07. Object - based land cover classification on the cloud computing platform at Non Nuoc Cao Bang global geopark
Abstract
Land cover is one of the most meaningful input factors for geopark management and monitoring. Previously, extracting of land cover data from remote sensing images has used commercial softwares. Due to the limitations of computer hardware and algorithms, commercial softwares increase the time and cost of mapping. The arrival of cloud computing platform Google Earth Engine (GEE) in 2010 has brought a breakthrough for analyzing and processing remote sensing images. Therefore, in this article, cloud computing technology is studied to build land covers in 2019 for Non Nuoc Cao Bang global geopark area. The classification results comprise 6 types of land covers, including: paddy, rural area, artificial forest, natural forest, water and other cultivated land. The classification accuracy is relatively high, overall accuracy is 83.2%, Kappa coefficient is 0.78. This classification results contribute significantly to the management and monitoring tasks in the geopark area.
Full text article
References
2. Blaschke, T (2010), Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sensing, 65,2–16.
3. Campos-Taberner, M.; García-Haro, F.J.; Camps-Valls, G.; Grau-Muedra, G.; Nutini, F.; Crema, A.; Boschetti, M. (2016), Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote Sens. Environ, 187, 102–118.
4. García-Haro, F.J.; Campos-Taberner, M.; Muñoz-Marí, J.; Laparra, V.; Camacho, F.; Sánchez-Zapero, J.; Camps-Valls, G. (2018), Derivation of global vegetation biophysical parameters from EUMETSAT Polar System. ISPRS J. Photogramm. Remote Sens, 139, 57–74.
5. Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2006). Random Forests for land cover classification. Pattern Recognition Letters, 27(4), 294–300.
6. Gonzalo Mateo-García , Luis Gómez-Chova, Julia Amorós-López, Jordi Muñoz-Marí and Gustau Camps-Valls (2018), Multitemporal Cloud Masking in the Google Earth Engine, Remote sensing.
7. Lalit Kumar and Onisimo Mutanga (2018), Usage, Trends, and Potential, Remote sensing
8. Masoud Mahdianpari, Bahram Salehi, Fariba Mohammadimanesh, Saeid Homayouni and Eric Gill (2019), The FirstWetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform, Remote sensing
9. Onisimo Mutanga, Lalit Kumar (2019), Google Earth Engine Applications, Remote sensing
10. Radhakrishna Achanta and Sabine S¨usstrunk (2017) Superpixels and Polygons using Simple Non-Iterative Clustering, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
11. Waske, B., and Braun, M. (2009). Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 64(5), 450–457.