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The presence and distribution of green vegetation cover in the biosphere are of paramount importance in investigating cause-effect phenomena at the land/atmosphere interface, estimating primary production rates as part of global carbon and water cycle assessments and evaluating soil protection and land use change over time. The fraction of green vegetation cover (FCover) as estimated from satellite observations has already been demonstrated to be an extraordinarily useful product for understanding vegetation cover changes, for supporting ecosystem service assessments over areas with variable extents and for processes spanning a variable period of time (abrupt events or long-term processes). This study describes a methodology implemented to estimate global FCover (from 2001 to 2015) by applying a linear spectral mixture analysis with global endmembers to an entire temporal series of MODIS satellite observations and gap-filling missing FCover observations in temporal series using the DINEOF algorithm. The resulting global MODV1 FCover product was validated with two global validation datasets and showed an overall good thematic absolute accuracy (RMSE = 0.146) consistent with the validation performance of other FCover global products. Basic statistics performed on the product show changes in average and trend values and allow for the quantification of gross vegetation loss and gain over different temporal scales. To demonstrate the capacity of this global product to monitor specific dynamics, a multitemporal analysis was performed on selected sites and vegetation responses (i.e., cover changes), and specific dynamics resulting from cause-effect phenomena are briefly discussed. The product is intended to be used for monitoring vegetation dynamics, but it also has the potential to be integrated in other modeling frameworks (e.g., the carbon cycle, primary production, and soil erosion) in conjunction with other spatial datasets such as those on climate and soil type.

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Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape.

Context Soil erosion is one of the main threats driving soil degradation across the globe with important impacts on crop yields, soil biota, biogeochemical cycles, and ultimately human nutrition. Objectives Here, using an empirical model, we present a global and temporally explicit assessment of soil erosion risk according to recent (2001-2013) dynamics of rainfall and vegetation cover change to identify vulnerable areas for soils and soil biodiversity. Methods We used an adaptation of the Universal Soil Loss Equation together with state of the art remote sensing models to create a spatially and temporally explicit global model of soil erosion and soil protection. Finally, we overlaid global maps of soil biodiversity to assess the potential vulnerability of these soil communities to soil erosion. Results We show a consistent decline in soil erosion protection over time across terrestrial biomes, which resulted in a global increase of 11.7% in soil erosion rates. Notably, soil erosion risk systematically increased between 2006 and 2013 in relation to the Electronic supplementary material The online version of this article (

Nature-based solutions are increasingly relevant tools for spatial and environmental planning, climate change adaptation (CCA), and disaster risk reduction (DRR). For this reason, a wide range of institutions, governments, and financial bodies are currently promoting the use of green infrastructure (GI) as an alternative or a complement to traditional grey infrastructure. A considerable amount of research already certifies the benefits and multi-functionality of GI: natural water retention measures (NWRMs), as GIs related specifically to the water sector are also known, are, for instance, a key instrument for the prevention and mitigation of extreme phenomena, such as floods and droughts. However, there are persisting difficulties in locating and identifying GI and one of the most promising solutions to this issue, the use of satellite-based data products, is hampered by a lack of well-grounded knowledge, experiences, and tools. To bridge this gap, we performed a review of the Copernicus Global Land Service (CGLS) products, which consist of freely-available bio-geophysical indices covering the globe at mid-to-low spatial resolutions. Specifically, we focused on vegetation and energy indices, examining previous research works that made use of them and evaluating their current quality, aiming to define their potential for studying GI and especially NWRMs related to agriculture, forest, and hydro-morphology. NWRM benefits are also considered in the analysis, namely: (i) NWRM biophysical impacts (BPs), (ii) ecosystem services delivered by NWRMs (ESs), and (iii) policy objectives (POs) expressed by European Directives that NWRMs can help to achieve. The results of this study are meant to assist GI users in employing CGLS products and ease their decision-making process. Based on previous research experiences and the quality of the currently available versions, this analysis provides useful tools to identify which indices can be used to study several types of NWRMs, assess their benefits, and prioritize the most suitable ones.

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