Monitoring Green Infrastructure for Natural Water Retention Using Copernicus Global Land Products

Abstract

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.

Assessment of Green Infrastructure in Riparian Zones Using Copernicus Programme

“…Just 4 bands were needed to calculate the biophysical variables applied to assess GI condition in terms of its vegetative health stage and water content [52]. Vegetation indices are calculated using the spectral bands that capture the Red and Near-Infrared (NIR) reflectance, as this part of the electromagnetic spectrum shows a higher sensitiveness to the leaf chlorophyll content [78,79].…”

“…A significant phase of the research was finding attributes that allowed to: (i) spatially identify GI sites that serve for water retention (1st output), (ii) recognize their role in the river riparian system (2nd output) and (iii) assign them an importance based on their conservation condition (3rd output) ( Table 5). With this aim, assumptions were made by: (i) selecting the common criteria used by the consulted approaches on indicators to detect ecosystems' condition [39,[57][58][59][60][61][62][63][64][65] (1st column) and (ii) linking it to the suitable Copernicus products [47], available ancillary data [75] or the most successfully used bio-geophysical indices to monitor vegetative surfaces [52,[78][79][80] (2nd column).…”

“…Moreover, S2 data has been used due to its high spatial resolution to overcome this weakness of the bio-geophysical indices offered in the CLMS [49]. The goal was to determine and evaluate ES condition indicators specified in the reviewed frameworks, such as the greening response and water stress [39], using indices that can be related to vegetation biophysical characteristics, like the Normalized Difference Vegetation Index (NDVI) [50][51][52][53].…”

“…Several indices were analysed complementarily to obtain a more accurate and reliable characterization of the environment [52]. These spectral indices (Table 5) were selected for being the most significantly used in vegetation and forestry studies, achieving representative and accurate results in previous experiences [52,86,87].…”

“…This index is defined using NIR and SWIR reflectance (Equation (3)) and, as NDVI and EVI, shows values in the range of −1 to +1, with higher values corresponding to higher leaf water content and vegetation cover [80]. The main reason for choosing this index was the easier observable monitoring of vegetative and forestry stages when observing their reflectance in the SWIR bands, as well as to identify water stress [52,87].…”