Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts

In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing ‘hidden’ or not easily discernible data concepts. PLSA provides a ‘bottom up’ approach to interoperability problems for users in the face of ‘top down’ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches

Can the effects of anthropogenic pressures and environmental variability on nekton fauna be detected in fishery data? Insights from the monitoring of the artisanal fishery within the Venice lagoon

Nekton communities in transitional ecosystems are naturally adapted to stressful conditions associated with high environmental variability. Human activities in these systems are likely to determine additional stress with a possible effect on fish fauna, hence on fisheries. In order to test the relative importance of natural and anthropogenic factors in determining changes in nekton community, catches (incl. bycatch) from artisanal fisheries (fyke nets) were monitored seasonally in different areas of the Venice lagoon (Italy) between 2001 and 2013. Changes in nekton community composition and in the biomass of target and non-target species/groups were analysed, and the results were related to temporal factors, environmental characteristics and to the variability in anthropogenic pressures. Statistical tests were carried out using a model-based analysis of both univariate and multivariate data. Results highlighted that temporal factors and environmental conditions (i.e. the main chemico-physical descriptors) are more relevant than anthropogenic pressures in explaining spatial and temporal changes in the lagoon nekton assemblage, but that several characteristics of the assemblage, in particular the biomass of some particular categories and of the whole assemblage, are sensitive to human impacts. A particularly negligible effect seemed to be associated with fishing effort, thus suggesting that the monitoring of the local artisanal fishery is suitable also to provide useful information on the evaluation of the status of nekton assemblage

Scoping biological indicators of soil quality Phase II. Defra Final Contract Report SP0534

This report presents results from a field assessment of a limited suite of potential biological indicators of soil quality to investigate their suitability for national-scale soil monitoring

Functional factor analysis for periodic remote sensing data

We present a new approach to factor rotation for functional data. This is achieved by rotating the functional principal components toward a predefined space of periodic functions designed to decompose the total variation into components that are nearly-periodic and nearly-aperiodic with a predefined period. We show that the factor rotation can be obtained by calculation of canonical correlations between appropriate spaces which make the methodology computationally efficient. Moreover, we demonstrate that our proposed rotations provide stable and interpretable results in the presence of highly complex covariance. This work is motivated by the goal of finding interpretable sources of variability in gridded time series of vegetation index measurements obtained from remote sensing, and we demonstrate our methodology through an application of factor rotation of this data.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS518 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Timescale effect estimation in time-series studies of air pollution and health: A Singular Spectrum Analysis approach

A wealth of epidemiological data suggests an association between mortality/morbidity from pulmonary and cardiovascular adverse events and air pollution, but uncertainty remains as to the extent implied by those associations although the abundance of the data. In this paper we describe an SSA (Singular Spectrum Analysis) based approach in order to decompose the time-series of particulate matter concentration into a set of exposure variables, each one representing a different timescale. We implement our methodology to investigate both acute and long-term effects of $PM_{10}$ exposure on morbidity from respiratory causes within the urban area of Bari, Italy.Comment: Published in at http://dx.doi.org/10.1214/07-EJS123 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Hyporheic invertebrates as bioindicators of ecological health in temporary rivers: a meta-analysis

Worldwide, many rivers cease flow and dry either naturally or owing to human activities such as water extraction. However, even when surface water is absent, diverse assemblages of aquatic invertebrates inhabit the saturated sediments below the river bed (hyporheic zone). In the absence of surface water or flow, biota of this zone may be sampled as an alternative to surface water-based ecological assessments. The potential of hyporheic invertebrates as ecological indicators of river health, however, is largely unexplored. We analysed hyporheic taxa lists from the international literature on temporary rivers to assess compositional similarity among broad-scale regions and sampling conditions, including the presence or absence of surface waters and flow, and the regional effect of hydrological phase (dry channel, non-flowing waters, surface flow) on richness. We hypothesised that if consistent patterns were found, then effects of human disturbances in temporary rivers may be assessable using hyporheic bioindicators. Assemblages differed geographically and by climate, but hydrological phase did not have a strong effect at the global scale. However, hyporheic assemblage composition within regions varied along a gradient of higher richness during wetter phases