Assessment of Green Infrastructure in Riparian Zones Using Copernicus Programme

Piedelobo, Laura; Taramelli, Andrea; Schiavon, Emma; Valentini, Emiliana; Molina, José-Luis; Xuan, A. Nguyen; González‐Aguilera, Diego

doi:10.3390/rs11242967

Cited by 27 publications

(10 citation statements)

References 61 publications

(114 reference statements)

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“…The increasing demand from national institutions for updated information to monitor ecosystems and detect their changes in time and space plays a crucial role in demonstrating spatial products as an essential tool for biodiversity assessments [28,43]. In this direction, the EU Copernicus Programme, especially through the Copernicus Land Monitoring Service (CLMS) [44] and the launch of Sentinel Earth observation satellite constellations, provides information services and it is promoting and supporting common frameworks for an updated land environmental monitoring at a European (local and in situ) [45,46] and global scale [47].…”

Section: Introductionmentioning

confidence: 99%

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

et al. 2021

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In the light of the “Biological Diversity” concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forest (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.

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Section: Introductionmentioning

confidence: 99%

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

et al. 2021

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“…In this sense and given the large study area, remote sensing is considered an ideal information source, especially in the current context of free access to data provided by satellite missions such as L8 (United States Geological Survey, USGS, Hunter Mill, VA, USA) and S2 (European Space Agency, ESA, Paris, France), which also justify its use in this type of application. [5][6][7][8][9][10].…”

Section: Case Studies: the Duero River Basin And The Groundwater Mancha Orientalmentioning

confidence: 99%

“…Especially, climate change-, natural hazards-and environmental protection-related SDGs represent focal challenges worldwide [2]. Currently, the development of multiscale data (i.e., with different resolutions) and open-source tools and models that help in evidence-based policy-and decision-making is mostly based on processing open data from the Landsat 8 (L8) [3] and Sentinel 2 (S2) satellite missions [4][5][6], e.g., using vegetation indices in agronomical applications, such as crop type mapping and monitoring [7,8], snow cover evolution [9] and monitoring vegetation changes [10], among others.…”

Section: Introductionmentioning

confidence: 99%

Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

et al. 2021

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Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2).

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“…To investigate the spectral characteristics of the field surface, we analyzed some images from the satellite Sentinel-2, obtaining the normalized difference vegetation index (NDVI) and the normal difference moisture index (NDMI) [63][64][65]. These indices have a geometric resolution of 10 m and, being sensitive to plant health and hydraulic stress, respectively [66,67], were used to improve the identification of the traces of paleomeanders by linking sedimentology to vegetation health of the area.…”

Section: Remote Sensingmentioning

confidence: 99%

Geophysical and Sedimentological Investigations Integrate Remote-Sensing Data to Depict Geometry of Fluvial Sedimentary Bodies: An Example from Holocene Point-Bar Deposits of the Venetian Plain (Italy)

et al. 2020

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Over the past few millennia, meandering fluvial channels drained coastal landscapes accumulating sedimentary successions that today are permeable pathways. Propagation of pollutants, agricultural exploitation and sand liquefaction are the main processes of environmental interest affecting these sedimentary bodies. The characterization of these bodies is thus of utmost general interest. In this study, we particularly highlight the contribution of noninvasive (remote and ground-based) investigation techniques, and the case study focuses on a late Holocene meander bend of the southern Venetian Plain (Northeast Italy). Electromagnetic induction (EMI) investigations, conducted with great care in terms of sonde stability and positioning, allowed the reconstruction of the electrical conductivity 3D structure of the shallow subsurface, revealing that the paleochannel ranges in depth between 0.8 and 5.4 m, and defines an almost 260 m-wide point bar. The electrical conductivity maps derived from EMI at different depths define an arcuate morphology indicating that bar accretion started from an already sinuous channel. Sedimentary cores ensure local ground-truth and help define the evolution of the channel bend. This paper shows that the combination of well-conceived and carefully performed inverted geophysical surveys, remote sensing and direct investigations provides evidence of the evolution of recent shallow sedimentary structures with unprecedented detail.

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Assessment of Green Infrastructure in Riparian Zones Using Copernicus Programme

Cited by 27 publications

References 61 publications

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping

Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

Geophysical and Sedimentological Investigations Integrate Remote-Sensing Data to Depict Geometry of Fluvial Sedimentary Bodies: An Example from Holocene Point-Bar Deposits of the Venetian Plain (Italy)

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