@InProceedings{RodriguesBenSoaKörFon:2020:ReSeIm,
author = "Rodrigues, Marcos Ant{\^o}nio de Almeida and Bendini, Hugo do
Nascimento and Soares, Anderson Reis and K{\"o}rting, Thales Sehn
and Fonseca, Leila Maria Garcia",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Remote sensing image time series metrics for distinction between
pasture and croplands using the random forest classifier",
booktitle = "Proceedings...",
year = "2020",
pages = "149--154",
organization = "IEEE Latin American GRSS \& ISPRS Remote Sensing
Conference (LAGIRS)",
keywords = "random forest, pasture, image time series.",
abstract = "Pasture and croplands play an important role in Brazils economic
and political scenarios, once its PIB (Raw Internal Product) is
mainly based on what is exported from the rural production, such
as meat and soybean, and government, with its regulations, is
partresponsible for the establishment and maintaining of the
conditions so that the trades can go well. In addition, these two
types of land use correspond together to aprox. one third of the
country extension. Moreover, frequently lands occupation is
subject of discussion concerning its potential use for the reason
of conflicts including Brazilian traditional communities, landless
people and big farmers. Considering it, mapping pasture and
croplands accurately is crucial for the country administration, in
both economic and political spheres. Certainly, remote sensing is
the very manner to tackle this issue, although this may not be an
easy task due to the spectral similarity between these patterns.
This work, hence, aims to distinct pasture from croplands in an
experimental subset area of Brazilian Cerrado biome, using remote
sensing metric images derived from one-year time series of the
Landsat 8 products. In order to achieve this goal, we utilized six
bands of the OLI sensor and calculated seven metrics, attaining a
compiled dataset with 42 layers. We performed an object-based
supervised classification with the Random Forest algorithm,
considering both spectral and geometrical attributes. Results
showed global accuracy of 80%, with Kappa index of 0.6, and the
potential time series have in separating targets spectrally
similar.",
conference-location = "Santiago, Chile",
conference-year = "22-26 mar.",
doi = "10.1109/lagirs48042.2020.9165671",
url = "http://dx.doi.org/10.1109/lagirs48042.2020.9165671",
isbn = "9781728143507",
label = "lattes: 5123287769635741 5
RodriguesBenSoaK{\"o}rFon:2020:ReSeIm",
language = "en",
targetfile = "rodrigues_remote.pdf",
urlaccessdate = "08 maio 2024"
}