Terrain Derivatives Research Articles

ESTIMATION OF SOIL ORGANIC CARBON DISTRIBUTION BY GEOSTATISTICAL AND DETERMINISTIC INTERPOLATION METHODS: A CASE STUDY OF THE SOUTHEASTERN SOILS OF NIGERIA

Soil organic carbon (SOC) plays a significant role in ecosystem protection and sustainable agriculture. The present study aims to estimate the spatial distribution of SOC using three different interpolation methods: ordinary kriging (OK), cokriging (COK), and inverse distance weighting (IDW). Sixty (n = 60) soil samples were collected from the depth of 0 30 cm and analyzed for SOC. The digital elevation model of the site was obtained from USGS explorer at 30 m spatial resolution and processed. Ten (10) terrain attributes were obtained, and a correlation matrix was conducted between SOC and terrain derivatives. The whole dataset was used to evaluate the model accuracy; root mean square error (RMSE) and mean error (ME) were the criteria adopted. Mean value of the SOC of the study area was generally low when compared to the standard rating for tropical soils (< 2%). SOC was significantly (p < 0.01) correlated with LS-factor (r = 0.34*), negatively correlated with elevation (r = 0.30*) and profile curvature (r = 0.30*). IDW performed better (RMSE = 0.75, ME = 0.004) followed by OK (RMSE= 0.78, ME = 0.004) and then COK (RMSE = 0.94, ME = 0.067). Conversely, COK produced the model with the smallest ME with terrain attributes (elevation, LS-factor, and profile curvature). The findings in the study showed that IDW is superior in SOC estimation. COK with the terrain attributes proved to have the capacity as a useful ancillary variable for improving the spatial structure of SOC maps of southeastern Nigeria.

A Vector Operation to Extract Second-Order Terrain Derivatives from Digital Elevation Models

Terrain derivatives exhibit surface morphology in various aspects. However, existing spatial change calculation methods for terrain derivatives are based on a mathematical scalar operating system, which may disregard the directional property of the original data to a certain extent. This situation is particularly true in second-order terrain derivatives, in which original data can be terrain derivatives with clear directional properties, such as slope or aspect. Thus, this study proposes a mathematical vector operation method for the calculation of second-order terrain derivatives. Given the examples of the first-order terrain derivatives of slope and aspect, their second-order terrain derivatives are calculated using the proposed vector method. Directional properties are considered and vectorized using the following steps: rotation-type judgment, standardization of initial direction, and vector representation. The proposed vector method is applied to one mathematical Gaussian surface and three different ground landform areas using digital elevation models (DEMs) with 5 and 1 m resolutions. Comparison analysis results between the vector and scalar methods show that the former achieves more reasonable and accurate second-order terrain derivatives than the latter. Moreover, the vector method avoids overexpression or even exaggeration errors. This vector operation concept and its expanded methods can be applied in calculating other terrain derivatives in geomorphometry.

Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data

Prediction and accurate digital soil mapping (DSM) of soil organic carbon (SOC) at a local scale is a key factor for any agro-ecological modelling. This study aims to use remote sensing and terrain derivatives to provide a reliable method for SOC prediction. An advanced geostatistical-based empirical Bayesian Kriging regression (EBKR) method was used and performance was compared with the artificial neural network (ANN) and hybrid ANN, i.e. ANN-OK (ordinary kriging) and ANN-CK (cokriging). The result showed that the hybrid ANN model performs better than ANN, whereas the EBKR method outperforms all other methods with the highest R 2 of 0.936. The DSM map shows that the highest SOC concentration was found in easternmost part of the study area with grass and agricultural land. This work shows the robustness of the EBKR prediction method over other techniques. The study will also aid the policymakers in adopting sustainable land use management.

A characteristic periglacial landform: Automated recognition and delineation of cryoplanation terraces in eastern Beringia

AbstractAutomated recognition and delineation of specific landforms and their constituent elements ranks among the most active areas of contemporary geomorphological research. This study contributes to that literature by applying semi‐ and fully automated recognition procedures to upland periglacial geomorphic landscapes. The Cryoplanation Terrace semi‐Automated Recognition (CTAR) algorithm utilizes basic terrain parameters to identify locations of cryoplanation terraces (CTs) from the high‐resolution ArcticDEM. Using a multistep process, candidate areas are identified based on morphometric characteristics. CTAR uses terrain derivatives to search ridges, hills, and mountains for flat areas bounded by abrupt breaks in slope. Because CTs are found exclusively in upland periglacial environments, some locations require that low‐lying areas be filtered out. To assess accuracy, CTAR was tested at five local study sites distributed across eastern Beringia, each containing multiple CTs delimited manually in a previous study. CTAR performed well, with an overall accuracy of 90%. A strong linear relationship exists between the size of CTAR‐delimited terraces and those identified in a previous study through air‐photo interpretation. In addition to identifying nearly all of the CTs in the five study areas, a fully automated version of the algorithm (GEE‐CTAR), implemented in Google Earth Engine, identified nearly 8,000 previously unmapped potential CTs in the Seward Peninsula region of western Alaska. The ability to identify CTs from digital elevation models provides a useful tool for recognizing and delineating upland periglacial topography. Objective recognition of large erosional landform elements created by periglacial processes is a critical step in developing the field of periglacial geomorphometry.

Digital mapping of soil parent material in a heterogeneous tropical area

Parent material is one of the five factors in soil formation. Studies on parent material allow interpreting soil genesis processes and improve our knowledge of specific soil attributes. However, soil parent material maps at detailed cartographic scale (finer than 1:100,000) are rare in tropical areas and it is usually inferred from poorly detailed geological data, which generally group different lithologies into single units. Thus, we propose a methodology to map soil parent material based on remote sensing and machine learning in a geologically very complex area. The study site covers 1378 km2 in São Paulo State, Brazil. Prediction models used data from 280 geological observation points, a digital elevation model (spatial resolution of 5 m, upscale to 30 m) and multitemporal Landsat images in a range of 30 years. We evaluated six classification algorithms, namely random forest, decision tree, support vector machine, multinomial logistic regression, K-means (unsupervised classification), and object-based image analysis with maximum likelihood classification. Environmental covariates were grouped to create different scenarios combining terrain derivatives, hydrologic covariates, topsoil spectral reflectance, and spatial coordinates. A bare soil image, elaborated using 30 years of Landsat data, was evaluated as a covariate to predict soil parent material. Predictions were validated using three different strategies: cross-validation, separate validation dataset (20%), and comparison with legacy geological maps (information from two areas with geological maps at fine scale). We also assessed the correspondence between the map of predicted soil parent material and data of soil particle size from 571 soil sampling points. Random forest algorithm presented the best validation performance, whereas the group of terrain derivatives and hydrologic covariates explained most of model variation. The produced parent material map was coherent with the spatial distribution of soil particle size across the study area.

Improved digital soil mapping with multitemporal remotely sensed satellite data fusion: A case study in Iran

Modeling and mapping of soil properties are critical in many environmental, climatic, ecological and hydrological applications. Digital soil mapping (DSM) techniques are now commonly applied to predict soil properties with limited data by developing predictive relationships with environmental covariates. Most studies derive covariates from a digital elevation model (named static covariates). Many works also include single-day remotely sensed satellite imagery. However, multitemporal satellite images can capture information about soil properties over time and bring additional information in predicting soil properties in DSM. We refer to covariates derived from multitemporal satellite images as dynamic covariates. The objective of this study was to assess the performance of DSM when using terrain derivatives (static covariates), single-date remotely sensed satellite indices (limited dynamic covariates), multitemporal satellite indices (dynamic covariates), and combinations of terrain derivatives and satellite indices (covariate fusion) as covariates in predicting soil properties and estimating uncertainty. Three soil properties are considered in this study: organic carbon (OC), sand content, and calcium carbonate equivalent (CCE). Inclusion of single and/or multitemporal remotely sensed satellite indices improved the prediction of soil properties over traditionally used terrain indices. Significant improvements were observed in the prediction of soil properties using two models, Cubist and random forest (RF). The increase in the R2 values for Cubist and RF were 126% and 78% for OC, 110% and 54% for sand, and 87% and 32% for CCE. The RMSE decreased by 34% and 27% for OC, 25% and 12% for sand, and 39% and 19% for CCE, when compared to the terrain indices only model. This also reduced the uncertainty of estimation and mapping. These clearly showed the advantage of using multitemporal satellite data fusion rather than simply using static terrain indices for DSM of soil properties to deliver a great potential in improving soil modeling and mapping for many applications.

Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery

Landform classification is one of the most important aspects in geomorphological research, dividing the Earth's surface into diverse geomorphological types. Thus, an accurate classification of landforms is a key procedure in describing the topographic characteristics of a given area and understanding their inner geomorphological formation processes. However, landform types are not always independent of one another due to the complexity and dynamics of interior and external forces. Furthermore, transitional landforms with gradually changing surface morphologies are widely distributed on the Earth's surface. With this situation, classifying these complex and transitional landforms with traditional landform classification methods is hard. In this study, a deep learning (DL) algorithm was introduced, aiming at automatically classifying complex and transitional landforms. This algorithm was trained to learn and extract landform features from integrated data sources. These integrated data sources contain different combinations of imagery, digital elevation models (DEMs), and terrain derivatives. The Loess Plateau in China, which contains complex and transitional loess landforms, was selected as the study area for data training. In addition, two sample areas in the Loess Plateau with complex and transitional loess hill and ridge landforms were used to validate the classified landform types by using the proposed DL method. Meanwhile, a comparative analysis between the proposed DL and random forest (RF) methods was also conducted to investigate their capabilities in landform classification. The proposed DL approach can achieve the highest landform classification accuracy of 87% in the transitional area with data combination of DEMs and images. In addition, the proposed DL method can achieve a higher accuracy of landform classification with better defined landform boundaries compared to the RF method. The classified loess landforms indicate the different landform development stages in this area. Finally, the proposed DL method can be extended to other landform areas for classifying their complex and transitional landforms.

Evaluating watershed-based optimized decision support framework for conservation practice placement in Plum Creek Minnesota

Targeting field-scale implementation of conservation practices (CPs) in agricultural watersheds is critical to meeting sustainable resource management goals. This not only needs accurate and extensive field-scale assessment, but also incorporation of willingness of the farmers to adopt the suite of practices. Many useful watershed models have not only lacked in accurate siting of field-scale practices based on rigorous physiographic/terrain criteria and hydro-conditioned digital elevation models (DEMs), but also limited in mathematically incorporating farmer’s uncertain, diverse and conflicting viewpoints into the model, which is pivotal to TMDL scenario assessment. This study develops an integrated decision support framework (DSF) to site CP opportunities at the field-scale using high-resolution Light Detection and Ranging (LiDAR)-hydro-conditioned DEM and other secondary data. Using python coding, DSF integrates three robust models namely Prioritize, Target and Measure Application (PTMApp), Agricultural Conservation Planning Framework (ACPF) and Hydrological Simulation Program FORTRAN-Scenario Application Manager (HSPF-SAM). The novel nature of this framework is its ability to facilitate precision planning at field-scale by siting the optimized conservation practices to achieve TMDL’s based on (i) rigorous terrain derivatives and siting criteria; (ii) simulation of accurate stream flow network (extensive hydro-conditioning and D infinity approach for flow accumulation); and (iii) incorporating diverse, conflicting and uncertain preferences of farmers using fuzzy logic. The framework offers a simplified interface to the locals to simultaneously address agronomical (siting practices to improve soil health and crop productivity), hydrological (improving water quality, TMDL’s), ecological (delineation of sites for riparian buffers) and socio-economic (farmer preferences and cost-analysis) objectives with moderate technical expertise. Besides field-scale practice siting, one of the superior features of the model is its flexibility to filter out even technically feasible cost-effective practices if they are not preferred by farmers, and thus giving a realistic assessment of TMDL’s. The model is applicable to any Hydrologic Unit Code (HUC-12) sub-watershed and has been demonstrated for Plum Creek watershed in Minnesota. Results showed 537 cost-effective practices including bioreactors, grassed waterways, nutrient removal wetlands, water and sediment control basins and riparian buffers yielded an 8.5% reduction in total nitrogen constrained with a budget of $250,000. Field-scale maps generated through this DSF empowers practitioners and farmers to work together to make implementation decisions based on sound agronomic, water quality, and economic considerations.

Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil

Detailed soil maps are essential for agricultural management, but they are scarce in many regions. Even with the recent development of digital soil mapping (DSM) strategies, providing an adequate spatial representation of soils is still a challenging task. Therefore, this work aims to define a DSM approach, which combines proximal and remote sensing data to describe the spatial variation of soil attributes and types. The study was carried out in a site at southeastern Brazil, where 326 sampling points were defined and collected at two depths. Soil Vis-NIR spectra and physico-chemical attributes were measured in laboratory. A bare surface synthetic image (SYSI) was created from multi-temporal Landsat images and later validated with lab. spectra. Geographically weighted regression was used to calibrate depth transfer functions, which were applied to SYSI, generating a subsurface soil synthetic image (SYSIsub). Soil attributes at both depths were mapped with SYSI, SYSIsub and terrain derivatives. A soil classification key was designed following the Brazilian Soil Classification System and boolean logic. Soils were classified based on the soil attributes maps and boolean key. Hence, Monte-Carlo simulation (MCS) was performed to evaluate the error propagation from predicted attribute maps to soil types map. Correlations between satellite and lab. spectra varied from 0.68 to 0.8, indicating good capacity of SYSI in retrieving bare soil reflectance. Depth transfer functions also had good performance, with R2 ranging from 0.62 to 0.72. The soil attribute maps with best performance were clay content (R2 = 0.63), iron concentration (R2 = 0.72) and soil color (hue R2 = 0.57; value R2 = 0.73; chroma R2 = 0.63). Soil organic matter and chemical attributes were poorly predicted, with R2 between 0.12 and 0.38. MCS indicated that uncertainties in attributes maps might result in confusion between Ferralsols and Acrisols, Regosols and Luvisols, as well as Luvisols and Acrisols. Comparison between digital and conventional maps of soil classes, presented satisfactory kappa (34.65%) and global accuracy (54.46%). The technique presents an improvement to DSM, as it integrates soil sensing and depth transfer functions into DSM.

Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment

The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB). Accurate maps of AGB are required for monitoring, reporting, and modelling of forest resources and carbon stocks. Previous research has linked plot-level AGB with environmental and remotely sensed data using pixel-based approaches. However, few studies focused on investigating possible improvements via object-based image analysis (OBIA) including terrain related data to predict AGB in topographically variable and mountainous regions, such as Atlantic forest in Minas Gerais, Brazil. OBIA is expected to reduce known uncertainties related to the positional discrepancy between the image and field data and forest heterogeneity, while terrain derivatives are strong predictors in forest ecosystems driving forest biomass variability. In this research, we compare an object-based approach to a pixel-based method for modeling, mapping and quantifying AGB in the Rio Doce basin, within the Brazilian Atlantic Forest biome. We trained a random forest (RF) machine learning algorithm using environmental, terrain, and Landsat Thematic Mapper (TM) remotely sensed imagery. We aimed to: (i) increase the precision of the AGB estimates; (ii) identify optimal variables that fit the best model, with the lowest root mean square error (RMSE, Mg/ha); (iii) produce an accurate map of the AGB for the study area, and subsequently (iv) describing the AGB spatial distribution as a function of the selected variables. The RF object-based model notably improved the AGB prediction by reducing the mean absolute error (MAE) from 28.64 to 20.95%, and RMSE from 33.43 to 20.08 Mg/ha, and increasing the R² (from 0.57 to 0.86) by using a combination of selected remote sensing, environmental, and terrain variables. Object-based modelling is a promising alternative to common pixel-based approaches to reduce AGB variability in topographically diverse and heterogeneous environments. Investigation of mapped outcomes revealed a decreasing AGB from west towards the east region of the Rio Doce Basin. Over the entire study area, we map a total of 195,799,533 Mg of AGB, ranging from 25.52 to 238 Mg/ha, following seasonal precipitation patterns and anthropogenic disturbance effects. This study provided reliable AGB estimates for the Rio Doce basin, one of the most important watercourses of the globally important Brazilian Atlantic Forest. In conclusion, we highlight that OBIA is a better solution to map forest AGB than the pixel-based traditional method, increasing the precision of AGB estimates in a heterogeneous and mountain tropical environment.

Detecting soil erosion in semi-arid Mediterranean environments using simulated EnMAP data

Tools that enhance the management of the soil resource are vital for sustainability and continued productivity. The assessment and monitoring of surface-soil condition provide key information required to effectively manage this resource. This research used a simulated Environmental Mapping and Analysis Program (EnMAP) hyperspectral image cube of an agricultural region in semi-arid Mediterranean Spain to classify soil erosion states. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to derive within-pixel fractions of eroded and accumulated soil states. Soil states (eroded, intermediate, accumulated) were classified with an overall accuracy of 70.3% using the unmixing fraction maps and terrain derivatives. The results generated in this research provided an outlook for the applicability of the future EnMAP sensor for soil erosion investigations in semi-arid Mediterranean environments.

Is high spatial resolution DEM data necessary for mapping palustrine wetlands?

ABSTRACTDigital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user’s and producer’s accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user’s and producer’s accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable.

Geomorphometric segmentation of complex slope elements for detailed digital soil mapping in southeast Brazil

Hillslope elements have considerable potential in predicting soil properties and types in the landscape, making them likely to be a useful basis for detailed soil mapping. The goal of this research was to apply a previously developed digital hillslope position (DHP) model, calibrate it as needed to a Brazilian landscape, and test its utility as a basis for identification of detailed soil map units. The study area covers 2500 ha and is located on the border between the municipalities of Piracicaba and Santa Bárbara d'Oeste, São Paulo state, Brazil. A digital elevation model, with spatial resolution of 5 m, was used to obtain slope gradient, profile curvature and relative elevation with different analysis scales. Hierarchical rules for these digital terrain derivatives were used to segment the landscape into hillslope positions. The user-calibrated hillslope position model was verified against local experience by identifying the hillslope position in the field and comparing it with the model classification using the Kappa statistic and a confusion matrix. Soil samples were collected across multiple hillslopes with different lithologies. The samples were analyzed for chemical composition and soil particle size separates. The measured soil properties were assessed for statistical significance by variance analysis among hillslope position, parent material, and the interaction between the two. Student's t-tests were performed iteratively across each hillslope position within a given parent material to identify specifically which soil properties were significantly different among the hillslope position map units. Variance analysis of soil samples located within the respective parent material map units identified significant differences for all soil properties measured, but only for some soil properties when categorized by DHP. Focusing on the parent material with a sufficient quantity of samples, there was always at least one hillslope position that was significantly different from the others for each soil property. Because each of these map units presented a significant difference in at least one soil property, they are useful for detailed soil mapping.

Drainage basin object-based method for regional-scale landform classification: a case study of loess area in China

Landform classification is one of the most important procedures in recognizing and dividing earth surface landforms. However, topographical homogeneity and differences in regional-scale landforms are often ignored by traditional pixel- and object-based landform classification methods based on digital elevation models (DEMs). In this work, a drainage basin object-based method for classifying regional-scale landforms is proposed. Drainage basins with least critical areas are first delineated from DEMs. Then, terrain derivatives of mean elevation, mean slope, drainage density, drainage depth, and terrain texture are employed to characterize the morphology of the drainage basins. Finally, a decision tree based on the topographical characteristics of the drainage basins is constructed and employed to determine the landform classification law. The experiment is validated in the landform classification of regional-scale loess areas in China. Results show that clear boundaries exist in different landforms at the regional scale. Landform type in a specific region shows significant topographical homogeneity under its specific regional geomorphological process. Classification accuracies are 87.3 and 86.3% for the field investigation and model validation, respectively. Spatial patterns of classified landforms and their proximity to sediment sources and other factors can be regarded as indicators of the evolutionary process of loess landform formation.

Extraction and Analysis of Different Terrain Derivatives from Point Cloud Airborne LiDAR Measurements of Reduced Data Volumes

Topographic parameters such as slope, aspect, profile curvature, contour curvature and drainage network are usually known as terrain derivatives that enjoy a wide range of engineering and environmental applications. Different Surveying Engineering techniques such as ground surveying, GPS, digital photogrammetry in addition to remote sensing technologies constitute main sources for digital elevation data exploited in extraction of different terrain derivatives at varying qualities. Airborne LiDAR is an efficient technology that can capture very dense and accurate digital elevation data, known as point cloud LiDAR data for extended areas in relatively short times with reasonable costs. However, processing of very high-density point cloud LiDAR data could lead to serious time and memory consumption problems. Thus, reductions of LiDAR data volumes are expected to lower data acquisition and data processing costs; but this might affect the qualities of the extracted terrain derivatives. This study aimed at extraction and evaluation of different terrain derivatives from airborne LiDAR measurements of reduced data volumes including slope, aspect and hillshade maps in addition to evaluation of the elevation residuals that arise due to reductions in LiDAR data volumes. Visual analysis of slope maps, aspect maps and hillshade maps showed clear differences in the sizes and shapes of the colour patches due to reductions in LiDAR data volumes which appears as degradations in the tones and textures in the maps that increases with increasing the amount of reductions from the original data volumes. Statistical analysis of slope maps showed that the standard deviation of the slopes has decreased by only 0.225% and 30.442% due to excluding 50% and 75% of the original LiDAR data respectively. This means that slope maps can withstand reductions in LiDAR data volumes down to 50% of the raw data without clear effects on the statistical properties of the produced slope maps. Error maps resulting due to omitting only 25% of the data is structured where colour patches are relatively small showing wide changes in the tone within the map compared to the error map resulting only from 50% of the LiDAR data which is less structured with smoothed tones. Statistical analysis of the error maps showed that the standard deviation of the elevation residuals increases with the decrease in LiDAR data volume referring to greater uncertainties of the extracted elevations. When LiDAR data volume is decreased from 75% of the original data down to 50% the standard deviation of the elevation residuals has increased by only 17%, however, when decreasing from 75% down to 25% the standard deviation of the elevation residuals has increased by about 101.5%.

Automatic recognition of loess landforms using Random Forest method

The automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. However, landform types are rather complex and gradual changes often occur in these landforms, thus increasing the difficulty in automatically recognizing and classifying landforms. In this study, small-scale watersheds, which are regarded as natural geomorphological elements, were extracted and selected as basic analysis and recognition units based on the data of SRTM DEM. In addition, datasets integrated with terrain derivatives (e.g., average slope gradient, and elevation range) and texture derivatives (e.g., slope gradient contrast and elevation variance) were constructed to quantify the topographical characteristics of watersheds. Finally, Random Forest (RF) method was employed to automatically select features and classify landforms based on their topographical characteristics. The proposed method was applied and validated in seven case areas in the Northern Shaanxi Loess Plateau for its complex and gradual changed landforms. Experimental results show that the highest recognition accuracy based on the selected derivations is 92.06%. During the recognition procedure, the contributions of terrain derivations were higher than that of texture derivations within selected derivative datasets. Loess terrace and loess mid-mountain obtained the highest accuracy among the seven typical loess landforms. However, the recognition precision of loess hill, loess hill–ridge, and loess sloping ridge is relatively low. The experiment also shows that watershed-based strategy could achieve better results than object-based strategy, and the method of RF could effectively extract and recognize the feature of landforms.

Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region.

The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area.

Landform planation index extracted from DEMs: A case study in ordos platform of China

Planation surface, a surface that is almost flat, is a kind of low-relief landforms. Planation surface is the consequence of the denudation and planation processes under a tectonic stable condition. The quantitative expression of the characteristics of planation surface plays a key role in reconstructing and describing the evolutionary process of landforms. In this study, Landform Planation Index (LPI), a new terrain derivative, was proposed to quantify the characteristics of planation surface. The LPIs were calculated based on the summit surfaces formed according to the clustering results of peaks. Ten typical areas in the Ordos Platform located in the central part of the Loess Plateau of China are chosen as the test areas for investigating their planation characteristics with the LPI. The experimental results indicate that the LPI can be effectively used to quantify the characteristics of planation surfaces. In addition, the LPI can be further used to depict the patterns of spatial differentiation in the Ordos Platform. Although the present Ordos Platform area is full of the high-density gullies, its planation characteristics is found to be well preserved. Furthermore, the characteristics of the planation surfaces can also reflect the original morphology of the Ordos Platform before the loess dusts deposition process evolved in this area. The statistical results of the LPI show that there is a gradually increasing tendency along with the increasing of slope gradient of summit surface. It indicates that the characteristics of planation surfaces vary among test areas with different landforms. These findings help to deepen the understanding of planation characteristics of the loess landform and its underlying paleotopography. Results of this study can be also served as an important theoretical reference value for revealing the evolutionary process of loess landform.

Sensitivity of DEM, slope, aspect and watershed attributes to LiDAR measurement uncertainty

Abstract Knowledge of LiDAR measurement uncertainty and its propagation into derived geospatial products is important for conscientious application of LiDAR products to environmental modeling, planning and decision making. This study simulates theoretical error limits of LiDAR elevations based on survey acquisition parameters and terrain conditions and applies a Monte Carlo simulation approach to determine the uncertainty in a DEM, grid based slope and aspect maps, watershed area and a stream network delineation. A cell-by-cell assessment of the DEM for hydrological network extraction is also provided through the Gyasi-Agyei et al. (1995) suitability metric. Results show that DEM uncertainty generally ranged between 0.025 and 0.060 m, slope uncertainty ranged between 0.6 and 1.5°, and aspect uncertainty ranged between 2.7 and 24.1° for the given case study site. DEM uncertainty and slope uncertainty were correlated with the incidence angle between the laser pulse and the terrain, described with a piecewise linear function. Uncertainty in aspect showed a correlation with terrain slope and could be described with a decreasing power function. The variation in watershed area was less than 1% of total watershed area indicating it was not sensitive to LiDAR measurement errors for the selected case study site. Stream network length variability was 1.5% of total length, but increased as the analyzed area decreased. Areas of 1 ha showed relative standard deviations of stream network length of ~ 26%. These results can be used to optimise LiDAR survey data collection configuration and in the design of tools to ensure the objectives and uncertainty requirements of projects requiring terrain derivative products are successfully achieved.

Comparative Study of Cartosat-DEM and SRTM-DEM on Elevation Data and Terrain Elements

The three dimensional space is represented by Digital Elevation Models (DEMs) in a discrete form. DEM plays an important role in characterizing the topography and to derive the watershed boundaries thereby to study the landscape with in the watershed. Over a decade, DEMs from satellite imageries like ASTER, SRTM, IKONOS, Cartosat etc. are becoming popular with wide applications. However, issue of accuracy of DEMs generated from the satellite imageries is the major concern of remote sensing researcher’s. The main purpose of this study is to compare the accuracy of DEM generated from two different satellite sources, 1) Optical based sensory satellite data – Cartosat-DEM. 2) Microwave based sensory satellite data – SRTM-DEM. The study was conducted over Southpennar watershed which has gradually undulating topography. The comparative study was done under two categories; they are through elevation data and terrain derivatives. The study reveals that in gradually undulating terrain, elevation values of Cartosat-DEM are lower than SRTM-DEM. whereas the stream parameter values of Cartosat-DEM are higher than SRTM-DEM. In visual analysis the contours obtained from Cartosat-DEM and SRTM-DEM aligns with each other irrespective of the resolution.