Glacier changes on the Tibetan Plateau are of great importance for regional climate and hydrology and even global ecological changes. It is urgent to understand the effect of climate warming on both clean and debris-covered glaciers on the Tibetan Plateau. This study used the double RF method and Landsat series images to extract clean glaciers and debris-covered glaciers on the Tibetan Plateau from 1985 to 2020 and analyzed their temporal and spatial changes under the background of climate change. The total area of glaciers on the Tibetan Plateau showed a retreating trend from 1985 to 2020, with an average retreat rate of −0.5 % yr⁻¹. The area of clean glaciers showed a significant retreating trend, with a retreat rate of −0.55 % yr⁻¹. The area of debris-covered glaciers showed an expanding trend, with an expanding rate of 0.62 % yr⁻¹. The clean glaciers retreated faster in the southeast and slower in the northwest, while the debris-covered glaciers expanded in most basins. The debris-covered glaciers were generally located at lower elevation areas than those of the clean glaciers. The slopes of clean glaciers were mainly in the range of 0–50°, while the slopes of debris-covered glaciers were mainly in the range of 0–30°. Climate warming was a main driver of glacier change. The clean glacier area was correlated negatively with average temperature in summer and positively with average precipitation in winter, while the debris-covered glacier area was correlated positively with both. The results of the study may provide a basis for scientific management of glaciers on the Tibetan Plateau in the context of climate warming.

Accurate identification and extraction of lake boundaries are the basis of the accurate assessment of lake changes and their responses to climate change. To reduce the effects of lake ice and snow cover, mountain shadows, cloud and fog shielding, alluvial and proluvial deposits, and shoals on the extraction of lake boundaries on the Tibetan Plateau, this study developed an RNSS water index to increase the contrast between the lake and similar surface objects of the spectral curve, and constructed a new method flow for lake extraction on the Tibetan Plateau based on image synthesis, topographic-spectral feature indexes, and machine learning algorithms. The lake extraction effects of three common machine learning classification algorithms were compared: the Cart decision tree, random forest (RF), and gradient boosting decision tree (GBDT). The results show that the new lake extraction method based on topographic-spectral characteristics and the GBDT classification method had the highest extraction accuracy for Tibetan Plateau lakes in 2016 and 2021. Its overall accuracy, Kappa coefficient, user’s accuracy, and producer’s accuracy for 2016 and 2021 were 99.81%, 0.887, 83.55%, 94.67% and 99.88%, 0.933, 89.18%, 98.24%, respectively, and the total area of lake extraction was the most consistent with the validation datasets. The three classification methods can effectively extract lakes covered by ice and snow, and the extraction effect was ranked as GBDT > RF > Cart. The lake extraction effect under mountain shadow was ranked as Cart > GBDT > RF, and the lake extraction effect under alluvial deposits and shoals was ranked as GBDT > RF > Cart. The results may provide technical support for extracting lakes from long time series and reveal the impact of climate change on Tibetan Plateau lakes.

Accurately assessing the dynamic changes of glaciers under the background of climate warming is of great significance for taking scientific countermeasures to cope with climate change. Aiming at the difficulties of glacier identification, such as mountain and cloud shadow, cloud cover and seasonal snow cover in high altitude areas, this paper proposes a reflectivity difference index for identifying glaciers in shadow and glacial lakes and a multi-temporal minimum band ratio index for reducing the influence of snow cover. It establishes a new large-scale glacier extraction method (so-called Double RF) based on the random forest algorithm of Google Earth Engine (GEE) and applies it to the Tibetan Plateau. The verification results based on 30% sample points show that overall accuracies of the first and second classification of 96.04% and 90.75%, respectively, and Kappa coefficients of 0.92 and 0.83, respectively. Compared with the real glacier dataset, the percentage of correctly extracted glacier area of the total area of glacier dataset (PGD) was 84.07%, and the percentage of correctly extracted glacier area of the total area of extracted glacier (PGE) was 89.06%; the harmonic mean (HM) of the two was 86.49%. The extraction results were superior to the commonly used glacier extraction methods: the band ratio method based on median composite image (Median_Band) (HM = 79.47%), the band ratio method based on minimum composite image (Min_Band) (HM = 81.19%), the normalized difference snow cover index method based on median composite image (Median_NDSI) (HM = 83.48%), the normalized difference snow cover index method based on minimum composite image (Min_NDSI) (HM = 84.08%), the random forest method based on median composite image (Median_RF) (HM = 83.87%) and the random forest method based on minimum composite image (Min_RF) (HM = 85.36%). The new glacier extraction method constructed in this study could significantly improve the identification accuracy of glaciers under the influences of shadow, snow cover, cloud cover and debris. This study provides technical support for obtaining long-term glacier distribution data on the Tibetan Plateau and revealing the impact of climate warming on glaciers on the Tibetan Plateau.

Highly accurate data on urban and rural settlement (URS) are essential for urban planning and decision-making in response to climate and environmental changes. This study developed an optimal random forest classification model for URSs based on spectral–topographic–radar polarization features using Landsat 8, NASA DEM, and Sentinel-1 SAR as the remote-sensing data sources. An optimal urban and rural settlement boundary (URSB) extraction technique based on morphological and pixel-level statistical methods was established to link discontinuous URSs and improve the accuracy of URSB extraction. An optimal random forest classification model for URSs was developed, as well as a technique to optimize URSB, using the Google Earth Engine (GEE) platform. The URSB of Xining, China, in 2020 was then extracted at a spatial resolution of 30 m, achieving an overall accuracy and Kappa coefficient of 96.21% and 0.92, respectively. Compared to using a single spectral feature, these corresponding metrics improved by 16.21% and 0.35, respectively. This research also demonstrated that the newly constructed Blue Roof Index (BRI), with enhanced blue roof features, is highly indicative of URSs and that the URSB was best extracted when the window size of the structural elements was 13 × 13. These results can be used to provide technical support for obtaining highly accurate information on URSs.

Global warming has changed plant phenology and induced variations in the productivity of terrestrial ecosystems. Recent studies have shown inconsistent results regarding the influence of phenological changes on plant production. We carried out a three-year in situ experiment in Inner Mongolia and used Stipa krylovii as an example to examine the phenological changes and their importance to plant production under changes in temperature and precipitation. We found that precipitation, temperature, and their interactions had no significant impact on the start of the growing season (SOS) or vegetative growth length (VGL). Precipitation had significant impacts on the end of the growing season (EOS), the length of the growing season (LOS), and reproductive growth length (RGL). The precipitation addition treatments of T2.0W + 50% (2 °C warming and 50% precipitation addition) and T1.5W + 50% (1.5 °C warming and 50% precipitation addition) significantly delayed the EOS by 6.7 d and 5.4 d, and significantly prolonged the LOS by 9.3 d and 9.3 d, respectively. Precipitation significantly changed the net CO₂ assimilation rate (P_n) of the heading stage. There was no significant difference in the dry mass among all the treatments. The SOS and VGL had significant negative impacts on the dry mass of Stipa krylovii, while temperature, precipitation, and the EOS had no significant direct effect on it. Our results imply that the SOS was more important than the EOS in regulating the plant production of Stipa krylovii. This study can facilitate the understanding of the response of productivity to phenological dynamics and improve the accuracy of simulating the terrestrial ecosystem carbon budget.

Clarifying the response of plant phenology to drought duration is helpful for accurately interpreting and predicting carbon sinks in ecosystems. Based on the response of different phenological periods of the dominant species Stipa krylovii to monthly, seasonal, and semiannual time scale drought in the typical steppe of Inner Mongolia from 1983 to 2018, the results revealed that (1) the start of the growing season (SOS) was characterized by an advance–delay–advance pattern, and the heading stage (HOS), flowering stage (FOS), and end of the growing season (EOS) all showed consistent advanced trends, which provided additional insight into the conclusions of previous studies that found the SOS was advanced in arid and semiarid regions. (2) The response mechanism of the SOS to the timing of drought was not consistent. Among the response mechanism, the SOS was delayed because of the January–February drought at different time scales but advanced because of the April drought at different time scales. The HOS/FOS was delayed by the June–July drought at different time scales, and the EOS was advanced by the August–September drought at different time scales. (3) More importantly, the SOS, HOS, FOS, and EOS were affected by predrought and its persistence, and the effects were greater the closer to the phenological periods that drought occurred. (4) At the monthly scale, droughts in January, June, and August were the critical drought periods affecting the SOS, HOS/FOS, and EOS, while seasonal and semiannual scale droughts in February, June–July, and September were the critical periods affecting the SOS, HOS/FOS, and EOS. The results of this study enrich our understanding of how drought at different time scales affects different phenological periods, providing a basis for improving plant phenological models.