Fine-scale vertical position as an indicator of vegetation in alkali grasslands – Case study based on remotely sensed data

Deák, Balázs; Valkó, Orsolya; Alexander, Cici; Mücke, Werner; Kania, Adam; Tamás, János; Heilmeier, Hermann

doi:10.1016/j.flora.2014.09.005

Cited by 66 publications

(59 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Despite the fact that certain classes (FAC, SAL, ACI and GLY) could be well-separated, it was not possible to distinguish most of the classes based on the NDVI scores alone. The reason for that might be that classes within the same vegetation group are generally characterized by a similar structure (species composition, plant life form, biomass, environmental moisture and cover of bare soil surface, see [26]), which likely resulted in considerable overlaps between their NDVI scores.…”

Section: Discussionmentioning

confidence: 99%

“…These classes are characterized by low vegetation cover and a high cover of bare ground, which is often covered by salt [26,27]. Meadows (ELY, ALO and BEC) are typical in sites with moist and moderately salty soil.…”

Section: Vegetation Classesmentioning

confidence: 99%

“…Meadows (ELY, ALO and BEC) are typical in sites with moist and moderately salty soil. They are tall grasslands with a medium amount of biomass, characterized by the high cover of a few grass species, such as Agrostis stolonifera, Alopecurus pratensis, Beckmannia eruciformis and Elymus repens [26,28]. In some parts of the study area, we detected meadow vegetation with weedy species, such as Cirisum arvense; we assigned them to the ACI class.…”

Section: Vegetation Classesmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

et al. 2015

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Vegetation Classesmentioning

confidence: 99%

Section: Vegetation Classesmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The National Park is a UNESCO World Heritage site, due to the large areas of connected open landscapes and the associated traditional pastoral practices. Typically, burial mounds are covered by loess grassland vegetation (Festucion rupicolae; Deák et al 2014). Due to their fertile chernozem soils, the majority of loess grasslands have been converted into arable fields in the region.…”

Section: Study Sitesmentioning

confidence: 99%

Cultural heritage and biodiversity conservation – plant introduction and practical restoration on ancient burial mounds

Valkó¹,

Tóth²,

Kelemen³

et al. 2018

Self Cite

View full text Add to dashboard Cite

Linking the conservation of cultural heritage and natural values provides a unique opportunity for preserving traditional landscapes and receives an increased awareness from stakeholders and society. Ancient burial mounds are proper objects of such projects as they are iconic landscape elements of the Eurasian steppes and often act as refugia for grassland specialist species. The aim of this project was to reintroduce grassland plant species to burial mounds for representing them as cultural monuments with the associated biodiversity for the public. The effectiveness of seed sowing, transplanting greenhouse-grown plants and individuals from threatened populations on burial mounds in Hortobágy National Park, Hungary was tested. The following questions were answered: (1) which method is the most effective for species introduction? (2) which species can establish most successfully? (3) how does management affect the species establishment rates? It was found advisable to use a combination of seed sowing and transplanting greenhouse-grown plants. Sowing was found as a cost-effective method for introducing large-seeded species, whilst introduction of greenhouse-grown transplants warranted higher establishment rates for a larger set of species. Transplanting adult individuals was more reliable regardless of management regimes, however this method is labour-intensive and expensive. Intensive management, like mowing with heavy machinery and intensive grazing, should be avoided in the first few years after introduction. The authors CONSERVATION IN PRACTICE Launched to accelerate biodiversity conservation A peer-reviewed open-access journalOrsolya Valkó et al. / Nature Conservation 24: 65-80 (2018) 66 highlighted the fact that introducing characteristic grassland species on cultural monuments offers a great opportunity to link issues of landscape and biodiversity conservation. This project demonstrated that, by the revitalisation of cultural monuments, cultural ecosystem services can also be restored.

show abstract

“…The water table level is closely related to microtopography and has a significant effect on local salt accumulation. Therefore, alkali habitats are a tightly-knit mosaic of many vegetation types such as alkali steppes, open alkali grasslands, alkali meadows and alkali reed stands [2][3][4]. Alkali steppes are dry, shortgrass vegetation types on moderately salty soils.…”

Section: Introductionmentioning

confidence: 99%

Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning

Zlinszky

Deák²,

Kania³

et al. 2015

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Natura 2000 Habitat Conservation Status is currently evaluated based on fieldwork. However, this is proving to be unfeasible over large areas. The use of remote sensing is increasingly encouraged but covering the full range of ecological variables by such datasets and ensuring compatibility with the traditional assessment methodology has not been achieved yet. We aimed to test Airborne Laser Scanning (ALS) as a source for mapping all variables required by the local official conservation status assessment scheme and to develop an automated method that calculates Natura 2000 conservation status at 0.5 m raster resolution for 24 km 2 of Pannonic Salt Steppe habitat (code 1530). We used multi-temporal (summer and winter) ALS point clouds with full-waveform recording and a density of 10 pt/m 2 . Some required variables were derived from ALS product rasters; others involved vegetation classification layers calculated by machine learning and fuzzy categorization. Thresholds separating favorable and unfavorable values of each variable required by the national assessment scheme were manually calibrated from 10 plots where field-based assessment was carried out. Rasters representing positive and negative scores for each input variable OPEN ACCESSRemote Sens. 2015, 7 2992were integrated in a ruleset that exactly follows the Hungarian Natura 2000 assessment scheme for grasslands. Accuracy of each parameter and the final conservation status score and category was evaluated by 10 independent assessment plots. We conclude that ALS is a suitable data source for Natura 2000 assessments in grasslands, and that the national grassland assessment scheme can successfully be used as a GIS processing model for conservation status, ensuring that the output is directly comparable with traditional field based assessments.

show abstract

Fine-scale vertical position as an indicator of vegetation in alkali grasslands – Case study based on remotely sensed data

Cited by 66 publications

References 23 publications

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Classification of Herbaceous Vegetation Using Airborne Hyperspectral Imagery

Cultural heritage and biodiversity conservation – plant introduction and practical restoration on ancient burial mounds

Mapping Natura 2000 Habitat Conservation Status in a Pannonic Salt Steppe with Airborne Laser Scanning

Contact Info

Product

Resources

About