An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

The aim of this article is to investigate and test the influence of oil spill volume and time gap (number of days between oil spill events and image acquisition date) on normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). This was carried out to determine the effect of these factors on vegetation condition affected by the oil spill. Based on regression analysis, it was shown that increase in the volume of oil spill resulted in increased deterioration of vegetation condition (estimated using NDVI and NDWI) in the study site. The study also tested how the length of time gap between the oil spill and image acquisition date influences the detectability of impacts of oil spill on vegetation. The results showed that the length of time between image acquisition and oil spill influenced the detectability of impacts of oil spill on vegetation condition. The longer the time between the date of image acquisition and the oil spill event, the lower the detectability of impacts of oil spill on vegetation condition. The NDVI seemed to produce better results than the NDWI. In conclusion, time and volume of oil spill can be important factors influencing the detection of pollution using vegetation indices (VIs) in an oil-polluted environment.     

Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data

Wildland fires are an annually recurring phenomenon in many terrestrial ecosystems. Accurate burned area estimates are important for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. High spatial and spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over three 2007 southern California burns were used to evaluate the sensitivity of different spectral indices at discriminating burned land shortly after a fire. The performance of the indices, which included both traditional and new band combinations, was assessed by means of a separability index that provides an assessment of the effectiveness of a given index at discriminating between burned and unburned land. In the context of burned land applications results demonstrated (i) that the highest sensitivity of the longer short wave infrared (SWIR) spectral region (1.9 to 2.5 μm) was found at the band interval from 2.31 to 2.36 μm, (ii) the high discriminatory power of the mid infrared spectral domain (3 to 5.5 μm) and (iii) the high potential of emissivity data. As a consequence, a newly proposed index which combined near infrared (NIR), longer SWIR and emissivity outperformed all other indices when results were averaged over the three fires. Results were slightly different between land cover types (shrubland vs. forest-woodland). Prior to use in the indices the thermal infrared data were separated into temperature and emissivity to assess the benefits of using both temperature and emissivity. Currently, the only spaceborne sensor that provides moderate spatial resolution (< 100 m) temperature and emissivity data is the Advanced Spaceborne and Thermal Emission Radiometer (ASTER). Therefore, our findings can open new perspectives for the utility of future sensors, such as the Hyperspectral Infrared (HyspIRI) sensor. However, further research is required to evaluate the performance of the newly proposed band combinations in other vegetation types and different fire regimes.     

Comparison of spectral indices obtained using multiple spectroradiometers

To determine the degree of comparability between three spectrometers (Analytical Spectral Devices FieldSpec Pro FR (FR), Analytical Spectral Devices HandHeld (HH), and UniSpec Spectral Analysis System (UN)), leaf spectra of three species (Cafea arabica, Lantana camara, Eriobotrya japonica), recorded from each instrument, were compared using two illumination, viewing, and field of view (FOV) scenarios. Scenario 1 eliminated differences due to illumination, viewing, and FOV conditions. Scenario 2 represented a ‘typical’ illumination and viewing set-up for each instrument. Six vegetation indices were computed from the raw spectra as well as spectra (1) interpolated to 1-nm intervals (the sampling interval of the FR) and (2) interpolated to 3.3 nm (the sampling interval of the UN). The spectra measured from the three instruments differed in both shape and amplitude, more so for scenario 2 than scenario 1. In many cases, indices obtained using one instrument differed significantly from the same indices obtained using the other two instruments (but the same leaves), regardless of scenario. The severity of these differences varied between indices. Interpolation was generally ineffective in ‘matching’ the spectra from the various instruments. Care should be exercised when comparing indices generated from spectra measured from different instruments.     

Spectral discrimination of healthy and Ganoderma-infected oil palms from hyperspectral data

Although hyperspectral remote sensing has been used to study many agricultural phenomena such as crop stress and diseases, the potential use of this technique for detecting Ganoderma disease infestations and damage to oil palms under field conditions has not been explored to date. This research was conducted to investigate the feasibility of using a portable hyperspectral remote-sensing instrument to identify spectral differences between oil-palm leaves with and without Ganoderma infections. Reflectance spectra of samples representative of three classes of disease severity were collected. The most significant bands for spectral discrimination were selected from reflectance spectra and first derivatives of reflectance spectra. The significant wavelengths were identified using one-way analysis of variance. Then, a Jeffries–Matusita (JM) distance measurement was used to determine spectral separability between the classes. A maximum likelihood classifier method was used to classify the three classes based on the most significant wavelength spectral responses, and an error matrix was finally used to assess the accuracy of the classification.     

Detection of DDoS attacks using optimized traffic matrix

Distributed Denial of Service (DDoS) attacks have been increasing with the growth of computer and network infrastructures in Ubiquitous computing. DDoS attacks generating mass traffic deplete network bandwidth and/or system resources. It is therefore significant to detect DDoS attacks in their early stage. Our previous approach used a traffic matrix to detect DDoS attacks quickly and accurately. However, it could not find out to tune up parameters of the traffic matrix including (i) size of traffic matrix, (ii) time based window size, and (iii) a threshold value of variance from packets information with respect to various monitored environments and DDoS attacks. Moreover, the time based window size led to computational overheads when DDoS attacks did not occur. To cope with it, we propose an enhanced DDoS attacks detection approach by optimizing the parameters of the traffic matrix using a Genetic Algorithm (GA) to maximize the detection rates. Furthermore, we improve the traffic matrix building operation by (i) reforming the hash function to decrease hash collisions and (ii) replacing the time based window size with a packet based window size to reduce the computational overheads. We perform experiments with DARPA 2000 LLDOS 1.0, LBL-PKT-4 of Lawrence Berkeley Laboratory and generated attack datasets. The experimental results show the feasibility of our approach in terms of detection accuracy and speed.     

A fibre optic corrosion fuse sensor using stressed metal-coated optical fibres

A novel optical fibre-based corrosion sensor based on the mechanical failure of stressed metal-coated optical fibres when exposed to a corrosive environment is presented. The current work discusses proof-of-principle tests carried out using commercially available aluminium-coated optical fibres. Tests have been undertaken in a non-corrosive environment, in aqua regia and in sea water. The failure times of stressed aluminium-coated fibres were found to correlate with the corrosivity of the test environment. The results are discussed in relation to microscopic investigations of the metal coatings in the aforementioned corrosive environments as a function of immersion time.     

Regional mapping of gross light-use efficiency using MODIS spectral indices

Direct estimation of photosynthetic light-use efficiency (LUE) from space would be of significant benefit to LUE-based models which use inputs from remote sensing to estimate terrestrial productivity. The Photochemical Reflectance Index (PRI) has shown promise in tracking LUE at the leaf- to small canopy levels, but its use at regional to global scales still remains a challenge. In this study, we used different formulations of PRI calculated from the MODIS ocean band centered at 531 nm and a set of alternative reference bands at 488, 551, and 678 nm to explore the relationship between PRI and LUE where LUE was measured at eight eddy covariance flux towers located in the boreal forest of Saskatchewan, Canada. The magnitude and variability of LUE was significantly lower at the times when useful MODIS ocean band images were available (i.e. around midday under clear-sky conditions) relative to the rest of the growing season. PRI₆₇₈ (reference band at 678 nm) showed the strongest relationship (r² = 0.70) with LUE_90a (i.e. 90-minute mean LUE calculated using Absorbed Photosynthetically Active Radiation, APAR), but only when all sites were combined. Overall, the relationships between the MODIS PRIs and LUE_90a were always stronger for observations closer to the backscatter direction and there were no significant differences in the strength of the correlations whether LUE was calculated based on incident PAR or on APAR. Predictions of ecosystem photosynthesis at the time of the MODIS overpasses were significantly improved by multiplying either PAR or APAR by MODIS PRI (r² improved from 0.09 to 0.44 and 0.54 depending on the PRI formulation).We used our PRI-LUE model to create a regional LUE_90a map for the three cover types covering 47,500 km² around the flux sites. The MODIS PRI-derived LUE_90a map appeared to capture more realistic spatial heterogeneity of LUE across the landscape compared to a daily LUE map derived using the look-up table in the MODIS GPP (MOD17) algorithm. While our LUE map is only a snapshot of minimum regional LUE_90a values, with appropriate gap-filling methods it could be used to improve regional-scale monitoring of GPP. Moreover, the strong relationship between midday and daily LUE on clear days (r² = 0.93) indicates that instantaneous MODIS observations of LUE_90a could be used to estimate daily LUE. Finally, pixel shadow fraction from the 5-Scale geometric-optical model was closely related to both MODIS PRI and tower-derived LUE suggesting that differences in stand leaf area and in diffuse illumination among flux sites play a role in the relationship we observed between LUE and MODIS PRI.     

Intercalibration of vegetation indices from different sensor systems

Spectroradiometric measurements were made over a range of crop canopy densities, soil backgrounds and foliage colour. The reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses. When Normalised Difference Vegetation Indices (NDVI) from the different instruments were compared, they varied by a few percent, but the values were strongly linearly related, allowing vegetation indices from one instrument to be intercalibrated against another. A table of conversion coefficents is presented for AVHRR, ATSR-2, Landsat MSS, TM and ETM+, SPOT-2 and SPOT-4 HRV, IRS, IKONOS, SEAWIFS, MISR, MODIS, POLDER, Quickbird and MERIS (see Appendix A for glossary of acronyms). The same set of coefficients was found to apply, within the margin of error of the analysis, for the Soil Adjusted Vegetation Index SAVI. The relationships for SPOT vs. TM and for ATSR-2 vs. AVHRR were directly validated by comparison of atmospherically corrected image data. The results indicate that vegetation indices can be interconverted to a precision of 1-2%. This result offers improved opportunities for monitoring crops through the growing season and the prospects of better continuity of long-term monitoring of vegetation responses to environmental change.     

Statistical process monitoring using improved PCA with optimized sensor locations

The emphasis of most PCA process monitoring approaches is mainly on procedures to perform fault detection and diagnosis given a set of sensors. Little attention is paid to the actual sensor locations to efficiently perform these tasks. In this paper, graph-based techniques are used to optimize sensor locations to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. Meanwhile, an improved PCA that uses two new statistics of PVR and CVR to replace the Q index in conventional PCA is introduced. The improved PCA can efficiently detect weak process changes, and give an insight to the root cause about the process malfunction. Simulation results of a CSTR process show that the improved PCA with optimized sensor locations is superior to conventional methods in fault resolution and sensibility.     

Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing

Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m^− 1) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted (R² = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models.     

Mapping mixed vegetation communities in salt marshes using airborne spectral data

The aim of this study is to evaluate a new neural network classifier using spectrally sampled image data to map mixed halophytic vegetation in tidal environments. The work is based on the concept of vegetation communities, mixtures of several species, characteristic of salt marshes. The study site is the Venice lagoon, and the material available is a spectrally sampled Compact Airborne Spectral Imager (CASI) image, in conjunction with ground truth for precise characterization of vegetation communities. Detailed observations of vegetation species and of their fractional abundance were collected for 36 Regions Of Interest (ROI): such field polygons are used for classification training and accuracy assessment. To select the most significant spectral channels, the Spectral Reconstruction method was applied to the image data: a set of 6 bands was selected as optimal for classification, out of the 15 available. The spatial heterogeneity of salt-marsh vegetation is significant and even at the spatial resolution of the airborne CASI image data, mixed pixels are observed. The Vegetation Community based Neural Network Classifier (VCNNC) is introduced to cope with a situation where no pure pixels exist, and was applied to the set of 6 selected bands. Both quantitative and qualitative comparisons of classification results of VCNNC with those of conventional Neural Network Classifier (NNC), trained and assessed on exactly the same data sets, shows that VCNNC's accuracy is substantially higher (≈ 91%) than that of NNC (≈ 84%), while the Kappa coefficient is 0.87 for VCNNC and 0.75 for the NNC method.     

Biomass estimation on grazed and ungrazed rangelands using spectral indices

Aboveground biomass was estimated on the shortgrass steppe of Eastern Colorado using Landsat TM Tasseled Cap green vegetation index (GVI), brightness index (BI), and wetness index (WI), the normalized difference vegetation index (NDVI) and the red waveband (RED), for two grazing treatments (moderately grazed or ungrazed). Field measurements of standing crop were obtained on six sites per grazing treatment. Ordinary least squares regression models of biomass as a function of one or more indices were tested for grazed, ungrazed, and combined grazed and ungrazed data. Biomass from grazed sites was linearly related to GVI, NDVI, WI, and RED indices (R2 0.62-0.67). Ungrazed sites produced no significant relations. With combined ungrazed and grazed data, biomass was not significantly related to GVI, NDVI, WI, or BI, and was poorly related to the RED index (R2 0.35). When grazing treatments were treated as dummy variables for the combined data, the RED index was moderately related to biomass (R2 0.70). These results suggest that information about grazing utilization is useful for estimating aboveground biomass in rangelands. The RED index appears to be sensitive to biomass variations for green vegetation and to a lesser extent dry or senescent vegetation when relatively bright soil backgrounds are present which is often the case for semi-arid environments such as the shortgrass steppe.     

Performance evaluation of spectral vegetation indices using a statistical sensitivity function

A great number of spectral vegetation indices (VIs) have been developed to estimate biophysical parameters of vegetation. Traditional techniques for evaluating the performance of VIs are regression-based statistics, such as the coefficient of determination and root mean square error. These statistics, however, are not capable of quantifying the detailed relationship between VIs and biophysical parameters because the sensitivity of a VI is usually a function of the biophysical parameter instead of a constant. To better quantify this relationship, we developed a “sensitivity function” for measuring the sensitivity of a VI to biophysical parameters. The sensitivity function is defined as the first derivative of the regression function, divided by the standard error of the dependent variable prediction. The function elucidates the change in sensitivity over the range of the biophysical parameter. The Student's t- or z-statistic can be used to test the significance of VI sensitivity. Additionally, we developed a “relative sensitivity function” that compares the sensitivities of two VIs when the biophysical parameters are unavailable.     

利用传感器噪声对复制遮盖篡改的检测

由于现今功能强大的图像编辑软件很容易就可以得到,所以对数字图像进行操作和编辑变得非常的容易,在一幅图像中添加或移掉一个重要的人或物并且不留任何痕迹是很有可能的.如果这些篡改图用在媒体或法律上,对社会将造成很大的影响.随着数码相机和摄像机的不断普及,验证数码图像变得越来越重要了.利用相机的传感器噪声对复制遮盖篡改图像进行检测,并根据其模板噪声进一步确定篡改区域.对多幅图像进行操作,实验证明效果不错.     

Mapping woodland species composition and structure using airborne spectral and LiDAR data

Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms. This paper investigates the unique thematic classes that can be derived using integrated airborne LiDAR and spectral data. The study area consists of a heterogeneous, semi‐natural broadleaf woodland on an ancient site and homogeneous broadleaf and conifer woodland on an adjoining plantation. A parcel‐based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data. The resultant 52 data clusters were amalgamated into 10 distinct thematic classes that contain information on species composition and vegetation structure. The thematic classes are relevant to the National Vegetation Classification (NVC) scheme for woodlands and scrub of Great Britain. Furthermore, in distinguishing structural subdivisions within the species‐based NVC classes, the thematic classification provides greater information for quantifying woodland habitat. The classes show degeneration from and regeneration to mature woodland communities and thus reflect the underlying processes of vegetation succession and woodland management. This thematic classification is ecologically relevant and is a forward development in woodland maps created from remote sensing data.     

Detection of buried ancient walls using airborne thermal video radiometry

This study examined the feasibility of using a very sensitive thermal video radiometer to derive information about subsoil objects from the air. In this study we mounted a thermal sensor onboard a helicopter and acquired digital data from an altitude of 1333 m over an archaeological site on the Golan Heights, Israel. The site, namely, Leviah Enclosure, is an Early Bronze Age settlement that is covered by a thin layer of soil. The buried structures, made from basalt, could not be observed from the ground or in aerial photos. However, in the thermal images, the buried basalt structures were significantly enhanced because they have different thermal characteristics than the ground's surface. Based on the thermal images, it was possible to generate a map to use for future excavation activity. Referring to the thermal maps, a selected area was excavated, and verification on the ground, using traditional archaeological methods revealed a positive agreement between the thermal-based map and the actual location of the buried structures. The research highlights the fact that this technology can contribute additional and useful information to the field of archaeology. Based on these results, further study is planned in order to examine the capability of the sensor under different conditions and to further excavate the entire Leviah Enclosure.     

Chemical warfare agents simulants detection with an optimized SAW sensor array

An E-nose based on surface acoustic wave (SAW) sensors has been developed, and sensitive polymer coatings have been optimized to detect simulants of chemical warfare agents (CWAs). The polymers selected have allowed to discriminate among simulants and classify them at low concentrations in air through Pattern Recognition Methods. Good detection responses have been achieved for very low concentrations, such as 0.05 ppm for Dimethyl methylphosphonate (DMMP) and 0.5 ppm for dipropylene glycol monomethyl ether (DPGME).     

The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings

Field spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress and disease. The objective of this study was to develop spectral indices for detection of Ganoderma disease in oil palm seedlings. The reflectance spectra of oil palm seedlings from three levels of Ganoderma disease severity were acquired using a spectroradiometer. Denoizing and data transformation using first derivative analysis was conducted on the original reflectance spectra. Then, comparative statistical analysis was used to select significant wavelength from transformed data. Wavelength pairs of spectral indices were selected using optimum index factor. The spectral indices were produced using the wavelength ratios and a modified simple ratio method. The relationship analysis between spectral indices and total leaf chlorophyll (TLC) was conducted using regression technique. The results suggested that six spectral indices are suitable for the early detection of Ganoderma disease in oil palm seedlings. Final results after regression with TLC showed that Ratio 3 is the best spectral index for the early detection of Ganoderma infection in oil palm seedlings. For future works, this can be used for the development of robust spectral indices for Ganoderma disease detection in young and mature oil palm using airborne hyperspectral imaging.     

Winter wheat vegetation indices calculated from combinations of seven spectral bands

Spectral reflectance data were obtained for winter wheat over a full growing season. Four irrigation treatments, applied to six genotypes, provided a variety of crop growth conditions. Leaf area index, green ground cover, total wet and total dry phytomass, and leaf phytomass measurements were taken monthly during the winter and biweekly during the spring. Reflectance measurements were made with a radiometer having three visible, two near-IR and two mid-IR bands. Vegetation indices, calculated from various band combinations, were linearly related to the five plant parameters. Of the 1240 vegetation indices formed, ratio indices had the higher (0.79–0.86) coefficients of determination (r²) than N-space greenness (0.61–0.81) when related to the plant parameters. The commonly used IR/red ratio produced considerably lower r² values than many of the other ratio indices. The mid-IR bands appeared more frequently in the ratio indices than in the greenness indices. The results show the relative merits of the seven bands, when combined into vegetation indices, to estimate various plant parameters.