Kharazmi Journal of Earth Sciences

Kharazmi Journal of Earth Sciences

Landuse change mapping based on drone and ultracam aerial images

Authors
Shahid Beheshti University
Abstract
The use of drone imagery has become widespread in studies related to various fields, including urban planning. The application of drone photogrammetry as an advanced tool for analyzing urban and construction changes is considered a novel method in spatial-based research. In this study, the potential of drone imagery, specifically DJI Phantom 4 Pro RTK with a relative accuracy of 10 cm, alongside ultra-cam aerial images with a relative accuracy of 20 cm, was explored for identifying and analyzing land-use changes in densely urbanized areas of the Tabriz region. To achieve this, two data sets were utilized, including a corrected orthomosaic image from the ultra-cam camera captured on June 9, 2013, and raw drone images from the same area on July 4, 2022. After geometric correction and the creation of orthophotos from the raw drone images, with horizontal accuracy of 8 cm and vertical accuracy of 14 cm, two classification algorithms, maximum likelihood and minimum distance, were applied to the orthomosaic image and the drone orthophoto. The next step involved using the thematic change detection method to extract land-use changes in the identified classes based on the two classification algorithms. Visual evaluation of the results revealed that the building class experienced the least change compared to other classes. Quantitative findings showed that the Kappa coefficient and overall accuracy for the maximum likelihood and minimum distance classification methods were 0.8924 and 94.17%, and 0.5273 and 93.08%, respectively. Additionally, quantitative analysis indicated that the greatest land-use change involved the conversion of buildings to roads, while the least change occurred in the transformation from roads to barren land.
Keywords
Subjects

Ahmad, A., Quegan, S., 2012. Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences 6 (129), 6425–6436.
Al-doski, J., Mansor, S. B., Mohd Shafri, H. Z., 2013. Change detection process and techniques. Civil and Environmental Research 3 (10), 37–45.
Arabi Aliabad, F.A., Malamiri, H.R.G., Shojaei, S., Sarsangi, A., Ferreira, C.S.S., Kalantari, Z., 2022. Investigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2. Remote Sensing 14 (13), 3227.
Boonpook, W., Tan, Y., Liu, H., Zhao, B., He, L., 2018. UAV-based 3D urban environment monitoring. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4, 37–43.
Boguszewski, A., Batorski, D., Ziemba-Jankowska, N., Dziedzic, T., Zambrzycka, A., 2021. LandCover.ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1102–1110.
Chavis, C., Anders, K.-H., Jankowski, P., 2012. Change detection for environmental monitoring using unmanned aerial vehicle (UAV) imagery. Marshall Plan Scholarship Program Final Project Report. Retrieved from https://www.marshallplan.at.
Gbopa, A.O., Ayodele, E.G., Okolie, C.J., Ajayi, A.O., Iheaturu, C.J., 2021. Unmanned aerial vehicles for three-dimensional mapping and change detection analysis. Geomatics and Environmental Engineering 15 (1), 41–61.
Ghimire, S., Wang, H., 2012. Classification of image pixels based on minimum distance and hypothesis testing. Computational Statistics & Data Analysis 56 (7), 2273–2287.
Han, D., Lee, S.B., Song, M., Cho, J.S., 2021. Change detection in unmanned aerial vehicle images for progress monitoring of road construction. Buildings 11 (4), 150.
Hester, D.B., 2008. Land cover mapping and change detection in urban watersheds using Quickbird high spatial resolution satellite imagery. North Carolina State University.
sLahooti, S., Lahoti, A., Saito, O., 2020. Application of unmanned aerial vehicles (UAVs) for urban green space mapping in urbanizing Indian cities. In Unmanned Aerial Vehicle: Applications in Agriculture and Environment, 177–188.
Liu, W., Ji, X., Liu, J., Guo, F., Yu, Z., 2022. A novel unsupervised change detection method with structure consistency and GFLICM based on UAV images. Journal of Geodesy and Geoinformation Science 5 (1), 91.
Lv, Zhiyong., Liu, T., Benediktsson, J.A., Falco, N., 2021. Land cover change detection techniques: Very-high-resolution optical images—a review. IEEE Geoscience and Remote Sensing Magazine 10 (1), 44–63.
Makuti, S., Nex, F., Yang, M.Y., 2018. Multi-temporal classification and change detection using UAV images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 651–658.
Peiman, R., 2011. Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: A case study on Pisa Province in Italy. International Journal of Remote Sensing 32 (15), 4365–4381.
Qin, R., 2014. An object-based hierarchical method for change detection using unmanned aerial vehicle images. Remote Sensing 6 (9), 7911–7932.
Qin, R., Tian, J., Reinartz, P., 2016. 3D change detection—approaches and applications. ISPRS Journal of Photogrammetry and Remote Sensing 122, 41–56.
Sedaghat, A., Mokhtarzade, M., Ebadi, H., 2011a. Uniform robust scale-invariant feature matching for optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 49 (11), 4516–4527.
Sedaghat, A., Mokhtarzade, M., Ebadi, H., 2011b. Mutual information similarity measure for scale and rotation invariant image matching. Journal of Geomatics Science and Technology 1, 14.
Soffianian, A., Madanian, M., 2015. Monitoring land cover changes in Isfahan Province, Iran using Landsat satellite data. Environmental Monitoring and Assessment 187, 1–15.
Wang, T., 2018. Building change detection with UAV images. 3rd International Conference on Electromechanical Control Technology and Transportation (ICECTT 2018), 564–567.
Wacker, A.G., Landgrebe, D.A., 1972. Minimum distance classification in remote sensing. LARS Technical Reports 25.
Yao, H., Qin, R., Chen, X., 2019. Unmanned aerial vehicle for remote sensing applications—a review. Remote Sensing 11 (12), 1443.
Zitova, B., Flusser, J., 2003. Image registration methods: A survey. Image and Vision Computing 21 (11), 977–1000.