Futures of global urban expansion: uncertainties and implications for biodiversity conservation

Urbanization will place significant pressures on biodiversity across the world. However, there are large uncertainties in the amount and location of future urbanization, particularly urban land expansion. Here, we present a global analysis of urban extent circa 2000 and probabilistic forecasts of urban expansion for 2030 near protected areas and in biodiversity hotspots. We estimate that the amount of urban land within 50 km of all protected area boundaries will increase from 450 000 km ^2 circa 2000 to 1440 000 ± 65 000 km ^2 in 2030. Our analysis shows that protected areas around the world will experience significant increases in urban land within 50 km of their boundaries. China will experience the largest increase in urban land near protected areas with 304 000 ± 33 000 km ^2 of new urban land to be developed within 50 km of protected area boundaries. The largest urban expansion in biodiversity hotspots, over 100 000 ± 25 000 km ^2 , is forecasted to occur in South America. Uncertainties in the forecasts of the amount and location of urban land expansion reflect uncertainties in their underlying drivers including urban population and economic growth. The forecasts point to the need to reconcile urban development and biodiversity conservation strategies

Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools

Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km(2), nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr(−1)), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.open

Low-Key Stationary and Mobile Tools for Probing the Atmospheric UHI Effect

The urban heat island (UHI) effect is created by a series of man-made surface modifications in urban areas that cause changes to the surface energy balance, resulting in higher urban surface air temperatures as compared with surrounding rural areas. Studying the UHI effect is highly amenable to hands-on undergraduate student research projects, because, among other reasons, there are low key measurement tools that allow accurate and regular stationary and mobile probing of air temperature. Here, we summarize the results of a student project at Texas A&M University that analyzed the atmospheric UHI of Bryan/College Station, a mid-size metro area in east Texas. Sling psychrometers were used for semi-regular twice daily stationary air temperature monitoring, and a low-cost electronic sensor and miniature data logger were used for mobile measurements. Stationary data from two similar, open mid-rise building locations showed typical UHI intensities of 0–2°C, while the mobile measurements identified situations with UHI intensities exceeding 6°C when traversing areas with high impervious surface fractions. Nighttime measurements showed the expected UHI intensity relations to wind speed and atmospheric pressure, while daytime data were more strongly related to urban morphology. The success of this research may encourage similar student projects that deliver baseline data to urban communities seeking to mitigate the UHI

Assessing Hazard Vulnerability, Habitat Conservation, and Restoration for the Enhancement ofmainland China’s Coastal Resilience

Worldwide, humans are facing high risks from natural hazards, especially in coastal regions with high population densities. Rising sea levels due to global warming are making coastal communities’ infrastructure vulnerable to natural disasters. The present study aims to provide a coupling approach of vulnerability and resilience through restoration and conservation of lost or degraded coastal natural habitats to reclamation under different climate change scenarios. The integrated valuation of ecosystems and tradeoffs model is used to assess the current and future vulnerability of coastal communities. The model employed is based on seven different biogeophysical variables to calculate a natural hazard index and to highlight the criticality of the restoration of natural habitats. The results show that roughly 25% of the coastline and more than 5 million residents are in highly vulnerable coastal areas of mainland China, and these numbers are expected to double by 2100. Our study suggests that restoration and conservation in recently reclaimed areas have the potential to reduce this vulnerability by 45%. Hence, natural habitats have proved to be a great defense against coastal hazards and should be prioritized in coastal planning and development. The findings confirm that natural habitats are critical for coastal resilience and can act as a recovery force of coastal functionality loss. Therefore, we recommend that the Chinese government prioritizes restoration (where possible) and conservation of the remaining habitats for the sake of coastal resilience to prevent natural hazards from escalating into disasters. Plain Language Summary: Coastal populations are especially at risk from sea-level rise (SLR), induced storm surges, and other natural hazards. Therefore, it becomes essential to analyze the current and future vulnerabilities of coastal regions to natural hazards. Furthermore, it is desirable for the policy and the decision making to propose the suitable approaches for the resilience enhancement. This paper analyzes the current and future vulnerability of mainland China’s coast to the SLR-induced natural hazards using a natural hazard index incorporating a coupled approach to vulnerability and resilience. The results show that the restoration of lost mangroves (where possible) and conservation of remaining coastal natural habitats can reduce the future coastal vulnerability by 45%. This study confirms that natural habitats are significant for coastal resilience and the governments should prioritize them for the sake of coastal resilience to mitigate the impacts of natural hazards. Includes supplemental material

Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: A Global Assessment

Urbanization is a global phenomenon and the book emphasizes that this is not just a social-technological process. It is also a social-ecological process where cities are places for nature, and where cities also are dependent on, and have impacts on, the biosphere at different scales from local to global. The book is a global assessment and delivers four main conclusions: Urban areas are expanding faster than urban populations. Half the increase in urban land across the world over the next 20 years will occur in Asia, with the most extensive change expected to take place in India and China Urban areas modify their local and regional climate through the urban heat island effect and by altering precipitation patterns, which together will have significant impacts on net primary production, ecosystem health, and biodiversity Urban expansion will heavily draw on natural resources, including water, on a global scale, and will often consume prime agricultural land, with knock-on effects on biodiversity and ecosystem services elsewhere Future urban expansion will often occur in areas where the capacity for formal governance is restricted, which will constrain the protection of biodiversity and management of ecosystem service

Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities: A Global Assessment

Urban Ecology; Urbanism; Sustainable Development; Complex Systems; Science, general; International Environmental La

A Meta-Analysis of Global Urban Land Expansion

The conversion of Earth's land surface to urban uses is one of the most irreversible human impacts on the global biosphere. It drives the loss of farmland, affects local climate, fragments habitats, and threatens biodiversity. Here we present a meta-analysis of 326 studies that have used remotely sensed images to map urban land conversion. We report a worldwide observed increase in urban land area of 58,000 km2 from 1970 to 2000. India, China, and Africa have experienced the highest rates of urban land expansion, and the largest change in total urban extent has occurred in North America. Across all regions and for all three decades, urban land expansion rates are higher than or equal to urban population growth rates, suggesting that urban growth is becoming more expansive than compact. Annual growth in GDP per capita drives approximately half of the observed urban land expansion in China but only moderately affects urban expansion in India and Africa, where urban land expansion is driven more by urban population growth. In high income countries, rates of urban land expansion are slower and increasingly related to GDP growth. However, in North America, population growth contributes more to urban expansion than it does in Europe. Much of the observed variation in urban expansion was not captured by either population, GDP, or other variables in the model. This suggests that contemporary urban expansion is related to a variety of factors difficult to observe comprehensively at the global level, including international capital flows, the informal economy, land use policy, and generalized transport costs. Using the results from the global model, we develop forecasts for new urban land cover using SRES Scenarios. Our results show that by 2030, global urban land cover will increase between 430,000 km2 and 12,568,000 km2, with an estimate of 1,527,000 km2 more likely

Built-up areas within and around protected areas: Global patterns and 40-year trends

Protected areas (PAs) are a key strategy in global efforts to conserve biodiversity and ecosystem services that are critical for human well-being. Most PAs have some built-up structures within their boundaries or in surrounding areas, ranging from individual buildings to villages, towns and cities. These structures, and the associated human activities, can exert direct and indirect pressures on PAs. Here we present the first global analysis of current patterns and observed long-term trends in built-up areas within terrestrial PAs and their immediate surroundings. We calculate for each PA larger than 5 km2 and for its 10-km unprotected buffer zone the percentage of land area covered by built-up areas in 1975, 1990, 2000 and 2014. We find that globally built-up areas cover only 0.12% of PA extent and a much higher 2.71% of the unprotected buffers as of 2014, compared to 0.6% of all land (protected or unprotected). Built-up extent in and around PAs is highest in Europe and Asia, and lowest in Africa and Oceania. Built-up area percentage is higher in coastal and small PAs, and lower in older PAs and in PAs with stricter management categories. From 1975 to 2014, the increase in built-up area was 23 times larger in the 10-km unprotected buffers than within PAs. Our findings show that the development of built-up structures remains limited within the boundaries of PAs but highlight the need to carefully manage the considerable pressure that PAs face from their immediate surroundings