基于GEE的1998～2018年京津冀土地利用变化对生态系统服务价值的影响

在京津冀地区可持续发展评估研究中,对生态系统服务价值进行动态估算具有重要意义.本文以京津冀地区为研究区,基于谷歌地球引擎(GEE)云平台采用分类决策树(CART)分类算法对研究区内1998、2003、2008、2013及2018年的Landsat TM/OLI影像进行监督分类得到5个时期土地利用数据并定量分析1998～2018年京津冀地区土地利用动态变化规律,再利用生态服务价值(ESV)当量估算方法定量估算京津冀地区的ESV并结合15 km×15 km尺度格网探明其时空动态变化.结果表明:①1998～2018年间,京津冀地区6种土地利用类型中建设用地(增加16. 67%)及草地(减少13. 73%)面积占比变化幅度最大,水体(减少0. 2%)面积占比变化幅度最小.②京津冀地区ESV总价值在1998～2003年间出现短暂增长(增加91. 97亿元),2003～2018年间持续降低(减少239. 07亿元),主要与建设用地面积在除1998～2003年的其余3个时间段扩张较快有关,6种土地利用类型中林地提供的ESV最高,建设用地及未利用土地提供的ESV最低.③基于15 km×15 km尺度格网的ESV时空分析表明,1998～2018年间京津冀地区ESV中等区逐渐较少,ESV较低区及较高区逐渐增加,且ESV较低区增速高于较高区.④1998～2018年间,京津冀地区6种土地利用类型对价值系数的敏感性系数(SI)范围为0～0. 40,且均低于1,表明本文ESV对修订后的生态系统服务价值系数缺乏弹性,证明本文定量估算ESV的结果是可靠的.在未来经济发展中,京津冀地区应合理优化土地利用格局,加强对林地、草地、水体及耕地的保护.研究可为制定可持续发展战略,建设生态友好型社会提供参考.     

基于Landsat TM/ETM+/OLI遥感大数据分析南京市1984—2015年大气能见度变化趋势

对南京市1984—2015年Landsat 4／5／7／8卫星TM／ETM＋／OLI传感器获取的遥感数据,利用ENVI遥感软件的FLAASH大气校正模块,进行了区域大气能见度（ VIS）遥感反演。结果表明,时间跨度达30余年的Landsat卫星遥感数据影像序列反演的VIS呈明显的下降趋势,20世纪80年代数值较高,“差”能见度（＜10 km）的观测率不到6％,21世纪以来VIS下降明显,“差”能见度的观测率为20％～25％。与2010—2015年南京市PM10、PM2．5监测数据进行了对比,在城市空气清洁及污染较轻时,星地监测结果有较好的一致性,但中到重污染天气时FLAASH算法反演VIS偏高,侧重于代表离主城区距离远的偏远乡野山林地区的能见度状况。     

基于TM资料对上海区域热环境特征的分析

为了揭示城市热岛(UHI)形成机制,以TM卫星遥感资料为主要数据源,结合地面自动气象站(AWS)实测的气象资料,利用地表能量平衡参数化(SEBAL)的方法估算了净辐射通量和土壤热通量,分析了上海区域热环境特征.结果表明,TM地表温度与AWS实测地表温度具有较好的一致性;不同类型地表其地表温度差异明显,城市道路是城市中重要热量来源,绿地和水体面积的增加能有效地减轻城市热岛强度,同时也表明了卫星遥感资料在城市气候环境研究中的潜在价值.     

A Comparison of Spatial and Spectral Image Resolution for Mapping Invasive Plants in Coastal California

We explored the potential of detecting three target invasive species: iceplant (Carpobrotus edulis), jubata grass (Cortaderia jubata), and blue gum (Eucalyptus globulus) at Vandenberg Air Force Base, California. We compared the accuracy of mapping six communities (intact coastal scrub, iceplant invaded coastal scrub, iceplant invaded chaparral, jubata grass invaded chaparral, blue gum invaded chaparral, and intact chaparral) using four images with different combinations of spatial and spectral resolution: hyperspectral AVIRIS imagery (174 wavebands, 4 m spatial resolution), spatially degraded AVIRIS (174 bands, 30 m), spectrally degraded AVIRIS (6 bands, 4 m), and both spatially and spectrally degraded AVIRIS (6 bands, 30 m, i.e., simulated Landsat ETM data). Overall success rates for classifying the six classes was 75% (kappa 0.7) using full resolution AVIRIS, 58% (kappa 0.5) for the spatially degraded AVIRIS, 42% (kappa 0.3) for the spectrally degraded AVIRIS, and 37% (kappa 0.3) for the spatially and spectrally degraded AVIRIS. A true Landsat ETM image was also classified to illustrate that the results from the simulated ETM data were representative, which provided an accuracy of 50% (kappa 0.4). Mapping accuracies using different resolution images are evaluated in the context of community heterogeneity (species richness, diversity, and percent species cover). Findings illustrate that higher mapping accuracies are achieved with images possessing high spectral resolution, thus capturing information across the visible and reflected infrared solar spectrum. Understanding the tradeoffs in spectral and spatial resolution can assist land managers in deciding the most appropriate imagery with respect to target invasives and community characteristics.     

SCS URBAN CURVE NUMBERS FROM A LANDSAT DATA BASE1

Estimating the Curve Numbers used in the Soil Conservation Service hydrologic models is a tedious and costly task. Recent advances in remote sensing and data processing have led to the development of readily available land cover data bases for many areas of the United States. This study evaluated the potential of using a Landsat data base to make the Curve Number estimation process more cost-effective and less tedious. Ten watersheds in the Washington, D.C., area were evaluated using a Landsat land cover data base developed by the U.S. Geological Survey. Results showed that these data can be useful. Predictions can be improved if ancillary data on residential lot size are included. It was concluded that this type of data base must be examined carefully before implementation.     

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.     

Landscape ecological planning through a multi-scale characterization of pattern: studies in the Western Ghats,South India

This article analyzes landscape pattern in the WesternGhats mountain ranges in south-western India at two scales,comparing small-scale, detailed studies of landscapepattern, with broader, regional-scale assessments of theWestern Ghats. Due in large part to their inaccessibility,relatively little is known about the landscapes of thisbiodiverse region, which also supports some of the highestpopulation densities in the world. A broad-scale NDVI-basedIRS 1B satellite image classification is used to analyzenorth-south and east-west trends across the entire WesternGhats and western coast of India, an area over 170 000 km². Northern and eastern landscapes are morefragmented compared to the southern and western slopes.Western slopes also have greater landscape diversity withland cover types more interspersed compared to the easternslopes. These differences can be related to north-south andeast-west variations in rainfall and plant distribution. Data from thirteen landscapes 10–50 km² in area, arefurther utilized to analyze trends in landscape pattern, anddescribe the geographical distribution of major natural andmanaged ecotope types. At this scale, very high levels ofintra-ecotope type variability in landscape pattern areencountered for all land cover types. Results at these twoscales are integrated to suggest a hierarchical stratifiedapproach for monitoring land cover and biodiversity in the region.     

Use of Satellite Imagery as Environmental Impact Assessment Tool: A case Study from the Nw Egyptian Red Sea Coastal Zone

Knowledge and detecting impacts of human activities on the coastal ecosystem is an essential management requirement and also very important for future and proper planning of coastal areas. Moreover, documentation of these impacts can help in increasing public awareness about side effects of unsustainable practices. Analysis of multidate remote sensing data can be used as an effective tool in environmental impact assessment (EIA). Being synoptic and frequent in coverage, multidate data from Landsat and other satellites provide a reference record and bird’s eye viewing to the environmental situation of the coastal ecosystem and the associated habitats. Furthermore, integration of satellite data with field observations and background information can help in decision if a certain activity has caused deterioration to a specific habitat or not. The present paper is an attempt to utilize remote sensing data for assessment impacts of some human activities on the major sensitive habitats of the NW Egyptian Red Sea coastal zone, definitely between Ras Gemsha and Safaga. Through multidate change analysis of Landsat data (TM & ETM+ sensors), it was possible to depict some of the human infringements in the area and to provide, in some cases, exclusive evidences for the damaging effect of some developmental activities.     

Improvements in Air Quality Modelling by Using Surface Boundary Layer Parameters derived from Satellite Land Cover Data

Many atmospheric dispersion models include only simpletreatment of surface features to estimate the wind profilesand stability parameters. Detailed characterisation of theland cover, particularly in large and complex urbanconurbations, is especially important, as the surfacefeatures can vary significantly over the area. This paperdiscusses the use of satellite land cover data to derivespatially resolved surface boundary layer (SBL) parameters.These parameters have been used in an air quality model,PEARL (Prediction Air Quality in Urban and RegionalLocations) for estimating monthly and annual COconcentrations. Land cover data, derived from LANDSATThematic Mapper Imagery, has been used to estimate SBLparameters (surface roughness length, albeedo, Bowen ratioand anthropogenic heat flux) for a study area of 10000km² encompassing Greater London and the surroundingcounties. The SBL parameters have been assigned according tomajor land cover types for the whole area at a spatialresolution of 1 × 1 km. Predictions from two versions of the PEARL model (one with land cover data and one without)have been compared with each other and with measured data forannual and monthly CO concentrations from seven London airquality monitoring sites. This comparison shows thatdifferences between predicted and observed values can bereduced by up to a factor of three. The use of SBLparameters derived from land cover data also yields moredetailed predicted annual CO spatial patterns especially inand around suburban areas. The performance of both versionsof the model for monthly CO concentrations has been comparedwith a range of statistical measures. This comparisonconfirms that improved agreement is observed betweenmodelled and measured monthly CO concentrations when use ismade of spatially resolved SBL parameters.