The Ordos Plateau, a typical semi-arid area in northern China, has experienced severe wind erosion events that have stripped the agriculturally important finer fraction of the topsoil and caused dust events that often impact the air quality in northern China and the surrounding regions. Both climate change and human activities have been considered key factors in the desertification process. This study used multi-spectral Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) remote sensing data collected in 2000, 2006, 2010 and 2015 to generate a temporal series of the modified soil-adjusted vegetation index (MSAVI), bare soil index (BSI) and albedo products in the Ordos Plateau. Based on these satellite products and the decision tree method, we quantitatively assessed the desertification status over the past 15 years since 2000. Furthermore, a quantitative method was used to assess the roles of driving forces in desertification dynamics using net primary productivity (NPP) as a commensurable indicator. The results showed that the area of non-desertification land increased from 6647 km² in 2000 to 15,961 km² in 2015, while the area of severe desertification land decreased from 16,161 km² in 2000 to 8,331 km² in 2015. During the period 2006–2015, the effect of human activities, especially the ecological recovery projects implemented in northern China, was the main cause of desertification reversion in this region. Therefore, ecological recovery projects are still required to promote harmonious development between nature and human society in ecologically fragile regions like the Ordos Plateau.

The mineralogical indices the Quartz Index (QI), Carbonate Index (CI) and Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data were applied to various geological materials for regional lithological mapping on the Tibetan Plateau. Many lithological and structural features are not currently well understood in the central Tibetan Plateau, including the distribution of mafic-ultramafic rocks related to the suture zones, the quartzose and carbonate sedimentary rocks accreted to the Eurasian continent, and sulfate layers related to the Tethys and neo-Tethys geological setting. These rock types can now be mapped with the interpretation of the processed ASTER TIR images described in this paper. A methodology is described for the processing of ASTER TIR data applied to a very wide region of the Tibetan Plateau. The geometrical and radiometric performance of the processed images is discussed, and the advantages of using ortho-rectified data are shown. The challenges of using ASTER data with a small footprint in addition to selecting an appropriate subset of scenes are also examined. ASTER scenes possess a narrow swath width when compared to LANDSAT data (60 km vs. 185 km, respectively). Furthermore, the ASTER data archive is vast, consisting of approximately three million images. These details can present an added level of complexity during an image processing workflow. Finally, geological interpretations made on the maps of the indices are compared with prior geological field studies. The results from the investigations suggest that the indices perform well in the classification of quartzose rocks based on the carbonate and mafic mineral content, in addition to the granitic rocks based on the feldspar content.

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

Monitoring the change of post-seismic landslides could provide valuable information for geological disaster treatment. The 2017 Jiuzhaigou Ms 7.0 earthquake has triggered a large number of landslides in the Jiuzhaigou United Nations Educational, Scientific and Cultural Organization (UNESCO) Natural Heritage site, which provides a unique opportunity for monitoring the spatio-temporal characteristics and exploring the impact factors of post-seismic landslides change. In this study, the spatio-temporal characteristics of landslides and their post-seismic changes are analyzed using multi-source, multi-temporal, and multi-scale remote sensing data combining with the field study. The Support Vector Machine classification, visual interpretation, field investigation, and Geographic Information System technology are employed to extract landslides and analyze their spatial distribution patterns. Moreover, the Certainty Factor method is used to explore the susceptibility of landslides and to find key impact factors. Our results show that the net increase area of landslide is 1.2 km² until September 27th, 2019, which are induced by the expansion of coseismic landslide, the post-seismic landslide, and the expansion of vegetation degradation. Moreover, the area expansion of the coseismic and post-seismic landslides is mainly related to the increase of debris flow induced by the post-seismic torrential rainfalls. The highest net increase rate of post-seismic landslide change does not distribute on the regions with the highest density of coseismic landslides. The susceptibility of post-seismic landslide change is greatly influenced by slope, altitude, aspect, peak ground acceleration fault, and strata. It is higher in the coseismic landslide area with low susceptibility. This study also suggests that the potential landslides will most likely occur in the unstable slope region affected by the additional driving force. Therefore, great attention should be paid to identify and prevent the potential landslides on unstable slopes in addition to treatments of the sliding slopes. This study provides a good example for the monitoring and assessment of post-seismic landslides in mountainous regions with a steep slope and deep valley.

The Altai Mountains are a heritage-rich archaeological landscape with monuments in almost every valley. Modern nation state borders dissect the region and limit archaeological landscape analysis to intra-national areas of interest. Remote sensing can help to overcome these limitations. Due to its high precision, Synthetic Aperture Radar (SAR) data can be a very useful tool for supporting archaeological prospections, but compared to optical imagery, the detectability of sites of archaeological interest is limited. We analyzed the limitations of SAR using TerraSAR-X images in different modes. Based on ground truth, the discernibility of burial mounds was analyzed in different SAR acquisition modes. We show that very-high-resolution TerraSAR-X staring spotlight images are very well suited for the task, with >75% of the larger mounds being discernible, while in images with a lower spatial resolution only a few large sites can be detected, at rates below 50%.

Methane (CH₄) is one of important greenhouse gases that affects the global radiative balance after carbon dioxide (CO₂). Previous studies have demonstrated the detection of known sources of CH₄ emission using the hyperspectral technology based on in situ vertical CH₄ profile or ground CH₄ emissions data. However, those approaches have not yet to detect the unknown terrestrial sources of CH₄ emission at local-scale or regional-scale. In this paper, the Shortwave Airborne Spectrographic Imager (SASI) was employed to detect concentrated sources of CH₄ emissions based on the absorption of CH₄ in the shortwave infrared (SWIR) region. As a result, a band ratio (namely R_CH4, R_CH4 = Band91/Band78) determined through wavelet transform singularity detection has proposed for detection of the terrestrial CH₄ emissions sources using SASI hyperspectral radiance image data, and elevated CH₄ locations in the oil shale retorting plants were identified. Additionally, SASI surface reflectance data and multiple reference spectra in the spectral angle mapper (SAM) were used to classify surface sources of CH₄ release. High-resolution Google Earth imagery and thermal imaging camera (FLIR GF320) had also verified that the CH₄ releasing sources are mainly the oil shale mining field and the retorting plant. Therefore, the high-resolution imaging hyperspectral spectrometer can provide a powerful tool for detecting terrestrial CH₄ release sources at local-scale to reduce the greenhouse gas emissions related to hydrocarbon development.