Antonio T. Monteiro

Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system

Abstract There is an increasing need of effective monitoring systems for habitat quality assessment. Methods based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making. Here, we evaluate the ability of Earth observation (EO) data, based on a new automated, knowledge-driven system, to predict several indicators for oak woodland habitat quality in a Portuguese Natura 2000 site. We collected in-field data on five habitat quality indicators in vegetation plots from woodland habitats of a landscape undergoing agricultural abandonment. Forty-three predictors were calculated, and a multi-model inference framework was applied to evaluate the predictive strength of each data set for the several quality indicators. Three indicators were mainly explained by predictors related to landscape and neighbourhood structure. Overall, competing models based on the products of the automated knowledge-driven system had the best performance to explain quality indicators, compared to models based on manually classified land cover data. The system outputs in terms of both land cover classes and spectral/landscape indices were considered in the study, which highlights the advantages of combining EO data with RS techniques and improved modelling based on sound ecological hypotheses. Our findings strongly suggest that some features of habitat quality, such as structure and habitat composition, can be effectively monitored from EO data combined with in-field campaigns as part of an integrative monitoring framework for habitat status assessment.

Assessing climate change impacts on wildfire exposure in Mediterranean areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981-2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981-2010, 2011-2040, and 2041-2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041-2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

Systemic delivery of bone marrow mesenchymal stem cells for in situ intervertebral disc regeneration

Cell therapies for intervertebral disc (IVD) regeneration presently rely on transplantation of IVD cells or stem cells directly to the lesion site. Still, the harsh IVD environment, with low irrigation and high mechanical stress, challenges cell administration and survival. In this study, we addressed systemic transplantation of allogeneic bone marrow mesenchymal stem cells (MSCs) intravenously into a rat IVD lesion model, exploring tissue regeneration via cell signaling to the lesion site. MSC transplantation was performed 24 hours after injury, in parallel with dermal fibroblasts as a control; 2 weeks after transplantation, animals were killed. Disc height index and histological grading score indicated less degeneration for the MSC‐transplanted group, with no significant changes in extracellular matrix composition. Remarkably, MSC transplantation resulted in local downregulation of the hypoxia responsive GLUT‐1 and in significantly less herniation, with higher amounts of Pax5+ B lymphocytes and no alterations in CD68+ macrophages within the hernia. The systemic immune response was analyzed in the blood, draining lymph nodes, and spleen by flow cytometry and in the plasma by cytokine array. Results suggest an immunoregulatory effect in the MSC‐transplanted animals compared with control groups, with an increase in MHC class II+ and CD4+ cells, and also upregulation of the cytokines IL‐2, IL‐4, IL‐6, and IL‐10, and downregulation of the cytokines IL‐13 and TNF‐α. Overall, our results indicate a beneficial effect of systemically transplanted MSCs on in situ IVD regeneration and highlight the complex interplay between stromal cells and cells of the immune system in achieving successful tissue regeneration. Stem Cells Translational Medicine 2017;6:1029–1039

Integrating land cover structure and functioning to predict biodiversity patterns: a hierarchical modelling framework designed for ecosystem management

ContextLand-use/land-cover (LU/LC) dynamics is one of the main drivers of global environmental change. In the last years, aerial and satellite imagery have been increasingly used to monitor the spatial extent of changes in LU/LC, deriving relevant biophysical parameters (i.e. primary productivity, climate and habitat structure) that have clear implications in determining spatial and temporal patterns of biodiversity, landscape composition and ecosystem services.ObjectivesAn innovative hierarchical modelling framework was developed in order to address the influence of nested attributes of LU/LC on community-based ecological indicators.MethodsFounded in the principles of the spatially explicit stochastic dynamic methodology (StDM), the proposed methodological advances are supported by the added value of integrating bottom-up interactions between multi-scaled drivers.ResultsThe dynamics of biophysical multi-attributes of fine-scale subsystem properties are incorporated to inform dynamic patterns at upper hierarchical levels. Since the most relevant trends associated with LU/LC changes are explicitly modelled within the StDM framework, the ecological indicators’ response can be predicted under different social-economic scenarios and site-specific management actions. A demonstrative application is described to illustrate the framework methodological steps, supporting the theoretic principles previously presented.ConclusionsWe outline the proposed multi-model framework as a promising tool to integrate relevant biophysical information to support ecosystem management and decision-making.

QPhenoMetrics: An open source software application to assess vegetation phenology metrics

Abstract Phenology is one of the most reliable indicators of vegetation dynamics. Assessing and monitoring the dynamics of phenology is relevant to support several decisions in order to improve the efficiency of several farming practices. An open source application – QPhenoMetrics - implemented in QGIS software that estimates vegetation phenology metrics is presented, using Earth Observation Systems (EOS) based time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology. QPhenoMetrics is characterized by freely-usable and updatable code, acceptance of satellite images or text formats, time-series analysis toolbox allowing the selection of region of interest with statistical quality assessment for Vegetation Indices (VI), and estimation of ensemble metrics. The application is structured in three components: (i) input data; (ii) pre-processing of the VI time-series and several fitting methods and (iii) computation of the phenological metrics. QPhenoMetrics produces a plot with the VI time-series and corresponding phenology metrics, and a spreadsheet is created with a list of NDVI or EVI values estimated using the selected fitting method. To evaluate the application, two main Portuguese crops, vineyards and maize, and MOD13 data from MODIS sensor during 2011–2012 were considered. QPhenoMetrics was validated with vineyard phenology observations (2007–2011). A comparative analysis with software products TimeSat and Spirits was also performed. It was concluded that QPhenoMetrics can be very useful for common users to extract phenology information for 16 daily MODIS data in HDF format, text files with NDVI/EVI data and ASCII files, through a simple and intuitive graphic interface. Furthermore, the user can evaluate the quality assessment of VI of the images used. QPhenoMetrics is an effective open source tool that in addition to being free, is readily modifiable by user according to the study requirements.

Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends

Spatiotemporal ecological modelling of terrestrial ecosystems relies on climatological and biophysical Earth observations. Due to their increasing availability, global coverage, frequent acquisition and high spatial resolution, satellite remote sensing (SRS) products are frequently integrated to in situ data in the development of ecosystem models (EMs) quantifying the interaction among the vegetation component and the hydrological, energy and nutrient cycles. This review highlights the main advances achieved in the last decade in combining SRS data with EMs, with particular attention to the challenges modellers face for applications at local scales (e.g. small watersheds). We critically review the literature on progress made towards integration of SRS data into terrestrial EMs: (1) as input to define model drivers; (2) as reference to validate model results; and (3) as a tool to sequentially update the state variables, and to quantify and reduce model uncertainty. The number of applications provided in the literature shows that EMs may profit greatly from the inclusion of spatial parameters and forcings provided by vegetation and climatic-related SRS products. Limiting factors for the application of such models to local scales are: (1) mismatch between the resolution of SRS products and model grid; (2) unavailability of specific products in free and public online repositories; (3) temporal gaps in SRS data; and (4) quantification of model and measurement uncertainties. This review provides examples of possible solutions adopted in recent literature, with particular reference to the spatiotemporal scales of analysis and data accuracy. We propose that analysis methods such as stochastic downscaling techniques and multi-sensor/multi-platform fusion approaches are necessary to improve the quality of SRS data for local applications. Moreover, we suggest coupling models with data assimilation techniques to improve their forecast abilities. This review encourages the use of SRS data in EMs for local applications, and underlines the necessity for a closer collaboration among EM developers and remote sensing scientists. With more upcoming satellite missions, especially the Sentinel platforms, concerted efforts to further integrate SRS into modelling are in great demand and these types of applications will certainly proliferate.

Climate Change Impacts on Water Resources and Reservoir Management: Uncertainty and Adaptation for a Mountain Catchment in Northeast Portugal

Reservoirs often play an important role in mitigating water supply problems. However, the implications of climate change are not always considered in reservoir planning and management. This study aimed to address this challenge in the Alto Sabor watershed, northeast Portugal. The study analysed whether or not the shortage of water supply can be effectively addressed through the construction of a new reservoir (two-reservoir system) by considering future climate projections. The hydrological model Soil and Water Assessment Tool (SWAT) was calibrated and validated against daily-observed discharge and reservoir volume, with a good agreement between model predictions and observations. Outputs from four General Circulation Models (GCM) for two scenarios (RCP 4.5 and 8.5) were statistically downscaled and bias-corrected with ground observations. A general increase in temperature is expected in the future while the change in precipitation is more uncertain as per the differences among climatic models. In general, annual precipitation would slightly decrease while seasonal changes would be more significant, with more precipitation in winter and much less in spring and summer. SWAT simulations suggest that the existence of two-reservoir will better solve the water supply problems under current climate conditions compared to a single-reservoir system. However in the future, the reliability of this solution will decrease, especially due to the variability of projections from the different climatic models. The solution to water supply problems in this region, adopted taking only present-day climate into account, will likely be inefficient for water supply management under future climate conditions.