Intra-Annual Sentinel-2 Time-Series Supporting Grassland Habitat Discrimination

Abstract

The present study aims to discriminate four semi-arid grassland habitats in a Mediterranean Natura 2000 site, Southern Italy, involving 6210/E1.263, 62A0/E1.55, 6220/E1.434 and X/E1.61-E1.C2-E1.C4 (according to Annex I of the European Habitat Directive/EUropean Nature Information System (EUNIS) taxonomies). For this purpose, an intra-annual time-series of 30 Sentinel-2 images, embedding phenology information, were investigated for 2018. The methodology adopted was based on a two-stage workflow employing a Support Vector Machine classifier. In the first stage only four Sentinel-2 multi-season images were analyzed, to provide an updated land cover map from where the grassland layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated the four grassland habitats by analyzing several input features configurations. These included multiple spectral indices selected from the time-series and the Digital Terrain Model. The results obtained from the different input configurations selected were compared to evaluate if the phenology information from time-series could improve grassland habitats discrimination. The highest F1 values (95.25% and 80.27%) were achieved for 6210/E1.263 and 6220/E1.434, respectively, whereas the results remained stable (97,33%) for 62A0/E1.55 and quite low (75,97%) for X/E1.61-E1.C2-E1.C4. However, since for all the four habitats analyzed no single configuration resulted effective, a Majority Vote algorithm was applied to achieve a reduction in classification uncertainty.

Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

“…Considering SVM classifiers, Buck et al (2015) [8] obtained an UA lower than 53% and an F1-score of 89.11%. Tarantino et al (2021) [13] considered the same input dataset and training/validation reference data of the current study using an SVM classifier; lower accuracies for all of the habitat classes resulted. In detail, these authors obtained F1-score values of 89.11%, 97.29%, 72.82%, and 57.81% for Type_1 to Type_4 grassland habitats, respectively.…”

“…Each image was cropped according to the boundary of the area of interest, and then all of the images were stacked, obtaining a single raster file of 40 layers. Only those pixels belonging to a pre-existing grassland layer, obtained by applying the automatic procedure proposed in [13], were considered.…”

“…Some studies have already addressed natural and semi-natural grassland ecosystems monitoring at medium-high spatial resolution, exploiting both optical and SAR data by using machine learning approaches [5][6][7][8][9][10][11][12][13]. In particular, a Support Vector Machine (SVM) classifier was used to analyze, both separately and in combination, a series of four optical (5 to 30 m) and five SAR images (12 m), for discriminating natural grasslands from croplands in areas affected by frequent clouds [5].…”

“…They compared five conventional machine learning algorithms, i.e., linear regression, K nearest neighbors, Kernel Ridge Regression (KRR), Random Forest (RF) and Gaussian Process (GP), finding RF to be the best performing in predicting grassland plant diversity. Tarantino et al (2021) [13] considered the same input dataset and training/validation reference data of the present work by using an SVM classifier.…”

Figure 6 from: Tomaselli V, Silletti G, Forte L (2021) A new association of Satureja montana L. subsp. montana dominated garrigues in Puglia (SE Italy). Plant Sociology 58: 1-14. https://doi.org/10.3897/pls2021582/01

“…The vegetation of the upper part of Alta Murgia consists mainly of widespread submediterranean xeric grasslands physiognomically characterized by Stipa austroitalica (Forte et al 2005) and, depending on the level of soil nutrient, of grasslands with dominant Ferula communis, Asphodelus ramosus, Charybdis pancration (Tarantino et al 2021). Scattered shrubs or trees determine physiognomic types of shrub-steppe or steppe-woodland (Bianco 1962).…”