Urban areas are expected to triple by 2030 in order to accommodate 60% of the global population. Anthropogenic landscape modifications expand coverage of impervious surfaces inducing the urban heat island (UHI) effect, a critical twenty first century challenge associated with increased economic expenditure, energy consumption, and adverse health impacts. Yet, omission of UHI measures from global climate models and metropolitan planning methodologies precludes effective sustainable development governance. We present an approach that integrates Earth observation and climate data with three-dimensional urban models to determine optimal tree placement (per square meter) within proposed urban developments to enable more effective localized UHI mitigation. Such data-driven planning decisions will enhance the future sustainability of our cities to align with current global urban development agendas.

Urban areas are Earth’s fastest growing land use that impact hydrological and ecological systems and the surface energy balance. The identification and extraction of accurate spatial information relating to urban areas is essential for future sustainable city planning owing to its importance within global environmental change and human–environment interactions. However, monitoring urban expansion using medium resolution (30–250 m) imagery remains challenging due to the variety of surface materials that contribute to measured reflectance resulting in spectrally mixed pixels. This research integrates high spatial resolution orthophotos and Landsat imagery to identify differences across a range of diverse urban subsets within the rapidly expanding Perth Metropolitan Region (PMR), Western Australia. Results indicate that calibrating Landsat-derived subpixel land-cover estimates with correction values (calculated from spatially explicit comparisons of subpixel Landsat values to classified high-resolution data which accounts for over [under] estimations of Landsat) reduces moderate resolution urban area over (under) estimates by on an average 55.08% for the PMR. This approach can be applied to other urban areas globally through use of frequently available and/or low-cost high spatial resolution imagery (e.g. using Google Earth). This will improve urban growth estimations to help monitor and measure change whilst providing metrics to facilitate sustainable urban development targets within cities around the world.

Urban land cover is one of the fastest global growing land cover types which permanently alters land surface properties and atmospheric interactions, often initiating an urban heat island effect. Urbanisation comprises a number of land cover changes within metropolitan regions. However, these complexities have been somewhat neglected in temperature analysis studies of the urban heat island effect, whereby over-simplification ignores the heterogeneity of urban surfaces and associated land surface temperature dynamics. Accurate spatial information pertaining to these land cover change—temperature relationships across space is essential for policy integration regarding future sustainable city planning to mitigate urban heat impacts. Through a multi-sensor approach, this research disentangles the complex spatial heterogeneous variations between changes in land cover (Landsat data) and land surface temperature (MODIS data), to understand the urban heat island effect dynamics in greater detail for appropriate policy integration. The application area is the rapidly expanding Perth Metropolitan Region (PMR) in Western Australia (WA). Results indicate that land cover change from forest to urban is associated with the greatest annual daytime and nighttime temperature change of 0.40 °C and 0.88 °C respectively. Conversely, change from grassland to urban minimises temperature change at 0.16 °C and 0.77 °C for annual daytime and nighttime temperature respectively. These findings are important to consider for proposed developments of the city as such detail is not currently considered in the urban growth plans for the PMR. The novel intra-urban research approach presented can be applied to other global metropolitan regions to facilitate future transition towards sustainable cities, whereby urban heat impacts can be better managed through optimised land use planning, moving cities towards alignment with the 2030 sustainable development goals and the City Resilience Framework (CRF).

Earth observation data can provide valuable assessments for monitoring the spatial extent of (un)sustainable urban growth of the world’s cities to better inform planning policy in reducing associated economic, social and environmental costs. Western Australia has witnessed rapid economic expansion since the turn of the century founded upon extensive natural resource extraction. Thus, Perth, the state capital of Western Australia, has encountered significant population and urban growth in response to the booming state economy. However, the recent economic slowdown resulted in the largest decrease in natural resource values that Western Australia has ever experienced. Here, we present multi-temporal urban expansion statistics from 1990 to 2015 for Perth, derived from Landsat imagery. Current urban estimates used for future development plans and progress monitoring of infill and density targets are based upon aggregated census data and metrics unrepresentative of actual land cover change, underestimating overall urban area. Earth observation provides a temporally consistent methodology, identifying areal urban area at higher spatial and temporal resolution than current estimates. Our results indicate that the spatial extent of the Perth Metropolitan Region has increased 45% between 1990 and 2015, over 320 km². We highlight the applicability of earth observation data in accurately quantifying urban area for sustainable targeted planning practices.

As an unprecedented stream of decametric hyperspectral observations becomes available from recent and upcoming spaceborne missions, effective algorithms are required to retrieve vegetation biophysical and biochemical variables such as leaf area index (LAI) and canopy chlorophyll content (CCC). In the context of missions such as the Environmental Mapping and Analysis Program (EnMAP), Precursore Iperspettrale della Missione Applicativa (PRISMA), Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), and Surface Biology Geology (SBG), several retrieval algorithms have been developed based upon the turbid medium Scattering by Arbitrarily Inclined Leaves (SAIL) radiative transfer model. Whilst well suited to cereal crops, SAIL is known to perform comparatively poorly over more heterogeneous canopies (including forests and row-structured crops). In this paper, we investigate the application of hybrid radiative transfer models, including a modified version of SAIL (rowSAIL) and the Invertible Forest Reflectance Model (INFORM), to such canopies. Unlike SAIL, which assumes a horizontally homogeneous canopy, such models partition the canopy into geometric objects, which are themselves treated as turbid media. By enabling crown transmittance, foliage clumping, and shadowing to be represented, they provide a more realistic representation of heterogeneous vegetation. Using airborne hyperspectral data to simulate EnMAP observations over vineyard and deciduous broadleaf forest sites, we demonstrate that SAIL-based algorithms provide moderate retrieval accuracy for LAI (RMSD = 0.92–2.15, NRMSD = 40–67%, bias = −0.64–0.96) and CCC (RMSD = 0.27–1.27 g m⁻², NRMSD = 64–84%, bias = −0.17–0.89 g m⁻²). The use of hybrid radiative transfer models (rowSAIL and INFORM) reduces bias in LAI (RMSD = 0.88–1.64, NRMSD = 27–64%, bias = −0.78–−0.13) and CCC (RMSD = 0.30–0.87 g m⁻², NRMSD = 52–73%, bias = 0.03–0.42 g m⁻²) retrievals. Based on our results, at the canopy level, we recommend that hybrid radiative transfer models such as rowSAIL and INFORM are further adopted for hyperspectral biophysical and biochemical variable retrieval over heterogeneous vegetation.

In the event of a crisis, such as COVID-19, the decisions and subsequent actions taken by the local government are one of the primary sources of support to the local population. Yet the processes through which these decisions are reached and the data engineering advancements made for and during events are poorly reported. Understanding the capabilities and constraints in which city officials operate is essential for impactful academic research alongside global city comparison and discussion on best practices in reaching optimal and data-informed decisions. This is especially pertinent for the global South, where informality in housing and the economy presents further challenges to appropriate resource distribution in a crisis. Here, we present insights into the City of Cape Town’s data-driven response and subsequent data engineering and analytical developments throughout the COVID-19 pandemic. This is based upon a review of internal documentation including a close-out report which summarised semi-structured interviews with staff involved in the data work stream. The paper reports on the deliverables produced during 2020 by the data work stream and outlines specific challenges the city faced and its data-informed response in the areas of (1) quantifying costs for COVID-19 initiatives, (2) dealing with a surge in fatalities, (3) guiding scarce public resources to respond to an evolving crisis, and (4) data sharing. We demonstrate the real-term value of incorporating data into the decision-making process and conclude by outlining key factors that cities and researchers must consider as a part of the usual business to effectively assist their populations during times of stress and crisis.