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%.

In 2003, the Three Gorges Project (TGP, China), currently the world's largest hydroelectric power plant by total capacity, went into operation. Due to large-scale impoundment of the Yangtze River and its tributaries and also due to resettlement, extensive environmental impacts like land use change and increase of geohazards are associated with the TGP. Within the Yangtze Project,we investigate these effects for the Xiangxi (香溪) catchment which is part of the Three Gorges Reservoir. The aim of this study is to evaluate the susceptibility for mass movement within the Xiangxi River backwater area using geographic information system (GIS). We used existing mass movements and the conditioning factors (geology, elevation, slope, curvature, land use, and land use change) for analyzing mass movement susceptibility. Mass movements and geology were mapped in the field to establish a mass movement inventory and a geological map. Land use and digital elevation model (DEM) were obtained from remote-sensing data. We determined the relation between mass movements and the conditioning factors by using the frequency ratio method and found strong relation between mass movements and both natural and human-influenced conditioning factors.

Large dam projects attract worldwide scientific attention due to their environmental impacts and socioeconomic consequences. One prominent example is the Three Gorges Dam (TGD) at the Yangtze River in China. Due to considerable resettlements, large-scale expansion of infrastructure and shifts in land use and management, the TGD project has irreversible impacts on the Upper Yangtze River Basin and strongly challenges the environmental conditions of this fast-developing region. Soil erosion and landslides are major geo-hazards. Knowing the extent and consequences of those geo-hazards for the landscape is essential to predict and evaluate their risk potential and allows for the development of strategies for a sustainable future land use in the Three Gorges Region (TGR). In this context, our research objectives are (1) to better understand the mechanisms of soil erosion, landslides, and diffuse matter fluxes in the TGR and their anthropogenic and environmental control factors, (2) to predict their hazard potential by combining spatial and temporal, scenario-driven high-resolution modeling in combination with multiscale earth observation data, and (3) to develop a multicomponent approach for the assessment and monitoring of geogene structures and processes. The paper describes the workflow of the project and introduces case studies, representing the current state of our research. It is shown that land-use changes as well as the water-level fluctuations of the reservoir are the crucial drivers for the soil erosion and landslide hazard. Furthermore, we present a framework aiming at the establishment of a monitoring and measuring network as well as an early warning system.

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