Quantitative descriptions of stream network and river catchment characteristics provide valuable context for enabling geomorphologically-informed sustainable river management. For countries where high-quality topographic data are available, there are opportunities to enable open access availability of baseline products from systematic assessment of morphometric and topographic characteristics. In this study, we present a national-scale assessment of fundamental topographic characteristics of Philippine river systems. We applied a consistent workflow using TopoToolbox V2 to delineate stream networks and river catchments using a nationwide digital elevation model (DEM) acquired in 2013 and generated through airborne Interferometric Synthetic Aperture Radar (IfSAR). We assessed morphometric and topographic characteristics for 128 medium- to large-sized catchments (catchment area > 250 km²) and organised the results in a national-scale geodatabase. The dataset realises the potential of topographic data as part of river management applications, by enabling variations in hydromorphology to be characterised and contextualised. The dataset is used to reveal the diversity of stream networks and river catchments in the Philippines. Catchments have a continuum of shapes (Gravelius compactness coefficient ranges from 1.05 to 3.29) with drainage densities that range from 0.65 to 1.23 km/km². Average catchment slope ranges from 3.1 to 28.1° and average stream slope varies by more than an order of magnitude from 0.004 to 0.107 m/m. Inter-catchment analyses show the distinctive topographic signatures of adjacent river catchments; examples from NW Luzon highlight topographic similarity between catchments whereas examples from Panay Island shown marked topographic differences. These contrasts underline the importance of using place-based analyses for sustainable river management applications. By designing an interactive ArcGIS web-application to display the national-scale geodatabase, we improve data accessibility and enable users to freely access, explore and download the data (https://glasgow-uni.maps.arcgis.com/apps/webappviewer/index.html?id=a88b9ca0919f4400881eab4a26370cee). The national-scale geodatabase provides a baseline understanding of fundamental topographic characteristics in support of varied geomorphological, hydrological and geohazard susceptibility applications.

Unoccupied aerial vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion (SfM) photogrammetry. Advances in UAV payloads and the advent of solid‐state LiDAR have enabled consumer‐grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV‐acquired images. We evaluate the application of a DJI Zenmuse L1 solid‐state LiDAR sensor on a Matrice 300 RTK UAV to generate digital elevation models (DEMs). To assess flying height (60–80 m) and speed parameters (5–10 ms⁻¹) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter and classify the point clouds to produce a raster DEM product. A dense control network showed that there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multiscale Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of −0.015 m on unvegetated gravel bars. Multiscale Model to Model Cloud Comparison (M3C2) residuals compared UAV LiDAR and Terrestrial Laser Scanner point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid‐state LiDAR was effective at penetrating sparse canopy‐type vegetation but was less penetrable through dense ground‐hugging vegetation (e.g. heather, thick grass). Whilst UAV solid‐state LiDAR needs to be supplemented with bathymetric mapping to produce wet–dry DEMs, by itself, it offers advantages to comparable geomatics technologies for kilometre‐scale surveys. Ten best practice recommendations will assist users of UAV solid‐state LiDAR to produce bare earth DEMs.

Channel re-meandering is commonly used in river restoration. However, few investigations have combined multi-temporal, reach-scale data from hydrological and topographic monitoring, and hydraulic numerical modelling to evaluate this restoration approach, particularly for low to medium energy gravel-bed rivers in low sediment supply settings. The Eddleston Water project focuses on the application and evaluation of Natural Flood Management measures to reduce flood risk to downstream communities and improve riverine physical habitat; providing a natural laboratory to examine re-meandering. A 1.6 km section of river was re-meandered between 2013 and 2016, increasing river length by 18 %. Assessment of the impact on flood attenuation was made using analysis of hydrological time series (2011-2020), comparing repeat (2018 and 2020) reach-scale topographic surveys of the channels to map geomorphic change, and numerically modelling two-dimensional flood extents and dynamics for flood events of different magnitude. Hydrological analysis showed enhanced flow attenuation was only statistically significant for in-channel events. Numerical hydraulic modelling showed a small impact on flood attenuation for a Q5 event (<15 min peak travel time delay, <1.2 % peak attenuation); effects were just discernible for a Q20 event. Repeat topographic surveys and sediment budgets identified overall bed degradation but showed physical habitat diversity was maintained. Whilst hydraulic effects were relatively small, they should be considered in the context of re-meandering being only undertaken on a small proportion of the river network. However, insights from hydraulic modelling show re-meandering design must consider interactions between channel and floodplain topography to maximise connectivity and thus attenuation.

Quantifying riverscape topography is challenging because riverscapes comprise of both wet and dry surfaces. Advances have been made in demonstrating the capability of mounting topo-bathymetric LiDAR (Light Detection and Ranging) sensors on crewed, occupied aircraft to quantify riverscape topography. However, only recently has miniaturisation of electronic components enabled topo-bathymetric LiDAR to be mounted on consumer-grade Unoccupied Aerial Vehicles (UAVs). We evaluate the capability of a demonstration YellowScan Navigator topo-bathymetric, full waveform LiDAR sensor, mounted on a DJI Matrice 600 UAV, to survey a 1 km long reach of the braided River Feshie, Scotland. Ground-truth data, with centimetre accuracy, were collected across wet areas using an echo-sounder, and in wet and dry areas using RTK-GNSS (Real-Time Kinematic Global Navigation Satellite System). The processed point cloud had a density of 62 points/m². Ground-truth mean errors (and standard deviation) across dry gravel bars were 0.06 ± 0.04 m, along shallow channel beds were −0.03 ± 0.12 m and for deep channels were −0.08 m ± 0.23 m. Geomorphic units with a concave three-dimensional shape (pools, troughs), associated with deeper water, had larger negative errors and wider ranges of residuals than planar or convex units. The case study demonstrates the potential of using UAV topo-bathymetric LiDAR to enhance survey efficiency but a need to evaluate spatial error distribution.

Unoccupied Aerial Vehicles (UAVs) with passive optical sensors have become popular for reconstructing topography using Structure from Motion photogrammetry (SfM). Advances in UAV payloads and the advent of solid-state LiDAR have enabled consumer-grade active remote sensing equipment to become more widely available, potentially providing opportunities to overcome some challenges associated with SfM photogrammetry, such as vegetation penetration and shadowing, that can occur when processing UAV acquired images. We evaluate the application of a DJI Zenmuse L1 solid-state LiDAR sensor on a Matrice 300 RTK UAV to generate Digital Elevation Models (DEMs). To assess flying height (60-80 m) and speed parameters (5-10 ms-1) on accuracy, four point clouds were acquired at a test site. These point clouds were used to develop a processing workflow to georeference, filter, and classify the point clouds to produce a raster DEM product. A dense control network showed there was no significant difference in georeferencing from differing flying height or speed. Building on the test results, a 3 km reach of the River Feshie was surveyed, collecting over 755 million UAV LiDAR points. The Multi-Curvature Classification algorithm was found to be the most suitable classifier of ground topography. GNSS check points showed a mean vertical residual of -0.015 m on unvegetated gravel bars. M3C2 residuals compared UAV LiDAR and TLS point clouds for seven sample sites demonstrating a close match with marginally zero residuals. Solid-state LiDAR was effective at penetrating sparse canopy-type vegetation but was less penetrable through dense ground-hugging vegetation such as heather or thick grass. Whilst UAV solid-state LiDAR needs to be supplemented with bathymetric mapping to produce spatially continuous wet-dry DEMs, by itself it offers several advantages to comparable geomatics technologies for km-scale surveys. Ten best practice recommendations will assist users of UAV solid-state LiDAR to produce bare earth DEMs in geomorphic environments.