The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

Linden, Sebastian van der; Rabe, Andreas; Held, Matthias; Jakimow, Benjamin; Leitão, Pedro J.; Okujeni, Akpona; Schwieder, Marcel; Suess, Stefan; Hostert, Patrick

doi:10.3390/rs70911249

Cited by 195 publications

(114 citation statements)

References 68 publications

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“…Multivariate techniques require large numbers of cases; however, expensive analyses of the examined materials in the laboratories can limit the number of measurements. Our approach considers all bands and we can use any kind of equations involving a [27] in EnMap-Box software [28] and the approach was successfully applied in a study by Buchorn et al [29] using field spectroscopy. Beside the similarity (as HypDA also determines indices based on regression between a measured variable and the spectral bands), HypDA provides several methods to investigate the prerequisites of regression analysis.…”

Section: Resultsmentioning

confidence: 99%

An interactive tool for semi-automatic feature extraction of hyperspectral data

Kovács

Szabó

2016

Open Geosciences

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Abstract:The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in -Hyperspectral Data Analyst (HypDA) -for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas.

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Section: Resultsmentioning

confidence: 99%

An interactive tool for semi-automatic feature extraction of hyperspectral data

Kovács

Szabó

2016

Open Geosciences

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“…Contudo, recentemente, esta limitação está sendo superada com a introdução de rotinas computacionais de código aberto, como o ImageRF (Waske et al 2012) e ImageSVM (Van der Linden et al 2015), de interface amigável em aplicativos de livre acesso, para que a grande maioria dos usuários possa fazer uso destes algoritmos. Essas ferramentas são baseadas na Linguagem de Dados Interativa (Interactive Data Language -IDL) e podem ser inteiramente integradas dentro de softwares comerciais, como IDL/ENVI® (Environment for Visualizing Images), ou também podem ser utilizadas como uma plataforma livre e independente denominada ENMap-Box (Van der Linden et al 2015).…”

Section: Introductionunclassified

“…Essas ferramentas são baseadas na Linguagem de Dados Interativa (Interactive Data Language -IDL) e podem ser inteiramente integradas dentro de softwares comerciais, como IDL/ENVI® (Environment for Visualizing Images), ou também podem ser utilizadas como uma plataforma livre e independente denominada ENMap-Box (Van der Linden et al 2015).…”

Section: Introductionunclassified

Abordagens Para Classificação Do Estádio Sucessional Da Vegetação Do Parque Nacional De São Joaquim Empregando Imagens Landsat-8 E Rapideye

et al. 2017

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Resumo:A classificação remota dos diferentes estádios sucessionais da vegetação ainda constitui um desafio devido à similaridade espectral destas classes. Este artigo tem o objetivo de avaliar o desempenho de imagens Landsat-8 e RapidEye para a classificação do estádio sucessional da vegetação em um fragmento de Floresta Ombrófila Mista, localizado no Parque Nacional de São Joaquim-SC. Para isto, três grupos de variáveis gerados a partir de cada imagem foram avaliados, sendo: (1) composto somente pelas bandas espectrais puras; (2) composto pelas métricas texturais GLCM geradas a partir das bandas espectrais; e (3) composto pelas variáveis dos dois grupos anteriores, além de dois índices de vegetação no caso da imagem Landsat-8, e três índices para a RapidEye. Cada grupo foi testado com os classificadores florestas randômicas (Random Forest-RF), máquinas de vetor de suporte (Support Vector Machine -SVM) e máxima verossimilhança (Maxver). Todos os experimentos alcançaram resultados satisfatórios, com índice Kappa variando de 0,66 a 0,88 e acurácia de usuário e produtor superiores a 50%. O melhor resultado alcançado foi com a imagem Landsat-8, grupo 3, associado ao algoritmo RF. A medida de importância das variáveis obtida com o algoritmo RF mostrou que as métricas texturais média, contraste e dissimilaridade destacaram-se na classificação para ambas as imagens.Palavras-chave: Sucessão florestal; Máquinas de Vetor de Suporte; Florestas Randômicas; Atributos. Abstract:The remote classification of the different vegetation successional stages still represents a challenging task in face of the similar spectral response of such classes. This paper is committed to evaluate the performance of Landsat-8 and RapidEye images in the classification of

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“…In this study, the classification was carried out using EnMAP-Box, an IDL based tool for remotely sensed imagery classification [72].…”

Section: Random Forest and Support Vector Machine For Classificationmentioning

confidence: 99%

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

Hasituya¹,

Chen

2017

Remote Sensing

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Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Therefore, timely and effective mapping of plastic-mulched farmland (PMF) is of great interest to policy-makers to leverage the trade-off between economic profit and adverse environmental impacts. However, it is still challenging to implement remote-sensing-based PMF mapping due to its changing spectral characteristics with the growing seasons of crops and geographic regions. In this study, we examined the potential of multi-temporal Landsat-8 imagery for mapping PMF. To this end, we gathered the information of spectra, textures, indices, and thermal features into random forest (RF) and support vector machine (SVM) algorithms in order to select the common characteristics for distinguishing PMF from other land cover types. The experiment was conducted in Jizhou, Hebei Province. The results demonstrated that the spectral features and indices features of NDVI (normalized difference vegetation index), GI (greenness index), and textural features of mean are more important than the other features for mapping PMF in Jizhou. With that, the optimal period for mapping PMF is in April, followed by May. A combination of these two times (April and May) is better than later in the season. The highest overall, producer's, and user's accuracies achieved were 97.01%, 92.48%, and 96.40% in Jizhou, respectively.

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The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

Cited by 195 publications

References 68 publications

An interactive tool for semi-automatic feature extraction of hyperspectral data

An interactive tool for semi-automatic feature extraction of hyperspectral data

Abordagens Para Classificação Do Estádio Sucessional Da Vegetação Do Parque Nacional De São Joaquim Empregando Imagens Landsat-8 E Rapideye

Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data

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