Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers is challenging; recent developments in machine learning approaches show promise to tackle this issue. This manuscript presents a proof of concept of machine learning approaches applied to multi-spectral images to detect the snow line and quantify its average altitude. The tested approaches include the combination of different image processing and classification methods, and takes into account cast shadows. The efficiency of these approaches is evaluated on mountain glaciers in the European Alps by comparing the results with manually annotated data. Solutions provided by the different approaches are robust when compared to the ground truth’s snow lines, with a Pearson’s correlation ranging from 79% to 96% depending on the method. However, the tested approaches may fail when snow lines are not continuous or exhibit a strong change of elevation. The major advantage over the state of the art is that the proposed approach does not require one calibration per glacier.

Classifiers trained on airborne hyperspectral imagery are proficient in identifying tree species in hyperdiverse tropical rainforests. However, spectral fluctuations, influenced by intrinsic and environmental factors, such as the heterogeneity of individual crown properties and atmospheric conditions, pose challenges for large-scale mapping. This study proposes an approach to assess the instability of airborne imaging spectroscopy reflectance in response to environmental variability. Through repeated overflights of two tropical forest sites in French Guiana, we explore factors that affect the spectral similarity between dates and acquisitions. By decomposing acquisitions into subsets and analyzing different sources of variability, we analyze the stability of reflectance and various vegetation indices with respect to specific sources of variability. Factors such as the variability of the viewing and sun angles or the variability of the atmospheric state shed light on the impact of sources of spectral instability, informing processing strategies. Our experiments conclude that the environmental factors that affect the canopy reflectance the most vary according to the considered spectral domain. In the Short Wave Infra-Red (SWIR) domain, solar angle variation is the main source of variability, followed by atmospheric and viewing angles. In the Visible and Near Infra-Red (VNIR) domain, atmospheric variability dominates, followed by solar angle and viewing angle variabilities. Despite efforts to address these variabilities, significant spectral instability persists, highlighting the need for more robust representations and improved correction methods for reliable species-specific signatures.

Summary: Airborne imaging spectroscopy can identify tree species in hyperdiverse tropical forests, but accuracy varies greatly among species. We asked which ecological and evolutionary attributes make a tropical tree species spectrally separable. In moist tropical forest in French Guiana, we fitted the learning curve of accuracy against the number of labelled crowns for 169 species, then tested six hypothesised determinants on 45 species rarefied to ten crowns, at species, species-pair and crown scales. Dispersed sampling and larger crowns increased accuracy, and liana infestation more than halved the odds of correct identification. Phenological regularity, how closely conspecific crowns share one leaf cycle, in step or not, was the strongest species-level correlate and survived every multivariate analysis. It did not act by making conspecifics uniform on the acquisition date: regular species were spectrally more distinct from other species, and within species the crowns that best followed the species’ leaf cycle were classified correctly. Congeners reduced accuracy; leaf and bark traits did not predict separability. Separability depends on the consistency of a species’ signal, how faithfully its crowns express a shared leaf cycle, how widely it is sampled and how free its crowns are from contamination, rather than on its measured functional traits.

Airborne hyperspectral imaging is a promising method for identifying tropical species, but spectral variability between acquisitions hinders consistent results. This paper proposes using Self-Supervised Learning (SSL) to encode spectral features that are robust to abiotic variability and relevant for species identification. By employing the state-of-the-art Barlow-Twins approach on repeated spectral acquisitions, we demonstrate the ability to develop stable features. For the classification of 40 tropical species, experiments show that these features can outperform typical reflectance products in terms of robustness to spectral variability by 10 points of accuracy across dates.

Ice shelves buttress the discharge of Antarctica’s outlet glaciers, mitigating their contribution to sea-level rise. Yet progressive damage through fracturing remains largely absent from projections, partly because long-term observations of ice-shelf fractures remain limited despite their critical influence on ice-shelf stability. Here, we introduce BASIS (BAsal and Surface Ice-shelf fracture extraction System), a deep-learning model for mapping surface crevasses and basal-fracture across Antarctic ice shelves. Unlike existing approaches based on classical edge detectors or models trained on edge-based datasets, BASIS is designed to delineate the full extent of fracture features, including diffuse basal-fracture signatures and highly damaged, chaotic regions. We train, validate, and test BASIS, based on DeepLabv3, using a unique manually labeled fracture dataset spanning 14 Antarctic ice shelves and the full Landsat observation era. Model performance approaches inter-annotator agreement, while maintaining the distinction between basal and surface fractures. We quantify biases associated with sensor transitions and observation availability and develop a framework for producing consistent multi-sensor fracture records. Annual mosaics reveal the spatiotemporal evolution of ice-shelf damage from the 1980s onward and show that basal-fracture signatures dominate the mapped damage patterns across the ice shelves examined here. At Pine Island Glacier Ice Shelf, BASIS resolves successive stages of shear-margin damage together with a progressive increase in basal-fracture. At Mertz and Borchgrevink ice shelves, it reveals extensive networks of basal-fracture signatures that are poorly represented in existing continent-wide products based on Sentinel-1 SAR imagery. Finally, BASIS demonstrates promising cross-sensor transferability to Sentinel-2 and ASTER optical imagery and, despite having been trained exclusively on Landsat data, also recovers coherent fracture patterns from SAR imagery. These results provide a foundation for large-scale, multi-sensor monitoring of Antarctic ice-shelf damage across unprecedented spatial and temporal scales.