Timescale Effects of Radial Growth Responses of Two Dominant Coniferous Trees on Climate Change in the Eastern Qilian Mountains

To explore the difference in the response of the radial growth of Pinus tabulaeformis and Picea crassifolia on different timescales to climate factors in the eastern part of Qilian Mountains, we used dendrochronology to select four different timescales (day, pentad (5 days), dekad (10 days), and month) for exploration. The primary conclusions were as follows: (1) According to an investigation of the dynamic correlations between radial growth and climate conditions, drought during the growing season has been the dominant limiting factor for radial growth across both species in recent decades; (2) climate data at the dekad scale are best for examining the correlations between radial growth and climate variables; and (3) based on basal area increment, P. tabuliformis in the study area showed a trend of first an increase and then a decrease, while P. crassifolia showed a trend of continuous increase (BAI). As the climate continues to warm in the future, forest ecosystems in arid and semi-arid areas will be more susceptible to severe drought, which will lead to a decline in tree growth, death, and community deterioration. As a result, it is critical to implement appropriate management approaches for various species based on the peculiarities of their climate change responses.

The Optical Characterization and Distribution of Dissolved Organic Matter in Water Regimes of Qilian Mountains Watershed

The constituents and content of dissolved organic matter (DOM) in the Qilian Mountain watershed were characterized with a spectroscopic technique, especially 3-DEEM fluorescence assisted by parallel factor (PARAFAC) analysis. The level of DOM in the surrounding area of Qinghai lake (thereafter the lake in this article specifically refers to Qinghai Lake)was highest at 9.45 mg C·L−1 and about 3 times less (3.09 mg C·L−1) in a cropland aquatic regime (the lowest value). In general, DOM was freshly autochthonously generated by plankton and plant debris, microorganisms and diagenetic effects in the aquatic environment (FI > 1.8). Component 1 (humic acid-like) and 3 (fulvic acid-like) determined the humification degree of chromophoric dissolved organic matter (CDOM). The spatial variation of sulfate and nitrate in the surrounding water regime of the lake revealed that organic molecules were mainly influenced by bacterial mediation. Mineral disintegration was an important and necessary process for fluorescent fraction formation in the cropland water regime. Exceptionally, organic moiety in the unused land area was affected by anespecially aridclimate in addition to microbial metabolic experience. Salinity became the critical factor determining the distribution of DOM, and the total normalized fluorescent intensity and CDOM level were lower in low-salinity circumstances (0.2–0.5 g·L−1) with 32.06 QSU and 1.38 m−1 in the grassland area, and higher salinity (0.6~0.8 g·L−1) resulted in abnormally high fluorescence of 150.62 QSU and absorption of 7.83 m−1 in the cropland water regime. Climatic conditions and microbial reactivity controlled by salinity were found to induce the above results. Our findings demonstrated that autochthonous inputs regulated DOM dynamics in the Qilian Mountains watershed of high altitude.

Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

Spatial and Temporal Analyses of Vegetation Changes at Multiple Time Scales in the Qilian Mountains

The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10−3, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale.

Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the eastern regions of the Qilian Mountains as an example to explore the possibility of tree species classification with satellite-derived images. We used Sentinel-2 images to classify the study area’s major vegetation types, particularly four tree species, i.e., Sabina przewalskii (S.P.), Picea crassifolia (P.C.), Betula spp. (Betula), and Populus spp. (Populus). In addition to the spectral features, we also considered terrain and texture features in this classification. The results show that adding texture features can significantly increase the separation between tree species. The final classification result of all categories achieved an accuracy of 86.49% and a Kappa coefficient of 0.83. For trees, the classification accuracy was 90.31%, and their producer’s accuracy (PA) and user’s (UA) were all higher than 84.97%. We found that altitude, slope, and aspect all affected the spatial distribution of these four tree species in our study area. This study confirms the potential of Sentinel-2 images for the fine classification of tree species. Moreover, this can help monitor ecosystem biological diversity and provide references for inventory estimation.

Assessing Soil Organic Carbon Stock Dynamics under Future Climate Change Scenarios in the Middle Qilian Mountains

Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m–2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO2 emissions in the Qilian Mountains.