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Article

National Scale Land Cover Classification for Ecosystem Services Mapping and Assessment, Using Multitemporal Copernicus EO Data and Google Earth Engine

1
School of Rural and Surveying Engineering, Laboratory of Photogrammetry and Remote Sensing Unit (PERS lab), The Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Forest Remote Sensing and Geospatial Analysis Laboratory, Democritus University of Thrace, 68200 Orestiada, Greece
3
Department of Biology, Laboratory of Botany, University of Patras, 26504 Patras, Greece
4
Ministry of Environment & Energy, Directorate of Biodiversity and Natural Environment Management, 11526 Athens, Greece
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3303; https://doi.org/10.3390/rs12203303
Received: 6 September 2020 / Revised: 7 October 2020 / Accepted: 7 October 2020 / Published: 11 October 2020
Land-Use/Land-Cover (LULC) products are a common source of information and a key input for spatially explicit models of ecosystem service (ES) supply and demand. Global, continental, and regional, readily available, and free land-cover products generated through Earth Observation (EO) data, can be potentially used as relevant to ES mapping and assessment processes from regional to national scales. However, several limitations exist in these products, highlighting the need for timely land-cover extraction on demand, that could replace or complement existing products. This study focuses on the development of a classification workflow for fine-scale, object-based land cover mapping, employed on terrestrial ES mapping, within the Greek terrestrial territory. The processing was implemented in the Google Earth Engine cloud computing environment using 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Furthermore, the relevance of different training data extraction strategies and temporal EO information for increasing the classification accuracy was also evaluated. The different classification schemes demonstrated differences in overall accuracy ranging from 0.88% to 4.94% with the most accurate classification scheme being the manual sampling/monthly feature classification achieving a 79.55% overall accuracy. The classification results suggest that existing LULC data must be cautiously considered for automated extraction of training samples, in the case of new supervised land cover classifications aiming also to discern complex vegetation classes. The code used in this study is available on GitHub and runs on the Google Earth Engine web platform. View Full-Text
Keywords: remote sensing; seasonal; Random Forests; OBIA; machine learning; big data; multispectral; radar; GEE; object remote sensing; seasonal; Random Forests; OBIA; machine learning; big data; multispectral; radar; GEE; object
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MDPI and ACS Style

Verde, N.; Kokkoris, I.P.; Georgiadis, C.; Kaimaris, D.; Dimopoulos, P.; Mitsopoulos, I.; Mallinis, G. National Scale Land Cover Classification for Ecosystem Services Mapping and Assessment, Using Multitemporal Copernicus EO Data and Google Earth Engine. Remote Sens. 2020, 12, 3303. https://doi.org/10.3390/rs12203303

AMA Style

Verde N, Kokkoris IP, Georgiadis C, Kaimaris D, Dimopoulos P, Mitsopoulos I, Mallinis G. National Scale Land Cover Classification for Ecosystem Services Mapping and Assessment, Using Multitemporal Copernicus EO Data and Google Earth Engine. Remote Sensing. 2020; 12(20):3303. https://doi.org/10.3390/rs12203303

Chicago/Turabian Style

Verde, Natalia, Ioannis P. Kokkoris, Charalampos Georgiadis, Dimitris Kaimaris, Panayotis Dimopoulos, Ioannis Mitsopoulos, and Giorgos Mallinis. 2020. "National Scale Land Cover Classification for Ecosystem Services Mapping and Assessment, Using Multitemporal Copernicus EO Data and Google Earth Engine" Remote Sensing 12, no. 20: 3303. https://doi.org/10.3390/rs12203303

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