08. APPLICATION OF MACHINE LEARNING WITH OBJECT-BASED IMAGE ANALYSIS FOR LAND USE AND LAND COVER MAPPING IN DAK NONG UNESCO GLOBAL GEOPARK, VIET NAM
Giới thiệu
Land use and land cover (LULC) information is a fundamental component of environmental research related to urban planning, green infrastructure sustainability and natural hazards assessment. In particular, remote sensing technology has demonstrated a powerful capacity for LULC modelling with a corresponding increase in sensor number and type. The integration of many algorithms into the object - based classification method to create different sets of machine learning algorithms has proven very effective for image feature extraction from satellite data. In this study, the Random Trees model was used in combination with object - based image analysis (OBIA) to map LULC in Dak Nong UNESCO Global Geopark, Vietnam, using Landsat imagery data for the period from 2005 to 2022. The accuracy results show an overall accuracy (OA) of 83.97 % (2005), 85.38 % (2015) and 86.75 % in 2022 while the results of the Kappa coefficient were 0.82 (2005), 0.83 (2015) and 0.84 (2022). Accordingly, it was concluded that the method proposed here is useful for LULC detection and can be applied to other areas with similar characteristics. The derived maps can also inform as a document to UNESCO and national - level decision making.
Toàn văn bài báo
Trích dẫn
[2]. M. J. Santos et al., (2021). The role of land use and land cover change in climate change vulnerability assessments of biodiversity: A systematic review. Landsc. Ecol., vol. 36, no. 12, p. 3367 - 3382, Dec. 2021. Doi: 10.1007/s10980-021-01276-w.
[3]. Md. J. Faruque et al., (2022). Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sens. Appl. Soc. Environ., vol. 25, p. 100699, Jan. 2022. Doi: 10.1016/j.rsase.2022.100699.
[4]. E. A. Alshari and B. W. Gawali (2021). Development of classification system for LULC using remote sensing and GIS. Glob. Transit. Proc., vol. 2, no. 1, p. 8 - 17, Jun. 2021. Doi: 10.1016/j.gltp.2021.01.002.
[5]. A. Rahman et al., (2020). Performance of different machine learning algorithms on satellite image classification in rural and urban setup. Remote Sens. Appl. Soc. Environ., vol. 20, p. 100410, Nov. 2020. Doi: 10.1016/j.rsase.2020.100410.
[6]. V.-M. Pham, S. Van Nghiem, Q.-T. Bui, T. M. Pham and C. Van Pham (2019). Quantitative assessment of urbanization and impacts in the complex of Hue Monuments, Vietnam. Appl. Geogr., vol. 112, p. 102096, Nov. 2019. Doi: 10.1016/j.apgeog.2019.102096.
[7]. Q.-T. Bui, M. V. Pham, Q.-H. Nguyen, L. X. Nguyen and H. M. Pham (2019). Whale optimization algorithm and adaptive neuro - fuzzy inference system: A hybrid method for feature selection and land pattern classification. Int. J. Remote Sens., vol. 40, no. 13, p. 5078 - 5093, Jul. 2019. Doi: 10.1080/01431161.2019.1578000.
[8]. A. E. Maxwell, T. A. Warner and F. Fang (2018). Implementation of machine - learning classification in remote sensing: An applied review. Int. J. Remote Sens., vol. 39, no. 9, p. 2784 - 2817, May 2018. Doi: 10.1080/01431161.2018.1433343.
[9]. P. Du, A. Samat, B. Waske, S. Liu and Z. Li (2015). Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS J. Photogramm. Remote Sens., vol. 105, p. 38 - 53, Jul. 2015. Doi: 10.1016/j.isprsjprs.2015.03.002.
[10]. B. E. Lefulebe, A. Van der Walt and S. Xulu (2022). Fine - scale classification of urban land use and land cover with planet scope imagery and machine learning strategies in the city of Cape town, South Africa. Sustainability, vol. 14, no. 15, p. 9139, Jul. 2022. Doi: 10.3390/su14159139.
[11]. D. C. Duro, S. E. Franklin and M. G. Dubé (2012). Multi - scale object - based image analysis and feature selection of multi - sensor earth observation imagery using random forests. Int. J. Remote Sens., vol. 33, no. 14, p. 4502 - 4526, Jul. 2012. Doi: 10.1080/01431161.2011.649864.