Retrievalof Agriculture Greenhouse based on GF-2 Remote Sensing Images

Timely and accurately acquisition of the area and spatial distribution of greenhouse in the agricultural regions using remote sensing technique is a novel solution,which would be valuable for the local authorities taking measures to adjust regional agricultural structure and to prevent and control environmental pollution.In this study,the nearest neighbor method based on object\|oriented thought is used to extract greenhouses in Guantao County of Handan City with GF-2 satellite image.The random verification shows that the accuracy of extraction in greenhouses is 95.65%,and the area of the greenhouse is 21.11 km².Since auxiliary facilities around greenhouses were also included in the area of greenhouses issued by local authority,the extraction results need to be revised by calculating the ratio of greenhouse in the greenhouse area.As a result,the final area of greenhouses is 33.68km²with the area accuracy of 87.80% (compared with the official statistics:30 km²).Greenhouses in Guantao County were obviously spatially clustered in some zones along traffic arteries and main rivers,especially around the Zhaizhuang village (about 0.93 km²).Using Chinese high-resolution satellites images to extract information of greenhouses can be effective and feasible with suitable method,and can provide technical support for decision makers to the spatial planning and management of agricultural greenhouse and the supervision and control of agricultural pollution.     

Mono-window Algorithm Applicability in Fujian Sea and Its Surrounding Areas Sea Surface Temperature Retrieval

On the basis of four quarters HJ\|1b thermal infrared remote sensing images during 2013~2014,each of the spring,summer,autumn and winter,mono\|window algorithm was adopted to retrieve Sea Surface Temperature(SST).To verify the feasibility and accuracy of this algorithm,the derived results were compared with the measured SST data,show that the average absolute error is 0.86 ℃ and the correlation coefficient R² is 0.971 5.The different levels influence on derived results caused by the uncertainty of water vapor and atmospheric temperature is analyzed,indicate that if the water vapor error ranges between -2~0 g/cm²and the temperature variation is between -2 ℃~2 ℃,the sea surface temperature error will be within 5%,the high retrieving accuracy can still be achieved;The sensitivity of the water vapor in winter shows higher than in summer,while the sensitivity of atmospheric temperature demonstrates lower than in summer.Therefore,mono\|window algorithm is good applicable in the SST retrieval in Fujian sea and its surrounding areas,which is of great significance to Fujian environmental monitoring.     

HJ-1A NDVI与MODIS NDVI时间序列提取植被物候特征对比研究

以HJ-1A和MODIS为数据源,通过动态阈值法提取物候特征参数,对HJ-1A NDVI和MODIS NDVI时间序列进行植被物候特征提取进行定性和定量比较,通过比较结果,提出HJ-1A NDVI数据在该应用中存在的问题,促进国产中空间高时间分辨率影像数据在植被物候信息提取研究中的应用,提高其在生态系统研究中的应用价值。结果表明:在SOS、EOS和LOS以及TOMS几个主要的物候时间点上,MODIS NDVI时间序列的标准差较小,所得物候数据更为集中,偏离度较小,所得物候数据较稳定;而HJ-1A NDVI时间序列所得物候数据的标准差较大,数据偏离程度较大,而在POS、BOS和AOS等表征植被生命周期中生长幅度数据上,其标准差较小,离散程度小。     

Research on Glacier Change Monitoring in Nianchu River Basin

This study extracted the local glacier information over the Nianchu river basin in the Tibet in 1996 and 2005 by using ice index,snow index and water index of Landsat TM\|5 multi\|spectral images,and Synthetic Aperture Radar (SAR)intensity and coherence information of ERS\|1/2 and Envisat ASAR.The optimized features were determined by their classification accuracies based on Support Vector Machine (SVM)classifier.The result showed that the composition of multi\|spectral and SAR features could effectively discriminate the water and ice from other types,with overall accuracies of 84% and 85% in 1996 and 2005,respectively.Based on the thematic information of these two years,the changes of the local glacial area and boundary were detected.The result showed that the glacial area of the Nianchu river basin was reduced by 154.7 km²,which mainly caused by the climate warming.     

基于HJ-1B卫星数据的南京市地表温度反演研究

地表温度(LST)是全球变化的过程参数,应用HJ-1B-RS热红外数据,采用辐射传输法(RTE)、覃志豪单窗算法(Qins’)和普适性单通道算法(JM&S)对南京市地表温度进行反演。结果表明:3种算法均能较好地反映南京地区的地表温度趋势。RTE反演精度最高,与MODIS地温产品的差值多集中在2.1 K左右;Qins’的反演结果略低,温差多集中在3.87 K左右;而JM&S的结果明显偏低,温差多集中在5.96 K左右。结合土地利用类型图对地表温度进行分析,RTE温度结果中,温度最高的建设用地与温度最低的水体的温度相差4.1 K;Qins’温度结果中建设用地与水体的温度相差4.38 K;JM&S温度结果中建设用地与水体的温度相差2.15 K。RTE和Qins’更能体现不同土地利用类型之间的温度差异及对城市热岛的贡献。     

IBI and CA Model based Extraction of Urban Built\|up Areaand Its Application：A Case Study in Shanghai

At present,the automatic extraction of urban built\|up area is still a problem.Taking Shanghai city as an example,this research uses the binary image,which was derived from an IBI(index\|based built\|up index)image,as the geographical cellular automaton initial input data.This was then imported to the extended cellular automata model for further processing.After a series of processing such as thresholding,corrosion and connectivity test,the urban built\|up area was simulated and can be extracted.Based on this method,the urban built\|up area of Shanghai in five different years between 1987 and 2015 was extracted using multi\|temporal Landsat images of Shanghai.In addition,the urban growth intensity index,the concentric buffers analysis and radar chart analysis were calculated and used to analysis spatiotemporal pattern of Shanghai urban expansion in the 28 study years.The result shows that the built\|up area of Shanghai was expanded from 195 km² in 1987 to 1759 km² in 2015,which is eight times as large as that in 1987.In the period between 1987 and 2002,the urban built\|up area expanded mainly along the North\|South axis of the Huangpu River.However,after 2002 the expansion rate was increased more quickly,and the direction of the expansion was shifted to an east to west direction.Generally speaking,Shanghai has experienced a rapid urban expansion in the recent three decades.     

MODIS浒苔遥感监测误差分析研究

近年来,我国沿海浒苔灾害频发,影响日趋严重,MODIS TERRA/AQUA等中低分辨率卫星遥感影像成为浒苔遥感监测最主要的数据源之一。但低分辨率影像存在 大量混合像元,导致浒苔提取误差。本文先从理论上分析了植被指数算法的非线性对浒苔信息提取的影响,发现像元中浒苔覆盖比例为0.2~0.4时,NDVI最大,会有 0.8的偏差,且浒苔越厚误差越大。然后以环境减灾卫星（HJ 1A/B）作为真实值,对MODIS浒苔监测误差进行定量分析。结果表明：MODIS影像难以兼顾大块聚集和零 散分布的浒苔,业务化监测中提取面积可能会被夸大21.88%,在面积准确的情况下,也会有21.67%的浒苔信息被错误提取。本文最后提出通过选择两个阈值来解决此 问题。     

卫星遥感海冰监测技术在河北省近海海域的应用

卫星遥感技术已成为海冰监测的重要技术手段之一。介绍了应用EOS系列卫星、HY-1B卫星和HJ-1A/B卫星等遥感数据提取河北省近海海域海冰分布、海冰类型、海冰厚度等海冰信息的方法。重点研究了应用HJ-1A/B卫星遥感数据进行海冰分类,及根据海冰类型与厚度的对应关系估算海冰厚度的方法,并以2013年1月17日的卫星遥感数据为例,提取海冰信息,制作相关海冰专题产品。结果表明:利用多种卫星遥感数据进行海冰监测,既可相互补充又能提高监测效率和准确率,制作更精细化的海冰监测产品,为河北海洋管理部门制定防灾减灾措施提供重要依据。     

基于HJ-1CCD影像的呼伦贝尔草原区MODIS/FPAR产品验证与分析

植物吸收性光合有效辐射分量(FPAR)是描述植被结构以及与之相关的物质与能量交换过程的基本生理变量。选取内蒙古呼伦贝尔谢尔塔拉镇针茅和羊草草甸草原为研究对象,利用2013年5次地面实测实验,通过HJ-I CCD高分辨率卫星影像建立统计模型,从而实现对该研究区MODIS/FPAR产品的验证与分析。1km HJ/FPAR"真值"与MODIS/FPAR产品值的变化趋势基本一致,并且二者具有较好的一致性,R2达到了0.6762。MODIS/FPAR产品能够很好地反映呼伦贝尔草甸草原在整个生长季的长势及物候变化,这是因为研究区样地尺度上HJ/FPAR和MODIS/FPAR产品值不但变化趋势相一致,而且相关系数R2高达0.9148。无论在同尺度(1km)还是整个研究区尺度,MODIS/FPAR均有一定的高估现象。研究结果对了解和进一步使用该地区的MODIS/FPAR产品具有重要的指导意义。     

HJ数据的LBV变换及其在面向对象分类中的应用

为了探讨环境卫星影像在分类中的应用潜力,通过对其地物光谱进行分析,计算推导出了适用于环境卫星数据的LBV变换公式,并且将变换后得到的LBV图像应用到面向对象分类中。实验结果表明:推导的针对HJ\|1B影像的LBV变换公式具有普适性,并且经过LBV变换后的影像有效地弥补了环境卫星数据光谱分辨率不高的缺点,在分割参数相同的情况下,分割效果明显好于原始影像分割结果。利用变换后的LBV图像进行面向对象分类,可以很好地提取出水体、植被、城镇和建筑用地4大类,总体分类精度达到93%,Kappa系数为0.8894,表明经LBV变换后的HJ影像在面向对象分类中具有很大的应用潜力。     

卫星遥感大气CO2的技术与方法进展综述

综合分析了国际上卫星遥感观测大气二氧化碳（CO²）含量的主要技术与方法,讨论了现有星载大气CO²探测器（传感器）的主要理论基础、反演方法,归纳了仪器的主要性能指标、观测方式和观测目标,分析了影响CO²遥感精度的主要因素。具体研究下列3类星载CO2探测器：① 技术相对成熟的、观测要素既包含CO²也包含其他微量气体的综合性星载被动探测仪器,例如大气红外垂直探测仪(AIRS)、大气制图扫描成像吸收光谱仪(SCIAMACHY)、超高光谱红外大气探测干涉仪(IASI);② 针对大气中CO²混合比或者对流层下层CO²含量进行专门观测的星载被动探测器：极轨碳观测卫星(Orbiting Carbon Observatory,OCO)和温室气体观测卫星(Greenhouse gas Observing Satellite,GOSAT)搭载的被动红外探测器;③ ASCENDS和A\|SCOPE等国际卫星计划正在研制中的星载主动激光雷达探测器。 进一步介绍了我国在高光谱仪器研制方面具备的研究基础。最后初步分析了星载CO²探测结果的验证、资料同化方法和未来的发展趋势。     

基于SVM模型的妫水河叶绿素a浓度的遥感反演

以北京市妫水河为研究区,基于2011年9月25日和2012年9月30日的两期叶绿素a浓度实测数据和准同步的环境一号卫星(HJ-1A)多光谱数据,分别构建一元线性和多元支持向量机模型(SVMM),通过决定系数R²和平均相对误差对模型的精度进行检验,用模型进行水体叶绿素a浓度的反演,并分析其时空分布特征。研究表明:在样本数较少的情况下,SVM具有很强的非线性映射能力,能够取得较好的预测结果,更适用于反演叶绿素a浓度。时间分布上,研究区叶绿素a浓度呈增加趋势,均值上升了6.86 μg/L;空间分布上,深水区叶绿素a浓度值低于浅水区,上游高于下游。国产HJ-1A CCD2多光谱数据以其4 d的时间分辨率,在水质动态变化监测方面具有优势。     

基于MODIS大气产品的天宫一号高光谱成像仪数据大气校正研究

天宫一号(TG-1)搭载的高光谱成像仪获取了大量的高光谱数据,可用于国土资源、农林业和油气矿产等领域的研究。但由于遥感成像时会受到大气的干扰,因此需要首先进行大气校正,消除大气的影响,才能进行遥感定量分析与应用。利用准同步的中分辨率成像光谱仪MODIS(Moderate Resolution Imaging Spectroradiometer)大气参数产品,结合6S辐射传输模型对天宫一号高光谱成像仪数据进行大气校正,并利用地面测量光谱和同步MODIS反射率数据对结果进行了验证。结果表明:经过大气校正后,天宫一号高光谱成像仪数据和地面测量光谱一致性较好,所有样点的相关系数都大于0.97,最大均方根误差为0.088。和MODIS反射相比,各波段回归直线的斜率接近1,且R²都大于0.8。     

Mass Change of Glaciers in Mt.Qomolangma(Everest) Region from 1974 to 2012 as derived from Remote Sensing Data

In this study,glacier mass changes are investigated for the period 1974~2012 for 14 glaciers larger than 3 km^2on south and north slopes in Mt.Qomolangma(Everest) region based on ZY-3 images,SRTM DEMs and topographic maps.In general,a continuous mass loss(-0.31±0.03 m w·e·a^-1) for glaciers on south and north slopes of Mt.Qomolangma could be observed between 1974 and 2012.The mass budget of 14 glaciers was -0.27±0.03 w·e·a^-1 for the period 1974~1999 and -0.35±0.06 w·e·a^-1 during 1999~2012.Glaciers on the south slope lost mass at a rate of -0.38±0.03 w·e·a^-1,[JP]was larger than glaciers on the north slope which was at a rate of 0.27±0.03 w·e·a^-1.And these glaciers change are heterogeneous and differ spatially.The main reason for negative mass budget may be attributed to the increasing air temperature,heterogeneous glacier mass balances were responded to different climate conditions.Debris-covered regions obviously exhibited higher thinning rates on the north slope about 5 500~6 000 m.However,the dependence of mass change on altitude is not significant in other regions.The main reason for this may be attributed to the heterogeneous debris thickness except for different climate conditions.Glacial lake expansion is the result of glacier rapid ablation,and also accelerates glacier melting.     

欧亚大陆中高纬度区逐日无云积雪产品研发及验证

利用多源遥感数据,结合光学遥感数据高空间分辨率及被动微波数据不受云干扰的优势,利用MODIS逐日积雪标准产品和AMSR-E雪水当量产品,生成了欧亚大陆中高纬度区500m分辨率的逐日无云积雪产品,并利用更高分辨率的Landsat-TM数据生成的积雪产品作为"真值"影像,对研发的逐日无云积雪覆盖产品的精度进行了验证。结果表明:MOD10A1和MYD10A1受云影响均较为严重,无法直接用于地表积雪面积的监测。而本研究合成的逐日无云产品具有较好的精度,与TM积雪图具有较高的一致性。但不同的土地覆盖类型对积雪分类精度有一定的影响。其中,裸地和草原覆盖区精度最好,Kappa系数分别为0.655和0.644,均为高度一致性;其次精度较好的是灌丛和耕地覆盖区,Kappa系数分别为0.584和0.572,均为中等的一致性;而森林覆盖区由于受到高大植被的影响,Kappa系数仅为0.389,合成产品相对TM积雪产品明显高估了森林区积雪面积。整体Kappa均值达到0.569,接近高度一致,研究结果对实时监测欧亚大陆积雪面积具有一定的应用价值。     

呼伦贝尔草甸草原MODIS/LAI产品验证

以内蒙古呼伦贝尔草甸草原为研究区域,利用2013年6期地面实测数据,结合HJ-1A/B CCD高分辨率影像,经过辐射校正与模型建立,对研究区域草原生长季的MODIS/LAI产品进行验证。结果表明:在时间上,MODIS/LAI产品能够较好地反映草原的长势与物候变化。在空间上,由于MODIS/LAI产品输入数据的不确定性,MODIS/LAI产品与地面情况存在一定偏差(ΔLAI=0.59m2/m2),在呼伦贝尔草甸草原草场整个生长季都存在高估现象,平均相对误差为40%。在生长初期和末期,较大的地表异质性使MODIS/LAI产品高估现象较严重;生长中期高估现象减小,相对误差在30%以内。研究结果对了解该地区的MODIS/LAI产品精度与使用该地区MODIS/LAI产品具有重要指导意义。     

环境一号卫星CCD相机水体信息采集特性分析

利用覆盖我国4大海区共88景图像数据,针对4波段CCD相机(HJ-1A/CCD1、HJ-1A/CCD2、HJ-1B/CCD1、HJ-1B/CCD2)在水体中的信息采集特性进行分析。结果表明:(1)HJ-1A和B星虽不是水色卫星,但其CCD相机在水体中依然有一定的信息,可作为水色遥感器服务于水环境;(2)HJ-1B/CCD1在南海海域水体信息采集过程存在明显的太阳耀斑现象,因而在水色信息提取时太阳耀斑是一个不可忽略的因子;(3)无论是在一类水体还是在二类水体,各遥感器的近红外波段皆存在水体信息采集为零的现象,其中HJ1B-CCD2尤为明显。利用近10景涵盖了HJ-1A/CCD1、HJ-1A/CCD2、HJ-1B/CCD1、HJ-1B/CCD2数据,以及同时过境的EOS/MODIS,采用基于遥感器入瞳处总辐亮度的交叉定标方法进行交叉辐射定标,获取适用于水体目标的交叉辐射定标参数。最后根据瑞利散射和气溶胶散射的计算值,以及获取的交叉定标系数,反推出HJ1B-CCD2可能接受到的灰度值。     

基于HJ-1卫星CCD数据的厦门海域叶绿素a浓度反演

针对HJ-1A/B卫星CCD数据,建立适合于厦门海域的叶绿素a浓度反演模型,将为持续监测该海域的赤潮提供时间序列的叶绿素a浓度数据。基于2013年7月31日厦门海域水体实测光谱与叶绿素a浓度同步测量数据,及HJ\|1B卫星CCD2光谱响应函数,对各波段遥感反射率与叶绿素a浓度的相关性进行比较,证实蓝、绿波段比值与叶绿素a浓度相关性最高。对OC3模型在内的5种模型的反演结果和实测叶绿素a浓度做相关性分析,发现各模型相关系数均达到0.7以上。利用2013年7月30日实测数据对同期厦门海域HJ-1B卫星CCD2数据叶绿素a浓度反演结果进行精度验证,结果表明本地化的10指数模型在反演叶绿素a浓度动态范围较大的区域具有更高的精度。     

基于多源时空信息的西宁和拉萨近70年来城市扩展过程重现及地表覆盖变化分析

西宁和拉萨城市作为青藏高原人类活动的热点地区,其发展历程对青藏高原社会经济发展具有重要影响。研究基于遥感影像、城市规划图和历史地图等资料重建了西宁和拉萨城市1949基准年、1978基准年、1990年、2000年、2010年和2018年城市扩展及2000年以来城市不透水层和绿地空间组分数据,分析了1949基准年以来西宁和拉萨主城区城市扩展的时空特征,揭示了社会经济因素和政策因素对城市土地利用/覆盖变化的影响。研究结果表明：(1)新中国成立以来,西宁和拉萨主城区持续扩展,均呈现非线性的增长态势,城市土地面积分别从1949基准年的1.98 km²和1.10 km²增长到2018年的75.65 km²和76.04 km²;西宁主城区城市扩展呈现十字状的扩展态势,拉萨呈现出圈层外延式的扩展模式;(2)自2000年来,西宁和拉萨城市绿化水平显著提升。2000～2018年,西宁和拉萨城市不透水层面积分别从36.91 km²和21.56 km²增加到55.34 km     

Land Change Patterns and Driving Mechanism in Beijing since 1990 based on GEE Platform

Land-cover and land-use dynamics is a key component for global change,and it is a significant form of the impact of human activities on physical environment.Basing Google Earth Engine platform and Classification And Regression Tree method,selected seven types of cultivated land,forest,grassland,wetland,water body,artificial surface and bare land as classification system,the paper used Landsat 5 TM and Landsat 8 OLI images to interpret the land\|cover and land\|use since 1990 of Beijing.Simultaneously,the paper analyzed and summarized the character of land\|use changing and driving force.The results show that：(1) GEE has outstanding advantages in remote sensing data analysis and processing at regional scales.(2) The CART method has high accuracy of remote sensing classification,and the overall accuracy of validation of 6 land cover products is above 93%.The spatial consistency of 2010 products and GlobeLand30\|2010 data showed that the spatial consistency ratios of woodland,water body and cultivated land were 84.28%,74.75%and 73.56% respectively.The spatial consistency of the distribution is 74.0%.(3) The main land types in Beijing were cultivated land,woodland and artificial surface,and the area accounted for about 90%.During the period from 1990 to 2016,the artificial surface and woodland area increased,and the cultivated land and water were shrinking.The artificial surface area increase of 1 371 km²,and cultivated land shrinkage 40%;On Beijing plain area,artificial surface by the circle of “spread pie” expansion trend to “blossom everywhere” expansion trend;The expansion of the artificial surface is mainly achieved through the encroachment of cultivated land.We constructed a multidimensional stepwise linear equation model to analyze the driving force of land type change,indicated that rapid population growth,rapid economic development,government\|related policies and other socio\|economic development factors jointly drive the Beijing land-cover/land-use evolution process.