A 30 m global map of elevation with forests and buildings removed

Abstract

Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∽30m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61m to 1.12m, and in forests from 5.15m to 2.88m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.

Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study

“…In addition to the issue of aggregation bias, our results draw attention to key uncertainties in the estimation of expected flood losses that cannot be easily resolved using the property‐level datasets commonly available to flood researchers (e.g., tax assessor data). Although joint observation of building footprints and terrain elevation is now feasible and continuously improving (Hawker et al, 2022), missing data on other key characteristics—such as FFE and the presence of basements—remains a major source of uncertainty and potential bias in the estimation of flood losses (Figure 6). To shed further light on the relative importance of different data imputation assumptions, flood loss estimation analyses and products need to acknowledge more fully, and attempt to model, these sources of biases and uncertainties inherent in estimation strategies, including relevant uncertainties in the underlying flood hazard model (Tate et al, 2015).…”

Quantitative Stress Test of Compound Coastal‐Fluvial Floods in China's Pearl River Delta

“…(2021), only 152 deltas are included in this delta database, unfortunately none of them are within China. Therefore, once the required information of defenses is accessible for the PRD, together with the latest released Forest And Building removed Copernicus DEM (FABDEM) (Hawker et al., 2022), the goal of simulating a more comprehensive physical process of compound flood, especially the flood flowing over defense structures and inundating the adjacent floodplain or urban area would be possible. The full‐impact risk assessment could thus be further addressed to inform risk management and resilience planning in river deltas.…”

“…However, so far inventories of these defenses are very scarce in river deltas, significantly impairing the accuracy of flood inundation maps and obstructing the consideration of vulnerability in risk assessment. (Hawker et al, 2022), the goal of simulating a more comprehensive physical process of compound flood, especially the flood flowing over defense structures and inundating the adjacent floodplain or urban area would be possible. The full-impact risk assessment could thus be further addressed to inform risk management and resilience planning in river deltas.…”

Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting

“…The accuracy of HD model simulations is affected by topobathy errors and uncertainties associated with LiDAR-derived DEMs. Recent advances in remote sensing and machine learning (ML) techniques have shown the benefits of using satellite, radar, and unmanned aerial imagery to correct elevations errors associated with building artifacts, flood defense structures, forests, and wetlands ( Cooper et al., 2019 ; Hawker et al., 2022 ; Liu et al., 2021 ; Zhao et al., 2022 ). In addition, these techniques have been used to estimate bathymetry in rivers, near shore, and intertidal zones for ungauged sites with satisfactory results ( Kasvi et al., 2019 ; Legleiter and Harrison, 2019 ; Ma et al., 2020 ; Moramarco et al., 2019 ; Neal et al., 2021 ).…”