Geomatics and satellite remote sensing offer useful analysis tools for several technical-scientific fields. This work, with reference to a regional case of study, investigates remote sensing ...potentialities for describing relationships between environment and diseases affecting wildlife at landscape level in the light of climate change effects onto vegetation. Specifically, the infectious keratoconjunctivitis (IKC) of chamois (Rupicapra rupicapra L.) in Aosta Valley (NW Italy) was investigated at the regional level. IKC (Mycoplasma conjunctivae) is a contagious disease for domestic and wild ruminants (Caprinae and Ovinae). Two types of analysis were performed: one aimed at exploring by remotely sensed data phenological metrics (PMs) and evapotranspiration (ET) trends of vegetation in the area; one investigating the correlation between PMs and ET, versus IKC prevalence. The analysis was based on TERRA MODIS image time series ranging from 2000 to 2019. Ground data about IKC were available for a shorter time range: 2009–2019. Consequently, PMs and ET trend investigations were focused on the whole times range (2000–2019); conversely, correlation analysis was achieved with reference to the reduced 2009–2019 period. The whole study was based on freely available data from public archives. MODIS products, namely MOD13Q1 v.6 and MOD16A2, were used to derive PM and ET trends, respectively. Shuttle Radar Topography Mission (SRTM) Digital Terrain Model (DTM) was used to describe local topography; CORINE Land Cover map was adopted to describe land use classes. PMs and ET (as derivable from EO data) proved to significantly changed their values in the last 20 years, with a continuous progressive trend. As far as correlation analysis was concerned, ET and some PMs (specifically, End of Season (EOS) and Length of Season (LOS) proved significantly condition IKC prevalence. According to results, the proposed methodology can be retained as an effective tool for supporting public health and eco-pathological sectors. Specifically, it can be intended for a continuous monitoring of effects that climatic dynamics determine onto wild animals in the Alpine area, included diseases and zoonosis, moving future environmental management and planning towards the One Health perspective.
Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the ...annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer.
Operational services based on SAR data from satellite missions are showing to have the potentialities of becoming a real scenario; nevertheless, the complexity of data pre-processing remains one of ...the main reasons for its slow uptake by a wider user community. Google Earth Engine (GEE) web-based platform allows an immediate access to SAR imagery (namely, Sentinel-1 - S1) making users able to directly focus on the expected application. SAR side-looking acquisition mode generates many geometric distortions within recorded images, especially in mountain areas, determining a different degree of reliability of deductions. Consequently, a mapping of these areas is desirable for a correct interpretation of derived information. In this work a trigonometry-based method for mapping was implemented in GEE. With reference to a time series made of 60 S1 images covering the whole Piemonte Region (NW Italy) in 2020, some maps of distortions were generated using the 30 m gridded SRTM DTM as topographic surface descriptor. S1 images, belonging to the analyzed time series, were acquired from both ascending and descending orbits. In particular, active/passive shadows, active/passive layover and foreshortening masks were computed and mapped. Distortion maps were finally intersected with land cover classes to test the correspondent degree of analysability by SAR data. The results show that such methodology can be proficiently used to mask unreliable observations, making possible to a priori be informed about the areas of a given territory that can be reasonably and reliably monitored by SAR data.
Remotely piloted aerial systems (RPAS) have been recognized as an effective low-cost tool to acquire photogrammetric data of low accessible areas reducing collection and processing time. Data ...processing techniques like structure from motion (SfM) and multiview stereo (MVS) techniques, can nowadays provide detailed 3D models with an accuracy comparable to the one generated by other conventional approaches. Accuracy of RPAS-based measures is strongly dependent on the type of adopted sensors. Nevertheless, up to now, no investigation was done about relationships between camera calibration parameters and final accuracy of measures. In this work, authors tried to fill this gap by exploring those dependencies with the aim of proposing a prediction function able to quantify the potential final error in respect of camera parameters. Predictive functions were estimated by combining multivariate and linear statistical techniques. Four photogrammetric RPAS acquisitions were considered, supported by ground surveys, to calibrate the predictive model while a further acquisition was used to test and validate it. Results are preliminary, but promising. The calibrated predictive functions relating camera internal orientation (I.O.) parameters with final accuracy of measures (root mean squared error) showed high reliability and accuracy.
Monitoring large-scale flood damage can be complicated and costly. Damages caused by floods affect also the agricultural sector. Permanence, height and quantity of stagnant water can significantly ...influence crop yield. Many studies exploit satellite data to map flooded areas, but only a few are focused on the timing of water persistence. This work refers to the river Sesia flooding event which occurred on 3 October 2020 in Northwest Italy with the aim of detecting damages to local crops. The analysis was based on Sentinel-1 data processed by Google Earth Engine platform. In particular, the Otsu's method was applied to test the difference between pre- and post-event images. Areas that were mapped as flooded were successively analysed to estimate local water persistence: specifically, 1-2-6 days after the event. According to the available Corine Land Cover 2018 dataset, it was found that flood mainly affected agricultural areas (about 3288 ha). Since damage also relies on water persistence, a focus area was selected to test the effectiveness of S1 multi-temporal in mapping its distribution. Results show that only 3.5% of the agricultural fields in the focus area remained underwater for at least 6 days and 69% for only 1 day.
Vegetation phenology is that branch of science that describes periodic plant life cycle events across the growing seasons. Remote sensing typically monitors these significant events by means of time ...series of vegetation indices, permitting to characterize vegetation dynamics. It is well known that vegetation in urban areas, i.e., green spaces in general, may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities, and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we mapped phenological behavior of urban green areas to explore yearly persistence of their potential favorable effect, such as heat reduction, air purification, noise mitigation, and promotion of physical/social activities and improvement of mental health. The study area corresponds to the municipality of Torino (about 800,000 inhabitants, NW, Italy). Renouncing to a rigorous at-species level phenological description, this work investigated macro-phenology of vegetated areas for the 2018, 2019 and 2020 years with reference to the new free and open Copernicus HR-VPP dataset. Vegetation type, deduced with reference to the 2019 BDTRE official technical map of the Piemonte Region, was considered and related to the correspondent macro-phenology using a limited number of metrics from the HR-VPP dataset. Investigation was aimed at exploring their capability of providing synthetic and easy-to-use information for urban planners. No validation was achieved about phenological metrics values (assuming their accuracy correspondent to the nominal one reported in the associated manuals). Nevertheless, a spatial validation was operated to investigate the capability of the dataset to properly recognize vegetated areas, thus providing correspondent metrics. Preliminary results showed a spatial inconsistency related to the HR-VPP dataset, that greatly overestimates (about 50%) vegetated areas in the city, assigning metric values to pixels that, if compared with technical maps, do not fall within vegetated areas. The work found out that, among HR-VPP metrics, LOS (Length Of Season) and SPROD (Seasonal Productivity) well characterized vegetation patches, making it possible to clearly read vegetation behavior, which can be effectively exploited to zone the city and make management of green areas and real estate considerations more effective.
Greening is a subsidy provided by the Common Agricultural Policy (CAP), related to mowing and designed to protect environment. National or regional paying agencies (PP) monitor and verify compliance ...of farmers' declarations with CAP rules. In this work, an operational procedure is proposed aimed at supporting PPs in detecting, mapping and quantifying the number of times mowing occurred in a meadow field. In particular, 72,539 meadows fields within the Piemonte region (NW - Italy) were analysed with a time series of Sentinel-2 (S2) data. The procedure is based on the processing of filtered and regularized time series of NDVI maps. The Fast Fourier Transform (FFT) was applied at field level to decompose the local NDVI temporal profile. The frequency (
corresponding to the maximum amplitude (
was therefore considered.
value was used to detect not-mowed meadows by thresholding based on the application of the non-parametric Kolmogorov-Smirnov test. Mowing counting were achieved with reference to
and the correspondent map (called MCM) generated for the study area. MCM was, finally, tested against the validation set (285 fields). Results showed an Overall Accuracy (OA) > 87%, confirming the effectiveness of the proposed procedure in detecting, mapping and quantifying the number of times mowing occurred.
Changes in land use and land cover as well as feedback on the climate deeply affect the landscape worldwide. This phenomenon has also enlarged the human-wildlife interface and amplified the risk of ...potential new zoonoses. The expansion of the human settlement is supposed to affect the spread and distribution of wildlife diseases such as canine distemper virus (CDV), by shaping the distribution, density, and movements of wildlife. Nevertheless, there is very little evidence in the scientific literature on how remote sensing and GIS tools may help the veterinary sector to better monitor the spread of CDV in wildlife and to enforce ecological studies and new management policies in the near future. Thus, we perform a study in Northwestern Italy (Aosta Valley Autonomous Region), focusing on the relative epidemic waves of CDV that cause a virulent disease infecting different animal species with high host mortality. CDV has been detected in several mammalian from Canidae, Mustelidae, Procyonidae, Ursidae, and Viverridae families. In this study, the prevalence is determined at 60% in red fox (
,
= 296), 14% in wolf (
,
= 157), 47% in badger (
,
= 103), and 51% in beech marten (
,
= 51). The detection of CDV is performed by means of real-time PCR. All the analyses are done using the TaqMan approach, targeting the chromosomal gene for phosphoprotein, gene P, that is involved in the transcription and replication of the virus. By adopting Earth Observation Data, we notice that CDV trends are strongly related to an altitude gradient and NDVI entropy changes through the years. A tentative model is developed concerning the ground data collected in the Aosta Valley region. According to our preliminary study, entropy computed from remote-sensing data can represent a valuable tool to monitor CDV spread as a proxy data predictor of the intensity of fragmentation of a given landscape and therefore also to monitor CDV. In conclusion, the evaluation from space of the landscape variations regarding the wildlife ecological corridors due to anthropic or natural disturbances may assist veterinarians and wildlife ecologists to enforce management health policies in a One Health perspective by pointing out the time and spatial conditions of interaction between wildlife. Surveillance and disease control actions are supposed to be carried out to strengthen the usage of geospatial analysis tools and techniques. These tools and techniques can deeply assist in better understanding and monitoring diseases affecting wildlife thanks to an integrated management approach.
Land cover (LC) maps are crucial to environmental modeling and define sustainable management and planning policies. The development of a land cover mapping continuous service according to the new ...EAGLE legend criteria has become of great interest to the public sector. In this work, a tentative approach to map land cover overcoming remote sensing (RS) limitations in the mountains according to the newest EAGLE guidelines was proposed. In order to reach this goal, the methodology has been developed in Aosta Valley, NW of Italy, due to its higher degree of geomorphological complexity. Copernicus Sentinel-1 and 2 data were adopted, exploiting the maximum potentialities and limits of both, and processed in Google Earth Engine and SNAP. Due to SAR geometrical distortions, these data were used only to refine the mapping of urban and water surfaces, while for other classes, composite and timeseries filtered and regularized stack from Sentinel-2 were used. GNSS ground truth data were adopted, with training and validation sets. Results showed that K-Nearest-Neighbor and Minimum Distance classification permit maximizing the accuracy and reducing errors. Therefore, a mixed hierarchical approach seems to be the best solution to create LC in mountain areas and strengthen local environmental modeling concerning land cover mapping.
Urban environment has been increasingly recognised as a health determinant able to promote healthy or unhealthy lifestyles. The growing use of technology and urbanization is influencing people ...behaviours, making them more sedentary. In children, this may be even more relevant as childhood is a critical period for creating bases for lifelong health and well-being. Given the potential for the urban environment to influence health, we investigated the association between some key characteristics of the urban environment and sedentary behaviour in school-aged children. We recruited 331 healthy children (9–11 years, 52% males), whose parents were asked to quantify their time spent in several sedentary activities. We derived two sedentary behaviour outcomes: the total daily sedentary time and the screen time. Exposure to less urbanized and more vegetated area was derived by combining key environmental attributes using Principal Component Analysis. Independently of age, sex and BMI children living in less urbanized and more vegetated areas reported 12 min less of daily sedentary time (β: −12, 95% CI from −22 to −2; p = 0.02) and were less likely to exceed the recommended daily screen time (2 h/day) (OR: 0.86 95% CI 0.74–1, 00; p = 0.056). A stronger association was found in children whose mothers were highly educated, suggesting that maternal education level acts as effect modifier. Our findings highlight that environmental characteristics may shape children’s health by influencing their lifestyles, and should be considered in Public Health strategy to prevent sedentary behaviour and promote more sustainable and healthier cities.
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•Urban environment is associated with sedentary behaviors in children.•Children living in greener and less urbanized areas were 15 min/day less sedentary.•Children living in greener/less urbanized areas less likely exceeded 2 h/day of screens.•Association between environment and sedentary behavior is modified by maternal education.
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