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.
China witnessed a rapid urbanization progress in the past 20 years. Urbanization can greatly influence urban environment, especially thermal environment. Causes for urban heat island (UHI) formation vary in different climatic regions. Understanding characteristics of spatial distribution of urban land surface temperature (ULST) and influencing factors of UHI formation is important for sustainable urban development. In this study, twelve megalopolises in China, each with a population over 5 million, were selected as research sites. Built-up areas and ULST of these megalopolises were extracted from remote sensing data of earth observation satellites of Landsat program for the three periods of 2000–2017. Spatial variation characteristics of ULST were analyzed. Influences of soil moisture, surface water availability, surface albedo, vegetation, energy consumption, gross domestic product, latitudes, precipitation, topography, and distance to the downtown on ULST were investigated through multivariate regression analysis. Results show that: all the cities expanded continuously from 2000 to 2017, but expansion rates varied significantly among the megalopolises. Urban expansion has significant effects on the spatial distribution of ULST. ULST changes more quickly when the expanded area and expansion rate are higher. Simultaneously, spatial distribution of ULST was found to relate to the shape of an urban boundary. Circularly expanding cities showed the most concentrated distribution of high-temperature regions. Correlation analysis revealed that surface water use, evapotranspiration, electric energy consumption, surface albedo, and vegetation activity were the primary influencing factors on ULST, while GDP, latitude, distance, and precipitation had no significant effects on ULST. Among the primary influencing factors, surface water was the main controlling factor on ULST in cities with more surface water distribution. Building area played a more important role in ULST in cities with less surface water distribution. In addition, vegetation area played a relatively more important role in ULST in semi-humid cities than humid cities.
It is possible to manage the forest ecosystem and promote sustainable development by keeping track of spatio-temporal fluctuation in the forest area and its ecosystem service value (ESV). The forest ecology of Ganzi Tibetan Autonomous Prefecture (Ganzi Prefecture), which is located in the northern Hengduan Mountains region, i.e., China’s most important ecological functional area, has seen significant alteration during the past 20 years. However, little is known about how the forest and its ESV evolve. We obtained data regarding Ganzi Prefecture’s forests using visual interpretation of remote sensing images derived from 1997, 2007, and 2017, and we evaluated the spatial–temporal changes in the forest ESV from 1997 to 2017 using global value coefficients and adjusted local value coefficients. The results revealed that (1) from 1997 to 2017, the forest area of Ganzi Prefecture increased by 6729.95 km2, and the forest growth rate was 336.50 km2/a, while (2) from 1997 to 2017, the forest ESV in Ganzi Prefecture experienced an overall increase of 257.59 × 108 yuan. The primary driver of the forest ESV increase was the implementation of forestry ecological engineering and protection policies. (3) Finally, the spatial distribution of the forest ESV revealed that the forest ESV density increased during this period, with the most significant increase occurring in Yajiang. The forest ESV was scattered with the highest density in Yajiang and the lowest density in Shiqu. This study emphasizes how crucial forest ecosystems are to Ganzi Prefecture’s mechanisms for maintaining life. It provided a scientific basis for the sustainable management of the forest ecosystem in the Hengduan Mountains.
Traditionally, investigations into the climatic response of various tree species have spanned different regions. However, dendrochronological studies within a single region, characterized by minor climatic differences, have received comparatively less attention. Therefore, this study collected 230 tree cores from four prevalent conifer species (P. yunnanensis, A. forrestii, P. likiangensis, and T. dumosa) in the Lugu Lake Wetland Nature Reserve of southwestern China, a region undergoing climate warming and drying. This study employed dendrochronological methods to investigate tree growth–climate static responses, individual tree responses to climate, and dynamic tree–climate interactions. Our findings revealed that as the trend of warming and drying persists, tree growth exhibits an initial increase followed by a subsequent decrease. Dynamic response analyses, along with standardized assessments, indicate that in the early stages of warming, tree growth benefits from elevated temperatures. However, in the later stages of warming, the combined effects of warming and drying become more pronounced. During this phase, the facilitating impact of temperature diminishes, while the controlling influence of moisture conditions intensifies. Looking ahead, with the ongoing intensification of warming and drying, tree growth in the region is anticipated to become increasingly reliant on the water supply. This shift may lead to the decline or mortality of tree species intolerant to drought, such as T. dumosa.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)