Hongli Zhang

Global warming and eutrophication jointly intensify algal blooms in lakes, yet many predictive approaches emphasize contemporaneous environmental conditions and may underrepresent ecological memory. Here, we ask whether major environmental drivers operate on distinct response timescales. Using generalized additive models (GAMs) and 2020–2025 climate and water-quality data from two contrasting Chinese lakes—shallow, wind-disturbed Lake Taihu and deep, semi-closed Lake Dianchi—we quantified lagged (1–3 months) and 3-month cumulative effects of climatic, nutrient, and water-quality drivers on chlorophyll-a (Chl-a) dynamics. We identified clear timescale partitioning among drivers: total phosphorus exhibited a lag-dominated response, with the strongest effect at a two-month lag; maximum temperature showed a relatively weak contemporaneous association but the strongest cumulative contribution over three months; and conductivity was strongly lagged in Taihu (partial r = 0.407, p < 0.001) but not in Dianchi. Partial-dependence analysis indicated that, across a typical spring temperature range of 10.1–28.4 °C, a 1 °C increase in air temperature corresponded to an estimated 0.05 mg/m³ increase in Chl-a, suggesting that the temperature signal was expressed more strongly through cumulative exposure than through contemporaneous variation. Time-blocked validation showed that these response patterns were detectable but not temporally stationary, particularly in Taihu. Together, the results indicate that algal blooms are characterized by driver-specific temporal memory and that the expression of this memory may depend on lake physical context. Effective monitoring and management should therefore incorporate driver-specific response timescales rather than relying solely on instantaneous environmental conditions.

Rural tourism serves as a catalyst for rural revitalization and plays an important role in poverty alleviation in Inner Mongolia. This study analyzes the spatial distribution of key villages and star-rated rural (pastoral) households in Inner Mongolia as rural tourism destinations, clarifies their distribution patterns, and contributes to the development of rural tourism in Inner Mongolia. By selecting rural tourism destinations in Inner Mongolia, importing the coordinate points obtained from Baidu Map API into Arc GIS 10.8, and using indices such as the nearest neighbor index, location quotient, kernel density analysis, the geographic concentration index, and the geographic correlation ratio for quantitative analysis, this study explores the distribution patterns and identifies the driving factors influencing the distribution of rural tourism destinations in Inner Mongolia using geographic detectors. The results are:(1) Rural tourism destinations in Inner Mongolia exhibit agglomerated distribution in space, with uneven distribution among different leagues and cities. (2) The spatial density of rural tourism destinations in Inner Mongolia shows the characteristics of “a single core center, with decreasing density toward the periphery, and dispersed secondary cores, and dispersed secondary cores.” The actual geographic concentration index G is 30.23, while the distribution index G 0 under uniform conditions is 29.14. Since G is greater than G 0, a certain degree of agglomeration exists in the spatial distribution of rural tourism destinations in Inner Mongolia, but the overall degree of agglomeration is not strong. (3) Tourism infrastructure, economic development, tourism potential, and natural factors collectively influence the spatial distribution of rural tourism destinations in Inner Mongolia. Among them, economic development, tourism potential, and tourism infrastructure have significant impacts on the distribution of key villages. In terms of subsystems, the number of A-level scenic resources has the highest explanatory power (q=0.848), followed by the number of tourists received (q=0.788), employment in the tertiary industry (q=0.687), and the resident population (q=0.603), which are the main factors influencing the spatial differentiation of rural tourism destinations.

As a major coastal economic province in the east of China, it is of great significance to clarify the temporal and spatial patterns of regional development in Shandong Province in recent years to support regional high-quality development. Nightlight remote sensing data can reveal the spatio-temporal patterns of social and economic activities on a fine pixel scale. We based the nighttime light patterns at three spatial scales in three geographical regions on monthly nighttime light remote sensing data and social statistics. Different cities and different counties in Shandong Province in the last 10 years were studied by using the methods of trend analysis, stability analysis and correlation analysis. The results show that: (1) The nighttime light pattern was generally consistent with the spatial pattern of construction land. The nighttime light intensity of most urban, built-up areas showed an increasing trend, while the old urban areas of Qingdao and Yantai showed a weakening trend. (2) At the geographical unit scale, the total nighttime light in south-central Shandong was significantly higher than that in eastern and northwest Shandong, while the nighttime light growth rate in northwest Shandong was significantly highest. At the urban scale, Liaocheng had the highest nighttime light growth rate. At the county scale, the nighttime light growth rate of counties with a better economy was lower, while that of counties with a backward economy was higher. (3) The nighttime light growth was significantly correlated with Gross Domestic Product (GDP) and population growth, indicating that regional economic development and population growth were the main causes of nighttime light change.

Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R² = 0.93, p < 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R² = 0.90, p < 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R² = 0.53, p = 0.04), EOS was delayed by 0.18 d (R² = 0.82, p = 0.002), and the LOS increased by 0.28 d (R² = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.

The seasonal variations of forest canopy spectral characteristics are critical to improving the utilization of remote sensing methodology to quantify forest physiology, especially forest carbon sink. However, the seasonal variations of forest canopy spectra are poorly understood. Combined field survey and EO-1 Hyperion imageries, we extracted the spectral curves of seven forest types of Changbai Mountain in China in seven periods. We also calculated various remote sensing indexes and analyzed their seasonal change of spectral characteristics among different forest types. Optimal indexes were selected to indicate the seasonal variation of forest carbon fluxes. Our results showed that there were differences in spectral curves among forest types. The reflectance of coniferous forests was lower than that of broad-leaved forests in growing season. Changbai Scotch pine forest owned the lowest spectral reflectance, whereas the reflectance of Mongolian oak forest was the highest, especially in the near-infrared region. The red edge slope (RES) of broad-leaved forest was higher than coniferous forest in spring and summer. The RES of broad-leaved and coniferous forests was similar in autumn. The red edge position of various forest types showed slight shift in different seasons. Four typical forest types showed different spectral characteristics with seasonal changes. The seasonal variation of coniferous forest spectral curves was not obvious. The seasonal variation of broad-leaved forest spectra was the largest. Most of the spectral indexes can indicate the seasonal variation characteristics of each forest type. Enhanced vegetation index (EVI) is better than normalized difference vegetation index (NDVI) to indicate the forest phenology. Seasonal curves of spectral indexes were different in all forest types. Spectral indexes of coniferous forests were most stable throughout the year. The curves of each index in broad-leaved forests showed significant difference in autumn, which may be influenced by the understory vegetation after their defoliation. For broad-leaved Korean (BK) pine forest, the scaled value of photochemical reflectance index (SPRI)*EVI owned the highest correlation with gross primary productivity (R = 0.99 and P < 0.01) and net ecosystem exchange (R = − 0.77 and P < 0.05), respectively. SPRI*NDVI showed the highest correlation with ecosystem respiration (R = 0.96 and P < 0.01). The seasonal variation of carbon fluxes of different forest types retrieved from the optimal remote sensing index were consistent, but their peaks occurred at different times.