基于Fragstats4的景观格局指数与地表温度的相关性——以石家庄市为例

以石家庄市城市景观为研究对象,选取1987年、2004年和2019年Landsat遥感影像数据,采用监督分类方法将研究区分为绿地、水体、不透水地表和未利用地四类景观,采用单窗算法和劈窗算法反演地表温度(LST)。从景观生态学角度出发,利用Fragstats4.2计算4种类型的景观格局指数,并对景观粒度和移动窗口的尺度选择进行探讨分析;利用ArcGIS空间分析方法和统计分析方法,分析4种类型的景观格局指数与LST的相关性。结果表明:1987—2019年绿地斑块类型面积(CA)、最大斑块面积指数(LPI)和聚集度指数(AI)逐渐下降,不透水地表CA、LPI和AI逐渐增加,随着城市化进程的加快,绿地面积逐渐减少和裂化,绿地景观优势在不断下降,不透水地表面积在逐渐增加和聚合,不透水地表景观优势在不断加强,逐渐形成优势景观。斑块百分比指数(PLAND)、LPI和AI与LST表现出一致的极显著相关关系,绿地和水体为负相关,不透水地表和未利用地为正相关;斑块破碎化指数(SPLIT)则相反,绿地和水体为正相关,不透水地表和未利用地为负相关。LST与PLAND和LPI的相关系数明显高于LST与AI和SPLIT的相关系数,说明一个优势景观对地表温度的影响效果明显大于几个比较分散或破碎的景观。     

安徽省植被和地表温度季节变化及空间分布特征

卫星遥感广泛应用于宏观、大范围、动态连续的植被和地表温度监测研究。利用2001—2008年MODIS卫星遥感资料, 分析了安徽省归一化植被指数 (NDVI) 和地表温度 (LST) 的季节、月变化和空间分布特征,探讨了代表城市区域的NDVI和LST时空分布及其相关性。结果表明:安徽省NDVI和LST季节变化显著,具有典型地域特征;受当地气候影响,植被、农作物类型地域差异较大,导致LST季节变化以及空间分布不同;城市中心向郊区过渡时,植被覆盖度在不断增加,伴随着NDVI的增加,LST下降;城市LST明显高于郊区值,呈现热岛效应。研究表明,当地气候和植被分布共同决定了LST的分布状况,这将为安徽省合理进行农业区划、科学监测生态环境以及有效评估土地利用与热岛效应提供重要参考依据。     

2005年7月珠三角地表温度场的遥感监测分析

城市热环境及其热效应是当前城市气候与环境中最为重要的研究内容之一。分析地表温度（LST）是研究热环境的一个有效手段。选取2005年7月中旬高温期间MODIS数据,利用推广分裂窗算法反演LST,采用最大值合成方法制作珠江三角洲地区热场分布序列图。结果发现：珠三角地区增温初期的高温区主要分布在珠三角外围的城市,呈由外向内的增温过程;珠三角地区沿河道区域的增温幅度最高最快,增温盛期的地表最高温度主要分布在河网密集地区;城市化和工业化程度高的市区热岛效应明显。研究结果符合目前珠三角经济区的发展状况,对城市区域布局和规划有积极意义。     

1984—2014年北京地区不透水地表的时空变化及其温度效应研究

研究城市地表覆盖与地表温度（LST）的关系对改善城市生态环境具有重要科学意义。在Landsat TM数据支持下,利用线性光谱混合分析模型提取不透水地表信息,结合LST和地表热通量,分析不透水地表覆盖度（ISA）和LST的时空变化特征及其相互关系,探讨不透水地表对LST的影响机理。结果表明:1984—2014年北京不透水地表面积迅速增长,中覆盖度比例下降,高覆盖度比例增加;LST从市中心向郊区递减,高温区向外扩张;LST和ISA呈显著正相关,但不是简单的线性关系;ISA处于0.6~0.9时LST上升速率最快,减少ISA在此范围内的不透水地表集中分布可缓解高温区集中的现象。     

基于哨兵2/3卫星高分辨率地表温度的降尺度方法

针对卫星遥感数据提取或生成地表温度（land surface temperature, LST）存在的时空分辨率矛盾,利用哨兵2/3卫星产品数据,其中哨兵3数据提供高时间分辨率影像,哨兵2数据提供高空间分辨率信息,并利用SNAP70及Excel2010软件,建立归一化植被指数与LST的相关关系,利用统计降尺度方法,成功将LST的空间尺度从1 000 m降至10 m,生成高时间分辨率10 m空间分辨率的LST。将原始1 000 m分辨率哨兵3地表温度图与降尺度到10 m空间分辨率的地表温度图对比,可以发现：降尺度地表温度图可以覆盖大部分原始1 000 m分辨率的地表温度信息,说明降尺度结果较好地保留了原始LST影像热特征的分布情况;而且,所生成的高空间分辨率的地表温度产品地物特征清晰,纹理明显。利用地面国家气象自动观测站实测0 cm地温数据验证降尺度结果,可以看出：误差平均值为26 K,误差值较小,说明降尺度结果精度较高。     

利用ASTER数据分析南京城市地表温度分布

城市环境日益受到人们重视, 南京是长江下游人口密集的城市, 研究南京市地表温度分布对了解南京城市气候, 改善生活环境, 为城市发展规划提供有效的气象服务具有一定科学意义。该文利用2002年8月21日10:30 (北京时) ASTER热红外数据, 在ENVI软件的支持下, 通过劈窗算法反演南京城市地表温度, 进一步生成城市地表温度分布等温线图。用同时相ETM+数据进行验证, 二者十分吻合, 说明ASTER反演结果可靠。结果表明:南京市存在明显的热岛效应, 城市地表温度分布差异大; 不同下垫面的地表温度差异明显, 城区地表温度总体高于郊区, 植被覆盖密集区地表温度低于植被稀疏地, 具有较大水域面积和较密植被的城中各大公园形成多个冷岛, 长江水体温度最低; 随着城市的扩大, 新城区热岛效应更加明显。水体和密集植被能显著改善城市环境。     

沈阳市区土地利用类型与地表温度关系研究

利用2010年8月Landsat TM数据计算沈阳市三环内地表温度(Land Surface Temperature,LST),基于目视解译与监督分类结合的方法将研究区划分为建筑用地、绿地、农田、弃地、道路和水体6类土地利用类型;以城市中心点为原点,分析城市中心不同距离圆环的LST及其与土地利用类型配置的关系,采用多元线性回归法和偏最小二乘回归法建立LST预测模型,并进行模型验证。结果表明:沈阳市区建筑用地、绿地、农田、道路和中心城区的平均LST均随着距中心原点的距离增加而减小;同一级别缓冲区内,建筑和道路的LST较高,水体的LST最低。城市的"摊饼"式发展,也造成了增强热岛效应的建筑用地和道路面积百分比随着距中心原点距离的增加逐渐降低。研究区范围内最主要的土地利用类型为建筑用地,其次为道路。对比分析表明,多元线性回归法和偏最小二乘回归法建立的模型均能较好的预测LST,采用偏最小二乘回归方法对沈阳地区土地利用类型与LST进行回归分析效果更理想。     

西安市城市热岛效应卫星遥感分析

基于Landsat卫星的ETM<'+>(增强型主题成像传感器)数据计算西安市亮度温度,采用监督分类法对西安市影像进行土地利用/覆盖变化分类;在此基础上对西安市城市热岛的空间分布特征及城市热岛与土地利用/覆盖变化的关系进行研究.结果表明:西安市城区地表温度明显比郊区地表温度高,由市中心向外呈现地表温度逐渐降低的趋势.城市地表温度与土地利用类型密切相关,不同地表覆盖类型的地表温度差异显著,城市用地和裸地是城市热岛强度的主要贡献因素,水体和林地具有较好的降温作用.     

长三角地区城市用地扩展及城乡热环境的特征分析

利用1993年和2004年长三角地区的卫星遥感资料,分析了该地区的3个主要区域南京、上海、苏锡常及其周边的土地利用类型变化,定量地评价城市用地扩展程度.结合2004年地表温度(LST)卫星资料,揭示了城乡LST空间分布特征及其差异.结果表明:上海、苏锡常和南京11a期间城市建设用地动态度K分别为204.0%、354.3%和99.2%,苏锡常城市扩展程度最快;不同土地覆盖类型的LST不同,城市用地LST最高,其次作物地,林地最低;城乡之间平均地表温差具有季节变化,冬季最大,而秋季最小.     

近15年北京夏季城市热岛特征及其演变

根据北京地区20个地面气象观测台站1990-2004年7月的气温资料,分析了最近15年来北京夏季城市热岛的最新特征和变化趋势,也分析了城市热岛与气温,城郊地表温度差与地表温度,气温和地表温度间的关系。结果表明：北京夏季夜间出现了强热岛,郊区城市也出现了热岛现象,但白天城市热岛相对夜间不明显。夜间城市热岛强度呈逐年增强趋势,但白天这种趋势不明显。夜间城市热岛与气温呈正相关,气温高的年份,城市热岛强度相对也大;夜间城郊地表温差与地表温度呈正相关,地表温度越高,城郊地表温差越大;夜间,气温与地表温度呈正相关,气温越高,地表温度也高。白天,这些相关相对夜间来说不那么明显。研究成果对北京城市发展规划和高温灾害的防治有一定的科学参考价值。     

利用ASTER数据反演南京城市地表温度

利用针对ASTER数据的分裂窗算法,反演了南京城市地表温度,并用实际观测资料和同步的MODIS数据对反演结果进行了验证,结果表明：基于ASTER数据的地表温度反演结果与实际观测资料相差0.9℃,与MODIS数据的反演结果具有较好的空间一致性;基于ASTER数据反演的当日南京地表温度在23～56℃范围内,城市地表温度普遍高于35℃,市内公园地表温度略低,多位于30～35℃,长江水体温度低于30℃,地表温度存在明显的空间差异;南京城市夏季白天存在明显＂热岛效应＂,热岛强度的空间差异与南京城市发展、规划有关。     

The surface urban heat island in the city of Brno (Czech Republic) derived from land surface temperatures and selected reasons for its spatial variability

Thermal infrared images from Landsat satellites are used to derive land surface temperatures (LST) and to calculate the intensity of the surface urban heat island (UHI) during the summer season in and around the city of Brno (Czech Republic). Overall relief, land use structure, and the distribution of built-up areas determine LST and UHI spatial variability in the study area. Land-cover classes, amount and vigor of vegetation, and density of built-up areas are used as explanatory variables. The highest LST values typically occur in industrial and commercial areas, which contribute significantly to surface UHI intensity. The intensity of surface UHI, defined as the difference between mean LST for urban and rural areas, reached 4.2 and 6.7 °C in the two images analyzed. Analysis of two surface characteristics in terms of the amount of vegetation cover, represented by normalized difference vegetation index, demonstrates the predominance of LST variability (56–67 % of explained variance) over the degree of urbanization as represented by density of buildings (37–40 % of LST variance).     

Modeling land surface phenology in a mixed temperate forest using MODIS measurements of leaf area index and land surface temperature

The development of satellite-derived vegetation indices and metrics has enabled researchers to monitor land surface phenology (LSP). While the use of satellite data to monitor LSP is prevalent, there has been minimal effort to model LSP in temperate climates using satellite observations of the land surface. Satellite-derived LSP models are beneficial for studying past and future changes in phenology and related ecosystem processes (e.g., water, energy, and carbon fluxes). The purpose of this study was to model LSP during the spring in a mixed temperate forest using satellite-derived measurements of leaf area index (LAI) and land surface temperature (LST). As part of the model validation process, the use of LST as a proxy for air temperature to model LSP was also investigated. The results indicate that LST derived from the MODIS Terra sensor at 10:30?a.m. (local solar time) can be used to develop a LSP model that predicts the full profile of LAI from winter dormancy to maturity and the date when LAI reaches half of the annual maximum (LAI_50%) with relatively low error. In addition, the modeled LAI values closely tracked in situ observations of the phenological development of the dominant deciduous tree species located in the study area where the model was developed. A comparison of LST and daily maximum air temperature at two levels above the ground surface revealed distinct differences and nonlinearities in the relationship between these two variables. However, accumulated growing degree-days calculated from each of these variables were similar because the largest differences between LST and daily maximum air temperature occurred prior to the beginning of heat accumulation. Consequently, the model predictions of LAI_50% derived from the use of LST and daily maximum air temperature were similar. When the developed model was applied in two other mixed forests, the errors were larger due to substantial interannual variability in the relationship between LAI and heat accumulation and systematic differences in this relationship between sites. Although the model cannot be successfully applied in these other mixed forests, the ability of the model to capture a consistent relationship between satellite estimates of LAI and LST in the study area where it was developed demonstrates that satellite observations of the land surface can be used in certain locations to create LSP phenology models. When validated, the models can be used to examine past and future changes in phenology and related ecosystem processes.     

Statistical estimation of high-resolution surface air temperature from MODIS over the Yangtze River Delta,China

High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance, urban climate change, and so on. This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) to estimate air temperature at a high resolution over the Yangtze River Delta region, China. It is found that daytime LST is highly correlated with maximum air temperature, and the linear regression coefficients vary with the type of land surface. The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models. The estimated air temperature shows a clear spatial structure of urban heat islands. Spatial patterns of LST and air temperature differences are detected, indicating maximum differences over urban and forest regions during summer. Validations are performed with independent data samples, demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C, and the uncertainty is about 3.1°C, if using all valid LST data. The error is reduced by 0.4°C (15%) if using best-quality LST with errors of less than 1 K. The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.     

Introduction of a sub-grid hydrology in the ISBA land surface model

In atmospheric models, the partitioning of precipitation between infiltration and runoff has a major influence on the terrestrial water budget, and thereby on the simulated weather or climate. River routing models are now available to convert the simulated runoff into river discharge, offering a good opportunity to validate land surface models at the regional scale. However, given the low resolution of global atmospheric models, the quality of the hydrological simulations is much dependent on various processes occurring on unresolved spatial scales. This paper focuses on the parameterization of sub-grid hydrological processes within the ISBA land surface model. Five off-line simulations are performed over the French Rhône river basin, including various sets of parameterizations related to the sub-grid variability of topography, precipitation, maximum infiltration capacity and land surface properties. Parallel experiments are conducted at a high (8 km by 8 km) and low (1° by 1°) resolution, in order to test the robustness of the simulated water budget. Additional simulations are performed using the whole package of sub-grid parameterizations plus an exponential profile with depth of saturated hydraulic conductivity, in order to investigate the interaction between the vertical soil physics and the horizontal heterogeneities. All simulations are validated against a dense network of gauging measurements, after the simulated runoff is converted into discharge using the MODCOU river routing model. Generally speaking, the new version of ISBA, with both the sub-grid hydrology and the modified hydraulic conductivity, shows a better simulation of river discharge, as well as a weaker sensitivity to model resolution. The positive impact of each individual sub-grid parameterization on the simulated discharges is more obvious at the low resolution, whereas the high-resolution simulations are more sensitive to the exponential profile with depth of saturated hydraulic conductivity.