hrvatski jezikClear Cookie - decide language by browser settings

Global Open Data Remote Sensing Satellite Missions for Land Monitoring and Conservation: A Review

Radočaj, Dorijan; Obhođaš, Jasmina; Jurišić, Mladen; Gašparović, Mateo (2020) Global Open Data Remote Sensing Satellite Missions for Land Monitoring and Conservation: A Review. Land, 9 (11). ISSN 2073-445X

PDF - Published Version - article
Available under License Creative Commons Attribution Non-commercial.

Download (12MB) | Preview


The application of global open data remote sensing satellite missions in land monitoring and conservation studies is in the state of rapid growth, ensuring an observation with high spatial and spectral resolution over large areas. The purpose of this study was to provide a review of the most important global open data remote sensing satellite missions, current state-of-the-art processing methods and applications in land monitoring and conservation studies. Multispectral (Landsat, Sentinel-2, and MODIS), radar (Sentinel-1), and digital elevation model missions (SRTM, ASTER) were analyzed, as the most often used global open data satellite missions, according to the number of scientific research articles published in Web of Science database. Processing methods of these missions’ data consisting of image preprocessing, spectral indices, image classification methods, and modelling of terrain topographic parameters were analyzed and demonstrated. Possibilities of their application in land cover, land suitability, vegetation monitoring, and natural disaster management were evaluated, having high potential in broad use worldwide. Availability of free and complementary satellite missions, as well as the open-source software, ensures the basis of effective and sustainable land use management, with the prerequisite of the more extensive knowledge and expertise gathering at a global scale.

Item Type: Article
Uncontrolled Keywords: multispectral ; Sentinel-2 ; Landsat 8 ; MODIS ; synthetic aperture radar ; Sentinel-1 ; digital elevation model ; SRTM ; machine learning ; spectral index
Subjects: BIOTECHNICAL SCIENCES > Biotechnology
BIOTECHNICAL SCIENCES > Interdisciplinary Technical Sciences
Divisions: Division of Experimental Physics
Depositing User: Jasmina Obhođaš
Date Deposited: 10 Nov 2020 09:21
DOI: 10.3390/land9110402

Actions (login required)

View Item View Item


Downloads per month over past year

Increase Font
Decrease Font
Dyslexic Font