Coarse Digital Elevation Model Research Articles

Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing of acquisition and resolution of digital elevation models (DEM) that are used for topographic correction. We use the fine-resolution data (2 m) from the Airborne Coastal Observatory (ACO), an imaging spectroscopy and lidar remote sensing platform, to quantify the expected error from other airborne and satellite scale platforms. First, we describe our retrieval workflow - the Imaging Spectroscopy Snow and Ice Algorithm (ISSIA) that yields spatial retrievals of broadband albedo (BBA), optical grain size, and radiative forcing by light absorbing particles (RFLAP) using the coincident 2 m lidar DEM over mountain glaciers. We use two acquisitions over Place Glacier, British Columbia, Canada that represent typical spring and summer conditions as a baseline from which synthetic datasets were generated to represent data from coarser-scale imaging spectroscopy platforms. Each synthetic dataset was motivated by specific airborne and satellite platform scenarios and quantifies four sources of error: (1) DEM spatial resolution and elevation accuracy, (2) coincident vs non-coincident DEM collection, (3) imagery spatial resolution, and (4) spectral resolution. Use of a 30 m DEM introduced the most error in steep mountain terrain, which primarily increased error in the BBA and RFLAP retrievals. The timing of DEM acquisition mattered most for high-resolution imagery and in areas surrounding the glacier toe where mass loss occurred more rapidly. Coarse-resolution imagery did not fully capture spatial variability in surface properties and topography, which lowered variance and increased overall errors. Coarsening of spectral resolution from 5 nm to 10 nm had minimal impact on surface property retrieval and associated errors were independent of DEM or imagery resolution. A simple energy analysis showed that surface albedo error, due to topographic correction with coarse and non-coincident DEMs, resulted in melt rate errors ranging from 6 to 20% for airborne and 11–20% for satellite scenarios.

Topographic Position-based Stream definition (TPS): A simple method to address spatial variability of drainage density in stream networks

ABSTRACT Defining river networks from a digital elevation model (DEM) is a critical step in many hydrological studies. Most methods rely on analyses of flow accumulation based on a single drainage area threshold value. Landscape heterogeneity hinders the definition of drainage pixels by exceeding a threshold. In this study, we propose a method that automatically accounts for variable drainage density in the definition of river networks from relatively coarse DEMs by using the topographic position index (TPI) as an auxiliary variable, therefore calling it the Topographic Position-based Stream definition (TPS). We tested the TPS method with the 90 m resolution MERIT-DEM (Multi-Error-Removed Improved-Terrain DEM) in Brazil, applying it to three river basins with contrasting landforms and climates. The method improved drainage representation in all analysed regions. It successfully represented stream networks with intra-basin variable drainage density by using a single DEM and three parameters, showing potential for hydrological applications at multiple spatial scales.

Hydro‐Geomorphic Metrics for High Resolution Fluvial Landscape Analysis

AbstractTopographic metrics are designed to quantify scale‐relevant relationships between geometric properties of landscapes to reveal the processes shaping them. They have long been derived from topographic flow routing algorithms, initially developed for coarse Digital Elevation Models (DEMs), whose resolution (≥30 m) and poor precision did not resolve correctly flow patterns and channel flow width. Since high resolution and precision DEMs make the description of meter‐scale flow patterns possible, new methods are required to analyze high resolution landforms structures such as hillslope‐channel connections, channel width or floodplains. Here, we investigate the potential of 2D hydraulic simulations based on the shallow water equations to replace the classical slope versus drainage area analysis, to analyze river morphology and to identify floodplains. We apply the Floodos model to the 1 m resolution DEM of the Elder Creek catchment, California, from which we derive three hydro‐geomorphic metrics accounting for the river geometry: a specific drainage area extended to channels, an effective flow width and the hydraulic slope. We analyze the Elder Creek catchment through what we call the hydraulic slope‐area diagram allowing a better identification of hillslope‐channel connections than the slope‐area approach. The effective flow width is analyzed along the drainage network and is characterized by a power‐law relationship consistent with previous observations. We derive metrics based on a multi‐runoff approach to automatically identify floodplains and evaluate along‐stream variations in hydraulic geometry. The hydro‐geomorphic metrics offer a geomorphic analysis suitable for high resolution DEMs and opens up new perspectives in fluvial landscape analysis.

How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

The evaluation of the potential of global data products for snow hydrological modelling in ungauged high-alpine catchments

Abstract. For many ungauged mountain regions, global datasets of different meteorological and land surface parameters are the only data sources available. However, their applicability in modelling high-alpine regions has been insufficiently investigated so far. Therefore, we tested a suite of globally available datasets by applying the physically based Cold Regions Hydrological Model (CRHM) for a 10-year (September 2000–August 2010) period in the gauged high-alpine Research Catchment Zugspitze (RCZ), which is 12 km2 and located in the European Alps. Besides meteorological data, snow depth is measured at two stations. We ran CRHM with a reference run with in situ-measured meteorological data and a 2.5 m high-resolution digital elevation model (DEM) for the parameterization of the surface characteristics. Regarding different meteorological setups, we used 10 different globally available datasets (including versions of ERA, GLDAS, CFSR, CHIRPS) and additionally one transferred dataset from a similar station in the vicinity. Regarding the different DEMs, we used ALOS (Advanced Land Observing Satellite) and SRTM (Shuttle Radar Topography Mission) (both 30 m) as well as GTOPO30 (1 km). The following two main goals were investigated: (a) the reliability of simulations of snow depth, specific snow hydrological parameters and runoff with global meteorological products and (b) the influence of different global DEMs on snow hydrological simulations in such a topographically complex terrain. The range between all setups in mean decadal temperature is high at 3.5 ∘C and for the mean decadal precipitation sum at 1510 mm, which subsequently leads to large offsets in the snow hydrological results. Only three meteorological setups, the reference, the transferred in situ dataset and the CHIRPS dataset, substituting precipitation only, showed agreeable results when comparing modelled to measured snow depth. Nevertheless, those setups showed obvious differences in the catchment's runoff regime and in snow depth, snow cover, ablation period, the date, and quantity of maximum snow water equivalent in the entire catchment and in specific parts. All other globally available meteorological datasets performed worse. In contrast, all globally available DEM setups reproduced snow depth, the snow hydrological parameters and runoff quite well. Differences occurred mainly due to differences in radiation model input due to different spatial realizations. Even though SRTM and ALOS have the same spatial resolution, they showed considerable differences due to their different product origins. Despite the fact that the very coarse GTOPO30 DEM performed relatively well on the catchment mean, we advise against using this product in such heterogeneous high-alpine terrain since small-scale topographic characteristics cannot be captured. While global meteorological data are not suitable for sound snow hydrological modelling in the RCZ, the choice of the DEM with resolutions in the decametre level is less critical. Nevertheless, global meteorological data can be a valuable source to substitute single missing variables. For the future, however, we expect an increasing role of global data in modelling ungauged high-alpine basins due to further product improvements, spatial refinements and further steps regarding assimilation with remote sensing data.

Enhancing the Resolution of Digital Elevation Models Using Surface Normal from Planetary Images

A digital elevation model (DEM) can be generated using a gradient of a single image and a stereo image pair from an imaging sensor or laser altimeter sensor on an orbiter. These DEMs are affected by intrinsic problems such as stereo noise in the triangulation process or low spatial resolution from sparse measurement points. Various image-based techniques are presently used to enhance the resolution of DEMs, but they can be very challenging since calculating each surface point direction vector from one or more partially overlapped images requires many assumptions. In this paper, an approach is proposed to enhance the resolution of a coarse DEM, using the information of a surface normal extracted from an ortho-rectified planetary image dataset. The surface normal extracted from the ortho-rectified planetary image dataset and coarse DEM are well-aligned, and the surface normal also offers direction vectors of uniform quality at every surface point.The keypoint of this improvement is the separation of the surface normal as a map, to overcome the ambiguity in the role of surface direction information. The coarse DEM is then optimized using the high-resolution surface normal information, so that it displays the characteristics of the terrain in the surface normal map. For performance analysis, the results obtained with the proposed method were compared with a DEM generated using the socet set next generation automatic terrain extraction (NGATE) program of BAE systems. Using the proposed method, the details of the terrain in the high-resolution surface normal image and the output DEM were well estimated.

A river channel terrain reconstruction method for flood simulations based on coarse DEMs

To mitigate the unavailability of accurate river channel terrain data for flood modeling, this work develops a new approach to construct channel terrain based on available global coarse digital elevation models (DEMs). Compared to other established methods, the approach constructs the river surface using the sparse cross-section data from available coarse DEMs and not measurements. For a 134 km river reach of the Yangtze River, the absolute percentage error of simulated peak discharge with the coarse DEMs is 76.77%, and the delay time of peak discharge is 13 h; however, with the reconstructed terrain data are 12.13% and 0 h. They are 9.70% and −1 h on a 382 km river reach. The simulation results show that the approach can be used to reliably predict the flood propagation process without fine terrain data. The proposed approach can be used to improve flood management in areas without fine river terrain data.

Effects of DEM resolutions on soil erosion prediction using Chinese Soil Loss Equation

Slope length and slope steepness are the two topographic factors (L and S) used in the Universal Soil Loss Equation (USLE) for soil erosion prediction. These factors are usually extracted from the digital elevation models (DEM). We hypothesized that different DEM resolutions and sources influence extracted topographic factors and therefore soil loss estimation. The objective of this study was to quantify the effect of different DEM resolutions on the topographic factor calculation and consequently soil loss prediction on different geomorphological types in China and United States. We selected three typical landforms of plains, hills and mountains in both countries. We used 1) open source DEMs of 3-, 10-, 30-, and 90-m in the United States, and 2) DEMs generated with 1:10,000 and 1:50,000 topographic maps in China to extract topographic factors using a LS software tool. The Chinese Soil Loss Equation (CSLE) was used to calculate average annual soil loss. The results showed that different DEM resolutions had different effects on the extracted L and S factors. Slope length and the L factor were overestimated as DEM resolutions coarsened, and the trend of the overestimation in different landforms was the same. Slope steepness and the S factor were underestimated as DEM resolution decreased in all landforms. However, the LS factor as a product of L and S factors, which reflects relative soil loss if the other factors are held constant, are less sensitive to DEM resolutions than either L or S factor alone due to partial error cancelation. Thus, the LS factor is not as greatly affected by DEM resolutions as either L or S factor alone. The relative soil loss errors are similar for all DEMs, and “good results for wrong reasons” are obtained with coarser DEMs. Overall results indicate that except for the 90 m DEM for certain occasion, all DEMs may be used to estimate soil loss for all types of terrains for practical application, as far as the relative soil loss errors are concerned. However, in the range of 3–90 m resolutions, for the sake of good practice and solid scientific support, higher resolution DEMs if available ought to be used to minimize estimation errors of L and S factors and consequently to increase confidence of soil loss prediction.

Effects of Digital Elevation Model Resolution on Watershed-Based Hydrologic Simulation

This paper investigated effects of Digital Elevation Model (DEM) resolution on flow simulation by applying the HSPF watershed model to three U.S. watersheds with different topographic conditions. Each watershed was delineated automatically and manually with four DEMs of the resolution ranging from 3.5 to 100 m. Results indicated that the simulated flow decreased with lowering DEM resolutions due to the reduction in the delineated drainage area particularly in low gradient watersheds. The DEM resolution impact was minimal when the manual method for watershed delineation was applied. The parameter uncertainty was found to be substantially greater than the resolution uncertainty in two out of three tested watersheds, indicating that the calibration of water balance parameters can alleviate the adverse effects of coarse DEM resolution for watersheds with high to moderate gradients. The findings are important to reducing the uncertainty, caused by DEM resolutions, in watershed modelling results, serving as guidelines for watershed modelling-based water resources management.

Minor topography governing erosional distribution of SOC and temperature sensitivity of CO2 emissions: comparisons between concave and convex toposequence

Erosion processes spatially redistribute soil particles and the associated carbon across landscapes. Their spatial redistribution pattern is governed by the transport distances of individual displaced soil particles, which is not only dependent on their settling velocity, but also affected by slope topography. However, the potential impacts of fine-scale variation of slope topography on the erosion-induced lateral and carbon fluxes are often over-generalized by coarse digital elevation models. In this study, two topo-sequences, convex and concave, over a long gentle slope in the northeast China were investigated. Surface soils were sampled at predetermined space intervals from upslope to downslope along the two toposequences, and then fractionated by the settling velocity of individual fractions into four classes: > 250, 63 – 250, 20 – 63 and < 20 μm. The soil organic carbon (SOC) and δ13 C of the unfractionated soils and all the settling classes were measured, and their CO2 emission rates were also determined at six temperature gradients: 5°C, 10°C, 15°C, 20°C, 25°C and 30°C. Our results show that: 1) The soil fractions along the upper lying convex segment showed a coarsening effect toward the knee point and then a fining trend at the slope toe, whilst the soil compositions along the lower lying concave segment stayed fairly comparable as the slope descended. 2) The net loss of surface soil along the eroding convex segment resulted in depleted SOC and more positive δ13 C signatures than that along the depositional concave segment. 3) The CO2 emission rates of almost all the settling fractions were enhanced compared with that of the unfractionated soil, and the settling class-specific CO2 emission rates and their temperature sensitivity (Q10 ) also differed along the two topo-sequences. This demonstrates that fine scale topographic variations had a strong control over the lateral and vertical carbon fluxes, which has been often disguised by coarse grid size in digital elevation models or average sediment delivery ratios. Topography-dependency must be properly accounted for when calculating slope-scale carbon balances.

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.

Baseline Estimation Using DEM Prior Knowledge and Capability Analysis for Gaofen-3 Repeat-Pass SAR Interferometry

In this paper, the study on the digital elevation model (DEM) reconstruction using Gaofen-3 satellite repeat-pass synthetic aperture radar (SAR) interferometry is presented. The coherence of the single-look complex (SLC) images and residuals of interferogram provide a powerful guarantee for interferometric processing for Gaofen-3. Baseline estimation is the key factor to generate accurate DEM with SAR interferometry. This paper uses the coarse orbit data and the precise orbit data of Gaofen-3 to carry on the baseline estimation and uses the prior coarse DEM information to refine the baseline estimation. Then, the topographic height is reconstructed with more precise spatial baseline. An experiment is conducted to verify the conclusion, and some mountain and flatten region are chosen as the study area. The results show that the DEM errors based on the prior DEM method are rapidly converged compared with the ones based on the orbit data method. Therefore, benefiting from the precise orbit data of the Gaofen-3 satellite, the accuracy of the baseline estimation can be improved further by using the method based on prior DEM information; thus, higher accuracy of DEM can be achieved so that Gaofen-3 repeat-pass data have some good characteristics, such as high imaging quality, stable spatial baseline, and high correlation, which has a good repeat-pass SAR interferometry application prospect.

Effects of DEM Resolution on Modeling Coastal Flood Vulnerability

This article examines whether Digital Elevation Model (DEM) resolution affects the accuracy of predicted coastal inundation extent using LISFLOOD-FP, with application to a sandy coastline in New Jersey. DEMs with resolution ranging from 10 to 100 m were created using coastal elevation data from NOAA, using the North American Vertical Datum of 1988. A two-dimensional hydrodynamic flood model was developed in LISFLOOD-FP using each DEM, all of which were calibrated and validated against an observed 24-h tidal cycle and used to simulate a 1.5 m storm surge. While differences in predicted inundated area from all models were within 1.0%, model performance and computational time worsened and decreased with coarser DEM resolution, respectively. This implied that using a structured grid model for modeling coastal flood vulnerability is based on two trade-offs: high DEM resolution coupled with computational intensity, but higher precision in model predictions, and vice versa. Furthermore, water depth predictions from all DEMs were consistent. Using an integrated numerical modeling and GIS approach, a two-scale modeling strategy, where a coarse DEM is used to predict water levels for projection onto a fine DEM was found to be an effective, and computationally efficient approach for obtaining reliable estimates of coastal inundation extent.

Hydrography‐Driven Coarsening of Grid Digital Elevation Models

AbstractA new grid coarsening strategy, denoted as hydrography‐driven coarsening, is developed in the present study. The hydrography‐driven coarsening is designed to retain the essential hydrographic features of valleys and channels observed in high‐resolution digital elevation models: (1) depressions are filled in the considered high‐resolution digital elevation model, (2) the obtained topographic data are used to extract a reference grid network composed of all surface flow paths, (3) the Horton order is assigned to each link of the reference grid network, and (4) within each coarse grid cell, the elevation of the point lying along the highest‐order path of the reference grid network and displaying the minimum distance to the cell center is assigned to this coarse grid cell center. The capabilities of the hydrography‐driven coarsening to provide consistent surface flow paths with respect to those observed in high‐resolution digital elevation models are evaluated over a synthetic valley and two real drainage basins located in the Italian Alps and in the Italian Apennines. The hydrography‐driven coarsening is found to yield significantly more accurate valley and channel profiles than existing coarsening strategies. In absolute terms, the obtained valleys and channels compare favorably with those observed. In addition, the proposed coarsening strategy is found to reduce drastically the impact of depression‐filling in the obtained coarse digital elevation models. The hydrography‐driven coarsening strategy is therefore advocated for all those cases in which the relief of the extracted drainage network is an important hydrographic feature.

Regional topographic classification in the North Shaanxi Loess Plateau based on catchment boundary profiles

Topographic classification and mapping are fundamental topics in geomorphology and have many promising applications. This study aimed to achieve landform classification using the catchment boundary profile (CBP) in the area of the North Shaanxi Province. CBPs were extracted, and seven indices were proposed and calculated to describe the profile features. Spatial maps of these indices were generated by interpolating 81 sample areas in the North Shaanxi Province of China. Finally, a topographic regional map was generated through multi-resolution segmentation after integrating the spatial maps of the seven indices. Results showed that the CBP indices were typical and representative to describe the terrain characteristics in the North Shaanxi Province and revealed their diverse spatial patterns. The investigated area was classified into 14 topographic units. The requirements of maximizing internal homogeneity while minimizing external homogeneity in terms of morphological characteristics were satisfied through the analysis of the terrain texture, topographic features, and land surface parameters. A comparison with the reference map showed that the regional classification differed from the geomorphologic map, given the different classification systems. However, the regional classification provided new knowledge of topographic features and spatial patterns in the macro scale. A comparison with other classification method indicated the advantage of the CBP-based method, which could distinguish the landforms of loess tableland, loess ridge, and loess hill that are the typical characteristics of loess landforms. The sensitivity analysis revealed that the differences in CBP indices, except for roughness and fractal dimension (FD) from different landforms, would increase with the digital elevation model (DEM) resolution becoming coarser, which indicates that the CBP-based method could be applied on a coarser resolution DEM if the differences of roughness and FD from different landforms could be identified at this coarse DEM level.

A Robust Imaging Algorithm for Squint Mode Multi-Channel High-Resolution and Wide-Swath SAR With Hybrid Baseline and Fluctuant Terrain

In this paper, the squint mode multi-channel (MC) synthetic aperture radar (SAR) with hybrid baseline and fluctuant terrain is proposed and studied for high-resolution and wide-swath (HRWS) imaging. During the imaging process, due to the cross-track baseline and fluctuant terrain, the azimuth signal reconstruction is the kernel problem for this imaging mode. To deal with this problem, in this paper a robust azimuth signal reconstruction approach is proposed, where terrain elevation of scene is considered. At first, the pre-processing of the linear range cell migration correction (RCMC) and topography-independent phase compensation is implemented in the azimuth time domain. After that, combining the azimuth echo signal characteristics, the local polynomial Fourier transform (LPFT) is utilized to obtain the coarse-focused SAR image. Then, based on joint pixel pair vector and robust Capon beamforming (RCB), a Doppler ambiguity suppression approach is proposed to reconstruct the Doppler ambiguity-free azimuth signal in LPFT frequency domain, during which the influence of the cross-track baseline component and fluctuant terrain is eliminated using the coarse digital elevation model (DEM) for the imaging scene. At last, the chirp scaling imaging algorithm is utilized to focus the SAR image. The effectiveness of the proposed imaging approach is demonstrated via simulated and real measured squint mode MC-HRWS SAR data.

Shading-based DEM refinement under a comprehensive imaging model

This paper introduces an approach to refine coarse digital elevation models (DEMs) based on the shape-from-shading (SfS) technique using a single image. Different from previous studies, this approach is designed for heterogeneous terrain and derived from a comprehensive (extended) imaging model accounting for the combined effect of atmosphere, reflectance, and shading. To solve this intrinsic ill-posed problem, the least squares method and a subsequent optimization procedure are applied in this approach to estimate the shading component, from which the terrain gradient is recovered with a modified optimization method. Integrating the resultant gradients then yields a refined DEM at the same resolution as the input image. The proposed SfS method is evaluated using 30m Landsat-8 OLI multispectral images and 30m SRTM DEMs. As demonstrated in this paper, the proposed approach is able to reproduce terrain structures with a higher fidelity; and at medium to large up-scale ratios, can achieve elevation accuracy 20–30% better than the conventional interpolation methods. Further, this property is shown to be stable and independent of topographic complexity. With the ever-increasing public availability of satellite images and DEMs, the developed technique is meaningful for global or local DEM product refinement.

The conservation value of elevation data accuracy and model sophistication in reserve design under sea-level rise.

Many studies have explored the value of using more sophisticated coastal impact models and higher resolution elevation data in sea-level rise (SLR) adaptation planning. However, we know little about to what extent the improved models and data could actually lead to better conservation outcomes under SLR. This is important to know because high-resolution data are likely to not be available in some data-poor coastal areas in the world and running more complicated coastal impact models is relatively time-consuming, expensive, and requires assistance by qualified experts and technicians. We address this research question in the context of identifying conservation priorities in response to SLR. Specifically, we investigated the conservation value of using more accurate light detection and ranging (Lidar)-based digital elevation data and process-based coastal land-cover change models (Sea Level Affecting Marshes Model, SLAMM) to identify conservation priorities versus simple "bathtub" models based on the relatively coarse National Elevation Dataset (NED) in a coastal region of northeast Florida. We compared conservation outcomes identified by reserve design software (Zonation) using three different model dataset combinations (Bathtub-NED, Bathtub-Lidar, and SLAMM-Lidar). The comparisons show that the conservation priorities are significantly different with different combinations of coastal impact models and elevation dataset inputs. The research suggests that it is valuable to invest in more accurate coastal impact models and elevation datasets in SLR adaptive conservation planning because this model-dataset combination could improve conservation outcomes under SLR. Less accurate coastal impact models, including ones created using coarser Digital Elevation Model (DEM) data can still be useful when better data and models are not available or feasible, but results need to be appropriately assessed and communicated. A future research priority is to investigate how conservation priorities may vary among different SLR scenarios when different combinations of model-data inputs are used.

Producing Image Map of Thebes Necropolis Cultural Heritage Site: The Mortuary Temple of King Ramses III at Habo Luxor, Egypt

In the recent past, archaeologists used in their study traditional methods. Now, remote sensors have become essential equipment in archaeology studies, either used on board satellites in space or on airborne. Producing modern maps for the archaeological sites is very essential for develop and protect it. The Orientation of ancient Egyptians constructions is still surprising among astronomers and scholars in the world to find out how ancient Egyptians oriented the axis of their constructions with high accuracy. Main research objective is using remote sensing data and techniques to assess producing image map and to determine the direction of the axis of Heritage site. The Mortuary Temple of King Ramses III at Habo Luxor, Egypt has been selected as study area. The study area covered with QuickBird 0.6m resolution slandered pansharped images. A coarse digital elevation model DEM has been used release the topographic relief. Also, ground control points GCPs and check points CPs have been used for geometric correction and image map accuracy assessment. The proposed methodology involves many steps included geometric correction of QuickBird satellite imagery using an appropriate mathematical model and asses the scale of the produced image map. After that, the direction of the main axis of the heritage site has been determined. A comparative study has been performed between traditional method based on direct GPS measurements and the suggested method. The obtained results of the study showed that the accuracy of the produced image map using second order polynomial function gives TRMS 4.288m which satisfies theoretical large scale mapping of 1: 10 000. The difference between axis direction of the temple from the produced image and from GPS direct measurements was ± 0 deg 59 min 7 sec.

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.