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.     

Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing

Coral reef benthic communities are mosaics of individual bottom-types that are distinguished by their taxonomic composition and functional roles in the ecosystem. Knowledge of community structure is essential to understanding many reef processes. To develop techniques for identification and mapping of reef bottom-types using remote sensing, we measured 13,100 in situ optical reflectance spectra (400-700 nm, 1-nm intervals) of 12 basic reef bottom-types in the Atlantic, Pacific, and Indian Oceans: fleshy (1) brown, (2) green, and (3) red algae; non-fleshy (4) encrusting calcareous and (5) turf algae; (6) bleached, (7) blue, and (8) brown hermatypic coral; (9) soft/gorgonian coral; (10) seagrass; (11) terrigenous mud; and (12) carbonate sand. Each bottom-type exhibits characteristic spectral reflectance features that are conservative across biogeographic regions. Most notable are the brightness of carbonate sand and local extrema near 570 nm in blue (minimum) and brown (maximum) corals. Classification function analyses for the 12 bottom-types achieve mean accuracies of 83%, 76%, and 71% for full-spectrum data (301-wavelength), 52-wavelength, and 14-wavelength subsets, respectively. The distinguishing spectral features for the 12 bottom-types exist in well-defined, narrow (10-20 nm) wavelength ranges and are ubiquitous throughout the world. We reason that spectral reflectance features arise primarily as a result of spectral absorption processes. Radiative transfer modeling shows that in typically clear coral reef waters, dark substrates such as corals have a depth-of-detection limit on the order of 10-20 m. Our results provide the foundation for design of a sensor with the purpose of assessing the global status of coral reefs.     

Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping

Tillage practices can affect the long term sustainability of agricultural soils as well as a variety of soil processes that impact the environment. Crop residue retention is considered a soil conservation practice given that it reduces soil losses from water and wind erosion and promotes sequestration of carbon in the soil. Spectral unmixing estimates the fractional abundances of surface targets at a sub-pixel level and this technique could be helpful in mapping and monitoring residue cover. This study evaluated the accuracy with which spectral unmixing estimated percent crop residue cover using multispectral Landsat and SPOT data. Spectral unmixing produced crop residue estimates with root mean square errors of 17.29% and 20.74%, where errors varied based on residue type. The model performed best when estimating corn and small grain residue. Errors were higher on soybean fields, due to the lower spectral contrast between soil and soybean residue. Endmember extraction is a critical step to successful unmixing. Small gains in accuracy were achieved when using the purest crop residue- and soil-specific endmembers as inputs to the spectral unmixing model. To assist with operational implementation of crop residue monitoring, a simple endmember extraction technique is described.     

Implementation of real-time constrained linear discriminant analysis to remote sensing image classification

In this paper, we investigate the practical implementation issues of the real-time constrained linear discriminant analysis (CLDA) approach for remotely sensed image classification. Specifically, two issues are to be resolved: (1) what is the best implementation scheme that yields lowest chip design complexity with comparable classification performance, and (2) how to extend CLDA algorithm for multispectral image classification. Two limitations about data dimensionality have to be relaxed. One is in real-time hyperspectral image classification, where the number of linearly independent pixels received for classification must be larger than the data dimensionality (i.e., the number of spectral bands) in order to generate a non-singular sample correlation matrix R for the classifier, and relaxing this limitation can help to resolve the aforementioned first issue. The other is in multispectral image classification, where the number of classes to be classified cannot be greater than the data dimensionality, and relaxing this limitation can help to resolve the aforementioned second issue. The former can be solved by introducing a pseudo inverse initiate of sample correlation matrix for R^-1 adaptation, and the latter is taken care of by expanding the data dimensionality via the operation of band multiplication. Experiments on classification performance using these modifications are conducted to demonstrate their feasibility. All these investigations lead to a detailed ASIC chip design scheme for the real-time CLDA algorithm suitable to both hyperspectral and multispectral images. The proposed techniques to resolving these two dimensionality limitations are instructive to the real-time implementation of several popular detection and classification approaches in remote sensing image exploitation.     

The use of airborne remote sensing for benthic cartography: Advantages and reliability

Airborne remote sensing is a useful tool for the production of biocenosis maps. The use of an image processing system, integrating bathymetric data, makes it possible to considerably refine the charting, through a layer of water of variable thickness and quality. Other limiting factors may have an impact on the quality of results. The identification of these factors makes it possible to propose a scale of reliability. Four examples of aerial teledetection surveys provide the basis for ( i ) assessing the reliability of the maps, ( ii ) determining the reasons for this variation in reliability, and ( iii ) using the scale as a means for assessing the reliability of a given map. The factors used are such that the reliability scale could subsequently be applied to the cartography of other marine assemblages.     

Short-term changes of remote sensing reflectancein a shallow-water environment: observations from repeated airborne hyperspectral measurements

An atmospheric correction algorithm has been developed for the Airborne Imaging Spectrometer for Applications (AISA) imagery over optically shallow waters in Sugarloaf Key of the Florida Keys. The AISA data were collected repeatedly during several days in May 2012, October 2012, and May 2013. Non-zero near-infrared (NIR) remote-sensing reflectance (R_rs) was accounted for through iterations, based on the relationship of field-measured R_rs between the NIR and red wavelengths. Validation showed mean ratios of 0.94–1.002 between AISA-retrieved and in situ R_rs in the blue to red wavelengths, with uncertainties generally <0.003 sr^–1. Such an approach led to observations of short-term changes in AISA-retrieved R_rs from repeated measurements over waters with bottom types of seagrass meadow, sand, and patch reef. Some of these changes are larger than twofold the R_rs uncertainties from AISA retrievals, therefore representing statistically significant changes that can be well observed from airborne measurements. Through radiative transfer modelling, we demonstrated that short-term R_rs changes within 1 hour resulted primarily from sediment resuspension, while tides played a relatively minor role due to the small variation in tidal heights. A sensitivity analysis indicated that although R_rs generally increases with decreasing tide height but increasing suspended sediments, more changes were observed over sandy bottom than over seagrass. The case study suggests that repeated airborne measurements may be used to study short-term changes in shallow-water environments, and such a capacity may be enhanced with future geostationary satellite missions specifically designed to observe coastal ecosystems.     

Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within-class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21,000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.     

Spectral characteristics and feature selection of hyperspectral remote sensing data

Hyperspectral remote sensing data with bandwidth of nanometre (nm) level have tens or even several hundreds of channels and contain abundant spectral information. Different channels have their own properties and show the spectral characteristics of various objects in image. Rational feature selection from the varieties of channels is very important for effective analysis and information extraction of hyperspectral data. This paper, taking Shunyi region of Beijing as a study area, comprehensively analysed the spectral characteristics of hyperspectral data. On the basis of analysing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from hyperspectral remote sensing data was proposed.     

Multi-resolution spectral analysis of wildfire potassium emission signatures using laboratory, airborne and spaceborne remote sensing

Thermal remote sensing studies of actively burning wildfires are usually based on the detection of Planckian energy emissions in the MIR (3-5 μm), LWIR (8-14 μm) and/or SWIR (1.0-2.5 μm) spectral regions. However, vegetation also contains a series of trace elements which present unique narrowband spectral emission lines in the visible and near infrared wavelength range when the biomass is heated to high temperatures during the process of flaming combustion. These spectral lines can be discriminated by detector systems that are less costly than the longer wavelength, actively cooled instruments more typically used in EO-based active fire studies. The main trace element resulting in the appearance of spectral emission lines appears to be potassium (K), with features at 766.5 nm and 769.9 nm. Here we study K-emission line spectral signature in laboratory scale fires using a field spectrometer, at a series of moderately-sized woodland and shrubland fires using airborne imagery from a new compact hyperspectral imager (HYPER-SIM.GA) operating at a relatively fine spectral sampling interval (1.2 nm), and at large open wildfires using the EO-1 satellite's Hyperion sensor. We derive a metric based on band differencing of the spectral signal both close to and outside of the K-line region in order to quantify the magnitude of the K-emission signature, and find that variations in this metric appear to track quite well with the commonly used measures of fire radiometric temperature and fire radiative power (FRP). We find that substantial flaming activity is required to generate a potassium emission signature, but that once present this can be detected using airborne remote sensing even through a substantial smoke layer that apparently obscures fire across the remainder of the VIS spectral range. Being specific to flaming combustion, detection of the K-emission line signature could prove useful in refining estimates of the gases released in open wildfires, since trace gas emission factors can vary substantially between flaming and smouldering stages. Finally, we demonstrate the first identification of the K-emission line signature from space using the EO-1 Hyperion instrument, but find it detectable only in certain instances. We conclude that a finer spectral and spatial resolution than that offered by Hyperion is required for improved detection performance. Nevertheless, our results point to the potential effectiveness of airborne and spaceborne K-emission signature detection as a complement to the more common thermal remote sensing approaches to wildfire detection and analysis. Sensors targeting this application should consider careful placement of the measurement wavelengths around the location of the K-line wavelengths, in part to minimise influences from the nearby oxygen A-band features.     

Deep feature learning versus shallow feature learning systems for joint use of airborne thermal hyperspectral and visible remote sensing data

Recently, the development of various remote sensing sensors has provided more reliable information and data for identification of different ground classes. Accordingly, multisensory fusion techniques are applied to enhance the process of information extraction from complementary airborne and spaceborne remote sensing data. Most of previous research in the literature has focused on the extraction of shallow features from a specific sensor and on classification of the resulted feature space using decision fusion systems. In recent years, Deep Learning (DL) algorithms have drawn a lot of attention in the machine learning area and have had different remote sensing applications, especially on data fusion. This study presents two different feature-learning strategies for the fusion of hyperspectral thermal infrared (HTIR) and visible remote sensing data. First, a Deep Convolutional Neural Network (DCNN)-Support Vector Machine (SVM) was utilized on the features of two datasets to provide the class labels. To validate the results with other learning strategies, a shallow feature model was used, as well. This model was based on feature fusion and decision fusion that classified and fused the two datasets. A co-registered thermal infrared hyperspectral (HTIR) and Fine Resolution Visible (Vis) RGB imagery was available from Quebec of Canada to examine the effectiveness of the proposed method. Experimental results showed that, except for the computational time, the proposed deep learning model outperformed shallow feature-based strategies in the classification performance that was based on its accuracy.     

Estimating the depth of buried hot features from thermal IR remote sensing data: a conceptual approach

Estimating the depth of buried thermal features from remotely sensed data is a difficult problem. The theoretical basis discussed in this letter shows that if the time of initiation of the heat source is known, then utilizing two sets of thermal Infrared ( IR) data of different time instances for the same anomaly area, the depth of the buried heat source can be computed. However, if the thermal IR data are collected at time intervals close to the time of initiation of heat source, the knowledge of initial conditions of heat source is obviated. In this latter case, a double ratioing technique, utilizing three sets of thermal IR remote sensing data, acquired at different times for the same area, can be used for estimating the depth of buried heat source. Respective sample sets of characteristic curves designed for depth estimation are presented along with results of sensitivity analysis tests, performed on simulated data.     

Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

The potential of canopy reflectance modelling to retrieve simultaneously several structural variables in managed Norway spruce stands was investigated using the “Invertible Forest Reflectance Model”, INFORM. INFORM is an innovative extension of the FLIM model, with crown transparency, infinite crown reflectance and understory reflectance simulated using physically based sub-models (SAILH, LIBERTY and PROSPECT). The INFORM model was inverted with hyperspectral airborne HyMap data using a neural network approach. INFORM based estimates of forest structural variables were produced using site-specific ranges of stand structural variables. A relatively simple three layer feed-forward backpropagation neural network with two input neurons, one neuron in the hidden layer and three output neurons was employed to map leaf area index (LAI), crown coverage and stem density.To identify the optimum 2-band spectral subset to be used in the inversion process, all 2-band combinations of the HyMap dataset were systematically evaluated for model inversion. Field measurements of structural variables from 39 forest stands were used to validate the maps produced from HyMap imagery. Using two HyMap wavebands at 837 nm and 1148 nm the obtained accuracy of the LAI map amounts to an rmse of 0.58 (relative rmse = 18% of mean, R² = 0.73). With HyMap data resampled to Landsat TM spectral bands and using two “optimum” bands at 840 nm and 1650 nm, rmse was 0.66 and relative rmse 21%. In contrast to approaches based on empirical relations between spectral vegetation indices and structural variables, the main advantage of the inversion approach is that it does not require previous calibration.     

Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat

ABSTRACT

The nitrogen nutrition index (NNI) is a quantitative and reliable indicator of the nitrogen nutrition distribution or status of crops. The timely and accurate estimation of the NNI is crucial in agriculture management. In this study, the quantitative analysis and hyperspectral remote sensing modelling of the NNI were conducted, in which the hyperspectral remote sensing data and NNI data at different growth stages of winter wheat were measured using ground and unmanned aerial vehicle (UAV) carrying high spectrometer equipment. First, the NNIs of the four growth stages of winter wheat were calculated and statistically analyzed. Then, the hyperspectral characteristics at different growth stages and various NNIs were examined. Second, the representation wavebands of the hyperspectral data, which were sensitive to the NNI of winter wheat, were acquired and evaluated. In addition, hyperspectral models were established and comparatively assessed for the NNI estimation. Finally, the hyperspectral characteristics and the remote sensing estimation of the NNIs were determined on the basis of UAV-based hyperspectral data. The results are as follows. (1) As the NNIs of winter wheat changed, the characteristic of the red shift, the variations in the red edge position, and the near-infrared waveband range of the hyperspectral data became apparent. (2) The green band, red edge, and near-infrared were sensitive to the NNIs of winter wheat, and they could be effectively used for estimating the NNI. Moreover, the multiple statistical regression models, which were based on representative wavebands, performed well in estimating the NNI results for the different growth stages of winter wheat.     

Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data

Due to the noise that is present in remote sensing data, a robust method to retrieve information is needed. In this study, the active learning method (ALM) is applied to spectral remote sensing reflectance data to retrieve in‐water pigment. The heart of the ALM is a fuzzy interpolation method that is called the ink drop spread (IDS). Three datasets (SeaBAM, synthetic and NOMAD) are used for the evaluation of the selected ALM approach. Comparison of the ALM with the ocean colour 4 (OC4) algorithm and the artificial neural network (ANN) algorithm demonstrated the robustness of the ALM approach in retrieval of in‐water constituents from remote sensing reflectance data. In addition, the ALM identified and ranked the most relevant wavelengths for chlorophyll and pigment retrieval.     

Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shortwave infrared subsystem can acquire images of active fires during daytime and night-time from a polar orbit, providing useful data on fire properties at a nominal spatial resolution of 30 m. Binary fire/no-fire counts of ASTER pixels have also been useful in evaluating the performance of widely-used fire products from the Moderate-Resolution Imaging Spectroradiometer (MODIS), which have a nominal spatial resolution of 1 km. However, the ASTER fire pixels are actually mixed pixels that can contain flaming, smouldering and non-burning components, and ASTER fire pixel counts provide no information about the sizes or temperatures of these subpixel components. This paper uses multiple endmember spectral mixture analysis (MESMA) to estimate subpixel fire sizes and temperatures from a night-time ASTER image of a fire in California, USA, demonstrating new methods that can provide information on fires not available from other sources. As a fire's size and its temperature exert strong influences on its gas and aerosol emissions, ecological impact and spreading rates, these MESMA estimates from ASTER imagery could contribute valuable new information towards monitoring, forecasting and understanding the behaviour and impacts of many fires worldwide.     

Simultaneous estimation of benthic fractional cover and shallow water bathymetry in coral reef areas from high-resolution satellite images

This article describes the development of a technique to estimate shallow water benthic cover and depth simultaneously from high-resolution satellite images of reef areas, specifically from the high-resolution sensor onboard IKONOS. The technique to derive the estimates of five bottom benthic cover types (sand, coral, seagrass, macroalgae and pavement) and depth from the four-band images uses a coupling of radiative transfer (RT) theory and spectral unmixing implemented in an iterative manner. To resolve the cover types for the unmixing, the method employed a combinatorial approach to select benthic cover composition. The estimation technique was applied to two reef areas around the coast of the Ishigaki in southern Ryukyus, namely, the Fukido River mouth area and the Shiraho Reef. The IKONOS images of Fukido River mouth area and Shiraho Reef were acquired in 2003 and 2002, respectively. The accuracy of the fractional cover and the depth estimates from the satellite images are then presented and compared with sea truth data and depth measurements. The results indicate good correspondence between estimated and measured depths, while the estimates for the benthic cover were at reasonable levels of accuracy.     

非相关线性判别分析用于蛋白质组数据的分类及特征挑选

提出一种非相关线性判别分析(ULDA)结合统计卡方检验(CHI2)的方法用于蛋白质组质谱数据的分类及特征挑选.首先以卡方检验为过滤器去除无类间差别的变量,然后用ULDA进行样本分类与特征筛选,通过对两组数据的分析,最终选择出的特征变量在这两组数据中的特异性分别为98.2%和95.74%,灵敏度均为100%.结果表明本文提出的方法能较好地处理变量数很大的蛋白质组数据,同时表明最后选择的特征变量有可能作为潜在的生物标记物,为相关疾病的早期诊断提供线索.     

Ronda peridotite massif: Methodology for its geological mapping and lithological discrimination from airborne hyperspectral data

The Ronda peridotite massif in the Sierra Bermeja, southern Spain, was imaged in July 1991 by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) during the Europe 1991 Multispectral Airborne Campaign. Principal component analysis was used (i) to extract the most spectrally extreme pixels for empirical line calibration; (ii) to physically interpret the image spectral information; and (iii) to define an optimal endmember selection based on both spatial and spectroscopic characteristics. Two successive spectral mixture analyses that allow one to focus on subtle spectral variations related to bedrock and soil lithology were applied. The first spectral mixture analysis was used to identify the major surface constituents in the image and extract the geological target to be investigated, i.e. the peridotite massif; and the second one was used to model the spectral variability within the designated target. Although mineralogical variations observed in the rocks were at a sub-pixel scale for the airborne survey, spatially organized units could be identified within the major outcrops of the peridotite massif from their spectral variations. A mineralogical interpretation of these spectral variations is proposed in relation with the field observations, in terms of relative abundance variations in the pyroxene/olivine ratio among the pixels. This work demonstrates that the proposed methodology makes possible the spectral distinction of lithological units within an ultramafic body, despite the occurrence of partial vegetation cover and multiple sub-pixel mixtures. Furthermore, it shows that hyperspectral data can provide such information, resulting in a very cost-effective method of petrologic mapping.     

Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data

This study employs data from the airborne Thermal Infrared Multispectral Scanner (TIMS) to measure thermal (i.e., longwave) energy responses, emitted or upwelling, from discrete surfaces that are typical of the city landscape within Salt Lake City, Utah, over a single diurnal time period (i.e., a single day/night-time sequence). These data are used to quantify the disposition of thermal energy for selected urban surfaces during the daytime and night-time, and the amount of change in thermal response or flux recorded between day and night. An analysis is presented on the thermal interrelationships observed for common urban materials for day, night, and flux, as identified from the TIMS data through the delineation of discrete surface type polygons. The results from the study illustrate that such factors as heat capacity, thermal conductivity, and the amount of soil moisture available have a profound impact on the magnitude of thermal energy emanating from a specific surface and on the dynamics of longwave energy response between day and night.