Remote sensing of aquatic vegetation: theory and applications

Aquatic vegetation is an important component of wetland and coastal ecosystems, playing a key role in the ecological functions of these environments. Surveys of macrophyte communities are commonly hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. Also, many vegetation characteristics can be estimated from reflectance measurements, such as species composition, vegetation structure, biomass, and plant physiological parameters. However, proper use of these methods requires an understanding of the physical processes behind the interaction between electromagnetic radiation and vegetation, and remote sensing of aquatic plants have some particular difficulties that have to be properly addressed in order to obtain successful results. The present paper reviews the theoretical background and possible applications of remote sensing techniques to the study of aquatic vegetation.     

Multi-objective optimization of air quality monitoring

A new method for multi-objective optimization of air quality monitoring systems based on satellite remote sensing of the troposphere is described in this work. The technique uses atmospheric turbidity as surrogate for air pollution loading. Through inverse chemical modeling and ancillary information the respective patterns of primary gaseous and particle pollutants are inferred. The optimization algorithm uses the resulting maps of ambient air pollution as input. It focuses on the gain of information with regard to human exposure to high pollution, potential impact on cultural heritage, compliance to ambient air quality standards, monitoring key point and area source emissions, as well as on the associated cost. Application of the method in Brescia, Italy showed its significant potential for improving the cost-effectiveness of air quality monitoring networks at the urban and regional scales.     

An instrumented station for the survey of ozone and climate change in the southern tropics

The assessment of changes induced by human activities on Earth atmospheric composition and thus on global climate requires a long-term and regular survey of the stratospheric and tropospheric atmospheric layers. The objective of this paper is to describe the atmospheric observations performed continuously at Reunion Island (55.5 degrees east, 20.8 degrees south) for 15 years. The various instruments contributing to the systematic observations are described as well as the measured parameters, the accuracy and the database. The LiDAR systems give profiles of temperature, aerosols and ozone in the troposphere and stratosphere, probes give profiles of temperature, ozone and relative humidity, radiometers and spectrometers give stratospheric and tropospheric integrated columns of a variety of atmospheric trace gases. Data are included in international networks, and used for satellite validation. Moreover, some scientific activities for which this station offers exceptional opportunities are highlighted, especially air mass exchanges nearby dynamical barriers: (1) On the vertical scale through the tropical tropopause layer (stratosphere-troposphere exchange). (2) On the quasi-horizontal scale across the southern subtropical barrier separating the tropical stratospheric reservoir from mid- and high latitudes.     

Global and Regional Distribution of Carbon Monoxide from MOPITT: Seasonal Distribution at 700 hPa

The Measurement of Pollution in the Troposphere (MOPITT) instrument is an eight-channel gas correlation radiometer, which was launched on the Earth Observing System (EOS) Terra satellite in 1999. Carbon monoxide (CO) is one of the important trace gases because its concentration in the troposphere directly influences the concentrations of tropospheric hydroxyl (OH), which controls the lifetimes of tropospheric trace gases. CO traces the transport of global and regional pollutants from industrial activities and large scale biomass burning. The global and regional distributions of CO were analyzed using the MOPITT data for East Asia, which were compared with the ozone distributions. In general, seasonal CO variations are characterized by a peak in the spring, which decrease in the summer. This work also revealed that the seasonal cycles for CO are at a maximum in the spring and a minimum in the summer, with average concentrations ranging from 118 to 170 ppbv. The monthly average for CO shows a similar profile to that for O3. This fact clearly indicates that the high concentration of CO in the spring is possibly due to one of two causes: the photochemical production of CO in the troposphere, or the transport of the CO into East Asia. The seasonal cycles for CO and O3 in East Asia are extensively influenced by the seasonal exchanges of different air mass types due to the Asian monsoon. The continental air masses contain high concentrations of O3 and CO, due to the higher continental background concentrations, and sometimes to the contribution from regional pollution. In summer this transport pattern is reversed, where the Pacific marine air masses that prevail over Korea bring low concentrations of CO and O3, which tend to give the apparent summer minimums.     

湖南省生态环境遥感监测平台建设思考

简述了国内外遥感监测平台的发展现状,基于湖南省生态环境遥感监测现状进行了分析,指出遥感监测平台存在遥感监测与评估技术缺乏、监测能力薄弱以及监测网络建设不完善等问题。从平台建设架构出发,构建了数据平台,对卫星遥感数据、地面调查/监测资料、无人机航测数据进行管理、存储、分析等;业务平台,主要围绕水、气、土壤等整个生态环境监测体系开展相应的监测和管理,还可针对重点区域,环境督察等业务进行拓展;展示平台,主要是通过各种终端设备和硬件对各类业务进行可视化。提出发射独立高分遥感卫星、深化遥感监测应用研究、加强各部门协同合作等建议,以期为湖南省生态环境遥感监测平台建设与发展提供参考。     

基于TROPOMI的扬州市对流层甲醛和二氧化氮时空分布特征分析

以2020年1月—2021年9月对流层观测仪（TROPOMI）卫星观测资料反演获取的对流层甲醛（HCHO）、二氧化氮（NO₂）柱浓度数据为依据,采用统计方法分析了扬州市HCHO和NO₂柱浓度的时空分布特征。结果表明,扬州市对流层HCHO、NO₂平均柱浓度分别为903.01×10¹³, 633.77×10¹³mole/cm²;受太阳紫外辐射影响,HCHO柱浓度变化特征表现为6月最高、1月最低;受气象条件和人为排放强度影响,NO₂则表现为1月最高、8月最低。2021年1—9月扬州市对流层HCHO、NO₂柱浓度月均值同比2020年分别增长4.0%,40.6%。空间分布特征显示,扬州市对流层HCHO和NO₂浓度高值区主要分布在扬州市南部,且浓度高值区域与重点排污企业分布情况较为一致,多为电力供热、工业锅炉、冶金、石化与化工、表面涂层等行业。相关性分析显示,对流层HCHO与气温、臭氧浓度呈显著正相关,而NO₂与气温、臭氧浓度呈显著负相关。     

On applications of remote sensing for environmental monitoring

Modern airborne and satellite remote sensing techniques offer attractive opportunities to coastal monitoring systems. Improvements of the evaluation of larger scales phenomena and processes due to the synopticity of the remote sensing data are of particular interest. However, some uncertainties and limitations about remote sensing must be considered. Microwave, infrared and visible radiation methods and their applications are briefly discussed and some applications are demonstrated. Special attention is paid to the remote sensing of various pollutants in the sea, in particular with respect to oil pollution.Promising developments of the remote sensing methods for coastal monitoring are to be expected from the European remote sensing satellite missions ERS 1 and ERS 2.Combination of these observations with simultaneous in situ measurements from ships (sea truth) appears to be most advantageous for the interpretation of the collected data.     

多源专题数据在国家重点生态功能区生态环境状况评价中的应用

为了建立一套生态遥感快速监测方法,实现生态环境状况评价,以2015年广西40个国家重点生态功能区为例,以全国第一次地理国情普查、高分影像、地貌为基础数据,利用RPC模型及网络刺点进行影像纠正,使用空间分析等程序化处理方法进行代码转换和信息抽取,实现了广西国情普查成果转化,得到了2014年和2015年生态遥感数据,形成一套较为完整的生态遥感快速监测方法,最终得到2015年全区重点生态功能县生态环境状况指数, 40个重点生态功能县中有15个县生态环境质量状况等级为"优",25个为"良"。     

地表水环境遥感监测关键技术与系统

介绍了地表水环境遥感监测的关键技术与系统及其典型应用,其代表性机理模型和应用示范成果主要来自于中国科学院遥感与数字地球研究所的高光谱遥感团队在最近几年中取得的一些研究进展,主要包括建立了基于改进双峰法的水体分布自动化遥感提取方法,实现了简单、高效和高精度的水体提取;提出了大型湖泊长时序水量估算方法,并以青藏高原湖区为例,重建了典型湖泊面积、水位和水量序列;发展了基于“软分类”的典型内陆水体叶绿素a浓度反演方法,构建了基于生物光学模型的高度浑浊水体悬浮物浓度遥感反演半解析方法,提高了反演方法的区域和季节适用性;构建了基于水色指数的大范围湖库营养状态和透明度遥感监测方法,实现了全球大型湖库营养状态遥感监测,以及全国大型湖库透明度遥感监测;在此基础上,开发了地表水环境遥感监测系统,提高了水环境遥感监测效率,促进了卫星遥感在水环境监测中的高精度业务化应用。     

水体中泥沙对藻类光谱特征位置的影响分析

自然水体叶绿素a浓度的遥感反演中,泥沙的存在影响着反演精度.如何消除这种影响是提高叶绿素a遥感反演精度的关键,而了解泥沙对藻类光谱特征的影响是消除影响的前提.文章在人工控制条件下获取了不同泥沙浓度下藻类光谱曲线,通过分析光谱曲线特征位置的漂移和数值变化,总结泥沙对藻类光谱的影响并提出了消除影响的可能性.     

遥感技术在大气环境监测中的应用综述

综合论述了近20多年来国内外对大气环境遥感监测的研究现状,介绍了应用于大气环境遥感监测的多种方法并着重阐述了被动式空基遥感和主动式地基遥感在大气环境遥感中的应用以及探测气溶胶的卫星传感器的发展历程和特点。最后,对我国大气环境遥感研究中存在问题和发展前景进行了讨论。     

遥感监测土壤湿度综述

遥感技术具有大面积同步观测,时效性、经济性强等特点,为大面积动态监测土壤湿度提供了可能。本文对近年来国内外遥感监测土壤湿度的理论、方法的发展和应用进行了回顾,重点介绍了目前已经比较成熟和广泛应用的基于可见光与热红外波段的植被指数方法以及在干旱、半干旱地区的应用,通过对比分析了各种遥感监测方法的优缺点,指出了土壤湿度遥感监测方法存在的不足,展望了土壤湿度遥感监测方法的发展趋势。     

郑州市对流层NO2柱浓度时空变化及其影响因素

基于郑州市2005—2015年的OMI遥感反演资料以及地面相关监测数据,研究了郑州市对流层NO_2的时空分布特征,并利用灰色关联法对郑州市NO_2柱浓度变化的主要影响因素进行分析。与地面观测数据对比检验显示,对流层NO_2柱浓度年均值数据与近地面监测站NO_2浓度的实测年均值数据呈显著的正相关,相关系数分别为0.884 6和0.940 2,表明OMI数据资料可以较好地反映地面NO_2浓度的变化。郑州市的对流层NO_2柱浓度在2005—2013年间呈现波动变化且2013—2015年NO_2柱浓度显著减小的特征。季节变化上NO_2柱浓度主要表现为冬季秋季春季夏季的特点。郑州市对流层NO_2柱浓度的空间变化分布主要表现为由北部向南部逐渐递减的趋势,年际变化上高值区与低值区变化不够显著,中值区近年来不断扩大。灰色关联度分析结果显示,汽车保有量与对流层NO_2柱浓度的灰色关联度最低为0.571,而标准煤消耗量、工业用电量以及采暖供热量与对流层NO_2柱浓度的灰色关联度比较高,分别为0.956、0.828、0.862,即大气中工业过程及采暖期煤炭燃烧排放的NO_2占较大比例,汽车尾气排放所占的比例相对较小。     

水质参数的遥感反演和遥感监测

遥感技术由于具有快速、宏观、低成本和周期性的优点,便于探测水质的时空变化,已成为水质参数监测的重要手段。目前能够直接进行遥感反演的水质参数主要是悬浮物浓度、叶绿素a浓度、可溶解性有机物等光学活性物质,并已经建立了许多反演模型。但是这些模型直接用于水质的遥感监测仍存在一些问题。今后,利用3S技术将地面观测和遥感观测结合起来,可望推动水色遥感的实际应用。     

Retrieval And Monitoring of Atmospheric Trace Gas Concentrations in Nadir and Limb Geometry Using the Space-Borne Sciamachy Instrument

The Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) onboard the European Envisat spacecraft performs continuous spectral observations of reflected, scattered and transmitted sunlight in various observation geometries. A unique feature of SCIAMACHY is the capability of probing the atmosphere in three different observation geometries:The nadir, limb, and occultation measurement modes. In nadir mode, column densities of trace gases are retrieved with a spatial resolution of typically 30× 60 km using the Differential Optical Absorption Spectroscopy (DOAS) technique (Platt and Perner, 1983). Alternating with the nadir measurement, vertical profiles of absorber concentration in the stratosphere are derived in limb and occultation. In this paper we present an overview over some applications of SCIAMACHY data in space-based monitoring of atmospheric pollution. The DOAS algorithms for the retrieval of total column amounts from nadir spectra are briefly described and case studies of pollution events are presented. We also illustrate the technique used to derive stratospheric concentration profiles from limb observations and show comparisons with other remote sensing systems. Special emphasis will be given to techniques, which take advantage of SCIAMACHY's different viewing geometries. In particular, we will discuss the potential and limits of strategies to infer tropospheric abundances of O₃and NO₂.     

Vegetation, Soil, and Animal Indicators of Rangeland Health

We studied indicators of rangeland health on benchmark sites with long, well documented records of protection from stress by domestic livestock or histories of environmental stress and vegetation change. We measured ecosystem properties (metrics) that were clearly linked to ecosystem processes. We focused on conservation of soil and water as key processes in healthy ecosystems, and on maintenance of biodiversity and productivity as important functions of healthy ecosystems. Measurements from which indicators of rangeland health were derived included: sizes of unvegetated patches, cover and species composition of perennial grasses, cover and species composition of shrubs and herbaceous perennials, soil slaking, and abundance and species composition of the bird fauna. Indicators that provided an interpretable range of values over the gradient from irreversibly degraded sites to healthy sites included: bare patch index, cover of long-lived grasses, palatability index, and weighted soil surface stability index. Indicators for which values above a threshold may serve as an indicator of rangeland health include: cover of plant species toxic to livestock, cover of exotic species, and cover of increaser species. Several other indicator metrics were judged not sensitive nor interpretable. Examples of application of rangeland health indicators to evaluate the success of various restoration efforts supported the contention that a suite of indicators are required to assess rangeland health. Bird species diversity and ant species diversity were not related to the status of the sample site and were judged inadequate as indicators of maintenance of biodiversity.     

遥感技术在水生态环境管理的应用与前景

随着遥感数据源的不断丰富,遥感技术不断提高,可以解决越来越多的水环境问题。指出了当前水生态环境管理方面的主要需求,结合目前遥感技术的发展,对国内外的水环境遥感研究进展进行综述。以湖泊富营养化监测与评估、核电站温排水遥感监测及城市黑臭水体遥感监测为案例,具体阐述遥感在水环境管理中的应用方法及成效。未来水生态环境管理发展趋势将以水污染防治为主向水污染防治和水生态修复与保护并重发展。基于此趋势,提出遥感在水生态修复的应用潜力,利于更多地方部门积极有效应用遥感技术,解决水生态环境问题。     

大气污染物排放调查监测研究进展

系统总结了当前大气污染物排放调查监测的主要方法及其在全球、区域、局地等不同尺度的典型应用,分析了地面监测、模型模拟与遥感反演3种方法受排放源多样、成分复杂、时空变异显著等因素影响,在尺度效应、成分解析等方面存在的问题。从优化监测方法、改进监测模式、融合监测技术等角度,提出了构建现代化大气污染物排放调查监测网络、开展污染物在不同排放阶段的变化解析研究及在不同圈层间的迁移全过程研究等建议。     

Can tree species diversity be assessed with Landsat data in a temperate forest?

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.     

Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples

The present study focuses on developing models to predict lichen species richness in a UNESCO Biosphere Reserve of the Swiss Pre-Alps following a gradient of land-use intensity combining remote sensing data and regression models. The predictive power of the models and the obtained r ranging from 0.5 for lichens on soil to 0.8 for lichens on trees can be regarded as satisfactory to good, respectively. The study revealed that a combination of airborne and spaceborne remote sensing data produced a variety of ecological meaningful variables.