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Title: MODELS FOR DESCRIBING THE DYNAMICS OF FOREST VEGETATION BASED ON REMOTE SENSING TECHNIQUES
MODELS FOR DESCRIBING THE DYNAMICS OF FOREST VEGETATION BASED ON REMOTE SENSING TECHNIQUES

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Ciprian Buzna; Marinel Nicolae Horablaga; Mihai Valentin Herbei; Florin Sala
10.5593/sgem2022V/3.2/s14.42
10.5593/sgem2022V/3.2
1314-2704
978-619-7603-54-5
English
22
3.2
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Forest Ecosystems
The study analyzed forest vegetation in the "Bazos Dendrological Park" area, Timis County, Romania, in order to describe the seasonal variation of the vegetation through imaging analysis based on satellite images (Sentinel 2). The study took place in the period 2021-2022, and each year 7 sets of images (T1 - T7) were taken between the months of April and August. NDMI, NDVI and NBR indices were calculated from the analysis of satellite images. Among the calculated indices, very strong correlations were found between NBR and NDMI (r=-0.928, year 2021), between NBR and NDVI (r=0.947, year 2021; r=0.928, year 2022). Moderate correlations were found between NDVI and NDMI (r=-0.769, year 2021), and weak correlations were found between NDMI and t (r=-0.655, year 2021), between NDVI and NDMI (r=0.617, year 2022). Other weak intensity correlations were also recorded. The variation of the NDVI indices in relation to NDMI and the NBR index in relation to NDMI or to NDVI was described by polynomial equations of 2nd degree, under statistical safety conditions (p<0.001, R2>0.9 for the year 2021; p=0.007, R2 >0.9 in the case of NDVI vs NDMI; p=0.014, R2=0.877 in the case of NBR vs NDVI, respectively p<0.001, R2>0.9 in the case of NBR vs NDMI for the year 2022). In relation to the time interval (t, days), spline models faithfully described the variation of the calculated indices during the study period, under statistical safety conditions ( ? = .0 0061 in the case of NDMI vs t, ? = 0017.0 in the case of NDVI vs t, ? = 0067.0 in the case of NBR vs t, under the conditions of 2021; ? = 0317.0 in the case of NDMI vs t, ? = 0024.0 in the case of NDVI vs t, ? = 0077.0 in the case of NDMI vs t, under the conditions of 2022).
[1] Lechner A.M., Foody G.M., Boyd D.S., Applications in remote sensing to forest ecology and management, One Earth, vol. 2/issue 5, pp 405-412, 2020.
[2] Popescu C.A., Herbei M.V., Sala F., Remote sensing in the analysis and characterization of spatial variability of the territory. A study case in Timis County, Romania, Scientific Papers: Management, Economic Engineering in Agriculture & Rural Development, vol. 20/issue 1, pp 505-514, 2020.
[3] Mohammadpour P., Viegas D.X., Viegas C., Vegetation Mapping with Random Forest using Sentinel 2 and GLCM texture feature - A case study for Lousa Region, Portugal, Remote Sensing, vol. 14, 4585, 2022.
[4] Zhang L., Hu Q., Tang Z., Using Sentinel-2 imagery and Machine Learning Algorithms to assess the inundation status of Nebraska Conservation Easements during 2018–2021, Remote Sensing, vol. 14, 4382, 2022.
[5] Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings third Earth Resources Technology Satellite-1 Symposium, Greenbelt 1974, NASA SP–351/issue 1, pp 3010-3017, 1974.
[6] Wilson E.H., Sader S.A., Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sensing of Environment, vol. 80, pp 385-396, 2002,
[7] Skakun R.S., Wulder M.A., Franklin S.E., Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage, Remote Sensing of Environment, vol. 86/issue 4, pp 433-443, 2003.
[8] Key C.H., Benson N.C, Landscape assessment: Remote sensing of severity, the Normalized Burn Ratio; and ground measure of severity, the composite Burn Index."FIREMON: Fire Effects Monitoring and Inventory System, RMRS-GTR, Ogden, UT: USDA Forest Service, Rocky Mountain Research Station, 2005.
[9] Piragnolo M., Pirotti F., Zanrosso C., Lingua E., Grigolato S., Responding to largescale forest damage in an alpine environment with Remote Sensing, Machine Learning, and Web-GIS, Remote Sensing, vol. 13/issue 8, 1541, 2021.
[10] Tudoran G.M., Dobre A.C., Cic?a A., Pascu I.S., Development of mathematical models for the estimation of dendrometric variables based on Unmanned Aerial Vehicle optical data: A Romanian case study, Forests, vol. 12, 200, 2021.
[11] Stoddart J., de Almeida D.R.A., Silva, C.A., Gorgens E.B., Keller M., Valbuena R., A conceptual model for detecting small-scale forest disturbances based on ecosystem morphological traits, Remote Sensing, vol. 14, 933, 2022.
[12] Planet Team, Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com, 2017.
[13] Hammer O., Harper D.A.T., Ryan P.D., PAST: Paleontological statistics software package for education and data analysis, Palaeontologia Electronica, vol. 4/issue1, pp 1- 9, 2001.
[14] Mlynarczyk A., Konatowska M., Krolewicz S., Rutkowski P., Piekarczyk, J., Kowalewski W., Spectral indices as a tool to assess the moisture status of forest habitats, Remote Sensing, vol. 14, 4267, 2022.
[15] Sousa D., Davis F.W., Easterday K., Reynolds M., Riege L., Butterfield H.S., Katkowski M., Predictive ecological land classification from multi-decadal satellite imagery, Frontiers in Forests and Global Change, vol. 5, 867369, 2022.
The authors thank to the GEOMATICS Research Laboratory from University of Life Sciences "King Michael I" from Timisoara, Romania for the facility of the software use for this study.
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https://doi.org/10.5593/sgem2022V/3.2/s14.42
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Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 06-08 December, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
361-368
06-08 December, 2022
website
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environment, forest vegetation, remote sensing, Sentinel 2, spline models