Abstract
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.
Highlights
Information on the spatial explicit distribution of ecosystem services (ES) is important for their conservation and management, policy design, implementation and monitoring, fulfillment of reporting processes to national and international mechanisms, as well as for communicating complex information to the public in order to increase awareness and engagement in biodiversity conservation and natural capital protection [1,2,3,4]
Global open-access land cover products generated through Earth Observation (EO) data e.g., GlobeLand30 [12], S2GLC [13], Copernicus Global Land Cover [14], as well as continental/regional products provided through the Copernicus land monitoring service, such as the Corine Land Cover [15], provide information that can be potentially used as relevant to ES mapping and assessment processes at global, national, regional, and local scales
Different classification schemes were evaluated for fine-scale, land-cover mapping at a national scale, following a fine-scale (21 classes) classification nomenclature
Summary
Information on the spatial explicit distribution of ecosystem services (ES) is important for their conservation and management, policy design, implementation and monitoring, fulfillment of reporting processes to national and international mechanisms, as well as for communicating complex information to the public in order to increase awareness and engagement in biodiversity conservation and natural capital protection [1,2,3,4]. Global open-access land cover products generated through Earth Observation (EO) data e.g., GlobeLand30 [12], S2GLC [13], Copernicus Global Land Cover [14], as well as continental/regional products provided through the Copernicus land monitoring service, such as the Corine Land Cover [15], provide information that can be potentially used as relevant to ES mapping and assessment processes at global, national, regional, and local scales Such operational, land-cover efforts might present limitations related to the use of diverse input data, accuracy variability over different areas and different classes, coarse update intervals and outdatedness in comparison to the real world [16], coarse thematic resolution and/or class diversity, and coarse minimum mapping units (MMU). The above shortcomings raise the need for timely and on-demand land-cover datasets that could replace or complement available, freely distributed products, providing timely information to the end-users and addressing some, if not all, of the above shortcomings [17]
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