Multiresolution Index Of Valley Bottom Flatness Research Articles

Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.

Integration of Sentinel-1/2 and topographic attributes to predict the spatial distribution of soil texture fractions in some agricultural soils of western Iran

This research was intended to examine the use of radar data obtained from Sentinel-1 imagery with different combinations of topographic attributes and multispectral data to predict soil texture fractions (STFs) in some agricultural soils located in the southern part of Kurdistan province, in western Iran. For this purpose, by applying the stratified random sampling technique, 216 soil samples were collected from the upper soil layer (0–30 cm) of the studied area. The particle size fractions were measured using the hydrometer method. Then, a comprehensive set of environmental variables including Sentinel-1, Sentinel-2 and digital elevation model (DEM) were derived. These environmental variables were included in four datasets; these included: dataset A (Sentinel-2 and DEM), dataset B (Sentinel-1 and DEM), dataset C (Sentinel-1 and Sentinel-2), and data set D (Sentinel-1, Sentinel-2 and DEM). After selecting the parsimonious variables in each dataset based on Pearson correlation coefficients and variance inflation factor (VIF) analyses, three machine learning models including random forest (RF), support vector machine (SVM), Cubist, and the ensemble of individual models were used to create the statistical link between environmental variables and STFs. The results showed that the ensemble modeling method with the dataset D had the highest accuracy in comparison to individual models in the prediction of clay (RMSE = 4.07%, MAE= 3.22%, ME= −0.08, CCC= 0.53), silt (RMSE = 5.96%, MAE=4.63%, ME=−0.52, CCC=0.49), and sand fraction (RMSE = 8.32%, MAE=6.46%, ME= 0.52, CCC=0.55). In addition, the results of variable importance revealed that all three types of environmental variables contributed to the prediction of the spatial distribution of STFs. These results also showed that elevation, Band 11 and VH_1 (which is the one provided by the radar data obtained from Sentinel-1), respectively, were the most important variables in the prediction of clay and sand fractions. In addition, Band 11, elevation, and multi-resolution valley bottom flatness index (MrVBF) were identified as the most important variables contributing to the prediction of the silt fraction. This study also demonstrated the effectiveness of backscatter coefficients obtained from Sentinel-1 in digital soil mapping (DSM) in the prediction of soil properties. Finally, the map of each fraction with its uncertainty and also, the soil texture map were generated from the ensemble model and dataset D. These maps could be used as the decision tools for agricultural management.

Soil organic carbon content and stock change after half a century of intensive cultivation in a chernozem area

Soil organic carbon (SOC) is a significant soil parameter controlling soil quality and productivity and also playing an essential role in the global carbon cycle. Assessment of long-term SOC content and stocks changes is only possible by comparing historical and present data. The Czech Republic holds a national database of agricultural soils completed during the 1960s, which we used to assess SOC changes. SOC content and stock changes were detected after more than 50 years of intensive cultivation in a rolling chernozem landscape prone to soil erosion by re-sampling soil profiles from the 1960s and assessing these changes in relation to local topography. Each re-sampled soil profile was classified based on its position within the terrain, into one of three distinct slope positions namely shoulder slope (SH), backslope (BS), and foot slope (FS) which refers to a predisposition to soil erosion (SH, BS) or deposition (FS). Despite previous studies reporting SOC content decline over the past several decades, our results showed an increase in SOC content in all slope positions, being the most pronounced at FS position. However, median value for the SOC stock significantly decreased at SH positions primarily due to intensive erosion (decreased by 2.82 kg·m−2). At the FS positions, the median value for the SOC stock increased but not significantly (increased by 5.51 kg·m−2). Changes in the SOC stock were primarily driven by changes in the topsoil depth, which significantly decreased at the SH positions by 22 cm; the opposite result was found for the FS position. The local topography as a driver of SOC stock changes was further supported by the significant correlation between SOC stock changes and topographical attributes (predominantly with Multiresolution Valley Bottom Flatness index, Topographic position index or Topographic wetness index) showing increasing SOC stocks in terrain depressions or valley bottoms due to soil accumulation. We concluded that soil erosion is a dominant process in our study area, explaining the long-term changes in SOC stock.

Modelling of soil depth and hydraulic properties at regional level using environmental covariates- A case study in India

Soil hydraulic properties are important for agroecosystem modelling, irrigation scheduling, drought risk assessment, and preparation of proper land use planning. Field capacity (FC) and permanent wilting point (PWP) are the two vital soil hydraulic properties that determine the availability of water for plant growth. In the present study, we evaluated four data mining algorithms for mapping of field capacity and permanent wilting point along with soil depth over 16.2 M ha of Andhra Pradesh state, South India. Class pedo-transfer function was developed based on the soil texture classes (760 observations) and the profile total field capacity (TFC) and total profile permanent wilting point (TPWP) were computed for 1 m soil depth. Multi-linear Regression (MLR), Cubist, Support Vector Machine (SVM), and Random Forest (RF) models were evaluated using 2267 soil profile datasets collected over the study area. Along with Landsat-8 data and climatic datasets, terrain attributes such as plan curvature, profile curvature, topographic wetness index (TWI), topographic position index (TPI), Multi-resolution Index of Valley Bottom Flatness (MrVBF), and Multi-resolution Ridge Top Flatness (MrRTF) were used as environmental covariates. The models were calibrated using 80% of total field observations and validated using 20% of observations. The validation results showed that RF outperformed all other models for the prediction of both soil depth and profile TFC and TPWP. RF explained 39% of the variation of total field capacity and permanent wilting point with RMSE of 133 mm and 89 mm, respectively. The highest mean TFC and TPWP were found in Vertisols (256 and 154 mm, respectively) and Inceptisols (216 and 126, mm respectively). The high-resolution (250 m) maps of hydraulic properties and soil depth are useful for land use planning and different crop modelling studies.

Spatial prediction of soil depth using environmental covariates by quantile regression forest model.

Prediction of soil depth for larger areas provides primary information on soil depth and its spatial distribution that becomes vital for land resource management, crop, nutrient, and ecosystem modeling. The present study assessed the spatial distribution of soil depth over 160,205 km2 of Andhra Pradesh, India, using 20 covariables by quantile regression forest (QRF). An aggregate of 2854 soil datasets compiled from various physiographic units were randomly partitioned into 80:20 ratio for calibration (2283 samples) and validation (571 samples). Landsat imagery, terrain datasets (8), and bioclimatic factors (11) were utilized as covariates. The QRF model outputs signified that precipitation, multi-resolution index of valley bottom flatness (MrVBF), mean diurnal range, isothermality, and elevation were the most important variables influencing soil depth variability across the landscape. Spatial prediction of soil depth by QRF model yielded a ME of - 1.81cm, RMSE of 34cm, PICP of 90.2, and a R2 value of 42% as compared to ordinary kriging which results in a ME of - 0.14cm, a RMSE of 37cm, and a R2 value of 32%. As soil depth is spatially dynamic and has significant correlation with terrain and environmental covariates, better prediction was possible by the QRF model. However, high-density bioclimatic variables could be utilized along with high-resolution terrain variables to improve the predictive accuracy.

Quaternary formations mapping in the region of Volta Grande do Rio Uruguai (Brazil)

Quaternary formations (detrital and weathered materials) are an important natural resource for different areas of scientific investigation, from understanding their relation to erosive processes and morphodynamic processes that create landforms or to understanding the history of the first human settlements (geoarcheology). Quaternary coverings can be formed in situ or be transported by external geologic agents. Regarding soils, Quaternary formations are related to landscape topography and are transformed according to the characteristics of this topography. Hence, classifying and mapping these soils is not always easy. The present article aims to map the Quaternary formations along a stretch of the Uruguay River basin known as Volta Grande (SC/RS-Brazil), by using topographic attributes derived from the SRTM GL1-Up Sampled digital elevation model, soil particle-size analysis, and a generated Multiresolution Index of Valley Bottom Flatness (MRVBF) index . The results of the analysis show that: (i) colluvium is the predominant Quaternary formation in the study area; (ii) there is a predominance of clay, corroborating previous studies of the region; (iii) the spatial distribution of the study area’s Quaternary formations reflect local slope dynamics based on morphology and topographic position; and, (iv) the existence of colluvium-alluvium on the Uruguay River’s banks indicates that slope attributes contributed to the pedogeomorphological dynamics of the study area and not only fluvial dynamics. Based on the results, the methodology applied in this study might be useful for pedogeomorphological studies, notably in the analysis and mapping of Quaternary formations, despite some of its limitations.

Torrential Flood Water Management: Rainwater Harvesting through Relation Based Dam Suitability Analysis and Quantification of Erosion Potential

In this study, a relation-based dam suitability analysis (RDSA) technique is developed to identify the most suitable sites for dams. The methodology focused on a group of the most important parameters/indicators (stream order, terrain roughness index, slope, multiresolution valley bottom flatness index, closed depression, valley depth, and downslope gradient difference) and their relation to the dam wall and reservoir suitability. Quantitative assessment results in an elevation-area-capacity (EAC) curve substantiating the capacity determination of selected sites. The methodology also incorporates the estimation of soil erosion (SE) using the Revised Universal Soil Loss Equation (RUSLE) model and sediment yield at the selected dam sites. The RDSA technique identifies two suitable dam sites (A and B) with a maximum collective capacity of approximately 1202 million m3. The RDSA technique was validated with the existing dam, Gomal-Zam, in the north of Sanghar catchment, where RDSA classified the Gomal-Zam Dam in a very high suitability class. The SE estimates show an average of 75 t-ha−1y−1 of soil loss occurs in the study area. The result shows approximately 298,073 and 318,000 tons of annual average sediment yield (SY) will feed the dam A and B respectively. The SE-based sediment yield substantiates the approximate life of Dam-A and Dam-B to be 87 and 90 years, respectively. The approach is dynamic and can be applied for any other location globally for dam site selection and SE estimation.

Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran

In the current study, the variations of soil classes at the first and second levels of WRB (World Reference Base for soil resource) soil classification system were investigated by two machine learning including multinomial logistic regression (MLR) and random forest (RF) models in an arid floodplain which covers an area approximately 600 km2 located in Sistan region, Iran. The model’s performance was tested using 10-fold cross-validation by calculation of overall model accuracy and the kappa statistic. Three main Reference Soil Groups (RSGs) including Cambisols, Fluvisols, and Solonchaks at the first level, and 18 WRB soil groups at the second level were identified. Results showed that the overall accuracy at the first level of WRB was 53% and 49% with a kappa of 0.26 and 0.19 for MLR and RF models, respectively. At the second level of WRB, the overall accuracy was 11% and 21% with a kappa of 0 and 0.09 for MLR and RF models, respectively. Also, results showed that the MLR model had better performance (overall accuracy = 53%) at the first level of WRB, but the RF model showed better prediction (overall accuracy = 21%) at the second level of WRB. Multiresolution Valley Bottom Flatness Index (MrVBF), Normalized Difference Salinity Index (NDSI), Multiresolution of Ridge Top Flatness Index (MrRTF), convergence index, and channel network base level were among top covariates used for prediction at two levels of WRB. Results revealed the complexity of soil variations in this floodplain. Using other covariates such as soil texture and salinity maps can improve the prediction power. Increasing the size of sampling is recommended to improve the accuracy of the models in predicting the second level of WRB in this area.

Using machine learning algorithms to map the groundwater recharge potential zones

Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model; DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.

Groundwater spring potential assessment using new ensemble data mining techniques

The growing demand and exigency for groundwater resources warrant the demarcation of groundwater spring potential zones (GSPZ) for effective sustainable strategy in groundwater identification, conservation, and management. Here we utilized a novel data mining (DM) ensemble to generate groundwater spring potential maps (GSPMs) by combining RF-BRT (random forest-boosted regression tree), MARS-SVM (multivariate adaptive regression spline-support vector machine) and, FDA-GLM-MDA (functional data analysis-generalized linear model-mixture discriminant analysis). Initially, an aggregate of 1726 groundwater spring locations was collected from the regional water company of Tehran Province and field investigation, in which 1208 springs (70%) were taken for training purposes and the remaining 518 (30%) springs were applied for the validation process. Twelve conditioning factors including DEM (digital elevation model)/elevation, fault density, aspect, rainfall, distance from rivers, distance from faults, slope, MRVBF (multiresolution index of valley bottom flatness), TWI (topographic wetness index), lithology, land use/land cover, and permeability were utilized for mapping process and their importance in predicting the groundwater spring potential. The variable importance (VI) analysis using SVM (support vector machine) reveals that the most significant conditioning factors in the prediction process are rainfall, TWI, DEM-elevation, distance from rivers, slope, distance from faults, and MRVBF. The GSPMs generated from novel data-mining (DM) ensembles were validated using the cut-off reliant (recall, fallout, F-measure, accuracy, precision, specificity, TSS: true skill statistic, Cohen’s kappa, fourfold plot, CCI: corrected classified instances) and cut-off independent (ROC-AUC: receiver operating characteristic-area under the curve) measures. The outcome of the validation measures shows that RF-BRT has the superior values of recall, F-measure, overall accuracy, precision, specificity, TSS, Cohen’s kappa, fourfold plot, CCI followed by MARS-SVM, and FDA-GLM-MDA whereas the AUC value of RF-BRT (0.955), MARS-SVM (0.934), and FDA-GLM-MDA (0.914) also display similar result. The GSPMs generated using the novel DM ensemble models in our study can be utilized by policymakers in implementing the strategies for effective land use planning and sustainable groundwater management.

A hillslope based digital soil mapping approach, for hydropedological assessments

Urban sprawl and the accompanying development is set to increase pressure on the hydrological system. In turn, the hydrology of an area affects the infrastructure. Hydropedology, can assist to sustainably manage the infrastructure-hydrology interaction. Even though the hillslope scale is accepted as ideal for hydropedological assessments, regional soil maps have been used when hydropedology assessments were undertaken for larger areas. This study uses a hillslope based approach within a digital soil mapping method to conduct a hydropedological assessment for a large (12,000 ha) urban area under developmental pressure within Johannesburg, South Africa. One hundred and thirty-three hillslopes within the study area were delineated and mean and standard deviation of terrain attributes were calculated for each hillslope. Based on these values, the conditioned Latin hypercube sampling (cLHS) method was used to select 30 hillslopes on which soil observations would be made, in a transect, at the surveyor's discretion. A hydropedological soil map was created with the multinomial logistic regression algorithm and the 142 soil observations made. The soil map attained an acceptable 69% validation point accuracy and a Kappa value of 0.59, and was used to create a hillslope conceptual hydrological response map. The chi-square test and QQ-plots indicated that the cLHS selected hillslopes represented the hillslopes of the study site well, but the observations only represented some of the terrain attributes well. Despite the range of the observation dataset covering at least 98.7% of the pixels for the multi resolution index of valley bottom flatness (MRVBF), topographic wetness index (TWI) and altitude above channel network layers, their site distribution differed statistically from that of the observation distribution. The hillslope based survey approach allowed for an acceptable soil map to be created, while allowing for hillslope based hydropedological interpretations to be made. The “cost” for the hillslope based approach was that the distribution of some covariates for the observations differed from their distributions over the entire site.

Predicting soil types and soil properties with limited data in the Uasin Gishu Plateau, Kenya

Digital soil mapping (DSM) approaches can be used to create new soil maps or enhance existing maps, particularly in areas where only general soil maps are available. In this study, we utilized a knowledge-based inference soil mapping approach to develop a first-generation digital soil map for part of the Uasin Gishu Plateau in western Kenya. We calculated the following environmental covariates from the Shuttle Radar Topographic Mission (SRTM) 30 m digital elevation model (DEM): slope gradient, multi-resolution valley bottom flatness index (MrVBF), multi-resolution ridgetop flatness index (MrRTF), topographic position index (TPI), elevation, and profile curvature. These covariates were then used along with existing soil information and expert knowledge from soil scientists familiar with the area to produce new raster-based maps of soil types, effective soil depth, soil moisture storage capacity and soil drainage class. The soil type maps predicted using clustering analysis and fuzzy logic methods showed good agreement with field observations based on the overall accuracy values. The fuzzy logic map performed slightly better (kappa coefficient (k) = 0.68; overall accuracy = 0.76) than the map based on clustering analysis (k = 0.59; overall accuracy = 0.68). The accuracy for the effective soil depth fuzzy logic map was higher (R2 = 0.56; RMSE = 11; ME = 1.1) compared to the existing soil map (R2 = 0.34; RMSE = 27; ME = 8). Seven major soil types occur in the study area: Ferralsols, Nitisols, Gleysols, Luvisols, Acrisols, Cambisols and Regosols, according to the World Reference Base soil classification system. This study generated detailed and improved predictions of soil types and properties at 30 m grid resolution. These maps should be more useful for soil, crop and land use management decisions than existing maps.

Description and spatial inference of soil drainage using matrix soil colours in the Lower Hunter Valley, New South Wales, Australia.

Soil colour is often used as a general purpose indicator of internal soil drainage. In this study we developed a necessarily simple model of soil drainage which combines the tacit knowledge of the soil surveyor with observed matrix soil colour descriptions. From built up knowledge of the soils in our Lower Hunter Valley, New South Wales study area, the sequence of well-draining → imperfectly draining → poorly draining soils generally follows the colour sequence of red → brown → yellow → grey → black soil matrix colours. For each soil profile, soil drainage is estimated somewhere on a continuous index of between 5 (very well drained) and 1 (very poorly drained) based on the proximity or similarity to reference soil colours of the soil drainage colour sequence. The estimation of drainage index at each profile incorporates the whole-profile descriptions of soil colour where necessary, and is weighted such that observation of soil colour at depth and/or dominantly observed horizons are given more preference than observations near the soil surface. The soil drainage index, by definition disregards surficial soil horizons and consolidated and semi-consolidated parent materials. With the view to understanding the spatial distribution of soil drainage we digitally mapped the index across our study area. Spatial inference of the drainage index was made using Cubist regression tree model combined with residual kriging. Environmental covariates for deterministic inference were principally terrain variables derived from a digital elevation model. Pearson’s correlation coefficients indicated the variables most strongly correlated with soil drainage were topographic wetness index (−0.34), mid-slope position (−0.29), multi-resolution valley bottom flatness index (−0.29) and vertical distance to channel network (VDCN) (0.26). From the regression tree modelling, two linear models of soil drainage were derived. The partitioning of models was based upon threshold criteria of VDCN. Validation of the regression kriging model using a withheld dataset resulted in a root mean square error of 0.90 soil drainage index units. Concordance between observations and predictions was 0.49. Given the scale of mapping, and inherent subjectivity of soil colour description, these results are acceptable. Furthermore, the spatial distribution of soil drainage predicted in our study area is attuned with our mental model developed over successive field surveys. Our approach, while exclusively calibrated for the conditions observed in our study area, can be generalised once the unique soil colour and soil drainage relationship is expertly defined for an area or region in question. With such rules established, the quantitative components of the method would remain unchanged.

Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming

ABSTRACTSpatial information on soil salinity is increasingly needed for decision making and management practices in arid environments. In this article, we attempted to investigate soil salinity variation via a digital soil mapping approach and genetic programming in an arid region, Chah-Afzal, located in central Iran. A grid sampling strategy with 2-km distance was used. In total, 180 soil surface samples were collected and then analyzed. A symbolic regression was then adopted to correlate electrical conductivity (ECe) with a suite of auxiliary data including predicted maps of apparent electrical conductivity (vertical: ECav and horizontal: ECah), Landsat spectral data and terrain attributes derived from a digital elevation model. The accuracy of the genetic programming model was evaluated using root mean square error (RMSE), mean error (ME), and coefficient of determination (R2) based on an independent validation data set (20% of database or thirty soil samples). In general, results showed that ECah had the strongest influence on the prediction of soil salinity followed by salinity index wetness index, Landsat Band 3, multi-resolution valley bottom flatness index, elevation, and normalized difference vegetation index. Furthermore, results indicated that the genetic programming model predicted ECe over the study area accurately (R2 = 0.87, ME = −1.04 and RMSE = 16.36 dSm−1). Overall, it is suggested that similar applications of this technique could be used for mapping soil salinity in other arid regions of Iran.

Evolution of neotectonic activity of East Anatolian Fault System (EAFS) in Bingöl pull-apart basin, based on fractal dimension and morphometric indices

This study is an attempt of a semi-automatic geomorphological GIS analysis based on morphometric indices. In the study, 10-m-resolution Digital Elevation Models (DEMs) are used to assess the neotectonic signals regarding the recent topographic developments and to attach additional significance to active tectonics in the Bingöl basin area. The methodology incorporates the determination of the structural similarities of the faults in the basins using fractal concepts and the application of four morphometric indices (Stream Power Index (SP), Multi-resolution Index of Valley Bottom Flatness (MRVBF), Terrain Ruggedness Index (TRI) and Valley Width-to-Height Ratio (Vf)). In order to detect the deviation from spatial randomness of the applied indices, the weighted correlation coefficient Moran’s I is used and the results are interpreted at a confidence interval of 99%. The spatial distribution of integrated index values is evaluated with the tectonically active fault zones in order to determine the probable activity and the structural deformation in the basin.The applied methodology reveals that the fractal analysis of the fault lines and the spatial analysis of the morphometric indices proved to be effective tools in analyzing the tectonic influence of the fault system on the basin area. Within the basin area, a relatively lesser degree of tectonic activity is observed, in contrast with the high tectonic activity outside the basin.

Detection of former landfills in gravel plain using geomorphometric analysis and High-Resolution LiDAR DTM

The article represents the application of geomorphologic approach to discover the potential areas of buried waste in agricultural landscape of Ljubljana gravel plain. Some former waste disposal sites or landfills are underground sites characterized by heterogeneous old waste buried in formerly concave landforms: old inactive gravel pits or paleo-riverbeds. They form different types of anthropogenic landforms. They were primary recognized and located with the terrain visualization (analytical shading, hypsometry) of LiDAR data and in continuation with geomorphometric analysis and classification of fluvial terrain. Due to subsidence of heterogeneous waste, terrain of former landfill sites is bumpy and uncharacteristic of fluvial surface morfology or terrain. The geomorphometric analysis was applied to differentiate the anthropogenic landforms (gravel pits, filled gravel pits …) from natural alluvial landforms with combination of two geomorphometrics: multiresolution index of valley bottom flatness (MrVBF) and convergence index and high density LiDAR data. Result is the automatically derived classification of terrain in to three classes: (1) bumpy terrain, typical for areas with high terrain potential for landfill, (2) flat terrain, typical for dry paleo riverbeds and (3) »agricultural« terrain, typical for intensive fields and meadows. By comparing the results of geomorphometric analysis with the results of visual analysis the 26 of 46 visually detected anthropogenic landforms are overlapping the areas of high terrain potential for landfill and among these 8 objects were proved with geohistorical analysis of archive aerial photographs.

Índices para a representação da paisagem como apoio para levantamento pedológico em ambiente de geoprocessamento

O mapeamento de solos tem ganhado destaque dentro da comunidade científica, pois, à medida em que a preocupação com o meio ambiente aumenta, ocorre a necessidade de se entender cada vez mais a distribuição dos solos na paisagem, tal como seu potencial e limitações de uso. Desta forma, o principal objetivo do trabalho foi aplicar índices de representação da paisagem com o apoio de geoprocessamento, suporte na delimitação dos diferentes compartimentos da paisagem. Foram utilizados índices primários Altitude above channel network (AACN) e secundários Channel network base level (CNBL, Multiresolution índex of valley bottom flatness (MRVBF) e Wetness índex (ITW), cujo estudo foi a Fazenda Experimental Canguiri, no município de Pinhais, região Metropolitana de Curitiba. Para correlacionar os atributos químicos e granulométricos por grupo de amostragem, totalizando 17 pontos (Sugamosto, 2002) foi gerada, no Software Statistica, uma matriz de correlação linear simples (Pearson) com os índices de representação da paisagem, os quais foram não apenas utilizados na análise de agrupamentos mas também eficientes no apoio do mapeamento dos solos, a nível de subordem do Sistema Brasileiro de Classificação de Solo.

Digital soil mapping at pilot sites in the northwest coast of Egypt: A multinomial logistic regression approach

The study examines a digital soil mapping approach for the production of soil maps by using multinomial logistic regression on soil and terrain information at pilot sites in the Northwestern Coastal region of Egypt. The aim is to reproduce the original map and predict soil distribution in the adherent landscape. Reference soil maps produced by conventional methods at Omayed and Nagamish areas were used. Spectral and terrain parameters were calculated and logit models of the soil classes were developed. Predicted soil classes’ maps were produced. Software’s IDRISI/SAGA/SATISTCA/SPSS were used. The terrain and spectral parameters were found to be significantly influential and the selection of the land surfaces predictors was satisfactory. The McFadden pseudo R-squares ranged from 0.473 to 0.496. The most significant terrain parameters influencing the spatial distribution of the soil classes were elevation, valley depth, multiresolution ridgetop flatness index, multiresolution valley-bottom flatness index, and SAGA wetness index. However, the most influential spectral parameters are the first two principal components of the six Landsat Enhanced Thematic Mapper bands. The overall accuracy of the predicted soil maps ranged from 72% to 74% with a Kappa Index ranging from 0.62 to 0.64. The developed probability models were successfully used to predict the spatial distribution of the soil mapping units at pixel resolutions of 28.5 m × 28.5 m and 90 m × 90 m at adjacent unvisited areas at Matrouh and Alamin. The developed methodology could contribute to the allocation and to the soil digital mapping and management of new expansion sites in remote desert areas of Egypt.