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。     

Research on Using End-member Abundance to Estimate Summer Maize Planting Area

Multispectral satellite remote sensing data of low or moderate spatial resolution are widely used in large range crop planting area extraction.For those areas with complex structure，when the low or moderate spatial resolution remote sensing data sources is used to extract the planting area of target crop，mixed pixel is the main obstacle factor to restrict the area extracting precision.Extracting it on sub\|pixel scale could overcome the restriction of low or moderate spatial resolution and develop the extraction precision.However，the extraction method of target crop planting area on sub\|pixel scale now usually directly use the end\|member abundance to instead the percentage of planting area.Therefore it may cause some errors.On the basis of previous researches，taking Hebi City，Henan Province as the study area，which located in Huang\|Huai\|Hai plain，has the largest summer maize planting area and the complex planting structure.Taking FY3/MERSI data as the main information source.Using the method of spectral matched adaptive best end\|member combination of pixel unmixing to extract the summer maize end\|member abundances.Making regression modeling in various equation forms between summer maize end\|member abundances in pixel and the percentage of planting area.Then select the optimal regression equation form to build regression model，and estimate the actual summer maize ground planting area.Summing up the correlation coefficient when the model was building，significance test and the RMS errors condition of sample point verification.Then choose the cubic model to estimate the planting area of summer maize in the study area.It is proved by remote sensing estimation that the area precision of summer maize planting area is 97.1%，the position precision is 82.5%.     

Analysis of the Spatial and Temporal Distribution Variatio of Formaldehyde by the OMI Satellite Sensor

In recent years，the atmospheric environmental issues become increasingly significant.Formaldehyde (HCHO) as a kind of carcinogen，its global testing to understand the spatial and temporal distribution and content in the atmosphere，has important significance for the detection of air quality and public safety.The study on the use of satellite AURE mounted OMI (Ozone Monitoring Instrument) a new generation of atmospheric detection sensors，data for the 2005~2014 January，April，July，October Tianshui vertical columns of tropospheric HCHO concentrations of trace data for each year.By VISON，GIS and other software combined with the product handling，explores the spatial distribution of Tianshui area HCHO，the time variation and their influencing factors.The results show that: the study area，the vertical columns of tropospheric HCHO concentration in 2005 showing sustained growth trend in 2012，2012~2014 chronology exhibit significantly decreased;winter and summer HCHO vertical column concentrations significantly higher than the spring and autumn，which highest in summer and winter followed; the eastern part of the study area and adjacent areas in Shanxi\|parts of Gangu County，Wushan County，exhibits a significantly higher value and lower central region of HCHO Tianshui vertical column density，and in 2014 the performance of HCHO concentration in the study area values are generally higher.Studies have shown that remote sensing is important for large\|scale atmospheric environmental monitoring.     

Relationships between Remotely Sensed Chlorophyll Index and Maximum Carboxylation Rate

Process-based ecological models，which simulate carbon exchange at the land\|surface，were powerful and indispensable tools for calculating regional and global spatiotemporal variations of terrestrial Gross Primary Productivity(GPP).Vcmax(maximum carboxylation rate)，one of the most critical parameters in the ecological models，was of significance in accurate calculation of GPP.However，the traditional methods of obtaining Vcmax is time\|consuming and laborintensive.In this study，correlations between three types of chlorophyll index(Modified Transformed Chlorophyll Absorption in Reflectance Index，Transformed Chlorophyll Absorption in Reflectance Index，and MERIS Terrestrial Chlorophyll Index) and canopy Vcmaxwas analyzed for different sites，and correlations between MTCI and canopy Vcmax for different time series of the same site.Results showed there were strong relationships between chlorophyll indices and canopy Vcmax.In the three types f chlorophyll index，the results show the most obvious correlations between MTCI and canopy Vcmax.For different time series of the same site，the relationship varies with different plant type.Results indicated that the remotely sensed chlorophyll index has ability to estimate Vcmax with spatial and temporal variations.     

基于四波段半分析算法和Hyperion遥感影像反演太湖叶绿素a浓度

水体叶绿素a浓度不仅是水质状况的重要指标,也是制定水环境保护和水资源开发利用方案的重要依据。以2004年8月19日太湖水质浓度实验数据和同步的Hyperion影像为数据基础,研究适用于Hyperion影像的四波段半分析算法。由模型参数标定数据集(37组)对四波段半分析算法参数的拟合分析和模型检验数据集(5组)对算法精度的评估可知,基于指数拟合方法获取的四波段半分析算法具有较高的叶绿素a浓度估算精度(相关系数为0.8913,平均绝对误差为1.1109μg/L,对应的平均相对误差为5.69%,其对应的4个波段波长分别为671.02nm、701.55nm、711.72nm和742.25nm)。用以上四波段半分析算法从Hyperion影像中提取的叶绿素a浓度呈湖心低、沿湖区域高的格局。与22.23 μg/L的年均叶绿素a浓度相比较,2004年8月19日的叶绿素a浓度处于年际较高水平。     

东中国海遥感叶绿素数据重构方法研究

由于天气等各种因素,卫星遥感叶绿素数据中的大面积无规律缺失问题一直是遥感数据领域的研究热点,阻碍了卫星数据的应用。因此,卫星遥感数据的重构和再分析成为一个重要课题,在关注海域获得时空连续的完整数据对于扩展遥感数据的应用范围,提高其数据利用效率有着重要意义。针对这一系列问题,基于对东中国海叶绿素时空多尺度(包括天气过程时间尺度)变化机制研究的需要,结合多变量DINEOF方法和最优插值等数学方法的优点,成功构建和发展了多尺度最优插值、二次订正的多变量DINEOF方法,简称DINEOF-OI方法。对于目标缺测数据点重构过程中,如何有效分配时间序列上与空间场中的观测数据对重构数据的影响权重,取决于研究的具体目标问题,是研究的重要思路创新。基于这一方法对东中国海近10a的卫星遥感叶绿素数据成功进行了重构试验,并较成功地刻画了东中国海海表面叶绿素的包括天气尺度在内的多尺度变化特征。     

长江口及邻近海域悬浮颗粒物对叶绿素a遥感反演算法的影响分析

利用水介质光辐射传输数值模型Hydrolight,结合前人对长江口及邻近海域水体的生物—光学模型研究,模拟不同光学水体的遥感反射率,并分析遥感反射率对悬浮颗粒物(SPM)的敏感性以及SPM对4种叶绿素a(Chla)反演算法(二波段、三波段、荧光基线高度(FLH)和综合叶绿素指数(SCI)算法)的影响。结果表明:由Hydrolight模拟得到的遥感反射率与现场同步实测的遥感反射率的均方根误差小于0.01sr-1,其中可实现遥感反射率在550~725nm波段较精确的模拟。遥感反射率对SPM的敏感性随着Chla浓度的升高而降低。二波段、三波段算法适合低SPM浓度水体的Chla浓度反演,FLH算法反演Chla浓度时易受SPM的影响,而SCI算法在中、高SPM浓度水体中消除SPM的影响进而反演Chla的潜力较好。     

The Comparison of Reflectance based on Different Terrain Correction

Selecting the Xinglong Mountain which locates in the southeast of Lanzhou city，GanSu province as an example.Using the Landsat8 satellite image as the data source，the Cosine method，C and the modified Minnaert methods were used for each band in the study area.Comparing with the results of the field measurement reflectance and the statistical characteristics of image，the result showed that the cosine method has a perfect correction in the visible and short wave infrared wavelength，the C correction has a serious over\|correct，however.In the Near Infrared Wavelength，the better result obtained by C correction，and the cosine method has over\|correct otherwise.Comparing with the correct effect of whole bands，the modified Minnaert method has an ideal correct effect.The comparison of before and after correction we found that there is a smaller difference for three methods in the smaller slope，and the over correct is mostly in the south.What’s more，with the increase of the slope，the over correct is more obviously，but，there is a little over correct which used the modified Minnaert method in the whole area，it’s more suitable in large slope and the complicated area.     

Change Detection of Forest Coverage in Sichuan GiantPanda Sanctuaries Using PALSAR Time Series Data

Time series features of SAR backscattering in the period of 2007 to 20011 were used to monitor the change of forest coverage in Wolong and Caopo Nature Reserve in Sichuan Prov.China.Firstly，PALSAR data were separated into 4 groups；then，a decision tree approach was derived using the J48 algorithm for the forest fine mapping，resulting in the overall classification accuracy(OA)and kappa coefficient(KC)equaling to 94.31% and 0.89，respectably.After that，four time\|series maps of forest coverage in study area were obtained in the observation period of 2007\|2011.Change detection demonstrated that the proportion of the forest coverage was reduced from 56.66% to 48.11%(that is 8.55% declining)caused by the Wenchuan Earthquake.However，the ecological system was recovering since the second year of the event，such as the forest coverage increased up to 48.52%(equals to a recovered square of 9.19 km² that is amounting to 13.24%of the damaged area).This study implied that Wenchuan Earthquake could be a negative impact on the deforestation of the study area；nonetheless the ecological system is recovering nowadays jointly contributed by the combination of natural and anthropogenic impactors.     

Piecewise Convex Mulutiple-model Hyperspectral Imagery ENDmember\|extraction based on Discrete Particle Swarm Optimization

Piecewise COnvex Multiple\|Model ENDmember(PCOMMEND) spectral unmixing can well solve unmixing of the nonconvex hyperspectral data，which improves the calculation accuracy of the standard linear mixed model based on the convex geometry model.the number of piecewise convex is not sure in the practical application，which limits the calculation ccuracy of unmixing and the wrong endmembers will sometimes extracted，in view of the situation，the Discrete Particle Swarm Optimization(D\|PSO)is proposed to unmix the piecewise convex mulutiple\|model hyperspectral imagery，D\|PSO is the intelligent algorithm of random search，and is able to find the global optimal solution of convex function，which reduce the unmixing error caused by the uncertainty number of the convex section，experiments on the simulative data and real data has indicate D\|PSO improves the accuracy of the extracting endmember and estimating the proportion.     

Research on Extraction of Burned Area based on Saliency Enhancement

The information of burned area is significant for post disaster assessment，ecosystems protection and restoration.So far，the existing detection methods of burned areas are less practical.Based on the FY\|3C MERSI satellite data，various characteristics of the burned area are fully utilized and a new method of burned area mapping is created through saliency enhancement.Two burned areas in the northwestern United States were selected as research areas.Three burned\|area\|sensitive vegetation indices(NDVI，GEMI and NDVIT) were combined with the saliency features of the images to enhance and extract the burned areas.Visual interpretations are used to evaluate the experimental results of the proposed method，and compared with the results of NBR threshold method.The results show that the kappa coefficient of the saliency enhancement method in the two research areas reaches more than 0.68，0.2 higher than NBR threshold method.Experiments show that the saliency enhancement method for mapping burned area is high，and the influence of vegetation change caused by non\|burned has little influence on it，and the method has a certain stability compared with the NBR threshold method.     

秋季太湖叶绿素a浓度荧光反演研究

基于2007年11~12月太湖全湖实测水质参数和光谱数据,首先利用高斯方程对遥感反射率进行过滤分解,找出叶绿素a(chl\|a)吸收峰675 nm以后的荧光反射峰(Fluorescence Peak:FP),再以662 nm处的反射率为基准,采用归一化荧光高度法进行叶绿素a浓度(C chl-a)反演,得到chl-a反演模型。基于高斯分解获取的chl-a的荧光反射峰值R(FP)与662 nm处的反射率R (662)比值［R(FP)/R(662)］与C chl-a之间存在显著的相关性,该模型为秋季太湖水体C chl-a的最佳反演模型。在高悬浮泥沙条件下,该模型能够较好地表示出叶绿素荧光高度与叶绿素浓度之间的关系,为C chl-a反演提供新的方法和依据,并为传感器敏感波段的选取和设置提供参考。     

Landsat 8 Data Mountain Shadow Removal based onBrightness Recovering Model

Mountain region in remotely sensed imagery are usually covered by shadows，which reduce the accuracy of information extraction.Therefore，in this paper a method based on intensity restoration is putting forward necessarily.First，Shadow Detection (SD) was constructed by the Max function and the band ratio to identify shadows.Thus，mountain shadows were extracted combined with the slope factor and SD，through the grid randomly arranged verification point verification accuracy.Second，the intensity curve model of the shadow area was fitted by ground data of the shadow and the transition rules of pixel intensity from the shadow to non\|shaded area.Third，the intensity restoration model was established by the derivative function of intensity curve to remove shadows.The results of the model on Changting Landsat 8 imagery indicated the extraction accuracy of the mountain shadow was 99.06% and the Kappa coefficient was 98%；According to the cluster analysis，the restoration and non\|shaded samples were the same type；Processed by the intensity restoration model，the average intensity of the shadow was increased by 13%，and the standard deviation was reduced by 80% and the clustering distances was reduced by 96%.respectively，average intensity of the shadow increased by 6.7%，the standard deviation was reduced by 73.7% and the clustering distances was reduced by 88.3% when compared with ATCOR_3，and average intensity of the shadow reduced by 1.8%，the standard deviation was increased by 6.7% and the clustering distances was reduced by 90% when compared with unitary linear restoration model.In the process of removing the mountain shadows，the intensity restoration method is neither replacing the shaded pixels nor interference with non\|shaded pixels and could preserve the spectral and intensity characteristics of shaded pixels better.  ；     

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.     

Comparison of RCM Precipitation Correction Methods for Hydrologic Simulation in Cold Region

It is a significant way to improve the accuracy of hydrologic simulation in the area having sparse observation sites by using the regional climate model precipitation driving hydrological model.As a result，a critical issue arises，which is how to correct the precipitation data coming from Regional Climate Model (RCM) based on the observation in the hydrology research in cold region.However，no systematic studies have been conducted to compare different precipitation correction methods and evaluate their impact on the hydrologic simulation in the cold region yet.Due to this，two kinds of mainstream regional climate model precipitation correction methods，Quantile Mapping (QM) and the Optimal Interpolation (OI)，have been compared and evaluated in Manas River basin between 2004 to 2009.The results show that both methods have their own advantages and disadvantages in statistical significance.The correction result of QM is good in low precipitation value and the average annual precipitation is more reasonable at spatial distribution.But when comes the high precipitation value，the result is not stable.Moreover，the correlation coefficient (R) and Root Mean Square Error(RMSE) doesn’t improve.Compared to R 0.37 and RMSE 2.80 mm/d，the modified R is 0.36 and RMSE is 2.70 mm/d;OI can improve R and RMSE significantly，one increases to 0.85 and the other reduces to1.46 mm/d after the correction.Despite that，OI also has its limitations.It gets more tiny precipitation relative to observation and the improvement of spatial distribution is not obvious.Using precipitation data before and after the correction to drive the hydrological models in a same set of model parameters.The results show that QM improves the simulation slightly，Nash-Sutcliffe efficiency coefficient (NSE) changes from 0.63 to 0.65 compared before，while OI is comparatively better，NSE increases to 0.71.This study is helpful to solve the problem of quality optimization in the preparation of hydrological simulation precipitation data and improve the precision of hydrologic simulation in cold region.     

松花湖水体叶绿素a含量与反射光谱特征关系初探

根据2008年7月在松花湖实测的水体反射光谱及实验室分析得到的叶绿素浓度数据,对松花湖水体反射光谱特征与叶绿素浓度之间的关系进行探讨与分析。研究结果表明:水体叶绿素浓度与各波长点处反射率相关性均较好,并选择700 nm处反射率建立单波段模型。而700 nm和677 nm波长处反射率比值、685 nm处光谱一阶微分、700 nm波长处波峰几何特征具有较好的相关性,给出了松花湖水体叶绿素浓度估算模型,为松花湖水体叶绿素浓度反演监测提供了一定的理论基础与参考。     

Retrieving Aerosol Optical Depth Over Land basedon GOCI Data Onboard Geostationary Satellite

The aerosol optical depth were retrieved using deep\|blue algorithm based on the GOCI data of 72 clear\|sky days in 2013 over the Yangtze River Delta，and then were validated by field data from two measured sites.The results show that the GOCI data can be used for inversion of land aerosols，and both sites show high accuracy.The fitting degree of Beichenlou station of Nanjing University of Information Science and Technology is 0.659，and that of AERONET station is 0.747，which shows good agreement compared with MODIS aerosol products.At the same time，it is found that the aerosol products based on the GOCI data of the stationary satellite have the ability to monitor the diurnal variation of aerosol and provide the basis for the aerosol dynamic diffusion and the climate effect study.      

Comparative Study on Thermal Environment between Old Urban Areas and New Urban Areas based on Landsat 8 Remote Sensing Image：A Case Study of Chengdu City

A large number of new urban areas have emerged under the rapid urbanization background in recent years in China，and the characteristics of urban thermal environment have significant changes.To analyze the thermal environment diversities between old urban areas and new urban areas and explore the impacting factors，we chose Chengdu City in Sichuan Province as a typical study area.Key surface parameters，including Land Surface Temperature (LST)，building index and vegetation index were derived based on Landsat 8 satellite image acquired on August 13，2014.The comparison study was conducted to analyze the differences of the parameters related to thermal environment changes，and the results indicated that：①Overall,the average surface temperature of the old urban area was higher than that of the new urban area.Regarding the spatial distribution，the central and northern region had higher temperature than the southern region for the old urban areas.In the new urban areas，although high temperature spots can be found in the central west or north，this region generally had a relatively low temperature.②The old urban area emphasized a higher Urban\|Heat\|Island\|Ratio\|Index (URI)，which revealed the descending trend of surface temperature via analyzing the thermal field profile from the old to the new.This showed that the urban thermal environment effect of the old city was stronger than the new urban area.③“Heat Island Effect” was easy to emerge in those areas with high density urban construction and little urban vegetation coverage，whereas reasonable urban landscape planning and layout would help to perfect the urban thermal environment.The comparison of the thermal environment effects between the new urban areas and the old urban areas shows the new urban planning in Chengdu has positively contributed to improve the thermal environment in the new urban areas，which can provide important reference for the future urban planning.