Evaluating Hyperspectral Imager for the Coastal Ocean (HICO) data for seagrass mapping in Indian River Lagoon,FL

Differentiation between benthic habitats, particularly seagrass and macroalgae, using satellite data is complicated because of water column effects plus the presence of chlorophyll-a in both seagrass and algae that result in similar spectral patterns. Hyperspectral imager for the coastal ocean data over the Indian River Lagoon, Florida, USA, was used to develop two benthic classification models, Slope_RED and Slope_NIR. Their performance was compared with iterative self-organizing data analysis technique and spectral angle mapping classification methods. The slope models provided greater overall accuracies (63–64%) and were able to distinguish between seagrass and macroalgae substrates more accurately compared to the results obtained using the other classifications methods.     

基于实测光谱估测密云水库水体叶绿素a浓度

水中叶绿素a浓度是衡量水体初级生产力和富营养化程度的最基本的指标。利用野外便携式地物光谱仪对密云水库水体进行反射光谱测量并同步采集水样。通过分析叶绿素a浓度与光谱反射特征的相关关系,建立了叶绿素a反演模型。结果表明,利用单波段光谱反射率、光谱比值指数或微分光谱比值能够可靠反演叶绿素a浓度;但微分光谱与叶绿素a浓度相关性更高,更适用于密云水库水体叶绿素a浓度的高光谱反演。     

Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC)

The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm², similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.     

Noise-signal index threshold: a new noise-reduction technique for generation of reference spectra and efficient hyperspectral image classification

Reference spectra of terrestrial targets are usually collected using field spectro-radiometers for mineral abundance mapping and target detection. These spectra often have noise that masks characteristic absorption and reflection features and affects the efficiency of material mapping. This work aims at obtaining an empirical technique for reduction of high-frequency noise from field spectra. The proposed noise correction technique uses a ‘normalized’ measure R_n , where R_n  = (L_n  ? F_n )/L_n for each band (n) calculated from field and laboratory spectra of test material, with F_n and L_n being the depth of the absorption feature in field and laboratory spectra, respectively. On the basis of the assumption of the constancy of this ratio in neighbouring bands, an empirical algorithm that approximates the ratio R_n of a noisy band to the corrected ratio of an adjacent band is used to obtain the noise-corrected field spectra. The classification accuracy increases significantly when noise reduced field spectra are used as reference spectra.     

遥感技术在沿海滩涂提取中的应用

基于遥感技术,利用TM多光谱影像和SPOT全色影像,在遥感影像处理软件ERDAS 8.7中进行几何配准、IHS融合,采用监督分类的方法,对福州市长乐地区的沿海滩涂进行自动提取,并结合地理信息系统软件Arc-GIS DeskTop 8.3制作滩涂提取专题图。结果表明,遥感技术用于沿海滩涂监测,具有科学性和直观性,是传统方法无法比拟的,能更好地为滩涂管理相关部门的规划和管理提供实时、准确的依据。     

Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China

Abstract

Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean. In this study, we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China. A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses. Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater. Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness [20th band (R ²=0.92938, n=49, p<0.01), 26th band (R ²=0.93806, n=49, p<0.01), 29th band (R ²=0.93288, n=49, p<0.01)]. By using these statistical models, we successfully determined the normalized oil slick thickness with the Hyperion image. Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water. The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water. The high, stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion.     

An assessment of chlorophyll-a concentration spatio-temporal variation using Landsat satellite data,in a small tropical reservoir

Traditional approaches to monitoring aquatic systems are often limited by the need for data collection which often is time-consuming, expensive and non-continuous. The aim of the study was to map the spatio-temporal chlorophyll-a concentration changes in Malilangwe Reservoir, Zimbabwe as an indicator of phytoplankton biomass and trophic state when the reservoir was full (year 2000) and at its lowest capacity (year 2011), using readily available Landsat multispectral images. Medium-spatial resolution (30 m) Landsat multispectral Thematic Mapper TM 5 and ETM+ images for May to December 1999–2000 and 2010–2011 were used to derive chlorophyll-a concentrations. In situ measured chlorophyll-a and total suspended solids (TSS) concentrations for 2011 were employed to validate the Landsat chlorophyll-a and TSS estimates. The study results indicate that Landsat-derived chlorophyll-a and TSS estimates were comparable with field measurements. There was a considerable wet vs. dry season differences in total chlorophyll-a concentration, Secchi disc depth, TSS and turbidity within the reservoir. Using Permutational multivariate analyses of variance (PERMANOVA) analysis, there were significant differences (p < 0.0001) for chlorophyll-a concentration among sites, months and years whereas TSS was significant during the study months (p < 0.05). A strong positive significant correlation among both predicted TSS vs. chlorophyll-a and measured vs. predicted chlorophyll-a and TSS concentrations as well as an inverse relationship between reservoir chlorophyll-a concentrations and water level were found (p < 0.001 in all cases). In conclusion, total chlorophyll-a concentration in Malilangwe Reservoir was successfully derived from Landsat remote sensing data suggesting that the Landsat sensor is suitable for real-time monitoring over relatively short timescales and for small reservoirs. Satellite data can allow for surveying of chlorophyll-a concentration in aquatic ecosystems, thus, providing invaluable data in data scarce (limited on site ground measurements) environments.     

杭州湾HJ CCD影像悬浮泥沙遥感定量反演

利用环境小卫星CCD(HJ CCD)影像对杭州湾悬浮泥沙浓度(SSC)进行了反演研究。通过对杭州湾水体遥感反射率(Rrs)与SSC进行相关性分析发现,在690nm和830nm左右出现显著的反射峰,分别位于HJ CCD影像的第3和第4波段范围内;大于700nm波长处的Rrs与SSC相关性较好。基于实测Rrs和SSC之间的相关关系,利用第4和第3波段比值作为遥感因子建立SSC反演模型,模型决定系数达到0.90。借鉴近红外-短波红外(NIR-SWIR)结合的大气校正方法反演出的准同步MODIS气溶胶数据,实现了HJ CCD影像的大气校正,第3、第4波段的大气校正结果相对误差分别为5.54%和6.97%。结果显示,HJ CCD影像反演的SSC相对误差为7.12%;杭州湾悬浮泥沙浓度要显著高于长江口,且内部差异明显。研究表明,通过适当的大气校正方法和反演算法,HJ CCD影像可用于杭州湾悬浮泥沙浓度的估计。     

高光谱数据的降维及Tabu搜索算法的应用

在高光谱影像的分类过程中,如何有效地降低特征空间的维数,又能保证原始数据所包含的丰富地物信息是一项十分重要而繁琐的工作。深入分析了这种降维的必要性,并针对当前常用的降维方法存在的问题,提出了运用Tabu搜索算法获取对分类最为有利的特征波段的思想。考虑到高光谱数据的特点,指出了算法运行中应该注意的若干关键参数设置问题。实验表明,Tabu搜索算法在求解质量和执行效率方面都有着良好的表现,可以用于高光谱数据的降维处理。     

高光谱数据的降维及Tabu搜索算法的应用

在高光谱影像的分类过程中,如何有效地降低特征空间的维数,又能保证原始数据所包含的丰富地物信息是一项十分重要而繁琐的工作.深入分析了这种降维的必要性,并针对当前常用的降维方法存在的问题,提出了运用Tabu搜索算法获取对分类最为有利的特征波段的思想.考虑到高光谱数据的特点,指出了算法运行中应该注意的若干关键参数设置问题.实验表明,Tabu搜索算法在求解质量和执行效率方面都有着良好的表现,可以用于高光谱数据的降维处理.     

Application of Aqua MODIS sensor data for estimating chlorophyll a in the turbid Case 2 waters of Lake Erie using bio-optical models

There is considerable interest in accurately estimating water quality parameters in turbid (Case 2) and eutrophic waters such as the Western Basin of Lake Erie (WBLE). Lake Erie is a large, open freshwater body that supports diverse ecosystem, and over 12 million people in the mid-western part of the United States depend on it for drinking water, fisheries, navigational, and recreational purposes. The increasing utilization of the freshwater has deteriorated the water severely and currently the lake is experiencing recurring harmful algal blooms (HABs). Improving the water quality of Lake Erie requires the use of robust monitoring tools that help water quality managers understand sources and pathways of influxes that trigger HABs. Satellite-based remote sensing sensor such as the moderate resolution imaging spectroradiometer (MODIS) may provide frequent and synoptic view of the water quality indices. In this study, data set from field measurements was used to evaluate the performance of 14 existing ocean color algorithms. Results indicated that MODIS data consistently underestimated the chlorophyll a concentrations in the WBLE, with the largest source of errors from dissolved organic matter and xanthophyll accessory pigments in this data set. Most of the global algorithms, including OC4v4 and the Baltic model, generated near-identical statistical parameters with an average R² of ~0.57 and RMSE ~2.9 μg/l. MODIS performed poorly (R² ~0.18) when its NIR/red bands were used. A slightly improved model was developed using similar band ratio approach generating R² of ~0.62 and RMSE ~1.8 μg/l.     

Mapping waterholes and testing for aridity using a remote sensing water index in a southern African semi-arid wildlife area

Abstract

Waterholes are a key resource that influences wildlife distribution in semi-arid ecosystems. Mapping waterholes can guide intervening decisions for supplementing water resources and managing wildlife distribution patterns. Although remote sensing provides a key to mapping distribution of waterholes, efficiency of existing remotely sensed methods for detecting waterholes have to be evaluated and even new ones developed. In this study, we evaluated performance of the Modified Normalized Difference Water Index (MNDWI) and Superfine Water Index (SWI) at selected optimum thresholds. Kappa results indicated that MNDWI detects waterholes better than SWI. We further validated MNDWI detected waterholes by testing response of waterhole area to temporal rainfall variability and waterhole persistence to spatial rainfall variability. Extent of MNDWI-detected waterholes varied in relation to temporal rainfall variability (p < 0.05). Waterhole persistence was not associated with spatial rainfall variability which could be explained by differences in waterhole types or low spatial rainfall variability.     

Mapping of bauxite mineral deposits in the northern region of Saudi Arabia by using Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data

Mineral deposit mapping is very essential for sustainable and eco-friendly exploitation of natural resources. The Kingdom of Saudi Arabia has abundant natural resources such as natural gas, oil and minerals. It reserves high quantity of minerals such as phosphates, bauxites, copper, gold and other industrial minerals. The red soil regions located in Hail and Qassim provinces of Saudi Arabia have rich amount of bauxite (major aluminum ore) deposits. In order to initiate the focus on mapping of mineral deposits along this area, standardized hyper-spectral analysis has been carried out by using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data. The spectral signature of gibbsite (major element in bauxite) samples is analyzed with reference to the spectral features of gibbsite in the visible near infrared and short-wave infrared bands electromagnetic spectrum. Advanced hyper-spectral transformations such as minimum noise fraction function and pixel purity index have been performed to identify the target end-member. The existence of the mineral is confirmed by comparing the spectral signatures of the end-member with the predefined spectral plots of ASTER and United States Geological Survey spectral libraries. Finally, the end-members are mapped and their abundance is estimated in 0–1 scale. The study has opened up new areas for mapping of bauxite deposits in the area and leads to eco-friendly exploitation of natural resources. It also validates the high potential of ASTER multispectral satellite data for the exploration and mapping of mineral resources.     

Model selection in Bayesian framework to identify the best WorldView-2 based vegetation index in predicting green biomass of salt marshes in the northern Gulf of Mexico

Coastal wetlands are among the most productive ecosystems globally but have experienced dramatic degradation and loss within the past several decades. Vegetation biomass of coastal wetlands is not only the key component of blue carbon storage but also plays an important role in vertical accretion, important for maintaining these habitats under relative sea-level rise. Remote sensing offers a cost-effective approach to study vegetation biomass at a broad spatial scale. We developed statistical models to predict peak aboveground green biomass of Spartina alterniflora and Juncus roemerianus, two dominant species of salt marshes using WorldView-2 satellite imagery at the Grand Bay National Estuarine Research Reserve (NERR) on the Mississippi coast in the northern Gulf of Mexico. The model accounted for nested data structures in the sampled biomass, assimilated uncertainties from data, parameters and model structures, and helped determine the best vegetation index among a variety of commonly-used indices to predict aboveground green biomass. We developed a series of mixed-effects models, which included different combinations of fixed effect(s), random intercept, and random slope(s). The fixed effects were species and one of the 60 vegetation indices derived from a WorldView-2 image obtained on 6 October 2012. The random effect used was site. We implemented the models in a Bayesian framework and selected the best model structure and vegetation index based on minimum posterior predictive loss and deviance information criterion. The results showed that the best vegetation index to predict peak green biomass was the green chlorophyll index derived from the reflectance values of band 8 (near-infrared) and band 3 (green), and its effect on biomass prediction varied among sites. The inclusion of species as a fixed effect improved the model prediction. The study demonstrated the need to account for spatial dependence of data in developing a robust model, and the importance of the second WorldView-2 near-infrared band (860–1040 nm) in predicting aboveground green biomass for the Grand Bay NERR. The analysis using mixed-effects modeling in Bayesian inference which coherently combined field and WorldView-2 data with uncertainties accounted for provides a robust and nondestructive tool for resource managers to monitor the status of coastal wetlands at a high spatial resolution in a timely manner. Through this study, we hope to emphasize the importance of appropriately accounting for nested data structures using mixed-effects models and promote wider application of Bayesian inference to facilitate assimilation of uncertainties in remote sensing applications.     

Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region

Using NOAA/AVHRR 10-day composite NDVI data and 10-day meteorological data, including air temperature, precipitation, vapor pressure, wind velocity and sunshine duration, at 19 weather stations in the three-river-source region in the Qinghai–Tibetan Plateau in China from 1982 to 2000, the variations of NDVI and climate factors were analyzed for the purpose of studying the correlation between climate change and vegetation growth as represented by NDVI in this region. Results showed that the NDVI values in this region gradually grew from the west to the east, and the distribution was consistent with that of moisture status. The growing season came earlier due to climate warming, yet because of the reduction of precipitation, maximal NDVI during 1982–2000 did not show a significant change. NDVI related positively to air temperature, vapor pressure and precipitation, but negatively related to sunshine duration and wind velocity. Furthermore, the response of NDVI to climate change showed time lags for different climate factors. Water condition and temperature were found to be the most important factors effecting the variation of NDVI during the growing season in both the semi-arid and the semi-humid areas. In addition, NDVI had a better correlation with vapor pressure than with precipitation. The ratio of precipitation to evapotranspiration, representing water gain and loss, can be regarded as a comprehensive index to analyze NDVI and climate change, especially in areas where the water condition plays a dominant role.     

海水背景下不同浓度的甲烷含量高光谱定量反演

在油气资源遥感探测中,通过烃渗漏引起的海表面甲烷气浓度异常来探测海底气藏是最直接的方法之一。为了更好地识别海表甲烷异常,提高遥感反演精度,对海表甲烷气含量进行定量光谱分析研究。设计室内甲烷波谱测试平台,获取海水背景下不同含量甲烷高光谱数据为数据,对光谱数据预处理及进行比值导数光谱法,并提取光谱吸收特征参数,对甲烷含量与光谱参数之间进行相关性分析,构建甲烷含量的反演模型。比值导数光谱法确实抑制了海水背景信息,突出了甲烷特征。1650—1664 nm和2180—2210 nm波段范围的光谱参数与甲烷含量相关性显著;其中,波谷、波深、面积、斜率与甲烷含量显著相关。基于2180—2210 nm波段范围建立的波谷、波深、面积、斜率四元回归方程y=-14.356 - 5931.796x₁ - 4325.081x₂+241.481x₃+7531.973x₄拟合效果最好,R²为0.9817;且在此波段范围内基于波深建立的单变量甲烷反演模型y = 2047.571x - 9.758,R²为0.9741,比基于其他变量所建立的反演模型效果要好。成功获取了和海水背景下甲烷含量线性相关显著的对应波段和吸收特征,可为利用多/高光谱遥感预测勘探海表面甲烷气浓度提供一定的理论和技术依据。     

印度洋海啸灾害遥感监测与评估——以印度尼西亚亚齐省为例

介绍了运用新一代中分辨率小卫星数据———英国灾害监测小卫星(DMC,DisasterMonitoringConstellation)数据源,以印度尼西亚亚齐省为例,采用遥感数据作为信息源,对2004-12-26印尼苏门答腊岛西北海域发生的里氏9·0级的强烈地震所引发的印度洋海啸灾害进行了监测评估。所获得的小卫星图像预处理后,通过对图像中受损失地物的光谱信息分析、受损失信息与环境背景信息的对比分析,进而建立其判读标志;并在此基础上进行损失程度分级判读、统计分析等,实现了从小卫星图像上对海啸灾情损失的遥感快速监测评估。其监测评估结果为中国开展国际援助提供了客观依据,其技术方法为海啸灾害及其它灾害的遥感快速监测评价提供了技术思路,也为中国即将发射运行的灾害和环境监测预报小卫星星座及中国DMC小卫星的应用提供必要的经验及技术支持。     

曹妃甸近海表层悬浮泥沙遥感定量监测研究

在曹妃甸近海水体实测光谱数据分析的基础上,与同步采集的水体悬浮泥沙浓度进行相关分析,选用合适的TM数据的遥感变量参数,根据2008年实测的四个剖面29个站点的海洋水色数据建立了水体悬浮泥沙浓度与TM数据对应的遥感定量反演模型,并用18个独立样本点进行了精度评价.研究结果表明:曹妃甸近海水体光谱曲线基本呈现单峰现象,敏感...     

Spatial variations of small phytoplankton contributions in the Northern Bering Sea and the Southern Chukchi Sea

Phytoplankton size classes (hereafter, PSCs) were derived from satellite ocean color data using a present phytoplankton abundance-based optical algorithm in the northern Bering and southern Chukchi Seas to characterize the spatial and seasonal variations of the different PSC and investigate the contributions of small phytoplankton to the total phytoplankton biomass. The comparison results showed that the phytoplankton abundance-based method approach could reasonably classify the three PSCs (pico-, nano-, and micro phytoplankton). The satellite maps of the dominant PSCs were derived using long-term satellite ocean color data. The general spatial distribution showed that the large (micro-) phytoplankton were dominant in the coastal waters and the west side of the Bering strait, while the small size (nano- or pico-) phytoplankton were dominant in the open ocean waters. Nano- and microphytoplankton were dominant in May and October in most of the study area, while pico-phytoplankton were dominant in the summer months in the open ocean waters. The annual variation in small phytoplankton dominance had a strong positive relationship with the annual mean sea surface temperature (SST), which is consistent with the increasing dominance of small phytoplankton biomass as water temperature increases. Microphytoplankton have an apparent increasing trend in the southeastern Chukchi Sea but slightly decreasing trends in Chirikov and St. Lawrence Island Polynya (SLIP). In contrast, there were increasing trends in picophytoplankton in Chirikov and SLIP, which seems to be related to increasing annual SST. It is crucial to monitor changes in dominant groups of phytoplankton community in the Bering and Chukchi Seas as important biological hotspots responding to the recent changes in environmental conditions.     

Analysis of elevation changes in relation to surface characteristics for six glaciers in Northern Labrador,Canada using advanced space-borne thermal emission and reflection radiometer imagery

Advanced space-borne thermal emission and reflection radiometer imagery and Digital Elevation Models were used to analyse surface elevation changes of six glaciers in Northern Labrador. Results indicate an average surface thinning of0.94 ± 0.49 m y^?1 (water equivalent) between 2000 and 2009. Three glaciers had an average elevation change of ?1.16 ± 0.55 m y^?1 (water equivalent) whichis three times the thinning rate found in a study from 1981 to 1983 ?0.36 ± 0.10 m y^?1 water equivalent). Analysis of surface characteristics in relation to elevation changes shows expected results of rapid thinning in bare ice areas and near zero change in accumulation areas. Debris covered areas of three glaciers show expected results of moderate thinning, but three other glaciers indicate high rates of thinning. Variability in thinning rates suggests possible influences in the type ofdebris and/or variations in climate such as increased rainfall.