Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

Zlinszky, András; Schroiff, Anke; Kania, Adam; Deák, Balázs; Mücke, Werner; Vári, Ágnes; Székely, Balázs; Pfeifer, Norbert

doi:10.3390/rs6098056

Cited by 68 publications

(75 citation statements)

References 82 publications

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“…This type of habitat mapping is based on remotely sensed data such as multispectral images [4], airborne hyperspectral imagery [5], light detection and ranging (LiDAR) [6,7], radar [8] and in some cases even a combination of these [9,10]. Nowadays, hyperspectral sensors have a high potential for monitoring of the environment [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

et al. 2015

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Abstract:Alkali landscapes hold an extremely fine-scale mosaic of several vegetation types, thus it seems challenging to separate these classes by remote sensing. Our aim was to test the applicability of different image classification methods of hyperspectral data in this complex situation. To reach the highest classification accuracy, we tested traditional image classifiers (maximum likelihood classifier-MLC), machine learning algorithms (support vector machine-SVM, random forest-RF) and feature extraction (minimum noise fraction (MNF)-transformation) on training datasets of different sizes. Digital images were acquired from an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400-1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. For the classification, we established twenty vegetation classes based on the dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset with various training sample sizes between 10 and 30 pixels. In order to select the optimal number of the transformed features, we applied SVM, RF and MLC classification to 2-15 MNF transformed bands. In the case of the original bands, SVM and RF classifiers provided high accuracy irrespective of the number of the training pixels. We found that SVM and RF produced the best accuracy when using the first nine MNF transformed bands; involving further features did not increase classification accuracy. SVM and RF provided high accuracies with the transformed bands, especially in the case of the OPEN ACCESS Remote Sens. 2015, 7 2047 aggregated groups. Even MLC provided high accuracy with 30 training pixels (80.78%), but the use of a smaller training dataset (10 training pixels) significantly reduced the accuracy of classification (52.56%). Our results suggest that in alkali landscapes, the application of SVM is a feasible solution, as it provided the highest accuracies compared to RF and MLC. SVM was not sensitive in the training sample size, which makes it an adequate tool when only a limited number of training pixels are available for some classes.

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Section: Introductionmentioning

confidence: 99%

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

et al. 2015

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“…Grasslands require very high spatial resolution, but also pose problems to remote sensing since they are spectrally very similar. Using airborne hyperspectral imagery, Burai et al successfully mapped up to 20 grassland classes [21] and Zlinszky et al reached a spatial resolution up to 0.5 m using dual-season full-waveform airborne laser scanning [22,23]. Grasslands also necessitate an alternative approach to categorization: successful examples include using fuzzy set theory to determine probabilities of class membership [23], or the study of Neumann et al where floristic composition was investigated using species ordination without using classes at all [24].…”

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confidence: 99%

Remote Sensing and GIS for Habitat Quality Monitoring: New Approaches and Future Research

et al. 2015

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Habitat quality is the ability of the environment to provide conditions appropriate for individual and species persistence. Measuring or monitoring habitat quality requires complex integration of many properties of the ecosystem, where traditional terrestrial data collection methods have proven extremely time-demanding. Remote sensing has known potential to map various ecosystem properties, also allowing rigorous checking of accuracy and supporting standardized processing. Our Special Issue presents examples where remote sensing has been successfully used for habitat mapping, quantification of habitat quality parameters, or multi-parameter modelling of habitat quality itself. New frontiers such as bathymetric scanning, grassland vegetation classification and operational use were explored, various new ecological verification methods were introduced and integration with ongoing OPEN ACCESS

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“…These modules are part of a full data processing chain using Python as a glue language. The full software solution (under the working name "Vegetation Classification Studio") has been used for classification tasks in various habitats from airborne LIDAR data (Zlinszky et al, 2015a(Zlinszky et al, , 2014.…”

Section: Methodsmentioning

confidence: 99%

Will It Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

Zlinszky¹,

Kania²

2016

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

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ABSTRACT:Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with "dominance profiles" visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.

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Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

Cited by 68 publications

References 82 publications

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Remote Sensing and GIS for Habitat Quality Monitoring: New Approaches and Future Research

Will It Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

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