@Article{GueriniFoKuplQuad:2020:EsNaGr,
author = "Guerini Filho, Marildo and Kuplich, Tatiana Mora and Quadros,
Fernando L. F. de",
affiliation = "{Universidade Federal do Rio Grande do Sul (UFRGS)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal
de Santa Maria (UFSM)}",
title = "Estimating natural grassland biomass by vegetation indices using
Sentinel 2 remote sensing data",
journal = "International Journal of Remote Sensing",
year = "2020",
volume = "41",
number = "8",
pages = "2861--2876",
month = "Apr.",
abstract = "Estimation of natural grassland biomass was carried out in a
region located in the Brazilian Pampa, using field and remote
sensing data and statistical models. The study was conducted in a
grassland with a rotational grazing system, with grazing rest
interval based on accumulated thermal sums 375 and 750 Degrees Day
(DD). One image of the MSI (MultiSpectral Instrument) sensor
aboard the Sentinel-2 satellite was evaluated and calibrated by 57
sampled biomass units collected in the field. Initially, the image
was preprocessed, with extraction of the reflectance values of the
Sentinel-2 bands, re-sampling of the pixels to 20 metres and
calculation of vegetation indices. Data statistical analyses
indicated significant correlations between field and remote
sensing data. Multiple linear regression analyses were applied at
each grazing rest interval using the remote sensing variables as
predictors (independent) of the biomass (dependent). Among the
variables, it is important to highlight the significant
correlation of the red-edge bands with the biomass. The equations
for estimating green biomass-presented coefficients of
determination (R2 ) of R2 = 0.51 for the rest interval 375 DD and
R2 = 0.65 for the rest interval 750 DD, while the senescent and
total biomass generated adjustments with R2 \≤ 0.50 for the
two rest intervals. Biomass estimates results were satisfactory,
regardless of the interval evaluated. Sampling schemes at
different seasons of the year and further spectral and field
variables (spectral and biomass) are suggested to improve even
more the accuracy of the estimates.",
doi = "10.1080/01431161.2019.1697004",
url = "http://dx.doi.org/10.1080/01431161.2019.1697004",
issn = "0143-1161",
language = "en",
targetfile = "guerini_estimating.pdf",
urlaccessdate = "27 abr. 2024"
}