Forest tree species discrimination in western Himalaya using EO-1 Hyperion

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

doi:10.1016/j.jag.2013.11.011

Cited by 55 publications

(50 citation statements)

References 35 publications

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“…Alternatively, several authors investigated the ability to identify tree species using airborne hyperspectral imagery [27][28][29][30], LiDAR data [31] or a combination of multiple sources [32][33][34][35]. Spectral variability between species related to differences in biochemical properties are better preserved using hyperspectral data which allow continuous sampling of the electromagnetic spectrum.…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

et al. 2016

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Abstract:Mapping forest composition is a major concern for forest management, biodiversity assessment and for understanding the potential impacts of climate change on tree species distribution. In this study, the suitability of a dense high spatial resolution multispectral Formosat-2 satellite image time-series (SITS) to discriminate tree species in temperate forests is investigated. Based on a 17-date SITS acquired across one year, thirteen major tree species (8 broadleaves and 5 conifers) are classified in a study area of southwest France. The performance of parametric (GMM) and nonparametric (k-NN, RF, SVM) methods are compared at three class hierarchy levels for different versions of the SITS: (i) a smoothed noise-free version based on the Whittaker smoother; (ii) a non-smoothed cloudy version including all the dates; (iii) a non-smoothed noise-free version including only 14 dates. Noise refers to pixels contaminated by clouds and cloud shadows. The results of the 108 distinct classifications show a very high suitability of the SITS to identify the forest tree species based on phenological differences (average κ = 0.93 estimated by cross-validation based on 1235 field-collected plots). SVM is found to be the best classifier with very close results from the other classifiers. No clear benefit of removing noise by smoothing can be observed. Classification accuracy is even improved using the non-smoothed cloudy version of the SITS compared to the 14 cloud-free image time series. However conclusions of the results need to be considered with caution because of possible overfitting. Disagreements also appear between the maps produced by the classifiers for complex mixed forests, suggesting a higher classification uncertainty in these contexts. Our findings suggest that time-series data can be a good alternative to hyperspectral data for mapping forest types. It also demonstrates the potential contribution of the recently launched Sentinel-2 satellite for studying forest ecosystems.

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Section: Introductionmentioning

confidence: 99%

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

et al. 2016

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“…Previous works [e.g. Pignatti et al, 2009;Petropoulos et al, 2012;George et al, 2014] have highlighted the usefulness of hyperspectral satellite imagery to improve forest cover classification. However, the experimental trials have resulted in low classification accuracy (even lower than 70% in some cases), not suitable for operative uses.…”

Section: Discussionmentioning

confidence: 99%

“…Since many of the 242 bands of Hyperion are not usable for the classification due to high noise ratio [George et al, 2014], a pre-processing phase was applied [Petropoulos et al, 2012]. In the first step, the Level 1 L1T GeoTIFF Hyperion imagery was converted into ENVI format files using the Hyperion_tools.sav toolkit available in ENVI image processing environment [ITT Visual Information Solutions, 2008].…”

Section: Methodsmentioning

confidence: 99%

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Puletti

Camarretta

Corona

2016

European Journal of Remote Sensing

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The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic.

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“…To check whether any important information was lost by the selected optimum bands, the SVM classification results obtained using the selected optimal bands were compared to that obtained using all bands, according to [42]. For TG-1, the classification map using 17 bands matched 94.72% with the classification map using 111 bands; for Hyperion, the classification map using 27 bands matched 96.03% with the classification map using 158 bands.…”

Section: Dimensionality Reduction Analysismentioning

confidence: 99%

Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

Liu

et al. 2016

Remote Sensing

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Abstract:The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier.

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Forest tree species discrimination in western Himalaya using EO-1 Hyperion

Cited by 55 publications

References 35 publications

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests

Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

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