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Marine geomorphological maps are useful to understand seafloor structure for example in the context of ecological studies, resources management or conservation planning. Although techniques to build such maps are increasingly sophisticated, manual techniques are still largely used. Automated approaches are needed to get reproducible maps in a reasonable time. This work provides statistical learning approaches based framework to build automatically geomorphological maps. We used bathymetric data to build Digital Bathymetric Model (DBM) and compute terrain attributes characteristic of seafloor geomorphology. Then, we used clustering based algorithms to select automatically ground truth locations from a reference geomorphological map manually made. Finally a supervised classification model random forest based was used to build predictive models for seafloor geomorphology typologies. Subsequently we studied the effect of DBM resolution, sample size and sampling method of the ground truth locations, in the quality of map production via a series of simulations. Results showed that the proposed framework allowed to build efficiently relevant seafloor geomorphological maps. The best compromise between the sampling effort and the quality of the resulting maps was obtained with 100 m DBM resolution, 200 data points sample size and using a complexity-dependent sampling method and led to a map matching at 90% the reference one. [ABSTRACT FROM AUTHOR]

Landslides are devastating events with global implications, impacting human lives and infrastructure. Understanding their patterns of occurrence and identifying susceptible regions is crucial to minimizing their impact. Probabilistic susceptibility mapping is a widely used method for studying landslides, providing a detailed spatial overview. By characterizing the terrain attributes of areas where landslides have and have not occurred, and extrapolating these patterns to the entire study area, a landslide probability map is generated. Although these methods are proven to be efficient, they are often implemented using paid software or restricted access programming languages, which makes it difficult for other researchers to reproduce them. This article presents a free and open-access approach to mapping landslide susceptibility using the Google Earth Engine platform, encompassing all the necessary modeling steps. To illustrate how it works, we carried out a case study for the municipalities of São Sebastião and Ilhabela, in southeastern Brazil. The landslide susceptibility map resulting from this application obtained a ROC curve value of 0.931. 5.4% of the study area is highly susceptible to landslides, and these areas are distributed across the municipalities. Ultimately, this method offers a quick, accessible and low-cost computational solution for an initial mapping of landslide susceptibility.

This editorial published in the journal Frontiers in Marine Science discusses the field of marine geomorphometry, which involves the quantitative characterization of the seabed terrain. The editorial highlights advancements in seabed mapping technologies and the increasing availability of data, which provide opportunities for innovation in marine geomorphometry. The document introduces a research topic on Frontiers in Marine Geomorphometry, aiming to disseminate novel approaches and applications of quantitative analysis of seafloor mapping datasets. The editorial emphasizes the importance of understanding, monitoring, and managing the oceans, especially in the context of the United Nations' Decade of Ocean Science for Sustainable Development. The document concludes by discussing various applications of marine geomorphometry, including habitat mapping, ecological analysis, and geohazard assessment, and mentions state-of-the-art approaches such as deep learning for automating marine geomorphometric and morphological analyses. [Extracted from the article]

Knowledge of soil volumetric water content (VWC) on agricultural soils as influenced by different soil management practices is important, but the measurement outputs of frequently used traditional sampling techniques are restricted to point-based measurements with limited spatial coverage. Remote sensing (RS) techniques are therefore being explored because of their greater spatial and temporal availability as well as their ability to cover large-scale areas. But a general limitation for RS data is the presence of vegetation cover, cloud cover and the effect of topography. To minimize the effects of these factors during field sampling, the use of spectral indices and terrain attributes have proven successful in the estimation of several soil properties; however, the impact of these approaches for the estimation and mapping of soil VWC, an important factor in crop growth and development, especially where different soil management practices are employed, remains limited. To contribute to the knowledge base of RS under varying soil systems, this study explores the possibility of combining in-situ measurements with remotely sensed (explanatory variables) data obtained under four different tillage practices to produce an estimated soil VWC that represents the entire study field. The tillage practices used include reduced till (RT), strategic till (ST), no-till (NT) and conventional till (CT). From these tillage plots, three explanatory datasets, namely Sentinel-2 (S2), spectral indices (SI) and terrain attributes (TA), were collected as predictors. In addition, each of the explanatory variables was structured into four groups, representing each of the four tillage methods. The eXtreme Gradient Boosting (XGBoost) model was used, and the best results were selected based on the root mean squared error (RMSE), the coefficient of determination (R2) and the mean absolute error (MAE). Prior to soil VWC prediction, the Pearson correlation matrix was used to determine the linear relationship between each of the selected explanatory variables (S2 bands, SI and TA) and soil VWC. Furthermore, spatial distribution maps of soil VWC were constructed using the inverse distance weighting (IDW) interpolation technique. For soil VWC estimation, the TA outperformed the SI and S2 datasets (R2 = 0.84). Similarly, the spatial distribution maps obtained from the TA data show a study area with a large concentration of VWC compared with the other datasets. The study also identified CT as the tillage approach that most impacts soil VWC estimation because all predicted results were poor without the addition of data from the CT plot. According to our findings, using TA data collected from various tillage management systems to estimate soil VWC is very promising because they can be used as predictors to improve soil VWC estimation and mapping. This study demonstrates conclusively that remote sensing data collected from different tillage management systems can be used as predictors to enhance the estimation and mapping of soil VWC, complementing the basis for the development of reliable and consistent precision irrigation management systems. [ABSTRACT FROM AUTHOR]

Watershed dynamics are governed by geomorphic indices, which include both surface and subsurface characteristics. In this study, several hydrological indices investigate the dynamics of the Asan watershed in Uttarakhand, India focusing on the interplay between human activities and natural processes using geomorphic indices and remote sensing techniques. The research aims to achieve a planned sustainable development by understanding various geo-environmental aspects related to stream network analysis, evaluation of different terrain attributes and land-use patterns. Utilizing the use of highresolution satellite data, such as DEM and satellite images, enhances the impact on land surface management and strengthens the overall research. The Asan watershed, designated as the 6 th ordered dendritic network, exhibits the dominance of moderately permeable strata with significant discharge potential and a tectonic tilt that favours fluvial erosion. Structural influence is observed to be more pronounced in longitudinal profiles of various higher-order streams. The research shed light on rapid industrialization and urbanisation. Through the generation of decadal LULC maps from 1980 to 2016, the study reveals a four-fold increase in the urban area over the last four decades. Economic, topographic, and commercial factors contribute to these dynamic changes, which alarmed environmental scientists and planners for effective management and development. [ABSTRACT FROM AUTHOR]

Aims: The relative importance of species within an ecosystem shows spatio‐temporal variability related to both the terrain features and numerous rapidly changing factors. Accordingly, functional and species patterns may show some level of persistence, or, due to various disturbances, fluctuations. Communities with high species richness were found to maintain a higher degree of stability than species‐poor or perturbed vegetations; however, diversity and its stability may be spatio‐temporally variable. Based on these considerations, our aim was to assess the responses of a sandy pasture, both species‐wise and at the community level, to the relatively invariant habitat, and to the more rapidly changing environmental conditions. Location: Hungary. Methods: We surveyed the vegetation in an area of about 1 ha by means of high‐resolution spatio‐temporal sampling: we recorded the surface aerial cover (%) of the plant species during 15 campaigns covering spring, summer and autumn aspects for seven years (2013–2019) in 80 × 60 m grids. The biological activity of the ecosystem (above‐ground biomass, soil respiration) was also followed up together with the environmental limiting factors. Results: During the study period, which was characterized by significant warming, the grassland showed a balanced physiological performance with year‐to‐year variability. Fluctuating differentiation of the plant species with different environmental optima along with the terrain attributes was assumed to be responsible for this balanced physiological performance. Although community‐level diversity remained stable, some species favoring cooler, wetter positions disappeared. Conclusions: Terrain features of the study area within 1.5 m or less elevation differences created considerably heterogeneous conditions for a high number (114) of plant species with different ecological needs to co‐occur. The stability of the diversity was found to show terrain relatedness, that is spatial as well as temporal patterns compared to the null model of spatio‐temporal independence, and the ecosystem functions followed these patterns. [ABSTRACT FROM AUTHOR]

Soil erosion by water is a main form of land degradation worldwide. The problem has been addressed, among others, in the United Nations Sustainability Goals. However, for mitigation of erosion consequences and adequate management of affected areas, reliable information on the magnitude and spatial patterns of erosion is needed. Although such need is often addressed by erosion modelling, precise erosion monitoring is necessary for the calibration and validation of erosion models and to study erosion patterns in landscapes. Conventional methods for quantification of rill erosion are based on labour‐intensive field measurements. In contrast, remote sensing techniques promise fast, non‐invasive, systematic and larger‐scale surveying. Thus, the main objective of this study was to develop and evaluate automated and transferable methodologies for mapping the spatial extent of erosion rills from a single acquisition of remote sensing data. Data collected by an uncrewed aerial vehicle was used to deliver a highly detailed digital elevation model (DEM) of the analysed area. Rills were classified by two methods with different settings. One approach was based on a series of decision rules applied on DEM‐derived geomorphological terrain attributes. The second approach utilized the random forest machine learning algorithm. The methods were tested on three agricultural fields representing different erosion patterns and vegetation covers. Our study showed that the proposed methods can ensure recognition of rills with accuracies between 80 and 90% depending on rill characteristics. In some cases, however, the methods were sensitive to very small rill incisions and to similar geometry of rills to other features. Additionally, their performance was influenced by the vegetation structure and cover. Besides these challenges, the introduced approach was capable of mapping rills fully automatically at the field scale and can, therefore, support a fast and flexible assessment of erosion magnitudes. [ABSTRACT FROM AUTHOR]

Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security. [ABSTRACT FROM AUTHOR]

Soil depth is a crucial parameter in slope stability analysis in mountainous areas. The drilling survey is the most reliable method for determining soil depth, but it requires a high cost for the vast geographical area. Therefore, this study proposes a soil depth prediction model for mountainous areas that uses Terrain Attributes (TAs) from digital maps. Gangwon-Do, a predominantly mountainous region in South Korea, is selected as the study target area. The study area is classified by parent rock type into igneous rocks, metamorphic rocks, and sedimentary rocks. The correlation with TAs is analyzed through multi-collinearity using drilling data published in the Korea drilling information database. In addition, the most suitable combination of variables is selected through multi-collinearity analysis, and the regression model using STI, TWI, and SLOPE is found to be the most appropriate model (VIF < 10). The proposed model for soil depth shows significance at p < 0.001, and the correlation coefficient ( R 2 ) is figured out for igneous rock (0.702), metamorphic rock (0.686), and sedimentary rock (0.693). In addition, the reliability of the proposed model was verified by using data from regions not included in the model development, and the correlation coefficients were igneous rock (0.867), metamorphic rock (0.801), and sedimentary rock (0.814). The model proposed is more suitable for Korean topography than the existing statistical models; it can help to increase the accuracy of slope stability analysis. [ABSTRACT FROM AUTHOR]

Geomorphological map plays a key role to illustrate landscape evolutionary history along with the guidelines of sustainable landuse planning. The Chota Nagpur Plateau is situated in the eastern part of the Indian subcontinent and it is a storehouse of valuable rocks and minerals of Precambrian origin. Classification of geomorphological units is highly required for sustainable landuse planning and natural hazards management. So, the principal objective of this scientific study is to classify the Chota Nagpur Plateau region into micro geomorphological units by applying automated and semi-automated methods. Digital elevation model (DEM) data and satellite imageries (from United States Geological Survey) have been used to improve the precision of this map. Geomorphological mapping techniques such as terrain attributes classification, Topographical Position Index (TPI) and Slope Position Index (SPI) have been applied to extract the major geomorphological units or features. TPI values show that the maximum area comes under the valley bottom with stream (35.26%) followed by the high ridge (24.55%) whereas minimum area coverage is found in the open slope zone (0.02%). Local ridges and mid-slope ridges lie in 8.77% and 4.94% area respectively. The result has been verified through field verification techniques with the help of GPS data. This high-accuracy map should be applied for regional planning and natural hazards management. These techniques also give very good results for terrain classification as well as landforms evolution.

Soil texture is an important property that controls the mobility of the water and nutrients in soil. This study examined the capability of machine learning (ML) models in estimating soil texture fractions using different combinations of remotely sensed data from Sentinel-1 (S1), Sentinel-2 (S2), and terrain-derived covariates (TDC) across two contrasting agroecological regions in Southwest Germany, Kraichgau and the Swabian Alb. Importantly, we tested the predictive power of three different ML models: the random forest (RF), the support vector machine (SVM), and extreme gradient boosting (XGB) coupled with the remote sensing data covariates. As expected, ML model performance was not consistent regarding the input covariates, soil texture fractions, and study regions. For example, in the Swabian Alb, the SVM model performed the best for the sand content with S2 + TDC (RMSE = 3.63%, R2 = 0.42), and XGB best predicted the clay content with S1 + S2 + TDC (RMSE = 6.84%, R2 = 0.64). In Kraichgau, the best models for sand (RMSE = 7.54%, R2 = 0.79) and clay contents (RMSE = 6.14%, R2 = 0.48) were obtained using XGB and SVM, respectively. Moreover, the results indicated that TDC were critical in estimating soil texture fractions, especially in Kraichgau, which indicated that topography plays an important role in defining the spatial distribution of soil properties. In contrast, the contribution of remote sensing data better predicted the silt and clay content in the Swabian Alb. The transferability of a region-specific model to the other region was low as indicated by poor predictive performance. The resulting soil-texture-fraction maps could be a significant source of information for efficient land resource management and environmental monitoring. Nonetheless, further research to evaluate the added value of the Sentinel imagery and to better analyze the spatial transferability of machine learning models is highly recommended. [ABSTRACT FROM AUTHOR]

Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using portable X-ray fluorescence (pXRF) spectrometry, magnetic susceptibility (MS), and terrain attributes (TA) via random forest algorithm. Samples were collected in Brazil from soils with high, moderate, and low weathering degrees (Oxisols, Ultisols, Inceptisols, respectively), and analyzed by pXRF and MS and for texture and available micronutrients. Seventeen TA were generated from a digital elevation model of 12.5 m spatial resolution. Predictions were made via: (i) TA; (ii) TA + pXRF; (iii) TA + MS; (iv) TA + MS + pXRF; (v) MS + pXRF; and (vi) pXRF; and validated via root mean square error (RMSE) and coefficient of determination (R2). The best predictions were achieved by: pXRF dataset alone for available Cu (R² = 0.80) and clay (R2 = 0.67) content; MS + pXRF dataset for available Fe (R2 = 0.68) and sand (R2 = 0.69) content; TA + pXRF + MS dataset for available Mn (R2 = 0.87) content. PXRF data were key to the best predictions. Soil property maps created from these predictions supported the adoption of sustainable soil management practices. [ABSTRACT FROM AUTHOR]

Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0-30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance of soil classes depends on the pedodiversity level and on the difference in SOC stock between the classes. The contribution of forest types as predictors is limited when one (mostly coniferous) type dominates. Better prediction results can be obtained in smaller, but consistent regions, like some natural forest areas.

Soil characteristics depend on its various physical, chemical, and biological parameters. Soil organic matter content (SOMC) is one of the main parameters of soil that affects all of these parameters. Therefore, this study was performed to estimate SOMC of irrigated lands in the Damavand Absard region of Tehran province using terrain attributes and standardized spectral reflectance (ZPC1) through two modeling approaches of random forest (RF) and artificial neural network (ANN). Soil sampling (0–30 cm) was performed at 60 points using random sampling method, and SOMC was measured in the laboratory. The results of SOMC modeling showed that the RF model predicted SOMC more accurately than the ANN model. Modeling SOMC using RF model showed that the R2, RMSE, and MAE values for training data were 0.81, 0.23%, and 0.22%, respectively. Furthermore, the R2, RMSE, and MAE values for test data were 0.78, 0.20%, and 0.21%, respectively. Modeling SOMC stocks by RF model showed that the efficient factors for estimating the SOC stocks were ZPC1, Slope, Normalized height, Relative Slope Position, elevation, Vertical Distance to Channel Network, Standardized height, and Slope length (LS) factor. According to the obtained results, the combination of terrain attributes and ZPC1 data can be used for managing and monitoring the SOMC in the irrigated lands of Iran with sufficient accuracy. Based on the results of this study, we conclude that the farmers can reference these maps to manage the SOMC in the Damavand Absard region due to high importance of this area for irrigated agriculture. [ABSTRACT FROM AUTHOR]

High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils. [ABSTRACT FROM AUTHOR]

Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security.

Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0–30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance of soil classes depends on the pedodiversity level and on the difference in SOC stock between the classes. The contribution of forest types as predictors is limited when one (mostly coniferous) type dominates. Better prediction results can be obtained in smaller, but consistent regions, like some natural forest areas. [ABSTRACT FROM AUTHOR]

Agroforestry systems play an important role in sustainable agroecosystems. However, accurately and adequately quantifying the relationships between environmental factors and tree growth in these systems are still lacking. Objectives of this study were to quantify environmental factors affecting growth of four tree species and to develop functional soil maps (FSM) for each species in an agroforestry site. The diameter at breast height, absolute growth rate (AGR), and neighborhood competition index of 259 trees from four species (northern red oak [Quercus rubra], pecan [Carya illinoinensis], cottonwood [Populus deltoides], and sycamore [Platanus occidentalis]) were determined. A total of 51 topsoil samples were collected and analyzed, and 12 terrain attributes were derived from the digital elevation model. The relationships between AGR, soil, topography, and tree size were analyzed using Spearman correlation. Based on correlation analysis, FSM for each species were generated using the k-means cluster method by overlaying correlated soil and terrain attribute maps. Results showed tree size and terrain attributes were driving factors affecting tree growth rate relative to soil properties. The spatial variations in AGR among functional units were statistically compared within tree species and the areas with larger AGR were identified by the FSM. This study demonstrated that FSM could delineate areas with different AGR for the oak, cottonwood, and sycamore trees. The AGR of pecan trees did not vary among functional units. The generated FSM may allow land managers to more precisely establish and manage agroforestry systems. [ABSTRACT FROM AUTHOR]

The redistribution of hillslope soil water during and after rainstorms is affected by soil properties and topography. Therefore, understanding how soil‐terrain attributes affect the soil volumetric water content (VWC) distribution under various catchment storages is a prerequisite for accurate hydrological modeling. Herein, the relationships between soil‐terrain attributes and soil VWC were examined in a steep (average slope = 60%), forested, zero‐order catchment. Detailed topography, soil properties, and runoff, and high frequency (6‐min) soil moisture data observed from July 2016 to November 2017 were employed along with resampled VWC data in three time‐steps (daily, hourly, and 6‐min intervals) for correlation analysis between soil‐terrain attributes and measured VWC. The results showed that daily aggregated data overlooked the highly heterogeneous flow conditions under dry‐wet transitions, leading to a constantly high correlation between VWC and the topographic wetness index (TWI). In comparison, the 6‐min data captured short‐lived flow processes, highlighting that, in reality, VWC‐TWI correlations fluctuated frequently as a function of catchment storages and precipitation characteristics, with four wetting patterns identified. Under wet‐dry transitions, the VWC‐TWI correlations increased significantly (from 0.3 to 0.6) as the hillslope gradually drained of lateral subsurface water. Soil water behavior consists of diurnal oscillations superimposed on a declining trend, which demonstrates the existence of unsaturated flow and which contributed to the increase in the strength of the correlations. Finally, through correlation analysis of all drying (n = 4) and wetting (n = 45) periods, we found that soil‐terrain attributes and VWC correlations can be used to identify the dominant soil water processes in various catchment storages. Key Points: The soil‐terrain attributes and soil moisture correlations were influenced by storage and precipitation inputs during wetting periodsSoil water behavior consists of diurnal oscillations superimposed on a declining trend during drying periodsTopography remains important in soil water redistribution during wet‐dry transitions [ABSTRACT FROM AUTHOR]

Knowledge of environmental factors controlling soil organic carbon (SOC) stocks can help predict spatial distribution SOC stocks. So, this study was carried out to select the best environmental factors to model and estimate the spatial distribution of SOC stocks in northwestern Iran. Soil sampling was performed at 210 points by multiple conditioned Latin Hypercube method (cLHm) and SOC stocks were measured. Also, environmental factors, including terrain attributes, moisture index, and normalized difference vegetation index (NDVI), were calculated. SOC stocks were modeled using random forest (RF) and partial least squares regression (PLSR) models. Modeling SOC stocks by RF model showed that the efficient factors for estimating the SOC stocks were slope height (slph), terrain surface texture (texture), standardized height (standh), elevation, relative slope position (rsp), and normalized height (normalh). Also, the PLSR model selected standardized height (standh), relative slope position (rsp), slope, and channel network base level (chnl base) to model SOC stocks. In both RF and PLSR methods, the standh and rsp factors were suitable parameters for estimating the SOC stocks. Predicting the spatial distribution of SOC stocks using environmental factors showed that the R2 values for RF and PLSR models were 0.81 and 0.40, respectively. The result of this study showed that in areas with complex land features, terrain attributes can be good predictors for estimating SOC stocks. These predictors allow more accurate estimates of SOC stocks and contribute considerably to the effective application of land management strategies in arid and semiarid area. [ABSTRACT FROM AUTHOR]

The spatial modelling of soil properties provides us with essential and useful information relevant to soil fertility management and environmental protection. The study aims to investigate the ability of empirical Bayesian kriging and principal component analysis, multiple linear regressions with environmental covariates in the modelling of soil properties distribution. For this study, thirty (n = 30) soil samples were obtained at 0–30 cm depth and nine (9) soil-environmental covariates derived from the digital elevation model (Shutter Radar Topography Mission at 30 m resolution) in southeastern Nigeria. The summary statistics revealed high sand content (> 70%) which revealed that the soils of the humid tropics developed on the coastal plain parent material are coarse-textured. Pearson correlation matrix revealed a significant but weak correlation between soil properties and soil-environmental variables. Using empirical Bayesian kriging interpolation, the cross-validation results revealed an acceptable prediction for magnesium, potassium, phosphorus, pH and total nitrogen (R2 > 0.5 with RMSE closer to 0). The principal component analysis reveals that principal component 1 to principal component 5 could interpret 78.1% of the total variability of soil properties. Modelling each soil property using multiple linear regression with the derived soil-environmental covariates, the study noted that only magnesium gave the best model fit with 50.9% of the soil-environmental covariates explaining its variability, while other soil properties presented unacceptable models. Therefore, to improve soil property prediction through multiple linear regression, more observation points are recommended to interpret better the performance of multiple linear regression over flat terrain system. [ABSTRACT FROM AUTHOR]

The objective of this study was to, through the distribution of some soil properties, model cation exchange capacity (CEC) in soils formed on gently undulating coastal plain sands of southeastern Nigeria using genetic horizon functions and terrain attributes. A total of 19 profile pits were prepared, described and 104 genetic horizons were identified and sampled, processed and analysed in the laboratory. Data were generated on the soil characteristics, including particle size fractions, hydraulic conductivity, bulk density, organic carbon, pH and electrical conductivity. Terrain attributes that were generated from digital elevation model include aspect, compound topographic index (CTI), Flow direction, curvatures, slope and stream power index (SPI). Data generated were analysed using descriptive statistics, correlation and regression. The terrain attributes were modified with genetic horizon depths, bulk density and clay content for the modelling process. Sand content, bulk density and cation exchange capacity possess geogenic rather than pedogenic characteristics and were normally distributed. The indication is that the two groups of terrain attributes depended on the mass per unit area of soil and clay content in their influence on these ultisol profiles. Paired comparison, root mean square error and normalized root mean square error indicated that the model was a good fit and could be useful in the prediction of soil properties and management of coastal plain sands.

Soil depth is a crucial parameter in slope stability analysis in mountainous areas. The drilling survey is the most reliable method for determining soil depth, but it requires a high cost for the vast geographical area. Therefore, this study proposes a soil depth prediction model for mountainous areas that uses Terrain Attributes (TAs) from digital maps. Gangwon-Do, a predominantly mountainous region in South Korea, is selected as the study target area. The study area is classified by parent rock type into igneous rocks, metamorphic rocks, and sedimentary rocks. The correlation with TAs is analyzed through multi-collinearity using drilling data published in the Korea drilling information database. In addition, the most suitable combination of variables is selected through multi-collinearity analysis, and the regression model using STI, TWI, and SLOPE is found to be the most appropriate model (VIF < 10). The proposed model for soil depth shows significance at p < 0.001, and the correlation coefficient (R2) is figured out for igneous rock (0.702), metamorphic rock (0.686), and sedimentary rock (0.693). In addition, the reliability of the proposed model was verified by using data from regions not included in the model development, and the correlation coefficients were igneous rock (0.867), metamorphic rock (0.801), and sedimentary rock (0.814). The model proposed is more suitable for Korean topography than the existing statistical models; it can help to increase the accuracy of slope stability analysis.

Landslides, like most natural disturbances, facilitate the evolution of new plant species. Hence, a detail characterization of topographic conditions can improve the prediction and mapping of species in such complex terrains. Within the Outer (Flysch) Upper Carpathian region, south Poland, we analyze the role of convergence points prepared in a previous study from slope and slope exposition (aspect) data, derived from a 1 m digital elevation model. Convergence points reflected microscale variability in topographic conditions and were analyzed in this study as convergence point density (CPD). Our objective was to use CPD to predict species richness on forested landslides using three geostatistical methods; Ordinary kriging (OK), Ordinary cokriging (OCK), and regression kriging (RK). Our results showed a relatively high correlation (r ∼ 0.65) between species richness and CPD compared to slope or slope exposition or with both. OCK and RK generally improved prediction. However, the application of cokriging in such terrains remains challenging and will not be appropriate, particularly if species richness has a small sample size. RK outperformed OK and OCK, decreasing the root mean square error (RMSE) by 33% and 10%, respectively. RK was also more robust to topographic heterogeneity and the limited number of observations than OCK. We conclude that a denser sampling of species composition or a more robust indicator is needed to improve these results. Notwithstanding these limitations, our results can be used as the first step to support short-term conservation efforts, especially when time-dependent changes in species composition are unimportant.

Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in predicting SOC concentration distribution with terrain, contributing approximately one-third of the overall prediction at the local scale. At the continental scale, climate is only 1.2 times more important than terrain in predicting SOC distribution, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation, respectively. We underscore that terrain attributes, while being integral to the SOC distribution at all scales, are stronger predictors at the local scale with explicit spatial arrangement information. While this observational study does not assess causal mechanisms, our analysis nonetheless presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.

Soil texture is an important property that controls the mobility of the water and nutrients in soil. This study examined the capability of machine learning (ML) models in estimating soil texture fractions using different combinations of remotely sensed data from Sentinel-1 (S1), Sentinel-2 (S2), and terrain-derived covariates (TDC) across two contrasting agroecological regions in Southwest Germany, Kraichgau and the Swabian Alb. Importantly, we tested the predictive power of three different ML models: the random forest (RF), the support vector machine (SVM), and extreme gradient boosting (XGB) coupled with the remote sensing data covariates. As expected, ML model performance was not consistent regarding the input covariates, soil texture fractions, and study regions. For example, in the Swabian Alb, the SVM model performed the best for the sand content with S2 + TDC (RMSE = 3.63%, R2 = 0.42), and XGB best predicted the clay content with S1 + S2 + TDC (RMSE = 6.84%, R2 = 0.64). In Kraichgau, the best models for sand (RMSE = 7.54%, R2 = 0.79) and clay contents (RMSE = 6.14%, R2 = 0.48) were obtained using XGB and SVM, respectively. Moreover, the results indicated that TDC were critical in estimating soil texture fractions, especially in Kraichgau, which indicated that topography plays an important role in defining the spatial distribution of soil properties. In contrast, the contribution of remote sensing data better predicted the silt and clay content in the Swabian Alb. The transferability of a region-specific model to the other region was low as indicated by poor predictive performance. The resulting soil-texture-fraction maps could be a significant source of information for efficient land resource management and environmental monitoring. Nonetheless, further research to evaluate the added value of the Sentinel imagery and to better analyze the spatial transferability of machine learning models is highly recommended.

Despite the increasing adoption of proximal sensors worldwide, rare works have coupled proximal with remotely sensed data to spatially predict soil properties. This study evaluated the contribution of proximal and remotely sensed data to predict soil texture and available contents of micronutrients using portable X-ray fluorescence (pXRF) spectrometry, magnetic susceptibility (MS), and terrain attributes (TA) via random forest algorithm. Samples were collected in Brazil from soils with high, moderate, and low weathering degrees (Oxisols, Ultisols, Inceptisols, respectively), and analyzed by pXRF and MS and for texture and available micronutrients. Seventeen TA were generated from a digital elevation model of 12.5 m spatial resolution. Predictions were made via: (i) TA; (ii) TA + pXRF; (iii) TA + MS; (iv) TA + MS + pXRF; (v) MS + pXRF; and (vi) pXRF; and validated via root mean square error (RMSE) and coefficient of determination (R2). The best predictions were achieved by: pXRF dataset alone for available Cu (R² = 0.80) and clay (R2 = 0.67) content; MS + pXRF dataset for available Fe (R2 = 0.68) and sand (R2 = 0.69) content; TA + pXRF + MS dataset for available Mn (R2 = 0.87) content. PXRF data were key to the best predictions. Soil property maps created from these predictions supported the adoption of sustainable soil management practices.

The upstream of Minjiang River is an important ecological barrier and one of the main water sources of the Chengdu Plain and upstream Yangtze River. However the ecological balance is vulnerable in this area. Terrain undulation has a certain impact on vegetation coverage in mountain areas. However, it is not clear how the influence of topography on vegetation coverage changes with pixel scale. This study proposed a framework by combining Discrete Wavelet Transform (DWT) and Geographically Weighted Regression (GWR) to detect the relationships between topography and vegetation in a typical fragile ecological area. The satellite-derived Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) were combined to examine the terrain undulation impacts on vegetation coverage at different scales in a small watershed of the Upper Minjiang River. The study results showed that approximate components of NDVI had consistently strong spatial autocorrelation at multi-scale. Elevation and south-facing index of aspect were positively correlated with NDVI, whereas slope was negatively correlated with NDVI. The order of correlation between terrain attributes and NDVI was elevation > aspect > slope. The correlation of NDVI with elevation and slope increased as the decomposition scale increased, whereas the correlation between NDVI and south-facing index of aspect did not show obvious change rule with increasing scale. The spatial heterogeneity of impact degree of elevation decreased as the scale parameter increased, but slope and aspect did not show obviously smoothing effect as the scale parameter increased. The findings will help identifying the favorable topographic position for vegetation growth and its controlling mechanisms and provide a new insight for ecological restoration practices.

The paper presents the results of research on the relationship between topography of undulated morainic plateau of postglacial landscape and distribution of organic carbon stocks in soil toposequences. The mean value of the soil organic carbon stocks (SOCS) for Retisols/Luvisols (RT/LV) was statistically lower than for the Phaeozems/Gleysols (PH/GL) but for RT/LV a higher variation of SOCS in comparison to PH/GL was observed. On the basis of Pearson correlation coefficient, the cartographic depth to water (DTW), the topographic wetness index (TWI) and the saga wetness index (SWI) were the most strongly correlated with the SOCS from among 13 analysed topographic attributes. In addition, the DTW was more correlated with SOCS than other topographic variables. Moreover, the DTW based on the channel networks with 2 ha flow initiation thresholds better correlate with SOCS than DTW obtained on the basis of channel networks with 1 ha and 4 ha flow initiation thresholds. Using Stepwise multiple regression analysis (SMLR), we concluded that the topographic attributes controlling the soil water content and slope shape had most impact on SOCS of the undulated morainic plateau of agricultural ecosystem. In this landform, where the RT/LV and PH/GL soil sequences dominate, the SOCS can be estimated by the DTW, TWI and GC (general curvature) with an estimation error of 0.21 kg m−2. In view of the increasing availability of LiDAR data and power of GIS tools, the use of topographic metrics to assess spatial variability of soil properties will play an increasingly important role in the estimation of soil properties.

Digital Soil Mapping (DSM) is a subdiscipline of pedology, where soil cover is modelled through use of spatial - temporal relations between environmental covariates and soil. The process of quantitative terrain description used in DSM is called terrain parametrization, where terrain attributes (morphometric factors, Digital Terrain Model derivatives) are the most used predicators. Terrain parameterisation was used as a tool in the hydrological survey workshop long before computers had been in use. With the development of digitisation, it also began to be used to determine selected soil attributes, which was greatly facilitated by GIS applications. A significant breakthrough in the importance of terrain attributes in the creation of soil maps and models took place with the formalisation of rules for digital soil mapping. Literature describes over 50 indices, although only a few of them are commonly applied. This applies to single soil attributes as well as more advanced implementations in more sophisticated models such as artificial intelligence algorithms. The aim of the following article is to present the main components of DSM and to describe characteristics of the most commonly derivatives of DTM applied there, also refers to several examples of the use of terrain parameters in the context of DSM in terms of the resolution of the elevation model used. [ABSTRACT FROM AUTHOR]

High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils.

The objective of this study was to, through the distribution of some soil properties, model cation exchange capacity (CEC) in soils formed on gently undulating coastal plain sands of southeastern Nigeria using genetic horizon functions and terrain attributes. A total of 19 profile pits were prepared, described and 104 genetic horizons were identified and sampled, processed and analysed in the laboratory. Data were generated on the soil characteristics, including particle size fractions, hydraulic conductivity, bulk density, organic carbon, pH and electrical conductivity. Terrain attributes that were generated from digital elevation model include aspect, compound topographic index (CTI), Flow direction, curvatures, slope and stream power index (SPI). Data generated were analysed using descriptive statistics, correlation and regression. The terrain attributes were modified with genetic horizon depths, bulk density and clay content for the modelling process. Sand content, bulk density and cation exchange capacity possess geogenic rather than pedogenic characteristics and were normally distributed. The indication is that the two groups of terrain attributes depended on the mass per unit area of soil and clay content in their influence on these ultisol profiles. Paired comparison, root mean square error and normalized root mean square error indicated that the model was a good fit and could be useful in the prediction of soil properties and management of coastal plain sands. [ABSTRACT FROM AUTHOR]

Context: Forest microclimates differ from regional macroclimates because forest canopies affect energy fluxes near the ground. However, little is known about the environmental drivers of understorey temperature heterogeneity and its effects on species assemblages, especially at landscape scales. Objectives: We aimed to identify which temperature variables best explain the landscape-scale distribution of forest vegetation and to disentangle the effects of elevation, terrain attributes and canopy cover on understorey temperatures. Methods: We measured growing season air temperature, canopy cover and plant community composition within 46 plots established across a 400-km2 area in Czech Republic. We linked growing season maximum, mean and minimum temperatures with elevation, canopy cover and topographic proxies for heat load, topographic position, soil moisture and cold air drainage, and created fine-scale topoclimatic maps of the region. We compared the biological relevance of in situ measured temperatures and temperatures derived from fine-scaled topoclimatic maps and global WorldClim 2 maps. Results: Maximum temperature was the best predictor of understorey plant species composition. Landscape-scale variation in maximum temperature was jointly driven by elevation and terrain topography ( R adj. 2 = 0.79) but not by canopy cover. Modelled maximum temperature derived from our topoclimatic maps explained significantly more variation in plant community composition than WorldClim 2 grids. Conclusions: Terrain topography creates landscape-scale variation in maximum temperature, which in turn controls plant species assembly within the forest understorey. Maximum temperature is therefore an important but neglected microclimatic driver of species distribution across landscapes. [ABSTRACT FROM AUTHOR]

Introduction: Soil classification generally aims to establish a taxonomy based on breaking the soil continuum into homogeneous groups that can highlight the essential differences in soil properties and functions between classes.The two most widely used modern soil classification schemes are Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB).With the development of computers and technology, digital and quantitative approaches have been developed. These new techniques that include the spatial prediction of soil properties or classes, relies on finding the relationships between soil and the auxiliary information that explain the soil forming factors or processes and finally predict soil patterns on the landscape. These approaches are commonly referred to as digital soil mapping (DSM) (14). A key component of any DSM mapping activity is the method used to define the relationship between soil observation and auxiliary information (4). Several types of machine learning approaches have been applied for digital soil mapping of soil classes, such as logistic and multinomial logistic regressions (10,12), random forests (15), neural networks (3,13) and classification trees (22,4). Many decisions about the soil use and management are based on the soil differences that cannot be captured by higher taxonomic levels (i.e., order, suborder and great group) (4). In low relief areas such as plains, it is expected that the soil forming factors are more homogenous and auxiliary information explaining soil forming factors may have low variation and cannot show the soil variability. Materials and Methods: The study area is located in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province. According tothe semi-detailed soil survey (16), 120 pedons with approximate distance of 750 m were excavated and described according to the “field book for describing and sampling soils” (19). Soil samples were taken from different genetic horizons, air dried and grounded. Soil physicochemical properties were determined. Based on the pedon description and soil analytical data, pedons were classified according to the ST (20) and WRB (11). Terrain attributes, remote sensing indices, geology, soil and geomorphology map were considered as auxiliary information. All of the auxiliary information were projected onto the same reference system (WGS 84 UTM 39N) and resampled to 50×50 m according to the suggested resolution for digital soil maps (14). Four modeling techniques (multinomial logistic regression (MLR), artificial neural networks (ANNs), boosted regression tree (BRT) and random forest (RF)) were used for each taxonomic level to identify the relationship between soil classes and auxiliary information in each classification system. The models were trained with 80 percent of the data (i.e., 96 pedons) and their validation was tested by remaining 20 percent of the dataset (i.e., 24 pedons) that split randomly. The accuracy of the predicted soil classes was determined by using overall accuracy and Brier score.For each classification system, the model with the highest OA and the lowest BS values were considered as the most accurate model for each taxonomic level. Results and Discussion: The results confirmed that ST showedmore accessory soil properties compared to WRB. The ST described the cation-exchange activity, soil depth classes, temperature and moisture regime. The different models had the same ability for prediction of soil classes across all taxonomic levels based on ST. Among the studied models, MLR had the highest performance to predict soil classes based on WRB. For all the studied models and both classification system, OA values showed a decreasing trend with increasing the taxonomic levels. Predicted soil classes based on the ST had the higher accuracy. Different models selected different auxiliary information to predict soil classes. For most of the models and both classification systems, the terrain attributes were the most important auxiliary information at each taxonomic level. Conclusion: Results demonstrated that although ST showed more accessory soil properties compared to WRB, the DSM approaches have not enough accuracy for prediction of the soil classes at lower taxonomic levels. More investigations are needed in this issue to make a firm conclusion whether DSM approaches are appropriate for prediction of soil classes at the levels that are important for soil management. Prediction accuracy of soil classes can be influenced by the target taxonomic level and classification system, soil spatial variability in the study area, soil diversity, sampling density and the type of auxiliary information.

Geomorphological research offers a valuable first step to understand the complexity of seafloor patterns that are likely to support particular fauna, thereby providing value for future targeted studies on benthic communities and habitats. The objective of this study is to perform a geomorphological classification of a tropical continental shelf, located in the north of the State of Pernambuco, Brazil, using Benthic Terrain Modeler (BTM), as a first step to characterize benthic habitats. The classification of the seabed is based on the bathymetry, broad-scale and fine-scale BPI (Benthic Positioning Index), seafloor slope, and a decision table containing definitions and thresholds appropriate to the data input. The relationships between morphology and the deposition of surface sediments were also investigated. Results from the BTM analysis revealed eight types of benthic structures: Flat Plains, Depressions, Gentle Slopes, Steep Slopes, Flat Ridge Tops, Rock Outcrop Highs, Local Pinnacles in Depressions and Local Pinnacles on Broad Flats. The results from this study may therefore assist in selecting priority areas to investigate, supporting better marine spatial planning in the region, and, in the future, collaborating to determine biodiversity patterns in Brazilian waters.

Abstract Soil mappers have traditionally relied on tacit knowledge and qualitative assessment of soil-landscape relationships to obtain the physiographic context necessary to predict soil distribution and spatial patterns. This assessment implicitly utilizes a nested hierarchical approach based on differences in the phenomenon scale of soil forming factors where climate, landscape, parent material, and topography are examined in sequence to create a model of soil-landscape relationships. Our objective was to predict parent material distribution using expert knowledge paired with quantitative digital terrain attributes as part of a nested approach to digital soil mapping. The study took place at an 890-hectare research farm in Logan County, Arkansas, which is part of the Arkansas River Valley. Two major groups of parent material are identifiable in the Arkansas River Valley: residual sandstone and shale on erosional uplands, and silty/clayey pedisediment in depositional areas. A 5-m digital elevation model was used to derive thirteen terrain attributes for the study site. Three of the terrain attributes, namely topographic position index, multi-resolution valley bottom flatness, and vertical distance to channel network, were utilized as part of a rule-based approach to model parent material distribution based on preliminary reconnaissance and expert knowledge of the area. The model was validated by sampling 20 locations using a conditioned Latin hypercube sampling (cLHS) design to evaluate the prediction accuracy. Seventy-five percent of cLHS samples were accurately predicted to be residuum or pedisediment. The resulting map also had 90% agreement with the National Cooperative Soil Survey map; however, the digital map was able to provide more spatially explicit information especially on inclusions. Incorporating parent material distribution as part of a nested hierarchical approach to digital soil mapping aids in constraining and predicting soil properties, enables a more straightforward examination of physiographic context and can ultimately lead to more accurate digital soil maps for land management. Highlights • A rule-based model was capable of predicting parent material distribution. • Overall mapping accuracy of parent material was 75%. • There was 90% agreement of parent material compared with a traditional soil map. [ABSTRACT FROM AUTHOR]

ABSTRACT: Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear Regressions - MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km2) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil. Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R2 values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions.

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

The aim of this study is to compare the classification of the landscape of the area located between Al-Alamien and Ras El-Hekma to landform classes using two different classification methods; the terrain attributes method and the topographic position index (TPI) method. Terrain attributes classification method derived from a digital elevation model and were overlaid using cell statistics to generate a landform map with seven classes. The landform classes were: (1) Lower coastal plain (17.86%), (2) Upper coastal plain (43.61%), (3) Piedmont plain (2.33%), (4) Dissected escarpment (2.64%), (5) Plateau (26.79%), (6) Ridges and coastal bars (3.62%), and (7) Inland dunes (3.15%). The topographic position index (TPI) method which generate the landform classes uses only the digital elevation model. The generated landform classes were: (1) Valleys (2.1%), (2) Midslopes drainages and shallow valleys (1.1%), (3) Plains (92.77%), (4) Hills in plains (0.34%), (5) Mesas (2.44%), and (6) Ridges (1.25%). The comparison of the two methods showed that using the terrain attributes method was more detailed. [ABSTRACT FROM AUTHOR]

Landform attributes derived from digital elevation models (DEMs) are the most commonly used factors to predict soil depths on hillslopes. However, the selection of an appropriate algorithm to calculate terrain attributes and identify an optimal DEM resolution remains ambiguous. In this study, we propose a method to use high-resolution DEM and spatial soil depth data to obtain terrain attributes and identify the optimal DEM resolution to predict soil depths at hillslope scales. A geophysical method (ground penetrating radar, GPR) was used to investigate the reasons for the optimal DEM resolution findings. Point-scale soil depth from 116 sites and elevation data were collected from two adjacent headwater hillslopes (H1: 0.42 ha and H2: 0.31 ha) at the Hemuqiao hydrological experimental station in Southeast China. The elevation datasets were collected using a total station at a variable spacing level and were then used to derived DEMs at nine spatial resolutions: 0.25, 0.50, 0.75, 1.00, 2.00, 3.50, 5.00, 7.50 and 10.00 m. Nine primary and secondary topographic attributes using the nine spatial resolution DEMs were then derived. Two different algorithms (D8 and D ∞) for calculating contributing areas and related secondary topographic attributes were compared. We used and compared both linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN) models for soil depth prediction. Results demonstrated that the two models performed well for predicting soil depth. Specifically, MLR performed better than the non-linear model of ANNs. Additionally, we found that the multiple-direction algorithm (D ∞) allowed flow divergence and avoided abrupt changes in soil depth predictions (orphan cells) and should be adopted for model construction. The D ∞ algorithm performed better in divergent areas, such as ridges and side slopes, and the D ∞ algorithm also worked well in convergent areas, such as valleys. Moreover, our results demonstrated that moderate (e.g., 2.00 m) resolution topographic attributes, instead of the finest resolution, achieved the best prediction with the lowest root mean square error (RMSE) and mean absolute error (MAE) and the highest values of the coefficient of determination (R 2 ). Moreover, the GPR results indicated that the valley accumulated more soils than side-slope areas, and a sharp increase in soil depth was found in areas adjacent to the valley. Comparing the optimal DEM resolution and valley width obtained by GPR, we found that average valley width ( AVW ) should be considered a good measure for choosing the optimal DEM resolution for soil depth prediction. [ABSTRACT FROM AUTHOR]

The effect of soil moisture content on vegetation and therefore on growth is well known. Information about the growth of forest stands is key in forest planning and management, and is the concern of various stakeholders. One way to assess moisture content and its impacts on forest growth is to apply the Topographic Wetness Index (TWI) and the derived terrain attributes from the Digital Terrain Model (DTM). The TWI is an important terrain attribute, used in various ecological studies. In the current study, a total of 9987 tally trees within 197 sample plots in southeastern Finland and LiDAR (Light Detection and Ranging) −based TWI were selected to examine: 1) the effect of cell resolutions and focal statistics of neighborhood cells of DTM, on tree diameter increment, and 2) possibilities to improve the prediction accuracy of an existing single-tree growth model using the terrain attributes and TWI with the combined effects of three characteristics (i.e., cell resolutions, neighborhood cells and terrain attributes). The results suggest that the TWI with terrain attributes improved the growth estimation significantly, and within different site types the Root Mean Square Errors (RMSE) were lowered substantially. The best results were obtained for birch trees. The higher resolution of the DTM and the lower focal neighborhood cells were found to be the best alternative in computing the TWI. [ABSTRACT FROM AUTHOR]