Digital Elevation Model Derivatives Research Articles

Estimating topsoil texture fractions by digital soil mapping - a response to the long outdated soil map in the Philippines

Digital soil mapping for soil texture is mostly an understanding of how soil texture fractions vary in space as influenced by environmental variables mainly derived from the digital elevation model (DEM). In this study, topsoil texture models were generated and evaluated by multiple linear regression (MLR), ordinary kriging (OK), simple kriging (SK) and universal kriging (UK) using free and open-source R, System for Automated Geoscientific Analyses, and QGIS software. Comparing these models is the main objective of the study. The study site covers an area of 124 km2 of the Municipality of Barili, Cebu. A total of 177 soil samples were gathered and analyzed from irregular sample points. DEM derivatives and remote sensing data (Landsat 8) were used as environmental variables. Exploratory analyses revealed no outlier in the data. Skewness and kurtosis values of the untransformed data vary greatly between –3.85 to 7.20 and 1.8 to 70.7, respectively; an indication that variables are highly skewed with heavy tails. Thus, Tukey’s ladder of powers transformation was applied that resulted to normal or nearly normal distribution having skewness values close to zero and kurtosis values have lighter tails. All data analysis from MLR modeling, variography, kriging, and cross-validations of models were implemented using the transformed data. Forward selection, backward elimination, and stepwise selection methods were adapted for predictors selection in MLR. The MLR, OK, SK, and UK were applied and cross validated for topsoil texture prediction. Likewise, exponential, Gaussian, and spherical models were fitted for the experimental variograms. Backward elimination method for clay, sand, and silt have the lowest MAE and highest R2 in MLR. The UK fitted with exponential variogram model has the highest R2 of 0.878, 0.821, and 0.893 for clay, sand, and silt, respectively. These models can be adapted as a decision support for agricultural land use planning and crop suitability development in the area.

A Synergetic Analysis of Sentinel-1 and -2 for Mapping Historical Landslides Using Object-Oriented Random Forest in the Hyrcanian Forests

Despite increasing efforts in the mapping of landslides using Sentinel-1 and -2, research on their combination for discerning historical landslides in forest areas is still lacking, particularly using object-oriented machine learning approaches. This study was accomplished to test the efficiency of Sentinel-derived features and digital elevation model (DEM) derivatives for mapping old and new landslides, using object-oriented random forest. Two forest subsets were selected including a protected and non-protected forest in northeast Iran. Landslide samples were obtained from CORONA images and aerial photos (old landslides), and also field mensuration and high-resolution images (new landslides). Segment objects were generated from a set combination of Sentinel-1A, Sentinel-2A, and some topographic-derived indices using multiresolution segmentation algorithm. Various object features were derived from the main channels of Sentinel images and DEM derivatives in the seven main groups, including spectral layers, spectral indices, geometric, contextual, textural, topographic, and hydrologic features. A single database was created, including landslide samples and Sentinel- and DEM-derived object features. Roughly 20% of landslide-affected objects and non-landslide-affected objects were randomly selected as an input for training the random forest classifier. Two-thirds of the selected objects were assigned as learning samples for classification, and the remainder were used for testing the accuracy of landslide and non-landslide classification. Results indicated that: (1) The sensitivity of mapping historical landslides was 86.6% and 80.3% in the protected and non-protected forests, respectively; (2) the object features of Sentinel-2A and DEM obtained the highest importance with the total scores of 55.6% and 32%, respectively in the protected forests, and 65.4% and 21% respectively in the non-protected forests; (3) the features derived from the combination of Sentinel-1 and -2A demonstrated a total importance of 10% for mapping new landslides; and (4) textural features were obtained in approximately two-thirds of the total scores for mapping new landslides, however a combination of topographic, spectral, textural, and contextual features were the effective predictors for mapping old landslides. This research proposes applying a synergetic analysis of Sentinel- and DEM-derived features for mapping historical landslides; however, there are no uniformly pre-defined influential variables for mapping historical landslides in different forest areas.

Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques

The quality of digital elevation models (DEMs), as well as their spatial resolution, are important issues in geomorphic studies. However, their influence on landslide susceptibility mapping (LSM) remains poorly constrained. This work determined the scale dependency of DEM-derived geomorphometric factors in LSM using a 5 m LiDAR DEM, LiDAR resampled 30 m DEM, and a 30 m ASTER DEM. To verify the validity of our approach, we first compiled an inventory map comprising of 267 landslides for Sihjhong watershed, Taiwan, from 2004 to 2014. Twelve landslide causative factors were then generated from the DEMs and ancillary data. Afterward, popular statistical and machine learning techniques, namely, logistic regression (LR), random forest (RF), and support vector machine (SVM) were implemented to produce the LSM. The accuracies of models were evaluated by overall accuracy, kappa index and the receiver operating characteristic curve indicators. The highest accuracy was attained from the resampled 30 m LiDAR DEM derivatives, indicating a fine-resolution topographic data does not necessarily achieve the best performance. Additionally, RF attained superior performance between the three presented models. Our findings could contribute to opt for an appropriate DEM resolution for mapping landslide hazard in vulnerable areas.

Analysis of Relief Shading Tools and Methods for Terrain Representation

Abstract. One of the key roles in map creation belongs to terrain representation. Relief shading is a traditional manual technique allowing users to perceive the terrain in an intuitive and naturalistic way. With the advent of digital elevation models (DEM), analytical relief shading came into a wider use, since it is faster, requires less effort and delivers reproducible results. In contrast to manual relief shading, it often lacks clarity when representing heterogeneous landscapes with diverse landforms though. The goal of this ongoing project is to identify which relief shading techniques are suitable for depicting specific landforms. Techniques developed over time to surmount this limitation mainly include (but not limited to) terrain generalisation, light direction adjustments, contrast enhancement and terrain segmentation. Each of numerous methods is usually applied and performs better in a particular landscape and does not consider possible landscape diversity within an area hillshading is generated for. Thus, it is necessary to detect the right combination of tools and parameters providing the best results regarding the visual quality in each landscape type and to find a correlation between those methods and landforms they are applied to. In the frame of this project, existing methods to automatically perform relief shading are initially tested on sample areas. Having miscellaneous landforms within sample areas and rendering them in the best possible way leads us to understanding which features analytical hillshading has to inherit from the manual one. It is planned to generate shaded reliefs using different techniques and to show them to cartographers and laymen worldwide via an online survey to define which combination of tools and methods consistently provides better results in terms of visual quality in each landscape type. A large number of participants should minimise the subjectivity peculiar to relief shading depiction. As the project involves both laymen and specialists, the survey is complemented with an extra section of questions for specialists with a higher weight in the final feedback evaluation based on their deeper knowledge of the subject. Analysis of participants’ feedback is meant for creation of a series of rules to be applied to specific landform or landscape type. For landscape features the range of qualitative and quantitative parameters is used (e.g. DEM derivatives). Besides, the resulting shaded reliefs are also compared with the Swiss Federal Office of Topography manual shaded reliefs at different scales. As a result, a direct correlation between the values of landscape features and visual quality of relief shading is provided. Finally, it can be seen that a particular combination of tools and methods is ideal for a landscape type based on the level of its geomorphological complexity. This should give users a possibility to improve the automatically produced relief shading as a whole, to adjust its parts if different landforms are present, or to bring more focus to those landscape features that were not revealed by automatic hillshading.

Geomorphological mapping based on DEMs and GIS: A review

Abstract. Geomorphology is a scientific discipline dealing with the characteristics, origin, and evolution of landforms. It utilizes topographic data such as spot height information, contour lines on topographic maps, and DEMs (Digital Elevation Models). Topographic data were traditionally obtained by ground surveying, but introduction of aerial photogrammetry in the early 20th century enabled more efficient data acquisition based on remote sensing. In recent years, active remote sensing methods including airborne and terrestrial laser scanning and applications of satellite radar have also been employed, and aerial photogrammetry has become easier and popular thanks to drones and a new photogrammetric method, SfM (Structure from Motion). The resultant topographic data especially raster DEMs are combined with GIS (Geographic Information Systems) to obtain derivatives such as slope and aspect as well as to conduct efficient geomorphological mapping. Resultant maps can depict various topographic characteristics based on surface height and DEM derivatives, and applications of advanced algorithms and some heuristic reasoning permit semi-automated landform classification. This quantitative approach differs from traditional and more qualitative methods to produce landform classification maps using visual interpretation of analogue aerial photographs and topographic maps as well as field observations.For scientific purposes, landforms need to be classified based on not only shape characteristics but also formation processes and ages. Among them, DEMs only represent shape characteristics, and understanding formation processes and ages usually require other data such as properties of surficial deposits observed in the field. However, numerous geomorphological studies indicate relationships between shapes and forming-processes of landforms, and even ages of landforms affect shapes such as a wider distribution of dissected elements within older landforms. Recent introduction of artificial intelligence in geomorphology including machine learning and deep learning may permit us to better understand the relationships of shapes with processes and ages. Establishing such relationships, however, is still highly challenging, and at this moment most geomorphologists think landform classification maps based on the traditional methods are more usable than those from the DEM-based methods. Nevertheless, researchers of some other fields such as civil engineering more appreciate the DEM-based methods because they can be conducted without deep geomorphological knowledge. Therefore, the methods should be developed for interdisciplinary understanding. This paper reviews and discusses such complex situations of geomorphological mapping today in relation to historical development of methodology.

Application of soil properties, auxiliary parameters, and their combination for prediction of soil classes using decision tree model

Soil classification systems are very useful for a simple and fast summarization of soil properties. These systems indicate the method for data summarization and facilitate connections among researchers, engineers, and other users. One of the practical systems for soil classification is Soil Taxonomy (ST). As determining soil classes for an entire area is expensive, time-consuming, and almost impossible, this research has tried to predict the soil classes in each level of the ST system (up to family level) by using the data of 120 excavated pedons and some auxiliary parameters (such as derivatives of digital elevation model, i.e., DEM) in Shahrekord plain, central Iran. For this reason, the decision tree model was encoded and implemented in the MATLAB software for three conditions: use of soil properties, auxiliary parameters, and its combination. According to the results, soil class prediction error by using soil properties, auxiliary parameters, and its combination was estimated to be 0, 3.33 and 0% for order and suborder levels; 0.83, 15 and 0.83% for great group level; 3.33, 22.5 and 3.33% for subgroup level and 30, 52.5 and 30% for family level, respectively. In addition, the use of kriging maps of soil properties (instead of 120 observational points) decreased the prediction error of the modeling in all levels of the ST system. It seems that the effect of auxiliary parameters (in comparison to soil properties) is not very significant for predicting soil classes in low-relief areas.

Through-Water Dense Image Matching for Shallow Water Bathymetry

The introduction of Dense Image Matching (DIM) has reactivated the interest in photogrammetric surface mapping, as it allows the derivation of Digital Elevation Models with a spatial resolution in the range of the ground sampling distance of the aerial images. While the primary field of application is wide-area mapping of topography and urban scenes, charting bathymetry of clear and shallow water areas is equally feasible via application of multimedia photogrammetry. The article specifically investigates the potential of through-water DIM for high resolution mapping of generally low textured shallow water areas using modern techniques like semiglobal matching and off-the-shelf software. In a case study, the DIM-derived underwater surfaces of coastal and inland water bodies are compared to concurrently acquired laser bathymetry data. With an achieved penetration depth of more than 5 m and deviations in the dm-range compared to the laser data as reference, the results confirm the general feasibility of through-water DIM. However, sufficient bottom texture and favorable environmental conditions are a precondition for achieving accurate results.

A CASE STUDY ON THROUGH-WATER DENSE IMAGE MATCHING

Abstract. In the last years, the tremendous progress in image processing and camera technology has reactivated the interest in photogrammetrybased surface mapping. With the advent of Dense Image Matching (DIM), the derivation of height values on a per-pixel basis became feasible, allowing the derivation of Digital Elevation Models (DEM) with a spatial resolution in the range of the ground sampling distance of the aerial images, which is often below 10 cm today. While mapping topography and vegetation constitutes the primary field of application for image based surface reconstruction, multi-spectral images also allow to see through the water surface to the bottom underneath provided sufficient water clarity. In this contribution, the feasibility of through-water dense image matching for mapping shallow water bathymetry using off-the-shelf software is evaluated. In a case study, the SURE software is applied to three different coastal and inland water bodies. After refraction correction, the DIM point clouds and the DEMs derived thereof are compared to concurrently acquired laser bathymetry data. The results confirm the general suitability of through-water dense image matching, but sufficient bottom texture and favorable environmental conditions (clear water, calm water surface) are a preconditions for achieving accurate results. Water depths of up to 5 m could be mapped with a mean deviation between laser and trough-water DIM in the dm-range. Image based water depth estimates, however, become unreliable in case of turbid or wavy water and poor bottom texture.

Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using Random Forest classification

Wetlands are important ecosystems that provide many ecological benefits, and their quality and presence are protected by federal regulations. These regulations require wetland delineations, which can be costly and time-consuming to perform. Computer models can assist in this process, but lack the accuracy necessary for environmental planning-scale wetland identification. In this study, the potential for improvement of wetland identification models through modification of digital elevation model (DEM) derivatives, derived from high-resolution and increasingly available light detection and ranging (LiDAR) data, at a scale necessary for small-scale wetland delineations is evaluated. A novel approach of flow convergence modelling is presented where Topographic Wetness Index (TWI), curvature, and Cartographic Depth-to-Water index (DTW), are modified to better distinguish wetland from upland areas, combined with ancillary soil data, and used in a Random Forest classification. This approach is applied to four study sites in Virginia, implemented as an ArcGIS model. The model resulted in significant improvement in average wetland accuracy compared to the commonly used National Wetland Inventory (84.9% vs. 32.1%), at the expense of a moderately lower average non-wetland accuracy (85.6% vs. 98.0%) and average overall accuracy (85.6% vs. 92.0%). From this, we concluded that modifying TWI, curvature, and DTW provides more robust wetland and non-wetland signatures to the models by improving accuracy rates compared to classifications using the original indices. The resulting ArcGIS model is a general tool able to modify these local LiDAR DEM derivatives based on site characteristics to identify wetlands at a high resolution.

Performance evaluation and sensitivity analysis of ASTER and SRTM (30m) DEM derived terrain variables in landslide susceptibility assessment: a case from the Western Ghats

Performance and sensitivity of freely available equal resolution space borne digital elevation model derivatives in landslide susceptibility analysis were carried out in a selected part of the Western Ghats, India. ASTER and SRTM digital elevation models having 30m resolution were used to derive the terrain variables such as slope, aspect, relative relief, slope length and steepness, curvature, landform and stream networks. Most of the variables shown spatial variability in distribution pattern, which affects the results of geo-environmental processes analyzed. Sensitivity and performance of each variable derived from the digital elevation models were assessed by preparing a landslide susceptibility index (LSI) maps using the Information Value (InfoVal) technique and was validated through receiver operator characteristics (ROC) curve analysis. LSI maps generated point towards the capability of the SRTM digital elevation model, to correctly generate the terrain variables than the ASTER elevation surface, by giving the accuracy of LSI maps greater than those produced using the ASTER derived parameters (0.77 and 0.72 for SRTM; 0.67 and 0.65 for ASTER). The results of the present study suggest, the SRTM digital elevation data is more sensitive and suitable for terrain analysis and earth surface process modelling than the ASTER elevation data sets, though both possess equal resolutions .DOI: http://dx.doi.org/10.5755/j01.erem.73.2.16385

Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis (GEOBIA) partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests (RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values, the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of raster-geodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.

Combining reflectance spectroscopy and the digital elevation model for soil oxidizable carbon estimation

As soil oxidizable carbon (Cox) has several different absorptions in the visible– and near infrared (vis–NIR) region due to its complex composition, multivariate calibration techniques such as multiple linear regression (MLR), partial least squares regression (PLSR), support vector machines (SVM) or random forest (RF) can be advantageously used to obtain a good prediction. Besides that, the content of Cox is often affected by the character of the terrain, mainly due to prevailing water regime with associated transport and sedimentation processes. Therefore, the question arises; if predictive models calibrated by combining vis–NIR diffuse reflectance spectroscopy (vis–NIR DRS, 350–2500nm) and the digital elevation model (DEM) derivatives will provide a more accurate estimate of Cox. Focused on a sloping arable land (100ha) affected by distinct water erosion, we tested for this purpose two conceptually different predictive approaches that differ in the nature of the spectroscopic predictor variables. In Approach A that relied on absorption feature (AF) parameters, the inclusion of DEM derivatives resulted in improved Cox prediction using all the tested calibration techniques, i.e. MLR, RF, PLSR and SVM. The MLR prediction that was the most accurate among all others improved from R2cv=0.81 (vis–NIR DRS dataset) or 0.50 (DEM derivatives dataset) to 0.84 (combination of both). For that prediction especially AF centered at 500, 700, 900, 1800, 1900, 2200 and 2400nm, as well as elevation, LS factor and plan curvature were important. In contrast, in the Approach B that relied on reflectance (RF and PLSR) or normalized reflectance (SVM) values at each wavelength, no positive effect of inclusion of DEM derivatives was observed.

Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus

Whereas contemporary land use and land management planning require specific and quantitative georeferenced soil information, often only general-purpose and qualitative soil maps are available. With a view to fill this gap for topsoil clay and organic carbon content in the central plateaus of Burundi, we tested for a representative 15km2 hilly landscape, six types of SCORPAN models. The SCORPAN models were first applied as standalone trend models and next extended with a component accounting for the spatial autocorrelation of the residuals from the trend. Various sets of predictors, including class variables derived from the available soil map and continuous derivatives from a Digital Elevation Model (DEM) and from Landsat-imagery were incorporated. For clay, the best prediction method was a Residual Kriging (RK) using a Generalized Additive Model (GAM) as trend built with only DEM derivatives and spectral normalized difference vegetation index (NDVI). Furthermore, the classical and simplest RK, i.e. using a Least Squares Linear Regression (LR) trend built with only continuous covariates, outperformed all standalone trend models. For organic carbon, residuals from the trend models were not significantly auto-correlated, making RK meaningless. In this case the best model was a GAM combining lithologic units with DEM derivatives and NDVI. Overall, the contribution of soil map–derived predictors to the model performance was rather weak. It was concluded that, for prediction of specific soil characteristics in the study area, a SCORPAN approach is preferred the more as the performance can be boosted by kriging of trend residuals if auto-correlated.

Machine learning performance for predicting soil salinity using different combinations of geomorphometric covariates

Conventional methods of monitoring salt accumulation in irrigation schemes require regular field visits to collect soil samples for laboratory analysis. Identifying areas prone to salt accumulation by means of geomorphometry (i.e. terrain analyses using digital elevation models (DEMs)) can potentially save time and costs. This study evaluated the extent to which DEM derivatives and machine learning (ML) algorithms (k-nearest neighbour, support vector machine, decision tree (DT) and random forest) can be used for predicting the location and extent of salt-affected areas within the Vaalharts and Breede River irrigation schemes of South Africa. In accordance with local management policies, salt-affected areas were defined as regions with soil electrical conductivity (EC) values >4dS/m. Two DEMs, namely the one-arch second Shuttle Radar Topography Mission (SRTM) DEM and a photogrammetrically-extracted digital surface model (DSM), were used for deriving the derivatives. Wetness indices as well as hydrological and morphometric terrain analysis techniques were used to generate predictive variables. For comparative purposes, the predictive variables were also used as input to regression modelling and kriging with external drift (KED). Thresholds were applied to the regression models and KED results to obtain a binary classification. EC values based on in situ soil samples were used for model development, classifier training and accuracy assessment.The results show that KED achieved the highest overall accuracy (OA) in Vaalharts (79.6%), whereas KED and ML (DT) showed the most promise in the Breede River (75%). The findings suggest that the use of elevation data and its derivatives as input to geostatistics and ML holds much potential for monitoring salt accumulation in irrigated areas, particularly for simulating sub-surface conditions. More work is needed to investigate the potential of using ML and DEM-derivatives, along with other geospatial datasets such as satellite imagery (that have been shown to be effective for monitoring surface conditions), for the operational modelling of salt accumulation in large irrigation schemes.

Assessment of multi-scenario rockfall hazard based on mechanical parameters using high-resolution airborne laser scanning data and GIS in a tropical area

Rockfall hazard is a main threat for mountainous and hilly areas that can cause loss of life, damage to infrastructures, and traffic interruption. Rockfall frequency and magnitude vary both spatially and temporally; therefore, multi-scenarios related to rockfall characteristics (trajectories, frequency and kinetic energy) can provide early warnings by identifying the areas at risk for mitigation purposes. The aim of this study is to predict the areas at risk from future rockfall incidents and suggest suitable mitigation measures to prevent them. The most significant elements in rockfall analysis are slope topography interpretation or the digital elevation model (DEM) and the rockfall modeling approach. Light detection and ranging (LiDAR) techniques have been widely used in rockfall studies because of their capability to provide high-resolution information regarding slope surfaces. In the current study, airborne laser scanning (ALS) is used to obtain a high-density point cloud (4 pts./m2) of the study area for the construction of an accurate DEM via a geographic information system. Rockfall source areas were identified based on multi-criteria method including DEM derivatives (e.g., slope, aspect, curvature and topographic contrast) in addition to terrain type and aerial photos. A 3D rockfall model has been established to determine rockfall multi-scenarios based on their characteristics according to a range of restitution coefficient (normal and tangential) and friction angle values; these parameters are particularly crucial in rockfall simulation to delineate the spatial prediction of rockfall hazard areas along the Jelapang corridor of the North–South Expressway in Malaysia. In addition, a barrier location was suggested based on limited rockfall height and kinetic energy to mitigate rockfall hazards. Results show that rockfall trajectories (stopping distance) and, subsequently, their frequency and energy are increased; moreover, barrier efficiency is reduced when the values of the mechanical parameters (Rn, Rt, and friction angle) are increased. Nonetheless, the suggested barrier location is an efficient and mitigative measure to eliminate the rockfall effect.

Field‐Scale Mapping of Soil Carbon Stock with Limited Sampling by Coupling Gamma‐Ray and Vis‐NIR Spectroscopy

Core Ideas Proximal sensors have been used for high‐detail mapping of soil C stock at field scale. Laboratory Vis‐NIR spectroscopy allowed an increase in the number of data points. Topsoil spatial variability maps were obtained using field gamma‐ray spectroscopy. Only conventional laboratory analysis was used to calibrate the model (one sample/ha). High‐precision mapping of important soil services, such as soil organic C stocks, is basic for monitoring the effects of different soil management regimes and the effectiveness of agricultural policies. Proximal soil sensing methods have been often used in the last decades to limit costs, field work, and time and to obtain reliable and accurate maps. We tested the combined use of two proximal sensors, visible–near‐infrared (Vis‐NIR) and passive γ‐ray spectrometers, to obtain highly detailed maps of C stocks of the topsoil (CS30, 0–30 cm) of nine pairs of fields in western Sicily using a limited number of sampling sites per field for traditional laboratory analysis (about one sample per hectare). Laboratory Vis‐NIR diffuse reflectance spectroscopy allowed the number of data points per field to be increased, at the same time reducing the costs for laboratory analysis. The predictive model had a coefficient of determination (R2) of 0.77 and an error (RMSE) of 0.67 kg m−2. Data points predicted by Vis‐NIR on the fine earth (<2 mm) and corrected for gravel content (CS30pred) were interpolated within each field using geographically weighted multiple regression and two sets of covariates: (i) digital elevation model derivatives, such as elevation, slope, plan and profile curvature, and topographic wetness index; and (ii) elevation and γ‐ray total counts maps. Validation of 36 independent data points showed that the second method provided greater accuracy than the first. In particular, residual prediction deviation (RPD) showed a mean value of 2.19; however, three pairs of fields showed high error and low RPD. This methodology provides a cost‐effective tool to interpolate C stocks within arable fields, limiting laboratory analysis. The accuracy of the CS30pred maps allows monitoring of the effects of agricultural management and/or soil erosion on the soil C pool.

Colluvial soils as a soil organic carbon pool in different soil regions

Subsoil has been recognized as large reservoir of soil organic carbon in recent years. In our study, we investigated deep colluvial soils as a potentially important source of SOC due to high mass redistribution driven by soil erosion. Three agriculture plots from the Chernozem, Luvisol, and Cambisol regions were studied to assess the SOC storage in topsoil (0–25cm), at 2m depth (0–200cm), and over the total soil depth (0–450cm) as a function of relief. The study is based on 558 borings, and soil profile description and classification to facilitate the colluvial soil delineation. Among these locations, SOC content was measured at 230 sampling points. Prediction of the SOC stock for the plots was based on support vector machine algorithms using digital elevation model derivatives as predictors. Total SOC stock varied among the study plots. The highest relative SOC stock was measured in the Chernozem (CH) plot (144.7t·ha−1), while at the Luvisol (LU) plot, it reached 68.4t·ha−1 and was 73.4t·ha−1 at the Cambisol (CM) plot. The role of colluvial soils regarding their spatial extent and SOC stock differs among the studied plots. Colluvial soils at the CH plot represent an important soil cover both spatially (13%) and by the volume of SOC stock (37%). A moderate importance of colluvial soils is determined for the LU plot (12% of SOC stock), and a low importance for the CM plot (5% of SOC stock). SOC stock contained in topsoil and subsoil differs in each plot. In the Luvisol and Cambisol plots, more than one half of SOC is retained in topsoil (53.4%, 60.3%). In contrast, more than two thirds of the SOC stock (73.1%) occurs in subsoil in the Chernozem plot. Moreover, 19.0% of the total SOC stock occurs at depths below 2m. This finding indicates the importance of the incorporation of deep colluvial soil horizons in SOC stock estimations.

Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran

The main objective of this research was to establish a semiautomated object-based image analysis (OBIA) methodology for locating landslides. We have detected and delineated landslides within a study area in north-western Iran using normalized difference vegetation index (NDVI), brightness, and textural features derived from satellite imagery (IRS-ID and SPOT-5) in combination with slope and flow direction derivatives from a digital elevation model (DEM) and topographically oriented gray-level cooccurrence matrices (GLCMs). We utilized particular combinations of these information layers to generate objects by applying multiresolution segmentation in a sequence of feature selection and object classification steps. The results were validated by using a landslide inventory database including 109 landslide events. In this study, a combination of these parameters led to a high accuracy of landslide delineation yielding an overall accuracy of 93.07%. Our results confirm the potential of OBIA for accurate delineation of landslides from satellite imagery and, in particular, the ability of OBIA to incorporate heterogeneous parameters such as DEM derivatives and surface texture measures directly in a classification process. The study contributes to the establishment of geographic object-based image analysis (GEOBIA) as a paradigm in remote sensing and geographic information science.

Suitability of spaceborne digital elevation models of different scales in topographic analysis: an example from Kerala, India

Digital elevation model (DEM), deriving conventionally from contour data of topographic maps, provides sufficient information regarding the continuously varying topographic surface of the Earth. Though spaceborne DEMs are increasingly being used in earth-environmental-applications, suitability of various freely available spaceborne DEMs [e.g., advanced spaceborne thermal emission and reflection (ASTER), shuttle radar topography mapping mission (SRTM), global multi-resolution terrain elevation data (GMTED)] for topographic and geomorphometric analyzes in tropical regions is yet to be ascertained. In this paper, comparability of these spaceborne DEMs among themselves and also with the DEM (TOPO) prepared from digital contour data of topographic maps is assessed. Results show that various primary and secondary derivatives of ASTER and SRTM DEMs provide relatively better precision and substantial agreement with the corresponding parameters derived from TOPO. Among the spaceborne DEMs, SRTM has relatively higher vertical accuracy (root mean square error = 17.05 m), compared to ASTER (24.09 m) and GMTED (32.85 m). The vertical accuracy of all the spaceborne DEMs strongly depends on the relief and ruggedness of the terrain as well as the type of vegetation. It is proposed that in the absence of other available and acceptable elevation datasets, SRTM and ASTER are equally competent for geomorphometric analysis in tropical regions, while GMTED shows significant loss of terrain information due to coarser spatial resolution.

Flow network derivation from a high resolution DEM in a low relief, agrarian landscape

ABSTRACTDigital flow networks derived from digital elevation models (DEMs) sensitively react to errors due to measurement, data processing and data representation. Since high‐resolution DEMs are increasingly used in geomorphological and hydrological research, automated and semi‐automated procedures to reduce the impact of such errors on flow networks are required. One such technique is stream‐carving, a hydrological conditioning technique to ensure drainage connectivity in DEMs towards the DEM edges. Here we test and modify a state‐of‐the‐art carving algorithm for flow network derivation in a low‐relief, agricultural landscape characterized by a large number of spurious, topographic depressions. Our results show that the investigated algorithm reconstructs a benchmark network insufficiently in terms of carving energy, distance and a topological network measure. The modification to the algorithm that performed best, combines the least‐cost auxiliary topography (LCAT) carving with a constrained breaching algorithm that explicitly takes automatically identified channel locations into account. We applied our methods to a low relief landscape, but the results can be transferred to flow network derivation of DEMs in moderate to mountainous relief in situations where the valley bottom is broad and flat and precise derivations of the flow networks are needed. Copyright © 2013 John Wiley & Sons, Ltd.