Remote sensing tools (RS) can contribute to a better understanding of the diversity of natural and semi-naturals habitats, their spatial distribution, and their conservation status. RS can also provide a generic set of derived indicators to support local to regional habitat monitoring. Here we propose a set of synthetic continuous textural indices computed from high spatial resolution airborne images for the characterization of vegetation structure in very heterogeneous landscape mosaics. These indices are based on Fourier-based textural ordination (FOTO) of very-high-resolution images. We investigate the relationship between textural indices and a set of common landscape metrics derived from vegetation maps, identifying four strata of interest: bare soil, herbs, low ligneous, and high ligneous. We identify two continuous textural indices, the first one being related to vegetation strata fragmentation and the second being related to the dominance of high ligneous. The combination of these two textural indices with the Normalized Difference Vegetation Index (NDVI) provides a synoptic and accurate overview of the spatial organization of the different vegetation strata. The methodological approach presented herein has a generic value in response to national conservation targets in the context of mapping relevant habitat indicators.

Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited.

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications. This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries). The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.

Les haies jouent des rôles clés dans les paysages agricoles, mais leur caractérisation automatique par analyse spatiale est complexe. Dans cet article, nous décrivons les principales fonctionnalités d’un outil open source — HedgeTools — qui permet de calculer une diversité d’indicateurs contribuant à évaluer la multifonctionnalité des haies. Il permet de créer la géométrie des objets, de les redécouper en fonction de divers critères et d’extraire leurs caractéristiques à différents niveaux d’agrégation. HedgeTools vise à faciliter la gestion et la préservation des haies en permettant d’évaluer leur état et leurs fonctions dans les paysages, avec des perspectives d’amélioration et d’extension de ses fonctionnalités.