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4/2021 vol. 31
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ORIGINAL ARTICLE
The Application of Sentinel-2 Data for Automatic Forest Cover Changes Assessment – Białowieża Primeval Forest Case Study
Renata Pelc-Mieczkowska 1
 
 
 
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University of Warmia and Mazury in Olsztyn, Faculty of Geoengineering
 
 
Online publication date: 2021-12-30
 
 
Publication date: 2021-12-01
 
 
Civil and Environmental Engineering Reports 2021;31(4):148-166
DOI: https://doi.org/10.2478/ceer-2021-0054
 
 
Article (PDF)
References (49)
 
KEYWORDS
remote sensing
radiometric indices
Sentinel-2
forest cover
 
ABSTRACT
Sentinel-2 mission, as a part of European Space Agency Earth Observation Program Copernicus, designed specifically for Earth surface observations provides images in 13 bands. That imaging is used to analyse many subject areas as Land monitoring, Emergency management, Security and Climate change. In the presented paper the application of Sentinel-2 data for automatic forest cover changes detection has been analysed. As input data, B02, B03, B04 and B08 bands have been used to compute Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI). To track changes in the forest cover over the years, for each pixel the difference in the value of vegetation indices between consecutive years have been calculated. Then the threshold was set at the level of 0.15. The values of differences above the threshold mean a significant decrease in the quality of vegetation and may be considered areas of deforestation.
 
REFERENCES (49)
1.
Gascon, F et al. 2014. Copernicus Sentinel-2 mission: products, algorithms and Cal/Val. Earth observing systems XIX (Vol. 9218, p. 92181E). International Society for Optics and Photonics. https://doi.org/10.1117/12.206....
CrossRef
 
Google Scholar
 
 
2.
Szantoi, Z and Strobl, P 2019. Copernicus Sentinel-2 calibration and validation. European Journal of Remote Sensing, 52:1, 253-255. DOI: 10.1080/22797254.2019.1582840.
CrossRef
 
Google Scholar
 
 
3.
Drusch, M et al. 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote sensing of Environment 120, 25-36.
Google Scholar
 
 
4.
Addabbo, P, Focareta, M, Marcuccio, S, Votto, C and Ullo, SL 2016. Contribution of Sentinel-2 data for applications in vegetation monitoring. ACTA IMEKO 5(2), 44-54.
Google Scholar
 
 
5.
Spoto, F et al. 2012. Overview of sentinel-2. IEEE International Geoscience and Remote Sensing Symposium 1707-1710.
Google Scholar
 
 
6.
Masek, J, Ju, J, Roger, JC, Skakun, S, Claverie, M and Dungan, J 2018. Harmonized Landsat/Sentinel-2 products for land monitoring. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium 8163-8165.
Google Scholar
 
 
7.
Zheng, H 2017. Performance evaluation of downscaling Sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sensing 9(12), 1274.
Google Scholar
 
 
8.
Abdi, AM 2020. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing 57(1), 1-20.
Google Scholar
 
 
9.
Steinhausen, MJ, Wagner, PD, Narasimhan, B and Waske, B 2018. Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. International journal of applied earth observation and geoinformation. 73, 595-604.
Google Scholar
 
 
10.
Urban, M 2018. Surface moisture and vegetation cover analysis for drought monitoring in the Southern Kruger National Park using sentinel-1, sentinel-2, and landsat-8. Remote Sensing 10(9), 1482.
Google Scholar
 
 
11.
Dotzler, S Hill, J Buddenbaum, H and Stoffels, J 2015. The potential of EnMAP and Sentinel-2 data for detecting drought stress phenomena in deciduous forest communities. Remote Sensing 7(10), 14227-14258.
Google Scholar
 
 
12.
Immitzer, M Vuolo, F and Atzberger, C 2016. First experience with Sentinel- 2 data for crop and tree species classifications in central Europe. Remote sensing 8(3), 166.
Google Scholar
 
 
13.
Erinjery, JJ Singh, M and Kent, R 2018. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sensing of Environment 216, 345-354.
Google Scholar
 
 
14.
Daryaei, A, Sohrabi, H, Atzberger, C and Immitzer, M 2020. Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data. Computers and Electronics in Agriculture 177, 105686.
Google Scholar
 
 
15.
Feng, J, Dong, B, Qin, T, Liu, S, Zhang, J and Gong, X 2021. Temporal and Spatial Variation Characteristics of NDVI and Its Relationship with Environmental Factors in Huangshui River Basin from 2000 to 2018. Polish Journal of Environmental Studies.
Google Scholar
 
 
16.
Puletti, N, Chianucci, F and Castaldi, C 2018. Use of Sentinel-2 for forest classification in Mediterranean environments. Ann. Silvic. Res 42, 32-38.
Google Scholar
 
 
17.
Hościło, A and Lewandowska, A 2019. Mapping forest type and tree species on a regional scale using multi-temporal Sentinel-2 data. Remote Sensing 11(8), 929.
Google Scholar
 
 
18.
Lima, TA, Beuchle, R, Langner, A, Grecchi, RC, Griess, VC and Achard, F 2019. Comparing Sentinel-2 MSI and Landsat 8 OLI imagery for monitoring selective logging in the Brazilian Amazon. Remote Sensing 11(8), 961.
Google Scholar
 
 
19.
Masiliūnas, D 2017. Evaluating the potential of Sentinel-2 and Landsat Image time series for detecting selective logging in the Amazon. Wageningen University and Research Centre: Wageningen, The Netherlands.
Google Scholar
 
 
20.
Khovratovich, T Bartalev, S Kashnitskii, A Balashov, I and Ivanova, A 2020. Forest change detection based on sub-pixel tree cover estimates using Landsat-OLI and Sentinel 2 data. In IOP Conference Series: Earth and Environmental Science 507(1), 012011.
Google Scholar
 
 
21.
Zhang, Y et al. M. (2021). Tracking small-scale tropical forest disturbances: Fusing the Landsat and Sentinel-2 data record. Remote Sensing of Environment 261, 112470.
Google Scholar
 
 
22.
Pałaś, KW and Zawadzki, J 2020. Sentinel-2 Imagery Processing for Tree Logging Observations on the Białowieża Forest World Heritage Site. Forests 11(8), 857.
Google Scholar
 
 
23.
Huete, A, Didan, K, Miura, T, Rodriguez, EP, Gao, X and Ferreira, LG 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment 83(1-2), 195-213.
Google Scholar
 
 
24.
Strong, CJ, Burnside, NG and Llewellyn, D 2017. The potential of small- Unmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index. PloS one 12(10), e0186193.
Google Scholar
 
 
25.
Sripada, RP, Heiniger, RW, White, JG and Weisz, R 2005. Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agronomy Journal 97(5), 1443-1451.
Google Scholar
 
 
26.
Rouse, JW, Haas, RH, Schell, JA and Deering, DW 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 351(1974), 309.
Google Scholar
 
 
27.
Carlson, TN and Ripley, DA 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote sensing of Environment 62(3), 241-252.
Google Scholar
 
 
28.
Pettorelli, N, Vik, JO, Mysterud, A, Gaillard, JM, Tucker, CJ and Stenseth, NC 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution 20(9), 503-510.
Google Scholar
 
 
29.
Gitelson, AA and Merzlyak, MN 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research 22(5), 689-692.
Google Scholar
 
 
30.
Ballester, C, Brinkhoff, J, Quayle, WC and Hornbuckle, J 2019. Monitoring the Effects of Water Stress in Cotton Using the Green Red Vegetation Index and Red Edge Ratio. Remote Sensing 11(7), 873.
Google Scholar
 
 
31.
Price, JC 1993. Estimating leaf area index from satellite data. IEEE Transactions on Geoscience and Remote Sensing 31(3), 727-734.
Google Scholar
 
 
32.
Chen, JM and Black, TA 1992. Defining leaf area index for non-flat leaves. Plant, Cell & Environment 15(4), 421-429.
Google Scholar
 
 
33.
Haboudane, D, Miller, JR, Pattey, E, Zarco-Tejada, PJ and Strachan, IB 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote sensing of environment 90(3), 337-352.
Google Scholar
 
 
34.
Huete, AR 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment 25(3), 295-309.
Google Scholar
 
 
35.
Gilabert, MA, González-Piqueras, J, Garcıa-Haro, FJ and Meliá, J 2002. A generalized soil-adjusted vegetation index. Remote Sensing of environment 82(2-3), 303-310.
Google Scholar
 
 
36.
Dash, J and Curran, PJ 2007. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research 39(1), 100-104.
Google Scholar
 
 
37.
Dash, J, Jeganathan, C and Atkinson, PM 2010. The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India. Remote Sensing of Environment 114(7), 1388-1402.
Google Scholar
 
 
38.
Susantoro, TM, Wikantika, K, Saepuloh, A and Harsolumakso, AH 2018. Selection of vegetation indices for mapping the sugarcane condition around the oil and gas field of North West Java Basin, Indonesia. In IOP Conference Series: Earth and Environmental Science 149(1), 012001.
Google Scholar
 
 
39.
Lasaponara, R 2006. On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series. Ecological modelling 194(4), 429-434.
Google Scholar
 
 
40.
Anyamba, A and Tucker, CJ 2005. Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. Journal of arid environments 63(3), 596-614.
Google Scholar
 
 
41.
Susantoro, TM Wikantika, K Saepuloh, A and Harsolumakso, AH 2018. Utilization of vegetation indices to interpret the possibility of oil and gas microseepages at ground surface. In IOP Conference Series: Earth and Environmental Science 145(1), 012012.
Google Scholar
 
 
42.
Kumar, P, Rani, M, Pandey, PC, Majumdar, A and Nathawat, MS 2010. Monitoring of deforestation and forest degradation using remote sensing and GIS: A case study of Ranchi in Jharkhand (India). Report and opinion 2(4), 14-20.
Google Scholar
 
 
43.
Mkhabela, MS, Bullock, P, Raj, S, Wang, S and Yang, Y 2011. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151(3), 385-393.
Google Scholar
 
 
44.
Pettorelli, N 2013. The normalized difference vegetation index. Oxford University Press.
Google Scholar
 
 
45.
Sruthi, S and Aslam, MM 2015. Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia 4, 1258-1264.
Google Scholar
 
 
46.
Vicente-Serrano, SM, Cuadrat-Prats, JM and Romo, A 2006. Early prediction of crop production using drought indices at different time-scales and remote sensing data: application in the Ebro Valley (north-east Spain). International Journal of Remote Sensing 27(3), 511-518.
Google Scholar
 
 
47.
Kushida, K et al. 2010. Spectral indices for remote sensing of phytomass and deciduous shrub changes in Alaskan arctic tundra. In AGU Fall Meeting Abstracts 2010, GC43B-0977.
Google Scholar
 
 
48.
Vasudevan, A, Kumar, DA and Bhuvaneswari, NS 2016. Precision farming using unmanned aerial and ground vehicles. IEEE Technological Innovations in ICT for Agriculture and Rural Development 146-150.
Google Scholar
 
 
49.
Main-Knorn, M, Pflug, B, Louis, J, Debaecker, V, Müller-Wilm, U and Gascon, F 2017. Sen2Cor for sentinel-2. Image and Signal Processing for Remote Sensing XXIII 10427, 1042704.
Google Scholar
 
 
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