Séries Temporais De Evi Do Modis Para O Mapeamento De Uso E Cobertura Vegetal Do Oeste Da Bahia

Borges, Elane Fiúza; Sano, Edson Eyji

doi:10.1590/s1982-21702014000200030

Cited by 18 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An important aspect related to the Cerrado physiognomies is their seasonality. In order to represent the seasonality in the classification, Borges and Sano (2014) and Abade et al (2015) used time series of vegetation indices derived from MODIS images, and performed the physiognomy classification with Support Vector Machine and Multilayer Perceptron (Abade et al, 2015), and Spectral Angle Mapper (Borges and Sano, 2014). While the revisit time of MODIS is high, the spatial resolution of only 250 meters results in a mixture of physiognomies within single pixels, thus making proper detailing of classes impossible.…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation of Brazilian Savanna Vegetation Using High Spatial Resolution Satellite Data and U-Net

Neves¹,

Körting²,

Fonseca³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.

show abstract

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation of Brazilian Savanna Vegetation Using High Spatial Resolution Satellite Data and U-Net

Neves¹,

Körting²,

Fonseca³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Essa medida de concordância varia de < 0 (nenhuma concordância) a 1 (concordância total). A seguir são apresentadas as classificações do Índice de Kappa segundo Borges & Sano (2014)…”

Section: Métodosunclassified

Análise Temporal Da Cobertura Florestal E Uso Da Terra Na Apa De Guaratuba – Pr

Heimann¹,

Júnior²

2021

BIOFIX

View full text Add to dashboard Cite

O crescimento e ocupação desordenada das terras contribuem para a perda de diversidade biológica nos biomas brasileiros. A criação de áreas especialmente protegidas, como as Áreas de Proteção Ambiental, unidades de conservação da categoria uso sustentável, consiste em um instrumento eficiente para frear tal cenário. Nesse sentido, a APA de Guaratuba, no estado do Paraná, desempenha importante papel de proteção da biodiversidade e controle da ocupação deste espaço, no entanto, é essencial que existam mecanismos de avaliação periódica do seu estado de conservação, por meio da identificação de ameaças que possam surgir à área protegida. Deste modo, o presente estudo objetivou quantificar a dinâmica da ocupação da terra na Área de Proteção Ambiental de Guaratuba – PR. Mapas temáticos de classificação da cobertura da terra foram elaborados para os anos de 1992 e 2017, no intuito de analisar as alterações ocorridas na área de estudo neste mesmo período. Foram utilizadas imagens a partir do TM Landsat, realizou-se a correção atmosférica das imagens com base no método Dark Object Subtraction (DOS). O sistema de projeção adotado foi o Universal Transversa de Mercator – UTM, fuso 22 Sul, Datum WGS-84. Foram definidas as classes de uso e iniciou-se o processo de classificação das imagens, empregando o Software ArcGis para a classificação supervisionada, a partir do algoritmo Maximum Likelihood – Maxver. Os resultados mostram que, ao longo dos 25 anos as áreas ocupadas por floresta nativa, agricultura e áreas consolidadas aumentaram, ao passo que as áreas ocupadas com água, campos, mangue e reflorestamentos diminuíram. Os coeficientes Kappa determinados para as classificações tanto de 1992 quanto 2017 apresentaram qualidade “muito boa” ou “concordância substancial”, confirmando a acurácia da amostragem. Conclui-se que as principais modificações ocorreram nas classes agricultura e reflorestamento, havendo o aumento das áreas utilizadas para agricultura (0,60%) e diminuição dos reflorestamentos (-0,61%).

show abstract

“…This approach is useful for vegetation studies, especially in agricultural areas, since vegetation cover is quite dynamic in time, and the ability to capture these variations is essential to discriminate different types of crops, through its phenological characteristics. For this purpose, most studies have explored time series of vegetation indices like NDVI or EVI from MODIS data (Jakubauskas et al, 2002;Sakamoto et al, 2005;Wardlow et al, 2007;Esquerdo et al, 2011;Arvor et al, 2011;Körting, 2012;Risso et al, 2012;Coutinho et al, 2013;Borges & Sano, 2014;Tomás et al, 2015;Neves et al, 2016). However, there is a demand to produce more detailed maps, which can be obtained from higher spatial resolution satellites such as Landsat-like (Zheng et al, 2015;Peña et al, 2015;Pan et al, 2015).…”

Section: Overviewmentioning

confidence: 99%

“…Afterwards, the time series were smoothed through the double logistic filter (Zhang et al, 2003;Eklundh, 2004). This function is recommended for smoothing image time series on cropland areas in the Brazilian Cerrado (Borges & Sano, 2014).…”

Section: Removal Of Outliers and Null Values And Filteringmentioning

confidence: 99%

Using Landsat 8 Image Time Series for Crop Mapping in a Region of Cerrado, Brazil

Bendini¹,

Sanches²,

Körting³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

show abstract

Séries Temporais De Evi Do Modis Para O Mapeamento De Uso E Cobertura Vegetal Do Oeste Da Bahia

Cited by 18 publications

References 34 publications

Semantic Segmentation of Brazilian Savanna Vegetation Using High Spatial Resolution Satellite Data and U-Net

Semantic Segmentation of Brazilian Savanna Vegetation Using High Spatial Resolution Satellite Data and U-Net

Análise Temporal Da Cobertura Florestal E Uso Da Terra Na Apa De Guaratuba – Pr

Using Landsat 8 Image Time Series for Crop Mapping in a Region of Cerrado, Brazil

Contact Info

Product

Resources

About