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

Zlinszky, András; Kania, Adam

doi:10.5194/isprs-archives-xli-b2-335-2016

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…Research, conducted on four other species (Kopeć et al 2019), indicates that validation polygons with a low proportion of the target species (below 50% cover) were classified correctly only in the range between 9 and 40%. Detection of a species with a small percentage cover is possible using fuzzy methods, which assign a probability of membership for each class to each pixel (Zlinszky and Kania 2016).…”

Section: Applications and Limitationsmentioning

confidence: 99%

Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: a serious threat to peat bog plant communities

Kopeć

Sabat-Tomala

Michalska-Hejduk

et al. 2020

Wetlands Ecol Manage

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Remote sensing is increasingly widely used in nature conservation management. The research focuses on developing an optimal set of airborne raster data for the identification of the invasive alien species Spiraea tomentosa L. The plant species selected for the purposes of this study poses a serious threat to peat bog plant communities, moist coniferous forests, and meadows in Central Europe. The impact of the data acquisition time on the accuracy of classification and the percentage cover limit required for correct identification of a target species using the developed method were also investigated. The study area is located in the Lower Silesian forests in Poland and is protected as a Natura 2000 site. Airborne hyperspectral and laser scanning data were simultaneously acquired two times in the growing season (August and September 2016) parallel to on-ground reference data collection. The 1 m resolution HySpex images with spectral range of 0.4–2.5 μm were corrected atmospherically, radiometrically and geometrically. Airborne Laser Scanning (ALS) data acquired at 7 points/m2 were used to generate several products, e.g. Canopy Height Model (CHM), rasters representing morphometric features of the area (Multiresolution Index of the Ridge Top Flatness or Valley Bottom Flatness—MRRTF, MRVBF), wetness relations (Topographic Wetness Index—TWI) and the availability of light (Total Insolation—TI), intensity of laser pulse reflection and geometric relations of vegetation points (i.a. Vegetation Cover, Vegetation Mean Intensity). The Random Forest (RF) classification and different raster datasets were used to identify the target species. As a result, the highest accuracy was obtained for the scenario based on HySpex images acquired in September. The accuracy (f1 score) for the target species achieved 83%. The developed method for the identification of Spiraea tomentosa has a great potential for application and can be used for monitoring peat bogs threatened by invasion of alien plants.

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Section: Applications and Limitationsmentioning

confidence: 99%

Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: a serious threat to peat bog plant communities

Kopeć

Sabat-Tomala

Michalska-Hejduk

et al. 2020

Wetlands Ecol Manage

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Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation

Feilhauer

Zlinszky

Kania³

et al. 2020

Remote Sens Ecol Conserv

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Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature's fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel's class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research.

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Applied soft classes and fuzzy confusion in a patchwork semi-arid ecosystem: Stitching together classification techniques to preserve ecologically-meaningful information

Enterkine,

Caughlin,

Dashti

et al. 2024

Remote Sensing of Environment

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

Cited by 7 publications

References 19 publications

Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: a serious threat to peat bog plant communities

Application of airborne hyperspectral data for mapping of invasive alien Spiraea tomentosa L.: a serious threat to peat bog plant communities

Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation

Applied soft classes and fuzzy confusion in a patchwork semi-arid ecosystem: Stitching together classification techniques to preserve ecologically-meaningful information

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