Laura Poggio

Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human-landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests - biodiversity value for conservation and timber production potential - with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas.

A GIS-based human health risk assessment for urban green space planning— An example from Grugliasco (Italy)

The need to develop approaches for risk-based management of soil contamination, as well as the integration of the assessment of the human health risk (HHR) due to the soil contamination in the urban planning procedures has been the subject of recent attention of scientific literature and policy makers. The spatial analysis of environmental data offers multiple advantages for studying soil contamination and HHR assessment, facilitating the decision making process. The aim of this study was to explore the possibilities and benefits of spatial implementation of a quantitative HHR assessment methodology for a planning case in a typical urban environment where the soil is contaminated. The study area is located in the city of Grugliasco a part of the Turin (Italy) metropolitan area. The soils data were derived from a site specific soil survey and the land-use data from secondary sources. In the first step the soil contamination data were geo-statistically analysed and a spatial soil contamination data risk modelling procedure designed. In order to spatially assess the HHR computer routines were developed using GIS raster tools. The risk was evaluated for several different land uses for the planned naturalistic park area. The HHR assessment indicated that the contamination of soils with heavy metals in the area is not sufficient to induce considerable health problems due to typical human behaviour within the variety of urban land uses. An exception is the possibility of direct ingestion of contaminated soil which commonly occurs in playgrounds. The HHR evaluation in a planning case in the Grugliasco Municipality confirms the suitability of the selected planning option. The construction of the naturalistic park presents one solution for reducing the impacts of soil contamination on the health of citizens. The spatial HHR evaluation using GIS techniques is a diagnostic procedure for assessing the impacts of urban soil contamination, with which one can verify planning options, and provides an important step in the integration of human health protection within urban planning procedures.

Effects of landscape configuration on mapping ecosystem service capacity: a review of evidence and a case study in Scotland

ContextHumans structure landscapes for the production of food, fibre and fuel, commonly resulting in declines of non-provisioning ecosystem services (ESs). Heterogeneous landscapes are capable of providing multiple ESs, and landscape configuration—spatial arrangement of land cover in the landscape—is expected to affect ES capacity. However, the majority of ES mapping studies have not accounted for landscape configuration.ObjectivesOur objective is to assess and quantify the relevance of configuration for mapping ES capacity. A review of empirical evidence for configuration effects on the capacity of ten ESs reveals that for four ESs configuration is relevant but typically ignored in ES quantification. For four ESs we quantify the relevance of configuration for mapping ESs using Scotland as a case study.MethodsEach ES was quantified through modelling, respectively ignoring or accounting for configuration. The difference in ES capacity between the two ES models was determined at multiple spatial scales.ResultsConfiguration affected the capacity of all four ESs mapped, particularly at the cell and watershed scale. At the scale of Scotland most local effects averaged out. Flood control and sediment retention responded strongest to configuration. ESs were affected by different aspects of configuration, thus requiring specific methods for mapping each ES.ConclusionsAccounting for configuration is important for the assessment of certain ESs at the cell and watershed scale. Incorporating configuration in landscape management provides opportunities for spatial optimization of ES capacity, but the diverging response of ESs to configuration suggests that accounting for configuration involves trade-offs between ESs.

Modelling high resolution RS data with the aid of coarse resolution data and ancillary data

Abstract In environmental applications, the data have a large variety of resolutions carrying information at different scales. Various approaches have been used to include in models information from sources at different scales combining multi-resolution products in order to integrate the spatio-temporal variability of sub-pixel pattern. A methodology is proposed for the integration of the results obtained with a geostatistical downscaling algorithm, based on block-to-point-kriging, in a General Additive Models interpolation framework to enhance the spatio-temporal resolution of remote sensing data. This allows a good reproduction of the overall spatial pattern of the target images and of their local values. The developed framework was tested using MODIS land surface temperature (LST) with the thermal band of Landsat in a situation of high contamination of clouds for the high resolution dataset. The method proved to be flexible and able to blend data from different sensors maintaining the finer spatial structure of the higher resolution data. The method combines strengths from different approaches: (1) it uses of information held in covariates to provide more accurate results; (2) it is applicable to a variety of remote sensing products as the method does not rely on predetermined functional relationships; (3) it can cope with cloud-rich high resolution images as only a subset of high resolution pixels is needed. This approach is general and can be used with numerous combinations of high and low resolution images, such as MODIS-derived variables, using related band ratios from Landsat or other higher resolution sensors. This approach is a valuable addition to space–time measuring and modelling of ecosystems functions from remote sensing.

Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Abstract Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.

Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas

Soil is very important for many land functions. To achieve sustainability it is important to understand how soils vary over space in the landscape. Remote sensing data can be instrumental in mapping and spatial modelling of soil properties, resources and their variability. The aims of this study were to compare satellite sensors (MODIS, Landsat, Sentinel-1 and Sentinel-2) with varying spatial, temporal and spectral resolutions for Digital Soil Mapping (DSM) of a set of soil properties in Scotland, evaluate the potential benefits of adding Sentinel-1 data to DSM models, select the most suited mix of sensors for DSM to map the considered set of soil properties and validate the results of topsoil (2D) and whole profile (3D) models. The results showed that the use of a mixture of sensors proved more effective to model and map soil properties than single sensors. The use of radar Sentinel-1 data proved useful for all soil properties, improving the prediction capability of models with only optical bands. The use of MODIS time series provided stronger relationships than the use of temporal snapshots. The results showed good validation statistics with a RMSE below 20% of the range for all considered soil properties. The RMSE improved from previous studies including only MODIS sensor and using a coarser prediction grid. The performance of the models was similar to previous studies at regional, national or continental scale. A mix of optical and radar data proved useful to map soil properties along the profile. The produced maps of soil properties describing both lateral and vertical variability, with associated uncertainty, are important for further modelling and management of soil resources and ecosystem services. Coupled with further data the soil properties maps could be used to assess soil functions and therefore conditions and suitability of soils for a range of purposes.

Soil legacy data rescue via GlobalSoilMap and other international and national initiatives

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1km in 2014, followed by an update at a resolution of 250m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

Example of Bayesian Uncertainty for Digital Soil Mapping

Any model for digital soil mapping suffers from different types of errors, including interpolation errors, so it is important to quantify the uncertainty associated with the maps produced. The most common approach is some form of regression kriging (RK) or variation involving geostatistical simulation. Another way of assessing the spatial uncertainty lies in the Bayesian approach where the uncertainty in the results is described by the posterior density. The aim of this paper is to present an example of a Bayesian approach for uncertainty estimation when mapping the topsoil organic matter content in the Grampian region of Scotland (UK, about 12,100 km2). The chosen approach uses (Bayesian) latent Gaussian models fitted using integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) models approach for coping with spatial correlation (INLA_SPDE). For practical comparison purposes, the results of INLA_SPDE were compared with the results of an extension of the scorpan kriging approach, i.e., (1) combining generalized additive models (GAM) with Gaussian simulations and (2) traditional RK. The results were assessed using in-sample and out-of-sample measures and compared for distribution similarity, spatial structure reproduction, computational load, and uncertainty ranges. We conclude that the Bayesian framework using INLA offers a viable alternative to existing methods and an improvement over traditional RK.

Enhancing the WorldClim data set for national and regional applications

Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim. The main aim of this study was to devise and evaluate a computationally efficient approach to produce new high resolution (100m) estimates of current and future climatic variables to be used at the national and regional scale. The test area was Great Britain, where local data are available and of good quality. Present and future climate surfaces were produced. For the present, the approach involved the integration, via spatial interpolation, of local climate information and WorldClim to reduce bias. For future climate scenarios the approach involved spatially downscaling of WorldClim (1km) to a finer resolution of 100m. The main advantages of the proposed approach are: 1. finer resolution, 2. locally adapted to the study area with use of higher number of meteorological stations and improved accuracy and bias, and 3. computationally efficient while making use of the existing resources provided by WorldClim. Two applications were presented to illustrate the practical consequences of improvements obtained with this method. The first is a measure of rainfall intensity, i.e. the R-factor, widely applied in erosion and catchment-scale studies. The second is an application to species distribution modelling, involving a range of bioclimatic variables. The results highlighted the importance of considering the spatial variability and structure of the data integrated in the modelling, and using data adapted to the geographical extent of the analysis, whenever possible. The results of the applications showed the advantage of using enhanced climatic data in applications such as the estimation of soil erosion, species range shift, carbon stocks and the provision of ecosystem services.

Comparison of Traditional and Geostatistical Methods to Estimate and Map the Carbon Content of Scottish Soils

The Scottish Government wish to preserve the carbon stocks already stored or sequestered in both organic and mineral soils and see land-use change as one of the key drivers affecting storage of soil organic carbon (SOC). A key component to develop any strategy to maintain the existing carbon stocks is the quantification of these stocks both in terms of the carbon content and its spatial distribution. To date, two different methods that use the same existing legacy data have been used to quantify carbon stocks in Scotland: a traditional approach and a hybrid generalised additive model (GAM)—geostatistical 3D model. Each of the methods revealed differences in the spatial patterns of SOC stocks. Understanding these differences will enable the development of more robust and accurate models that can be used to assess changes in stocks due to changing land use. Here, we compare these methods for the Scottish mainland, Western Isles, and Orkney. The traditional approach was based on calculating average organic carbon values from a subset (6000) of around 40,000 observations stored within the Scottish Soil Database. The total SOC stock was then determined by multiplying the areal extent of each soil series/land-use combination by the calculated profile stock. The uncertainty was also quantified based on standard error of the measured carbon contents and the uncertainty in the bulk density pedotransfer functions. A hybrid GAM-geostatistical 3D model combined the fitting of a GAM using a 3D smoother with related covariates and the kriging or Gaussian simulations of the residuals to spatially account for local details. The uncertainty was also calculated and was found to be large, indicating a wide range of credible values for each pixel. The deviation from the median ranges was between 5 and 75 % for the interpolated values depending on location.