One of the most important characteristics of porphyry copper deposits (PCDs) is the type and distribution pattern of alteration zones which can be used for screening and recognizing these deposits. Hydrothermal alteration minerals with diagnostic spectral absorption properties in the visible and near-infrared (VNIR) through the shortwave infrared (SWIR) regions can be identified by multispectral and hyperspectral remote sensing data. Six Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands in SWIR have been shown to be effective in the mapping of Al-OH, Fe-OH, Mg-OH group minerals. The five VNIR bands of Landsat-8 (L8) Operational Land Imager (OLI) are useful for discriminating ferric iron alteration minerals. In the absence of complete hyperspectral coverage area, an opportunity, however, exists to integrate ASTER and L8-OLI (AO) to compensate each other’s shortcomings in covering area for mineral mapping. This study examines the potential of AO data in mineral mapping in an arid area of the Duolong porphyry Cu-Au deposit(Tibetan Plateau in China) by using spectral analysis techniques. Results show the following conclusions: (1) Combination of ASTER and L8-OLI data (AO) has more mineral information content than either alone; (2) The Duolong PCD alteration zones of phyllic, argillic and propylitic zones are mapped using ASTER SWIR bands and the iron-bearing mineral information is best mapped using AO VNIR bands; (3) The multispectral integration data of AO can provide a compensatory data of ASTER VNIR bands for iron-bearing mineral mapping in the arid and semi-arid areas.

The fragile alpine vegetation in the Tibetan Plateau (TP) is very sensitive to environmental changes, making TP one of the hotspots for studying the response of vegetation to climate change. Existing studies lack detailed description of the response of vegetation to different climatic factors using the method of multiple nested time series analysis and the method of grey correlation analysis. In this paper, based on the Normalized Difference Vegetation Index (NDVI) of TP in the growing season calculated from the MOD09A1 data product of Moderate-resolution Imaging Spectroradiometer (MODIS), the method of multiple nested time series analysis is adopted to study the variation trends of NDVI in recent 17 years, and the lag time of NDVI to climate change is analyzed using the method of Grey Relational Analysis (GRA). Finally, the characteristics of temporal and spatial differences of NDVI to different climate factors are summarized. The results indicate that: (1) the spatial distribution of NDVI values in the growing season shows a trend of decreasing from east to west, and from north to south, with a change rate of −0.13/10° E and −0.30/10° N, respectively. (2) From 2001 to 2017, the NDVI in the TP shows a slight trend of increase, with a growth rate of 0.01/10a. (3) The lag time of NDVI to air temperature is not obvious, while the NDVI response lags behind cumulative precipitation by zero to one month, relative humidity by two months, and sunshine duration by three months. (4) The effects of different climatic factors on NDVI are significantly different with the increase of the study period.

The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation’s ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10°E from the west to the east, 0.28/10°N from the south to the north; (2) During 2000–2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1–2 months, relative humidity by 1–2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

The Three-North Shelter Forest (TNSF) program is a significant ecological safety barrier in northern China, where both climate change and anthropogenic activity contribute to the increase in vegetation coverage observed. However, comprehensive effects of these factors on vegetation have not been accurately quantified yet. This study utilized the Global Land Surface Satellite (GLASS) Advanced Very-High-Resolution Radiometer (AVHRR) Fractional Vegetation Cover (FVC) data, meteorological data, and spatial distribution of ecological engineering to analyze spatiotemporal variation of FVC and climate in the TNSF program region in China during the period 1982–2018. A partial correlation analysis and residual analysis were performed to determine the relative contribution of climate change and anthropogenic activity to the FVC and the overall effect of ecological governance. Results showed that since 1982, the average FVC in the TNSF program region was 0.201–0.253, with an average growth rate of 0.01·(10a)⁻¹. The FVC showed a significant increase in 66.45% of the TNSF region, and will continue to increase, while only 7.02% showed a significant decrease. The coefficient of variation showed a large spatial variation, with 30.86% being in very low stability regions, mainly distributed in Inner Mongolia and the Loess Plateau. A warm and wet climate is more conducive to increasing the FVC than the warm and dry climate, and ecological engineering has the largest impact on areas with an annual accumulated precipitation greater than 300 mm. A quantitative analysis revealed that climate change and anthropogenic activity contributed to the significant increase in the FVC in 15.58% and 46.81% of the TNSF region, respectively. Therefore, ecological governance projects, such as the TNSF program, play a crucial role in enhancing the FVC in this region.

Water-use efficiency (WUE) is not only an important indicator to connect the carbon and water cycles of a terrestrial ecosystem, but also a key parameter for an ecosystem to respond to climate change. It is crucial for understanding the mechanism of regional ecosystem response to environmental change by researching the influences of vegetation and climate change on WUE variation and revealing its drivers. Based on trend analysis, grey relational analysis, and ridge-regression analysis, this study analyzed the spatiotemporal variation characteristics of WUE in Inner Mongolia (IM) from 2001 to 2018 and determined the dominant influencing factors of WUE variation. The results showed that the annual mean WUE in IM was 1.39 g C m⁻² mm⁻¹ and it generally presented a rising trend, with an increasing rate of 0.0071 g C m⁻² mm⁻¹ yr⁻¹. Leaf-area index (LAI) and precipitation were the most important factors influencing WUE in IM, followed by relative humidity and wind speed. Temperature, water vapor pressure and sunshine duration slightly influenced WUE and they were relatively less important. According to the ridge-regression analysis, LAI, precipitation and relative humidity had a positive contribution to WUE variation, while the wind speed had a negative contribution. Regionally, LAI was the dominant cause of WUE variation. The contribution and relative contribution rate of LAI to WUE variation were 0.008 g C m⁻² mm⁻¹ yr⁻¹ and 44.57%, which were significantly higher than those of precipitation, relative humidity, and sunshine duration. Thus, vegetation primarily dominated WUE variability during the study period. The relative contribution rate of LAI varied across the different vegetation types and ranged from 25.26% in swamps to 51.29% in meadows. Our results improve the understanding of the effects of driving factors on WUE, which can help policymakers with water resource management and ecological restoration.