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.

The calculation and determination of the built-up area with the highest possible accuracy is of major importance in urban, suburban and agricultural studies. Until present, different methodologies of satellite image processing in their multispectral space (eg pixel-based classification) have been developed and used in Remote Sensing, allowing the identification and area measurement of the builtup area with very high accuracy. Accordingly, the indexes that have been developed are able to provide the identification and area measurement of the built-up area immediately and quickly, but with less space and measuring accuracy than other methodologies. In this paper the main indexes that are used in Remote Sensing are initially presented. Afterwards, a new index, BUI (Built-Up Index), is presented, whose development is based on the combination of the bands of Landsat ETM+: RED (band 3), SWIR1 (band 5) and SWIR2 (band 7). Its comparison with other indexes takes place in the urban, suburban and agricultural area of a Greek city, and its effectiveness is tested in four other cities (in Greece and Palestine). The results are encouraging, since this index allows the identification of the built-up area more accurately than the others. Finally, the accuracy of area measurement of the builtup area approaches accuracies obtained with other methodologies of Remote Sensing.

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Urban development and alterations in local/urban/sub-urban expansion plans is usually based on the analysis of spatial indicators and how they induce the future development in a specifi c area. For this structural reason the alterations of urban plans mainly occur under a surface observation and analysis of the spatial characteristics. Examining the issue under a more comprehensive framework we also focus on other characteristics such as social and economic policies, health and education, transportation, commercial, etc. that dominate on the development. Thus, urban sustainability indicators from a broad spectrum of activities should be introduced during urban planning. This paper investigates and analyzes the use of such urban sustainability indicators in spatial planning through the use of geographical information systems (GIS) designed for this reason.

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For many decades the multispectral images of the earth’s surface and its objects were taken from multispectral sensors placed on satellites. In recent years, the technological evolution produced similar sensors (much smaller in size and weight) which can be placed on Unmanned Aerial Vehicles (UAVs), thereby allowing the collection of higher spatial resolution multispectral images. In this paper, Parrot’s small Multispectral (MS) camera Sequoia+ is used, and its images are evaluated at two archaeological sites, on the Byzantine wall (ground application) of Thessaloniki city (Greece) and on a mosaic floor (aerial application) at the archaeological site of Dion (Greece). The camera receives RGB and MS images simultaneously, a fact which does not allow image fusion to be performed, as in the standard utilization procedure of Panchromatic (PAN) and MS image of satellite passive systems. In this direction, that is, utilizing the image fusion processes of satellite PAN and MS images, this paper demonstrates that with proper digital processing the images (RGB and MS) of small MS cameras can lead to a fused image with a high spatial resolution, which retains a large percentage of the spectral information of the original MS image. The high percentage of spectral fidelity of the fused images makes it possible to perform high-precision digital measurements in archaeological sites such as the accurate digital separation of the objects, area measurements and retrieval of information not so visible with common RGB sensors via the MS and RGB data of small MS sensors.

Purpose The purpose of this paper is to discuss unmanned aerial vehicle (UAV) and the comparison of image processing software. Design/methodology/approach Images from a drone are used and processed with new digital image processing software, Imagine UAV® of Erdas imagine 2015®. The products (Digital Surface Model and ortho images) are validated with check points (CPs) measured in the field with Global Positioning System. Moreover, similar products are produced by Agisoft PhotoScan® software and are compared with both the products of Imagine UAV and the CPs. Findings The results reveal that the two software tools are almost equivalent, while the accuracies of their products are similar to the accuracies of the external orientations of drone images. Originality/value Comparison of image processing software.