Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

Kluczek, Marcin; Zagajewski, Bogdan; Kycko, Marlena

doi:10.3390/rs14051209

Cited by 18 publications

(5 citation statements)

References 99 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem of comparing the utility of hyperspectral and multispectral images to vegetation mapping was analysed in terms of classification of other vegetation classes, such as plant communities. For example, the accuracies for HS and S2 were comparable when classifying plant communities in the Tatra Mountains; F1 varied from 0.76 to 0.90 depending on the dataset 52 . Similar accuracies for hyperspectral and multispectral data (0.90 and 0.93 respectively) were acquired in habitat mapping in parts of North West England 53 .…”

Section: Discussionmentioning

confidence: 98%

The utility of airborne hyperspectral and satellite multispectral images in identifying Natura 2000 non-forest habitats for conservation purposes

Jarocińska

Kopeć

Niedzielko

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Aerial hyperspectral and multispectral satellite data are the two most commonly used datasets to identify natural and semi-natural vegetation. However, there is no documented analysis based on data from several areas concerning the difference in the classification accuracy of non-forest Natura 2000 habitat with the use of aerial hyperspectral and satellite multispectral data. Also, there is no recommendation, on which habitat can be classified with sufficient accuracy using free multispectral images. This study aimed to analyse the difference in classification accuracy of Natura 2000 habitats representing: meadows, grasslands, heaths and mires between data with different spectral resolutions and the results utility for nature conservation compared to conventional maps. The analysis was conducted in five study areas in Poland. The classification was performed on multispectral Sentinel-2 (S2) and hyperspectral HySpex (HS) images using the Random Forest algorithm. Based on the results, it can be stated that the use of HS data resulted in higher classification accuracy, on average 0.14, than using S2 images, regardless of the area of the habitat. However, the difference in accuracy was not constant, varying by area and habitat characterisation. Greater differences in accuracy were observed for areas where habitats were characterised by high α-diversity or β-diversity. The HS and S2 data make it possible to create maps that provide a great deal of new knowledge about the distribution of Natura 2000 habitats, which is necessary for the management of protected areas. The obtained results indicate that by using S2 images it is possible to identify, at a satisfactory level, alluvial meadows and grassland. For heaths and mires, using HS data improved the results, but it is also possible to acquire general distribution of these classes, whereas HS images are obligatory for mapping salt, Molinia and lowland hay meadows.

show abstract

Section: Discussionmentioning

confidence: 98%

The utility of airborne hyperspectral and satellite multispectral images in identifying Natura 2000 non-forest habitats for conservation purposes

Jarocińska

Kopeć

Niedzielko

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The recent studies in the classification of ecological communities using satellite images have emphasized the usage of multi-temporal satellite images for improving performance [107][108][109]. Consequently, Kluczek et al [110] achieved an F1-score in the range of 76-90% for the classification of 13 mountain forest and non-forest plant communities. Another study by Bhatt et al [111] obtained a Kappa coefficient of 75% using the Random Forests classifier for the classification of habitat communities.…”

Section: Discussionmentioning

confidence: 99%

Countrywide Mapping of Plant Ecological Communities with 101 Legends including Land Cover Types for the First Time at 10 m Resolution through Convolutional Learning of Satellite Images

Sharma

2022

Applied Sciences

View full text Add to dashboard Cite

This paper presents next-generation mapping of plant ecological communities including land cover and agricultural types at 10 m spatial resolution countrywide. This research introduces modelling and mapping of land cover and ecological communities separately in small regions-of-interest (prefecture level), and later integrating the outputs into a large scale (country level) for dealing with regional distribution characteristics of plant ecological communities effectively. The Sentinel-2 satellite images were processed for cloud masking and half-monthly median composite images consisting of ten multi-spectral bands and seven spectral indexes were generated. The reliable ground truth data were prepared from extant multi-source survey databases through the procedure of stratified sampling, cross-checking, and noisy-labels pruning. Deep convolutional learning of the time-series of the satellite data was employed for prefecture-wise classification and mapping of 29–62 classes. The classification accuracy computed with the 10-fold cross-validation method varied from 71.1–87.5% in terms of F1-score and 70.9–87.4% in terms of Kappa coefficient across 48 prefectural regions. This research produced seamless maps of 101 ecological communities including land cover and agricultural types for the first time at a country scale with an average accuracy of 80.5% F1-score.

show abstract

“…RF algorithm was applied as a machine learning algorithm for vegetation classification in RStudio (version 2022.02.3+492). RF algorithm, an ensemble classifier that produces multiple decision trees, is commonly used in LULC due to its' high accuracy results achieved, solving highly non-linear problems on relatively small-size databases, and handling a large number of input features (Kluczek and Zagajewski 2022, Wójtowicz et al 2021, Adeli et al 2022. Furthermore, RF enables input feature ranking through random permutation, which has been studied in this paper using the RandomForest package in R (Zheng et al 2017).…”

Section: Image Classificationmentioning

confidence: 99%

Vegetation Mapping with Random Forest using Sentinel 2- A case study for Lousã region, Portugal

Mohammadpour¹,

Isbaex²,

Chuvieco³

et al. 2022

Advances in Forest Fire Research 2022

View full text Add to dashboard Cite

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousa district in Portugal. We used 10 multispectral bands with a spatial resolution of 10m, four vegetation indices including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying Principal Component Analysis (PCA) on the 10 S2A bands, four texture features including Mean (ME), Homogeneity (HO), Correlation (COR), and Entropy (EN)were derived for the first three Principal Components. After defining the land use classes by object-based, the Random Forest (RF) classifier was applied. The map accuracy will be evaluated by the confusion matrix, using the metrics of Overall Accuracy (OA), Producer Accuracy (PA), User Accuracy (UA), and Kappa Coefficient (K). The described classification methodology is expected to show a high overall accuracy for vegetation mapping.

show abstract

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

Cited by 18 publications

References 99 publications

The utility of airborne hyperspectral and satellite multispectral images in identifying Natura 2000 non-forest habitats for conservation purposes

The utility of airborne hyperspectral and satellite multispectral images in identifying Natura 2000 non-forest habitats for conservation purposes

Countrywide Mapping of Plant Ecological Communities with 101 Legends including Land Cover Types for the First Time at 10 m Resolution through Convolutional Learning of Satellite Images

Vegetation Mapping with Random Forest using Sentinel 2- A case study for Lousã region, Portugal

Contact Info

Product

Resources

About