Methods for identifying green infrastructure

A critical review of Natural Flood Management application and spatial prioritisation approaches in tropical island catchments

Tropical island communities face substantial hydrometrological threats, including flood inundation. Flood risk is increasing, driven by climate change but also other factors including urbanisation, land-cover and land-use (LCLU) change, making flood management challenging to address in practice. Protecting, restoring, and emulating the natural functions of catchments to reduce flood risk, also known as Natural Flood Management (NFM), is a promising method for improving flood management. Global NFM research is in its infancy and NFM research in tropical island states has tended to focus on individual catchment projects. Therefore, overall trends, challenges, and opportunities for NFM in tropical island catchments are poorly understood and, until now, have not been reviewed across these geographies. A particular gap in NFM understanding in tropical island catchments is how NFM options can be best implemented within any particular catchment - specifically where NFM should be located, how modelling can support these decisions and the influence of different catchment characteristics on these decisions. This literature review aims to explore what, where and how NFM has been used in catchments in tropical island states, with a specific focus on catchment characteristics and spatial modelling. This paper draws on research and interconnections between multiple environmental science spheres, by reviewing both academic and grey literature to better understand how NFM has been applied in tropical island states, with a primary focus on Pacific Island Countries and Territories (PICTs). The research highlights that some islands have greater potential for exploiting NFM due to their physical catchment characteristics and data availability. NFM spatial modelling approaches need to be further developed and adapted to specific tropical island community requirements to improve inland flood resilience at the pace needed and to ensure resources are directed optimally.

Improving biodiversity protection through artificial intelligence

Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, substantially improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resourcelimited world.

Improving biodiversity protection through artificial intelligence

Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, substantially improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resourcelimited world.

Green Infrastructure Planning Principles: Identification of Priorities Using Analytic Hierarchy Process

Green infrastructure planning has been receiving great attention since the end of the last century. Although green infrastructure has been known for its ability to respond to a wide range of environmental, social, and economic challenges, the concept and associated implementation measures are still being discussed among researchers, decision-makers, and practitioners. To help these discussions, several authors have identified green infrastructure planning principles to help these professional with planning procedures. However the perception of practitioners regarding these principles was never taken into consideration. Because of this, the purpose of this research is to learn about the priorities of urban planners regarding green infrastructure planning principles and their integration into spatial planning. To achieve this, an Analytic Hierarchy Process methodology was applied to urban planners working in the 17 municipalities of Lisbon Metropolitan Area, in order to prioritize the green infrastructure planning principles influencing GI design and development in urban areas. Experts were asked to prioritize eight primary green infrastructure planning principles: connectivity, multi-functionality, applicability, integration, diversity, multiscale, governance, and continuity. The results show that the most important green infrastructure planning principle for practitioners is connectivity, followed by multifunctionality and applicability. Both integration and multiscale principles were considered more important in municipalities with predominantly urban features.