A new approach for biocrust and vegetation monitoring in drylands using multi-temporal Sentinel-2 images. (August 2019)

• Record Type: Journal Article
• Authors: Panigada, Cinzia; Tagliabue, Giulia; Zaady, Eli; Rozenstein, Offer; Garzonio, Roberto; Di Mauro, Biagio; De Amicis, Mattia; Colombo, Roberto; Cogliati, Sergio; Miglietta, Franco; Rossini, Micol
• Is Part Of: Progress in physical geography. Volume 43:Number 4(2019)
• Page Start: 496
• Page End: 520
• Publication Date: 2019-08
• Subjects: Biological soil crust -- remote sensing -- satellite -- hyperspectral -- multispectral -- drylands -- continuum removal -- phenology; Physical geography -- Periodicals; 910.02
• DOI: 10.1177/0309133319841903
• Languages: English
• ISSNs: 0309-1333

Abstract

Drylands, one of the planet's largest terrestrial biomes, are suggested to be greatly threatened by climate change. Drylands are usually sparsely vegetated, and biological soil crusts (biocrusts) – that is, soil surface communities of cyanobacteria, mosses and/or lichens – can cover up to 70% of dryland cover. As they control key ecosystem processes, monitoring their spatial and temporal distribution can provide highly valuable information. In this study, we examine the potential of European Space Agency's (ESA) Sentinel-2 (S2) data to characterize the spatial and temporal development of biocrust and vascular plant greening along a rainfall gradient of the Negev Desert (Israel). First, the chlorophyll a absorption feature in the red region (CRred ) was identified as the index mostly sensitive to changes in biocrust greening but minimally affected by changes in soil moisture. This index was then computed on the S2 images and enabled monitoring the phenological dynamics of different dryland vegetation components from August 2015 to August 2017. The analysis of multi-temporal S2 images allowed us to successfully track the biocrust greening within 15 days from the first seasonal rain events in the north of Negev, and to identify the maximum development of annual vascular plants and greening of perennial ones. These results show potential for monitoring arid and semi-arid environments using the newly available S2 images, allowing new insights into dryland vegetation dynamics.