江苏省工地和裸地扬尘遥感监测方法研究与结果分析

应用卫星遥感影像结合无人机现场核查数据,对2020年江苏省各设区市主城区工地和裸地2类扬尘源的时空分布变化和污染、管控情况开展了连续性监测,为生态环境监测部门业务化应用提供了思路和方法.研究结果显示,遥感解译精度优于95％,扬尘源数量、面积均呈上升趋势,至第4季度总数达1760个、总面积162.53 km2,总体管控情...     

北京市建筑施工裸地的空间分布及扬尘效应

北京市作为中国典型的快速城市化大城市之一,大规模的城市建设使得城市内部存在大量的建筑施工裸地,工地扬尘效应造成了严重的大气颗粒物污染。以北京市平原区为研究区,首先利用高分辨率卫星数据反演了建筑施工裸地的空间分布格局,在此基础上,结合建筑施工裸地的扬尘排放因子,在空间上定量分析建筑施工裸地的扬尘效应。结果显示,2012年全市平原区建筑施工裸地总面积为140.77 km2,主要分布在城市核心区向外拓展的城乡过渡带。建筑施工裸地的扬尘效应分析中发现北京市平原区建筑施工裸地中TSP、PM10和PM2.5的排放量分别是31.53、16.66、9.16万吨,并且这些污染物的排放量在城市发展新区与功能拓展区中较高,生态涵养发展区较小。以北京市的建筑施工裸地为例,示范了以遥感为技术手段结合污染排放因子的城市内部典型面源污染的监测和污染排放定量化,为今后面源污染的快速准确监测开辟了新的研究思路。     

基于高分辨率影像的固体废物自动识别技术

随着经济快速发展,工业固废、非正规垃圾、未覆盖建筑渣土等固体废物急剧增加,对区域生态环境造成极大威胁。固废堆场具有面积小、分布散等特点,目前国内仍缺少针对各类固废堆场的遥感自动识别研究。为此,基于国产高分辨率卫星遥感数据,根据野外实地光谱采集结果,分别开展了未覆盖建筑渣土、工业固废及非正规垃圾的自动识别方法研究,提取研究区各类固废堆场。结果表明:未覆盖建筑渣土在蓝波段与绿波段分别存在"吸收谷"与"反射峰",基于该特征构建的比值指数模型,结合直方图双峰法阈值分割可以有效提取未覆盖建筑渣土区域;工业固废、非正规垃圾2种固废类型多样,光谱反射率没有明显规律,结合其纹理、色调等特征,采用面向对象多尺度分割、支持向量机监督分类方法能够较好地识别2种类型固废;基于自动化提取技术并结合人机交互判读方法,提取的研究区未覆盖建筑渣土、工业固废及非正规垃圾等3种固废堆场的精度分别达到96.83%、88.26%、85.71%,各类固体废物遥感识别精度较高,极大提高了固废监测效率。     

基于神东中心区植被覆盖变化的多时相遥感监测

准确、快速地获取植被覆盖信息是矿区生态恢复和建设的关键与重点。以神东中心区为研究对象,利用2002、2005、2007、2010、2012年Landsat TM/ETM+和HJ1A-CCD1五景同期遥感数据,采用像元二分模型法,归一化植被指数(NDVI)值反演植被覆盖度,对研究区生态环境变化规律进行分析。结果表明,神东中心区平均植被覆盖度整体呈上升趋势,区内绝大部分地表覆盖程度得到改善,改善区面积达64.01%,退化区面积只有15.34%。该方法快速、定量地反映矿区植被覆盖及变化情况,为矿区生态环境动态监测和治理提供技术支持。     

一种基于集成学习的城市新增建设用地快速提取方法

城市新增建设用地是热岛效应调控、大气污染和扬尘管控、生态服务变化的重要指示信息。为准确提取新增建设用地,提出一种运用多时相Sentinel遥感数据和集成学习算法的城市新增建设用地快速提取方法。基于多时相Sentinel-1和Sentinel-2遥感影像提取光谱特征、纹理特征和后向散射特征,进行面向对象分割、全域均值滤波和归一化后得到组合特征集,运用随机森林、旋转森林、支持向量机和极限学习机多分类器集成学习进行分类来提取新增建设用地。提取了南京市2017年4月至10月间的新增建设用地并统计了各行政区分布的面积,提取整体精度达0. 95,Kappa系数达0. 88。相比与基于像素方法,面向对象技术可有效降低"椒盐现象",提高斑块完整性;与分类后提取方法比较,直接变化提取方法可减少误差产生环节及误差累积,从而降低系统误差,提高提取精度。     

南充市大气PM10与PM2.5排放清单及特征

以四川省南充市为研究区域,通过实地调研、现场测试及结合统计年鉴等获得数据,采用排放因子法计算南充市2014年大气PM_(10)、PM_(2.5)排放量并建立排放清单。结果表明,南充市2014年扬尘源、移动源、生物质燃烧源、化石燃料固定燃烧源、工艺过程源排放总量PM_(10)分别为85 187、1 777、9 175、2 417、3 519 t,PM_(2.5)分别为16 093、1 619、7 322、914、1 585 t,PM_(10)贡献率分别为83.5%、1.7%、9.0%、2.4%、3.4%,PM_(2.5)贡献率分别为58.4%、5.9%、26.6%、3.3%、5.8%。城市区域扬尘源、生物质燃烧源、移动源、化石燃料固定燃烧源、工艺过程源对PM_(10)贡献分别为60.0%、12.5%、6.3%、8.6%、12.5%,对PM_(2.5)贡献分别为41.8%、21.6%、14.4%、8.1%、14.1%。南充市2014年大气PM_(10)、PM_(2.5)排放源总量和贡献率以及区域空间分布特征均存在差异。     

遥感技术在湖泊叶绿素ａ 监测中的应用研究———以太湖为例

水质遥感技术在湖泊水质监测领域内的应用具有十分积极的意义。在总结现有水质遥感反演方法的基础上，选取了遥感指数法和神经网络法两种理论完全不同的反演方法，构建太湖叶绿素a与MODIS影像波段间的函数关系，并从反演能力和反演精度两个角度对上述方法进行了比较研究。结果表明，神经网络模型的非线性特征能够敏感地把握住叶绿素a浓度变化在反射波谱信息上的微小响应，较为成功地反演出叶绿素a与反射光谱信息间的非线性关系。神经网络模型的反演能力和反演精度均优于遥感指数方法，具有较好的应用前景。     

基于RS与GIS的水土保持生态功能区生态状况评估

以河南省大别山水土保持国家重点生态功能区为研究区,基于RS与GIS获取2012年和2017年研究区植被覆盖、土地利用、土壤侵蚀等遥感解译数据,从生态功能、生态结构和生态压力3个方面,采用综合指数法,对研究区生态状况进行动态监测与评价。结果表明,2012—2017年间,研究区土地利用结构发生较明显的变化,林草地覆盖率上升9.85%,耕地和建设用地面积比上升13.23%,水域湿地面积比变化不大;植被覆盖指数上升 62.74%,中度以上土壤侵蚀面积比略有增加;生态状况变化度ΔF=4.95,整体生态状况等级由良变为优,生态状况变化度评级为显著变好。     

省级高空间分辨率扬尘源排放清单研究

通过对浙江省统一开展部署和行动，现场调查收集全省7 507个施工工地、3 923个堆场以及不同等级公路和城市道路的真实活动水平数据，并基于点源地理信息和路网信息图层，采用排放系数法和ArcGIS工具构建了浙江省2015年3 km×3 km高空间分辨率扬尘源排放清单。结果表明，2015年浙江省扬尘源PM₁₀和PM_2.5的排放量分别为24.26×10⁴ t和6.00×10⁴ t，其中PM₁₀和PM_2.5排放贡献均主要为施工扬尘和道路扬尘，施工扬尘分别贡献37.7%和39.3%，道路扬尘分别贡献36.5%和39.1%。从城市空间分布来看，杭州市、宁波市、温州市、绍兴市扬尘排放总量居于全省前四，舟山市最低，而城市主城区排放量显著高于郊区。     

基于改进MobileNetV3-SSD的河道排污口目标检测研究

为实现对水系入河排污口有效、准确的自动检测，提出一种基于改进MobileNetV3-SSD的深度学习模型。在MobileNetV3-SSD模型的基础上，使用K-means聚类算法和遗传算法，对先验框的宽高比进行调整，使得预测框更好地匹配真实框。引入多尺度特征融合模块，提高模型对小排污口的检测能力。引入改进的CBAM注意力模块，减少模型在排污口检测时计算的参数数量。使用可变形卷积替代普通卷积，自适应地捕获不同排污口的形态与尺度信息，提升模型的特征提取能力。实验结果表明，改进后MobileNetV3-SSD模型的平均精度为89.36%,F1分数为91.88%,较改进前分别提升4.83%和5.03%。     

无锡市国控环境空气自动监测站点周边裸土分布高分辨率遥感监测

以无锡市为研究区,使用过境时间相近的哨兵2号Sentinel-2和Landsat8影像,综合使用NDSI、NDISI、MNDWI、LST等指数进行决策树分类,获得10 m高空间分辨率的土地利用分类结果和裸土分布,裸土提取精度达到94.13%。统计了无锡市与8个国控环境空气自动监测站点1、2、3 km缓冲区范围内的裸土分布情况,并与各站点监测的大气颗粒物浓度进行相关性分析。结果表明,国控环境空气自动监测站点周边裸土面积对颗粒物浓度有较大影响,其中对PM₁₀浓度的影响明显大于PM_2.5;相比于1 km和3 km,2 km缓冲区范围内的裸土面积对PM₁₀浓度的影响最大,建议环境管理部门重点关注无锡地区国控监测站点周边2 km范围内的裸土扬尘源分布情况。     

常州市裸露地面风蚀扬尘排放清单及分布特征研究

以2014年为基准年,以常州市行政边界为研究区域,利用卫星遥感解译的土地利用类型和裸土面积信息,并通过国土、气象等部门提供的土壤类型、气象因子等相关资料获取本地化因子和参数,估算常州市行政区域内裸露地面土壤扬尘中颗粒物的年排放量。结果表明:常州市裸露地面主要分布在金坛市、溧阳市和武进区,市中心区域最少,裸露土地利用类型主要为农田、滩涂、裸露山体、荒地及未硬化或未绿化的空地等;2014年常州市行政区域内裸露地面风蚀扬尘中TSP、PM 10、PM 2.5的年排放量分别为62.66 t、5.63 t、0.24 t,其中,金坛市土壤起尘量最大,其次为武进区,再次为新北区、溧阳市,市中心区域土壤扬尘最少。     

Assessing and Monitoring the Health of Western Rangeland Watersheds

The most important function of watersheds in the western U.S. is the capacity to retain soil and water, thereby providing stability in hydrologic head and minimizing stream sediment loads. Long-term soil and water retention varies directly with vegetation cover. Data on ground cover and plant species composition were collected from 129 sites in the Rio Grande drainage of south-central New Mexico. This area was previously assessed by classification of Advanced Very High Resolution Radiometry (AVHRR) imagery. The classification of irreversibly degraded sites failed to identify most of the severely degraded sites based on size of bare patches and 35% of the sites classified as degraded were healthy based on mean bare patch size and vegetation cover. Previous research showed that an index of unvegetated soil (bare patch size and percent of ground without vegetative cover) was the most robust indicator of the soil and water retention function. Although the regression of mean bare patch size on percent bare ground was significant (p < 0.001), percent bare ground accounted for only 11% of the variability in bare patch size. Therefore bare patch size cannot be estimated from data on percent bare ground derived from remote sensing. At sites with less than 25% grass cover, and on sites with more than 15% shrub cover, there were significant relationships between percent bare soil and mean bare patch size (p < 0.05). Several other indicators of ecosystem health were related to mean bare patch size: perennial plant species richness (r = 0.6, p < 0.0001), percent cover of increaser species (r = 0.5, p < 0.0001) and percent cover of forage useable by livestock (r = 0.62, p < 0.0001). There was no relationship between bare patch size and cover of species that are toxic to livestock. In order to assess the ability of western rangeland watersheds to retain soil and water using remote sensing, it will be necessary to detect and estimate sizes of bare patches ranging between at least 0.5 m in diameter to several meters in diameter.     

Environmental change monitoring in the arid and semi-arid regions: a case study Al-Basrah Province, Iraq

In recent years, land use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. This research utilizes the integrated remote sensing and geographic information systems (GIS) in the southern part of Iraq (Basrah Province was taken as a case) to monitor, map, and quantify the environmental change using a 1:250,000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation land, sand land, urban area, unused land, and water bodies. Supervised classification and normalized difference buildup index, normalized difference vegetation index, normalized difference bare land index, the normalized differential water index, crust index (CI) algorithms, and change detection techniques were adopted in this research and used, respectively, to retrieve its class boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. The results showed that the urban area, sand lands, and bare lands had increased by the rate of 1.2%, 0.8%, and 0.4% per year, with area expansion from 3,299.1, 4,119.1 km², and 3,201.9 km² in 1990 to 3,794.9, 4,557.7, and 3,351.7 km² in 2003, respectively. While the vegetation cover and water body classes were about 43.5% in 1990, the percentage decreased to about 39.6% in 2003. This study demonstrates the effectiveness of the remote sensing and GIS technologies in detecting, assessing, mapping, and monitoring the environmental changes.     

南京市建筑扬尘排放清单研究

统计分析了2010年南京市各行政区建筑场地面积和工期，结合扬尘排放因子，建立了南京市建筑扬尘排放清单。研究表明，2010年南京市建筑扬尘TSP、PM10和PM2．5的排放量分别达2．53万t、1．40万t和0．95万t，占工业烟（粉）尘排放量的23％、13％和8．6％。郊区县建筑扬尘排放量较大，约占全市 TSP、PM10、PM2．5排放总量的72％；主城区排放强度较高。对不同建筑工程类型扬尘排放量估算表明，城市建设工程和市政工程是建筑扬尘的主要来源，城市建设工程中又以住宅类建设工程为主。对不同研究获得的建筑扬尘结果比较，发现扬尘排放因子选择和污染源活动水平统计是影响建筑扬尘结果的关键因素。     

Land cover change of watersheds in Southern Guam from 1973 to 2001

Land cover change can be caused by human-induced activities and natural forces. Land cover change in watershed level has been a main concern for a long time in the world since watersheds play an important role in our life and environment. This paper is focused on how to apply Landsat Multi-Spectral Scanner (MSS) satellite image of 1973 and Landsat Thematic Mapper (TM) satellite image of 2001 to determine the land cover changes of coastal watersheds from 1973 to 2001. GIS and remote sensing are integrated to derive land cover information from Landsat satellite images of 1973 and 2001. The land cover classification is based on supervised classification method in remote sensing software ERDAS IMAGINE. Historical GIS data is used to replace the areas covered by clouds or shadows in the image of 1973 to improve classification accuracy. Then, temporal land cover is utilized to determine land cover change of coastal watersheds in southern Guam. The overall classification accuracies for Landsat MSS image of 1973 and Landsat TM image of 2001 are 82.74% and 90.42%, respectively. The overall classification of Landsat MSS image is particularly satisfactory considering its coarse spatial resolution and relatively bad data quality because of lots of clouds and shadows in the image. Watershed land cover change in southern Guam is affected greatly by anthropogenic activities. However, natural forces also affect land cover in space and time. Land cover information and change in watersheds can be applied for watershed management and planning, and environmental modeling and assessment. Based on spatio-temporal land cover information, the interaction behavior between human and environment may be evaluated. The findings in this research will be useful to similar research in other tropical islands.     

基于深度全连接网络Himawari-8卫星气溶胶反演研究

利用葵花8(Himawari-8,H8)16个波段数据、卫星、太阳角度数据和深度学习技术,提出一种基于深度全连接网络(Deep Neural Networks,DNN)模型的AOD遥感反演方法(Himawari-DNN)。该方法直接建立H8影像本身与AERONET站点AOD数据间的关系,可避免传统AOD遥感反演方法中复杂过程,得到精度较高的反演结果。通过有效数据对所构建的模型做精度测试,同时将反演结果和实测数据对比分析,结果表明,模型反演结果与研究区内所有站点的观测值均具有较高的一致性(R^2均>0.89)。可见,应用DNN对H8气象静止卫星开展AOD反演具有一定的可行性。     

Externalities of Fugitive Dust

It is known that fugitive dust can cause human health and environmental problems, alone or in combination with other air pollutants. These problems are referred to as ‘external costs’ that have been traditionally ignored. However, there is a growing interest towards quantifying externalities to assist policy and decision-making. With this in mind, the present study aimed at discussing the environmental regulations that deal with fugitive dust, the impact of fugitive dust on human health and global climate system, and the available methods for calculating fugitive dust externalities. The damage cost associated with human health and global environmental problems was predicted based on the environmental strategy priority model. The damage cost estimated by the model ranged from 40 to 374 EUR/kg of emitted fugitive dust with a mean value of 120 EUR/kg of emitted fugitive dust. It was also found that PM_2.5 and PM₁₀ have contributed to about 60% and 36% of the estimated damage cost, respectively. The remaining 4% was attributed to both nitrate and sulfate aerosols.     

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed,Ethiopia

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very peculiar in that the area of Lake Hawassa increased from 91.9 km² in 1973 to 95.2 km² in 2011, while that of Lake Cheleleka whose area was 11.3 km² in 1973 totally vanished in 2011 and transformed into mud-flat and grass dominated swamp. The “change and no change” analysis revealed that more than one third (548.0 km²) of the total area was exposed to change between 1973 and 2011. This study was useful in identifying the major land cover changes, and the analysis pursued provided a valuable insight into the ongoing changes in the area under investigation.