Detecting the Phenology and Discriminating Mediterranean Natural Habitats With Multispectral Sensors—An Analysis Based on Multiseasonal Field Spectra

Due to their high degree of vegetation heterogeneity, fragmentation, and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in situ observations solely. Together with standardized ground plots and regular in situ measurements, remote sensing contributes to better understand the diversity of these habitats and their phenology. We used field spectroradiometry to simulate the radiometric signal corresponding to six multispectral satellites: 1) IKONOS, 2) Landsat 5 TM, 3) Landsat 8, 4) Pléiades, 5) Sentinel-2, and 6) WorldView-2. We compared the suitability of each sensor for the estimation of the cover fraction of photosynthetic vegetation (PV) observed for five types of habitats during a vegetation cycle from February to October 2013. We also analyzed the contribution of multiseasonal satellite acquisitions for habitat discrimination. We showed that multivariate regression applied to Worldview-2 reflectance produces the most accurate PV. This was explained by the higher number of spectral bands in the visible domain. Habitat discrimination based on monotemporal acquisitions showed better performances when PV was higher. Sentinel-2 and WorldView-2 outperformed other sensors for each individual date. Multitemporal acquisitions outperformed monotemporal acquisition for habitat discrimination. However, selecting all reflectance data acquired during the season resulted in suboptimal performances compared to more parsimonious combinations. Finally, all of them ranged between 86.6% and 89.2% classification accuracy with multiseasonal acquisitions. New strategies need to be designed to identify individual habitats of particular interest. Defining optimal multiseasonal remote-sensing acquisitions specific to each habitat and appropriate spectral and spatial resolution will contribute to improved discrimination of Mediterranean natural habitats.