MERIS satellite chlorophyll mapping of oligotrophic and eutrophic waters in the Laurentian Great Lakes

Chlorophyll-a (Chla) concentrations and ‘water-leaving’ reflectance were assessed along transects in Keweenaw Bay (Lake Superior) and in Green Bay (Lake Michigan) (two of the Laurentian Great Lakes, USA), featuring oligotrophic (0.4–0.8 mg Chla m^? 3) and eutrophic to hyper-eutrophic waters (11–131 mg Chla m^? 3), respectively. A red-to-NIR band Chla retrieval algorithm proved to be applicable to Green Bay, but gave mostly negative values for Keweenaw Bay. An alternative algorithm could be based on Chla fluorescence, which in Keweenaw Bay was indicated by enhanced reflectance near 680 nm. Bands 7, 8 and 9 of the Medium Resolution Imaging Spectrometer (MERIS) have been specifically designed to detect phytoplankton fluorescence in coastal waters. A quite strong linear relationship was found between Chla concentration and fluorescence line height (FLH) computed with these MERIS bands. The same relationship held for observations on oligotrophic waters elsewhere, but not for Green Bay, where the FLH diminished to become negative as Chla increased. The remote sensing application of the algorithms could be tested because a MERIS scene was acquired coinciding with the day of the field observations in Keweenaw Bay and one day after those in Green Bay. For Green Bay the pixel values from the red-to-NIR band algorithm compared well to the steep Chla gradient in situ. This result is very positive from the perspective of satellite use in monitoring eutrophic inland and coastal waters in many parts of the world. Implementation of the FLH relationship in the scene of Keweenaw Bay produced highly variable pixel values. The FLH in oligotrophic inland waters like Lake Superior appears to be very close to or below the MERIS detection limit. An empirical algorithm incorporating three MERIS bands in the blue-to-green spectral region might be used as an alternative, but its applicability to other regions and seasons remains to be verified. Moreover, none of the algorithms will be suitable for mesotrophic water bodies. The results indicate that Chla mapping in oligotrophic and mesotrophic areas of the Great Lakes remains problematic for the current generation of satellite sensors.     

Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes

A change in the spectral shape at 681 nm is used to distinguish blooms of cyanobacteria from blooms of other phytoplankton via MERIS satellite sensor imagery. During large cyanobacterial blooms, the spectral shape around 681 nm is not a positive quantity as scattering due to cyanobacteria overwhelms the fluorescence signal, thus creating a negative spectral shape. This relationship is consistent in both remotely sensed and in situ data.     

Reconciling the global terrestrial water budget using satellite remote sensing

Recent retrievals of multiple satellite products for each component of the terrestrial water cycle provide an opportunity to estimate the water budget globally. In this study, we estimate the water budget from satellite remote sensing over ten global river basins for 2003-2006. We use several satellite and non-satellite precipitation (P) and evapo-transpiration (ET) products in this study. The satellite precipitation products are the GPCP, TRMM, CMORPH and PERSIANN. For ET, we use four products generated from three retrieval models (Penman-Monteith (PM), Priestley-Taylor (PT) and the Surface Energy Balance System (SEBS)) with data inputs from the Earth Observing System (EOS) or the International Satellite Cloud Climatology Project (ISCCP) products. GPCP precipitation and PM (ISCCP) ET have less bias and errors over most of the river basins. To estimate the total water budget from satellite data for each basin, we generate merged products for P and ET by combining the four P and four ET products using weighted values based on their errors with respect to non-satellite merged product. The water storage change component is taken from GRACE satellite data, which are used directly with a single pre-specified error value. In the absence of satellite retrievals of river discharge, we use in-situ gauge measurements. Closure of the water budget over the river basins from the combined satellite and in-situ discharge products is not achievable with errors of the order of 5-25% of mean annual precipitation. A constrained ensemble Kalman filter is used to close the water budget and provide a constrained best-estimate of the water budget. The non-closure error from each water budget component is estimated and it is found that the merged satellite precipitation product carries most of the non-closure error.     

Technique of large-scale soil mapping in basaltic terrain using satellite remote sensing data

The present paper discusses the technique of large-scale soil mapping using remote sensing data. IRS-1C PAN merged data of two seasons, namely late Kharif (monsoon) and Rabi (post rainy season), were interpreted visually in conjunction with Survey of India (SOI) toposheet (1:50?000 scale) and available ground data to prepare the physiography–land use (PLU) map. The PLU delineation explained a three-tier approach comprising landform, slope and land use characteristics of a given parcel of land. The first letter in the PLU legend indicates the landform, the second letter denotes slope percentage and the third letter indicates the land use characteristics. Soils occurring in different PLU units were examined and a PLU–soil relationship was developed. The soil map depicting phases of soil series was prepared using ILWIS software.     

Measuring morphological polycentricity - A comparative analysis of urban mass concentrations using remote sensing data

Polycentricity belongs to the most versatile and fuzzy concepts in urban geography. It basically points to the existence of more than one center within a conurbation. Previous studies have mostly referred to the spatial distribution of employment density for (sub-) center identification. In contrast, our study draws on large area 3D building models derived from ubiquitous remote sensing data. We use stereoscopic Cartosat-1 digital surface models in combination with building footprints. These geoinformation reflect the spatial configuration of the built dimension and allow a physical approach to the concept of polycentricity. For (sub-) center identification we thoroughly analyze conceptually different kinds of threshold approaches (global, region-specific and distance-based) applied to concentrations of urban masses. After evaluating the advantages and disadvantages of the threshold approaches applied, we combine these methods to overcome their individual shortcomings. Last but not least, we establish a framework consisting of mapping techniques and site- and non-site specific statistics to evaluate polycentricity at fine-grained spatial intra-urban scale. In general we find that urban mass concentrations are a reasonable proxy for commonly used employment density data. We address the polycentricity issue across four German city regions—Frankfurt, Cologne, Stuttgart and Munich—and we find all of them to still be morphologically dominated by their core cities. Nevertheless, our analysis reveals striking differences of the urban spatial structure highlighting a rather monocentric pattern in the Munich region on the one hand, and a polycentric-dispersed distribution of urban mass concentrations in the Stuttgart region on the other hand.     

Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing

Various remote sensing products are used to identify spatial-temporal trends in snow cover in river basins originating in the Himalayas and adjacent Tibetan-Qinghai plateau. It is shown that remote sensing allows detection of spatial-temporal patterns of snow cover across large areas in inaccessible terrain, providing useful information on a critical component of the hydrological cycle. Results show large variation in snow cover between years while an increasing trend from west to east is observed. Of all river basins the Indus basin is, for its water resources, most dependent on snow and ice melt and large parts are snow covered for prolonged periods of the year. A significant negative winter snow cover trend was identified for the upper Indus basin. For this basin a hydrological model is used and forced with remotely sensed derived precipitation and snow cover. The model is calibrated using daily discharges from 2000 to 2005 and stream flow in the upper Indus basin can be predicted with a high degree of accuracy. From the analysis it is concluded that there are indications that regional warming is affecting the hydrology of the upper Indus basin due to accelerated glacial melting during the simulation period. This warming may be associated with global changes in air temperature resulting from anthropogenic forcings. This conclusion is primarily based on the observation that the average annual precipitation over a five year period is less than the observed stream flow and supported by positive temperature trends in all seasons.     

Quantifying light-fishing for Dosidicus gigas in the eastern Pacific using satellite remote sensing

The distribution and abundance of the fleet targeting Jumbo flying squid (Dosidicus gigas) in the Eastern Pacific is examined during the 1999 fishery season. The commercial fishery consists of a multinational jigging fleet, which fish at night using powerful lights to attract squid. The emission of light from these vessels can be observed using satellite-derived imagery obtained by the United States Defence Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). In order to quantify fishing effort using lights, data on the distribution and abundance of vessels were obtained via satellite tracking using the ARGOS system. The distribution of the fishery as derived from light signatures was found to closely resemble that derived from ship location data. By using ARGOS data to calibrate DMSP-OLS images, we are able to estimate fishing effort in terms of the ‘area illuminated’ by the fishing fleet. Light signatures derived from DMSP-OLS were successfully used to quantify fishing effort, estimating the number of vessels fishing to within ±2 in 85 out of 103 satellite images (83%). High seas fishing was also quantified, with light signatures corresponding to a single fishing vessel observed in 11 out of 103 satellite passes during the fishery season (July-December 1999). This study examines how much light (in terms of area) is emitted by a single squid fishing vessel, and may prove to be a valuable tool in assessing and policing fisheries using satellite remote sensing.     

Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis

Monitoring of coral reef bleaching has hitherto been based on regional-scale, in situ data. Larger-scale trends, however, must be determined using satellite-based observations. Using both a radiative transfer simulation and an analysis of multitemporal Landsat TM images, the ability of satellite remote sensing to detect and monitor coral reef bleaching is examined. The radiative transfer simulation indicates that the blue and green bands of Landsat TM can detect bleaching if at least 23% of the coral surface in a pixel has been bleached, assuming a Landsat TM pixel with a resolution of 30×30 m on shallow (less than 3 m deep) reef flats at Ishigaki Island, Japan. Assuming an area with an initial coral coverage of 100% and in which all corals became completely bleached, the bleaching could be detected at a depth of up to 17 m. The difference in reflectance of shallow sand and corals is compared by examining multitemporal Landsat TM images at Ishigaki Island, after normalizing for variations in atmospheric conditions, incident light, water depth, and the sensor's reaction to the radiance received. After the normalization, a severe bleaching event when 25-55% of coral coverage was bleached was detected, but a slight bleaching event when 15% of coral coverage was bleached was not detected. The simulation and data analysis agreed well with each other, and identified reliable limits for satellite remote sensing for detecting coral reef bleaching. Sensitivity analysis on solar zenith angle, aerosol (visibility) and water quality (Chl a concentration) quantified the effect of these factors on bleaching detection, and thus served as general guidelines for detecting coral reef bleaching. Spatial misregistration resulted in a high degree of uncertainty in the detection of changes at the edges of coral patches mainly because of the low (∼30 m) spatial resolution of Landsat TM, indicating that detection of coral reef bleaching by Landsat TM is limited to extremely severe cases on a large homogeneous coral patch and shallow water depths. Satellite remote sensing of coral reef bleaching should be encouraged, however, because the development and deployment of advanced satellite sensors with high spatial resolution continue to progress.     

Landscape evolution in the Yellow River Basin using satellite remote sensing and GIS during the past decade

Over the past decade, rapid landscape pattern change has taken place in many arid and semi-arid regions of China, such as the Yellow River Basin. In this paper, landscape evolution was investigated by the combined use of satellite remote sensing, geographic information system (GIS) and landscape modelling technologies. The aim was to improve our understanding of landscape changes so that sustainable land use could be established. First, the changes in various landscape metrics were analysed using the landscape structure analysis programme. Second, the mathematical methodology was explored and developed for landscape pattern change, which included: the status and trends change model for individual landscape types, the 1-km² area percentage data model and the transition matrix of landscape types. The results show that the area of the Yellow River Basin was about 794 000 km² during the period from 1990 to 2000; cropland, built-up land and unused land expanded significantly whereas woodland, grassland and water bodies contracted substantially. The area of cropland increased dramatically by 2817 km², and the areas of grassland and woodland decreased by 4669 and 33 km², respectively. Meanwhile, the landscape pattern in the study area also experienced numerous changes over the past decade. The major factors that caused the landscape changes in this area over the past decade were found to be governmental policies for environmental protection, population growth, and meteorological and environmental conditions.     

High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests

Satellite imagery with fine spectral and spatial resolution and high temporal resolution provides a unique opportunity to map and monitor biodiversity. The aim of this article is (i) to review the state of the use of high-resolution remote sensing for ecological studies in the Indian Himalayan Region (IHR) and (ii) to suggest further potential avenues of research in the region using this technology. It has been recognized that the recent improvements in remote sensing have enabled researchers to categorize and spatially map species and communities and to detect vegetation types based on ecological gradients and environmental drivers. Across the globe, the use of current high-resolution imagery in vegetation studies is increasing due to their improved efficiency, as fine-scale vegetation information is needed for both theoretical understanding of the processes involved and conservation towards the maintenance of ecological functions of natural ecosystems. Remote sensing is particularly useful in the IHR, where field-based mapping of forest vegetation and land cover using conventional techniques is a cost- and time-intensive effort. Mapping fine-scale patterns of tree species using high-resolution remote-sensing data has important implications for the understanding and monitoring of ecosystem function and biodiversity patterns in forests.     

Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.     

Multispectral satellite remote sensing of an oceanic cyclone in the northwestern Mediterranean

Radiance data in the visible waveband from the Nimbus-7 Coastal Zone Color Scanner (CZCS) has enabled us to identify mesoscale activity in the Gulf of Lions in 1979. After correction for atmospheric effects, chlorophyll-like pigment concentrations were derived from subsurface upwelling radiances by a previously tested algorithm. The mapping of oceanic phytoplankton distribution revealed a cyclonic eddy offshore from Marseilles, on 13 April 1979. It was centred at 42°52'N-5° 12'E and had a diameter of approximately 50 km. Our results seem to confirm the results of numerous hydrological cruises carried out in this area.     

可逆数据隐藏算法在卫星遥感图像上的应用

针对卫星遥感图像受环境因素影响导致图像对比度下降,以及现有可逆信息隐藏算法嵌入容量低等问题,提出一种新的可逆数据隐藏算法,实现在遥感图像安全传输的同时增强图像的对比度.算法为了提高嵌入率,改进已有算法的嵌入方式,同时在信息嵌入的过程中,逐步增强卫星遥感图像的对比度.实验结果表明:提出的算法和一些典型可逆数据隐藏.     

Influence of atmospheric correction on the estimation of biophysical parameters of crop canopy using satellite remote sensing

A quantitative approach has been made for the estimation of biophysical parameters of a vegetation canopy by the inversion of a vegetation canopy reflectance model. Model inversion has been done using a non-linear optimization scheme against directional reflectance data over the canopy. A quasi-Newton algorithm has been employed that searches the minimum of a function iteratively using the functional values only. The technique provides a reasonably good estimation of the biophysical parameters. A study has been conducted to quantify the error related to the estimation of biophysical parameters of vegetation with simulated satellite data corrected with improper values of atmospheric aerosol and water vapour contents. In the visible, atmospheric correction of satellite data with improper values of atmospheric aerosol content results in a modification of the amplitude and angular pattern of the directional reflectance for both low-density and high-density vegetation canopies. However, in the near-infrared, the atmospheric correction of data with improper values of aerosol and water vapour contents changes the amplitude of directional reflectance, but, no significant changes in angular pattern are noticed. This study indicates that parameter estimation can be significantly influenced by using improper values of both aerosol and water vapour contents during data correction in the visible and near-infrared regions of the solar spectrum. The estimation accuracy is higher for a low-density canopy than for a dense vegetation canopy. Retrievals of all the surface parameters are not equally affected by such improper atmospheric correction of data. Particularly, estimations of soil reflectance and leaf area index are significantly influenced by such improper correction for a high-density vegetation canopy. However, the accuracy of the retrieved parameter values is higher in the near-infrared than in the visible for both high-density and low-density canopies.     

高分辨率遥感图像投影分析的靠岸舰船检测

目的 高分辨率遥感图像中,靠岸舰船检测有着广泛的应用前景,其主要难点在于舰船与港口陆地在空间上紧邻,在颜色和纹理特征上相似,舰船与港口陆地难以分割。针对这种情况,利用港口岸线平直的几何特点和靠岸舰船多为舷靠的停泊特点,提出一种基于投影分析的靠岸舰船检测方法。方法 首先,对原始图像进行预处理,利用K-means聚类算法与区域生长算法相结合的方式得到海陆分割图像,利用Sobel算子与Otsu分割结合的方式获取边缘图像;然后,通过改进的Hough变换提取直线特征,结合港岸几何特性定位港口岸线;再将海陆分割后的二值图像向沿岸线和垂直岸线两个方向进行投影,根据沿岸线方向投影形态确定和分离并靠舰船,根据垂直岸线方向的投影形态定位舰船目标;最后,利用舰船尺寸、长宽比、最小外接矩形占空比特征去除虚警。结果 在15个港口场景不同分辨率的遥感图像测试集上,本文方法整体检测率达到85.4%,虚警率达17.2%;限定分辨率范围在24 m的情形下,检测率提高到93.5%,虚警率降低至5.3%。结论 本文方法简单有效,无需港口先验信息,适用于多尺度和多方向的靠岸舰船目标检测任务,对不同类型舰船形态差异具有鲁棒性,且能够分离并靠舰船。     

Use of satellite remote sensing in Malaysian forestry and its potential

The use of satellite remote sensing in Malaysian forestry and its potential are discussed under three headings (1) Application of satellite remote sensing in Malaysian forestry; (2) Current efforts in remote sensing research application; (3) Potentials of remote sensing techniques in monitoring logging operations and forest change; and remote sensing as a tool in rehabilitation and reforestation. It is concluded that there is a high potential of satellite remote sensing application in Malaysia, especially with the Landsat and SPOT data supported with aerial photographs. This is due to its fast delivery of relevant, timely and accurate information needed for sustainable forestry and a sound management decision.     

High-precision extraction of nearshore green tides using satellite remote sensing data of the Yellow Sea,China

Since 2007, a massive floating green tide has occurred repeatedly in the Yellow Sea. The work involved in monitoring and clearing such a phenomenon requires highly precise spatial information. Satellite remote sensing data can provide such detail but they can be limited by long revisit periods and small-scale observational coverage. Therefore, in operational monitoring of green tides, Moderate Resolution Imaging Spectroradiometer (MODIS) data have an important role because of their short revisit period (twice daily) and large-scale observational coverage (viewing swath width: 2330 km). However, errors exist in MODIS-derived areal detection of green tides because its coarse spatial resolution (250–1000 m) can lead to mixed-pixel problems (i.e. in many cases, the limit of spatial resolution results in single pixels covering multiple ground objects). In this study, a method for spectral unmixing was developed for remote green tide detection. This method is based on mathematical morphology, and it integrates both the spatial and spectral characteristics of the image in the process of endmember extraction. Experiments using simulated and actual MODIS images were undertaken to illustrate the advantages of the proposed method. Compared with traditional linear unmixing methods, the abundance and area of green tides extracted by the proposed method were closer to the ‘real’ condition. The average relative deviation of green tide area decreased from 84.0% to 21.0%. The results illustrate that the proposed method is able to work efficiently and that it can provide improved information on green tide abundance and area.     

Optical monitoring of phytoplankton blooms in Loch Striven,a eutrophic fjord

During May 1991, a mooring equipped with a blue-green colour sensor and two fluorometers was deployed in Loch Striven, Scotland. The colour sensor recorded the ratio of downwelling irradiances at 440 and 524 nm, which is strongly influenced by phytoplankton abundance. The fluorometers recorded the red fluorescent response of phytoplankton pigments to a blue flash. Profiles of water properties were made almost daily near the mooring, in order to assess the reliability of automatic bio-optical measurements of phytoplankton abundance. Each type of instrument had limitations, but all were able to estimate chlorophyll concentration with a precision of 10-14% over the observed ranges of 1 to 21 or 33 mg m ³, giving an r² (between the bio-optical signal and extracted chlorophyll) of about 0.8. There were large day-to-day, and, sometimes, hour-to-hour changes in amount of phytoplankton. These were largely the result of (non-tidal) water movements: biological changes were slow. Implications for the monitoring of phytoplankton blooms in coastal waters are discussed.     

Remote sensing of algal blooms by aircraft and satellite in Lake Erie and Utah Lake

During late summer, when the surface waters of Lake Erie reach their maximum temperature, an algal bloom is likely to develop. Such phenomena, which characterize eutrophic conditions, have been noticed on other shallow lakes using the Earth Resources Technology Satellite (ERTS-1). The concentration of the algae into long streamers provides additional information on surface circulations. To augment the ERTS Multispectral Scanner Subsystem (MSS) data of Lake Erie an aircraft was used to obtain correlative thermal-IR and additional multiband photographs. The algal bloom is highly absorptive in the visible wavelengths but reverses contrast with the surrounding water in the near-IR bands. The absorption of shortwave energy heats the dark brown algal mass, providing a hot surface target for the thermal-IR scanner. A large bloom of Aphanizomenon flos-aquae observed in Utah Lake together with recent bloom history in Lake Erie is used to verify the Great Lakes bloom.     

遥感技术在全国第一次河流湖泊普查中的应用

河流湖泊是水体环境的主要形式,是生活用水、生产用水、生态用水的重要来源,对社会经济的发展起着至关重要的作用.遥感技术为全国第一次河湖普查提供了重要的技术支撑,本次河流湖泊普查涉及到的遥感影像数据为中巴地球资源卫星(CBERS)数据及2.5m高空间分辨率影像.为全面查清我国湖泊数量、分布等基本情况,本文对全国第一次河湖清查阶段中利用CBERS数据进行湖泊对象清查进行了介绍;并着重讨论了ENVI\IDL环境下CBERS数据批处理模式的建立及应用,为同类CBERS数据的大范围、大批量数据应用提供一定的参考和依据;同时,全国1:5万分幅2.5m高空间分辨率影像作为重要的河流水系现势性检查数据,本文对利用此数据进行1:5万DLG数字水系的合理性检查进行了探讨,并利用ENVI\IDL实现了全国水系的自动化检查;作为水系复核工作中的有效工具,增强了河流水系提取的现势性,提高了河流水系的精度,实现了水系提取数据精度源于并高于1:5万DLG数字水系的目的.