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Paul Macarof, Stefan Groza and Florian Statescu

Abstract

In this paper is investigating correlation between land surface temperature and vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index 2 - EVI2 and Modified Soil Adjusted Vegetation Index - MSAVI) using Landsat images for august, the warmest month, for study area. Iaşi county is considered as study area in this research. Study Area is geographically situated on latitude 46°48'N to 47°35'N and longitude 26°29'E to 28°07'E. Land surface temperature (LST) can be used to define the temperature distribution at local, regional and global scale. First use of LST was in climate change models. Also LST is use to define the problems associated with the environment. A Vegetation Indices (VI) is a spectral transformation what suppose spatial-temporal intercomparisons of terrestrial photosynthetic dynamics and canopy structural variations. Landsat5 TM, Landsat7 ETM+ and Landsat8 OLI, all data were used in this study for modeling. Landsat images was taken for august 1994, 2006 and 2016. Preprocessing of Landsat 5/7/8 data stage represent that process that prepare images for subsequent analysis that attempts to compensate/correct for systematic errors. It was observed that the “mean” parameter for LST increased from 1994 to 2016 at approximately 5°C. Analyzing the data from VI, it can be assumed that the built-up area increased for the Iasi county, while the area occupied by dense vegetation has decreased. Many researches indicated that between LST and VI is a linear relationship. It is noted that the R2 values for the LST-VI correlations decrease from 1994 (i.g.R2= 0.72 for LST-NDVI) in 2016 (i.g.R2= 0.23 for LST-NDVI). In conclusion, these correlation can be used to study vegetation health, drought damage, and areas where Urban Heat Island can occur.

Open access

Nicoleta Iurist (Dumitraşcu), Florian Stătescu and Iustina Lateş

Abstract

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

Abstract

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

Paul Macarof, Cezarina Georgiana Bartic Lazăr and Florian Statescu

Abstract

The main goal of this paper is to detect snow in areas where was detecting and mapping, using Differential Radar Interferometry (DInSAR) technique, ground displacement. DInSAR is a powerful tool to detect and monitor ground deformation. Iaşi county is considered as study area in this research. Study area is geographically situated on latitude 46°48’N to 47°35’N and longitude 26°29’E to 28°07’E. For this paper, to detect and mapping grond displacement, was used Sentinel – 1 images, provided free by The European Space Agency (ESA), for January 2018, with vertical polarization (VV), ascending orbit and Interferometric Wide swath (IW) mode operated. SNAP was used to process the Sentinel – 1 images. Landsat-8 OLI was taken to detect areas cover with snow using Normalized Difference Snow Index (NDSI) - a numerical indicator that shows snow cover over land areas. ArcMap was used to create NDSI map after Landsat-8 data was preprocessed. The presence of snow has been observed both in the areas where it exists vertical displacement positive and negative.

Open access

Casiana Marcu, Florian Stătescu and Nicoleta Iurist

Abstract

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.