基于无人机高光谱遥感在太湖蓝藻水华监测中的一次应用

采用机载高光谱视频相机,在4个季节对太湖蓝藻进行7次、18个架次的有效拍摄。对拍摄到的高光谱影像进行辐射定标、几何拼接等预处理后,提取不同浓度蓝藻和水草等其他物体的高光谱数据,发现不同浓度的蓝藻光谱在680 nm后表现出较大差异。采用主成分分析（PCA）对高光谱数据降维后,结合k-近邻（kNN）分类算法,可实现对蓝藻的精准定位。定性识别结果经光谱预处理后,采用连续投影算法（SPA）进行特征波段提取,发现蓝藻光谱的季节差异主要表现在450 nm~570 nm和760 nm~910 nm波段。     

Mapping Invasive Aquatic Vegetation in the Sacramento-San Joaquin Delta using Hyperspectral Imagery

The ecological and economic impacts associated with invasive species are of critical concern to land managers. The ability to map the extent and severity of invasions would be a valuable contribution to management decisions relating to control and monitoring efforts. We investigated the use of hyperspectral imagery for mapping invasive aquatic plant species in the Sacramento-San Joaquin Delta in the Central Valley of California, at two spatial scales. Sixty-four flightlines of HyMap hyperspectral imagery were acquired over the study region covering an area of 2,139 km² and field work was conducted to acquire GPS locations of target invasive species. We used spectral mixture analysis to classify two target invasive species; Brazilian waterweed (Egeria densa), a submerged invasive, and water hyacinth (Eichhornia crassipes), a floating emergent invasive. At the relatively fine spatial scale for five sites within the Delta (average size 51 ha) average classification accuracies were 93% for Brazilian waterweed and 73% for water hyacinth. However, at the coarser, Delta-wide scale (177,000 ha) these accuracy results were 29% for Brazilian waterweed and 65% for water hyacinth. The difference in accuracy is likely accounted for by the broad range in water turbidity and tide heights encountered across the Delta. These findings illustrate that hyperspectral imagery is a promising tool for discriminating target invasive species within the Sacramento-San Joaquin Delta waterways although more work is needed to develop classification tools that function under changing environmental conditions.     

Monitoring and assessing of landscape heterogeneity at different scales

In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.     

Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey

In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.     

Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data

This study aims at the classification and water quality assessment of Harike wetland (Ramsar site) in India using satellite images from the Indian Remote Sensing satellite, Resourcesat (IRS P6). The Harike wetland is a converging zone of two rivers, Beas and Sutlej. The satellite images of IRS Linear Imaging Self Scanner (LISS) IV multispectral sensor with three bands (green, red, and near infrared (NIR)) and a spatial resolution of 5.8 m were classified using supervised image classification techniques. Field points for image classification and water sampling were recorded using a Garmin eTrex Global Positioning System. The water quality parameters assessed were dissolved oxygen, conductivity, pH, turbidity, total and suspended solids (SS), chemical oxygen demand, and Secchi disk transparency (SDT). Correlations were established between turbidity and SS, SS and SDT, and total solids and turbidity. Using reflectance values from the green, red, and NIR bands, we then plotted the water quality parameters with the mean digital number values from the satellite imagery. The NIR band correlated significantly with the water quality parameters, whereas, using SDT values, it was observed that the green and the red reflectance bands were able to distinguish the waters from the two rivers, which have different water qualities.     

Mapping giant salvinia with satellite imagery and image analysis

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer’s and user’s accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.     

Development of MODIS data-based algorithm for retrieving sea surface temperature in coastal waters

A new algorithm was developed for retrieving sea surface temperature (SST) in coastal waters using satellite remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua platform. The new SST algorithm was trained using the Artificial Neural Network (ANN) method and tested using 8 years of remote sensing data from MODIS Aqua sensor and in situ sensing data from the US coastal waters in Louisiana, Texas, Florida, California, and New Jersey. The ANN algorithm could be utilized to map SST in both deep offshore and particularly shallow nearshore waters at the high spatial resolution of 1 km, greatly expanding the coverage of remote sensing-based SST data from offshore waters to nearshore waters. Applications of the ANN algorithm require only the remotely sensed reflectance values from the two MODIS Aqua thermal bands 31 and 32 as input data. Application results indicated that the ANN algorithm was able to explaining 82–90% variations in observed SST in US coastal waters. While the algorithm is generally applicable to the retrieval of SST, it works best for nearshore waters where important coastal resources are located and existing algorithms are either not applicable or do not work well, making the new ANN-based SST algorithm unique and particularly useful to coastal resource management.     

Feature extraction using Hough transform for solid waste bin level detection and classification

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.     

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.     

基于高光谱分形分析的水稻镉污染动态监测

水稻中过量镉会影响叶绿素含量和细胞结构,进而改变水稻在光谱维上的特征表现.利用长春郊区实测大田水稻高光谱数据和同步测量的土壤、水稻生化参数,系统分析水稻冠层光谱曲线的分形特征及不同镉污染胁迫程度下光谱分维与两类光谱指数的变化情况,构建光谱分维、光谱指数与水稻叶片镉浓度的空间分布图,探讨光谱分维与光谱指数在提取镉污染胁迫等级信息中的相互关系及物理机制.研究表明,光谱分维相对于光谱指数能够更好地综合反映镉污染胁迫下水稻生理特征参数的变化,因而为有效地大范围动态监测水稻镉污染提供诊断依据.     

空间关联随机森林模型结合Sentinel-2影像估算潍北地区裸土期土壤盐分

土壤盐分含量(SSC)是评价土地退化和肥力水平的重要指标,实现SSC状态和空间分异的快速准确监测对区域环境的优化管理极为关键。选取潍北平原为研究区,野外采集233处土壤样品并获取同时相Sentinel-2多光谱影像,进一步将特征光谱波段和构建的最优光谱指数作为输入自变量,测试得到的SSC实测值为因变量,最后将空间关联函数引入到随机森林中去建立基于空间关联随机森林算法的SSC遥感估算模型,完成区域尺度上的SSC反演估算与空间制图。结果表明：影像的B3、B8和B11是SSC的特征波段,通过波段比值变换能够增强卫星光谱信号对SSC的吸收响应,筛选得到的最优光谱指数分别为RI₃₄(波段3和波段4的反射率比值)、RI₇₁₁(波段7和波段11的反射率比值)、ND₆₁₁(波段6和波段11的反射率归一化值)和D₄₅(波段4和波段5的反射率差值);仅用特征波段或最优光谱指数来构建模型不能取得满意的SSC估算精度,空间关联随机森林模型的SSC估算精度要高于随机森林模型;在将上述特征波段和最优光谱指数共同输入空间关联随机...     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.     

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.     

Shadow attenuation with high dynamic range images

Shadow often interferes with accurate image analysis. To mitigate shadow effects in near-earth imagery (2 m above ground level), we created high dynamic range (HDR) nadir images and used them to measure grassland ground cover. HDR composites were created by merging three differentially exposed images spanning a wide exposure range and resulted in lightened shadows. HDR images showed more detail; reduced the numbers of pure black, pure white, and pixels visually indistinguishable from black and white; reapportioned skewed luma values towards a normal distribution; and increased the Euclidean distance between litter and bare ground RGB values--allowing increased feature separation; all of which facilitated an increase in real feature classification through manual image analysis. Drawbacks to the method included decreased image sharpness due to minor misalignment of images or moving vegetation, time required to create HDR images, and difficulty with acquiring primary images from a moving platform. We conclude that HDR imagery can provide more accurate measurements of bare soil cover for ecosystem monitoring and assessment.     

Assessment of visual landscape quality using IKONOS imagery

The assessment of visual landscape quality is of importance to the management of urban woodlands. Satellite remote sensing may be used for this purpose as a substitute for traditional survey techniques that are both labour-intensive and time-consuming. This study examines the association between the quality of the perceived visual landscape in urban woodlands and texture measures extracted from IKONOS satellite data, which features 4-m spatial resolution and four spectral bands. The study was conducted in the woodlands of Istanbul (the most important element of urban mosaic) lying along both shores of the Bosporus Strait. The visual quality assessment applied in this study is based on the perceptual approach and was performed via a survey of expressed preferences. For this purpose, representative photographs of real scenery were used to elicit observers’ preferences. A slide show comprising 33 images was presented to a group of 153 volunteers (all undergraduate students), and they were asked to rate the visual quality of each on a 10-point scale (1 for very low visual quality, 10 for very high). Average visual quality scores were calculated for landscape. Texture measures were acquired using the two methods: pixel-based and object-based. Pixel-based texture measures were extracted from the first principle component (PC1) image. Object-based texture measures were extracted by using the original four bands. The association between image texture measures and perceived visual landscape quality was tested via Pearson’s correlation coefficient. The analysis found a strong linear association between image texture measures and visual quality. The highest correlation coefficient was calculated between standard deviation of gray levels (SDGL) (one of the pixel-based texture measures) and visual quality (r?=?0.82, P?<?0.05). The results showed that perceived visual quality of urban woodland landscapes can be estimated by using texture measures extracted from satellite data in combination with appropriate modelling techniques.     

Evaluating Change in Rangeland Condition using Multitemporal AVHRR Data and Geographic Information System Analysis

Coarse-scale, multitemporal satellite image data were evaluated as a tool for detecting variation in vegetation productivity, as a potential indicator of change in rangeland condition in the western U.S. The conterminous U.S. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data set was employed using the six-year time series 1989–1994. Normalized Difference Vegetation Index (NDVI) image bands for the state of New Mexico were imported into a Geographic Information System (GIS) for analysis with other spatial data sets. Averaged NDVI was calculated for each year, and a series of regression analyses were performed using one year as the baseline. Residuals from the regression line indicated 14 significant areas of NDVI change: two with lower NDVI, and 11 with higher NDVI. Rangeland management changes, cross-country military training activities, and increases in irrigated cropland were among the identified causes of change.     

Environmetric approaches for lake pollution assessment

The application of multivariate statistical methods to high mountain lake monitoring data has offered some important conclusions about the importance of environmetric approaches in lake water pollution assessment. Various methods like cluster analysis and principal components analysis were used for classification and projection of the data set from a large number of lakes from Rila Mountain in Bulgaria. Additionally, self-organizing maps of Kohonen were constructed in order to solve some classification tasks. An effort was made to relate the maps with the input data in order to detect classification patterns in the data set. Thus, discrimination chemical parameters for each pattern (cluster) identified were found, which enables better interpretation of the pollution situation. A methodology for application of a combination of different environmetric methods is suggested as a pathway to interpret high mountain lake water monitoring data.     

Multilayer perceptron neural network for flow prediction

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance.     

Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania,Romania

This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.     

湖库水生植被光学遥感监测研究进展和展望

水生植被在湖库生态系统中发挥稳定沉积物、净化水质、平衡水生生态系统等作用,监测水生植被变化对湖库生态环境的监测具有重要意义。梳理了国内外利用高光谱、多光谱光学卫星遥感数据提取湖库水生植被的方法,尤其是针对其中涉及的阈值确定问题进行总结分析,介绍了典型研究区水生植被时空分布和变化以及与水质的关系,最后给出一些水生植被遥感监测的展望。