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.

The environmental change in the wetlands in the southern Mongolian Plateau has important impacts on the environment of North China and even the entire Northeast Asia, from which the global climate change can be understood on a large scale, especially the climate change in the Mongolian Plateau. This study extracted the information on the wetlands from three stages of remote sensing images (also referred to as RS images) of the study area, including Enhanced Thematic Mapper Plus (ETM+) images of 2000, TM images of 2010, and Landsat 8 Operational Land Imager (OLI) images of 2018. As indicated by the extraction results, the area of wetlands decreased from 796.90 km² to 666.24 km² at a rate of 7.26 km²/a and the patch number of wetlands decreased from 731 to 316 in the study area in 2000‒2018. In terms of wetland types, the change of the wetlands was dominated by the decrease of lacustrine wetlands, of which the area and patch number decreased by 106.2 km² and 242, respectively. Furthermore, the area of the lacustrine wetlands decreased at the highest rate of 8.70 km²/a in 2010‒2018. From the perspective of spatial distribution, the wetlands in the western part shrunk more notably than those in the eastern part as a whole in the study area. Meanwhile, the decrease in the area of the wetlands mainly occurred in the northwest endorheic region, where the area and patch number of the wetlands decreased by 97.86 km² and 366, respectively, accounting for 74.90% and 88.19% of the total decreased area and the total decreased patch number of the wetlands in the study area, respectively.

It is a common responsibility of all countries to protect the ecological environment and promote sustainable development. Eco-geology, which provides a basic guarantee for the rational utilization of natural resources, ecological protection and restoration, has gradually become a new hot spot of geological survey institutions in various countries. Eco-geological survey in the Qinghai–Tibet Plateau plays an important role in regional ecological protection and global change research. Setting up workable budgets symbolizes the competence of geological survey organizations in fulfilling their responsibilities, and unreliable cost estimates can cause economic and political complications. The unclear scope definition and scarcity of project information available at early stages make it hard to generate reliable preliminary cost estimates. Hence, based on historical data and the practical experience of scientists who have been engaged in eco-geological survey in the Qinghai–Tibet Plateau for a long time, this research aimed to develop a cost estimation model using statistical learning methods for geological survey organizations to forecast preliminary costs during the early stages of an eco-geological survey project to fulfill different cost control and managerial functions. This research makes full use of modern remote sensing technology and reasonably arranges the input of ground survey, drilling, geophysical exploration and other workloads, supplemented by the existing ecological monitoring station data. It comprehensively sorts out the work means and workload of field and office work needed to complete the map work, and it puts forward a consistent index system and preliminary cost calculation framework. On this basis, based on the principle of matching quantity and price, a reasonable budget standard or market price is selected to calculate the budget estimate of a single map sheet, and then, through the regional adjustment coefficient, the budget estimate of a map sheet covering the whole Qinghai–Tibet Plateau is extended and calculated. The budget estimate will provide effective support for the declaration of eco-geological survey projects in the Qinghai–Tibet Plateau and the calculation of the scale of project funds so as to ensure the smooth development of eco-geological surveying in the region.