2021
DOI: 10.1088/1755-1315/876/1/012003
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Assessment of the health status of tree stands based on Sentinel - 2B remote sensing materials and the short-wave vegetation index SWVI

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Cited by 5 publications
(3 citation statements)
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“…Indeed, as previous research has shown, the S1A backscatter coefficient and the S2B vegetation indices were effective and frequent predictors. The C-band backscatter "VV" is very sensitive to soil moisture in open areas (Nonni et al, 2018), and it has been found to be useful in distinguishing the different types of vegetated or herbaceous land covers in the study area (crop, Thick Forest, Clear Drill, Water bodies) (Alekseev & Chernikhovskii, 2021;Khellouk et al, 2021). In the study field, a method based on a combination of remote sensing spectral indices, biological variables, and backscatter coefficients was created and tested, and it was shown to be effective in identifying LULCC and change detection.…”
Section: Sentinel Classificationmentioning
confidence: 99%
“…Indeed, as previous research has shown, the S1A backscatter coefficient and the S2B vegetation indices were effective and frequent predictors. The C-band backscatter "VV" is very sensitive to soil moisture in open areas (Nonni et al, 2018), and it has been found to be useful in distinguishing the different types of vegetated or herbaceous land covers in the study area (crop, Thick Forest, Clear Drill, Water bodies) (Alekseev & Chernikhovskii, 2021;Khellouk et al, 2021). In the study field, a method based on a combination of remote sensing spectral indices, biological variables, and backscatter coefficients was created and tested, and it was shown to be effective in identifying LULCC and change detection.…”
Section: Sentinel Classificationmentioning
confidence: 99%
“…Vegetation indices used in lithology mapping include the normalized difference vegetation index (NDVI), greenness and short-wave infrared vegetation index (VIGS), and short-wave infrared normalized vegetation index (SWVI) [28,75]. NDVI indicates vegetation coverage [78], VIGS detects vegetation stress from heavy metal elements [28], and SWVI reflects vegetation leaf water content [95].…”
Section: Spectral Featuresmentioning
confidence: 99%
“…In our approach, we combined the local deposition measurements and the remote sensing method. According to our knowledge, very few similar studies have been published (Alekseev and Chernikhovskii, 2021) [38]. In addition, our sample areas are also unique because the monitoring plots were located in the Pannonian Biogeographic Region, which is a meeting edge between the Central European deciduous forest zone and the continental forest-steppe region.…”
mentioning
confidence: 99%