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Article

Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

1
Department of Agricultural, Food, and Environmental Sciences, University of Perugia, 06121 Perugia, Italy
2
Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Località Feo di Vito, 89122 Reggio Calabria, Italy
3
Maiella National Park, Via Badia 28, 67039 Sulmona, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Georgios Mallinis
Remote Sens. 2021, 13(12), 2299; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122299
Received: 28 April 2021 / Revised: 8 June 2021 / Accepted: 9 June 2021 / Published: 11 June 2021
With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area. View Full-Text
Keywords: Google Earth Engine (GEE); land use land cover (LULC); Landsat 8; object-oriented classification; machine learning (ML); Random Forest (RF); SNIC (Simple Non-Iterative Clustering); GLCM (Gray Level Co-occurrence Matrix); pan-sharpening Google Earth Engine (GEE); land use land cover (LULC); Landsat 8; object-oriented classification; machine learning (ML); Random Forest (RF); SNIC (Simple Non-Iterative Clustering); GLCM (Gray Level Co-occurrence Matrix); pan-sharpening
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MDPI and ACS Style

Tassi, A.; Gigante, D.; Modica, G.; Di Martino, L.; Vizzari, M. Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote Sens. 2021, 13, 2299. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122299

AMA Style

Tassi A, Gigante D, Modica G, Di Martino L, Vizzari M. Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park. Remote Sensing. 2021; 13(12):2299. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122299

Chicago/Turabian Style

Tassi, Andrea, Daniela Gigante, Giuseppe Modica, Luciano Di Martino, and Marco Vizzari. 2021. "Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park" Remote Sensing 13, no. 12: 2299. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122299

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