The study, published in Discover Geoscience, focused on the Areal do Limeira watershed, a 61.3 square kilometer drainage basin in the center of Rio Grande do Sul state that feeds into the Ibicuí River. The area sits on sedimentary rocks of the Triassic Pirambóia Formation, which weather into exceptionally sandy soils with low clay content. That geology, combined with a temperate climate delivering an average of 1,545 millimeters of rain per year well distributed across all months, makes the terrain highly vulnerable. Gullies are dense in the watershed, reaching a density of 2.43 kilometers per square kilometer and covering roughly four percent of the entire area.

The researchers chose the Simulated Water Erosion model, known as SIMWE, a bivariate physical model designed to simulate erosion, transport, and deposition by overland flow across complex terrain, soil, and vegetation conditions. The model runs in two modules within the open-source GRASS GIS environment: one simulating overland water flow and one simulating sediment flow, built on the classical equations of Foster and Meyer and Bennett. Its sediment module generates erosion and deposition maps based on the Water Erosion Prediction Project framework, and it has been applied worldwide, from steep Mediterranean vineyards to the heavily eroded terraced landscapes of the Chinese Loess Plateau. In Brazil, however, it remains relatively little used, which made this application a test of the model’s value in data-scarce regions.

Building the model required an unusual combination of freely available data and hands-on fieldwork. The team used the FABDEM digital elevation model, a 30-meter resolution product with forests and buildings removed, extracting slope derivatives to characterize the terrain. They manually vectorized land use and land cover from Planet satellite imagery dated March 20, 2025, distinguishing grassland, native forest, silviculture, post-harvest silviculture, and crop fields, each assigned a Manning’s roughness coefficient reflecting its resistance to flow. Critically, they also went into the field, collecting two soil samples for each land use class at both top and mid-slope positions, running double-ring infiltration tests with custom-built equipment and constant-head permeability tests on undisturbed samples.

The simulation scenario assumed a rainfall intensity of 250 millimeters per hour, consistent with the extreme events the region can experience. Precipitation excess was calculated separately for each land use class by subtracting measured infiltration rates from rainfall intensity. Water bodies, roads, and residential areas were treated as impermeable, with all rainfall converted to runoff. The model’s outputs included water depth and discharge from the hydrological module, and sediment transport capacity, sediment flux, sediment concentration, and the erosion-deposition balance from the sediment module, all expressed as raster maps across the watershed.

The hydrological results showed surface runoff concentrating along the main drainage channels and in flatter sectors, particularly the alluvial plains in the northern part of the watershed. There, the model revealed a striking sensitivity to human engineering: embankments and drains built for irrigated rice cultivation redirect water across the landscape, creating artificial flow patterns that overlap the natural drainage network and intensify water depth and discharge wherever these structures act as barriers. Notably, the mapped gullies did cluster in areas of flow concentration at the upstream ends of the drainage system, and flow rates spiked inside the deepest reaches of individual gullies, where steep walls accelerate runoff during storms.

The sediment module told a consistent story. Transport capacity, sediment flux, and concentration all peaked near the main channels where runoff converges and sediment is plentiful, while the lowest transport capacity appeared at higher elevations and steeper slopes where little material is available to move. Surface roughness mattered too: crop fields and silviculture generated resistance that reduced sediment flow, yet topography overrode vegetation effects wherever flow concentrated toward drainage channels, even through cultivated land. Proximity of gullies to lower-order channels increased simulated sediment concentration, and the model’s deposition patterns matched areas of sediment accumulation observed in the field.

The overall erosion-deposition balance came down firmly on the side of loss. Integrating positive and negative pixel values across the watershed yielded a net soil loss of approximately 27,632 kilograms, with 441 kilograms lost within the mapped gullies themselves. That contrasts sharply with a previous Brazilian watershed-scale SIMWE application in which deposition dominated, covering 64 to 77 percent of the area. Here, erosive processes prevailed, underscoring the fragility of the sandy Pampa terrain. Yet the gullies themselves showed a narrower gap between erosion and deposition than the watershed as a whole, because the strongest erosion rates occurred in the main drainage channels rather than inside the gullies.

Soil texture emerged as the property most consistently linked to simulated erosion, even though it is not a direct model input. Every sample contained at least 60 percent sand, but crop fields averaged the sandiest, least clayey soils and showed the highest percentage of area occupied by erosion in the simulation. Grassland, with denser vegetation cover, fared best, while native forest showed unexpectedly high erosion at hilltop positions where loamy sand prevailed, contrasting with clay-rich sandy clay loam at mid-slope. Silviculture and post-harvest silviculture fell in between, with the harvested areas exposing bare soil that becomes highly vulnerable during heavy rain. The authors caution that the low number of samples limits statistical inference, but the descriptive association aligns with earlier findings that higher clay content reduces sediment transport capacity.

The practical implications are considerable. By pinpointing where runoff concentrates, where sediment moves, and where it redeposits, the model gives land managers in the Pampa a targeting tool for monitoring and conservation, particularly in agricultural sectors and zones threatened by gully expansion. The authors also flag post-harvest silviculture for special attention, since eucalyptus planting in the region surged by 268 percent between 2009 and 2012 and harvested stands leave soil exposed. The study’s limitations are candidly acknowledged: SIMWE cannot represent vertical variability in soil properties, the 30-meter elevation data is coarse, sediment parameters came from the literature, and no formal calibration or validation was performed. Future work should investigate the subsurface erosion mechanisms the model cannot capture, validate runoff and sediment patterns in the field, and test alternative land use and rainfall scenarios. For now, the message is clear: in this sandy, biodiverse, and increasingly cultivated biome, saving the soil may require looking beneath the surface as much as at it.

Subject of Research: Modeling water erosion susceptibility and gully dynamics in the Pampa biome of southern Brazil using the SIMWE model

Article Title: Modeling susceptibility to water erosion in the Pampa biome, southern Brazil

Article References: Schnorr, G. G., & Trentin, R. (2026). Modeling susceptibility to water erosion in the Pampa biome, southern Brazil. Discover Geoscience, 4(1), Article 356. https://doi.org/10.1007/s44288-026-00727-8

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00727-8

Keywords: soil erosion, SIMWE model, Pampa biome, gully erosion, hydrological modeling, GRASS GIS, sediment transport, digital elevation model, land use, Rio Grande do Sul, subsurface erosion, soil conservation

The review, authored by Bernardo Cândido of the University of Missouri, synthesised peer-reviewed studies published since 2005, tracing the methodological lineage from close-range digital photogrammetry through to today’s UAV-based workflows. Its central conclusion is deceptively simple: structure-from-motion photogrammetry directly measures soil-surface geometry and, in repeated surveys evaluated against uncertainty, detectable net surface change. It does not directly measure detachment, soil loss, sediment flux, sediment delivery or conservation success. Those are interpretive claims, and they become defensible only when detectability, mechanism, storage context and scale are explicitly constrained.

The technical reasons for this caution lie in the measurement chain itself. Structure from motion estimates camera geometry and sparse scene structure from overlapping images, and multi-view stereo then densifies that solution into a point cloud from which elevation models are derived. Each stage introduces uncertainty. Weak image networks, particularly largely parallel acquisition geometries, can introduce systematic deformation even when models look visually plausible, as James and Robson demonstrated in foundational work. Bare agricultural surfaces compound the problem: they are often planar, weakly textured and repetitive, so they constrain camera self-calibration less effectively than rougher natural terrain. Bright illumination and homogeneous texture can further degrade keypoint matching and introduce broad-scale vertical noise.

Georeferencing decisions matter just as much. Ground-control-point-based orientation, direct georeferencing from RTK or PPK camera positions, and stable local coordinate frames each carry distinct strengths and weaknesses, and camera-center coordinates used in bundle adjustment are observations rather than independent validation data. The review stresses that RTK or PPK positioning does not eliminate the need for spatially distributed checkpoints, and that a single global RMSE can coexist with poorer local precision. For multitemporal analysis, the decisive requirement is a stable date-to-date reference frame accompanied by independent, spatially explicit uncertainty assessment, because detectability varies across the scene rather than behaving as one uniform number.

The evidential strength of drone photogrammetry also varies systematically across erosion processes, and the review organises this variation into a four-level inference ladder. At the base sits surface reconstruction: recovering microtopography, roughness, rill form and many exposed gully shapes. Here the method is already robust. Studies have shown that structure from motion can recover bare-soil microtopography with useful accuracy, that rill geometry can be measured rapidly and non-destructively at plot scale, and that automated rill mapping across agricultural fields can achieve accuracy statistics above eighty percent, although performance drops for shallow incisions and where vegetation or linear surface features resemble rills.

Gullies, with their large relief signals, are particularly well suited to the technique, and early studies demonstrated volumetric analysis of headcut erosion in exposed sections. But strong relief does not eliminate geometric blind spots. UAV-only surveys become restricted on steep gully walls above roughly fifty to sixty degrees and on overhangs, and additional terrestrial imagery substantially improves the resulting three-dimensional models. Vegetation imposes a harder limit still: agreement between photogrammetric surfaces and terrestrial laser scanning degrades as ground cover increases and is significantly affected beyond about fifty-three percent cover. When soil visibility changes, the review notes, the target of the measurement changes too, and elevation differences recorded over vegetation or standing water cannot automatically be read as soil loss.

Diffuse, sheet and interrill erosion is the most demanding domain, because the expected topographic signal is small, spatially dispersed and often close to the magnitude of non-erosional change. Controlled experiments have shown that compaction and settlement can exceed the volume attributed to diffuse erosion under some conditions, and that part of the mobilised material may remain stored within convergent features rather than leaving the plot. On cultivated hillslopes, repeated surveys captured rainfall-related surface lowering, yet inferred mass loss decreased once soil settlement was accounted for through bulk-density measurements. In other words, soils can lower without exporting sediment, and a negative elevation difference that exceeds local uncertainty should first be reported as surface lowering, not erosion.

The strongest studies are those that close the inferential loop with independent evidence. One benchmark combined UAV monitoring of bare erosion plots under natural rainfall with direct sediment collection and spatially variable levels of detection, reporting strong correspondence between photogrammetric soil-loss estimates and measured sediment yield. Another paired drone surveys with RFID soil-particle tracking, runoff and sediment measurements at the plot outlet, and a sediment-connectivity index, revealing that short particle travel distances could coexist with increasing connectivity and higher sediment discharge. A post-fire Mediterranean catchment study combined photogrammetry with terrestrial LiDAR, GNSS and georadar to quantify erosion and sedimentation around a retention dam over several years. In each case, topography gained explanatory power because it was embedded in a broader process-observation framework.

Management claims sit at the top of the ladder and are the sparsest of all. A comparative study using post-storm drone imagery found that cover-cropped plots showed lower soil loss, shallower rills and smaller rill surface area than bare plots, a genuinely comparative design that moves beyond description. But even that study acknowledges that its reconstructed-baseline approach is constrained by vegetation conditions and requires stronger ground validation for quantitative use. Demonstrating that an intervention reduced erosion requires showing a change relative to a credible counterfactual, and the review finds that validated conservation-effectiveness claims remain much rarer than claims of detectable net surface change.

The practical implications reach beyond academic terminology. For soil conservation, drone surveys excel at spatial diagnosis, identifying runoff pathways, erosional source areas and depositional zones at the scale where interventions are actually placed. For erosion modelling, repeated UAV surveys provide spatially distributed time series that field-scale models have long lacked; one study using nine surveys found that WEPP reproduced observed erosion more closely than USLE, which systematically overestimated soil loss at the site. But surface lowering alone should not be assumed to represent sediment yield. The review’s agenda is clear: shared benchmark datasets across surface states, explicit separation of erosional from non-erosional change, standardised validation protocols for diffuse erosion, nested event-based monitoring, and reporting standards that keep every claim proportional to the evidence that supports it. Detailed surfaces are no longer the hard part; proving what they mean is.

Subject of Research: UAV structure-from-motion photogrammetry for measuring soil surfaces and inferring soil erosion

Article Title: A critical review of UAV structure from motion photogrammetry for measuring soil surfaces and inferring soil loss

Article References: Cândido, B. (2026). A critical review of UAV structure from motion photogrammetry for measuring soil surfaces and inferring soil loss. Discover Soil, 3(1), Article 140. https://doi.org/10.1007/s44378-026-00298-7

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00298-7

Keywords: UAV photogrammetry, structure from motion, soil erosion, soil microtopography, rill erosion, gully erosion, diffuse erosion, DEM of difference, sediment redistribution, soil conservation, uncertainty, remote sensing