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.

Improved sensor characteristics are generally assumed to increase the potential accuracy of image classification and information extraction from remote sensing imagery. However, the increase in data volume caused by these improvements raise challenges associated with the selection, storage, and processing of this data, and with the cost-effective and timely analysis of the remote sensing datasets. Previous research has extensively assessed the relevance and impact of spatial, spectral and temporal resolution of satellite data on classification accuracy, but little attention has been given to the impact of radiometric resolution. This study focuses on the role of radiometric resolution on classification accuracy of remote sensing data through different classification experiments over three different sites. The experiments were carried out using fine and low scale radiometric resolution images classified through a bagging classification tree. The classification experiments addressed different aspects of the classification road map, including among others, binary and multiclass classification schemes, spectrally and spatially enhanced images, as well as pixel and objects as units of the classification. In addition, the impact of image radiometric resolution on computational time and the information content in fine- and low-resolution images was also explored. While in certain cases, higher radiometric resolution has led to up to 8% higher classification accuracies compared to lower resolution radiometric data, other results indicate that higher radiometric resolution does not necessarily imply improved classification accuracy. Also, classification accuracy of spectral indices and texture bands is not related so much to the radiometric resolution of the original remote sensing images but rather to their own radiometric resolution. Overall, the results of this study suggest that data selection and classification need not always adhere to the highest possible radiometric resolution.

Spatial resolution of river and riverine area is an important aspect of hydraulic flood modeling that affects the accuracy of flood extent. This study compares the accuracy of Digital Elevation Models (DEMs) produced from three methods of land surveying measurements and their effect on the results of river flow modeling and mapping of floodplain. Four data sets have been used for the creation of the DEMs: Light Detection and Ranging (LiDAR) point cloud data (raw data and processed), classic land surveying and digitization of elevation contours from 1:5000 scale topographic maps. LiDAR offers advantages over traditional methods for representing a terrain. Optech ILRIS-3D (Intelligent Laser Ranging and Imaging System) is a land based LiDAR system and has been used in this study. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. In this study, geomorphologic filters, GIS operations and expert knowledge have been applied to produce the bare earth DEM. The HEC-GeoRAS and HEC-RAS software have been used as pre-and post-processing tools to prepare model inputs, simulate of river flow, and delineate flood inundation maps. The methodology has been applied in the suburban part of Xerias river at Volos-Greece, where typical hydrologic and hydraulic methods for ungauged watersheds have been used for flood modeling and inundation mapping. The results show that flood inundation area is significantly affected by the accuracy of DEM spatial resolution and could have significant impact on the delineation and mapping of flood hazard areas.LiDAR, Terrain spatial resolution, HEC-RAS, Flood Modeling and Mapping, AGL, Point cloud, DEM.

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