Computer-based monitoring platform for renewable energy systems: Four experimental application cases using NI LabVIEW

Sustainability and responsible environmental behaviour constitute a vital premise in the development of the humankind. In fact, during last decades, the global energetic scenario is evolving towards a scheme with increasing relevance of Renewable Energy Sources (RES) like photovoltaic, wind, biomass and hydrogen. Furthermore, hydrogen is an energy carrier which constitutes a mean for long-term energy storage. The integration of hydrogen with local RES contributes to distributed power generation and early introduction of hydrogen economy. Intermittent nature of many of RES, for instance solar and wind sources, impose the development of a management and control strategy to overcome this drawback. This strategy is responsible of providing a reliable, stable and efficient operation of the system. To implement such strategy, a monitoring system is required.The present paper aims to contribute to experimentally validate LabVIEW as valuable tool to develop monitoring platforms in the field of RES-based facilities. To this aim, a set of real systems successfully monitored is exposed.

Above ground biomass estimation in Mediterranean multiple use systems with high spatial resolution satellite images

Forest biomass has been having an increasing importance in the world economy and in the evaluation of the forests development and monitoring. It was identified as a global strategic reserve, due to its applications in bioenergy, bioproduct development and issues related to reducing greenhouse gas emissions. The estimation of above ground biomass is frequently done with allometric functions per species with plot inventory data. An adequate sampling design and intensity for an error threshold is required. The estimation per unit area is done using an extrapolation method. This procedure is labour demanding and costly. The mail goal of this study is the development of allometric functions for the estimation of above ground biomass with ground cover as independent variable, for forest areas of holm aok (Quercus rotundifolia), cork oak (Quercus suber) and umbrella pine (Pinus pinea) in multiple use systems. Ground cover per species was derived from crown horizontal projection obtained by processing high resolution satellite images, orthorectified, geometrically and atmospheric corrected, with multi-resolution segmentation method and object oriented classification. Forest inventory data were used to estimate plot above ground biomass with published allometric functions at tree level. The developed functions were fitted for monospecies stands and for multispecies stands of Quercus rotundifolia and Quercus suber, and Quercus suber and Pinus pinea. The stand composition was considered adding dummy variables to distinguish monospecies from multispecies stands. The models showed a good performance. Noteworthy is that the dummy variables, reflecting the differences between species, originated improvements in the models. Significant differences were found for above ground biomass estimation with the functions with and without the dummy variables. An error threshold of 10% corresponds to stand areas of about 40 ha. This method enables the overall area evaluation, not requiring extrapolation procedures, for the three species, which occur frequently in multispecies stands.

Above ground biomass functions with vegetation indices for multiple use systems of two evergreen oaks

Remote sensing is a promising approach for above ground biomass estimation, as forest parameters can be obtained indirectly. The analysis in space and time is quite straight forward due to the flexibility of the method to determine forest crown parameters with remote sensing. It can be used to evaluate and monitoring for example the development of a forest area in time and the impact of disturbances, such as silvicultural practices or deforestation. The vegetation indices, which condense data in a quantitative numeric manner, have been used to estimate several forest parameters, such as the volume, basal area and above ground biomass. The objective of this study was the development of allometric functions to estimate above ground biomass using vegetation indices as independent variables. The vegetation indices used were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Simple Ratio (SR) and Soil-Adjusted Vegetation Index (SAVI). QuickBird satellite data, with 0.70 m of spatial resolution, was orthorectified, geometrically and atmospheric corrected, and the digital number were converted to top of atmosphere reflectance (ToA). Forest inventory data and published allometric functions at tree level were used to estimate above ground biomass per plot. Linear functions were fitted for the monospecies and multispecies stands of two evergreen oaks (Quercus suber and Quercus rotundifolia) in multiple use systems, montados. The allometric above ground biomass functions were fitted considering the mean and the median of each vegetation index per grid as independent variable. Species composition as a dummy variable was also considered as an independent variable. The linear functions with better performance are those with mean NDVI or mean SR as independent variable. Noteworthy is that the two better functions for monospecies cork oak stands have median NDVI or median SR as independent variable. When species composition dummy variables are included in the function (with stepwise regression) the best model has median NDVI as independent variable. The vegetation indices with the worse model performance were EVI and SAVI.

Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe

Estimation of pasture productivity is an important step for the farmer in terms of planning animal stocking, organizing animal lots, and determining supplementary feeding needs throughout the year. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Two types of sensors were evaluated: an active optical sensor(OptRx®, which measures the NDVI, Normalized Difference Vegetation Index) and a capacitance probe (GrassMaster II which estimates plant mass). The results showed the potential of NDVI for monitoring the evolution of spatial and temporal patterns of vegetative growth of biodiverse pasture. Higher NDVI values were registered as pasture approached its greatest vegetative vigor, with a significant fall in the measured NDVI at the end of Spring, when the pasture began to dry due to the combination of higher temperatures and lower soil moisture content. This index was also effective for identifying different plant species (grasses/legumes) and variability in pasture yield. Furthermore, it was possible to develop calibration equations between the capacitance and the NDVI (R2 = 0.757; p < 0.01), between capacitance and GM (R2 = 0.799; p<0.01), between capacitance and DM (R2 = 0.630; p<0.01), between NDVI and GM (R2=0.745; p < 0.01), and between capacitance and DM (R2=0.524; p<0.01). Finally, a direct relationship was obtained between NDVI and pasture moisture content (PMC, in %) and between capacitance and PMC (respectively, R2 = 0.615; p<0.01 and R2=0.561; p <0.01) in Alentejo dryland farming systems.

Effect of trees in the soil nutrient concentration and pasture produtivity in Mediterranean silvo-pastoral systems

Silvo-pastoral are mixed systems of trees and grass, which have been proposed as a means to extend the benefits of forest to farmed land. Agro-forestry systems under semi-arid Mediterranean conditions, called montados in Portugal and dehesas in Spain, cover substantial areas in the world. These silvo-pastoral systems are the most extensive European agro-forestry system, as they cover 3.5–4.0 Mha in Spain and Portugal. Long-term studies are essential to assess the magnitude of the temporal nutrient flow dynamics in terrestrial ecosystems and to understand the response of these systems to fertilizer management. In order to implement the conservation task and recovery of resources through silvo-pastoral systems it is necessary to know and correct potential limiting factors, especially the soil factor, and this requires agronomic knowledge as well as the implmentation of the available new technologies. In this context, this task aims at a better understanding of the contribution of the two components of montado ecosystem (trees and herbaceous vegetation) on the soil nutrient and water dynamics, that allow for the interpretation of the variability of pasture dry matter yield and help the farmer in the management of tree density. Collaterally the task will evaluate and calibrate new technologies that simplify the monitoring of soil, grassland, trees and grazing animals.

Evaluation of DUALEM, OptRx and Grassmaster II sensors for monitoring the spatial variability and the temporal stability of a Mediterranean pasture

Site-specific management (SSM) is a form of precision agriculture whereby decisions on resource application and agronomic practices are improved to better match soil and crop requirements as they vary in the field. SSM enables the identification of regions (homogeneous management zones) within the area delimited by field boundaries. These subfield regions constitute areas that have similar permanent characteristics. Traditional soil and pasture sampling and the necessary laboratory analysis are time-consuming, labour-intensive and cost prohibitive, not viable from a SSM perspective because it needs a large number of soil and pasture samples in order to achieve a good representation of soil properties, nutrient levels and pasture quality and productivity. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of soil nutrients and pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Three types of sensors were evaluated in a 7ha pasture experimental field: an electromagnetic induction sensor (“DUALEM 1S”, which measures the soil apparent electrical conductivity, ECa), an active optical sensor ("OptRx®", which measures the NDVI, “Normalized Difference Vegetation Index”) and a capacitance probe ("GrassMaster II" which estimates plant mass). The results indicate the possibility of using a soil electrical conductivity probe as, probably, the best tool for monitoring not only some of the characteristics of the soil, but also those of the pasture, which could represent an important help in simplifying the process of sampling and support SSM decision making, in precision agriculture projects. On the other hand, the significant and very strong correlations obtained between capacitance and NDVI and between any of these parameters and the pasture productivity shows the potential of these tools for monitoring the evolution of spatial and temporal patterns of the vegetative growth of biodiverse pasture, for identifying different plant species and variability in pasture yield in Alentejo dry-land farming systems. These results are relevant for the selection of an adequate sensing system for a particular application and open new perspectives for other works that would allow the testing, calibration and validation of the sensors in a wider range of pasture production conditions, namely the extraordinary diversity of botanical species that are characteristic of the Mediterranean region at the different periods of the year.