Spatially explicit risk approach for multi-hazard assessment and management in marine environment: The case study of the Adriatic Sea

Smallholder oil palm plantation sustainability assessment using multi-criteria analysis and unmanned aerial vehicles

Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).

Ecosystem services at risk in Italy from coastal inundation under extreme sea level scenarios up to 2050: A spatially resolved approach supporting climate change adaptation

According to the latest projections of the Intergovernmental Panel on Climate Change, at the end of the century, coastal zones and low-lying ecosystems will be increasingly threatened by rising global mean sea levels. In order to support integrated coastal zone management and advance the basic "source-pathway-receptor-consequence" approach focused on traditional receptors (e.g., population, infrastructure, and economy), a novel risk framework is proposed able to evaluate potential risks of loss or degradation of ecosystem services (ESs) due to projected extreme sea level scenarios in the Italian coast. Three risk scenarios for the reference period (1969-2010) and future time frame up to 2050 under RCP4.5 and RCP8.5 are developed by integrating extreme water-level projections related to changing climate conditions, with vulnerability information about the topography, distance from coastlines, and presence of artificial protections. A risk assessment is then performed considering the potential effects of the spatial-temporal variability of inundations and land use on the supply level and spatial distribution of ESs. The results of the analysis are summarized into a spatially explicit risk index, useful to rank coastal areas more prone to ESs losses or degradation due to coastal inundation at the national scale. Overall, the Northern Adriatic coast is scored at high risk of ESs loss or degradation in the future scenario. Other small coastal strips with medium risk scores are the Eastern Puglia coast, Western Sardinia, and Tuscany's coast. The ESs Coastal Risk Index provides an easy-to-understand screening assessment that could support the prioritization of areas for coastal adaptation at the national scale. Moreover, this index allows the direct evaluation of the public value of ecosystems and supports more effective territorial planning and environmental management decisions. In particular, it could support the mainstreaming of ecosystem-based approaches (e.g., ecological engineering and green infrastructures) to mitigate the risks of climate change and extreme events while protecting ecosystems and biodiversity. Integr Environ Assess Manag 2022;18:1564-1577. © 2021 SETAC.

A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran

We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.

Establishment of an integrated decision-making method for planning the ecological restoration of terrestrial ecosystems

Ecological restoration of terrestrial ecosystems facilitates environmental protection and enhances sustainable development of land resources. With increasingly severe land degradation, new and effective methods must be developed for the restoration of ecological functions. In this study, we developed a regional risk assessment approach to support the planning of ecological restoration of a terrestrial ecosystem located in the Daye area in central China. The study area was divided into six sub-regions where ecological risks were characterized by building a non-linear model to represent ecological interactions among the risk components there. Socio-economic conditions in the areas were evaluated and presented using an analytic hierarchy process. Assessment of different stakeholders there was conducted based on multiple-criteria decision analysis. Then, integrated assessment was performed using the technique of order preference for an ideal solution. We divided the degraded land in Daye into areas with different priorities for restoration or rectification and presented corresponding sequential time intervals for the action. The results are as follows: (i) the top priority rectification areas (totaling 358 km²) are mainly distributed in northeast and northwest regions; (ii) the high priority rectification areas are concentrated in the central region spanning 226 km²; (iii) the medium priority rectification areas comprised a large amount of arable and forest land spanning 605 km²; and (iv) the low priority rectification areas cover the rest part of the Daye area spanning 195 km². The assessment tool was proven to be useful in planning regional ecological restoration in terrestrial ecosystems.

Resilience assessment framework for critical infrastructure in a multi-hazard environment: Case study on transport assets

The exposure of critical infrastructure to natural and human-induced hazards has severe consequences on world economies and societies. Therefore, resilience assessment of infrastructure assets to extreme events and sequences of diverse hazards is of paramount importance for maintaining their functionality. Yet, the resilience assessment commonly assumes single hazards and ignores alternative approaches and decisions in the restoration strategy. It has now been established that infrastructure owners and operators consider different factors in their restoration strategies depending on the available resources and their priorities, the importance of the asset and the level of damage. Currently, no integrated framework that accounts for the nature and sequence of multiple hazards and their impacts, the different strategies of restoration, and hence the quantification of resilience in that respect exists and this is an acknowledged gap that needs urgently filling. This paper provides, for the first time in the literature, a classification of multiple hazard sequences considering their nature and impacts. Subsequently, a novel framework for the quantitative resilience assessment of critical infrastructure, subjected to multiple hazards is proposed, considering the vulnerability of the assets to hazard actions, and the rapidity of the damage recovery, including the temporal variability of the hazards. The study puts forward a well-informed asset resilience index, which accounts for the full, partial or no restoration of asset damage between the subsequent hazard occurrences. The proposed framework is then applied on a typical highway bridge, which is exposed to realistic multiple hazard scenarios, considering pragmatic restoration strategies. The case study concludes that there is a significant effect of the occurrence time of the second hazard on the resilience index and a considerable error when using simple superimposition of resilience indices from different hazards, even when they are independent in terms of occurrence. This potentially concerns all critical infrastructure assets and, hence, this paper provides useful insights for the resilience-based design and management of infrastructure throughout their lifetime, leading to cost savings and improved services. The paper concludes with a demonstration of the importance of the framework and how this can be utilised to estimate the resilience of networks to provide a quantification of the resilience at a regional and country scale.

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.

Multi-scenario analysis in the Adriatic Sea: A GIS-based Bayesian network to support maritime spatial planning

Oceans are changing faster than even observed before. Unprecedented climate variability is interacting with long-term trends, all against a backdrop of rising anthropogenic use of marine space. The growth of maritime activities is taking place without the full understanding of complex interactions between natural and human-induced changes, leading to a progressive decline of biodiversity and degradation of marine ecosystems. Against this complex interplay, marine managers and policy makers are increasingly calling for new approaches and tools allowing a multi-scenario assessment of environmental impacts arising from the complex interaction between natural and anthropogenic drivers, also in consideration of multiple marine plans objectives. Responding to this need, for the Adriatic Sea we developed a GIS-based Bayesian Network to evaluate the probability (and related uncertainty) of cumulative impacts under four 'what-if' scenarios representing different marine management options and climate conditions. We addressed issues concerning consequences of potential planning measures, as well as management programmes required to achieve environmental status targets, as required by relevant EU acquis. Results from the scenario analysis highlighted that an integrated approach to maritime spatial planning is required, combining more sustainable management options of marine spaces and resources with climate adaptation strategies. This approach to planning would allow to reduce human pressures on the marine environment and rise resilience of natural ecosystems to climate and human-induced disturbances, which would result in an overall decrease of cumulative impacts.

Cumulative Impact Index for the Adriatic Sea: Accounting for interactions among climate and anthropogenic pressures

Assessing and managing cumulative impacts produced by interactive anthropogenic and natural drivers is a major challenge to achieve the sustainable use of marine spaces in line with the objectives of relevant EU acquis. However, the complexity of the marine environment and the uncertainty linked to future climate and socio-economic scenarios, represent major obstacles for understanding the multiplicity of impacts on the marine ecosystems and to identify appropriate management strategies to be implemented. Going beyond the traditional additive approach for cumulative impact appraisal, the Cumulative Impact Index (CI-Index) proposed in this paper applies advanced Multi-Criteria Decision Analysis techniques to spatially model relationships between interactive climate and anthropogenic pressures, the environmental exposure and vulnerability patterns and the potential cumulative impacts for the marine ecosystems at risk. The assessment was performed based on spatial data characterizing location and vulnerability of 5 relevant marine targets (e.g. seagrasses and coral beds), and the distribution of 17 human activities (e.g. trawling, maritime traffic) during a reference scenario 2000-2015. Moreover, projections for selected physical and biogeochemical parameters (temperature and chlorophyll 'a') for the 2035-2050 timeframe under RCP8.5 scenario, were integrated in the assessment to evaluate index variations due to changing climate conditions. The application of the CI-Index in the Adriatic Sea, showed higher cumulative impacts in the Northern part of the basin and along the Italian continental shelf, where the high concentration of human activities, the seawater temperature conditions and the presence of vulnerable benthic habitats, contribute to increase the overall impact estimate. Moreover, the CI-Index allowed understanding which are the phenomena contributing to synergic pressures creating potential pathways of environmental disturbance for marine ecosystems. Finally, the application in the Adriatic case showed how the output of the CI-Index can provide support to evaluate multi-risk scenarios and to drive sustainable maritime spatial planning and management.

Spatial congruence between multiple stressors in the Mediterranean Sea may reduce its resilience to climate impacts

Climate impacts on marine ecosystems may be exacerbated by other, more local stressors interacting synergistically, such as pollution and overexploitation of marine resources. The reduction of these human stressors has been proposed as an achievable way of retaining ecosystems within a “safe operating space” (SOS), where they remain resilient to ongoing climate change. However, the operability of an SOS requires a thorough understanding of the spatial distribution of these climate and human impacts. Using the Mediterranean Sea as a case study, we illustrate the spatial congruence between climate and human stressors impacting this iconic “miniature ocean” synergistically. We use long-term, spatially-explicit information on the distribution of multiple stressors to identify those highly impacted marine areas where human stressors should be prioritized for management if the resilience to climate impacts is to be maintained. Based on our spatial analysis, we exemplify how the management of an essential supporting service (seafood provision) and the conservation of a highly impacted Mediterranean sub-region (the Adriatic Sea) may benefit from the SOS framework.