Vegetation phenology is the most intuitive and sensitive biological indicator of environmental conditions, and the start of the season (SOS) can reflect the rapid response of terrestrial ecosystems to climate change. At present, the model based on mean temperature neglects the role of the daytime maximum temperature (TMAX) and the nighttime minimum temperature (TMIN) in providing temperature accumulation and cold conditions at leaf onset. This study analyzed the spatiotemporal variations of spring phenology for the boreal forest from 2001 to 2017 based on the moderate-resolution imaging spectro-radiometer (MODIS) enhanced vegetation index (EVI) data (MOD13A2) and investigated the asymmetric effects of daytime and nighttime warming on the boreal forest spring phenology during TMAX and TMIN preseason by partial correlation analysis. The results showed that the spring phenology was delayed with increasing latitude of the boreal forest. Approximately 91.37% of the region showed an advancing trend during the study period, with an average advancement rate of 3.38 ± 0.08 days/decade, and the change rates of different land cover types differed, especially in open shrubland. The length of the TMIN preseason was longer than that of the TMAX preseason and diurnal temperatures showed an asymmetrical increase during different preseasons. The daytime and nighttime warming effects on the boreal forest are asymmetrical. The TMAX has a greater impact on the vegetation spring phenology than TMIN as a whole and the effect also has seasonal differences; the TMAX mainly affects the SOS in spring, while TMIN has a greater impact in winter. The asymmetric effects of daytime and nighttime warming on the SOS in the boreal forest were highlighted in this study, and the results suggest that diurnal temperatures should be added to the forest terrestrial ecosystem model.

Extreme climate events undoubtedly have essential impacts on terrestrial ecosystems, but the spatiotemporal patterns of extreme climate events at regional scales are unclear. In this study, based on observations and 14 CMIP6 global climate models, we analyzed the spatiotemporal changes in extreme temperature events at the mainland China scale and different basin scales in historical and future periods, and their relative importance for the changes in mean temperature (T_mean). The results show that at the mainland China scale in the historical period, extreme cold days and extreme cold nights significantly decreased, while T_mean, extreme warm days, and extreme warm nights significantly increased. However, the rates of increase in T_mean and extreme temperature events in the Continental Basin, Southwest Basin and Yellow River Basin are higher than that at the mainland China scale. The multi-model ensemble is the best model for simulating extreme temperature events in mainland China. At the mainland China scale in the future, the trends of T_mean and extreme temperature events are slow, rapid, and extremely rapid under SSP1-2.6, SSP2-4.5 and SSP5-8.5, respectively. In addition, the changes in the Continental Basin and Songhua and Liaohe River Basin are larger than those at the mainland China scale. In the historical and future periods, the extreme temperature events that have a great influence on the T_mean at the Chinese mainland scale and different basin scales are all related to the minimum temperature. The findings from this study can provide references for formulating scientific and reasonable regional-scale climate change policies.

Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe).

The grasslands of Inner Mongolia are prone to wildfires, which can endanger the grassland ecosystem, as well as people’s lives and property. The amount of heat released by grassland fires must be determined for the quantitative evaluation of grassland fires. On the basis of a field survey of combustible load and an indoor heat release experiment, together with the acquisition of NDVI and fire area data, this study evaluated the amount of combustible load, plant heat release, potential heat release, and fire-caused heat release in Inner Mongolia grasslands. The following results were obtained: (1) The models for determining Inner Mongolia’s combustible load (Y) per unit area throughout the growing and nongrowing seasons were Y_i = 412.74NDVI_i^1.5917 and Y_j = −7.21t_j + Y₁₀ (i represents May–October of the growing season; j represents November–April of the following year). In the northern temperate zones, grasslands and meadows showed a decline in combustible load per unit area. The interannual combustible load variance increased between 2001 and 2016; (2) the per square meter average heat release of Stipa capillata, Cleistogenes squarrosa, Carex doniana, Leymus chinensis, and other plants was 0.51, 0.18, 0.17, 0.3, and 1.42 MJ/g, respectively. Unit weights were released at 2.13, 1.77, 2.06, 1.9, and 3.99 MJ/m²; (3) from 2001 to 2016, Inner Mongolia’s grassland fires predominantly occurred in northern temperate grasslands and meadows. Over the 16 year period, the total heat emission was 1.01 × 10¹² MJ, with variable decreasing trends noted in spring and fall. The main practical objectives of this paper were to provide basic data for fire spread modeling and suggest more scientific and effective fire management methods for the future.