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Accurate estimation of global precipitation is critical for the study of the earth in a changing climate. It is generally understood that instantaneous retrieval of precipitation using microwave sensors is more accurate in the tropics and mid latitudes, but the retrievals become difficult and uncertain at higher latitude and over frozen land. In the lack of reliable microwave-based precipitation estimates at high latitudes, retrievals from a single infrared band are commonly used as an alternative to fill the missing gaps. The present study shows that multi-spectral infrared, near-surface air temperature, and near-surface humidity data can add useful information to that obtained from a single infrared band and can significantly improve delineating precipitating from non-precipitating scenes, especially at higher latitudes over land. The role of surface air temperature and humidity is found to be more effective at higher latitudes, but multispectral data is effective across all latitudes. The study is performed using 4 years (2007–2010) of collocated multi-spectral data from the Moderate Resolution Imaging Spectroradiometer (MODIS), surface temperature and humidity data from the European Center for Medium Range Weather Forecast (ECMWF) analysis, and reference precipitation data from CloudSat, which can detect even very light precipitation within 80°S–80°N. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study the influence of the measurement noise (on the flux) and of model bias on the temperature estimation by multispectral method with two, three or four colors using a model based on the Wien's approximation (and flux ratio) and taking into account spectral emissivity up to 2-order variations (a special case will be considered: variation according to the Drude's law). For this purpose, in a first part the model and the method will be presented, then in a second part, the estimation of parameters from a regularized nonlinear least squares regression algorithm (Levenberg-Marquardt), and their statistical properties obtained using a Monte Carlo method (1000 estimates) will be discussed. The study will conclude with experimental validations in the InfraRed range (using a thermal camera). [ABSTRACT FROM AUTHOR]

Pedestrian detection is a crucial problem in human pose recovery and behavior analysis, especially in applications such as visual surveillance, robotics, and drive-assistance systems. Recently, most pedestrian detection approaches of machine learning and signal processing have achieved advanced performance in traditional natural images. However, there exists a limitation on the accuracy in pedestrian detection. The reason behind this is that supporting information for detecting pedestrian is limited. In fact, spectrum besides visible light can provide abundant discriminative information for pedestrian detection. Therefore, it is significative to exploit multi-spectral information for detection task. In this paper, a multi-spectral based pedestrian detection approach is proposed, which not only takes use of the information of red, green and blue (RGB) bands, but also incorporates the information of near-infrared spectrum into the detection process. Latent variable support vector machines (L-SVM) are employed to train the multi-spectral pedestrian detection model. Experiments are implemented on a new dataset containing 1826 multi-spectral image pairs. The experimental results illustrate that utilizing multi-spectral information achieves significant performance improvement in a pedestrian detection task compared with only using RGB information. [ABSTRACT FROM AUTHOR]

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R2) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAirTM platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAirTM imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system. [ABSTRACT FROM AUTHOR]

Bali Botanical Garden is the ex-situ plant conservation which has been used for research activities, environment education/ services and recreation. However, this site becomes a magnet of regional development in the surrounding Bedugul Basin. However, this is an isolated region which has limited spatial utilization. Land-use/ land Cover change with Landsat time series (1997 and 2014) can be figured out how this influences land conservation and conversion around it. The research method used digital image processing, GIS and field measurement/observation. Land-use/ Land Cover information was derived by supervised multispectral classification. NDVI analysis was used to classify the vegetation density. Field measurement made on the sample locations were selected based on purposive sampling method. It is as the green belt and green barrier which not only protecting the ecosystem in the upper but also controlling land changes in downstream. The natural region in this upperstream has well protected (94% constant, 5% lost and 1% increase of the vegetation cover) but this under area has changed by 25.46% as built areas. [ABSTRACT FROM AUTHOR]

Downy mildew (DM) caused by the biotrophic obligate oomycete Peronospora arborescens (Berk.) is one of the most economically limiting diseases of opium poppy ( Papaver somniferum L.) worldwide. The first symptoms appear as small chlorotic leaf lesions, which can evolve to curled and thickened tissues that become deformed and necrotic as the disease develops. The present study explored the use of high-resolution thermal and multi-spectral imagery as an indicator of DM infection. Work was conducted in two opium poppy field plots artificially infected by P. arborescens. Airborne thermal and multi-spectral imagery were acquired at 200 mm resolution on three dates in spring of 2009 using an unmanned aerial vehicle (UAV). Leaf reflectance and transmittance spectra of DM asymptomatic and symptomatic opium poppy leaves were measured using an integrating sphere. Simulation work was conducted with the coupled PROSPECT + SAILH radiative transfer model to assess the effects of the variability found in an opium poppy plot developing a DM epidemic on the normalized difference vegetation index (NDVI) and the green/red index (R/R) calculated from the multi-spectral imagery. The airborne flights enabled DM detection by using image-derived canopy temperature (Tc) normalized by air temperature (Tc − Ta) and the green/red index (R/R). T for each grid unit was calculated to estimate pure-vegetation temperature removing background and soil effects. T − Ta and R/R were assessed as a function of aggregated NDVI clusters to compare asymptomatic and symptomatic plants normalized by similar growth levels. Results demonstrated that Tc − Ta and the R/R index were related to physiological stress caused by DM infection. In addition, T − Ta was found to decrease as the NDVI increased and symptomatic plants reached significantly higher ( P < 0.05) temperatures for an NDVI ≥0.6. The R/R index was positively correlated with the NDVI, showing significantly higher values ( P < 0.05) in symptomatic plants with an NDVI ≥0.5. These results demonstrate the feasibility of detecting P. arborescens infection in opium poppy plants using high-resolution thermal and multi-spectral imagery acquired with an UAV. [ABSTRACT FROM AUTHOR]

[Display omitted] • An innovative strategy based on nano-effect spectroscopy and data fusion is proposed. • The strategy achieved identification of cultivation method and years of Dendrobium huoshanense. • Mid-level fusion strategy based on nano-effects significantly improves discriminative accuracy. • PLS-DA and OPLS-DA achieved 100% accuracy of cultivation years and methods identification. • The cultivation years of Dendrobium huoshanense was satisfactorily achieved by PLSR model. Dendrobium huoshanense (DHS) is an expensive Chinese herb with medicinal and edible functions. The quality of DHS is closely related to cultivation methods and years. In this work, nano-effect near and mid infrared spectra of 320 DHS samples with four cultivation methods and four years were obtained, and the feature vectors were obtained using a mid-level data fusion strategy with significant variable extraction and integration using variable importance for the projection (VIP). The feature vectors then combined with partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify the cultivation years and methods. The result showed that it is difficult to discriminate effectively by a single spectrum, and the accuracy of PLS-DA discrimination based on raw or simple fusion of raw spectra is only 95.5% and 84.4% or 99.6% and 88.5%, while the accuracy of discrimination based on nano-effect feature fusion spectra can be improved to 100%, and PLSR was used to quantify the cultivation year of the DHS with good linearity and accuracy. The nano-effect spectrum is based on the reaction of tetraphenyl zinc porphyrin (ZnTPP) with polysaccharides, flavonoids and alkaloids in DHS, which amplifies the spectral difference. This study demonstrates that multi-spectral feature vectors fusion method could potentially be a reliable analytical method to distinguish the cultivation methods and years in DHS herbs. [ABSTRACT FROM AUTHOR]

This paper describes selected results from over a dozen collaborative projects led by Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia spanning a 30-year history of developments in satellite, airborne, field and drill core sensing technologies and how these can assist explorers to measure and map valuable mineral information. The exploration case histories are largely from Australian test sites and describe how spectral sensing technologies have progressed from early “niche creation” systems, such as the field PIMA-II (Portable Field Mineral Analyzer) and airborne Geoscan, HyMap™ and OARS-TIPS (Operational Airborne Remote Sensing – Thermal Infrared Profiling Spectrometer) systems and drill-core HyLogger™ systems, to the current expanding array of pubic and commercial mineral mapping sensors, including the ASTER (Advanced Spaceborne Thermal Emission and Reflectance Radiometer) satellite system which has acquired imagery spanning the entire Earth’s land surface (

In the context of climate change, agricultural managers have the imperative to combine sufficient productivity with durability of the resources. Many studies have shown the interest of recent satellite missions as suitable tools for agricultural surveys. Nevertheless, they are not predictive methods. A system able to detect summer crops as early as possible is important in order to obtain valuable information for a better water management strategy. The detection of summer crops before the beginning of the irrigation period is therefore our objective. The study area is located near Toulouse (southwestern France), and is a region of mixed farming with a wide variety of irrigated and non-irrigated crops. Using the reference data for the years concerned, a set of fixed thresholds are applied to a vegetation index (the Normalized Difference Vegetation Index, NDVI) for each agricultural season of multi-spectral satellite optical imagery acquired at decametric spatial resolutions from 2006 to 2013. The performance (i.e., accuracy) is contrasted according to the agricultural practices, the development states of the different crops and the number of acquisition dates (one to three in the results presented here). The detection of summer crops reaches 64% to 88% with a single date, 80% to 88% with two dates and 90% to 99% with three dates. The robustness of this method is tested for several years (showing an impact of meteorological conditions on the actual choice of images), several sensors and several resolutions.

Achieving more timely, accurate and transparent information on the distribution and dynamics of the world’s land cover is essential to understanding the fundamental characteristics, processes and threats associated with human-nature-climate interactions. Higher resolution (~30–50 m) land cover mapping is expected to advance the understanding of the multi-dimensional interactions of the human-nature-climate system with the potentiality of representing most of the biophysical processes and characteristics of the land surface. However, mapping at 30-m resolution is complicated with existing manual techniques, due to the laborious procedures involved with the analysis and interpretation of huge volumes of satellite data. To cope with this problem, an automated technique was explored for the production of a high resolution land cover map at a national scale. The automated technique consists of the construction of a reference library by the optimum combination of the spectral, textural and topographic features and predicting the results using the optimum random forests model. The feature-rich reference library-driven automated technique was used to produce the Japan 30-m resolution land cover (JpLC-30) map of 2013–2015. The JpLC-30 map consists of seven major land cover types: water bodies, deciduous forests, evergreen forests, croplands, bare lands, built-up areas and herbaceous. The resultant JpLC-30 map was compared to the existing 50-m resolution JAXA High Resolution Land-Use and Land-Cover (JHR LULC) map with reference to Google Earth™ images. The JpLC-30 map provides more accurate and up-to-date land cover information than the JHR LULC map. This research recommends an effective utilization of the spectral, textural and topographic information to increase the accuracy of automated land cover mapping.

Since the existence of buried targets influences the thermal physical properties of the surrounding area, causing the temperature differences over time in the regional surface, which reflected in the infrared image is the gray value changes over time. To solve the problem, we research on the mathematical model of teinperature change of the region containing buried targets, and reveal the relationship between the regional temperature changes and thermal physical properties and buried position of the buried targets. We use real multi-temporal and multi-spectral infrared images to get the temperature distribution of the region surface which compared with the solution of the mathematical model. By continuously changing the value of the thermal model parameters, we can get a solution which consistent with the temperature distribution obtained by the real temporal infrared images, and then we complete the detection of subsurface targets. [ABSTRACT FROM AUTHOR]

Abstract: In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objectives of this study are to develop, demonstrate and validate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near real-time at the African continental scale as a first step towards global scale coverage. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. Two different validations were done at the continental scale over Africa: i) The algorithm validation checked the ability of the proposed algorithm to perform as effectively as human interpretation of the image: it showed an accuracy of 96.6% and no commission errors. ii) The product validation was carried out by using an independent dataset derived from high resolution imagery: the continental permanent water surface product showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at the African continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties. [Copyright &y& Elsevier]

Highlights: [•] A multi-spectral dataset is constructed containing RGB and near-infrared images. [•] The incorporation of near-infrared is proved to be valuable for saliency detection. [•] The best model for integrating RGB and near-infrared clues is analyzed. [ABSTRACT FROM AUTHOR]

Abstract: The United States Department of Energy (DOE) is the lead federal agency for the research, development, demonstration, and deployment (RDD&D) of carbon storage technologies. Monitoring, verification, accounting and assessment (MVA) is an essential element of geologic CO2 storage projects, since without MVA it is not possible to understand the fate of CO2 in the injection formation or to monitor any potential CO2 releases to underground sources of drinking water (USDW) or the atmosphere. Determining the location of CO2 in the subsurface and identifying indicators of the potential release of CO2 to the atmosphere are significant challenges requiring the adoption of existing technologies as well as novel approaches for monitoring large areas above a storage reservoir. Remote sensing technologies could offer a solution. Remote sensing refers to the use of monitoring tools that can gather data at a location remote to the area of interest, and could provide an option for non-invasive and large scale spatial monitoring. The National Energy Technology Laboratory (NETL) has been developing and deploying remote sensing applications over the past decade to improve monitoring of both geologic and terrestrial carbon storage projects. MVA remote sensing tools being developed or improved by NETL sponsored research include interferometric synthetic aperture radar (InSAR), tiltmeters/GPS, remote operated vehicles (ROVs), SEQURE™, light detection and ranging (LIDAR), and multi-spectral/hyper-spectral scanning. Benefits associated with NETL's remote sensing effort include development and deployment of non-intrusive tracking and monitoring technologies, reduced manpower requirements needed to meet potential regulated MVA requirements, and reduced costs to implement monitoring technologies. These technologies can provide early detection of CO2 releases which, in turn, will allow for further refinement of project monitoring protocols using more conventional detection and mitigation technologies to better pinpoint CO2 location. This paper addresses selected remote sensing techniques under NETL development and field deployment of these technologies. [Copyright &y& Elsevier]

Abstract: Visual saliency detection has been applied in many tasks in the fields of pattern recognition and computer vision, such as image segmentation, object recognition, and image retargeting. However, the accurate detection of saliency remains a challenge. The reasons behind this are that: (1) well-defined mechanism for saliency definition is rarely established; and (2) supporting information for detecting saliency is limited in general. In this paper, a multi-spectrum based saliency detection algorithm is proposed. Instead of only using the conventional RGB information as what existing algorithms do, this work incorporates near-infrared clues into the detection framework. Features of color and texture from both types of image modes are explored simultaneously. When calculating the color contrast, an effective color component analysis method is employed to produce more precise results. With respect to the texture analysis, texton representation is adopted for fast processing. Experiments are done to compare the proposed algorithm with other 11 state-of-the-art algorithms and the results indicate that our algorithm outperforms the others. [Copyright &y& Elsevier]

Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm. With this motivation, as the first contribution, the article presents a unified PAD algorithm for different kinds of attacks such as printed photos, a replay of video, 3D masks, silicone masks, and wax faces. The proposed algorithm utilizes a combination of wavelet decomposed raw input images from sensor and face region data to detect whether the input image is bonafide or attacked. The second contribution of the article is the collection of a large presentation attack database in the NIR spectrum, containing images from individuals of two ethnicities. The database contains 500 print attack videos which comprise approximately 1,00,000 frames collectively in the NIR spectrum. Extensive evaluation of the algorithm on NIR images as well as visible spectrum images obtained from existing benchmark databases shows that the proposed algorithm yields state-of-the-art results and surpassed several complex and state-of-the-art algorithms. For instance, on benchmark datasets, namely CASIA-FASD, Replay-Attack, and MSU-MFSD, the proposed algorithm achieves a maximum error of 0.92% which is significantly lower than state-of-the-art attack detection algorithms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Agarwal, Singh, Vatsa and Noore.)

In this study we use visible, short-wave infrared and thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data validated with high-resolution Quickbird (QB) and Worldview2 (WV2) for mapping debris cover in the eastern Himalaya using two independent approaches: (a) a decision tree algorithm, and (b) texture analysis. The decision tree algorithm was based on multi-spectral and topographic variables, such as band ratios, surface reflectance, kinetic temperature from ASTER bands 10 and 12, slope angle, and elevation. The decision tree algorithm resulted in 64 km2 classified as debris-covered ice, which represents 11% of the glacierized area. Overall, for ten glacier tongues in the Kangchenjunga area, there was an area difference of 16.2 km2 (25%) between the ASTER and the QB areas, with mapping errors mainly due to clouds and shadows. Texture analysis techniques included co-occurrence measures, geostatistics and filtering in spatial/frequency domain. Debris cover had the highest variance of all terrain classes, highest entropy and lowest homogeneity compared to the other classes, for example a mean variance of 15.27 compared to 0 for clouds and 0.06 for clean ice. Results of the texture image for debris-covered areas were comparable with those from the decision tree algorithm, with 8% area difference between the two techniques. [ABSTRACT FROM AUTHOR]

Abstract: Land surface temperature (LST) plays an important role in many fields. However, the limited spatial resolution of current thermal sensors impedes the utilization of LSTs. Based on a theoretical framework of thermal sharpening, this report presents an Enhanced Generalized Theoretical Framework (EGTF) to downscale LSTs using multi-spectral (MS) and multi-resolution images. MS proxy-sharpening and LST downscaling are combined under EGTF. Simulated images upscaled from Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are produced for indirect validations. Validation of MS proxy-sharpening shows that EGTF is better than the Gram-Schmidt (GS) and the Principle Component (PC) methods, yielding a lower root mean square error (RMSE) and ERGAS (erreur relative globale adimensionnelle de synthèse) and, thus, maintaining higher spectral similarity. For LST downscaling, validations show that EGTF has a higher accuracy than the Unmixing-Based Image Fusion (UBIF) method and indicate that the proxy-sharpening process improves the accuracy of downscaled LSTs. Further discussions regarding the selection of the moving-window size (MWS) demonstrate that the MWS could be determined by the range in a semi-variance analysis of scaling factor images. [Copyright &y& Elsevier]

Abstract: Aerial photography has made the single most important contribution to our improved appreciation of the density, diversity and distribution of archaeological sites in Britain since World War Two. This is particularly the case for areas of intensive lowland agriculture where ploughed-out sites are known mainly from marks in the crops growing above them. However, reconnaissance for such cropmarks is not equally effective throughout the lowlands, because of the particular conditions of drier weather, well-drained soils and arable agriculture required before they become visible, and is highly unpredictable. Given that the appearance of cropmarks is linked to moisture stress in growing plants, they are potentially detectable at bandwidths outside the visible spectrum and before they become apparent therein. This paper focuses on the application of two spectral enhancement techniques: Principal component analysis and Tasselled cap transformation. Comparing a range of imagery (CASI-2, ATM and digital vertical photographic data) from two case study areas in Lowland Scotland, each with very different environmental, agricultural and archaeological backgrounds to facilitate further comparisons, the paper demonstrates that multi-spectral/hyperspectral imagery can enhance the identification of otherwise invisible archaeological sites, particularly in the near-infrared part of the spectrum. However, the lower spatial resolution of such imagery, compared to photography, can make the often diffuse and incomplete cropmark traces more difficult to determine with confidence. [Copyright &y& Elsevier]

This paper addresses the problem of obtaining 3d detailed reconstructions of human faces in real-time and with inexpensive hardware. We present an algorithm based on a monocular multi-spectral photometric-stereo setup. This system is known to capture high-detailed deforming 3d surfaces at high frame rates and without having to use any expensive hardware or synchronized light stage. However, the main challenge of such a setup is the calibration stage, which depends on the lights setup and how they interact with the specific material being captured, in this case, human faces. For this purpose we develop a self-calibration technique where the person being captured is asked to perform a rigid motion in front of the camera, maintaining a neutral expression. Rigidity constrains are then used to compute the head's motion with a structure-from-motion algorithm. Once the motion is obtained, a multi-view stereo algorithm reconstructs a coarse 3d model of the face. This coarse model is then used to estimate the lighting parameters with a stratified approach: In the first step we use a RANSAC search to identify purely diffuse points on the face and to simultaneously estimate this diffuse reflectance model. In the second step we apply non-linear optimization to fit a non-Lambertian reflectance model to the outliers of the previous step. The calibration procedure is validated with synthetic and real data. [ABSTRACT FROM AUTHOR]

Abstract: In order to monitor red tide in Case 2 waters, this paper proposes a method of multi normalized difference indices combination to extractred tide information from multi-spectral remote sensing image, and uses HJ-CCD and MODIS data to extract red tide information in the Shenzhen near-shore waters and the Pearl River Estuary to verify its feasibility. Because the spatial resolution of HJ-CCD is better than MODIS, the extract result is better than MODIS. [Copyright &y& Elsevier]

Abstract: In this paper, from the water spectral reflectance characteristics, I select five multi-spectral remote sensing water information extraction methods, issue comparative analysis and applicability assessment in five typical experiment areas based on Landsat data. I have made a comprehensive comparison and algorithm applicability analysis on the five kinds of methods. The main conclusions are the following: MNDWI and The spectral relationship method between multi-band have the most widely applicability, MNDWI always use in the building shadow and bare land. The spectral relationship method between multi-band has the special effects on the mountain shadow areas. NDVI has an apparent superiority in the dense forest and grassland, the low-cost of single-band threshold make it have more advantages in the great plains areas, compromise and low noise of NDWI can be used in complicated areas. [Copyright &y& Elsevier]

Abstract: One of the important science requirements of the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission is to be able to measure ozone with two degrees of freedom in the troposphere and sensitivity in the lowest 2 km (lowermost troposphere, LMT), in order to characterize air quality and boundary layer transport of pollution. Currently available remote sensing techniques utilize backscattered solar ultraviolet (UV) radiances or thermal infrared (TIR) emissions to perform ozone retrievals. However, in the TIR, measurement sensitivity to the LMT requires high thermal contrast between the Earth’s surface and the near-surface (tens to hundreds of meters above surface) atmosphere, while in the UV, the measurement sensitivity to the LMT is low because of Rayleigh scattering. In this paper, we explore the feasibility of using multi-spectral intensity measurements in the UV, visible (VIS), mid infrared (MIR) and TIR, and polarization measurements in the UV/VIS, to improve tropospheric and lowermost tropospheric ozone retrievals. Simulations for 16 cloud and aerosol free atmospheric profiles spanning a range of ozone mixing ratios indicate that adding VIS measurements to UV measurements significantly enhances the sensitivity to lowermost tropospheric ozone, but only makes a slight improvement to the total degrees of freedom for signal (DFS). On the other hand, the combination of UV and TIR significantly improves the total DFS as well as the lowermost tropospheric DFS. The analysis presented here is a necessary and important first step for defining spectral regions that can meet the GEO-CAPE measurement requirements, and subsequently, the requirements for instrumentation. In this work, the principle of multi-spectral retrievals has been extended from previously published literature and we show that the UV + VIS, UV + TIR and UV + VIS + TIR combinations have the potential to meet the GEO-CAPE measurement requirements for tropospheric ozone. Our analysis includes errors from water and surface properties; further analysis is needed to include temperature, additional gas interferents, clouds, aerosols and more realistic surface properties. These simulations must be run on a much larger dataset, followed by OSSEs (Observing System Simulation Experiments), where simulated retrievals are assimilated into chemical-transport models, to quantitatively assess the impact of the proposed measurements for constraining the spatiotemporal distribution of ozone in the LMT for basic science studies and applications such as air quality forecasts. [Copyright &y& Elsevier]

Abstract: Defoliation is a key parameter of forest health and is associated with reduced productivity and tree mortality. Assessing the health of forests requires regular observations over large areas. Satellite remote sensing provides a cost-effective alternative to traditional ground-based assessment of forest health, but assessing defoliation can be difficult due to mixed pixels where vegetation cover is low or fragmented. In this study we apply a novel spectral unmixing technique, referred to as weighted Multiple Endmember Spectral Mixture Analysis (wMESMA), to Landsat 5-TM and EO-1 Hyperion data acquired over a Eucalyptus globulus (Labill.) plantation in southern Australia. This technique combines an iterative mixture analysis cycle allowing endmembers to vary on a per pixel basis (MESMA) and a weighting algorithm that prioritizes wavebands based on their robustness against endmember variability. Spectral mixture analysis provides an estimate of the physically interpretable canopy cover, which is not necessarily correlated with defoliation in mixed-aged plantations due to natural variation in canopy cover as stands age. There is considerable variability in the degree of defoliation as well as in stand age among sites and in this study we found that results were significantly improved by the inclusion of an age correction algorithm for both the multi-spectral (R 2 no age correction =0.55 vs R 2 age correction =0.73 for Landsat) and hyperspectral (R 2 no age correction =0.12 vs R 2 age correction =0.50 for Hyperion) image data. The improved accuracy obtained from Landsat compared to the Hyperion data illustrates the potential of applying SMA techniques for analysis of multi-spectral datasets such as MODIS and SPOT-VEGETATION. [Copyright &y& Elsevier]

Abstract: Crop descriptors, such as leaf area index, crop cover fraction, and leaf chlorophyll content, can be successfully estimated using appropriate spectral indices from the visible and near infrared spectral regions. However, these indices do not provide estimates of dry biomass, an important indicator of crop productivity. For estimating crop aboveground dry biomass and yield, this study developed an approach to integrate crop stressors and crop descriptors derived from optical remote sensing data with the Monteith radiation use efficiency model. Multi-temporal remote sensing data were acquired by the Compact Airborne Spectrographic Imager and the Landsat-5/7 Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) sensors to monitor the growth conditions of corn in the 2001 and 2006 growing seasons. The modified triangular vegetation index (MTVI2) derived from the remote sensing data was used to estimate the fraction of absorbed photosynthetically active radiation (f APAR ). A canopy structure dynamics model was then used to simulate the seasonal variation of f APAR . Crop water stress was estimated from the near and shortwave infrared reflectance of the Landsat images for a dry period in the 2001 growing season. By estimating leaf chlorophyll content using the Transformed Chlorophyll Absorption in Reflectance Index (TCARI) in combination with the Optimized Soil Adjusted Vegetation Index (OSAVI), different levels of nitrogen content could be identified. For the two growing seasons, the aboveground dry biomass and yield were linearly related with the cumulative absorbed photosynthetically active radiation (APAR) using the Monteith radiation use efficiency model. The cumulative APAR accounted for 96% of the corn aboveground dry biomass variability and 72% of the yield variability. Biomass and yield variability were partly explained by the variations in crop water stress intensity, which was dependent on soil texture. The seasonal radiation use efficiency was stable over the 2years and was about 3.9gMJ−1, with a confidence interval of 0.6gMJ−1 at the 95% confidence level. The assimilation of remotely sensed data into the radiation use efficiency model performed well for monitoring dry biomass accumulation and estimating corn yields. [Copyright &y& Elsevier]

Abstract: Magnetic resonance imaging (MRI) is a valuable instrument in medical science owing to its capabilities in soft tissue characterization and 3D visualization. A potential application of MRI in clinical practice is brain parenchyma classification. This work proposes a novel approach called “Unsupervised Linear Discriminant Analysis (ULDA)” to classify and segment the three major tissues, i.e. gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF), from a multi-spectral MR image of the human brain. The ULDA comprises two processes, namely Target Generation Process (TGP) and Linear Discriminant Analysis (LDA) classification. TGP is a fuzzy-set process that generates a set of potential targets from unknown information, and applies these targets to train the optimal division boundary by LDA, such that three tissues GM, WM and CSF are separated. Finally, two sets of images, namely computer-generated phantom images and real MR images are used in the experiments to evaluate the effectiveness of ULDA. Experiment results reveal that UDLA segments a multi-spectral MR image much more effectively than either FMRIB''s Automated Segmentation Tool (FAST) or Fuzzy C-means (FC). [Copyright &y& Elsevier]

Artificial Color is the use of technology to emulate the basic means by which animals acquire and use spectral information. It begins with sensing of the scene using multiple broad and overlapping spectral sensitivity functions analogous with cone cells in the human visual system. In this paper we develop an algorithm to obtain a set of near-optimal application-specific spectral sensitivity functions for Artificial Color systems. The user-designed sensitivity functions result in concurrent good intra-class packing and interclass integrity in the color space for reflectance classes with known statistics. This method leads to improved color discrimination and results in more robust spectral classifiers. [ABSTRACT FROM AUTHOR]

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations). [ABSTRACT FROM AUTHOR]

A visual analytics technique for the intuitive, hands-on analysis of massive, multi-dimensional and multi-variate data is presented. This multi-touch-based technique introduces a set of metaphors such as wiping, scratching, sandblasting, squeezing and drilling, which allow for rapid analysis of global and local characteristics in the data set, accounting for factors such as gesture size, pressure and speed. A case study is provided for the analysis of multi-spectral image data of cultural artefacts. By aligning multi-spectral layers in a stack, users can apply different multi-touch metaphors to investigate features across different wavelengths. With this technique, flexibly definable regions can be interrogated concurrently without affecting surrounding data. [ABSTRACT FROM AUTHOR]

The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%). [ABSTRACT FROM AUTHOR]