Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments.
<p>Freshwater environments have undergone important changes in recent years; the various pressures on land use, the effects of climate change and the over-exploitation of water resources are significantly affecting water resource availability and biodiversity in these fragile ecosystems. Constant monitoring of freshwater environments is crucial for their management and protection. This can be obtained by satellite remote sensing that is a powerful, cost-efficient and still under-exploited monitoring tool. The main idea of the research work is to investigate how different kind of satellite data can be exploited to achieve a better description of freshwater environments at adequate space scales and with high temporal resolution. The study-case is the Albufera wetland in Spain, one the most important protected areas in Europe for the presence of many migratory birds species. The Albufera Natural Park includes a lake surrounded by rice fields irrigated by periodic flooding that offer in some periods of the year&#160;suitable habitats for many species of birds and others water-related organisms, such as macroinvertebrates and fishes.</p><p>The continuous monitoring of the flooded area extension is a prerequisite to understand the link between the water presence and habitat availability. The study combines observation from multiple optical and synthetic aperture radar (SAR) sensors with spatial resolution between 3 and 30 m. Acquisitions from Landsat-8, Sentinel-2 satellites were used in the optical and infrared bands. The revisit time ranges between 5 and 10 days even if, in case of cloud cover, the revisit time increases consistently. An unsupervised classification method, based on the application of a threshold, was used, in particular multispectral indexes such as MNDWI, NDWI and NDVI were calculated. The NDWI and MNDWI indexes allowed to identify the presence of limpid and turbid water in the October-May period, while in the May-September period the NDVI was used to identify rice plants and therefore indirectly estimate the possible presence of water below the canopy. &#160;In order to increase the time resolution, also in periods with frequent cloud presence, Sentinel-1A and 1B and COSMO-SkyMed SAR images were also used. The Sentinel-1 constellation operates in C band with time resolution of about 5 days; while COSMO-SkyMed operates in X bands with time resolution of about 10 days. The images were processed with both unsupervised and supervised classification methods. The information obtained from images processing were compared with very high-resolution (0.30 m and 0.50 m) satellite images and field measurements in order to validate and calibrate the classification method. The classification obtained with multispectral and SAR data were also cross-validated, providing very satisfactory results. Combination of different satellite data allowed for a significant increase of the temporal resolution of the observations, also in presence of cloud cover. The result of the study showed the dynamic of flooding-drying of the wetland and the flooding duration in different areas of the Albufera Park. This dataset is extremely useful for the optimization of wetland management and for further investigation on the link between flooding duration and habitat availability.</p>
Knowledge about the frequency and duration of each flowing status of non-perennial rivers is severely limited by the small number of streamflow gauges and reliable prediction of surface water presence by hydrological models. In this study, multispectral Sentinel-2 images were used to detect and monitor changes in water surface presence along three non-perennial Mediterranean rivers located in southern Italy. Examining the reflectance values of water, sediment and vegetation covers, the bands in which these classes are most differentiated were identified. It emerged that the false-color composition of the Sentinel-2 bands SWIR, NIR and RED allows water surfaces to be clearly distinguished from the other components of the river corridor. From the false-color composite images, it was possible to identify the three distinct flowing status of non-perennial rivers: “flowing” (F), “ponding” (P) and “dry” (D). The results were compared with field data and very high-resolution images. The flowing status was identified for all archive images not affected by cloud cover. The obtained dataset allowed to train Random Forest (RF) models able to fill temporal gaps between satellite images, and predict the occurrence of one of the three flowing status (F/P/D) on a daily scale. The most important predictors of the RF models were the cumulative rainfall and air temperature data before the date of satellite image acquisition. The performances of RF models were very high, with total accuracy of 0.82–0.97 and true skill statistic of 0.64–0.95. The annual non-flowing period (phases P and D) of the monitored rivers was assessed in range 5 to 192 days depending on the river reach. Due to the easy-to-use algorithm and the global, freely available satellite imagery, this innovative technique has large application potential to describe flowing status of non-perennial rivers and estimate frequency and duration of surface water presence.
<p>Changes in fluvial morphology, such as the migration of channels and sandbars, are driven by many factors e.g. water, woody debris and sediment discharges, vegetation and management practice. Nowadays, increased anthropic pressure and climate change are accelerating the natural morphologic dynamics. Therefore, the monitoring of river changes and the assessment of future trends are necessary for the identification of the optimal management practices, aiming at the improvement of river ecological status and the mitigation of hydraulic risk. Satellite data can provide an effective and cost-effective tool for the monitoring of river morphology and its temporal evolution.</p><p>The main idea of this work is to understand which remote sensed data, and particularly which space and time resolutions, are more adapt for the observation of sandbars evolution in relatively large rivers. To this purpose, multispectral and Synthetic Aperture Radar (SAR) archive data, with different spatial resolution, were used. Preference was given to satellite data freely available. Moreover, the observations extracted by the satellite data were compared with ground data recorded by a fixed camera.</p><p>The study case is a sandy bar (area about 0.4 km<sup>2 </sup>and maximum width about 350 m) in a lowland reach of the Po River (Italy), characterized by frequent and relevant morphological changes. The bar shoreline changes were captured by a fixed video camera, installed on a bridge and operating for almost two years (July 2017 - November 2018). To this purpose, we used: Sentinel-2 multispectral images with a spatial resolution of 10 m, Sentinel-1 SAR images with a resolution of 5 x 20 m and CosmoSkyMed SAR images with a resolution of 5 m. It is worth noting that the Sentinel data of the Copernicus Programme are freely available while the CosmoSkyMed data of the Italian Space Agency (ASI) are freely distributed for scientific purpose after the successful participation to an open call. In order to validate the results provided by Sentinel and CosmoSkyMed data, we used very high resolution multispectral images (about 50 cm).</p><p>Multispectral images are easily interpreted, but are affected by the presence of cloud cover. For instance, in this analysis, the expendable multispectral images were equal to about 50% of the total archive. On the other hand, the SAR images provide information also in the presence of clouds and at night-time, but they have the drawback of more complex processing and interpretation. The shorelines extracted from the satellite images were compared with those extracted from photographic images, taken on the same day of the satellite acquisition. Other comparisons were made between different satellite images acquired with a temporal mismatch of maximum two days.</p><p>The results of the comparisons showed that the Sentinel-1 and Sentinel-2 data were both adequate for the shoreline changes observation. Due to the higher resolution, the CosmoSkyMed data provided better results. SAR data and multispectral data allowed for automatic extraction of the bar shoreline, with different degree of processing burden. The fusion of data from different satellites gave the opportunity of highly increase the sampling rate.</p>
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