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The microtopography of tidal freshwater forested wetlands (TFFWs) impacts biogeochemical processes affecting the carbon and nitrogen dynamics, ecological parameters, and habitat diversity. However, it is challenging to quantify low-relief microtopographic features that might only vary by a few tens of centimeters. We assess the high-resolution fine-scale microtopographic features of a TFFW with terrestrial LiDAR and aerial LiDAR to test a method appropriate to quantify microtopography in low-relief forested wetlands. Our method uses a combination of water-level and elevation thresholding (WALET) to delineate hollows in terrestrial and aerial LiDAR data. Close-range remote sensing technologies can be used for microtopography in forested regions. However, the aerial and terrestrial LiDAR technologies have not been used to analyze or compare microtopographic features in TFFW ecosystems. Therefore, the objectives of this study were (1) to characterize and assess the microtopography of low-relief tidal freshwater forested wetlands and (2) to identify optimal elevation thresholds for widely available aerial LiDAR data to characterize low-relief microtopography. Our results suggest that the WALET method can correctly characterize the microtopography in this area of low-relief topography. The microtopography characterization method described here provides a basis for advanced applications and scaling mechanistic models. [ABSTRACT FROM AUTHOR]

A notable characteristic of terrain in non‐urbanized deglaciated areas of northeastern North America is the microtopography created by processes related to surficial geology, deglaciation and mechanical disturbances to surface materials from excavating events, most of which are caused by tree throw in the modern landscape. The features are often on the scale of 1–4 m across and decimetres to a metre in depth, appearing as 'puddles' during intense or high‐magnitude precipitation events. Generalized storage capacity values have been summarized in textbooks for varied landscape conditions, but surprisingly little information is available about how microtopography and related surface water storage varies in dominant physiographic settings in deglaciated landscapes defined by slope, surficial geology and land cover conditions. The increasing availability of elevation data at a horizontal resolution of 2 m or higher has made it possible to remotely evaluate differences in terrain elevation and quantify upland surface water storage capacity from relatively small topographic depressions. Here, we describe and quantify these topographic features in several coastal and inland watersheds in the state of Maine (USA) with measurements of depression volume calculated from digital elevation models (DEMs) using a pit filling approach. Results show that microtopographic storage capacity varies with slope and land cover conditions in deglaciated terrain of northeastern North America. Basin‐average surface water depression storage capacity estimates range from ~4 mm to as low as 0.2 mm. Human interventions such as clearing land for agriculture are associated with lower microtopographic surface water storage capacity than forested landscapes in the region. [ABSTRACT FROM AUTHOR]

Microtopography plays an important role in various ecological, hydrologic, and biogeochemical processes. However, quantifying the characteristics of microtopography represents a data-intensive challenge. Over the last decade, high-resolution or close-range remote sensing data and techniques have emerged as powerful tools to quantify microtopography. Traditional field surveys were mostly limited to transects or small plots, using limited sets of observations but with the decrease in the cost of close-range remote sensing technologies and the increase in computing performance, the microtopography even in forested environments can be assessed. The main objective of this article is to provide a systematic framework for microtopographic studies using close-range remote sensing technologies. This is achieved by reviewing the application of close-range remote sensing to capture microtopography and develop microtopographic models in natural ecosystems. Specifically, to achieve the main objectives, we focus on addressing the following questions: (1) What terrain attributes represent microtopography in natural ecosystems? (2) What spatial resolution of terrain attributes is needed to represent the microtopography? (3) What methodologies have been adopted to collect data at selected resolutions? (4) How to assess microtopography? Current research, challenges, and applicability of close-range remote sensing techniques in different terrains are analyzed with an eye to enhancing the use of these new technologies. We highlight the importance of using a high-resolution DEM (less than 1 m2 spatial resolution) to delineate microtopography. Such a high-resolution DEM can be generated using close-range remote sensing techniques. We also illustrate the need to move beyond elevation and include terrain attributes, such as slope, aspect, terrain wetness index, ruggedness, flow accumulation, and flow path, and assess their role in influencing biogeochemical processes such as greenhouse gas emissions, species distribution, and biodiversity. To assess microtopography in terms of physical characteristics, several methods can be adopted, such as threshold-based classification, mechanistically-based delineation, and machine learning-based delineation of microtopography. The microtopographic features can be analyzed based on physical characteristics such as area, volume, depth, and perimeter, or by using landscape metrics to compare the classified microtopographic features. Remote sensing techniques, when used in conjunction with field experiments/data, provide new avenues for researchers in understanding ecological functions such as biodiversity and species distribution, hydrological processes, greenhouse gas emissions, and the environmental factors that influence those parameters. To our knowledge, this article provides a comprehensive and detailed review of microtopography data acquisition and quantification for natural ecosystem studies. [ABSTRACT FROM AUTHOR]

This article demonstrates the application of the methods unravelling microtopographic features, specifically, sunken linear landforms indicating remains of historical trackways in forests. These are related to the 'Magna Via' route in the vicinity of the Deserted Castle and the Peťuša Castle in Central Slovakia. The microtopography validation dataset indicating sunken linear landforms was used to evaluate the data overlap of tracks identified by the global positioning and navigation system (GNSS) and a model of tracks created with the least cost path (LCP) algorithm. Microtopographic features derived from a digital relief model generated by high‐resolution Light Detection and Ranging (LiDAR) scans (density of 5 points/pixel) allowed us to determine the exact position (submeter total vertical and horizontal accuracy) of certain segments of the GNSS tracks and LCP overlapping the sunken linear landforms. Moreover, the LCP model shows the most efficient trackways considering the travel costs depending on the slope parameter. [ABSTRACT FROM AUTHOR]

Predicting drought responses of individual trees in tropical forests remains challenging, in part because trees experience drought differently depending on their position in spatially heterogeneous environments. Specifically, topography and the competitive environment can influence the severity of water stress experienced by individual trees, leading to individual-level variation in drought impacts. A drought in 2015 in Puerto Rico provided the opportunity to assess how drought response varies with topography and neighborhood crowding in a tropical forest. In this study, we integrated 3 years of annual census data from the El Yunque Chronosequence plots with measurements of functional traits and LiDAR-derived metrics of microsite topography. We fit hierarchical Bayesian models to examine how drought, microtopography, and neighborhood crowding influence individual tree growth and survival, and the role functional traits play in mediating species’ responses to these drivers. We found that while growth was lower during the drought year, drought had no effect on survival, suggesting that these forests are fairly resilient to a single-year drought. However, growth response to drought, as well as average growth and survival, varied with topography: tree growth in valley-like microsites was more negatively affected by drought, and survival was lower on steeper slopes while growth was higher in valleys. Neighborhood crowding reduced growth and increased survival, but these effects did not vary between drought/non-drought years. Functional traits provided some insight into mechanisms by which drought and topography affected growth and survival. For example, trees with high specific leaf area grew more slowly on steeper slopes, and high wood density trees were less sensitive to drought. However, the relationships between functional traits and response to drought and topography were weak overall. Species sorting across microtopography may drive observed relationships between average performance, drought response, and topography. Our results suggest that understanding species’ responses to drought requires consideration of the microenvironments in which they grow. Complex interactions between regional climate, topography, and traits underlie individual and species variation in drought response.

Peatlands represent an important component of the global carbon cycle, storing 180–621 Gt of carbon (C). Small-scale spatial variations in elevation, frequently referred to as microtopography, influence ecological processes associated with the peatland C cycle, including Sphagnum photosynthesis and methane flux. Microtopography can be characterized with measures of topographic variability and by using conceptual classes (microforms) linked to function: most commonly hummocks and hollows. However, the criteria used to define these conceptual classes are often poorly described, if at all, and vary between studies. Such inconsistencies compel development of explicit quantitative methods to classify microforms. Furthermore, gradient-based characterizations that describe spatial variability without the use of microforms are lacking in the literature. Therefore, the objectives of this study were to (1) calculate peatland microtopographical elevation gradients and measures of spatial variability, (2) develop three microform classification methods intended for specific purposes, and (3) evaluate and contrast classification methods. Our results suggest that at spatial scales much larger than microforms, elevation distributions are unimodal and are well approximated with parametric probability density functions. Results from classifications were variable between methods and years and exhibited significant differences in mean hollow areal coverages of a raised ombrotrophic bog. Our results suggest that the conceptualization and classification of microforms can significantly influence microtopographic structural metrics. The three explicit methods for microform classification described here may be used and built upon for future applications. [ABSTRACT FROM AUTHOR]

Highlights • The forest-tundra ecotone exhibits myriad responses to environmental change. • We related TLS-derived microstructure to photosynthetic functioning of spruce trees. • Microstructure alone explained 28% of the variance in photosynthetic functioning. • Canopy roughness and insolation were most important and were scale-dependent. • Results suggest microstructure may modulate FTE trees' sensitivity to climate change. Abstract The forest-tundra ecotone (FTE) is exhibiting myriad responses to rapid environmental change. Microstructural variability (at cm to m length scales) of vegetation canopies and geomorphic features may modulate the response of FTE vegetation to regional climate changes. Understanding the influence of microstructure on tree function at the FTE is particularly relevant during vulnerable early growth stages. During these stages, individual trees are tightly coupled to conditions of the surface boundary layer, which can be more conducive to growth than the conditions above the boundary layer. Until recently, however, it has been difficult to characterize microstructure in a replicable, transferable manner. This study builds upon substantial research on ecological responses of trees at the FTE to growth environment conditions by integrating high-resolution terrestrial lidar scanning (TLS) to characterize microstructure. Our main goal was to use TLS technology to understand the effects of microstructure on photosynthetic functioning (i.e., chlorophyll fluorescence) of small-stature white spruce (Picea glauca (Moench) Voss) trees at the FTE. Our specific objectives were to: 1) determine how much variance in photosynthetic functioning is explained by microstructure; 2) identify microstructural metrics that most strongly control variance in photosynthetic functioning; and 3) determine the scales at which microstructural metrics most strongly drive variance in photosynthetic functioning. Random Forest modeling demonstrated that 28% of variance in photosynthetic functioning can be explained through variation in fine-scale environmental conditions that are modulated by microstructure alone. Insolation and canopy roughness were the most important predictors of photosynthetic functioning, and the sensitivity of photosynthetic functioning to canopy roughness was scale-dependent. This suggests that microstructure affects spatial heterogeneity in the boundary layer that may influence carbon assimilation of small-stature spruce trees. This research emphasizes the importance of quantifying microstructure in study systems where fine-scale heterogeneity of the growth environment may modulate plant responses to regional climate change. [ABSTRACT FROM AUTHOR]

Abstract The 190 km2 Calhoun Critical Zone Observatory in the Piedmont region of South Carolina, USA lies in an ancient, highly weathered landscape transformed by historic agricultural erosion. Following the conversion of largely hardwood forests to cultivated fields and pastures for ~200 years, excess runoff from fields led to extreme sheet, rill, and gully erosion across the landscape. Roads, terraces, and a variety of other human disturbances have increased the landscape's surface roughness. By the 1950s, cultivation-based agriculture was largely abandoned across most of the Southern Piedmont due to soil erosion, declining agricultural productivity, and shifting agricultural markets. Secondary forests, dominated by loblolly and shortleaf pines, have since regrown on much of the landscape, including the 1500 km2 Sumter National Forest, which was purchased from farmers and private land owners in the 1930s. Although this landscape was intensively farmed for approximately 150 years, there are a few hardwood forest stands and even entire small watersheds that have never been plowed and degraded by farming. Such relatively old hardwood stands and watersheds comprise relic landforms whose soils, regoliths, and vegetation are of interest to hydrologists, environmental historians, biogeochemists, geomorphologists, geologists, pedologists, and others interested in understanding the legacy of land-use history in this severely altered environment. In this work we champion the need for high-resolution terrain mapping and demonstrate how Light Detection And Ranging (LiDAR) digital elevation model (DEM) data and microtopographic terrain roughness analyses (MTRA) can be used to infer land use history and management. This is accomplished by analyzing fine scale variation in terrain slope across the 1190 km2 CCZO using data derived from three independent and overlapping LiDAR datasets at varying spatial resolutions. Terrain slope variability MTRA is further compared to three other methods of capturing and quantifying fine-scale surface roughness. We lastly demonstrate how these analyses can be employed in concert with historic aerial photography from the 1930's, contemporary Landsat remote sensing data, and ecological field data to identify reference relic landforms: hardwood stands, hillslopes, and small watersheds that have experienced minimal anthropogenic erosion for study and conservation. Highlights • Human activities roughen the ground surface via enhanced erosion. • Minimally eroded areas can be extremely difficult to identify within large areas. • Remote sensing can be used distinguish highly vs. minimally impacted areas. • Minimally impacted areas have great value for research and conservation. • Our work increases researchers' ability to locate and utilize these areas. [ABSTRACT FROM AUTHOR]

Soil surface roughness (SSR) is an important indicator that characterizes the microtopography feature of farmland after tillage. It has a high practical value for sowing and seedling raising, farmland management, and drainage irrigation in agricultural production. The traditional method often is prone to damage the surface microstructure and results in low efficiency and accuracy. In this study, a new method was proposed to address the limitations of traditional measurement methods of SSR. The proposed measurement and evaluation method of farmland microtopography feature information based on 3D lidar and inertial measurement unit (IMU) could be used to quickly obtain the global point cloud map containing the height data of the test field. Taking three different tillage methods of farmland as the research object, the surface root mean square height (RMSH), correlation length (CL), and their ratio were selected as roughness parameters to explore the anisotropy of microtopography features in different directions. The measurement method was then used to study the effects of sampling processing methods (number, interval, and length) on the measurement accuracy in both OX and OY directions. The results indicate that under the same accuracy requirements, for the 2 × 2 m area, the farmland with different microtopography features needs to be processed with different sample numbers, sample intervals, and sample lengths. The optimal combination of sample parameters for Test field I is sample number of 50, sample interval of 120 mm, and sample interval of 1600 mm, and that in Test field II is sample number of 50, sample interval of 160 mm, and sample interval of 1800 mm. For Test field III, the optimal combination is sample number of 100, sample interval of 40 mm, and sample length of 1200 mm. The experimental results compared with the traditional method illustrate the high accuracy and good feasibility of the proposed method for measuring and evaluating the microtopography feature information of the farmland. The results of the study help to understand the microtopography features and its parameterization of the farmland after tillage, which could further reveal the role and significance of SSR parameters in objectively evaluating farmland tillage quality and optimizing farmland management. • A new efficient parametrization of farmland microtopography was introduced. • The effects of tillage methods on soil roughness parameters were studied. • The proposed method was used for on-the-go soil surface roughness measurement. • A robust evaluation of parameters was performed from the 3D digital height model. [ABSTRACT FROM AUTHOR]

The use of LiDAR in the study of gap regimes has seen significant progress in recent years. Researchers have assumed that canopy gaps detected in this manner are ecologically equivalent to gaps sampled in situ by more traditional methods. However, those latter methods usually include canopy gaps only and ignore non-regenerating openings that are produced by causes limiting tree establishment. We developed a predictive model capable of discriminating between canopy gaps and non-regenerating openings using LiDAR-derived data. Selected predictive variables were related to conditions that limit tree establishment such as zones of moisture accumulation and steep slopes or to the resulting vegetation physiognomy. The model was applied to three old-growth maple forests to predict the fractions of canopy openings belonging to these two types. On average, non-regenerating openings represented 19.5% of the total area detected as canopy openings and occupied 1.37% of the sites. Canopy gaps formed 80.5% of the total area in canopy openings and covered 5.71% of sites that were studied. The non-regenerating opening seemed more frequent on thin surficial deposits. The canopy gap fraction was similar among study sites but had lower values than usually reported for temperate deciduous forest. [ABSTRACT FROM AUTHOR]

Microtopographic variability in peatlands has a strong influence on greenhouse gas fluxes, but we lack the ability to characterize terrain in these environments efficiently over large areas. To address this, we assessed the capacity of photogrammetric data acquired from an unmanned aerial vehicle (UAV or drone) to reproduce ground elevations measured in the field. In particular, we set out to evaluate the role of (i) vegetation/surface complexity and (ii) supplementary LiDAR data on results. We compared remote-sensing observations to reference measurements acquired with survey grade GPS equipment at 678 sample points, distributed across a 61-hectare treed bog in northwestern Alberta, Canada. UAV photogrammetric data were found to capture elevation with accuracies, by root mean squares error, ranging from 14-42 cm, depending on the state of vegetation/surface complexity. We judge the technology to perform well under all but the most-complex conditions, where ground visibility is hindered by thick vegetation. Supplementary LiDAR data did not improve results significantly, nor did it perform well as a stand-alone technology at the low densities typically available to researchers. [ABSTRACT FROM AUTHOR]

ALS-derived raster visualization techniques have become common in recent years, opening up new possibilities for subtle landform detection in earth sciences and archaeology, but they have also introduced confusion for users. As a consequence, the choice between these visualization techniques is still mostly supported by empirical knowledge. Some attempts have been made to compare these techniques, but there is still a lack of analytical data. This work proposes a new method, based on gradient modelling and spatial statistics, to analytically assess the efficacy of these visualization techniques. A selected panel of outstanding visualization techniques was assessed first by a classic non-analytical approach, and secondly by the proposed new analytical approach. The comparison of results showed that the latter provided more detailed and objective data, not always consistent with previous empirical knowledge. These data allowed us to characterize with precision the terrain for which each visualization technique performs best. A combination of visualization techniques based on DEM manipulation (Slope and Local Relief Model) appeared to be the best choice for normal terrain morphometry, occasionally supported by illumination techniques such as Sky-View Factor or Negative Openness as a function of terrain characteristics. [ABSTRACT FROM AUTHOR]

Defining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25 m LiDAR DEMs (40 bare-earth points m −2 ), resampled 1 m and 2 m LiDAR DEMs, and a radar generated 5 m DEM. Furthermore, the resampled 1 m and 2 m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m −2 ) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10 km 2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5 m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5 m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2 m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2 m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m −2 , even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered. [ABSTRACT FROM AUTHOR]

The conservation of species at risk of extinction requires data to support decisions at landscape to regional scales. There is a need for information that can assist with locating suitable habitats in fragmented and degraded landscapes to aid the reintroduction of at-risk plant species. In addition, desiccation and water stress can be significant barriers to the success of at-risk plant reintroduction programs. We examine how airborne light detection and ranging (LiDAR) data can be used to model microtopographic features that reduce water stress and increase resource availability, providing information for landscape planning that can increase the success of reintroduction efforts for a dryland landscape in Hawaii. We developed a topographic habitat-suitability model (HSM) from LiDAR data that identifies topographic depressions that are protected from prevailing winds (high-suitability sites) and contrasts them with ridges and other exposed areas (low-suitability sites). We tested in the field whether high-suitability sites had microclimatic conditions that indicated better-quality habitat compared to low-suitability sites, whether plant-response traits indicated better growing conditions in high-suitability sites, whether the locations of individuals of existing atrisk plant species corresponded with our habitat-suitability classes, and whether the survival of planted individuals of a common native species was greater in high-suitability, compared to low-suitability, planting sites. Mean wind speed in a high-suitability field site was over five times lower than in a low-suitability site, and soil moisture and leaf wetness were greater, indicating less stress and greater resource availability in high-suitability areas. Plant height and leaf nutrient content were greater in high-suitability areas. Six at-risk species showed associations with high-suitability areas. The survival of planted individuals was less variable among high-suitability plots. These results suggest that plant establishment and survival is associated with the habitat conditions identified by our model. The HSM can improve the survival of planted individuals, reduce the cost of restoration and reintroduction programs through targeted management activities in high-suitability areas, and expand the ability of managers to make landscape-scale decisions regarding land-use, land acquisition, and species recovery. [ABSTRACT FROM AUTHOR]

• In this study we have exploited LiDAR derived canopy height data at landscape level (230 km2). • Canopy of conifer plantation was higher than other forest types. • Taller trees were found in the concave valleys and at the intermediate elevations. • Along with topography, neighboring tree density and distance from canopy gap shape canopy height. • The results of this study will provide important insights about vulnerability of forests. Spatial variations of maximum canopy height are key indicators of many ecosystem processes such as above-ground biomass and productivity. With rapid advances in remote sensing techniques, such as airborne laser scanning (ALS), we can now measure canopy height, tree density, and canopy gaps from local to large scales. But these data are yet to be fully exploited for a better understanding of canopy height variations in relation to fine topographical heterogeneities and neighborhood conditions at the landscape level (>100 km2). In this study, we analyzed ALS data to extract spatial variations in canopy height and topographic features for a forest-dominated area of 230 km2 in Kyoto, Japan. The study area spanned an elevational range from 70 to 1000 m a.s.l and included four forest types, i.e., cedar or cypress plantations (PL), natural evergreen needle leaf forests (ENF), deciduous broadleaved forests (DBF), and evergreen broadleaved forests (EBF). PL had a greater mean canopy height than ENF, both of which were taller than DBF and EBF. Canopy height exhibited a hump-shaped relationship with elevation with the peak value at mid-altitude (461–560 m) in all forest types. Canopy height was greater by 5-10 m in the concave valleys than on the convex slopes and ridges. Trees were taller at greater distances from forest gaps and with lower tree density in the neighborhood. Local tree density, distance from the nearest canopy gap, and topographic curvatures were the most significant predictors in the best-fitted model (Random Forest: R2 = 0.41). The analytical approach developed in this study provides a useful tool for planning proper land use and maintaining a healthy forest stand at the regional scale. [ABSTRACT FROM AUTHOR]

• LiDAR scanning enables identification of previously undetectable erosional features. • Smoothing-via-Filling-Rough-Depressions (SvFRD) effectively quantifies gully volumes. • SvFRD estimates a pre-gully terrain surface based on local & landscape microtopography. • SvFRD was developed entirely using free software facilitating further development. Gully mapping techniques successfully identify gullies over a large range of breadths and depths in complex landscapes but practices for estimating gully volumes need further development. Gully gap-interpolation for estimation of gully volume does not often factor in landscape microtopography in the generation of the new surface. These approaches can thus overestimate large classical gully volumes, averaging over depressions, or underestimate volumes by creating overly-smooth highly curved surfaces. Microtopographic methodology was developed to estimate the pre-gully surface and gully volume across the Calhoun Critical Zone Observatory (CCZO) in South Carolina, USA. The CCZO is a Southern Piedmont landscape severely gullied by historic agriculture with upland Ultisols many meters deep. Our gully-mapping and gully-filling approaches used 1 m2 LiDAR elevation data and is based on the premise that gullies are local depressions on uplands which are deeply incised with high microtopographic roughness. Our smoothing-via-filling-rough-depressions (SvFRD) algorithm iteratively fills gullies until landscape microtopographic roughness is reduced and unchanging after a subsequent iteration. Results were evaluated in the context of prior landscape bulk erosion estimates ranging from 1483 to 3708 m3/ha as well as field surveys of gullies. Minimally eroded reference and highly-eroded post-agricultural terrain were compared to test gully-mapping and volume accuracy. Comparing gully-volume estimation techniques, inverse-distance-weighting (IDW) yielded the highest volume (1072 m3/ha) followed by ANUDEM (638 m3/ha) while spline-interpolation yielded the lowest estimate (555 m3/ha). SvFRD landscape gully volume estimates (615.5 m3/ha) were most similar to ANUDEM interpolation with roughness and gully extent results most similar to spline interpolation. Spline interpolation is effective and easily implemented but if microtopographic accuracy and mapping of fine-scale erosions features is desired to hindcast pre-gully terrain conditions, our depression-filling approach, implemented using free GIS and statistical software, is an effective method to estimate reasonable erosion volumes. [ABSTRACT FROM AUTHOR]

Soil surface measurements play an important role in the performance assessment of tillage operations and are relevant in both academic and industrial settings. Manual soil surface measurements are time-consuming and laborious, which often limits the amount of data collected. An experiment was conducted to compare two approaches for measuring and analysing the cross-sectional area and geometry of a furrow after a trailing shoe sweep. The compared approaches in this study were a manual pinboard and a Light Detection and Ranging (LiDAR) sensor. The experiments were conducted in coarse sand and loamy sand soil bins exposed to three levels of irrigation. Using the LiDAR, a system for generating 3D scans of the soil surface was obtained and a mean furrow geometry was introduced to study the geometrical variations along the furrows. A comparison of the cross-sectional area measurements by the pinboard and the LiDAR showed up to 41% difference between the two methods. The relation between irrigation and the resulting furrow area of a trailing shoe sweep was investigated using the LiDAR measurements. The furrow cross-sectional area increased by 11% and 34% under 20 mm and 40 mm irrigation compared to non-irrigated in the coarse sand experiment. In the loamy sand, the cross-sectional area increased by 17% and 15% by irrigation of 20 mm and 40 mm compared to non-irrigated measured using the LiDAR. [ABSTRACT FROM AUTHOR]