Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Abstract

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

“…The Maxver classifier uses the Maximum Likelihood (ML) algorithm which assumes that the digital numbers of a class in the image bands are normally distributed and calculates the probability of each pixel belonging to that class [42]. ML takes into account the mean and covariance vectors of the training sets of a class in a 3dimensional space and assigns each pixel to the class for which it has the highest probability of membership [43]. Since the Maxver classifier is a supervised classification technique, all pixels were assigned to the four land cover classes.…”

Dependência Espacial Do Ruído De Tratores Agrícolas Em Diferentes Rotações Do Motor

“…Os parâmetros do variograma são intervalo da distância em que o variograma atinge o limiar, ou seja, é a distância que a variável apresenta dependência espacial, efeito pepita reflete a erro analítico, indicando uma variabilidade inexplicada de um ponto a outro, o que pode ser devido tanto a erros nas medições ou micro variações não detectadas devido à distância de amostragem, componente estrutural quanto a variação depende da distância, limiar valor em que o variograma é estabilizada e é, aproximadamente, igual à da variância dos dados (SILVA et al, 2013).…”

Defining management zones for a papaya culture considering soil chemical attributes by fuzzy c-means

“…Additionally, the spatial dependency index (SDI) was calculated by [C1/(C0 + C1)]*100, where C1 is the structural variance, and C0 + C1 is the threshold. The SDI was classified as follows: weak (SDI ≤ 25%), moderate (25% < SDI ≤ 75%) and strong (SDI > 75%) (Silva et al, 2013).…”

“…Table 2 shows that the elements Ca and V had strong SDI. According to Silva et al (2013), with moderate and high R1 2 , respectively, but presented low values of R2 2 . All adjustments presented R1 2 higher than 50%, and 75% had R1 2 greater than 75%.…”