Agricultural land abandonment is an important environmental issue in Europe. The proper management of agricultural areas has important implications for ecosystem services (food production, biodiversity, climate regulation and the landscape). In the coming years, an increase of abandoned areas is expected due to socio-economic changes. The identification and quantification of abandoned agricultural plots is key for monitoring this process and for applying management measures. The Valencian Region (Spain) is an important fruit and vegetable producing area in Europe, and it has the most important citrus industry. However, this agricultural sector is highly threatened by diverse factors, which have accelerated land abandonment. Landsat and MODIS satellite images have been used to map land abandonment. However, these images do not give good results in areas with high spatial fragmentation and small-sized agricultural plots. Sentinel-2 and airborne imagery shows unexplored potential to overcome this thanks to higher spatial resolutions. In this work, three models were compared for mapping abandoned plots using Sentinel-2 with 10 m bands, Sentinel-2 with 10 m and 20 m bands, and airborne imagery with 1 m visible and near-infrared bands. A pixel-based classification approach was used, applying the Random Forests algorithm. The algorithm was trained with 144 plots and 100 decision trees. The results were validated using the hold-out method with 96 independent plots. The most accurate map was obtained using airborne images, the Enhanced Vegetation Index (EVI) and Thiam’s Transformed Vegetation Index (TTVI), with an overall accuracy of 88.5%. The map generated from Sentinel-2 images (10 m bands and the EVI and TTVI spectral indices) had an overall accuracy of 77.1%. Adding 20 m Sentinel-2 bands and the Normalized Difference Moisture Index (NDMI) did not improve the classification accuracy. According to the most accurate map, 4310 abandoned plots were detected in our study area, representing 32.5% of its agricultural surface. The proposed methodology proved to be useful for mapping citrus in highly fragmented areas, and it can be adapted to other crops.
Coastal lagoons are transitional ecosystems with complex spatial and temporal variability. Remote sensing tools are essential for monitoring and unveiling their variability. Turbidity is a water quality parameter used for studying eutrophication and sediment transport. The objective of this research is to analyze the monthly turbidity pattern in a shallow coastal lagoon along two years with different precipitation regimes. The selected study area is the Albufera de Valencia lagoon (Spain). For this purpose, we used Sentinel 2 images and in situ data from the monitoring program of the Environment General Subdivision of the regional government. We obtained Sentinel 2A and 2B images for years 2017 and 2018 and processed them with SNAP software. The results of the correlation analysis between satellite and in situ data, corroborate that the reflectance of band 5 (705 nm) is suitable for the analysis of turbidity patterns in shallow lagoons (average depth 1 m), such as the Albufera lagoon, even in eutrophic conditions. Turbidity patterns in the Albufera lagoon show a similar trend in wet and dry years, which is mainly linked to the irrigation practice of rice paddies. High turbidity periods are linked to higher water residence time and closed floodgates. However, precipitation and wind also play an important role in the spatial distribution of turbidity. During storm events, phytoplankton and sediments are discharged to the sea, if the floodgates remain open. Fortunately, the rice harvesting season, when the floodgates are open, coincides with the beginning of the rainy period. Nevertheless, this is a lucky coincidence. It is important to develop conscious management of floodgates, because having them closed during rain events can have several negative effects both for the lagoon and for the receiving coastal waters and ecosystem. Non-discharged solids may accumulate in the lagoon worsening the clogging problems, and the beaches next to the receiving coastal waters will not receive an important load of solids to nourish them.
Developing indicators to monitor environmental change in wetlands with the aid of Earth Observation Systems can help to obtain spatial data that is not feasible with in situ measures (e.g., flooding patterns). In this study, we aim to test Sentinel-2A/B images suitability for detecting small water bodies in wetlands characterized by high diversity of temporal and spatial flooding patterns using previously published indices. For this purpose, we used medium spatial resolution Sentinel-2A/B images of four representative coastal wetlands in the Valencia Region (East Spain, Mediterranean Sea), and on three different dates. To validate the results, 60 points (30 in water areas and 30 in land areas) were distributed randomly within a 20 m buffer around the border of each digitized water polygon for each date and wetland (600 in total). These polygons were mapped using as a base map orthophotos of high spatial resolution. In our study, the best performing index was the NDWI. Overall accuracy and Kappa index results were optimal for −0.30 threshold in all the studied wetlands and dates. The consistency in the results is key to provide a methodology to characterize water bodies in wetlands as generalizable as possible. Most studies developed in wetlands have focused on calculating global gain or loss of wetland area. However, inside of wetlands which hold protection figures, the main threat is not necessarily land use change, but rather water management strategies. Applying Sentinel-2A/B images to calculate the NDWI index and monitor flooded area changes will be key to analyse the consequence of these management actions.
Phytoplankton blooms are sporadic events in time and are isolated in space. This complex phenomenon is produced by a variety of both natural and anthropogenic causes. Early detection of this phenomenon, as well as the classification of a water body under conditions of bloom or non-bloom, remains an unresolved problem. This research proposes the use of Inherent Optical Properties (IOPs) in optically complex waters to detect the bloom or non-bloom state of the phytoplankton community. An IOP index is calculated from the absorption coefficients of the colored dissolved organic matter (CDOM), the phytoplankton (phy) and the detritus (d), using the wavelength (λ) 443 nm. The effectiveness of this index is tested in five bloom events in different places and with different characteristics from Mexican seas: 1. Dzilam (Caribbean Sea, Atlantic Ocean), a diatom bloom (Rhizosolenia hebetata); 2. Holbox (Caribbean Sea, Atlantic Ocean), a mixed bloom of dinoflagellates (Scrippsiella sp.) and diatoms (Chaetoceros sp.); 3. Campeche Bay in the Gulf of Mexico (Atlantic Ocean), a bloom of dinoflagellates (Karenia brevis); 4. Upper Gulf of California (UGC) (Pacific Ocean), a diatom bloom (Coscinodiscus and Pseudo-nitzschia) and 5. Todos Santos Bay, Ensenada (Pacific Ocean), a dinoflagellate bloom (Lingulodinium polyedrum). The diversity of sites show that the IOP index is a suitable method to determine the phytoplankton bloom conditions.
Wireless sensor networks are widely used for monitoring different processes, including agriculture, in order to reach sustainability. One of the keys to sustainable crops is water saving. In particular, saving water is extremely important in arid and semiarid regions. In those regions, citrus trees are cultivated, and drip irrigation is used to save water. In this paper, we propose a smart irrigation system for citrus trees using a WSN. We describe the employed sensors and nodes for this proposal. Next, we present the proposed architecture and the operational algorithms for the nodes. Moreover, we designed different algorithms for fault tolerance and energy saving functionalities. The energy saving algorithm is based on the relevance of the gathered data, which is analyzed in order to consider whether the information should be forwarded or not. A TPC-based protocol is proposed to perform the communication among the nodes of our system. In addition, we present different simulations of the proposed system. Particularly, we show the consumed bandwidth and the remaining energy in the different nodes. Finally, we test different energy configurations to evaluate the network lifetime and the remaining energy when the first node depletes its energy.
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