渭-库绿洲耕层土壤砷含量高光谱估测研究

以新疆渭干河-库车河三角洲绿洲耕层土壤为研究对象,通过对原始光谱反射率(R)、包络线去除(CR)和一阶微分(R′)变换光谱数据结合连续小波变换生成小波系数,筛选出与砷含量具有极显著相关性的特征波段(P<0.001),运用偏最小二乘回归、BP神经网络、随机森林回归及支持向量机回归分析方法建立耕层土壤砷含量的高光谱估测模型。结果表明,经一阶微分结合连续小波变换处理后,光谱数据与砷含量之间的相关性提升尤为明显。综合考虑各模型估测精度及稳定性,认为R′-CWT-2⁶-SVMR模型可作为研究区耕层土壤砷含量的最佳估算模型,其训练集和验证集的决定系数(R²)分别为0.753和0.740,均方根误差(RMSE)分别为1.350 mg/kg和1.819 mg/kg,相对分析误差(RPD)分别为2.03和2.00。     

基于实测光谱的海河悬浮物浓度反演研究

悬浮物浓度是评价水质优劣的重要指标之一。以天津滨海新区海河为研究区域,进行光谱数据测量和水体样本采集,并在实验室进行水质参数提取,得到归一化光谱反射率和光谱一阶微分数据。然后对光谱测量数据和悬浮物浓度实测值进行相关性分析,发现896 nm归一化光谱反射率和光谱反射率比值(R_(896)/R_(546))与悬浮物浓度相关性较好。最后,分别建立单波段、波段比值和一阶微分函数拟合模型,进行对比分析。结果表明,基于R_(896)/R_(546)的二次多项式模型拟合效果最好,方差齐性检验(F)值也是最高的,可用于该地区水体的悬浮物浓度反演和预测。     

博斯腾湖矿化度遥感反演及空间分布特征研究

以我国最大的内陆淡水湖——博斯腾湖为研究对象,利用Terra/MODIS L1B空间分辨率为250 m和500 m的遥感反射率数据及湖水矿化度实测数据建立线性回归模型,分析湖表面矿化度的空间分布特征。结果表明:空间分辨率为500 m的1~7个波段组合建立的多元线性回归模型相关性最高(R~2=0.70),模型验证结果显示,实测值和反演值的相关系数(R~2)为0.82,均方根误差(RMSE)为0.12。利用最优模型对博斯腾湖湖面矿化度进行反演,其分布存在明显的空间梯度,西北、东北和东南湖区矿化度较高,而西南湖区和湖区南部矿化度较低。     

Landsat5 TM遥感影像上太湖蓝藻水华反射光谱特征研究

利用ENVI遥感软件的FLAASH工具对2005年10月17日大规模蓝藻水华暴发的太湖Landsat5 TM影像进行大气校正处理,反演获得蓝藻水华和其他地物类型的遥感反射率图像,提取了不同集聚程度蓝藻水华的可见波段至近红外波段反射率数据,并与陆生植被、无蓝藻水面等地物的光谱反射率进行了比较。研究表明,与陆生植被、无蓝藻水面相比,蓝藻水华在TM2波段和TM4波段具有更高的反射率,在可见光波段整体反射率略高于陆生植被,在TM5、TM7波段则受水的影响反射率很低。从蓝藻水华、陆生植被的细胞生理结构、生长环境、复杂的遥感反射、散射及透射模型方面初步讨论了光谱差异的原因。     

遥感影像反演区域能见度及其与地面空气质量监测数据一致性研究

简述了基于"暗"像元和波段反射率比值关系的遥感反演区域能见度的原理和算法流程,以2010年获取的11景WRS 119/038幅江苏省太湖流域Landsat-7 ETM影像为数据源,在FLAASH大气校正软件中,开展了基于卫星遥感影像的区域能见度信息提取试验,并与30个地面空气自动监测站的PM10和1个能见度自动监测站数...     

环境卫星CCD影像在太湖湖泛暗色水团监测中的应用

太湖地区2009年5月11日、2010年8月21日、2011年7月28日和2011年9月24日的环境卫星CCD影像显示，在太湖西部沿岸带、竺山湖等水域存在湖泛暗色水团现象。由于环境CCD缺少辅助反演气溶胶信息的2.1um波段，试验了基于空气自动监测子站获得的与环境卫星CCD成像时间接近的地面能见度测量数据进行FLAASH大气校正的方法，反演结果总体上符合水体光谱特征。提取了湖泛水体、对照水体阳区在CCD各波段的光谱反射率数据统计特征。结果表明，和对照水体相比，湖泛水体在环境卫星CCD的可见光—近红外波段具有较低的反射率，与人眼观察湖泛水色暗黑的感官一致，另一方面，湖泛水域由于仍有一定的藻类存在，在环境卫星CCD近红外（波段4 ）具有比可见光（波段3）略高的反射率，其规律与基于Landsat ETM的湖泛暗色水团遥感分析结果相一致。     

基于不同时间尺度综合指数贡献率的攀枝花市大气污染特征分析

选取2016—2022年攀枝花市环境空气质量监测数据，基于年度、季度、月度、小时时间尺度评价环境空气质量综合指数贡献率。结果表明，攀枝花市污染类型已由传统的单一工业型污染转变为复合型污染，且污染物在不同时间尺度下的分布特征明显。从季度尺度来看，第一季度细颗粒物(PM_2.5)综合指数贡献率较大，第二和第三季度臭氧(O₃)综合指数贡献率较大，第四季度二氧化氮(NO₂)及PM_2.5综合指数贡献率较大。从月度尺度来看，O₃在3—8月的贡献较大，PM_2.5、NO₂、可吸入颗粒物(PM₁₀)、一氧化碳(CO)、二氧化硫(SO₂)在1—2月及11—12月的贡献较大。从小时尺度来看，O₃在13—19点贡献较大，PM₁₀、PM_2.5、CO在8—13点贡献较大，SO₂在9—13点贡献较大，NO₂在8—1...     

MODIS影像监测海州湾叶绿素a浓度的研究

利用海州湾海域2004～2006年的水质监测资料,选择同期成像质量良好的MODIS Terra 1B影像,分别在单波段因子和波段组合与叶绿素a浓度之间进行相关分析,发现近红外波段和绿波段、红波段的比值因子、差值因子以及仿植被指数因子与叶绿素a浓度相关性较高.用这些因子建立叶绿素a浓度的回归模型,其中因子F7(24)的二次多项式模型的R2达到0.801,平均相对精度达到65%以上,且模型具有较高的泛化能力,不仅证明了利用MODIS 1B影像监测海面叶绿素a浓度的可行性,而且对于监测该海域赤潮和富营养化状况具有重要的现实意义.     

太湖湖泛现象的卫星遥感监测

2010年8月20日太湖地区Landsat ETM影像显示,太湖西部沿岸带存在湖泛黑水团现象,对该景遥感影像进行了大气校正,提取了湖泛样区、其他水体样区的ETM各波段光谱反射率数据统计特征。结果表明,湖泛样区在可见光波长的ETM波段1、2、3具有很低的反射率,水色暗黑,与人眼观察一致,而在反射红外波长的ETM波段4则有比波段3高的反射率,差异植被指数DVI>0,其原因为湖泛黑水团中,虽然大量蓝藻死亡分解,然而水中还残留有一定数量的活体蓝藻,残余叶绿素及细胞造成了虽然较弱、但仍较为稳定的反射红外波长处的光谱反射能力。提出了识别湖泛现象的遥感判据为ρ0.485<0.05 andρ0.56<0.08 andρ0.66<0.065 and(ρ0.83-ρ0.66)>0 andρ0.83<0.1。     

基于实测光谱的矿业开发集中区土壤元素含量反演

采用ASD FieldSpec 3型便携式地物光谱仪对矿集区的296个土壤样本进行高光谱反射率测定,通过土壤光谱特征分析,提取污染元素潜在光谱特征,并与土样化学测试结果进行相关分析,得到As、Cd和Zn的光谱特征参数,并从Aster影像对应的特征波段上实现该3种元素含量的反演。结果表明:As、Cd和Zn预测值的相对误差平均值分别为0.116、0.106和0.088,该方法可为大面积的土壤环境质量调查提供技术参考。     

基于随机森林与神经网络的高光谱土壤重金属Zn含量反演

在河北省保定市白洋淀区域采集115个土壤样品进行重金属含量分析和室内光谱测量,分别将BP神经网络、随机森林、决策树、多元线性回归、K近邻回归、AdaBoost回归和偏最小二乘回归法应用于全部原始波谱数据和基于双层随机森林选择后的波段数据。结果表明,基于原始波谱数据的土壤重金属Zn元素含量的反演模型精度较低,而通过双层随机森林选择出光谱数据中与土壤重金属Zn信息相关的波段,减轻了网络模型的过拟合问题,提高了模型预测精度;与其他模型比较,结合双层随机森林和BP神经网络构建的反演模型对研究区土壤重金属Zn含量预测效果最佳。     

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir,Beijing, China

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R², RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.     

Research on the relationship between the fractional coverage of the submerged plant Vallisneria spiralis and observed spectral parameters

The present paper discusses the relationship between the coverage fraction of submerged plants and the observed spectral characteristics. The purpose of this paper is to validate a remote sensing technology to monitor the change in the plant composition of a water body. In the current study, the reflectance spectra of the submerged plant Vallisneria spiralis at different fraction coverages of the wetland in Hangzhou Bay were measured. The relationships between the fraction coverage of V. spiralis and simulated Quickbird normalized difference vegetation index (NDVI), red edge, and other spectral characteristic parameters were established. The results showed that the spectral reflectance characteristics of submerged plant V. spiralis were mainly in the visible light (490–650 nm) and near infrared (700–900 nm). The rate of change of the blue band curve and simulated Quickbird NDVI showed a higher correlation with the V. spiralis coverage, so estimation models of the fraction coverage were constructed using these parameters. The estimated fraction coverage of V. spiralis with different models were validated with ground data, and the accuracy of estimation models was assessed. The most suitable estimated fraction coverage of V. spiralis was obtained using the rate of change of the blue band curve and simulated Quickbird NDVI. The present work demonstrated a method to monitor the distribution and dynamical variation of submerged plants at the large scale.     

Total suspended matter observation in the Pearl River estuary from in situ and MERIS data

Based on the cruise data collected in the Pearl River estuary (PRE) in May 2008, an empirical two-band model by using the ratio of R _rs at 629 and 671 nm was established to retrieve total suspended matter (TSM) concentration with the determination coefficient (R²) of 0.854, mean relative error (MRE) of 7.483%, and root-mean-square error (RMSE) of 1.295 mg L^???1. To match with medium resolution imaging spectrometer (MERIS) bands, in situ remote sensing reflectance was re-sampled to the bandwidth of 10 nm. The relationship between TSM and re-sampled R _rs at 620 nm (MERIS band 6) and 665 nm (MERIS band 7) are obtained (R² = 0.748, RMSE = 1.697 mg L^???1, MRE = 8.785%, n = 13). Additionally, to map the spatial distribution of TSM in the PRE, MERIS level_1B data were calibrated using a multiple linear regression model based on in situ R _rs. Another dataset collected in the PRE in January 2004 was used to validate the two-band model and also applied to map TSM distribution from MERIS image. The comparison between measured TSM values and modeled ones showed satisfactory results (R² = 0.753, MRE = 22.199%, and RMSE = 2.603 mg L^???1).     

Novel hyperspectral reflectance models for estimating black-soil organic matter in Northeast China

This paper presents a novel method for estimating black-soil organic matter (SOM) in the black-soil zone of northeast China from hyperspectral reflectance models. Traditional black-soil property measurements are relatively slow, but the pressures of agricultural production and environmental protection require a quick method to collect black-soil organic matter content. SOM estimation using soil hyperspectral reflectance models can meet this requirement, based on the spectral characteristics of black-soil in Northeast China. On the basis of the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The concepts of curvature and ratio indices are also applied to compare and test the stability and accuracy of data modeling. The results show that the response of black-soil spectral reflectance from 400-1,100 nm to organic matter content is more marked than that from 1,100-2,500 nm. Specifically, the main response range of black-soil organic matter is between 620-810 nm, with a maximal spectral response at 710 nm. By comparing different models, we found that the normalized first derivate model is optimal for estimating SOM content, with a determination coefficient of 0.93 and root mean squared errors (RMSE) of 0.18%.     

Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data

The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R ² (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance.     

Mapping urban forest tree species using IKONOS imagery: preliminary results

A stepwise masking system with high-resolution IKONOS imagery was developed to identify and map urban forest tree species/groups in the City of Tampa, Florida, USA. The eight species/groups consist of sand live oak (Quercus geminata), laurel oak (Quercus laurifolia), live oak (Quercus virginiana), magnolia (Magnolia grandiflora), pine (species group), palm (species group), camphor (Cinnamomum camphora), and red maple (Acer rubrum). The system was implemented with soil-adjusted vegetation index (SAVI) threshold, textural information after running a low-pass filter, and brightness threshold of NIR band to separate tree canopies from non-vegetated areas from other vegetation types (e.g., grass/lawn) and to separate the tree canopies into sunlit and shadow areas. A maximum likelihood classifier was used to identify and map forest type and species. After IKONOS imagery was preprocessed, a total of nine spectral features were generated, including four spectral bands, three hue?Cintensity?Csaturation indices, one SAVI, and one texture image. The identified and mapped results were examined with independent ground survey data. The experimental results indicate that when classifying all the eight tree species/ groups with the high-resolution IKONOS image data, the identifying accuracy was very low and could not satisfy a practical application level, and when merging the eight species/groups into four major species/groups, the average accuracy is still low (average accuracy = 73%, overall accuracy = 86%, and ???=?0.76 with sunlit test samples). Such a low accuracy of identifying and mapping the urban tree species/groups is attributable to low spatial resolution IKONOS image data relative to tree crown size, to complex and variable background spectrum impact on crown spectra, and to shadow/shaded impact. The preliminary results imply that to improve the tree species identification accuracy and achieve a practical application level in urban area, multi-temporal (multi-seasonal) or hyperspectral data image data should be considered for use in the future.     

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。     

Application of a benthic observed/expected-type model for assessing Central Appalachian streams influenced by regional stressors in West Virginia and Kentucky

Stream bioassessments rely on taxonomic composition at sites compared with natural, reference conditions. We developed and tested an observed/expected (O/E) predictive model of taxonomic completeness and an index of compositional dissimilarity (BC index) for Central Appalachian streams using combined macroinvertebrate datasets from riffle habitats in West Virginia (WV) and Kentucky (KY). A total of 102 reference sites were used to calibrate the O/E model, which was then applied to assess over 1,200 sites sampled over a 10-year period. Using an all subsets discriminant function analysis (DFA) procedure, we tested combinations of 14 predictor variables that produced DF and O/E models of varying performance. We selected the most precise model using a probability of capture at >0.5 (O/E _0.5, SD?=?0.159); this model was constructed with only three simple predictor variables—Julian day, latitude, and whether a site was in ecoregion 69a. We evaluated O/E and BC indices between reference and test sites and compared their response to regional stressors, including coal mining, residential development, and acid deposition. The Central Appalachian O/E and BC indices both showed excellent discriminatory power and were significantly correlated to a variety of regional stressors; in some instances, the BC index was slightly more sensitive and responsive than the O/E _0.5 model. These indices can be used to supplement existing bioassessment tools crucial to detecting and diagnosing stream impacts in the Central Appalachian region of WV and KY.