Estimation of chlorophyll‐a concentration in Lake Tai,China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R ₆₆₇), chl‐a concentration was not accurately estimated at R ² = 0.494. Accuracy of estimation was improved to R ² = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R ₇₁₉) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R ² = 0.868 using R ₇₁₉/R ₆₆₇.     

Investigation of chlorophyll‐a and total suspended matter concentrations using Landsat ETM and field spectral measurement in Taihu Lake,China

The field spectral reflectance was measured with a spectroradiometer in Taihu Lake of China, and the water samples were collected in situ and analysed in the lab. The water quality results are correlated with the reflectivity to find out the spectral characteristics and evaluated for estimating chlorophyll‐a (CHL) and total suspended matter (TSM), on the basis of which the most appropriate single and combined bands with different pixel window sizes are selected from Landsat sensor to develop estimation models. And then the two best precise models, one with ETM3 dividing ETM1 and the other with ETM4 dividing ETM1, are used respectively to approximate the CHL and TSM concentrations. Additionally, the pixel window size has an effect on the precision to estimate the water quality parameters, 7×7 or 5×5 is mostly suitable to estimate CHL concentration whereas 3×3 or less is more suitable for estimating TSM concentration.     

Land degradation monitoring using multi‐temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China

Land degradation is one of the most pressing problems of environments. This research presents a methodology to monitor land degradation in a transition zone between grassland and cropland of northeast China, where soil salinization and grassland degradation, even desertification, have been observed in the past few decades. Landsat TM/ETM data in 1988, 1996 and 2001 were selected to determine the rate and status of grassland degradation and soil salinization together based on both decision tree (DT) classifier and the field investigation. The thermal radiance values of TM/ETM 6 data, the Normalized Difference Vegetation Index (NDVI), and new variables (brightness, greenness, and wetness) generated by the Kauth–homas Transforms (KT) algorithms from Landsat TM/ETM data served as the feature nodes of a DT classifer and contributed to improving the classification results. It showed an overall accuracy of more than 85% and a Kappa statistic of agreement of about 0.79 in 1996 and 2001 with the exception of about 0.69 in 1988. The statistical areas of land degradation in the observation periods revealed that land degradation, especially the salt‐affected soil, is accelerating. The distribution maps of land degradation in the years of 1988, 1996 and 2001 were generated respectively based on the classification results. Their change maps were created by the difference between the distribution maps from 1988 to 1996 and from 1996 to 2001 respectively. The changes of salt‐affected soil occurred near the water bodies due to variations of water sizes, and most of the degraded grassland appeared around the salt‐affected soil. Although climate variations play an important role in this region, human activities are also crucial to land degradation.     

Mapping the concentrations of total suspended matter in Lake Taihu,China, using Landsat‐5 TM data

Remote sensing techniques can be used to estimate and map the concentrations of suspended matter in inland water, providing both spatial and temporal information. Although an empirical approach to remote sensing of inland waters has been carried out frequently, satellite imagery has not been incorporated into routine lake monitoring programmes due in part to the lack of a standard prediction equation with multi‐temporal capacity for suspended matter. Empirical and physical models must be developed for each lake and its corresponding turbidity composition if they are to be compared over time, or with other bodies of water.

This study aimed to develop and apply multi‐temporal models to estimate and map the concentrations of total suspended matter (TSM) in Lake Taihu, China. Two Landsat‐5 Thematic Mapper (TM) images and nearly contemporaneous in situ measurements of TSM were used. A modified Dark‐Object Subtraction (DOS) method was used, and appeared to be adequate for atmospheric correction. The relationships were examined between TSM concentrations and atmospherically corrected TM band and band ratios. Results of this study show that the ratio TM4/TM1 has a strong relationship with TSM concentrations for lake waters with relatively low concentrations of phytoplankton algae. However, TM3 provided a strong predictive relationship with TSM concentrations despite varied water quality conditions. Different prediction models were developed and compared using multiple regression analysis. The Akaike Information Criteria (AIC) approach was used to choose the best models. The validation of the multi‐temporal capability of the best models indicated that it is feasible to apply the linear regression model using TM3 to estimate TSM concentrations across time in Lake Taihu, even if no in situ data were available.     

Estimation of states and parameters for a discrete-time system using quasilinearization†

An attempt is made here to demonstrate the usefulness of quasilinearization technique for the estimation of states and parameters of a discrete-time system. The estimation problem is viewed as a least-squared-errors problem which is then transformed into a Two Point Boundary Value (TPBV) problem by the use of the Discrete Maximum Principle. The TPBV problem is then solved by the quasilinearization technique.     

Bidirectional effect on a spectral image sensor for in‐field crop reflectance assessment

Varying illumination geometry affects spectral measurements of a target reflectance and the intensity of solar radiation is the most important factor for in‐field spectral measurements. This paper reports the effect of bidirectional electromagnetic radiation on an image‐based reflectance sensor designed for plant nitrogen assessment. The results show the nonlinearity of reflectance as a function of the solar zenith angle. Ambient illumination was analysed and compensated for using fixed nadir‐view positions of a solar radiometer and a 3‐charge‐coupled device (CCD) multispectral imaging sensor (MSIS). A compensation algorithm was developed to correct for the nonlinearity of both sensors. The compensated reflectance remained linearly consistent with varying the solar zenith angle throughout the daytime within a maximum standard deviation of 0.62% at all three (green, red and near‐infrared) spectral channels, when testing with a 20% reflectance panel. The consistent reflectance was recovered under both sunny and cloudy conditions.     

First algorithm for chlorophyll‐a retrieval from MODIS‐Terra imagery of Sun‐induced fluorescence in the Southern Ocean

The measurements of in situ samplers, the ENEA Light Detection and Ranging (Lidar) Fluorosensor (ELF) and Moderate Resolution Imaging Spectroradiometer on‐board the Terra satellite (MODIS‐Terra), carried out in the Southern Ocean during the Austral summer 2002–2003, were used to provide the first algorithm for chlorophyll‐a (Chl‐a) retrieval from MODIS‐Terra imagery of Sun‐induced fluorescence in the Southern Ocean. The results of the algorithm indicate that the standard MODIS‐Terra algorithm underestimated Chl‐a. The discrepancy (20%) is below the expected error of MODIS (35%).     

Evaluation of SeaWiFS chlorophyll‐a in the Black and Mediterranean Seas

The performance of NASA's OC2 and OC4 algorithms to estimate chlorophyll‐a concentrations from SeaWiFS radiometric measurements on the global scale was tested in two contrasted bio‐optical environments, the Black Sea and the Mediterranean Sea. The in situ bio‐optical measurements were made during October 1999 at 25 stations. Comparisons of the in situ measurements with the concurrent SeaWiFS retrievals indicate that the OC2 and OC4 algorithms are not working satisfactorily in both seas. Case 2 waters dominate the Black Sea and the failure of the algorithms is expected. On the other hand, failure of the algorithms in the case 1 waters of the Mediterranean Sea may be due to their specific optical properties. Modifying the OC4 algorithm to include SeaWiFS information at 412?nm yields a better performance in the Mediterranean Sea without degrading performance in the Black Sea. Combining a local algorithm adapted to oligotrophic waters of the Mediterranean Sea and OC4 provides the best results overall.     

Estimation of instantaneous air temperature above vegetation and soil surfaces from Landsat 7 ETM+ data in northern Germany

The temperature–vegetation index method (TVX method, also called contextual method) for the area-wide mapping of instantaneous air temperature is adopted for use with Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The method requires multispectral data consisting of bands in the red, near-infrared and thermal spectral regions, and no additional data. The approach is complemented with an iterative filtering routine for eliminating outliers and an interpolation algorithm for filling data gaps. The adopted method is applied to a multi-temporal dataset of nine ETM+?scenes, covering large parts of north-eastern Germany including the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site. Thus, for the first time the TVX method is applied to fine spatial resolution data and a central European region. The satellite-derived air temperatures (60 m spatial resolution) are compared with in situ measurements, showing an average error of about 3 K (root mean square, RMS), whereas the mean error in land surface temperature (LST) estimation is about 2 K. The results compare well with the in situ values throughout all seasons. The accuracy of about 3 K is in line with previously reported results for the TVX method (employing medium spatial resolution data) as well as for physically based approaches (ecosystem- or energy-balance models). Only remote sensing models incorporating in situ air temperature (as training data for neural networks or in multiple regression analysis) are reported to perform better in terms of RMS deviations. In the past, overestimation of air temperature by the TVX method was repeatedly observed. It is shown that the remote sensing approach tends to under- or overestimate the in situ air temperatures, depending on the in situ measurement heights. In conjunction with the attempt to assign the satellite-derived air temperature to a certain height above ground, the possibility of a simple correction for reference height is investigated. Over- and underestimations larger than 2 K seem to reflect existing differences in temperature rather than calculation errors. Furthermore, the dependence of the derived air temperature spatial pattern on different moving window sizes is shown. Possible sources of errors and limitations of the approach are discussed in detail.     

Detection of coal fire location and change based on multi‐temporal thermal remotely sensed data and field measurements

This study focuses on analysis methods for monitoring coal fires, using a combination of multi‐temporal thermal infrared data, high spatial resolution remote sensing data and field measurements. This technical note is prepared as a feasibility study for the detection of coal fire dynamics in the Inner Mongolia Autonomous Region in northern China.     

Estimation of Forest Aboveground Biomass and Canopy Heightin Jiangxi Province Using GLAS and Landsat TM Images

Aiming at the problem that optical remote sensing cannot estimate forest biomass exactly because it’s easily affected by the weather and hard to penetrate the canopy of the forest.Using Jiangxi forest as the study area,established forest canopy height and forest biomass model by GLAS waveform data,integrating multispectral data(TM) and filed survey data.The study results show:(1) using waveform feature parameter,terrain feature parameters and field survey data to build forest canopy height model can eliminate the terrain influence and obtain the discrete canopy height.(2) Combined with the NDVI and discrete canopy height can be carried out large scale continuous forest canopy height mapping.(3) Power function relationship between canopy height and forest biomass can be used to estimate forest biomass.In general,large\|footprint LiDAR combined with optical Landsat TM data can give full paly to the advantages of multi\|source remote sensing and improve the precision of forest biomass inversion.     

Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field‐based results

Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full‐range spectroradiometer (400–2500 nm) acquired above tree canopies. This study focused on whether these same species are also separable using hyperspectral data acquired using the airborne visible/infrared imaging spectrometer (AVIRIS). Stepwise discriminant techniques were used to reduce data dimensionality to a maximum of 10 spectral bands, followed by discriminant techniques to measure separability. Discriminatory variables were largely located in the visible and near‐infrared regions of the spectrum. Cross‐validation accuracies ranged from 65% (1 pixel radiance data) to as high as 85% (3×3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air‐ or space‐borne sensors.     

Feasibility of airborne imaging spectrometry for lake monitoring—a case study of spatial chlorophyll a distribution in two meso-eutrophic lakes

The spatial distribution of the sum of chlorophyll a and phaeophytin a concentrations (chl-a) under light wind (0–2 m s^?1) conditions was studied in two lakes with an AISA airborne imaging spectrometer. Chl-a was interpreted from AISA radiance data using an algorithm based on the near-infrared (700–710 nm) to red (660–665 nm) ratio. The results of Lake Lohjanjärvi demonstrate that the use of one monitoring station can result in over- or underestimation by 29–34% of the overall chl-a compared with an AISA-based estimation. In Lake Hiidenvesi, the AISA-based estimation for the mean chl-a with 95% confidence limits was 25.19±2.18 µg l^?1. The use of AISA data together with chl-a measured at 15 in situ sampling stations decreased the relative standard error of the mean chl-a estimation from 20.2% to 4.0% compared with the use of 15 discrete samples only. The relative standard error of the mean chl-a using concentrations at the three routine monitoring stations was 15.9 µg l^?1 (63.1%). The minimum and maximum chl-a in Lake Hiidenvesi were 2 and 101 µg l^?1, 6 and 70 µg l^?1 and 11 and 66 µmg l^?1, estimated using AISA data, data from 15 in situ stations and data from three routine in situ stations, respectively.     

Evaluation of the B‐method for determining actual evapotranspiration in a boreal forest from MODIS data

Boreal forests occupy about 11% of the terrestrial surface and represent an important contribution to global energy balance. The ground measurement of daily evapotranspiration (LE_d) is very difficult due to the limitations on experiments. The objective of this paper is to present and explore the applicability of the B‐method for monitoring actual LE_d in these ecosystems. The method shown in this paper allows us to determine the surface fluxes over boreal forests on a daily basis from instantaneous information registered in a conventional meteorological tower, as well as the canopy temperature (T _c) retrieved by satellite. Images collected by the MODIS (moderate resolution imaging spectroradiometer) on board EOS‐Terra have been used for this study. The parameters of the model were calibrated from the SIFLEX‐2002 (Solar Induced Fluorescence Experiment 2002) campaign dataset in a northern boreal forest in Finland. A study of these parameters was made on an hourly basis in order to make the method applicable, not only at midday but within an interval of 7 h around it. This is an important advance with respect to the original formulation of this approach since the overpass time of satellites can be very variable. The comparison between T _c ground measured with a thermal infrared radiometer, and T _c retrieved from land surface temperature (LST) MODIS data, showed an estimation error of ±1.4°C for viewing angles from 5 to 60°. A complete sensitivity analysis was carried out and an estimation error of about ±35%, corresponding to the interval 10.00–11.00 h UTC, was shown as the lowest in LE_d retrieval. Finally, the method was validated over the study site using 21 MODIS images for 2002 and 2003. The results were compared with eddy‐correlation ground measurements. An accuracy of ±1.0 mm/day and an overestimation of 0.3 mm/day were shown in the LE_d retrieval.     

Applications of Landsat‐5 TM imagery in assessing and mapping water quality in Reelfoot Lake,Tennessee

Water quality in Reelfoot Lake, Tennessee, was investigated in the field over 15 years ago. However, the spatial variations of water quality were not studied. The remote sensing technique has been proved a powerful tool in mapping spatial distributions of some water quality parameters such as chlorophyll‐a concentration. Additionally, different regression methods and various independent variables have been used to establish relationships between water quality parameters and spectral reflectance. The results from this study indicate that Landsat TM2 and TM3, as a set of independent variables in multivariate regression analysis, are good predictors of water quality in Reelfoot Lake. TM2 is positively correlated to water quality, and TM3 is negatively correlated to water quality. Poor water quality, or a high algae load, results in a high reflectance measured by TM2 and a low reflectance measured by TM3. Maps of spatial distribution of Secchi disk depth, turbidity, chlorophyll‐a, and total suspended solids present apparent spatial variations of water quality in the lake.     

Classifying depth‐layered geological structures on Landsat TM images by gravity data: a case study of the western slope of Songliao Basin,northeast China

Geological structures on remotely sensed images, Landsat Thematic Mapper (TM) images in this case, can be classified by quantitative depth information on the basis of the comparison of results from Landsat TM images and geophysical data. Although the lineaments with different depths can be visually interpreted together on Landsat TM images, the depth information and geological significance of these lineaments are however hard to obtain solely from the Landsat TM images of a study area under a thick cover, and it is of much importance for hydrocarbon exploration in the Western Slope Belt of Songliao Basin, northeast China. During the present study, the 3‐dimensional field source information, including location and depth information, is derived from 3‐dimensional Euler deconvolution of gravity data in particular. As an example, it may be quantitatively classified into four groups of depth range: <100?m, 100–500?m, 500–1000?m, >1000?m. It is then superimposed onto the lineaments map from Landsat TM images using a geographical information system (GIS). With a comprehensive analysis of the superimposed maps, we obtain validation and quantitative depth information of the geological structures delineated on the Landsat TM images. Four depth‐layered maps of geological structures with different depths are presented here. It is concluded that the number of structures with depth greater than 1000?m on the Landsat TM images is fewer than those at the other three depth ranges. The detection of geological structures on Landsat TM images attributed to depth information derived from the geophysical data may also be possible by this approach.     

In situ determination of sun‐induced chlorophyll a fluorescence quantum yield in the North China Sea

Sun‐induced chlorophyll a fluorescence signals were measured in the North China Sea using an underwater hyperspectral spectroradiometer. We combined these signals with other apparent and inherent optical properties to derive the quantum yield of chlorophyll a fluorescence in vivo, and determined that the maximum was around 0.06 and the average value reached 0.017, with most values ranging from 0.001 to 0.02, consistent with results obtained in other sea areas. In addition, we validated some empirical relationships with in situ measurements, and compared them with the former results. We found that the correlations between inherent optical properties and chlorophyll a concentration in this area are very different from those in other seas; however, the relationship between the diffuse attenuation coefficient for the instantaneous photosynthetically available radiation (IPAR) and that at 490 nm is comparatively stable.     

Land‐cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling

A land‐cover classification is needed to deduce surface boundary conditions for a soil–vegetation–atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster–Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster–Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.     

Variability of chlorophyll concentration in the Arabian Sea and Bay of Bengal as observed from SeaWiFS data from 1997–2000 and its interrelationship with Sea Surface Temperature (SST) derived from NOAA AVHRR

Variability of chlorophyll concentration in the Arabian Sea and Bay of Bengal has been studied using SeaWiFS eight‐day average, 9 km processed data for the period 1997–2000. The interrelationship with sea surface temperature (SST) was studied with the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived, best SST product. The chlorophyll pattern shows in general high concentrations during February to March in the Arabian Sea and November to December in the Bay of Bengal. Year‐to‐year variations in temperature show an inverse relation with chlorophyll, at different locations, even on a monthly basis. However, the intraannual variability in chlorophyll at different locations shows differences in the relationship with SST. The Arabian Sea showed an inverse relationship at most of the locations, while a positive relationship was observed in the northwest region during October to December and an inverse relationship during January to April. The Bay of Bengal showed positive relationships at northeast locations, whereas no definite assessment could be made for other locations due to the narrow range of chlorophyll concentration.A longer time series (～10 years) will be required to establish a more concrete relationship but definitely consistent patterns are emerging from this study. The results form an additional dimension to the criteria for partitioning the ocean, required for global productivity or biophysical coupled modelling.     

Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

Urban landscapes are expanding rapidly and are reshaping the distribution of many animal and plant species. With these changes, the need to understand and to include urban biodiversity patterns in research and management programmes is becoming vital. Recent studies have shown that remote sensing tools can be useful in studies examining biodiversity patterns in natural landscapes. The present study aimed to explore whether remote sensing tools can be applied in biodiversity research in an urban landscape. More specifically, the study examined whether the Landsat‐derived Normalized Difference Vegetation Index (NDVI) and linear spectral unmixing of urban land cover can predict bird richness in the city of Jerusalem. Bird richness was sampled in 40 1‐ha sites over a range of urban environments in 329 surveys. NDVI and the per cent cover of built‐up area were strongly and negatively correlated with each other, and were both very successful in explaining the number of bird species in the study sites. Mean NDVI in each site was positively correlated with the site bird species richness. A hump‐shaped relationship between NDVI and species richness was observed (when calculated over increasing spatial scales), with a maximum value (Pearson's R = 0.87, p<0.001, n = 40) at a scale of 15 ha. We suggest that remote sensing approaches may provide planners and conservation biologists with an efficient and cost‐effective method to study and estimate biodiversity across urban environments that range between densely built‐up areas, residential neighbourhoods, urban parks and the peri‐urban environment.