Spectral Variability and Discrimination Assessment in a Tropical Peat Swamp Landscape Using CHRIS/PROBA Data

Liesenberg, Veraldo; Boehm, Hans-Dieter Viktor; Gloaguen, Richard

doi:10.2747/1548-1603.47.4.541

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…In other words, nadir observations provides certain spectral separation between classes, but the spectral information provided by off-nadir angles is more relevant for the discrimination of successional stages of TDF. Other studies that used multiangular images for land cover mapping also noticed that better results are obtained from off-nadir angles than nadir observation, especially related to vegetation classes [12], [14], [15], [19], [42], and [43].…”

Section: Discussionmentioning

confidence: 91%

“…This problem has been encountered by many authors in the purpose of discriminating primary and secondary tropical rainforests with satellite imagery [39]- [41], [14]. Nevertheless, Kappa values for the overall land cover map and for the classes accuracies present successful results in the discrimination of both classes for the dry season, meaning that not only the input spectral data, but also the nonparametric method selected, have been determinant in the separation of intermediate and late stages of succession of TDF.…”

Section: Discussionmentioning

confidence: 93%

“…In 2008, Kneubühler et al [13] observed an improvement on map accuracy proportionally related to the addition of CHRIS/PROBA's angles of observation into the classification of Alpen boreal forests. CHRIS/PROBA has also been used in tropical areas for the identification of successional stages of regrowth within Indonesian rainforests [14]. The authors used a single image of the wet season and evaluated map accuracy of classifications performed by the use of single CHRIS/PROBA angle in front of multiangular classification (four CHRIS/PROBA angles together).…”

Section: Introductionmentioning

confidence: 99%

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Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees

Millán

Sánchez‐Azofeifa

Málvarez

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Most satellites for environmental monitoring point ground targets from a perpendicular position to the terrain, also known as nadir. The CHRIS/PROBA platform provides four extra angles of observation, plus nadir pointing: −55 • , −36 • , 0 • , +36 • , and +55 • . The purpose of this study is to investigate the best season (wet or dry season) and angle of observation to map tropical dry forest succession in Brazil. Nonparametric decision trees were used to build up classification maps based on principal component analysis (PCA) inputs. Our results indicate that the use of off-nadir data improves the map accuracy of successional stages of tropical dry forests and riparian forests. Particularly, extreme and negative angles of observation generate higher map accuracies, suggesting that tree shadows are enhancing spectral differences between the studied vegetation classes. Images from the dry season provide better total and classes' map accuracies for late and intermediate stages of tropical dry forests. On the other hand, some classes, such as riparian forests and early stage of tropical forests need the use of off-nadir angles of observation to reach a minimum accuracy and best scores are reached using wet season's images.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees

Millán

Sánchez‐Azofeifa

Málvarez

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The logging has led to the more homogeneous canopy structure in MF2; therefore, it has higher reflectance of red and NIR. On the other hand, shadows of emergent trees from MF1 and NF brought more shadowing effects in the canopy structure; therefore, the signal of red and NIR towards the sensor is decreased [52]. After preprocessing of the ETM+ scene, we calculated the normalized difference vegetation index (NDVI) [53], the principal component analysis (PCA) [54], a tasseled cap transformation (TCT) [55,56] and the gray level co-occurrence matrix (GLCM) [57].…”

Section: Landsat Processingmentioning

confidence: 99%

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Attarchi

Gloaguen

2014

Remote Sensing

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Abstract:The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, because topography can hinder AGB estimation in mountain terrain. Therefore, we focused on the use of atmospherically and topographically corrected multispectral Landsat ETM+ and Advanced Land-Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) to estimate forest AGB. We then evaluated 11 different multiple linear regression models using different combinations of corrected spectral and PolSAR bands and their derived features. The use of corrected ETM+ spectral bands and GLCM textures improves AGB estimation significantly (adjusted R 2 = 0.59; RMSE = 31.5 Mg/ha). Adding SAR backscattering coefficients as well as PolSAR features and textures increase substantially the accuracy of AGB estimation (adjusted R 2 = 0.76; RMSE = 25.04 Mg/ha). Our results confirm that topographically and atmospherically corrected data are indispensable for the estimation of mountain forest's physical properties. We also demonstrate that only the joint use of PolSAR and multispectral data allows a good estimation of AGB in those regions. OPEN ACCESSRemote Sens. 2014, 6 3694

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“…Em geral, os perfis espectrais das 15 classes de uso e cobertura do solo apresentados na Figura 3 corroboram aqueles reportados em Galvão et al (2009), Liesenberg et al (2010 e Ponzoni et al (2012). Uma análise desses perfis espectrais, mostra que uma classificação pautada apenas nas características espectrais dos objetos tenderia a apresentar inconsistências entre classes com perfis espectrais semelhantes.…”

Section: Resultsunclassified

Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático

Liempt

Messias

Liesenberg

et al. 2015

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi avaliar o desempenho dos classificadores digitais SVM e K-NN para a classificação orientada a objeto em imagens Landsat-8, aplicados ao mapeamento de uso e cobertura do solo da Alta Bacia do Rio Piracicaba-Jaguari, MG. A etapa de pré-processamento contou com a conversão radiométrica e a minimização dos efeitos atmosféricos. Em seguida, foi feita a fusão das bandas multiespectrais (30 m) com a banda pancromática (15 m). Com base em composições RGB e inspeções de campo, definiramse 15 classes de uso e cobertura do solo. Para a segmentação de bordas, aplicaram-se os limiares 10 e 60 para as configurações de segmentação e união no aplicativo ENVI. A classificação foi feita usando SVM e K-NN. Ambos os classificadores apresentaram elevados valores de índice Kappa (k): 0,92 para SVM e 0,86 para K-NN, significativamente diferentes entre si a 95% de probabilidade. Uma significativa melhoria foi observada para SVM, na classificação correta de diferentes tipologias florestais. A classificação orientada a objetos é amplamente aplicada em imagens de alta resolução espacial; no entanto, os resultados obtidos no presente trabalho mostram a robustez do método também para imagens de média resolução espacial.Termos para indexação: classificação orientada a objetos, gestão territorial, sensoriamento remoto, resolução espacial, uso e cobertura do solo. Comparative analysis of digital classifiers of Landsat-8 images for thematic mapping proceduresAbstract -The objective of this work was to evaluate the performance of SVM and K-NN digital classifiers for the object-based classification on Landsat-8 images, applied to mapping of land use and land cover of Alta Bacia do Rio Piracicaba-Jaguari, in the state of Minas Gerais, Brazil. The pre-processing step consisted of using radiometric conversion and atmospheric correction. Then the multispectral bands (30 m) were merged with the panchromatic band (15 m). Based on RGP compositions and field inspection, 15 land-use and land-cover classes were defined. For edge segmentation, the bounds were set to 10 and 60 for segmentation configuring and merging in the ENVI software. Classification was done using SVM and K-NN. Both classifiers showed high values for the Kappa index (k): 0.92 for SVM and 0.86 for K-NN, significantly different from each other at 95% probability. A major improvement was observed for SVM by the correct classification of different forest types. The object-based classification is largely applied on high-resolution spatial images; however, the results of the present work show the robustness of the method also for medium-resolution spatial images.Index terms: object-based classification, territorial management, remote sensing, spatial resolution, land use and land cover. IntroduçãoA gestão territorial demanda uma constante caracterização dos recursos naturais, além de seu monitoramento contínuo, com o objetivo de sua utilização de forma racional. A execução de projetos de levantamento e mapeamento da superfície terrestre têm-se beneficiado ...

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Spectral Variability and Discrimination Assessment in a Tropical Peat Swamp Landscape Using CHRIS/PROBA Data

Cited by 8 publications

References 41 publications

Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees

Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees

Improving the Estimation of Above Ground Biomass Using Dual Polarimetric PALSAR and ETM+ Data in the Hyrcanian Mountain Forest (Iran)

Análise comparativa de classificadores digitais em imagens do Landsat-8 aplicados ao mapeamento temático

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