Infrastructure Solutions, 7(1). doi:10.1007/s41062-021-00598-7.

Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), 117–124. doi:10.1016/j.ijsbe.2015.02.005.

Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. doi:10.1016/j.ejrs.2015.02.002.

Vázquez-Jiménez, R., Romero-Calcerrada, R., Novillo, C. J., Ramos-Bernal, R. N., & Arrogante-Funes, P. (2017). Applying the chi-square transformation and automatic secant thresholding to Landsat imagery as unsupervised change detection methods. Journal of Applied Remote Sensing, 11(1), 016016. doi:10.1117/1.jrs.11.016016.

Kwarteng, A. Y., & Chavez Jr, P. S. (1998). Change detection study of Kuwait City and environs using multi-temporal Landsat Thematic Mapper data. International Journal of Remote Sensing, 19(9), 1651-1662. doi:10.1080/014311698215162.

Hashim, F., Dibs, H., & Jaber, H. S. (2021). Applying Support Vector Machine Algorithm on Multispectral Remotely sensed satellite image for Geospatial Analysis. Journal of Physics: Conference Series, 1963(1). doi:10.1088/1742-6596/1963/1/012110.

International Association of Assessing Officers Technical Standards Committee. (2014). Guidance on international mass appraisal and related tax policy. Journal of Property Tax Assessment & Administration, 11(1), 5-33.

Yang, C., He, X., Yan, F., Yu, L., Bu, K., Yang, J., Chang, L., & Zhang, S. (2017). Mapping the influence of land use/land cover changes on the urban heat island effect-A case study of Changchun, China. Sustainability (Switzerland), 9(2). doi:10.3390/su9020312.

Maulik, U., & Chakraborty, D. (2017). Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques. IEEE Geoscience and Remote Sensing Magazine, 5(1), 33–52. doi:10.1109/MGRS.2016.2641240.

Dibs, H., & Hussain, T. H. (2018). Estimation and Mapping the Rubber Trees Growth Distribution using Multi Sensor Imagery with Remote Sensing and GIS Analysis. Journal of University of Babylon for Pure and Applied Sciences, 26(6), 109-123.

Fahad, K. H., Hussein, S., & Dibs, H. (2020). Spatial-Temporal Analysis of Land Use and Land Cover Change Detection Using Remote Sensing and GIS Techniques. IOP Conference Series: Materials Science and Engineering, 671(1), 012–046. doi:10.1088/1757-899X/671/1/012046.

Dibs, H., Hasab, H. A., Al-Rifaie, J. K., & Al-Ansari, N. (2020). An Optimal Approach for Land-Use / Land-Cover Mapping by Integration and Fusion of Multispectral Landsat OLI Images: Case Study in Baghdad, Iraq. Water, Air, & Soil Pollution, 231(9). doi:10.1007/s11270-020-04846-x.

Wang, M., Wan, Y., Ye, Z., & Lai, X. (2017). Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm. Information Sciences, 402, 50–68. doi:10.1016/j.ins.2017.03.027.

Prieto-Amparan, J. A., Villarreal-Guerrero, F., Martinez-Salvador, M., Manjarrez-Domínguez, C., Santellano-Estrada, E., & Pinedo-Alvarez, A. (2018). Atmospheric and radiometric correction algorithms for the multitemporal assessment of grasslands productivity. Remote Sensing, 10(2), 219. doi:10.3390/rs10020219.

Zhang, J., Dong, W., Wang, J. X., & Liu, X. N. (2014). A method to enhance the fog image based on dark object subtraction. Applied Mechanics and Materials, 543–547, 2484–2487. doi:10.4028/www.scientific.net/AMM.543-547.2484.

Roy, D. P., Li, J., Zhang, H. K., & Yan, L. (2016). Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data. Remote Sensing Letters, 7(11), 1023–1032. doi:10.1080/2150704X.2016.1212419.

Hashim, F., Dibs, H., & Jaber, H. S. (2022). Adopting Gram-Schmidt and Brovey Methods for Estimating Land Use and Land Cover Using Remote Sensing and Satellite Images. Nature Environment and Pollution Technology, 21(2), 867–881. doi:10.46488/NEPT.2022.v21i02.050.

Dibs, H., Hasab, H. A., Mahmoud, A. S., & Al-Ansari, N. (2021). Fusion Methods and Multi-classifiers to Improve Land Cover Estimation Using Remote Sensing Analysis. Geotechnical and Geological Engineering, 39(8), 5825–5842. doi:10.1007/s10706-021-01869-x.

Dibs, H., Idrees, M. O., & Alsalhin, G. B. A. (2017). Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery. Egyptian Journal of Remote Sensing and Space Science, 20(1), 21–30. doi:10.1016/j.ejrs.2017.01.004.

Nazmfar, H., & Jafarzadeh, J. (2018). Classification of Satellite Images in Assessing Urban Land Use Change Using Scale Optimization in Object-Oriented Processes (A Case Study: Ardabil City, Iran). Journal of the Indian Society of Remote Sensing, 46(12), 1983–1990. doi:10.1007/s12524-018-0850-7.

Xu, K., Tian, Q., Yang, Y., Yue, J., & Tang, S. (2019). How up-scaling of remote-sensing images affects land-cover classification by comparison with multiscale satellite images. International Journal of Remote Sensing, 40(7), 2784–2810. doi:10.1080/01431161.2018.1533656.

Mohan, B.S.S., Sekhar, C.C. (2012). Class-Specific Mahalanobis Distance Metric Learning for Biological Image Classification. Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, 7325, Springer, Berlin, Germany. doi:10.1007/978-3-642-31298-4_29.

Fu, G., Liu, C., Zhou, R., Sun, T., & Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing, 9(5), 498. doi:10.3390/rs9050498.

Tang, Y., Zhang, F., Engel, B. A., Liu, X., Yue, Q., & Guo, P. (2020). Grid-scale agricultural land and water management: A remote-sensing-based multiobjective approach. Journal of Cleaner Production, 265, 121792. doi:10.1016/j.jclepro.2020.121792.

Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11(1), 31-50.

Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19–28. doi:10.1016/j.neunet.2017.07.017.

Hopkins, P. (1988). Assessment of Thematic Mapper Imagery for Forestry Application under Lake States Conditions. Photogrammetric Engineering and Remote Sensing, 54(1), 61-68.

Liu, B., Yu, X., Zhang, P., Tan, X., Yu, A., & Xue, Z. (2017). A semi-supervised convolutional neural network for hyperspectral image classification. Remote Sensing Letters, 8(9), 839–848. doi:10.1080/2150704x.2017.1331053.

Cavallaro, G., Riedel, M., Richerzhagen, M., Benediktsson, J. A., & Plaza, A. (2015). On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4634–4646. doi:10.1109/JSTARS.2015.2458855.

Jog, S., & Dixit, M. (2016). Supervised classification of satellite images. Conference on Advances in Signal Processing, CASP 2016, 93–98. doi:10.1109/CASP.2016.7746144.

Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250–1265. doi:10.1016/j.asr.2012.06.032.

Dhingra, S., & Kumar, D. (2019). A review of remotely sensed satellite image classification. International Journal of Electrical and Computer Engineering (IJECE), 9(3), 1720. doi:10.11591/ijece.v9i3.pp1720-1731.

Li, H., Dou, X., Tao, C., Wu, Z., Chen, J., Peng, J., Deng, M., & Zhao, L. (2020). RSI-CB: A large-scale remote sensing image classification benchmark using crowdsourced data. Sensors (Switzerland), 20(6). doi:10.3390/s20061594.

Deng, C., & Wu, C. (2013). The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 86, 100–110. doi:10.1016/j.isprsjprs.2013.09.010.

Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 08(04), 611–622. doi:10.4236/ijg.2017.84033.

Nappo, N., Peduto, D., Mavrouli, O., van Westen, C. J., & Gullà, G. (2019). Slow-moving landslides interacting with the road network: Analysis of damage using ancillary data, in situ surveys and multi-source monitoring data. Engineering Geology, 260. doi:10.1016/j.enggeo.2019.105244.

Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. Egyptian Journal of Remote Sensing and Space Science, 18(2), 251–259. doi:10.1016/j.ejrs.2015.07.003.

Full Text: PDF

DOI: 10.28991/ESJ-2023-07-02-09


  • There are currently no refbacks.

Copyright (c) 2023 Nadhir Al-Ansari