@Article{AdamiRizMorTheFer:2010:AmPrEs,
author = "Adami, Marcos and Rizzi, Rodrigo and Moreira, Mauricio Alves and
Theodor Rudorff, Bernardo Friedrich and Ferreira, Camila
Cossetin",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Univ Fed
Pelotas, BR-96001970 Capao Do Leao, RS Brazil and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Amostragem probabil{\'{\i}}stica estratificada por pontos para
estimar a {\'a}rea cultivada com soja / Probabilistic stratified
point sampling to estimate soybean crop area",
journal = "Pesquisa Agropecu{\'a}ria Brasileira",
year = "2010",
volume = "45",
number = "6",
pages = "585--592",
month = "July",
note = "Scopus and {CAB Abstracts} and AGRIS and {DOAJ Directory of Open
Access Journals Free} and x",
keywords = "Estat{\'{\i}}sticas Agr{\'{\i}}colas, imagens de
sat{\'e}lite, Glycine max, modelagem, Sistema de
Informa{\c{c}}{\~a}o Geogr{\'a}fica. Glycine max, agricultural
statistics, satellite image, multitemporal images, modeling,
geographic information systems, Agriculture, Glycine max,
agricultural statistics, satellite image, multitemporal images,
modeling, geographic information systems, statistics, frame.",
abstract = "O objetivo deste trabalho foi avaliar o desempenho de um modelo
probabil{\'{\i}}stico de amostragem estratificada por pontos, e
definir um tamanho de amostra adequado para estimar a {\'a}rea
cultivada com soja no Rio Grande do Sul. A {\'a}rea foi
estratificada de acordo com a percentagem de soja cultivada em
cada munic{\'{\i}}pio do estado: menor que 20, de 20 a 40 e
maior que 40%. Foram avaliadas estimativas obtidas por meio de
seis tamanhos de amostras, resultantes da combina{\c{c}}{\~a}o
de tr{\^e}s n{\'{\i}}veis de signific{\^a}ncia (10, 5 e 1%) e
dois valores de erro amostral (5 e 2,5%). Para cada tamanho de
amostra, foram realizados 400 sorteios aleat{\'o}rios. As
estimativas foram avaliadas com base na {\'a}rea de soja obtida
de um mapa tem{\'a}tico de refer{\^e}ncia proveniente de uma
cuidadosa classifica{\c{c}}{\~a}o autom{\'a}tica e visual de
imagens multitemporais dos sat{\'e}lites TM/Landsat-5 e
ETM+/Landsat-7 dispon{\'{\i}}vel para a safra 2000/2001. A
{\'a}rea de soja no Rio Grande do Sul pode ser estimada por meio
de um modelo de amostragem probabil{\'{\i}}stica estratificada
por pontos, sendo que a melhor estimativa {\'e} obtida para o
maior tamanho amostral (1.990 pontos), com diferen{\c{c}}a de
apenas -0,14% em rela{\c{c}}{\~a}o {\`a} estimativa do mapa de
refer{\^e}ncia e um coeficiente de varia{\c{c}}{\~a}o de 6,98%.
ABSTRACT: The objective of this work was to evaluate the
performance of a probabilistic sampling model stratified by points
and to define an appropriate sample size to estimate the
cultivated soybean area in the state of Rio Grande do Sul, Brazil.
The area was stratified according to the percentage of soybean
cultivated in each state municipality: less than 20, from 20 to 40
and more than 40%. Estimates were evaluated based on six sample
sizes, resulting from the combination of three significance levels
(10, 5 and 1%) and two sampling errors (5 and 2,5%), choosing 400
random samples for each sample size. The estimates were compared
to a reference soybean thematic map available for the crop year
2000/2001 that was derived from a careful automatic and visual
classification of multitemporal TM/Landsat-5 and ETM+/Landsat-7
images. The soybean area in Rio Grande do Sul State can be
estimated through a probabilistic sampling model stratified by
points with best estimates obtained for the largest sample size
(1,990 points), which differed -0.14% in relation to the estimate
of the reference map and had a coefficient of variation of 6.98%.
Abstract:The objective of this work was to evaluate the
performance of a probabilistic sampling model stratified by points
and to define an appropriate sample size to estimate the
cultivated soybean area in the state of Rio Grande do Sul, Brazil.
The area was stratified according to the percentage of soybean
cultivated in each state municipality: less than 20, from 20 to 40
and more than 40%. Estimates were evaluated based on six sample
sizes, resulting from the combination of three significance levels
(10, 5 and 1%) and two sampling errors (5 and 2,5%), choosing 400
random samples for each sample size. The estimates were compared
to a reference soybean thematic map available for the crop year
2000/2001 that was derived from a careful automatic and visual
classification of multitemporal TM/Landsat-5 and ETM+/Landsat-7
images. The soybean area in Rio Grande do Sul State can be
estimated through a probabilistic sampling model stratified by
points with best estimates obtained for the largest sample size
(1,990 points), which differed -0.14% in relation to the estimate
of the reference map and had a coefficient of variation of
6.98%.",
doi = "10.1590/S0100-204X2010000600008",
url = "http://dx.doi.org/10.1590/S0100-204X2010000600008",
issn = "0100-204X",
language = "pt",
targetfile = "a08v45n6.pdf",
urlaccessdate = "08 maio 2024"
}