Mountains are complex ecological systems, with unique structures and functions, and have rich biodiversity, water and mineral resources, and opportunities for tourism. Mountain ecology is the study of mountain structures, functions, and processes, as well as the exploration, sustainable utilization and conservation of mountain resources. In this paper, we review the effects of major topographic elements on ecological factors, summarize the ecological benefits of mountains, and discuss the current research issues and topics for Mountain Ecology. It is emphasized that in topography of mountainous areas is the most critical controlling factor for ecological structures, functions, and processes. Topography affects the abundance and distribution of organisms by modifying solar radiation, heat, moisture, soil and fertility. For this reason, topography and topographyinduced ecological phenomena and processes are considered to be the most critical topics in mountain ecology. Topographic research addresses such issues as ecological complexity, biodiversity, mountain climate changes, mountain ecological engineering, mountain sustainable development, and quantification of topographic elements (elevation, slope, aspect, and location). Among these, developing appropriate methods for quantifying topographic elements is a key technique. We also suggest a need for a synthetic study of relationships between humans and the natural world based mountain sustainable development.

山地是一个生态复杂系统，它具有特定的结构和功能，拥有丰富的生物多样性资源、水资源、矿产资源和旅游资源。开展山地生态学研究对阐明山地系统的结构与功能、山地的生态现象与过程，以及合理开发利用和保护山地资源都有极为重要的意义。本文在简要分析山地地形的主要要素对生态因子影响的基础上，对山地的生态效应进行了归纳，探讨了山地生态学应包含的主要研究内容。作者认为，在山区，地形地貌是形成山地结构和功能以及各种生态现象和过程的最根本因素，它通过改变地表的光、热、水、土、肥等生态因子而发生作用。因此，山地生态学应把地形地貌与各种生态现象和过程的相互作用作为其核心的研究内容。作者提出，山地生态学研究主要包括： 山地生态复杂性与生物多样性、山地气候变化、山地生态工程、山区可持续发展综合研究以及山地生态学研究技术与方法论等内容。

The numbers and biomass of litter and soil invertebrate fauna were investigated in six plots at altitudes of 280 m, 330 m, 480 m, 610 m, 790 m and 870 m on Gunung Silam, Sabah, East Malaysia. There were relatively high numbers and biomass in the lower plots, where the Oligochaeta were a high proportion of the total invertebrate biomass. The biomass of other invertebrate groups was low in the soil. There was a marked effect of altitude, particularly for the Oligochaeta above 610 m. The low biomass of the Oligochaeta in the higher plots suggested that the importance of this group may be diminished at relatively low altitudes on small mountains. However, several other soil faunal groups showed no evidence of an altitudinal effect, and it is suggested that the stunted forests near the summits of small mountains such as Gunung Silam may have a different litter and soil fauna from forests of a similar physiognomy at high elevations on large mountains.

The varied altitudinal gradient of climate and vegetation is further complicated by mass elevation effect (MEE), especially in high and extensive mountain regions. However, this effect and its implications for mountain altitudinal belts have not been well studied until recently. This paper provides an overview of the research carried out in the past 5 years. MEE is virtually the heating effect of mountain massifs and can be defined as the temperature difference on a given elevation between inside and outside of a mountain mass. It can be digitally modelled with three factors of intra-mountain base elevation (MBE), latitude and hygrometric continentality; MBE usually acts as the primary factor for the magnitude of MEE and, to a great extent, could represent MEE. MEE leads to higher treelines in the interior than in the outside of mountain masses. It makes montane forests to grow at 4800-4900 m and snowlines to develop at about 6000 m in the southern Tibetan Plateau and the central Andes, and large areas of forests to live above 3500 m in a lot of high mountains of the world. The altitudinal distribution of global treelines can be modelled with high precision when taking into account MEE and the result shows that MEE contributes the most to treeline distribution pattern. Without MEE, forests could only develop upmost to about 3500 m above sea level and the world ecological pattern would be much simpler. The quantification of MEE should be further improved with higher resolution data and its global implications are to be further revealed.

Rocky Mountains is the Northern America's largest inland mountains. Its mass elevation effect (MEE) imposes substantial forcing on treeline altitude. As a result, its treeline elevation is much higher than other inland mountains and coastal mountains of the continent. However, most existing literatures on MEE are from a qualitative perspective. This study applied meteorological station records, NCAR/NCEP free air temperature and DEM data to calculate MEE-related temperature increases from inner to out of the Colorado Rocky Mountains (ΔT) and to quantify values of MEE factors, including latitude, mountain bass elevation (MBE), hydric continentality and surface openness. Thereby the magnitude of MEE was computed with ΔT and MEE factors. MEE's impacts on treeline distribution were estimated through isothermal line altitude of the warmest month 10℃ from inner to out of mountains of different ranges. Elevation of isothermal line was interpolated by MODIS land surface temperature data coupled with elevation and deduced temperature lapse rate. Results show that: (1) ΔT is an ideal indicator for MEE. ΔT at adopted stations ranges from 0.78 °C to 4.29°C, whereas mean value is 2.07 ℃. ΔT showed a descending trend from outer ranges toward inner ranges. The greatest ΔT occurred at the center ranges between 38°-39°N. (2) The resulted MEE model for the Colorado Rocky Mountains taking MBE and hydric continentality as independent variables has a high explanatory power of R2 of 71.2%. MBE had the most significant contribution to the model with 55.21%, whereas latitude and surface openness were eliminated. (3) The 10 ℃ isothermal line of the hottest month in the Colorado Rockies was higher in inner ranges than in outer ranges at all the three latitudes, with a difference of 400 m to 700 m. At 38°N and 39°N, differences of 10℃ isothermal line elevation between inner and outer ranges showed strong variation, whereas less at 40°N. This difference corresponds well to air temperature difference between the inner and outer mountain ranges. The findings suggest that MEE was crucial for driving treeline up to higher elevation in the Rocky Mountains. This study developed a quantitative model for the MEE of the Colorado Rocky Mountains and improved our understanding of MEE and its significance for treeline distribution.

落基山脉作为北美最大的内陆山地,其山体效应对林线分布具有很大影响,导致林线海拔远高于周围内陆山体及其他海岸山地。然而,以往落基山脉山体效应研究多集中于定性研究,但是山体效应如何量化,如何根据落基山脉的地形气候条件构建区域山体效应的定量化模型,目前鲜有研究。通过分析台站处山体增温及量化落基山脉山体效应的影响因子,并计算最热月均温10 ℃等温线的海拔高度,来定量化地估算科罗拉多落基山脉山体效应值大小及其对林线分布的影响。结果表明：① 用山体增温值表示山体效应大小是合理且比较理想的指标。科罗拉多落基山脉增温显著,所有台站的增温均值为2.07 ℃,增温幅度为0.78~4.29 ℃。② 科罗拉多落基山脉山体效应的主要影响因素为山体基面高度和降水大陆度,二者与山体增温构建的线性拟合模型具有较高的解释能力,判定系数高达71.2%。③ 科罗拉多落基山脉不同纬度带山体内外最热月10 ℃等温线分布高度对比表明,山体内部理想林线高度均高于山体外部的理想林线分布,内外分布差异为400~700 m。定量分析科罗拉多落基山脉的山体效应模型,优化了区域尺度的山体效应模型精度,有助于深入认识山体效应及其对垂直带分布的影响。

<p>This paper focuses on the method of quantifying the phenomenon of mass elevation effect (massenerhebungs effect). Geographers have taken notice of mass elevation effect and its influence on mountain altitudinal belts for more than 100 years. But so far, our knowledge on mass elevation effect has been very limited, let alone its quantitative effect on mountain altitudinal belts. Geographers and botanists have established many unitary or dibasic fitting models between mountain altitudinal belts' distribution and longitude or latitude, or both. But most of these models involve small scales and could not be expanded to other regions; while others are established for the northern hemisphere or the whole globe with very low precision. The reason is that these models neglect one of the most important factors controlling the distribution of altitudinal belts&mdash;mass elevation effect. It is well known that the higher the mountain range, the greater the mass elevation effect is. So, mountain's base altitude could be a represent of mass elevation effect. We collect 173 samples of forest line distribution, and use latitude, longitude and mountain base elevation (MBE) as independent variables to build a multiple linear regression equation for timberline altitude in the southeastern Eurasian continent. The result turned out that the contribution of latitude, longitude and mountain base elevation to timber line distribution reaches 30.60%, 26.53%, and 42.87%, respectively. North of northern latitude 32&deg;, the contribution for each of the three factors amounts to 53.08%, 21.25%, and 25.67%, respectively; to the south, the contribution is 14.94%, 48.98%, and 36.08%, respectively. The results indicate that MBE, serving as a proxy indicator of mass elevation effect, is a significant factor determining the elevation of altitudinal belts. Compared with other factors, it is more stable and independent in affecting forest line elevation. Of course, mass elevation effect is also determined by other factors, including mountain's volume, the distance to the edge of a land mass, the structures of the mountains nearby, etc. They need to be included in the study of mass elevation effect in the future.</p>

根据收集到173 个林线数据，采用纬度、经度和基面高度的三元一次方程拟合欧亚大陆东南部林线分布，计算各自的标准回归系数和贡献率，以此来确定山体基面高度(山体效应的简明表达形式) 对林线分布高度的影响。结果表明，纬度、经度和山体基面高度对林线分布高度的贡献率分别为30.60%、26.53%、42.87%。以北纬32o为界线，对其以北、以南区域也分别进行了分析，基面高度的贡献率达到24.10%和39.11%。分析不同尺度和区域山体基面高度作用于林线的贡献率不难发现：在欧亚大陆东南部以基面高度代表的山体效应对于林线高度的影响显著，明显地超过了纬度和经度。基面高度的作用受气候条件和海陆位置影响较小，不论大陆内部或沿海，基面高度分异对山地垂直带分异的影响都相对独立和稳定。该结果定量地表明了山体效应对林线分布高度的重要作用。

As a huge mountain range in the North-South boundary of China, the Qinling-Daba Mountains are characterized by prominent mass elevation effect (MEE) and play an important role in the azonality pattern of climate and ecology in central China. The essence of the MEE is the warming effect of mountains, as huge mountain and plateau absorbs more solar radiation compared with the free atmosphere of the same altitude and then releases in the form of long-wave radiation external heat, making the internal mountain temperature higher than the external in the same altitude of free atmosphere. Therefore, the temperature difference between the mountain interior and the periphery has been suggested as an appropriate indicator to quantify the MEE. To analyze MEE of the Qinling-Daba Mountains, MODIS land surface temperature (LST) data, STRM-1 DEM data and observation data from 118 meteorological stations were combined to estimate monthly mean air temperature by ordinary linear regression (OLS) and geographical weighted regression (GWR) methods in the Qinling-Daba Mountains. Air temperature at an altitude of 1500 m (the average elevation of the Qinling-Daba Mountains) in the interior of the Qinling-Daba Mountains was calculated by a fixed lapse rate and compared with that in the periphery. The results show that: (1) Compared with OLS method, the GWR method has higher accuracy with R 2 > 0.89 and the root mean squared error (RMSE) = 0.68-0.98 ℃. (2) The monthly mean temperature at the altitude of 1500 m estimated by GWR presents a gradual upward trend from east to west. In the western Qinling Mountains, the annual average temperature and temperature in July at the altitude of 1500 m increase about 6 ℃ and 4.5 ℃ compared with the eastern flank, while in the Daba Mountains, they are about 8°C and 5 ℃ higher in the west than in the east. (3) From south to north, with the Hanjiang River as the boundary, the monthly mean temperature at the altitude of 1500 m tends to rise from the rim of the mountains to the ridge. (4) Compared with the lower valleys in Hanzhong and western Henan, the monthly mean temperatures at the altitude of 1500 m are approximately 3.85-9.28 ℃, 1.49-3.34 ℃ and 0.43-3.05 ℃ higher those in the great undulating high mountains in the western Qinling-Daba Mountains, the great undulating middle-high mountains in the Qinling Mountains and the great undulating middle mountains in the Daba Mountains, respectively, and the average temperature difference is about 3.50 ℃. This shows that the MEE of the Qinling-Daba Mountains is obvious and its impact on the distribution patterns of mountain climate and ecology needs to be further studied.

秦巴山地作为横亘在中国南北过渡带的巨大山脉,其山体效应对中国中部植被和气候的非地带性分布产生了重要的影响,山体内外同海拔的温差是表征山体效应大小较为理想的指标。本研究结合MODIS地表温度（LST）数据、STRM-1 DEM数据和秦巴山地的118个气象站点的观测数据,分别采用普通线性回归（OLS）和地理加权回归（GWR）两种分析方法对秦巴山地的气温进行估算,在此基础上将秦巴山地各月气温转换为同海拔（1500 m,秦巴山地平均海拔）气温,对比分析秦巴山地的山体效应。结果表明：① 相比OLS分析,GWR分析方法的精度更高,各月回归模型的R ²均在0.89以上,均方根误差（RMSE）在0.68~0.98 ℃之间。② 利用GWR估算得到的同海拔气温,从东向西随海拔升高呈现了明显的升高的趋势,秦岭西部山地比东段升高约6 ℃和4.5 ℃;大巴山西部山地年均和7月份同海拔的气温较东段升高约8 ℃和5 ℃。③ 从南向北,以汉江为分界,秦岭与大巴山的同海拔的气温均呈现出由山体边缘向内部升高的趋势。④ 秦巴山地西部大起伏高山,秦岭大起伏高中山和大巴山大起伏中山,相比豫西汉中中山谷地,各月均同海拔气温分别升高了约3.85~9.28 ℃、1.49~3.34 ℃和0.43~3.05 ℃,平均温差约为3.50 ℃,说明秦巴山地大起伏中高山的山体效应十分明显。

Land Use /Cover Change (LUCC) directly reflects the dominant factor leading to global change&mdash;human activities.The study on LUCC will enhance the understanding of the interaction between global change and terrestrial ecosystems,and improve the accuracy of evaluating and predicting the responses of terrestrial ecosystems to global change.In this paper,based on the two well known balance equations&mdash;heat and water balance equations on the earth's surface,the authors discuss the feedback of LUCC on climate.The study shows:(a)Vegetation has the function of reducing runoff,storing water and mitigating the effects of global climate change.The effect of vegetation on precipitation may not be considered.(b)The effect of vegetation on climate depends on specific regions.(c)In the middle latitude areas,if changes of the surface albedo and runoff meet the following relationship,&Delta;f=5&Delta;&alpha;&times;10^-4g/cm²&middot;min vegetation will not have an effect on earths surface temperature.This result indicates that strategies concerning global change should be suitable to local conditions.

Spatial stratified heterogeneity is the spatial expression of natural and socio-economic process, which is an important approach for human to recognize nature since Aristotle. Geodetector is a new statistical method to detect spatial stratified heterogeneity and reveal the driving factors behind it. This method with no linear hypothesis has elegant form and definite physical meaning. Here is the basic idea behind Geodetector: assuming that the study area is divided into several subareas. The study area is characterized by spatial stratified heterogeneity if the sum of the variance of subareas is less than the regional total variance; and if the spatial distribution of the two variables tends to be consistent, there is statistical correlation between them. Q-statistic in Geodetector has already been applied in many fields of natural and social sciences which can be used to measure spatial stratified heterogeneity, detect explanatory factors and analyze the interactive relationship between variables. In this paper, the authors will illustrate the principle of Geodetector and summarize the characteristics and applications in order to facilitate the using of Geodetector and help readers to recognize, mine and utilize spatial stratified heterogeneity.

空间分异是自然和社会经济过程的空间表现,也是自亚里士多德以来人类认识自然的重要途径。地理探测器是探测空间分异性,以及揭示其背后驱动因子的一种新的统计学方法,此方法无线性假设,具有优雅的形式和明确的物理含义。基本思想是：假设研究区分为若干子区域,如果子区域的方差之和小于区域总方差,则存在空间分异性;如果两变量的空间分布趋于一致,则两者存在统计关联性。地理探测器q统计量,可用以度量空间分异性、探测解释因子、分析变量之间交互关系,已经在自然和社会科学多领域应用。本文阐述地理探测器的原理,并对其特点及应用进行了归纳总结,以利于读者方便灵活地使用地理探测器来认识、挖掘和利用空间分异性。

Climate change is a major driver of vegetation activity, and thus its complex processes become a frontier and difficulty in global change research. To understand the complex relationship between climate change and vegetation activity, the spatial distribution and dynamic characteristics of the response of NDVI to climate change from 1982 to 2013 in China were investigated by the geographically weighted regression (GWR) model. The GWR was run based on the combined datasets of satellite vegetation index (GIMMS NDVI) and climate observation (temperature and moisture) from meteorological stations nationwide. The results noted that the spatial non-stationary relationship between NDVI and surface temperature has appeared in China. The significant negative temperature-vegetation relationship was distributed in northeast, northwest and southeast parts of the country, while the positive correlation was more concentrated from southwest to northeast. And then, by comparing the normalized regression coefficients for different climate factors, regions with moisture dominants for NDVI were observed in North China and the Tibetan Plateau, and regions with temperature dominants for NDVI were distributed in the East, Central and Southwest China, where the annual mean maximum temperature accounts for the largest areas. In addition, regression coefficients between NDVI dynamics and climate variability indicated that the higher warming rate could result in the weakened vegetation activity through some mechanisms such as enhanced drought, while the moisture variability could mediate the hydrothermal conditions for the variation of vegetation activity. When the increasing rate of photosynthesis exceeded that of respiration, there was a positive correlation between vegetation dynamics and climate variability. However, the continuous and dynamic responding process of vegetation activity to climate change will be determined by spatially heterogeneous conditions in climate change and vegetation cover. Furthermore, the description of climate-induced vegetation activity from its rise to decline in different regions is expected to provide a scientific basis for initiating ecosystem-based adaptation strategies in response to global climate change.

为研究气候变化与植被活动之间的复杂关系,采用1982-2013年GIMMS NDVI与气象站点温度与水分的监测资料,应用基于像元的地理加权回归方法,探究了中国植被NDVI及其动态特征对气候变化响应的空间格局。中国植被NDVI与地表温度呈空间非平稳关系,在空间上的负相关关系主要集中在东北、西北及东南部分地区,空间正相关则更为集中和连片;针对不同气候指标的标准化系数对比可知,植被NDVI受水分控制作用较为显著的区域主要集中在北方地区以及青藏高原,温度的主导作用区域则分布在华东、华中及西南地区,其中年均最高气温对NDVI的主导区域范围最广;植被NDVI动态与气候变率的回归结果表明,增温速率的升高会通过加剧干旱等机制对植被活动产生抑制作用,水分变率对植被活动的强弱起到了重要的调节作用。

Environment Canada meteorological station hourly sampled air temperatures Tair at four stations in the southwest Yukon were used to identify cloud contamination in the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra clear-sky daytime land surface temperature (LST) and emissivity daily level-3 global 1-km grid product (MOD11A1, Collection 5) that is not flagged by the MODIS quality algorithm as contaminated. The additional cloud masking used qualitative ground-based sky condition observations, collected at two of the four stations, and coincident MODIS quality flag information. The results indicate that air temperature observed at a variety of discrete spatial locations having different land cover is highly correlated with MODIS LST collected at 1-km grid spacing. Quadratic relationships between LST and air temperature, constrained by ground observations of “clear” sky conditions, show less variability than relationships found under “mainly clear” and “mostly cloudy” sky conditions, and the more clouds observed in the sky coincides with a decreasing y intercept. Analysis of MODIS LST and its associated quality flags show a cold bias (&amp;lt;0°C) in the assignment of the ≤3-K-average LST error, indicating MODIS LST has a maximum average error of ≤2 K over a warm surface (&amp;gt;0°C). Analysis of two observation stations shows that unidentified clouds in MODIS LST are between 13% and 17%, a result that agrees well with previous studies. Analysis of daytime values is important because many processes are dependent on daylight and maximum temperature. The daytime clear-sky LST–Tair relationship observed for the good-quality confirmed cloud-free-sky MODIS LST quality flag can be used to discriminate cloud-contaminated grid cells beyond the standard MODIS cloud mask.

The glacier mass balance (GMB) is an important link between climate and water resources, which has remarkable regulation functions for river runoff. The research, using MOD11C3, TRMM 3B43 and other multi-source remote sensing data to drive the degree-day model, simulates the GMB processes and analyzes the recharge of glacial meltwater to runoff in the Manas River Basin (MRB) during 2000-2016. The results show that: (1) By constructing the temperature and precipitation inversion model, the accuracy of the meteorological remote sensing data can be effectively corrected, and the characteristics of climate change in the glacial region can be well described after downscaling. The annual average temperature and precipitation in the glacier area were -7.57 ℃ and 410.71 mm, respectively. The place at an altitude of 4200 m is a severe climate change zone. Above 4200 m, the temperature drop rates and precipitation gradients were -0.03 ℃/100 m and -2.66 mm/100 m, respectively; while below 4200 m, they were -0.57 ℃/100 m and 4.8 mm/100 m, respectively. Besides, at a higher altitude of 4700 m, the precipitation increased by 5.17 mm/100 m. (2) During the study period, the glaciers in the basin continued to be in a negative state, with a cumulative GMB of -9811.19 mm w.e. and an average annual GMB between -464.85 mm w.e. and -632.19 mm w.e. The vertical GMB increased by 244.83 w.e./100 m and 18.77 w.e./100 m in the ablation zone and the accumulation zone, respectively. From 2000 to 2002 and 2008 to 2010, the melting of glaciers slowed down, and the ablation was intensified from 2002 to 2008 and from 2010 to 2016. Strikingly, the loss of glaciers was most serious during the period 2005-2009. (3) The river runoff responded strongly to the change of GMB within the year, especially in July and August, namely, the GMB loss accounted for 75.4% of the total amount of the whole year, and the river runoff accounted for 55.1% of the annual total. The inter-annual glacial meltwater recharge rate fluctuated between 19% and 31%, which may be due to the differences of precipitation and snow melt water recharge rates in different years. The contribution rate of glacial meltwater of the MRB is close to that of other river basins on the northern slope of the Tianshan Mountains, which can further confirm the reliability of the GMB estimation results. Above all, the research can provide reference for the study of GMB in other river basins.

冰川物质平衡变化是连接气候和水资源的重要纽带,对河川径流有重要的调节功能。本文采用MOD11C3和TRMM 3B43等多源遥感数据驱动度日模型,模拟了2000—2016年玛纳斯河（简称玛河）流域冰川物质平衡过程,并分析了冰川融水对径流的补给规律。结果表明： ① 通过构建气温及降水反演模型能有效校正气象遥感原数据的精度,且经降尺度后能较精细刻画冰川区气候变化特征。冰川区年均气温和降水量分别为-7.57 ℃和410.71 mm,海拔4200 m处为气候变化剧烈地带,气温直减率以其为界上下分别为-0.03 ℃/100 m和-0.57 ℃/100 m,降水梯度分别为-2.66 mm/100 m和4.8 mm/100 m,海拔大于4700 m后降水又以5.17 mm/100 m递增。② 研究期内流域冰川持续呈负平衡状态,累积物质平衡达-9811.19 mm w.e.,年均物质平衡介于-464.85~-632.19 mm w.e.之间。垂向物质平衡在消融区和积累区分别以244.83 mm w.e./100 m、18.77 mm w.e./100 m递增。2000—2002年、2008—2010年冰川消融减缓,2002—2008年、2010—2016年消融加剧,其中2005—2009年期间冰川亏损最为强烈。③ 年内河川径流对冰川物质平衡变化响应强烈,尤以7月、8月物质平衡亏损最为严重占全年总量的75.4%,使得同期河川径流量占全年径流总量的55.1%。年际冰川融水补给率波动于19%~31%之间,可能是不同年份降水和积雪融水补给率差异较大所致。玛河与天山北坡其他河流冰川融水贡献率非常接近,也进一步证实了本研究物质平衡估算结果的可靠性。本研究可为其他流域冰川物质平衡研究提供借鉴和参考。

The Beijing-Tianjin-Hebei region is one of the main producing areas of high-quality winter wheat in China, but drought disasters frequently occur in this region under the influence of global warming. Accurate monitoring of drought in the Beijing-Tianjin-Hebei region can not only provide scientific guidance for regional agricultural production, but also play an important strategic role in guaranteeing national food security. Therefore, in this study, drought-causing factors such as precipitation, vegetation growth, soil moisture gain and loss were considered comprehensively. Firstly, the GWR (Geographical Weighted Regression) model was used to downscale TRMM (Tropical Rainfall Measuring Mission) 3B43 data, and the Precipitation Condition Index (<em>PCI</em>) with a 1-km resolution was obtained. Combining MODIS (Moderate-Resolution Imaging Spectroradiometer) data, the Vegetation Condition Index (<em>VCI</em>), Temperature Condition Index (<em>TCI</em>) were obtained. Finally, a comprehensive drought index (<em>CDI</em>) was constructed based on the multiple regression model to achieve spatial and temporal monitoring and evaluation. The results show that: (1) The annual and monthly data of the 1-km spatial resolution TRMM based on the GWR model and proportion coefficient method have been greatly improved in spatial resolution compared with the original TRMM data, and the accuracy of the data has also passed the test, which shows that the downscaling analysis improves the description ability of TRMM data to the spatial and temporal characteristics of precipitation in the study area. (2) The results of the monitoring model are basically consistent with the drought process. The correlation coefficient (<em>R</em> value) between <em>CDI</em> and Standard Precipitation Index (<em>SPI</em>) was 0.45-0.85, and the correlation coefficient between <em>CDI</em> and drought area of crops ranged from -0.81 to -0.86, and all of them passed the very significant test of <em>P</em>&#x0003C;0.01, and the <em>R</em> value was greater than 0.6 between the <em>CDI</em> and standardized unit yield of crop (<em>P</em>&#x0003C;0.05), which indicated that the comprehensive drought monitoring model constructed by this research was applicable in the Beijing-Tianjin-Hebei region.

Time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, were used to estimate and map the spatial distribution of monthly mean air temperatures of the Tibetan Plateau and neighboring areas. Time series and regression analyses of monthly mean land surface temperature (Ts) and monthly mean air temperature (Ta) were conducted using both ordinary linear regression (OLS) and geographical weighted regression (GWR) methods. Analysis shows that recorded Ta is rather closely related to Ts, and that the GWR method has a much better result (adjusted R² > 0.91, root mean square error (RMSE)=1.16-1.58℃) for estimating Ta than OLS. The GWR model, with MODIS Ts and altitude as independent variables, was thus used to estimate Ta for the Tibetan Plateau. For more than 80% of the stations, the Ta retrieved from Ts had residuals lower than 2℃. Analysis of the spatial pattern of retrieved Ta data showed that the mean Ta of the summer half year was higher than 0℃ even at high altitudes of 5000&plusmn;600 m of the plateau, especially in the warmest month (July) the Ta in high mountain areas with altitudes of 4000-5500 m could reach as high as 10 ℃. This may help explain why the highest timber line in the northern hemisphere is located on the Tibetan Plateau. According to our results, Ta in July was probably 6-10℃ warmer in the inner plateau than in the outer plateau at any given elevation which resulted from the heating up effect of the Plateau.

National key towns are the leader in the development of small towns. The formation of scientific and reasonable national key town layouts is of great significance for optimizing China's urbanization strategy. Taking the 1887 and 3675 national key towns published in 2004 and 2014 respectively as samples, this study used geodector and the geographically weighted regression (GWR) model to explore the changing characteristics of their distribution and analyze the influencing factors of national key town layouts and effects at the prefecture level. The results show that: 1) After the adjustment by the government, the balance of the layout and construction effect of national key towns is enhanced. The main gathering areas moved westward and northward, the distribution of cold spots and hotspots break through the "Hu Huanyong Line", and the degree of differentiation of economic radiation effect is weakened, reflecting the powerful influence of policy factors. In addition to the inter-county balance and regional preferential policies, the distribution of national key towns is also significantly affected by factors such as altitude, road network density, and resident population urbanization rate. 2) The combination of the factor detector, the GWR model, and the interaction detector can more accurately characterize the mode of action, direction, path, and intensity of the influencing factors. The layout of national key towns is not the result of the uniform, independent, and direct effect of the five significant factors, but the product of the synergistic effect of the interaction of the factors with spatial heterogeneity. 3) The synergy between the inter-county balance policy and other factors is the dominant force in the formation of the existing layout of the key towns; the effect of the regional preferential policy is overall positive, but the target areas need to be more accurately identified.

重点镇是小城镇发展的龙头,形成科学合理的重点镇布局对优化中国城市化战略格局有重要意义。论文以2004年和2014年分别公布的1887个和3675个全国重点镇为样本,对其分布及效应的变动特征进行探究,进而在地级尺度对重点镇布局的影响因子及其作用进行地理探测和局部空间回归。结果表明：① 经增补调整,中国重点镇布局及建设效应的均衡性增强,主要集聚区西移北扩,冷热点的分布突破“胡焕庸线”,经济辐射效应的分化程度减弱,体现出政策因素的有力影响。除县际均衡和区域倾斜政策外,重点镇的分布还受到海拔高度、公路网密度、常住人口城镇化率等因子的显著作用。② 因子探测器、GWR模型和交互作用探测器的结合能更精准地刻画影响因子的作用方式、方向、路径和强度。中国重点镇的布局不是5个显著性因子均匀、独立、直接作用的结果,而是影响均具空间异质性的各因子两两交互作用后增效的产物。③ 县际均衡政策与其他因子的协同作用是形成现有重点镇分布格局的主导力量;区域倾斜政策的效果总体较好,但目标区域还需更准确。

<p>Due to the influences of local topographical factors and terrain inter-shielding, calculation of DSR quantity over rugged terrains is very complex. Based on DEM (digital elevation model) data and meteorological observations, a distributed model for calculating direct solar radiation over rugged terrains is developed. This model gives an all-sided consideration on factors influencing DSR. Using the selected model, normals of annual DSR quantity with a resolution of 1 km&times;1 km for the Yellow River Basin was generated, with DEM data as the general characterization of the terrains. Characteristics of DSR quantity influenced by geographic and topographic factors over rugged terrains were analyzed thoroughly. The results suggest that influenced by local topographic factors, i.e. azimuth, slope and so on, the annual DSR quantity over mountainous area has a clear spatial difference; and the annual DSR quantity of sunny slope (or southern slope) of mountains is obviously larger than that of shady slope (or northern slope). The calculated DSR quantity of the Yellow River Basin is provided in the same way as other kinds of spatial information and can be employed as basic geographic data for relevant studies as well.</p>

Mountain elevation effect (MEE) is a major factor responsible for the spatial pattern of mountain altitudinal belts. Mountain basal elevation (MBE) was thought to be the most important factor of MEE. And it almost can be regarded as MEE itself. In Tibetan Plateau and its surrounding areas, the contours of snowline present an approximation of rings, a large degree change from its basic distribution pattern with latitude. It was thought to have a close relationship with MEE and MBE. In order to quantitative analyze the Influence of MBE to snowline, we compiled 142 snowline descriptions from literatures covering the Tibetan Plateau and its surrounding areas. Snowline elevation was related to longitude, latitude and MBE, to construct a multivariate linear regression equation. And then, the standard regression coefficient and relative contribution of each influencing factors were counted out, so as to compare the influence of three factors. Afterwards, we divided all samples into 5 subsets according to their MBE (0-1000 m, 1001-2000 m, 2001-3000 m, 3001~ 4000 m, 4001-5000 m), for the purpose of analyzing the effect of MBE to the snowlines. The results turned out that, (1) to the whole research area, the relative contribution of latitude, longitude and MBE to snowline distribution reach to 30.60%, 26.53%, and 42.87%, respectively; (2) as the uplift of MBE and the reduction of research scale, the determination coefficient (R²) of each subset model diminishes and retains a high domain (0.668-0.895), which illustrates the significant and scientificity of the model clearly; (3) the relative contribution of latitude decreases linearly with the increase of MBE (92.6%-48.99%, R²=0.855), while the effect of MBE increases obviously with its uplift (3.33%-31.76%, R²=0.582), the higher the MBE, the more significant influence to snowline.

山体效应是地理地带性之外，在大尺度上影响垂直带分布的主要因素，山体基面高度则是山体效应的第一影响因子。青藏高原及其周边地区，雪线呈现出中心高、周围低，与山体基面高度相一致的环状分布模式。为分析山体基面高度对雪线分布的影响，本文共收集青藏高原及周边地区雪线数据142个，采用纬度、经度和基面高度为自变量的三元一次方程拟合研究区雪线分布，计算各自的标准回归系数和相对贡献率，再将基面高度划分成5个子集（0~1000 m、1001~2000 m、2001~3000 m、3001~4000 m和4001~5000 m），分析基面高度不同的山地对雪线的影响差异。结果表明：① 在青藏高原，纬度、经度和基面高度对雪线高度分布的相对贡献率分别为51.49%、16.31%和32.20%；② 随着基面高度的增高，各子集模型的决定系数虽有逐渐降低的趋势，但仍保持在较高的值域（R²=0.895~0.668），说明模型的有效性；③ 随基面高度的抬升，纬度和山体基面高度对雪线分布高度的相对贡献率分别表现出降低（92.6%~48.99%，R²=0.855）和增大（3.33%~31.76%，R²=0.582）的趋势，表明基面高度越高，其对雪线分布高度的影响越大。

秦巴山地是中国的南北分界线,也是黄河和长江的分水岭,其山体效应的定量化影响秦巴山地山体垂直带的分布格局、非地带性因素的作用强度和机理,以及中国暖温带和北亚热带的具体位置的确定。山体基面高度是影响山体效应最重要和关键的地形因子,其定量化和数字化提取是秦巴山地山体效应定量化研究的重要内容。本研究针对秦巴山地山体效应的定量化研究,使用30 m分辨率的STRM-1数据,分别基于山体特征线和流域分区2种方法提取了秦巴山地的山体基面高度分区,并根据地形起伏度和坡度,确定基面范围,计算了山体基面高度值。结果表明：① 基于山体特征线的方法将秦巴山地分为93个基面高度分区,基于流域分区的方法将秦巴山地分为209个基面高度分区,根据2种分区结果提取的基面高度值相差不大且均体现了秦巴山地地势的特点;② 秦巴山地山体基面高度从东向西呈阶梯状递增的趋势;③ 从南到北,秦巴山地的东段和中段均呈先增高后降低的趋势,即从大巴山向北至汉江谷地降低,再向北至秦岭升高;④ 山地的不同侧翼的山体基面高度不同,秦岭南坡的基面高度（1000~1809 m）明显高于北坡（850~1300 m）。秦巴山地山体基面高度与其植被带分布上限联系密切,实现山体基面高度的数字化提取,为山体效应的定量化研究提供了重要的技术支持。

As an important climate resource, near surface air temperature plays an essential role in driving the vegetation growth, monitoring agro-meteorological disasters and global change. The spatially continuous air temperature images, which can be better derived from remotely sensed data than interpolated of meteorological data from limited ground stations, are required as the important input parameters by several environmental models at the regional scale. To serve and support the scientific research and business work concerning the distribution of air temperature at regional scale, in this paper, we firstly introduced the main five categories of methods to estimate air temperature from remotely sensed thermal data, including simple statistical approaches, the advanced statistical approaches, the temperaturevegetation index (TVX), the method based on surface energy balance and the one based on temperature lapse rate. Then, the approaches of maximum, minimum and mean air temperatures estimation were discussed in more details, followed by the comparison of air temperature estimation at different temporal and spatial scales. Finally, the problems and prospects for remotely sensed estimation of air temperature were summarized.

气温作为重要的气候资源之一，在植被长势、农业灾害和气候变化研究中发挥着举足轻重的作用。目前大尺度农业、气候模型都需要空间分布的气温作为输入参数。除了用有限的台站数据空间插值获取气温栅格化数据外，遥感技术更为连贯精细的气温空间观测提供了有力的数据支持。为服务区域尺度气温相关的科研及业务工作，论文首先介绍了目前国内外气温遥感估算的主要方法，包括简单统计法、高级统计法、温度-植被指数分析法（TVX法）、地表能量平衡法及大气温度廓线外推法。再根据实际应用需要，重点总结比较了最高、最低和平均气温及不同时空尺度气温的遥感估算特点。最后讨论了气温遥感估算在实际应用中存在的问题并探讨了未来的研究趋势。

In the context of global warming, land surface albedo has become one of the important input parameters for climate simulation. The radiation data of 34 sites provided by Chinese Ecosystem Research Network (CERN), GLASS land surface albedo products, ERA-Interim reanalysis data, MODIS EVI (MOD13A3) and meteorological data provided by China Meteorological Administration were used to analyze the variation characteristics of the land surface albedo in different ecosystems based on Sen's Slope trend analysis; correlations and relative importance between land surface albedo and climate variables were calculated using all-subsets regression and hierarchical partitioning methods. The results showed that the bare land and rock or gravel land had the largest slope value from 2000 to 2017, being -0.083% yr-1 in winter. Pixels with significant correlations between land surface albedo and precipitation, EVI, soil moisture, and temperature during the growing season accounted for 73%, 79%, 56%, and 86% of the total pixels, respectively. EVI was the dominant factor in the change of land surface albedo in arid and semi-arid regions, and the independent contribution rates of these two types of regions were 41% and 56.18%, respectively. The effects of precipitation and temperature on land surface albedo in Northeast China in July lagged about 2 months. The influence of soil moisture on land surface albedo lagged about 1-2 months in the desert areas of Inner Mongolia and Middle-Lower Yangtze River Plain.

在全球气候变暖的背景下,地表反照率已成为地表辐射平衡和气候研究的重要参数之一。利用中国陆地生态系统研究网络（Chinese Ecosystem Research Network,CERN）提供的34个站点辐射数据、GLASS地表反照率产品、ERA-Interim再分析资料、MODIS EVI（MOD13A3）和中国气象数据共享网提供的气象数据,基于Sen's Slope趋势分析方法,分析不同生态系统地表反照率的变化特征;利用全子集回归和分层分解方法计算地表反照率与各要素之间的相关性和相对重要性;探讨各气候因子对地表反照率的影响。结果表明,2000&#x02014;2017年裸土地和裸岩砾石地变化率最大,冬季斜率达-0.083% yr^-1。生长季地表反照率与降水、增强型植被指数（Enhanced Vegetation Index,EVI）、土壤水分和气温显著相关的像元分别占总像元的73%、79%、56%和86%。EVI是干旱和半干旱地区地表反照率变化的主导因素,其对地表反照率变化的独立贡献率分别为41%和56.18%。7月东北地区降水量和气温对地表反照率的影响大约滞后2个月;内蒙古沙漠地区和长江中下游平原土壤水分对地表反照率的影响大约滞后1~2个月。