Floodplain and terrace features can provide information about current and past fluvial processes, including channel response to varying discharge and sediment flux, sediment storage, and the climatic or tectonic history of a catchment. Previous methods of identifying floodplain and terraces from digital elevation models (DEMs) tend to be semi-automated, requiring the input of independent datasets or manual editing by the user. In this study we present a new method of identifying floodplain and terrace features based on two thresholds: local gradient, and elevation compared to the nearest channel. These thresholds are calculated statistically from the DEM using quantile–quantile plots and do not need to be set manually for each landscape in question. We test our method against field-mapped floodplain initiation points, published flood hazard maps, and digitised terrace surfaces from seven field sites from the US and one field site from the UK. For each site, we use high-resolution DEMs derived from light detection and ranging (lidar) where available, as well as coarser resolution national datasets to test the sensitivity of our method to grid resolution. We find that our method is successful in extracting floodplain and terrace features compared to the field-mapped data from the range of landscapes and grid resolutions tested. The method is most accurate in areas where there is a contrast in slope and elevation between the feature of interest and the surrounding landscape, such as confined valley settings. Our method provides a new tool for rapidly and objectively identifying floodplain and terrace features on a landscape scale, with applications including flood risk mapping, reconstruction of landscape evolution, and quantification of sediment storage and routing.

In many locations, our ability to study the processes which shape the Earth are greatly enhanced through the use of high-resolution digital topographic data. However, although the availability of such datasets has markedly increased in recent years, many locations of significant geomorphic interest still do not have high-resolution topographic data available. Here, we aim to constrain how well we can understand surface processes through topographic analysis performed on lower-resolution data. We generate digital elevation models from point clouds at a range of grid resolutions from 1 to 30 m, which covers the range of widely used data resolutions available globally, at three locations in the United States. Using these data, the relationship between curvature and grid resolution is explored, alongside the estimation of the hillslope sediment transport coefficient (D, in m² yr⁻¹) for each landscape. Curvature, and consequently D, values are shown to be generally insensitive to grid resolution, particularly in landscapes with broad hilltops and valleys. Curvature distributions, however, become increasingly condensed around the mean, and theoretical considerations suggest caution should be used when extracting curvature from landscapes with sharp ridges. The sensitivity of curvature and topographic gradient to grid resolution are also explored through analysis of one-dimensional approximations of curvature and gradient, providing a theoretical basis for the results generated using two-dimensional topographic data. Two methods of extracting channels from topographic data are tested. A geometric method of channel extraction that finds channels by detecting threshold values of planform curvature is shown to perform well at resolutions up to 30 m in all three landscapes. The landscape parameters of hillslope length and relief are both successfully extracted at the same range of resolutions. These parameters can be used to detect landscape transience and our results suggest that such work need not be confined to high-resolution topographic data. A synthesis of the results presented in this work indicates that although high-resolution (e.g., 1 m) topographic data do yield exciting possibilities for geomorphic research, many key parameters can be understood in lower-resolution data, given careful consideration of how analyses are performed.

Significance: Forest restoration has become a global conservation priority, particularly in the tropics where a significant proportion of remaining forest ecosystems are degraded. To achieve ambitious restoration targets via limited conservation funds, areas that will deliver the greatest biodiversity value must be prioritized. Here, we combine airborne laser scanning with an extensive camera trap dataset to target conservation and restoration across a degraded logged forest gradient. We demonstrate the importance of accounting for three-dimensional habitat structure when defining forest suitability and restoration potential for mammals. Consequently, we provide a robust quantitative framework to prioritize degraded forest restoration based on biodiversity considerations.

The impacts of degradation and deforestation on tropical forests are poorly understood, particularly at landscape scales. We present an extensive ecosystem analysis of the impacts of logging and conversion of tropical forest to oil palm from a large-scale study in Borneo, synthesizing responses from 82 variables categorized into four ecological levels spanning a broad suite of ecosystem properties: (i) structure and environment, (ii) species traits, (iii) biodiversity, and (iv) ecosystem functions. Responses were highly heterogeneous and often complex and nonlinear. Variables that were directly impacted by the physical process of timber extraction, such as soil structure, were sensitive to even moderate amounts of logging, whereas measures of biodiversity and ecosystem functioning were generally resilient to logging but more affected by conversion to oil palm plantation.