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Freshwater tufas in karstic environments denote an increasingly studied high-resolution terrestrial paleoclimate archive. Tufa morphology is, among other factors, climate-driven and sedimentary facies models are developed to ascribe present day environmental conditions to constrain sedimentary records of paleotufa. Therefore, knowledge about tufa morphology is needed but difficult to obtain due to the complex topo-bathymetry of tufa-bearing channel beds as a consequence of the complex nature of sedimentary processes shaping a patchy transition zone between land and water. Combined airborne small-footprint LiDAR topo-bathymetry (ALTB) is a new emerging remote sensing technique, which is applied here for the first time in a densely forested area to describe the morphology of a small tufa-bearing river (Kaisinger Brunnenbach) near Greding, Bavaria, Germany. We show that sub-metre footprint ALTB captures tufas of maximum total dam heights from 0.3 m up to 1.6 m. Based on a difference model of the channel bed inclusive tufas and the channel bed exclusive tufas, tufas are obtained as objects allowing the determination of maximum tufa height. Our results document that slope and openness as raster-based morphometric derivatives provide information about tufa orientation and morphology whereas longitudinal slope computed in two different window sizes enable a morphological channel bed characterization as well as the relation of tufa morphology to channel bed morphology. Hence, we demonstrate the suitability of ALTB to capture tufa barrages in a densely forested karstic river and suggest channel bed morphology is a crucial factor controlling tufa deposition and morphology.

In this study, a systematic analysis of cesium through crushed granite (0.297–0.840 mm) was constructed where synthetic groundwater and seawater were employed as the liquid phase. To build such models, multi-stage advection–dispersion column tests were conducted in order to determine the major transport processes and the calibration/validation of the hypothesized reactive transport models. The experimental breakthrough curves (BTCs) for HTO and Cs were both matched with their corresponding numerical BTCs by adjusting reaction and transport parameters such as forward or backward reaction rate constants (k f or k b), dispersivity (α), and porosity(θ).

Coral walls protect vegetation gardens from strong winds that sweep across Xiji Island, Taiwan Strait for half the year. Topographic parameters based on light detection and ranging (LiDAR)-based high-resolution digital elevation model (DEM) provide obvious correspondence with the expected form of landscape features. The information on slope, curvature, and openness can help identify the location of landscape features. This study applied the automatic landscape line detection to extract historic vegetable garden wall lines from a LiDAR-derived DEM. The three rapid processes used in this study included the derivation of topographic parameters, line extraction, and aggregation. The rules were extracted from a decision tree to check the line detection from multiple topographic parameters. Results show that wall line detection with multiple topographic parameter images is an alternative means of obtaining essential historic wall feature information. Multiple topographic parameters are highly related to low wall feature identification. Furthermore, the accuracy of wall feature detection is 74% compared with manual interpretation. Thus, this study provides rapid wall detection systems with multiple topographic parameters for further historic landscape management.

The river characteristics have played an important role for hydrological models. Sediments supplied from landslides may affect the river channel morphological changes in different reaches or magnitude; namely, the riverbed morphology can be related to disaster events. Measurements of riverbed morphology are made using high-resolution LiDAR DEM in this study to understand the relationship. Quantifying the morphology along a river channel has been proven difficult; till the airborne laser altimetry technology was implemented in the survey. In this study, the remote sensing techniques are employed to assist the studies on stream roughness distribution and morphological investigation. The results are proven to be valuable for hydrological and engineering applications.

Vegetative cover can contribute to improving the stability of steep slopes by reducing erosion; the vegetation growing condition may reflect the landslides spatial characteristics. The landslides and vegetation restoration evaluation in this study was implement by high resolution air-photo, SPOT-5 satellite image and air-borne LiDAR data then acquired for calculated vegetation restoration rate. The vegetation restoration results shown different spatially distribution on landslide area, the vegetation restoration rate where nearby riverbank is less than where in mountain area; the growing rate also shows the same appearance, the mean value with 3.2 meter in mountain area and the riverbank area with 1.1 meter restoration rate. It indicated that the vegetation restoration of mountain area appear well growing condition than in riverbank area. The steep slope appears with low vegetation restoration rate, it may have relations with higher frequency of landslides and lower disturbed by human in this area.

LiDAR DEM (Digital Elevation Model) data of two different periods, before and after a heavy rain event, were acquired to specify the exact boundary of the mass been moved, and to calculate the variations of ground elevation by using the DEM values for each photometric cellular element. With the modified Sediment Production Rate (SPR) equation, volumes of the mass wasted were as well being estimated. In addition, with the aids of spatial analysis tools of Geographic Information System (GIS), details of the landslide were well enhanced; such that, the magnitude and the peripheral extent of the landslide were characterized.

Monitoring and evaluation are the base work for environment protection and management. Maps are the most directly and lucid measure to represent and record geographic environment. As a result, By overlaying maps with different ages and processes of geographic change can be quantitative analyzed and should provides long-term scale, on the other hand, satellite image can be use for short-term investigation to know the wetland change which cause by hazard event. It is shown in this study that local spatial autocorrelation should provide objective classification in training stage, and the spatial autocorrelation also can enhance the pattern of vegetation and water body on image.

Using spectral-only information for landslides classification is usually confusing with houses, roads, and other bare lands because these ground features have similar spectral patterns on images. The terrain roughness can be measured by significant wavelengths; some studies have linked the relationships between terrain roughness and the landslide by using numerical analyses of topography data. In this study, airborne LiDAR data of 1m grid are used to explore the possibility of improvement of landslide classification, the LiDAR-derived data include DEM slope and terrain roughness indices including diversity, dominance and relative richness with different grid size data are used to improvement classification accuracy. The improvement of accuracy when including DEM slope is 22% in producer's accuracy and 27% in user's accuracy. The accuracy of diversity, dominance and relative richness indices all are improved when kernel sizes enlarge in Maximum Likelihood and Mahalanobis Distance algorithms.

The deliverables of an airborne LiDAR survey usually include all points, ground points, digital surface models (DSM) and digital elevation models (DEM). Indices of point clouds tested in this study include density of all points, density of ground points, density of only returns, and density of multiple returns. Shallow landslides are the most common landslides triggered by torrential rainfalls and explicit fresh scars after rainfall events. Multiple returns in forest area give the possibility of differentiating landslide scars from vegetated lands. Classification results from the indices derived from these four kinds of densities are verified by the result obtained by manual interpretation of the derived nDSM images. The experiment is carried out using the dataset obtained in I-Lan County after Typhoon Kalmaegi on 17 July 2008. The results show that a proper definition of the parameters for the indices is most critical for the detection of shallow landslides.