Climate warming has enhanced the winter greenness of evergreen vegetation on the Tibetan Plateau over the past 20 years. This response is primarily determined by the temperature sensitivity of vegetation, rather than the absolute extent of warming. However, how these factors shape the spatial patterns of sensitivity of vegetation to temperature (abbreviated as vegetation sensitivity) remains poorly understood. In this study, we analyzed the spatial pattern of vegetation sensitivity and its driving factors by using partial least squares regression, pixel-scale multiple linear regression, zone-level linear mixed models, and random forest model based on the winter normalized vegetation index (NDVIwinter) and multiple driving factors datasets. We found that the dominant factors influencing winter vegetation greenness are growing-season temperature and precipitation. The 72.5% of pixels exhibited positive vegetation sensitivity, with higher values in the south of the Himalayas and southeast edge. Vegetation sensitivity varied significantly across aridity zones, with the highest and lowest values in humid and arid zones, respectively. Furthermore, mixed forests showed the highest sensitivity. Random Forest analysis revealed that downward shortwave radiation (Ssrd) and the aridity index (AI) may contribute to spatial variability in NDVIwinter sensitivity to temperature, while the Ssrd and elevation were the most important environmental factors in humid and arid zones, respectively. Partial dependence plots indicated nonlinear relationships between key driving factors and NDVIwinter sensitivity, with precipitation and AI showing negative associations, while mean temperature exhibited a positive trend. The elevation and Ssrd displayed threshold-like responses, with sharp transitions in their influence on vegetation sensitivity. These findings clarify the complex regulatory roles of radiation, water availability, temperature, and topography in shaping the spatial heterogeneity of winter vegetation temperature sensitivity, and improve our understanding of alpine ecosystem responses to climate change.

Global vegetation greenness has increased since the 1980s, boosting productivity, facilitating CO2 absorption, and contributing to climate mitigation. The Tibetan Plateau plays a crucial role in maintaining carbon balance and climate stability. However, the dynamics in the coupling relationship between vegetation greenness and productivity remain poorly understood. In this study, we delved into the coupling relationship between vegetation greenness and productivity across the Tibetan Plateau, utilizing a long time series of MODIS Leaf Area Index (LAI) and Gross Primary Productivity (GPP) data, and further examined how these coupling relationships varied along environmental gradients. The results revealed that 77.9% of the pixels, primarily in northeastern and northern regions, exhibited concurrent increasing trends in both annual mean LAI and GPP during 2000–2021. Conversely, 19.8% of the pixels demonstrated inconsistent trends. Notably, annual mean LAI and GPP displayed a strong coupling strength of 0.73, which weakened during the late growing season. A critical transition threshold was observed, when LAI exceeded 6 m² m−2. Additionally, we identified decoupling in the southern and northwestern regions, attributed to pronounced leaf-shading effects and drought adaptation strategies. This study highlights the importance of understanding the coupling relationships of vegetation greenness and productivity in the carbon cycle.

The match relationship between urbanization and ecosystem services (ESs) is a cornerstone of achieving sustainable development. However, the evolution patterns of urbanization/ecosystem service (UES) synergies under economic polarization in the rapid urbanization process remain poorly understood. This study integrates bivariate local Moran’s index and correlation analysis methods to examine the match relationship between urbanization and three key ESs (water yield, carbon sequestration, and food production) from 2000 to 2020 and explores the impact of intra-city disparities on the match relationship of urbanization and ESs. The findings revealed that urbanization and three ecosystem services showed increasing trends during 2000–2020 simultaneously. The spatial aggregation pattern of urbanization and ecosystem services showed smaller variations from 2000 to 2020. There was a High-High aggregation between urbanization and water yield in urban built-up areas and primarily High-Low aggregations between urbanization, carbon sequestration, and food production. Furthermore, the impact of urbanization on ESs decreased with increasing urban polarization. In particular, the Beijing–Tianjin–Tangshan region still demonstrated pronounced economic polarization, suggesting disparities in economic development within its urban core. This study highlights the importance of mitigating the adverse effects of urban polarization on ESs and fostering resilient and sustainable urban ecosystems in rapidly developing regions.

The construction of ecological security pattern (ESP) is an effective way to ensure regional ecological security. Although the method of constructing the regional ESP based on ecosystem services (ESs) has been widely recognized and applied, the spatial characteristics of ESs supply–demand mismatch has not been well included into ESP construction. We constructed a regional ESP framework connecting demand sources and ecological sources from the perspective of ESs supply–demand mismatch. Taking the Wuhan urban agglomeration (WUA), findings indicated distinct spatial aggregations of ecological sources and demand sources due to the supply–demand mismatch of ESs. Ecological sources (12,406.29 km2 or 21.42%) were primarily located in the south and north of WUA, while demand sources (1,191.26 km2 or 2.07%) were concentrated mainly in the central. Two types of corridors jointly ensured regional ecological security. A total of 86 supply–supply corridors in the north ensured the supply ability of ESs by connecting ecological sources, while 35 supply–demand corridors in the south alleviated the supply–demand mismatch of ESs. The targeted implementation of ecological governance based on the corridor types provides a new approach to coordinate the mismatch of ESs supply–demand and enhance ecological security. However, 63.38 km2 of pinch points, recognized as high-flow areas within the corridors, primarily comprised fragmented landscapes, and barriers covering 99.67 km2 obstructed corridor flow, notably surrounding the demand sources. These regions should be prioritized for ecological conservation. Overall, this research framework provides a reliable scientific basis for configuring spatial landscape patterns and developing ecological strategies in urban agglomerations.

Climate warming is expected to increase growth and expansion of evergreen vegetation in many cold regions, with substantial influences on ecological and atmospheric processes. Nevertheless, the direction and magnitude of changes in productivity (greenness) of evergreen vegetation, as well as their potential drivers, remain unclear in many parts of the world. The woody evergreen vegetation on the Tibetan Plateau influences ecosystems and land surface processes, affecting regional and continental weather and climate through regulating land-air interactions. Here, we show that the remotely-sensed winter greenness of evergreen vegetation increased by 9.8% over 2000–2021 on the Tibetan Plateau, with significant (P < 0.05) greening across 55.8% of the areas with evergreen vegetation, which is more widespread than the increase of summer peak greenness, suggesting upslope shifts in treelines and shrublines and encroachment by evergreen woody plants. While our results show that warming was the principal climate driver of greening, the spatial pattern of greening was more related to the temperature sensitivity of greenness rather than temperature trends. Positive impacts of increasing precipitation on greenness were observed in a few areas classified as grasslands. Moreover, the magnitude of winter greening on the plateau was larger than that of the greening in the Arctic, where warming was faster, which indicates higher level of sensitivity of greenness to temperature of evergreen vegetation on the Tibetan Plateau. Our results highlight the high sensitivity of evergreen vegetation to climate warming and provide a new foundation for improving the understanding the responses and feedbacks of the Tibetan Plateau ecosystem to climate change.

Global warming could affect vegetation growth, while land surface temperature has exhibited an asymmetric warming pattern over the past 50 years. The Tibetan Plateau, known as "the roof of the world," experiences the surface warming almost twice as high as the global average. However, previous research has largely overlooked the impacts of this asymmetrical warming on vegetation growth and the temporal changes in these impacts. In this study, we assess the effects of asymmetric warming on vegetation growth at regional and different vegetation types by using partial correlation analysis, and reveal the temporal changes of impacts strength using time moving window. The results showed that there had been a significant greening trend (1.01 x 10(-3) yr(-1), p < 0.01) in the Normalized Difference Vegetation Index (NDVI) during the growing season, as well as significant warming trends in both maximum temperatures (T-max, 0.0354 degrees C yr(-1), p < 0.01) and minimum temperatures (T-min, 0.0333 degrees C yr(-1), p < 0.01) during 2000-2021. Under the background of asymmetric warming, the grassland showed stronger impacts of T-min than T-max on NDVI, while the opposite effects were observed for forests. Over time, the response of NDVI to T-max and T-min has intensified. This study highlights importance of asymmetric warming in the projection of climate change on vegetation in similarly vulnerable ecosystems worldwide.

Vegetation is an essential component of terrestrial ecosystems and supplies multiple ecosystem benefits and services. Several indices have been used to monitor changes in vegetation communities using remotely-sensed data. However, only a few studies have conducted a comparative analysis of different indices concerning vegetation greenness variation. Additionally, there have been oversights in assessing the change in greenness of evergreen woody species. In this study, we used the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of terrestrial vegetation (NIRv), and the leaf area index (LAI) data derived from MODIS data to examine spatial and temporal change in vegetation greenness in the growing season (May–September) and then evaluated the evergreen vegetation greenness change using winter (December–February) greenness using trend analysis and consistency assessment methods between 2000 and 2022 on the Tibetan Plateau, China. The results found that vegetation greenness increased in 80% of pixels during the growing season (northeastern, central-eastern, and northwestern regions). Nevertheless, a decline in the southwestern and central-southern areas was identified. Similar trends in greenness were also observed in winter in about 80% of pixels. Consistency analyses based on the four indexes showed that vegetation growth was enhanced by 29% and 30% of pixels in the growing season and winter, respectively. Further, there was relatively strong consistency among the different vegetation indexes, particularly between the NIRv and EVI. The LAI was less consistent with the other indexes. These findings emphasize the importance of selecting an appropriate index when monitoring long-term temporal trends over large spatial scales.

Phenological changes of evergreen vegetation affect ecosystem functions and land-atmosphere exchanges. Although recent studies have documented changes in the spring onset of photosynthesis derived from satellite solar-induced chlorophyll fluorescence (SOSSIF) of evergreen vegetation in the Northern Hemisphere, spatial variations in the response of the onset of spring photosynthesis to climate change remain poorly characterized. Using a continuous solar-induced chlorophyll fluorescence dataset, we found that SOSSIF advanced in more than 70.0% of surveyed areas with evergreen vegetation, represented as pixels and spread widely across the Tibetan Plateau. Warming temperatures contributed to advanced SOSSIF in more than 42.0% of surveyed areas, mainly in the southeast and areas between 90 and 97 degrees E. Increasing precipitation contributed to advanced SOSSIF in about 15.0% of surveyed areas, mainly along the southeastern edge of the plateau. A negative partial correlation between SOSSIF and temperature (RP(SOSSIF, T)) was observed in more than 65.0% of surveyed areas, and stronger negative RP(SOSSIF, T) was found in areas with a larger positive interannual correlation between preseason temperature and precipitation, likely due to the better match between favorable temperature and water conditions in these areas. The positive RP(SOSSIF, T) was likely associated with low water availability. The partial correlation between SOSSIF and precipitation (RP(SOSSIF, P)) was spatially diverse, with negative RP(SOSSIF, P) in about half of surveyed areas. Stronger negative RP(SOSSIF, P) was observed in areas with a stronger negative interannual correlation between preseason precipitation and solar radiation, which was probably caused by the trade-off between water and light availability to maximize the benefits from precipitation. The positive RP(SOSSIF, P) was likely due to the lack of solar radiation. This study provides new explanations for spatial variations in the response of SOSSIF to temperature and precipitation, contributing to assessments of vegetation phenology and global carbon cycle modeling.

土壤湿度是地表水热交换过程和水文循环中的一个关键组成部分,获取高时空分辨率的土壤湿度数据一直是当前研究的热点.SMAP(Soil Moisture Passive and Active)主被动微波土壤湿度产品的精度高,但存在着空间分辨率低和时间分辨率缺失的问题,这限制了其在区域尺度上的应用,为解决这一问题得到更高时空分辨率的土壤湿度产品,本文利用广义回归神经网络模型(GRNN)模拟了 MODIS地表温度、反射率、植被指数光学/热红外遥感数据以及高程、坡度、坡向、经纬度数据与SMAP土壤湿度的关系,从而将京津冀地区SMAPL2土壤湿度产品的时间分辨率由不连续(4～20 d)提升至1 d,空间分辨率由3 km提升至1 km,并扩展其在京津冀地区的空间覆盖范围.研究发现:①GRNN模型总体验证结果表明土壤湿度估算值与SMAP原始值的相关性较高(r=0.7392),均方根误差(RMSE)为0.0757 cm3/cm3;②不同季节典型日期的GRNN模型估算结果精度相差较大,春季处的相关性相比其他季节最低,精度相对较高(r春=0.6152,RMSE4=0.0653cm3/cm3),秋季和夏季土壤湿度估算精度较为接近(r夏=0.6957,r秋=0.7053,RMSE夏=0.0754cm3/cm3,RMSE秋=0.0694cm3/cm3),冬季的估算精度最高(r冬=0.8214,RMSE冬=0.0367cm3/cm3);③2016年京津冀夏秋季节的土壤湿度较其他季节要显著提高,空间分布上坝上高原区域较低,而沿海地区的土壤湿度明显较高.本研究对京津冀地区的生态水文、气候预测以及干旱监测等应用领域具有重要价值.

Wetlands are some of the most highly productive ecosystems on earth and are one of the most important environments for human survival and development. However, with the acceleration of industrialization and urbanization, wetlands have been constantly occupied and destroyed and have even disappeared in some areas, so it is necessary to establish a reasonable and scientific ecological risk assessment system for wetlands. In this study, 181 counties in Beijing-Tianjin-Hebei urban agglomeration (Jing-Jin-Ji) were selected as the research objects, and an index system with 22 indexes was established from two aspects: the external hazard degree and the vulnerability of the wetland itself. The ecological risk of the wetlands during 6 periods from 1990 to 2015 was comprehensively evaluated by long-term data and multiple index factors. The results showed that (1) due to the decrease in the hazard degree and the increase in the vulnerability, the ecological risk of the wetlands in Jing-Jin-Ji first increased and then decreased; (2) more than 60% of the counties were at the middle or middle-high risk level, especially in the center of urban development, which generally had higher risk level than other regions; (3) most wetland parks were at the middle-high risk level, while wetland reserves, reservoirs and lakes were mostly in the low risk or middle-low risk level. Therefore, in recent years, the implementation of wetland protections and restoration policies and projects has played a significant role in reducing the ecological risk of the wetlands. In the future, more targeted management and protection measures need to be formulated and followed up in time.

通过湿地可恢复性评价可获知湿地恢复的难易程度,为湿地修复的选址与实施提供理论支撑.基于湿地类型和生态要素数据,利用湿地类型丧失区和湿地要素受损区构建了受损湿地可恢复性评价方法,定量评价湿地受损区尺度的可恢复性;借助区域内地形条件、城镇化影响和生态重要性指标,利用源-汇理论和最小累积阻力模型构建了湿地所在区域可恢复性评价方法,定量评价区域尺度的湿地可恢复性;从湿地受损区和湿地所在区域2个尺度,构建湿地自然可恢复性评价方法,并对天津滨海新区湿地可恢复性进行评价和分析.结果 表明:在湿地受损区尺度,滨海新区稳定湿地区面积为640.30 km2,在整个恢复区面积中占比最大;其次是最难恢复区,面积为381.85 km2,占比为22.15％;最难恢复区和较难恢复区面积约占整个恢复区的42.36％.在湿地所在区域尺度,滨海新区湿地可恢复性在中等和高等安全水平下,较易恢复区面积均为1 695.65km2,占比为82.02％,难恢复区面积分别为67.55和129.05 km2,占比为3.27％和6.24％.

城市化进程的加快及对湿地资源的不合理开发利用造成湿地严重受损,开展湿地资源的时空变化监测对于湿地的合理开发和可持续发展具有重要作用.基于多时期土地利用数据和长时间序列的Landsat影像数据,利用马尔科夫转移矩阵和GIS空间分析法分析湿地类型受损状况,利用趋势分析法分析长时间序列和不同阶段的湿地植被、水体和土壤湿度要素受损状况,构建了湿地受损遥感识别方法,综合湿地类型和湿地要素受损状况,分析湿地受损模式,并在天津滨海新区进行案例研究.结果 显示:滨海新区湿地类型受损主要分为3个时期(1980s-2000年、2000-2009年、2009-2015年),期间湿地经历了从受损到恢复的过程,1980s-2015年滨海新区湿地面积增加了41.40 km2;1984-2015年湿地要素均呈退化趋势,植被、水体和土壤显著退化的面积分别为364.66、221.28和253.94 km2.在不同时间段内,影响湿地面积受损的主导因素不同,3个时期滨海新区湿地受损分别是植被、水体和植被占主要地位.

气候变化和人类活动的加剧,使地球上重要的生态系统——湿地被不断侵占与破坏,当前建立湿地退化的科学评估方法十分迫切且必要.基于区域生态风险理论,从胁迫度和退化度2个角度建立了包含22项指标的湿地退化多指标评估体系,并对天津市1990-2015年湿地退化风险进行评估.结果 表明:由于胁迫度的波动变化和湿地自身退化度的上升,天津市1990-2015年湿地退化风险呈先下降后上升的趋势,2015年的退化风险较1990年增加了10.08％,且原本湿地退化风险较低的区域呈现风险快速上升的现象.亟待出台更严格的相关政策和建立更完善的管理制度,以加强天津市湿地的保护与恢复.

Wetlands play a critical role in the environment. With the impacts of climate change and human activities, wetlands have suffered severe droughts and the area declined. For the wetland restoration and management, it is necessary to conduct a comprehensive analysis of wetland loss. In this study, the Xiong’an New Area was selected as the study area. For this site, we built a new method to identify the patterns of wetland loss integrated the landscape variation and wetland elements loss based on seven land use maps and Landsat series images from the 1980s to 2015. The calculated results revealed the following: (1) From the 1980s to 2015, wetland area decreased by 40.94 km2, with a reduction of 13.84%. The wetland loss was divided into three sub stages: the wet stage from 1980s to 2000, the reduction stage from 2000 to 2019 and the recovering stage from 2009 to 2015. The wetland area was mainly replaced by cropland and built-up land, accounting for 98.22% in the overall loss. The maximum wetland area was 369.43 km2 in the Xiong’an New Area. (2) From 1989 to 2015, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil moisture monitoring index (SMMI) showed a degradation, a slight improvement and degradation trend, respectively. The significantly degraded areas were 80.40 km2, 20.71 km2 and 80.05 km2 by the detection of the remote sensing indices, respectively. The wetland loss was mainly dominated by different elements in different periods. The water area (NDWI), soil moisture (SMMI) and vegetation (NDVI) caused the wetland loss in the three sub-periods (1980s–2000, 2000–2009 and 2009–2015). (3) According to the analysis in the landscape and elements, the wetland loss was summarized with three patterns. In the pattern 1, as water became scarce, the plants changed from aquatic to terrestrial species in sub-region G, which caused the wetland vegetation loss. In the pattern 2, due to the water area decrease in sub-regions B, C, D and E, the soil moisture decreased and then the aquatic plants grew up, which caused the wetland loss. In the pattern 3, in sub-region A, due to the reduction in water, terrestrial plants covered the region. The three patterns indicated the wetland loss process in the sub region scale. (4) The research integrated the landscape variation and element loss appears potential in the identification of the loss of wetland areas.

湿地生态系统与森林、海洋并称为地球三大生态系统,是地球上最重要的生态系统之一.近些年来,随着人类活动与城市发展的影响,湿地生态系统安全受到巨大的威胁.开展湿地景观演变与人为干扰的关系研究,对雄安新区湿地生态环境的保护具有重要意义.本文利用20世纪80年代末(1980s)—2015年间7期土地利用数据,基于移动窗口法分析雄安新区湿地景观演变时空分异及其与人为干扰的关系研究.研究结果表明,1980s—2015年间雄安新区湿地景观时空格局发生了较大的变化.从时间上看,湿地面积整体呈现减少的趋势,湿地破碎化程度逐渐增加,斑块形状变化复杂,连接度减小;从空间分布格局看,2000年以后湿地景观斑块个数增加,中部和东北部湿地破碎度逐渐增加,从中部湿地核心区向边缘破碎度逐渐增加.1980s—2015年间雄安新区人为干扰度整体呈现减少的趋势,其中1980s—2000年间人为干扰强度在西南部和中部有所减弱;2000—2015年间人为干扰度呈现先增加后减少的趋势,新时期下由于湿地保护政策的实行,湿地发展转向低强度可持续利用发展阶段,湿地周边区域受建设用地扩张的影响,人为干扰度增强,人为干扰度与湿地分布具有良好的对应关系.

Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km2 in 1989 and a minimum of 44 km2 in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

The accuracy assessment of global gridded precipitation products is the first step in local application. Based on the daily precipitation observation data of 824 weather stations in China during 1979-2015, this study intends to evaluate quantitatively the accuracy of newly released Multi-Source Weighted-Ensemble Precipitation (MSWEP) products in China by using a variety of indicators such as relative bias and correlation coefficient. Moreover, a corrected Mann-Kendal trend monitoring method was used to analyze the reliability of MSWEP products in trend analysis of precipitation. The main conclusions show that:① Precipitation estimated from MSWEP is higher than that of the gauged precipitation in China. However, there is an underestimate in the North China region. ② MSWEP daily rainfall data have overestimation and underestimation of slight-precipitation and heavy-precipitation events, respectively. ③ There is a good relation between monthly/annual gauge precipitation and MSWEP. ④ There is an obvious spatial difference between the trends of annual precipitation based on MSWEP and site observations in China. However, in spring, autumn and winter, the spatial distribution of the trend is in good agreement.

城镇化发展极大地改变了区域生境分布格局和功能,从而影响区域生态安全.因此,开展生境质量的评估,对于城市生态安全保障具有重要作用.基于InVEST生境质量模型,评估了京津冀区域2005-2015年生境质量时空变化格局.研究结论如下:1)2005-2015年,京津冀生境面积减少7134.2 km2,占2005年生境面积的3.7％.耕地、草地和水域生境面积分别减少5081.0、1695.1、421.6 km2,占对应类型生境面积的4.7％,4.9％和7.2％.2)京津冀生境质量从0.88降至0.83,下降幅度达5.69％.其中,耕地生境质量下降最为严重,其次为水域生境质量.3)生境质量下降区域主要沿北京-保定-石家庄-邢台-邯郸等经济发展迅速的区域分布,表现为城市扩张侵占原有生境.4)京津冀高生境质量斑块破碎度增加,低生境质量斑块集聚度增加.整体上看,京津冀区域生境斑块破碎度增加.

湿地是水陆生态系统的转换区,是地球上生物多样性和生产力最高的生态系统之一。在快速城镇化和社会经济发展的推动下,京津冀地区湿地生态环境面临较大地威胁。利用1980年代末到2015年7期土地生态遥感解译数据,运用GIS空间分析和主成分分析方法,分析了京津冀地区湿地景观时空变化及其驱动力。研究结果表明:（1）1980s末—2015年期间,京津冀地区湿地面积的变化呈现从略微增长到快速减少趋势,近十年减少趋势略有减缓。湿地总面积减少了2695.05 km2,较1980s末年减少了20.08%;河北省湿地面积减少最多,且天然湿地减少占据主导地位,其次是天津市和北京市。（2）湿地面积损失较为严重的区域主要分布在环渤海区域、北京市和河北省张家口市和唐山市。湿地受损主要是水田和滩涂向非湿地转换引起的。（3）水田和水库坑塘构成的人工湿地是京津冀地区优势景观类型。湖泊、河渠、滩地破碎度增加,且空间分布离散,连通性差。（4）选取9个驱动因素指标进行主成分分析,人类活动是影响湿地景观格局变化的主要因素,城市扩张和农业发展是侵占湿地的主要表现形式,此外气候和政策等因素也对湿地变化存在一定影响。