This paper studied the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological classification in the shallowly covered Eastern Yanshan Mountain of Eastern China. The interpreted objects include quartz sandstone, carbonate rock, gneiss, and andesite. An integrated approach was employed to process the remote sensing data: First, the matched filtering method, accompanied by the ASTER library and imagery spectra as reference, was used to enhance the targeted information, namely the remotely visible (typical) rock outcrops. As the andesite is frequently weathered to iron-rich minerals in the field, so the high-resolution SPOT6 3/1 image, instead of the ASTER image, was used to locate the outcrops. Second, the fractal digital number-frequency algorithm was developed to preliminarily extract the lithology anomaly patches; and the obtained geological anomalies were generalized into three types: anomalies related to outcrops, anomalies caused by different forms of the same lithology, and random noise. Third, digital elevation model derived slope masks, in combination with the spatial intersection operation, was used to eliminate the pseudo-outcrop anomalies and noise. Finally, the accuracy assessment was conducted by referencing the local rock-outcrop database, and the misclassification rates for quartz sandstone, carbonate rock, gneiss, and andesite are 8.9%, 12.5%, 23%, and 48.3%, respectively. This study has contributed a useful case study for remote-sensing lithology mapping in shallowly covered areas, and the proposed method should have a great potential to be applied to many similar cases.

Lithological mapping in weathered and vegetated coastal zone is always a crucial but challenging issue for geological remote sensing. This study tested the applicability of Sentinel-2A and ASTER GDEM data for lithological mapping in the coastal zone of Shanwei district, southern China. Here, a comprehensive scheme for tuff and granite outcrops extraction and lithological classification was employed to process the remote sensing data: 1) Using the matched filtering with image-derived spectra as references, the weathered but representative outcrop-related patches were preliminarily extracted based on the fractal “DN-A” (digital number-area) algorithm; 2) The image masks of NDXI (NDVI, NDSI and MNDWI) in conjunction with spatial intersection, were conducted to minimize the influence of vegetation coverage; 3) Considering terrain features caused by differential weathering, slope analysis have allowed us to discriminate from tuff to granite outcrops; 4) A set of empirical verification criteria were established to assess the classification accuracy, which are exactly 82.4~83.2% of tuff and 91.3~92.3% of granite. The peak values of tuff and granite patch areas are 0–0.1 km² (51%) and 0–0.5 km² (60%), and 90% of patches are < 0.6 km² of tuff and < 3.5 km² of granite, respectively. The protolith of extracted weathered outcrops were further identified by several excavated trenches. This research has contributed a useful case study for remote-sensing lithological mapping in weathered and vegetated coastal zone, and the proposed approach should have a great possibility to be extrapolated for other similar cases.

As the traditional geological cross section measurement, the artificial traverse method was recently substituted by using point coordinates data. However, it is still the crux of the matter that how to acquire the high-precision point coordinates data quickly and economically. Thereby, the backpack Lidar is presented on the premise of the principle of using point coordinates in this issue. Undoubtedly, Lidar technique, one of booming and international active remote sensing techniques, is a powerful tool in obtaining precise topographic information, high-precision 3-D coordinates and building a real 3-D model. With field practice and date processing indoors, it is essentially accomplished that geological sections maps could be generated simply, accurately and automatically in the support of relevant software such as ArcGIS and LiDAR360.

Accurate terrain models are critical for studying the formation and development of slot canyons. However, for slot canyon landforms, it is challenging to generate comprehensive and high-resolution morphological data by individual observation due to the inaccessibility of steep walls on either side and the complexity of the field observation environment, such as variable-slope terrain, partial vegetation cover, and lack of satellite signal. Off-the-shelf surveying techniques, including Unmanned Aerial Vehicles (UAV) photogrammetry and Backpack Mobile Laser Scanning (BMLS), facilitate slot canyon surveys and provide better observations. This paper proposes an integrated scheme to generate comprehensive and centimeter-resolution slot canyon terrain datasets (e.g., color point clouds, Digital Elevation Models (DEM), and 3D mesh) using BMLS and fine UAV photogrammetry. The results show that the fine flight of UAVs based on a rough model can avoid collision with obstacles or flying into restricted areas, allowing users to perform tasks faster and safer. Data integration of BMLS and UAV photogrammetry can obtain accurate terrain datasets with a Root Mean Squared Error (RMSE) of point cloud registration of 0.028 m. Such high-resolution integration terrain datasets reduce local data shadows produced solely by individual datasets, providing a starting point to revealing morphological evolution and genesis of slot canyons.