Fine-resolution Digital Elevation Model Research Articles

Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India

The number of rainfall-induced landslides and the resulting casualties are increasing worldwide. Efficient Landslide Early Warning Systems (LEWS) are the best way to reduce the risk due to such events, but the number of operational LEWS is still limited. A new data-driven approach for spatio-temporal landslide forecasting on a regional scale is proposed, integrating Landslide Susceptibility Maps (LSMs) using RF algorithm and probabilistic hydro-meteorological thresholds, considering both rainfall severity and antecedent soil wetness. The proposed method is also compared with two deterministic process-based approaches: Transient Rainfall Infiltration and Grid-based Regional Slope Stability (TRIGRS) and SHALSTAB, considering the spatial variability in soil thickness and properties, along with the rainfall data. The quantitative comparison is carried out for two test areas in the Western Ghats of India (Idukki and Wayanad), for two different spatial resolutions. The efficiency and area under the curve (AUC) values from a receiver operating characteristic curve (ROC) were used to evaluate the performance of different models. The results for Idukki indicate that the efficiency values of the data-driven approach were improved by 4.67 % by using fine resolution DEM (digital elevation model) of 12.5 m resolution, while in the case of TRIGRS and SHALSTAB models, the improvements were 3.39 % and 1.83 %, respectively. For Wayanad, the improvement in efficiencies was further lesser, 2.59 % in the case of data-driven model, and 0.95 % and 0.73 % in the cases of TRIGRS and SHALSTAB, respectively. The maximum efficiency and AUC values were obtained by the data-driven model for both regions, with a spatial resolution of 12.5 m. The maximum efficiency values were obtained as 81.21 % and 83.33 % for Idukki and Wayanad, respectively, and the corresponding AUC values were 0.92 and 0.93. The results indicate that the model proposed in this study, with data-driven approach performs better than the process-based approaches and can bypass the complexities involved in modeling.

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used.

Granularity of Digital Elevation Model and Optimal Level of Detail in Small-Scale Cartographic Relief Presentation

One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. Recent advancements in the coverage, quality, and resolution of global DEMs facilitate the overall improvement of the detail and reliability of terrain-related research. At the same time, geographic problem solving is conducted in a wide variety of scales, and the data used for mapping should have the corresponding level of detail. Specifically, at small scales, intensive generalization is needed, which is also true for elevation data. With the widespread accessibility of detailed DEMs, this principle is often violated, and the data are used for mapping at scales far smaller than what is appropriate. Small-scale relief shading obtained from fine-resolution DEMs is excessively detailed and brings an unclear representation of the Earth’s surface instead of emphasizing what is important at the scale of visualization. Existing coarse-resolution global DEMs do not resolve the issue, since they accumulate the maximum possible information in every pixel, and therefore also require reduction in detail to obtain a high-quality cartographic image. It is clear that guidelines and effective principles for DEM generalization at small scales are needed. Numerous algorithms have been developed for the generalization of elevation data represented either in gridded, contoured, or pointwise form. However, the answer to the most important question—When should we stop surface simplification?—remains unclear. Primitive error-based measures such as vertical distance are not effective for cartography, since they do not account for the landform structure of the surface perceived by the map reader. The current paper approached the problem by elaborating the granularity—a newly developed property of DEMs, which characterizes the typical size of a landform represented on the DEM surface. A methodology of estimating the granularity through a landform width measure was conceptualized and implemented as software. Using the developed program tools, the optimal granularity was statistically learned from DEMs reconstructed for multiple fragments of manually drawn 1:200,000, 1:500,000, and 1:1,000,000 topographic maps covering different relief types. It was shown that the relative granularity should be 5–6 mm at the mapping scale to achieve the clearness of relief presentation typical for manually drawn maps. We then demonstrate how the granularity measure can be used effectively as a constraint during DEM generalization. Experimental results on a combination of contours, hypsometric tints, and hill shading indicated clearly that the optimal level of detail in small-scale cartographic relief presentation can be achieved by DEM generalization constrained by granularity in combination with fine DEM resolution, which facilitates high-quality rendering.

Processing of VENµS Images of High Mountains: A Case Study for Cryospheric and Hydro-Climatic Applications in the Everest Region (Nepal)

In the Central Himalayas, glaciers and snowmelt play an important hydrological role, as they ensure the availability of surface water outside the monsoon period. To compensate for the lack of field measurements in glaciology and hydrology, high temporal and spatial resolution optical remotely sensed data are necessary. The French–Israeli VENµS Earth observation mission has been able to complement field measurements since 2017. The aim of this paper is to evaluate the performance of different reflectance products over the Everest region for constraining the energy balance of glaciers and for cloud and snow cover mapping applied to hydrology. Firstly, the results indicate that a complete radiometric correction of slope effects such as the Gamma one (direct and diffuse illumination) provides better temporal and statistical metrics (R2 = 0.73 and RMSE = 0.11) versus ground albedo datasets than a single cosine correction, even processed under a fine-resolution digital elevation model (DEM). Secondly, a mixed spectral-textural approach on the VENµS images strongly improves the cloud mapping by 15% compared with a spectral mask thresholding process. These findings will improve the accuracy of snow cover mapping over the watershed areas downstream of the Everest region.

Innovative method for linking anthropisation process to vulnerability

Coastal areas have been gradually modified by humans to face emerging social needs. This phenomenon can be traced in the Amalfi Coast (Southern Italy) through the presence of terraces, constructed to make its steep slope habitable and arable. The current research is aimed to investigate the anthropisation process in Minori, and to identify the most vulnerable areas where debris flows. Two photogrammetric datasets, acquired in 1956 and 2017, respectively, were processed to generate two fine resolution digital elevation models and orthophotos, applied to calculate the normalised remaining vegetation index, the digital elevation model of differences, the new vulnerability expeditious index. The two orthophotos were also classified by applying the OBIA approach for extracting terraced areas. An increment of 800% of man-made infrastructures was detected, while the extension and the location of terraces was not subjected to any transformation. This implies a strong increment of vulnerability in the Minori municipality.

Assessment and comparison of Digital Elevation Model (DEM) products in varying topographic, land cover regions and its attribute: a case study in Shikoku Island Japan

Digital Elevation Model (DEM) is the model of Earth’s surface and is significantly variable for science application. The DEMs based on free provided are the 10 m DEM produced by the Geographical Survey Institute of Japan (GSI-DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM, Shuttle Radar Topography Mission (SRTM), Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS), and Global 30 Arc-Second Elevation (GTOPO30) that are normally used in scientific researches. These terrain data have been generated by different methods, such as Stereoscopic Photogrammetry, RADAR-SAR interferometry, LIDAR, and GPS. The generated data contain an error from collecting and processing methods that have related to morphology relief and land cover type. In this study, the six open-source DEMs and their attributes are evaluated by the referent elevation points observed by GPS, in Shikoku Island Japan. It was revealed that the error of DEMs is an effect on the terrain characteristic. Overall, the accuracy values of fine-resolution DEMs based on the RMSE value are 5.9 m, 9.9 m, and 10.1 m for GSI-DEM, ASTER, and SRTM, respectively. The coarse-resolution DEMs revealed the accuracy of RMSE as 18.2 m, 69.4 m, and 61.8 m for GMTED2010, HydroSHEDS, and GTOPO30, respectively. The accuracy of the slope and river network is also evaluated. This approach might be used to recommend for a new generation of DEM and revealed for the accuracy of the Earth’s surface.

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

Drainage Network Analysis and Structuring of Topologically Noisy Vector Stream Data

Drainage network analysis includes several operations that quantify the topological organization of stream networks. Network analysis operations are frequently performed on streams that are derived from digital elevation models (DEMs). While these methods are suited to application with fine-resolution DEM data, this is not the case for coarse DEMs or low-relief landscapes. In these cases, network analysis that is based on mapped vector streams is an alternative. This study presents a novel vector drainage network analysis technique for performing stream ordering, basin tagging, the identification of main stems and tributaries, and the calculation of total upstream channel length and distance to outlet. The algorithm uses a method for automatically identifying outlet nodes and for determining the upstream-downstream connections among links within vector stream networks while using the priority-flood method. The new algorithm was applied to test stream datasets in two Canadian study areas. The tests demonstrated that the new algorithm could efficiently process large hydrographic layers containing a variety of topological errors. The approach handled topological errors in the hydrography data that have challenged previous methods, including disjoint links, conjoined channels, and heterogeneity in the digitized direction of links. The method can provide a suitable alternative to DEM-based approaches to drainage network analysis, particularly in applications where stream burning would otherwise be necessary.

Effects of DEM Source, Spatial Resolution and Drainage Area Threshold Values on Hydrological Modeling

Automated computation of hydrological and morphometric parameters of any watershed are not only depended on Digital Elevation Model (DEM) resolution but also affected by their sources and drainage Area Threshold Value (ATV). In present study, the effects of different resolution of freely available DEMs are analysed in QSWAT model at different ATVs. The performance of QSWAT model and processing time were also evaluated. DEM derived from ASTER, CARTOSAT and SRTM were used at 30, 90 and 180 m spatial resolutions with six different ATV’s, in QSWAT model. Result shows that DEM resolutions and ATVs have significant impacts on hydrological and morphometric parameters. QSWAT output like sub-basin count and surface runoff were less impacted by DEM resolutions while these components were affected by ATV, while HRU counts and sediment yield were affected by DEM resolution. It was also observed that fine resolution DEM increases processing time at smaller threshold values as compared to coarser resolution DEM. Result of this study could be used for selection of optimum DEM resolution and ATV.

Evaluating metrics of local topographic position for multiscale geomorphometric analysis

The field of geomorphometry has increasingly moved towards the use of multiscale analytical techniques, due to the availability of fine-resolution digital elevation models (DEMs) and the inherent scale-dependency of many DEM-derived attributes such as local topographic position (LTP). LTP is useful for landform and soils mapping and numerous other environmental applications. Multiple LTP metrics have been proposed and applied in the literature; however, elevation percentile (EP) is notable for its robustness to elevation error and applicability to non-Gaussian local elevation distributions, both of which are common characteristics of DEM data sets. Multiscale LTP analysis involves the estimation of spatial patterns using a range of neighborhood sizes, traditionally achieved by applying spatial filtering techniques with varying kernel sizes. While EP can be demonstrated to provide accurate estimates of LTP, the computationally intensive method of its calculation makes it unsuited to multiscale LTP analysis, particularly at large neighborhood sizes or with fine-resolution DEMs. This research assessed the suitability of three LTP metrics for multiscale terrain characterization by quantifying their computational efficiency and by comparing their ability to approximate EP spatial patterns under varying topographic conditions. The tested LTP metrics included: deviation from mean elevation (DEV), percent elevation range (PER), and the novel relative topographic position (RTP) index. The results demonstrated that DEV, calculated using the integral image technique, offers fast and scale-invariant computation. DEV spatial patterns were strongly correlated with EP (r2 range of 0.699 to 0.967) under all tested topographic conditions. RTP was also a strong predictor of EP (r2 range of 0.594 to 0.917). PER was the weakest predictor of EP (r2 range of 0.031 to 0.801) without offering a substantial improvement in computational efficiency over RTP. PER was therefore determined to be unsuitable for most multiscale applications. It was concluded that the scale-invariant property offered by the integral image used by the DEV method counters the minor losses in robustness compared to EP, making DEV the optimal LTP metric for multiscale applications.

Historic Low Wall Detection via Topographic Parameter Images Derived from Fine-Resolution DEM

Coral walls protect vegetation gardens from strong winds that sweep across Xiji Island, Taiwan Strait for half the year. Topographic parameters based on light detection and ranging (LiDAR)-based high-resolution digital elevation model (DEM) provide obvious correspondence with the expected form of landscape features. The information on slope, curvature, and openness can help identify the location of landscape features. This study applied the automatic landscape line detection to extract historic vegetable garden wall lines from a LiDAR-derived DEM. The three rapid processes used in this study included the derivation of topographic parameters, line extraction, and aggregation. The rules were extracted from a decision tree to check the line detection from multiple topographic parameters. Results show that wall line detection with multiple topographic parameter images is an alternative means of obtaining essential historic wall feature information. Multiple topographic parameters are highly related to low wall feature identification. Furthermore, the accuracy of wall feature detection is 74% compared with manual interpretation. Thus, this study provides rapid wall detection systems with multiple topographic parameters for further historic landscape management.

Modeling deep soil properties on California grassland hillslopes using LiDAR digital elevation models

Topography strongly regulates soil formation at the hillslope scale through its effects on sediment redistribution and biological activities. Spatially explicit land surface parameters (LSPs) such as slope and curvature hold potential for modeling the resulting soil carbon (C) and nitrogen (N) distributions, but their representation of deep soil profiles remains largely unexplored. In this study we examine relationships between deep soil profile C and N stocks and LSPs derived from a fine-resolution digital elevation model (DEM) on prototypical rolling hillslope catenas. Consistent with other studies we found that soil thickness was the primary controller of soil organic C and N stocks and was best predicted by mean curvature. Specifically, subsoil thickness, instead of A horizon thickness, explained variability of soil C and N on hillslopes. In addition, our results suggest that, along ridge to toeslope catenas, the processes mediating soil C and N distribution varied from convex to concave positions. Convex ridge positions appeared to favor processes that enrich soil profiles with high C and N concentrations despite their drier position, while concave hollow and toeslope positions favored cumulic processes, despite their conceptually moister conditions in which enrichment processes would be favored. Our data also point to slope aspect as a weak but potentially geomorphically important covariate in modeling soil thickness and C and N stocks using LSPs. Overall, LSPs of curvature and aspect explained 51% of the variability in soil thickness, while curvature and aspect explained 50% of the variability in soil organic C stocks. Our results suggest that diffusive sediment transportation likely exerts a first-order control on soil thickness and soil organic C and N stocks in many semi-arid landscapes. Our data also highlight the importance of subsoil in mapping soil C and N stocks and other soil properties. Quantitative modeling of soil C and N as in our study supports examination of additional ecosystem properties at fine spatial scales.

The effect of DEM and land use spatial resolution on simulated streamflow and sediment

<div> <p>The Hydrologic Simulation Program-FORTRAN (HSPF) model is widely used to develop management strategies for water resources and to evaluate the hydrologic effect of various management scenarios. The spatial resolution of the input data used to parameterize HSPF model may induce uncertainty in model outputs. In this study, the impact of spatial resolutions of Digital Elevation Model (DEM) and land use map on the uncertainty of HSPF predicted flow and sediment were evaluated. DEM resolution can affect stream length, watershed area and average slope, whereas land use data resolution can lead to redistribution of land use information. Results showed that finer resolution DEM and land use maps can generate higher flow volumes and sediment loads compared to modelling scenarios using inputs of coarse resolution. The relative change in model performance between the baseline scenario (high-resolution) and scenarios of coarser resolution described uncertainties due to DEM and land use spatial information, and the probability density function of these uncertainties was used to estimate these uncertainties. Modelled flow and sediment uncertainty due to DEM resolution seems to follow a log-normal and a general extreme value distribution respectively, whereas modelled flow and sediment uncertainty due to land use resolution seems to both follow a general extreme value distribution. Overall, results highlight the need for a high-resolution DEM and land use maps in the application of the HSPF model while they provide useful information for reducing the model uncertainties.</p> </div> <p> </p>

An integrated approach to estimate shortwave solar radiation on clear-sky days in rugged terrain using MODIS atmospheric products

Downward surface shortwave radiation (DSSR) is important in various disciplines and is influenced by the atmosphere and mountainous topography. In this paper, an integrated approach that incorporates a mountainous spectral radiation scheme and an atmospheric broadband transmittance model based on a fine-resolution digital elevation model (DEM) and MODIS atmospheric products is proposed to estimate DSSR. This approach accurately considers important terrain and atmospheric factors and avoids any dependence on ground-based observations except for validation purposes. The results demonstrate that the radiation components exhibit very high spatial heterogeneity and are largely affected by different terrain orientations (especially topographic obstructions), slope, the cosine of the solar illumination angle, and atmospheric environments. Ground-based measurements for 56 clear-sky days from two observation stations are used for validation in the Dayekou watershed of the Heihe River Basin. The experiments show that this algorithm performs well; both the mean bias error percentage (MBE%; −6.2%) and the root-mean-square difference percentage (RMSD%; 7.5%) are less than 10%.

Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin

Abstract. The intensity, duration, and geographic extent of floods in Bangladesh mostly depend on the combined influences of three river systems, the Ganges, Brahmaputra and Meghna (GBM). In addition, climate change is likely to have significant effects on the hydrology and water resources of the GBM basin and may ultimately lead to more serious floods in Bangladesh. However, the assessment of climate change impacts on the basin-scale hydrology by using well-calibrated hydrologic modeling has seldom been conducted in the GBM basin due to the lack of observed data for calibration and validation. In this study, a macroscale hydrologic model H08 has been applied over the basin at a relatively fine grid resolution (10 km) by integrating the fine-resolution DEM (digital elevation model) data for accurate river networks delineation. The model has been calibrated via the analysis of model parameter sensitivity and validated based on long-term observed daily streamflow data. The impacts of climate change (considering a high-emissions path) on runoff, evapotranspiration, and soil moisture are assessed by using five CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs (global circulation models) through three time-slice experiments; the present-day (1979–2003), the near-future (2015–2039), and the far-future (2075–2099) periods. Results show that, by the end of 21st century, (a) the entire GBM basin is projected to be warmed by ~4.3 °C; (b) the changes of mean precipitation (runoff) are projected to be +16.3% (+16.2%), +19.8% (+33.1%), and +29.6% (+39.7%) in the Brahmaputra, Ganges, and Meghna, respectively; and (c) evapotranspiration is projected to increase for the entire GBM (Brahmaputra: +16.4%, Ganges: +13.6%, Meghna: +12.9%) due to increased net radiation as well as warmer temperature. Future changes of hydrologic variables are larger in the dry season (November–April) than in the wet season (May–October). Amongst the three basins, the Meghna shows the highest increase in runoff, indicating higher possibility of flood occurrence. The uncertainty due to the specification of key model parameters in model predictions is found to be low for estimated runoff, evapotranspiration and net radiation. However, the uncertainty in estimated soil moisture is rather large with the coefficient of variation ranging from 14.4 to 31% among the three basins.

Capacity assessment of the Qattara Depression: Egypt as a sink for the global sea level rise

One of the most prominent geomorphological features in the Western Desert of Egypt is the occurrence of the Qattara Depression (20,800 km2 or 2% of Egypt’s area), which has the deepest point in Africa. Topographic analysis of the depression was carried out using fine resolution digital elevation models acquired from the Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER-GDEM) in order to assess the capacity of the depression. Results showed that the lower point in the depression occurs at −136 m and its capacity at the sea level is 1340 billion m3, which equals 10 times the live storage capacity of the Lake Nasser behind the Aswan High Dam. Although the depression has the aptitude to be a reservoir for any augmentation in the sea level, detailed environmental impact assessment studies should be implemented to address geologic, climatic, biologic as well as socio-economic impacts.

Sediment delivery estimates in water quality models altered by resolution and source of topographic data.

Moderate-resolution (30-m) digital elevation models (DEMs) are normally used to estimate slope for the parameterization of non-point source, process-based water quality models. These models, such as the Soil and Water Assessment Tool (SWAT), use the Universal Soil Loss Equation (USLE) and Modified USLE to estimate sediment loss. The slope length and steepness factor, a critical parameter in USLE, significantly affects sediment loss estimates. Depending on slope range, a twofold difference in slope estimation potentially results in as little as 50% change or as much as 250% change in the LS factor and subsequent sediment estimation. Recently, the availability of much finer-resolution (∼3 m) DEMs derived from Light Detection and Ranging (LiDAR) data has increased. However, the use of these data may not always be appropriate because slope values derived from fine spatial resolution DEMs are usually significantly higher than slopes derived from coarser DEMs. This increased slope results in considerable variability in modeled sediment output. This paper addresses the implications of parameterizing models using slope values calculated from DEMs with different spatial resolutions (90, 30, 10, and 3 m) and sources. Overall, we observed over a 2.5-fold increase in slope when using a 3-m instead of a 90-m DEM, which increased modeled soil loss using the USLE calculation by 130%. Care should be taken when using LiDAR-derived DEMs to parameterize water quality models because doing so can result in significantly higher slopes, which considerably alter modeled sediment loss.

Predicting Ephemeral Gully Location and Length Using Topographic Index Models

Abstract. Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: TSA = 30 for S1 and TSA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Predicting Ephemeral Gully Location and Length Using Topographic Index Models

<abstract><title><italic>Abstract.</italic></title> Ephemeral gullies (EGs) are incised channels resulting from concentrated overland flow that often form in a similar location every year. These erosional features add to producers’ management efforts and costs. Locating EGs and predicting their length is crucial for estimating sediment load and planning conservation strategies. Since topography plays an important role in the formation of EGs, this study investigated the prediction of EG location and length in two agricultural areas (S1 and S2) in two different physiographic regions using four topographic index models: compound topographic index (CTI), slope area (SA), wetness topographic index (WTI), and slope area power (SAP). The impacts of digital elevation model (DEM) resolution, agricultural land use mask data source, and topographic model critical thresholds were also evaluated. Automated geospatial models were developed to locate and derive EG length. Results show that the SA model predicted EG occurrence and length better than other models tested. The SA and CTI model predictions had similar patterns in terms of locating EG trajectory; however, the CTI model had greater discontinuity along the trajectory. The method developed to derive length in this study was sensitive to discontinuity, so the performance of the CTI model was poor. Finer-resolution DEMs (2 m) predicted EG location and lengths better than coarser-resolution DEMs (10 m or greater). Use of actual field-level reconnaissance data instead of NASS data for agricultural land use masking decreased false negative classification by 16% or more for all models. Detailed calibration of the SA model yielded different optimal thresholds for the two study regions: T_SA = 30 for S1 and T_SA = 50 for S2. Topographic index models were found to be useful in locating EGs and estimating expected lengths, but site-specific calibration of the topographic index model threshold was required, which might limit the general utility of these methods.

Developing a Fine-Resolution Digital Elevation Model to Support Hydrological Modeling and Ecological Studies in the Northern Everglades

Accurate high-resolution terrain data are essential for hydrological modeling in lowlands. This study integrates elevation survey data and vegetation data at the point and 50 m scales to develop a fine-resolution digital elevation model (DEM) for the northern Everglades of Florida. The terrain was divided into two vertical strata (lowland and highland) based on a 50 m scale vegetation map. The DEM in highlands was interpolated with all the survey points and later adjusted using an association between vegetation and hydroperiod (the number of days per year that land is flooded). The DEM in lowlands was interpolated with elevation surveys tagged as lowland types. The two DEMs were then combined, forming a new DEM with a 7.7 cm mean absolute validation error—a significant (2.3 cm) improvement over the previous DEM.