Terrestrial laser scanning (TLS) is often used to monitor landslides and other gravitational mass movements with high levels of geometric detail and accuracy. However, unstructured TLS point clouds lack semantic information, which is required to geomorphologically interpret the measured changes. Extracting meaningful objects in a complex and dynamic environment is challenging due to the objects' fuzziness in reality, as well as the variability and ambiguity of their patterns in a morphometric feature space. This work presents a point-cloud-based approach for classifying multitemporal scenes of a hillslope affected by shallow landslides. The 3D point clouds are segmented into morphologically homogeneous and spatially connected parts. These segments are classified into seven target classes (scarp, eroded area, deposit, rock outcrop and different classes of vegetation) in a two-step procedure: a supervised classification step with a machine-learning classifier using morphometric features, followed by a correction step based on topological rules. This improves the final object extraction considerably.
Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHS adapt ) in a small catchment (4.2 km 2 ) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHS adapt with the original cLHS design (cLHS orig ). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHS adapt and cLHS orig , while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHS adapt by adding available legacy samples (cLHS adapt+ ) and compared the prediction accuracies. Based on an external validation set cLHS val (n = 20), the coefficient of determination (R 2 ) of the cLHS adapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R 2 -values of the RF predictions based on cLHS adapt+ , using additional legacy samples, are marginally increased on average by 5%.
Predicting taxonomic classes can be challenging with dataset subject to substantial irregularities due to the involvement of many surveyors. A data pruning approach was used in the present study to reduce such source errors by exploring whether different data pruning methods, which result in different subsets of a major reference soil groups (RSG) – the Plinthosols – would lead to an increase in prediction accuracy of the minor soil groups by using Random Forest (RF). This method was compared to the random oversampling approach. Four datasets were used, including the entire dataset and the pruned dataset, which consisted of 80% and 90% respectively, and standard deviation core range of the Plinthosols data while cutting off all data points belonging to the outer range. The best prediction was achieved when RF was used with recursive feature elimination along with the non-oversampled 90% core range dataset. This model provided a substantial agreement to observation, with a kappa value of 0.57 along with 7% to 35% increase in prediction accuracy for smaller RSG. The reference soil groups in the Dano catchment appeared to be mainly influenced by the wetness index, a proxy for soil moisture distribution.
Below vegetation, throughfall kinetic energy (TKE) is an important factor to express the potential of rainfall to detach soil particles and thus for predicting soil erosion rates. TKE is affected by many biotic (e.g. tree height, leaf area index) and abiotic (e.g. throughfall amount) factors because of changes in rain drop size and velocity. However, studies modelling TKE with a high number of those factors are lacking. This study presents a new approach to model TKE. We used 20 biotic and abiotic factors to evaluate thresholds of those factors that can mitigate TKE and thus decrease soil erosion. Using these thresholds, an optimal set of biotic and abiotic factors was identified to minimize TKE. The model approach combined recursive feature elimination, random forest (RF) variable importance and classification and regression trees (CARTs). TKE was determined using 1405 splash cup measurements during five rainfall events in a subtropical Chinese tree plantation with five-year-old trees in 2013. Our results showed that leaf area, tree height, leaf area index and crown area are the most prominent vegetation traits to model TKE. To reduce TKE, the optimal set of biotic and abiotic factors was a leaf area lower than 6700 mm2, a tree height lower than 290 cm combined with a crown base height lower than 60 cm, a leaf area index smaller than 1, more than 47 branches per tree and using single tree species neighbourhoods. Rainfall characteristics, such as amount and duration, further classified high or low TKE. These findings are important for the establishment of forest plantations that aim to minimize soil erosion in young succession stages using TKE modelling.
Soil erosion by water outlines a major threat to the Three Gorges Reservoir Area in China. A detailed assessment of soil conservation measures requires a tool that spatially identifies sediment reallocations due to rainfall-runoff events in catchments. We applied EROSION 3D as a physically based soil erosion and deposition model in a small mountainous catchment. Generally, we aim to provide a methodological frame that facilitates the model parametrization in a data scarce environment and to identify sediment sources and deposits. We used digital soil mapping techniques to generate spatially distributed soil property information for parametrization. For model calibration and validation, we continuously monitored the catchment on rainfall, runoff and sediment yield for a period of 12 months. The model performed well for large events (sediment yield > 1 Mg) with an averaged individual model error of 7.5%, while small events showed an average error of 36.2%. We focused on the large events to evaluate reallocation patterns. Erosion occurred in 11.1% of the study area with an average erosion rate of 49.9 Mg ha À1 . Erosion mainly occurred on crop rotation areas with a spatial proportion of 69.2% for 'corn-rapeseed' and 69.1% for 'potato-cabbage'. Deposition occurred on 11.0%. Forested areas (9.7%), infrastructure (41.0%), cropland (corn-rapeseed: 13.6%, potatocabbage: 11.3%) and grassland (18.4%) were affected by deposition. Because the vast majority of annual sediment yields (80.3%) were associated to a few large erosive events, the modelling approach provides a useful tool to spatially assess soil erosion control and conservation measures.
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