This paper shows the work carried out to obtain a methodology capable of monitoring the Common Agricultural Policy (CAP) aid line for the protection of steppe birds, which aims to improve the feeding and breeding conditions of these species and contribute to the improvement of their overall biodiversity population. Two methodologies were initially defined, one based on remote sensing (BirdsEO) and the other on Machine Learning (BirdsML). Both use Sentinel-1 and Sentinel-2 data as a basis. BirdsEO encountered certain impediments caused by the land’s slope and the crop’s height. Finally, the methodology based on Machine Learning offered the best results. It evaluated the performance of up to 7 different Machine Learning classifiers, the most optimal being RandomForest. Fourteen different datasets were generated, and the results they offered were evaluated, the most optimal being the one with more than 150 features, including a time series of 8 elements with Sentinel-1, Sentinel-2 data and derived products, among others. The generated model provided values higher than 97% in metrics such as accuracy, recall and Area under the ROC Curve, and 95% in precision and recall. The methodology is transformed into a tool that continuously monitors 100% of the area requesting aid, continuously over time, which contributes positively to optimizing the use of administrative resources and a fairer distribution of CAP funds.
The Mar Menor is a coastal lagoon of great socio-ecological and environmental value; in recent years, different localized episodes of hypoxia and eutrophication have modified the quality of its waters. The episodes are due to a drop in dissolved oxygen levels below 4 mg/L in some parts of the lagoon and a rise in chlorophyll a to over 1.8 mg/L. Considering that monitoring the Mar Menor and its watershed is essential to understand the environmental dynamics that cause these dramatic episodes, in recent years, efforts have focused on carrying out periodic measurements of different biophysical parameters of the water. Taking advantage of the data collected and the versatility offered by neural networks, this paper evaluates the performance of a dozen advanced neural networks oriented to time series forecasted for the estimation of dissolved oxygen and chlorophyll a parameters. The data used are obtained in the water body by means of sensors carried by a multiparameter oceanographic probe and two agro-climatic stations located near the Mar Menor. For the dissolved oxygen forecast, the models based on the Time2Vec architecture, accompanied by BiLSTM and Transformer, offer an R2 greater than 0.95. In the case of chlorophyll a, three models offer an R2 above 0.92. These metrics are corroborated by forecasting these two parameters for the first time step out of the data set used. Given the satisfactory results obtained, this work is integrated as a new biophysical parameter forecast component in the monitoring platform of the Mar Menor Observatory developed by IMIDA. The results demonstrate that it is feasible to forecast the concentration of chlorophyll a and dissolved oxygen using neural networks specialized in time series forecasts.
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