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Open access

Nicoleta Iurist (Dumitraşcu), Florian Stătescu and Iustina Lateş


Earth observation and space analysis of land areas, oceanic and atmospheric phenomena is a necessity nowadays.

European Space Agency (ESA) is developing a new family of satellites, called Sentinel, in order to perform the operational needs of the environmental monitoring program, Copernicus. Since 2014 until now ESA have successfully launched four satellites, which have a proven track record.

This paper contains information about Sentinel constellation, features of the satellite images and also the applications of Sentinel satellite images. This paper also describes how to purchase satellite data and the software that can be used to view and analysis data are named.

The aim of this paper is to analyze the changes of land cover and land use of study area, in two different periods, based on Sentinel satellite images.

Open access

Paul Macarof and Florian Statescu


This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

Open access

Casiana Marcu, Florian Stătescu and Nicoleta Iurist


Lidar has provided significant benefits for forest development and engineering operations and provides a good means to collect information on forest stands.

A common analysis using LiDAR data computes the CHM as a difference between DSM and DTM, create a DTM from the ground returns and a DSM from the first returns and subtract the two rasters, but how exactly are generated the DTM and the DSM. Irregular height variations, called data pits are present in the CHM and appear when the first Lidar return is far below the canopy. The purpose of this study is an approach that computes the CHM directly from height-normalized LiDAR points.