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Sample records for airborne hyperspectral remote

  1. Remote sensing of soil moisture using airborne hyperspectral data

    USGS Publications Warehouse

    Finn, Michael P.; Lewis, Mark (David); Bosch, David D.; Giraldo, Mario; Yamamoto, Kristina H.; Sullivan, Dana G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, Craig; Williams, Michael S.

    2011-01-01

    Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

  2. Remote sensing of soil moisture using airborne hyperspectral data

    USGS Publications Warehouse

    Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.

    2011-01-01

    Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

  3. Automation of hyperspectral airborne remote sensing data processing

    NASA Astrophysics Data System (ADS)

    Kozoderov, V. V.; Egorov, V. D.

    2014-12-01

    An automated system is proposed for discriminating the spectral radiances registered by the hyperspectral airborne instruments based on average spectra and their interclass variability while distinguishing pixels related to the illuminated and shaded elements of the crown trees for various species and ages. Maps of the ground-based inventory for the selected area of airborne remote sensing are used as prior information. The system automatically forms databases of the selected classes of objects using the contours of these objects drawn on the image under processing. An opportunity to distinguish these classes is demonstrated in the red edge region of the spectra transition from the chlorophyll spectral band to the maximum of the spectral vegetation reflectivity.

  4. Remote sensing of soil moisture using airborne hyperspectral data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Institute for Technology Development (ITD) has developed an airborne hyperspectral sensor system that collects electromagnetic reflectance data of the terrain. The system consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near Infrare...

  5. Airborne hyperspectral systems for solving remote sensing problems

    NASA Astrophysics Data System (ADS)

    Rodionov, I. D.; Rodionov, A. I.; Vedeshin, L. A.; Vinogradov, A. N.; Egorov, V. V.; Kalinin, A. P.

    2014-12-01

    A retrospective of airborne hyperspectrometer projects carried out in the ZAO Reagent Scientific Technical Center is presented. Hyperspectral devices developed during the period since the end of 1990s to the present day are described. The line of hyperspectrometers designed in recent times covers the ranges from ultraviolet (0.2 μm) to near infrared (1.0 μm). These devices can be mounted on airborne and automobile carriers, including small-size ones. By now, the developments of hyperspectral devices in ZAO Reagent have reached the finished state and have been prepared for serial production. Their technical characteristics permit one to speak of the creation of wide-range high-aperture ultraspectral high spatial resolution equipment with a possibility of real-time data processing on board.

  6. Application research of using CASI/SASI airborne hyperspectral remote sensing on lithology identification

    NASA Astrophysics Data System (ADS)

    Zhou, Jiajing; Qin, Kai

    2016-04-01

    Remote sensing provides an advanced method for lithology identification, which is one of the important research fields in geological prospecting. In theory, each lithology is of individual spectrum characteristics. Based on the spectral differences between them, we can identify different lithologies by remote sensing images. At present, the studies on lithology identification by remote sensing are primarily conducted on the multispectral images, such as Landsat 7 ETM+, SPOT-5, QuickBird and WorldView-2. Hyperspectral remote sensing images provide richer information, making it easier to identify the lithologies, but studied rarely. CASI/SASI is an airborne hyperspectral system covering a wavelength range of 0.38-2.45μm. With hundreds of bands, the hyperspectral images are useful to identify the spectrum characteristics of lithology. In addition, images are of high spatial resolution, with CASI of about 1m and SASI of about 2-2.5m, which make lithology identification more accurate. CASI/SASI hyperspectral data was collected in Beishan metallogenic belt in northwest China, as same as the ground spectral data of the lithologies. After data preprocessing, we divided different lithologies using CASI/SASI hyperspectral images and lithology spectrum, identified some important lithologies related to mineralization, and successfully found a few new ore clues.

  7. Airborne Hyperspectral Remote Sensing for Identification Grassland Vegetation

    NASA Astrophysics Data System (ADS)

    Burai, P.; Tomor, T.; Bekő, L.; Deák, B.

    2015-08-01

    In our study we classified grassland vegetation types of an alkali landscape (Eastern Hungary), using different image classification methods for hyperspectral data. Our aim was to test the applicability of hyperspectral data in this complex system using various image classification methods. To reach the highest classification accuracy, we compared the performance of traditional image classifiers, machine learning algorithm, feature extraction (MNF-transformation) and various sizes of training dataset. Hyperspectral images were acquired by an AISA EAGLE II hyperspectral sensor of 128 contiguous bands (400-1000 nm), a spectral sampling of 5 nm bandwidth and a ground pixel size of 1 m. We used twenty vegetation classes which were compiled based on the characteristic dominant species, canopy height, and total vegetation cover. Image classification was applied to the original and MNF (minimum noise fraction) transformed dataset using various training sample sizes between 10 and 30 pixels. In the case of the original bands, both SVM and RF classifiers provided high accuracy for almost all classes irrespectively of the number of the training pixels. We found that SVM and RF produced the best accuracy with the first nine MNF transformed bands. Our results suggest that in complex open landscapes, application of SVM can be a feasible solution, as this method provides higher accuracies compared to RF and MLC. SVM was not sensitive for the size of the training samples, which makes it an adequate tool for cases when the available number of training pixels are limited for some classes.

  8. Mapping mine tailings using airborne geophysical and hyperspectral remote sensing data

    NASA Astrophysics Data System (ADS)

    Shang, Jiali

    Mine tailings are the waste products from mining operations. Most mine tailings contain a considerable amount of reactive sulphides which can cause acid mine drainage (AMD) when exposed to air and water. AMD constitutes a threat both to the environment and to public health. Increased awareness of AMD has led to growing activities in mine-tailing monitoring and reclamation worldwide. Mining companies in Canada are required to provide information to provincial governments about their waste disposal and control activities. There is an urgent need to develop new automated ways to provide information on short- to long-term evolution of tailings, thus enabling the mining companies to monitor their tailings more effectively. The overall goal of the thesis is to explore the potential of hyperspectral remote sensing and geophysical techniques for mapping variations within and immediately outside of the tailings. Data used for this study are from three sources: airborne geophysical data, hyperspectral casi and Probe-1 data, and field data. This study has contributed to both the remote sensing data analysis techniques and the understanding of mine-tailing surface and subsurface processes. Specifically, this study has the following important findings: (1) Airborne magnetic and electromagnetic data can provide information regarding the subsurface distribution of mine tailings on the basis of sulphide mineral content. A procedure has been developed in this study to use these data sources for rapidly surveying large tailings areas. This procedure can minimize expenditures for mining companies when designing remedial plans for the closure of the mines. This study has also identified regions of enhanced conductivity that extend beyond the tailing containment area. This information indicates seepage pathways, and is important for monitoring the effectiveness of tailing containment structures. (2) High-spatial-resolution hyperspectral casi (Compact Airborne Spectrographic Imagery

  9. [Remote sensing of chlorophyll fluorescence at airborne level based on unmanned airship platform and hyperspectral sensor].

    PubMed

    Yang, Pei-Qi; Liu, Zhi-Gang; Ni, Zhuo-Ya; Wang, Ran; Wang, Qing-Shan

    2013-11-01

    The solar-induced chlorophyll fluorescence (ChlF) has a close relationship with photosynthetic and is considered as a probe of plant photosynthetic activity. In this study, an airborne fluorescence detecting system was constructed by using a hyperspectral imager on board an unmanned airship. Both Fraunhofer Line Discriminator (FLD) and 3FLD used to extract ChlF require the incident solar irradiance, which is always difficult to receive at airborne level. Alternative FLD (aFLD) can overcome the problem by selecting non-fluorescent emitter in the image. However, aFLD is based on the assumption that reflectance is identical around the Fraunhofer line, which is not realistic. A new method, a3FLD, is proposed, which assumes that reflectance varies linearly with the wavelength around Fraunhofer line. The result of simulated data shows that ChlF retrieval error of a3FLD is significantly lower than that of aFLD when vegetation reflectance varies near the Fraunhofer line. The results of hyperspectral remote sensing data with the airborne fluorescence detecting system show that the relative values of retrieved ChlF of 5 kinds of plants extracted by both aFLD and a3FLD are consistent with vegetation growth stage and the ground-level ChlF. The ChlF values of aFLD are about 15% greater than a3FLD. In addition, using aFLD, some non-fluorescent objects have considerable ChlF value, while a3FLD can effectively overcome the problem.

  10. [Remote sensing of chlorophyll fluorescence at airborne level based on unmanned airship platform and hyperspectral sensor].

    PubMed

    Yang, Pei-Qi; Liu, Zhi-Gang; Ni, Zhuo-Ya; Wang, Ran; Wang, Qing-Shan

    2013-11-01

    The solar-induced chlorophyll fluorescence (ChlF) has a close relationship with photosynthetic and is considered as a probe of plant photosynthetic activity. In this study, an airborne fluorescence detecting system was constructed by using a hyperspectral imager on board an unmanned airship. Both Fraunhofer Line Discriminator (FLD) and 3FLD used to extract ChlF require the incident solar irradiance, which is always difficult to receive at airborne level. Alternative FLD (aFLD) can overcome the problem by selecting non-fluorescent emitter in the image. However, aFLD is based on the assumption that reflectance is identical around the Fraunhofer line, which is not realistic. A new method, a3FLD, is proposed, which assumes that reflectance varies linearly with the wavelength around Fraunhofer line. The result of simulated data shows that ChlF retrieval error of a3FLD is significantly lower than that of aFLD when vegetation reflectance varies near the Fraunhofer line. The results of hyperspectral remote sensing data with the airborne fluorescence detecting system show that the relative values of retrieved ChlF of 5 kinds of plants extracted by both aFLD and a3FLD are consistent with vegetation growth stage and the ground-level ChlF. The ChlF values of aFLD are about 15% greater than a3FLD. In addition, using aFLD, some non-fluorescent objects have considerable ChlF value, while a3FLD can effectively overcome the problem. PMID:24555390

  11. Overview of hyperspectral remote sensing for mapping marine benthic habitats from airborne and underwater sensors

    NASA Astrophysics Data System (ADS)

    Dierssen, Heidi M.

    2013-09-01

    The seafloor, with its diverse and dynamic benthic habitats varying on meter to centimeter scales, is difficult to accurately monitor with traditional techniques. The technology used to build imaging spectrometers has rapidly advanced in recent years with the advent of smaller sensors and better signal-to-noise capabilities that has facilitated their use in mapping fine-scale benthic features. Here, the use of such sensors for hyperspectral remote sensing of the seafloor from both airborne and underwater platforms is discussed. Benthic constituents provide a so-called optical fingerprint with spectral properties that are often too subtle to be discerned with simple color photographs or multichannel spectrometers. Applications include the recent field validation of the airborne Portable Remote Imaging SpectroMeter (PRISM), a new imaging sensor package optimized for coastal ocean processes in Elkorn Slough California. In these turbid sediment-laden waters, only subtle spectral differences differentiate seafloor with sediment from that with eelgrass. The ultimate goal is to provide robust radiometric approaches that accurately consider light attenuation by the water column and are able to be applied to diverse habitats without considerable foreknowledge.

  12. Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

    NASA Astrophysics Data System (ADS)

    Krause, Keith Stuart

    The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

  13. CASI/SASI airborne hyperspectral remote sensing anomaly extraction of metallogenic prediction research in Gansu Beishan South Beach area

    NASA Astrophysics Data System (ADS)

    Che, Yongfei; Zhao, Yingjun

    2014-11-01

    Hyperspectral remote sensing has one of the technical advantages atlas. The known deposits of Gansu Beishan South Beach deposits as the study area, based on the theory of wall rock alteration, using airborne hyperspectral remote sensing data (CASI/SASI), extracted mineralization alteration information and analysis. Based on airborne hyperspectral remote sensing mineral mapping results in the study area, Combining analysising of possible mineral formation fluid properties, spatial distribution characteristics and time evolution with analysising of mineral formation environment (lithology and tectonic environment), construction of the South Beach gold deposit location model, the deposit location model as a guide, comprehensive analysis of mineralization geological background and surface geochemical data, delineated mineralization favorable areas. The field investigation showed that signs of altered development of strong in the delineation of the mineralization favorable areas and metallogenic potential of better, is worth paying attention to the prospecting target area. Further explanation that the hyperspectral remote sensing can provide accurate and reliable information for the prospecting, and is worthy of further mining the ore prospecting potential.

  14. Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

    NASA Astrophysics Data System (ADS)

    Warren, Mark A.; Taylor, Benjamin H.; Grant, Michael G.; Shutler, Jamie D.

    2014-03-01

    Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

  15. Concept and integration of an on-line quasi-operational airborne hyperspectral remote sensing system

    NASA Astrophysics Data System (ADS)

    Schilling, Hendrik; Lenz, Andreas; Gross, Wolfgang; Perpeet, Dominik; Wuttke, Sebastian; Middelmann, Wolfgang

    2013-10-01

    Modern mission characteristics require the use of advanced imaging sensors in reconnaissance. In particular, high spatial and high spectral resolution imaging provides promising data for many tasks such as classification and detecting objects of military relevance, such as camouflaged units or improvised explosive devices (IEDs). Especially in asymmetric warfare with highly mobile forces, intelligence, surveillance and reconnaissance (ISR) needs to be available close to real-time. This demands the use of unmanned aerial vehicles (UAVs) in combination with downlink capability. The system described in this contribution is integrated in a wing pod for ease of installation and calibration. It is designed for the real-time acquisition and analysis of hyperspectral data. The main component is a Specim AISA Eagle II hyperspectral sensor, covering the visible and near-infrared (VNIR) spectral range with a spectral resolution up to 1.2 nm and 1024 pixel across track, leading to a ground sampling distance below 1 m at typical altitudes. The push broom characteristic of the hyperspectral sensor demands an inertial navigation system (INS) for rectification and georeferencing of the image data. Additional sensors are a high resolution RGB (HR-RGB) frame camera and a thermal imaging camera. For on-line application, the data is preselected, compressed and transmitted to the ground control station (GCS) by an existing system in a second wing pod. The final result after data processing in the GCS is a hyperspectral orthorectified GeoTIFF, which is filed in the ERDAS APOLLO geographical information system. APOLLO allows remote access to the data and offers web-based analysis tools. The system is quasi-operational and was successfully tested in May 2013 in Bremerhaven, Germany.

  16. Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

    NASA Astrophysics Data System (ADS)

    Zhou, Xianfeng; Huang, Wenjiang; Kong, Weiping; Ye, Huichun; Luo, Juhua; Chen, Pengfei

    2016-11-01

    Timely and accurate assessment of canopy nitrogen content (CNC) provides valuable insight into rapid and real-time nitrogen status monitoring in crops. A semi-empirical approach based on spectral index was extensively used for nitrogen content estimation. However, in many cases, due to specific vegetation types or local conditions, the applicability and robustness of established spectral indices for nitrogen retrieval were limited. The objective of this study was to investigate the optimal spectral index for winter wheat (Triticum aestivum L.) CNC estimation using Pushbroom Hyperspectral Imager (PHI) airborne hyperspectral data. Data collected from two different field experiments that were conducted during the major growth stages of winter wheat in 2002 and 2003 were used. Our results showed that a significant linear relationship existed between nitrogen and chlorophyll content at the canopy level, and it was not affected by cultivars, growing conditions and nutritional status of winter wheat. Nevertheless, it varied with growth stages. Periods around heading stage mainly worsened the relationship and CNC estimation, and CNC assessment for growth stages before and after heading could improve CNC retrieval accuracy to some extent. CNC assessment with PHI airborne hyperspectra suggested that spectral indices based on red-edge band including narrowband and broadband CIred-edge, NDVI-like and ND705 showed convincing results in CNC retrieval. NDVI-like and ND705 were sensitive to detect CNC changes less than 5 g/m2, narrowband and broadband CIred-edge were sensitive to a wide range of CNC variations. Further evaluation of CNC retrieval using field measured hyperspectra indicated that NDVI-like was robust and exhibited the highest accuracy in CNC assessment, and spectral indices (CIred-edge and CIgreen) that established on narrow or broad bands showed no obvious difference in CNC assessment. Overall, our study suggested that NDVI-like was the optimal indicator for winter

  17. Fusion of full waveform Laserscanning and airborne hyperspectral remote sensing data for the characterization of forest stands

    NASA Astrophysics Data System (ADS)

    Buddenbaum, Henning

    2010-05-01

    Hyperspectral data offer the maximum spectral reflectance information available from remote sensing. A continuous spectrum of narrow bands with near-laboratory quality is recorded for each pixel. This data can be used for difficult classification tasks or detailed quantitative analyses, e.g. determination of chlorophyll or water content in leaves. But in forested areas, discerning between different age classes of the same tree species is still error-prone. Airborne Laserscanning measures the three-dimensional position of every reflecting object and can be used to map tree heights and crown volumes. These are highly correlated with tree age and timber volume. In addition, Laserscanner data can be used to differentiate between coniferous and deciduous trees either by analysing crown shapes that lead to different surface roughness or by exploiting the intensity information of laser echoes from the crowns. But a more detailed determination of tree species is not possible using Laserscanning alone. The combination of hyperspectral and Laserscanning data promises the possibility to map both tree species and age classes. We used a HyMap data set with 122 bands recorded in 2003 and a full waveform Laserscanning recorded in 2005 in the same area, Idarwald Forest in South-western Germany. To combine both datasets, we defined voxels above the HyMap pixels, containing the mean laser intensity in slices of 50 cm height. These voxels form a second hyperspectral dataset of 76 bands with the same geometry as the HyMap image, so that they could be fused into a 198 band image. The joined image performed better in a classification of tree species and age classes than each of the single images and also better than a dataset consisting of the hyperspectral image and a tree height map. Apart from classification, it can also be used to derive tree heights and crown base heights and to estimate biomass, leaf area index and timber volume and to characterize the vertical forest structure.

  18. Airborne Hyperspectral Imaging System

    NASA Technical Reports Server (NTRS)

    Behar, Alberto E.; Cooper, Moogega; Adler, John; Jacobson, Tobias

    2012-01-01

    A document discusses a hyperspectral imaging instrument package designed to be carried aboard a helicopter. It was developed to map the depths of Greenland's supraglacial lakes. The instrument is capable of telescoping to twice its original length, allowing it to be retracted with the door closed during takeoff and landing, and manually extended in mid-flight. While extended, the instrument platform provides the attached hyperspectral imager a nadir-centered and unobstructed view of the ground. Before flight, the instrument mount is retracted and securely strapped down to existing anchor points on the floor of the helicopter. When the helicopter reaches the destination lake, the door is opened and the instrument mount is manually extended. Power to the instrument package is turned on, and the data acquisition computer is commanded via a serial cable from an onboard user-operated laptop to begin data collection. After data collection is complete, the instrument package is powered down and the mount retracted, allowing the door to be closed in preparation for landing. The present design for the instrument mount consists of a three-segment telescoping cantilever to allow for a sufficient extended length to see around the landing struts and provide a nadir-centered and unobstructed field of view for the hyperspectral imager. This instrument works on the premise that water preferentially absorbs light with longer wavelengths on the red side of the visible spectrum. This property can be exploited in order to remotely determine the depths of bodies of pure freshwater. An imager flying over such a lake receives light scattered from the surface, the bulk of the water column, and from the lake bottom. The strength of absorption of longer-wavelength light depends on the depth of the water column. Through calibration with in situ measurements of the water depths, a depth-determining algorithm may be developed to determine lake depth from these spectral properties of the

  19. Novel compact airborne platform for remote sensing applications using the Hyper-Cam infrared hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Turcotte, Caroline S.; Puckrin, Eldon; Aube, Françoys; Farley, Vincent; Savary, Simon; Chamberland, Martin

    2013-05-01

    High resolution broad-band imagery in the visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, which leads to an additional means of detecting and identifying targets based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology, which yields high spectral resolution and enables a high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 μm spectral range. The Hyper-Cam has been recently integrated and flown on a novel airborne gyro-stabilized platform inside a fixed-wing aircraft. The new platform, more compact and more advanced than its predecessor, is described in this paper. The first results of target detection and identification are also presented.

  20. Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK) on the Vision II turbine rotorcraft UAV over the Florida Keys

    NASA Astrophysics Data System (ADS)

    Holasek, R. E.; Nakanishi, K.; Swartz, B.; Zacaroli, R.; Hill, B.; Naungayan, J.; Herwitz, S.; Kavros, P.; English, D. C.

    2013-12-01

    As part of the NASA ROSES program, the NovaSol Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK) was flown as the payload on the unmanned Vision II helicopter. The goal of the May 2013 data collection was to obtain high resolution visible and near-infrared (visNIR) hyperspectral data of seagrasses and coral reefs in the Florida Keys. The specifications of the SHARK hyperspectral system and the Vision II turbine rotorcraft will be described along with the process of integrating the payload to the vehicle platform. The minimal size, weight, and power (SWaP) specifications of the SHARK system is an ideal match to the Vision II helicopter and its flight parameters. One advantage of the helicopter over fixed wing platforms is its inherent ability to take off and land in a limited area and without a runway, enabling the UAV to be located in close proximity to the experiment areas and the science team. Decisions regarding integration times, waypoint selection, mission duration, and mission frequency are able to be based upon the local environmental conditions and can be modified just prior to take off. The operational procedures and coordination between the UAV pilot, payload operator, and scientist will be described. The SHARK system includes an inertial navigation system and digital elevation model (DEM) which allows image coordinates to be calculated onboard the aircraft in real-time. Examples of the geo-registered images from the data collection will be shown. SHARK mounted below VTUAV. SHARK deployed on VTUAV over water.

  1. Airborne multispectral and hyperspectral remote sensing: Examples of applications to the study of environmental and engineering problems

    SciTech Connect

    Bianchi, R.; Marino, C.M.

    1997-10-01

    The availability of a new aerial survey capability carried out by the CNR/LARA (National Research Council - Airborne Laboratory for the Environmental Research) by a new spectroradiometer AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) on board a CASA 212/200 aircraft, enable the scientists to obtain innovative data sets, for different approach to the definitions and the understanding of a variety of environmental and engineering problems. The 102 MIVIS channels spectral bandwidths are chosen to meet the needs of scientific research for advanced applications of remote sensing data. In such configuration MIVIS can offer significant contributions to problem solving in wide sectors such as geologic exploration, agricultural crop studies, forestry, land use mapping, idrogeology, oceanography and others. LARA in 1994-96 has been active over different test-sites in joint-venture with JPL, Pasadena, different European Institutions and Italian University and Research Institutes. These aerial surveys allow the national and international scientific community to approach the use of Hyperspectral Remote Sensing in environmental problems of very large interest. The sites surveyed in Italy, France and Germany include a variety of targets such as quarries, landfills, karst cavities areas, landslides, coastlines, geothermal areas, etc. The deployments gathered up to now more than 300 GBytes of MIVIS data in more than 30 hours of VLDS data recording. The purpose of this work is to present and to comment the procedures and the results at research and at operational level of the past campaigns with special reference to the study of environmental and engineering problems.

  2. The information of oil and gas micro-seepage in Dongsheng region of inner Mongolia based on the airborne hyperspectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Tian, Shu-Fang; Chen, Jian-Ping; Zhou, Mi

    2008-11-01

    The technology of hyper-spectral remote sensing which has higher spatial resolution characteristic, and optimizes the qualification of identifying and extracting salt mines, not only enhances the capacity of natural scenes detection and recognition, but also advances the level of quantitative remote sensing. It has important meaning for using the technology of hyper-spectral remote sensing to quantitative extraction. The paper investigate gas micro-seepage based on the Airborne Hyper-spectral Remote Sensing in Dongsheng of Inner Mongolia on the basis of gas micro-seepage theory using EO-1 Hyperion data collected by Satellite-Borne Sensor which has highest spatial resolution presently in the world. On the basis of data pretreated this paper adopts band math extracted the distribution of oil and gas micro-seepage using diagnostic assimilating spectrum of alteration minerals by the numbers. With eigenvector length model evaluates the research area comprehensive index, oil and gas micro-seepage information model of the research area is established and key regions of oil and gas micro-seepage are confirmed, which offers academic gist for oil and gas resource exploitation of Dongsheng.

  3. APEX - the Hyperspectral ESA Airborne Prism Experiment

    PubMed Central

    Itten, Klaus I.; Dell'Endice, Francesco; Hueni, Andreas; Kneubühler, Mathias; Schläpfer, Daniel; Odermatt, Daniel; Seidel, Felix; Huber, Silvia; Schopfer, Jürg; Kellenberger, Tobias; Bühler, Yves; D'Odorico, Petra; Nieke, Jens; Alberti, Edoardo; Meuleman, Koen

    2008-01-01

    The airborne ESA-APEX (Airborne Prism Experiment) hyperspectral mission simulator is described with its distinct specifications to provide high quality remote sensing data. The concept of an automatic calibration, performed in the Calibration Home Base (CHB) by using the Control Test Master (CTM), the In-Flight Calibration facility (IFC), quality flagging (QF) and specific processing in a dedicated Processing and Archiving Facility (PAF), and vicarious calibration experiments are presented. A preview on major applications and the corresponding development efforts to provide scientific data products up to level 2/3 to the user is presented for limnology, vegetation, aerosols, general classification routines and rapid mapping tasks. BRDF (Bidirectional Reflectance Distribution Function) issues are discussed and the spectral database SPECCHIO (Spectral Input/Output) introduced. The optical performance as well as the dedicated software utilities make APEX a state-of-the-art hyperspectral sensor, capable of (a) satisfying the needs of several research communities and (b) helping the understanding of the Earth's complex mechanisms.

  4. PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING

    EPA Science Inventory

    The use of airborne hyperspectral remote sensing imagery for automated mapping of submersed aquatic vegetation in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery, together with in-situ spectral refl...

  5. Remote distinction of a noxious weed (musk thistle: Carduus nutans) using airborne hyperspectral imagery and the support vector machine classifier

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote detection of invasive plant species using geospatial imagery may significantly improve monitoring, planning, and management practices by eliminating shortfalls such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion ex...

  6. Airborne Hyperspectral Imagery for the Detection of Agricultural Crop Stress

    NASA Technical Reports Server (NTRS)

    Cassady, Philip E.; Perry, Eileen M.; Gardner, Margaret E.; Roberts, Dar A.

    2001-01-01

    Multispectral digital imagery from aircraft or satellite is presently being used to derive basic assessments of crop health for growers and others involved in the agricultural industry. Research indicates that narrow band stress indices derived from hyperspectral imagery should have improved sensitivity to provide more specific information on the type and cause of crop stress, Under funding from the NASA Earth Observation Commercial Applications Program (EOCAP) we are identifying and evaluating scientific and commercial applications of hyperspectral imagery for the remote characterization of agricultural crop stress. During the summer of 1999 a field experiment was conducted with varying nitrogen treatments on a production corn-field in eastern Nebraska. The AVIRIS (Airborne Visible-Infrared Imaging Spectrometer) hyperspectral imager was flown at two critical dates during crop development, at two different altitudes, providing images with approximately 18m pixels and 3m pixels. Simultaneous supporting soil and crop characterization included spectral reflectance measurements above the canopy, biomass characterization, soil sampling, and aerial photography. In this paper we describe the experiment and results, and examine the following three issues relative to the utility of hyperspectral imagery for scientific study and commercial crop stress products: (1) Accuracy of reflectance derived stress indices relative to conventional measures of stress. We compare reflectance-derived indices (both field radiometer and AVIRIS) with applied nitrogen and with leaf level measurement of nitrogen availability and chlorophyll concentrations over the experimental plots (4 replications of 5 different nitrogen levels); (2) Ability of the hyperspectral sensors to detect sub-pixel areas under crop stress. We applied the stress indices to both the 3m and 18m AVIRIS imagery for the entire production corn field using several sub-pixel areas within the field to compare the relative

  7. Detection of soil properties with airborne hyperspectral measurements of bare fields.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Airborne remote sensing data, using a hyperspectral (HSI) camera, were collected for a flight over two fields with a total of 128 ha. of recently seeded and nearly bare soil. The within-field spatial distribution of several soil properties was found by using multiple linear regression to select the ...

  8. Hyperspectral remote sensing of wild oyster reefs

    NASA Astrophysics Data System (ADS)

    Le Bris, Anthony; Rosa, Philippe; Lerouxel, Astrid; Cognie, Bruno; Gernez, Pierre; Launeau, Patrick; Robin, Marc; Barillé, Laurent

    2016-04-01

    The invasion of the wild oyster Crassostrea gigas along the western European Atlantic coast has generated changes in the structure and functioning of intertidal ecosystems. Considered as an invasive species and a trophic competitor of the cultivated conspecific oyster, it is now seen as a resource by oyster farmers following recurrent mass summer mortalities of oyster spat since 2008. Spatial distribution maps of wild oyster reefs are required by local authorities to help define management strategies. In this work, visible-near infrared (VNIR) hyperspectral and multispectral remote sensing was investigated to map two contrasted intertidal reef structures: clusters of vertical oysters building three-dimensional dense reefs in muddy areas and oysters growing horizontally creating large flat reefs in rocky areas. A spectral library, collected in situ for various conditions with an ASD spectroradiometer, was used to run Spectral Angle Mapper classifications on airborne data obtained with an HySpex sensor (160 spectral bands) and SPOT satellite HRG multispectral data (3 spectral bands). With HySpex spectral/spatial resolution, horizontal oysters in the rocky area were correctly classified but the detection was less efficient for vertical oysters in muddy areas. Poor results were obtained with the multispectral image and from spatially or spectrally degraded HySpex data, it was clear that the spectral resolution was more important than the spatial resolution. In fact, there was a systematic mud deposition on shells of vertical oyster reefs explaining the misclassification of 30% of pixels recognized as mud or microphytobenthos. Spatial distribution maps of oyster reefs were coupled with in situ biomass measurements to illustrate the interest of a remote sensing product to provide stock estimations of wild oyster reefs to be exploited by oyster producers. This work highlights the interest of developing remote sensing techniques for aquaculture applications in coastal

  9. Target detection algorithm for airborne thermal hyperspectral data

    NASA Astrophysics Data System (ADS)

    Marwaha, R.; Kumar, A.; Raju, P. L. N.; Krishna Murthy, Y. V. N.

    2014-11-01

    Airborne hyperspectral imaging is constantly being used for classification purpose. But airborne thermal hyperspectral image usually is a challenge for conventional classification approaches. The Telops Hyper-Cam sensor is an interferometer-based imaging system that helps in the spatial and spectral analysis of targets utilizing a single sensor. It is based on the technology of Fourier-transform which yields high spectral resolution and enables high accuracy radiometric calibration. The Hypercam instrument has 84 spectral bands in the 868 cm-1 to 1280 cm-1 region (7.8 μm to 11.5 μm), at a spectral resolution of 6 cm-1 (full-width-half-maximum) for LWIR (long wave infrared) range. Due to the Hughes effect, only a few classifiers are able to handle high dimensional classification task. MNF (Minimum Noise Fraction) rotation is a data dimensionality reducing approach to segregate noise in the data. In this, the component selection of minimum noise fraction (MNF) rotation transformation was analyzed in terms of classification accuracy using constrained energy minimization (CEM) algorithm as a classifier for Airborne thermal hyperspectral image and for the combination of airborne LWIR hyperspectral image and color digital photograph. On comparing the accuracy of all the classified images for airborne LWIR hyperspectral image and combination of Airborne LWIR hyperspectral image with colored digital photograph, it was found that accuracy was highest for MNF component equal to twenty. The accuracy increased by using the combination of airborne LWIR hyperspectral image with colored digital photograph instead of using LWIR data alone.

  10. Remote sensing applications with NH hyperspectral portable video camera

    NASA Astrophysics Data System (ADS)

    Takara, Yohei; Manago, Naohiro; Saito, Hayato; Mabuchi, Yusaku; Kondoh, Akihiko; Fujimori, Takahiro; Ando, Fuminori; Suzuki, Makoto; Kuze, Hiroaki

    2012-11-01

    Recent advances in image sensor and information technologies have enabled the development of small hyperspectral imaging systems. EBA JAPAN (Tokyo, Japan) has developed a novel grating-based, portable hyperspectral imaging camera NH-1 and NH-7 that can acquire a 2D spatial image (640 x 480 and 1280 x 1024 pixels, respectively) with a single exposure using an internal self-scanning system. The imagers cover a wavelength range of 350 - 1100 nm, with a spectral resolution of 5 nm. Because of their small weight of 750 g, the NH camera systems can easily be installed on a small UAV platform. We show the results from the analysis of data obtained by remote sensing applications including land vegetation and atmospheric monitoring from both ground- and airborne/UAV-based observations.

  11. Water turbidity estimation from airborne hyperspectral imagery and full waveform bathymetric LiDAR

    NASA Astrophysics Data System (ADS)

    Pan, Z.; Glennie, C. L.; Fernandez-Diaz, J. C.

    2015-12-01

    The spatial and temporal variations in water turbidity are of great interest for the study of fluvial and coastal environments; and for predicting the performance of remote sensing systems that are used to map these. Conventional water turbidity estimates from remote sensing observations have normally been derived using near infrared reflectance. We have investigated the potential of determining water turbidity from additional remote sensing sources, namely airborne hyperspectral imagery and single wavelength bathymetric LiDAR (Light Detection and Ranging). The confluence area of the Blue and Colorado River, CO was utilized as a study area to investigate the capabilities of both airborne bathymetric LiDAR and hyperspectral imagery for water turbidity estimation. Discrete and full waveform bathymetric data were collected using Optech's Gemini (1064 nm) and Aquarius (532 nm) LiDAR sensors. Hyperspectral imagery (1.2 m pixel resolution and 72 spectral bands) was acquired using an ITRES CASI-1500 imaging system. As an independent reference, measurements of turbidity were collected concurrent with the airborne remote sensing acquisitions, using a WET Labs EcoTriplet deployed from a kayak and turbidity was then derived from the measured backscatter. The bathymetric full waveform dataset contains a discretized sample of the full backscatter of water column and benthic layer. Therefore, the full waveform records encapsulate the water column characteristics of turbidity. A nonparametric support vector regression method is utilized to estimate water turbidity from both hyperspectral imagery and voxelized full waveform LiDAR returns, both individually and as a fused dataset. Results of all the evaluations will be presented, showing an initial turbidity prediction accuracy of approximately 1.0 NTU. We will also discuss our future strategy for enhanced fusion of the full waveform LiDAR and hyperspectral imagery for improved turbidity estimation.

  12. Hyperspectral remote sensing of vegetation

    USGS Publications Warehouse

    Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

    2011-01-01

    Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

  13. Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

    USGS Publications Warehouse

    Williams, D.J.; Rybicki, N.B.; Lombana, A.V.; O'Brien, T. M.; Gomez, R.B.

    2003-01-01

    The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to realtime resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

  14. Airborne Hyperspectral Imaging of Seagrass and Coral Reef

    NASA Astrophysics Data System (ADS)

    Merrill, J.; Pan, Z.; Mewes, T.; Herwitz, S.

    2013-12-01

    This talk presents the process of project preparation, airborne data collection, data pre-processing and comparative analysis of a series of airborne hyperspectral projects focused on the mapping of seagrass and coral reef communities in the Florida Keys. As part of a series of large collaborative projects funded by the NASA ROSES program and the Florida Fish and Wildlife Conservation Commission and administered by the NASA UAV Collaborative, a series of airborne hyperspectral datasets were collected over six sites in the Florida Keys in May 2012, October 2012 and May 2013 by Galileo Group, Inc. using a manned Cessna 172 and NASA's SIERRA Unmanned Aerial Vehicle. Precise solar and tidal data were used to calculate airborne collection parameters and develop flight plans designed to optimize data quality. Two independent Visible and Near-Infrared (VNIR) hyperspectral imaging systems covering 400-100nm were used to collect imagery over six Areas of Interest (AOIs). Multiple collections were performed over all sites across strict solar windows in the mornings and afternoons. Independently developed pre-processing algorithms were employed to radiometrically correct, synchronize and georectify individual flight lines which were then combined into color balanced mosaics for each Area of Interest. The use of two different hyperspectral sensor as well as environmental variations between each collection allow for the comparative analysis of data quality as well as the iterative refinement of flight planning and collection parameters.

  15. High spectral resolution airborne short wave infrared hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Wei, Liqing; Yuan, Liyin; Wang, Yueming; Zhuang, Xiaoqiong

    2016-05-01

    Short Wave InfraRed(SWIR) spectral imager is good at detecting difference between materials and penetrating fog and mist. High spectral resolution SWIR hyperspectral imager plays a key role in developing earth observing technology. Hyperspectral data cube can help band selections that is very important for multispectral imager design. Up to now, the spectral resolution of many SWIR hyperspectral imagers is about 10nm. A high sensitivity airborne SWIR hyperspectral imager with narrower spectral band will be presented. The system consists of TMA telescope, slit, spectrometer with planar blazed grating and high sensitivity MCT FPA. The spectral sampling interval is about 3nm. The IFOV is 0.5mrad. To eliminate the influence of the thermal background, a cold shield is designed in the dewar. The pixel number of spatial dimension is 640. Performance measurement in laboratory and image analysis for flight test will also be presented.

  16. Determination of pasture quality using airborne hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Pullanagari, R. R.; Kereszturi, G.; Yule, Ian J.; Irwin, M. E.

    2015-10-01

    Pasture quality is a critical determinant which influences animal performance (live weight gain, milk and meat production) and animal health. Assessment of pasture quality is therefore required to assist farmers with grazing planning and management, benchmarking between seasons and years. Traditionally, pasture quality is determined by field sampling which is laborious, expensive and time consuming, and the information is not available in real-time. Hyperspectral remote sensing has potential to accurately quantify biochemical composition of pasture over wide areas in great spatial detail. In this study an airborne imaging spectrometer (AisaFENIX, Specim) was used with a spectral range of 380-2500 nm with 448 spectral bands. A case study of a 600 ha hill country farm in New Zealand is used to illustrate the use of the system. Radiometric and atmospheric corrections, along with automatized georectification of the imagery using Digital Elevation Model (DEM), were applied to the raw images to convert into geocoded reflectance images. Then a multivariate statistical method, partial least squares (PLS), was applied to estimate pasture quality such as crude protein (CP) and metabolisable energy (ME) from canopy reflectance. The results from this study revealed that estimates of CP and ME had a R2 of 0.77 and 0.79, and RMSECV of 2.97 and 0.81 respectively. By utilizing these regression models, spatial maps were created over the imaged area. These pasture quality maps can be used for adopting precision agriculture practices which improves farm profitability and environmental sustainability.

  17. Detection of gaseous plumes in airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Agassi, Eyal; Hirsch, Eitan; Chamberland, Martin; Gagnon, Marc-André; Eichstaedt, Holger

    2016-05-01

    The thermal hyperspectral sensor Hyper-Cam was mounted on a light aircraft and measured continuous releases of several atmospheric tracers from a height of 2 km. A unique detection algorithm that eliminates the need for clear background estimation was operated over the acquired data with excellent detection results. The data-cubes were acquired in a "target mode", which is a unique method of operation of the Hyper-Cam sensor. This method provides multiple views of the plume which can be exploited to enhance the detection performance. These encouraging results demonstrate the utility of airborne LWIR hyperspectral imaging for efficient detection and mapping of effluent gases for environmental monitoring.

  18. Benthic habitat mapping using hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Vélez-Reyes, Miguel; Goodman, James A.; Castrodad-Carrau, Alexey; Jiménez-Rodriguez, Luis O.; Hunt, Shawn D.; Armstrong, Roy

    2006-09-01

    Benthic habitats are the different bottom environments as defined by distinct physical, geochemical, and biological characteristics. Remote sensing is increasingly being used to map and monitor the complex dynamics associated with estuarine and nearshore benthic habitats. Advantages of remote sensing technology include both the qualitative benefits derived from a visual overview, and more importantly, the quantitative abilities for systematic assessment and monitoring. Advancements in instrument capabilities and analysis methods are continuing to expand the accuracy and level of effectiveness of the resulting data products. Hyperspectral sensors in particular are rapidly emerging as a more complete solution, especially for the analysis of subsurface shallow aquatic systems. The spectral detail offered by hyperspectral instruments facilitates significant improvements in the capacity to differentiate and classify benthic habitats. This paper reviews two techniques for mapping shallow coastal ecosystems that both combine the retrieval of water optical properties with a linear unmixing model to obtain classifications of the seafloor. Example output using AVIRIS hyperspectral imagery of Kaneohe Bay, Hawaii is employed to demonstrate the application potential of the two approaches and compare their respective results.

  19. Airborne Hyperspectral Survey of Afghanistan 2007: Flight Line Planning and HyMap Data Collection

    USGS Publications Warehouse

    Kokaly, Raymond F.; King, Trude V.V.; Livo, K. Eric

    2008-01-01

    Hyperspectral remote sensing data were acquired over Afghanistan with the HyMap imaging spectrometer (Cocks and others, 1998) operating on the WB-57 high altitude NASA research aircraft (http://jsc-aircraft-ops.jsc.nasa.gov/wb57/index.html). These data were acquired during the interval of August 22, 2007 to October 2, 2007, as part of the United States Geological Survey (USGS) project 'Oil and Gas Resources Assessment of the Katawaz and Helmand Basins'. A total of 218 flight lines of hyperspectral remote sensing data were collected over the country. This report describes the planning of the airborne survey and the flight lines that were flown. Included with this report are digital files of the nadir tracks of the flight lines, including a map of the labeled flight lines and corresponding vector shape files for geographic information systems (GIS).

  20. An airborne real-time hyperspectral target detection system

    NASA Astrophysics Data System (ADS)

    Skauli, Torbjorn; Haavardsholm, Trym V.; Kåsen, Ingebjørg; Arisholm, Gunnar; Kavara, Amela; Opsahl, Thomas Olsvik; Skaugen, Atle

    2010-04-01

    An airborne system for hyperspectral target detection is described. The main sensor is a HySpex pushbroom hyperspectral imager for the visible and near-infrared spectral range with 1600 pixels across track, supplemented by a panchromatic line imager. An optional third sensor can be added, either a SWIR hyperspectral camera or a thermal camera. In real time, the system performs radiometric calibration and georeferencing of the images, followed by image processing for target detection and visualization. The current version of the system implements only spectral anomaly detection, based on normal mixture models. Image processing runs on a PC with a multicore Intel processor and an Nvidia graphics processing unit (GPU). The processing runs in a software framework optimized for large sustained data rates. The platform is a Cessna 172 aircraft based close to FFI, modified with a camera port in the floor.

  1. 1994-1995 CNR LARA project airborne hyperspectral campaigns

    SciTech Connect

    Bianchi, R.; Cavalli, R.M.; Fiumi, L.

    1996-08-01

    CNR established a new laboratory for airborne hyperspectral imaging devoted to environmental problems and since the end of last June 1994 the project (LARA Project) is fully operative to provide hyperspectral data to the national and international scientific community. The Daedalus AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) instrument, acquired by CNR (Italian National Research Council) in the framework of its LARA (Airborne Laboratory for Environmental Studies) Project, has been intensively operative. A number of MIVIS deployments have been carried out in Italy and Europe in cooperation with national and international institutions on a variety of sites, including active volcanoes, coastlines, lagoons and ocean, vegetated and cultivated areas, oil polluted surfaces, waste discharges, and archeological sites. One year of activity has shown the high system efficiency, from the survey to data preprocessing and dissemination.

  2. Real-time airborne hyperspectral imaging of land mines

    NASA Astrophysics Data System (ADS)

    Ivanco, Tyler; Achal, Steve; McFee, John E.; Anger, Cliff; Young, Jane

    2007-04-01

    DRDC Suffeld and Itres Research have jointly investigated the use of visible and infrared hyperspectral imaging (HSI) for surface and buried land mine detection since 1989. These studies have demonstrated reliable passive HSI detection of surface-laid mines, based on their reflectance spectra, from airborne and ground-based platforms. Commercial HSI instruments collect and store image data at aircraft speeds, but the data are analysed off- line. This is useful for humanitarian demining, but unacceptable for military countermine operations. We have developed a hardware and software system with algorithms that can process the raw hyperspectral data in real time to detect mines. The custom algorithms perform radiometric correction of the raw data, then classify pixels of the corrected data, referencing a spectral signature library. The classification results are stored and displayed in real time, that is, within a few frame times of the data acquisition. Such real-time mine detection was demonstrated for the first time from a slowly moving land vehicle in March 2000. This paper describes an improved system which can achieve real-time detection of mines from an airborne platform, with its commensurately higher data rates. The system is presently compatible with the Itres family of visible/near infrared, short wave infrared and thermal infrared pushbroom hyperspectral imagers and its broadband thermal infrared pushbroom imager. Experiments to detect mines from an airborne platform in real time were conducted at DRDC Suffield in November 2006. Surface-laid land mines were detected in real time from a slowly moving helicopter with generally good detection rates and low false alarm rates. To the authors' knowledge, this is the first time that land mines have been detected from an airborne platform in real time using hyperspectral imaging.

  3. Estimation of leaf nitrogen and silicon using hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Mokhele, Tholang A.; Ahmed, Fethi B.

    2010-11-01

    The potential to estimate the nutrient status in important agricultural crops such as maize and sugarcane is of significant interest. In South African sugarcane agriculture, just like in global ecosystem, the estimation of Nitrogen (N) and Silicon (Si) is very important. These nutrients are one of the factors influencing the prevalence of the stalk borer Eldana saccharina Walker (Lepidoptera: Pyralidae). Therefore, the researchers aim at estimating leaf N and Si concentration as well as their ratio in sugarcane using hyperspectral remote sensing (spectroradiometry) for monitoring E. saccharina. A hand-held Analytical Spectral Devices (ASD) Field Spec® 3 spectroradiometer was used to take leaf spectral measurements of sugarcane plants from a potted-plant trial taking place under shade house conditions. In this trial, nitrogen and silicon nutrient applications as well as varieties used were known. In addition, watering regimes and artificial infestation of E. saccharina were carefully controlled. The study results indicate that the Red-edge Index (R740/R720) is linearly related to N concentration (R2 = 0.81, Root Mean Square Error (RMSE) = 0.103) for N37 with the highest correlation coefficient. For Si, the index (R750-R560)/(R750+R560) was linearly related to Si concentration (R2 = 0.53, RMSE = 0.118) for N25. Finally, the N:Si ratio was linearly correlated to the index (R1075-R730)/(R1075+R730) (R2 = 0.67, RMSE = 1.508) for N37, hence this index can be used for early detection of E. saccharina damage or for identifying sugarcane that is prone to attack by E. saccharina. It was concluded that hyperspectral remote sensing has potential for use in estimating the N:Si ratio and E. saccharina potential infestations can be monitored rapidly and nondestructively in sugarcane under controlled conditions. It is recommended that an advanced study be conducted in field conditions using airborne and/or spaceborne hyperspectral sensors.

  4. DETECTION AND IDENTIFICATION OF TOXIC AIR POLLUTANTS USING AIRBORNE LWIR HYPERSPECTRAL IMAGING

    EPA Science Inventory

    Airborne longwave infrared LWIR) hyperspectral imagery was utilized to detect and identify gaseous chemical release plumes at sites in sourthern Texzas. The Airborne Hysperspectral Imager (AHI), developed by the University of Hawaii was flown over a petrochemical facility and a ...

  5. [Hyperspectral remote sensing monitoring of grassland degradation].

    PubMed

    Wang, Huan-jiong; Fan, Wen-jie; Cui, Yao-kui; Zhou, Lei; Yan, Bin-yan; Wu, Dai-hui; Xu, Xi-ru

    2010-10-01

    The distributing of China's grassland is abroad and the status of grassland degradation is in serious condition. So achieving real-time and exactly grassland ecological monitoring is significant for the carbon cycle, as well as for climate and on regional economies. With the field measured spectra data as data source, hyperspectral remote sensing monitoring of grassland degradation was researched in the present article. The warm meadow grassland in Hulunbeier was chosen as a study object. Reflectance spectra of leaves and pure canopies of some dominant grassland species such as Leymus chinensis, Stipa krylovii and Artemisia frigid, as well as reflectance spectra of mixed grass community were measured. Using effective spectral feature parametrization methods, the spectral feature of leaves and pure canopies were extracted, so the constructive species and degenerate indicator species can be exactly distinguished. Verification results showed that the accuracy of spectral identification was higher than 95%. Taking it as the foundation, the spectra of mixed grass community were unmixed using linear mixing models, and the proportion of all the components was calculated, and the errors were less than 5%. The research results of this article provided the evidence of hyperspectral remote sensing monitoring of grassland degradation.

  6. [Progress in leaf area index retrieval based on hyperspectral remote sensing and retrieval models].

    PubMed

    Zhang, Jia-Hua; Du, Yu-Zhang; Liu, Xu-Feng; He, Zhen-Ming; Yang, Li-Min

    2012-12-01

    The leaf area index (LAI) is a very important parameter affecting land-atmosphere exchanges in land-surface processes; LAI is one of the basic feature parameters of canopy structure, and one of the most important biophysical parameters for modeling ecosystem processes such as carbon and water fluxes. Remote sensing provides the only feasible option for mapping LAI continuously over landscapes, but existing methodologies have significant limitations. To detect LAI accurately and quickly is one of tasks in the ecological and agricultural crop yield estimation study, etc. Emerging hyperspectral remote sensing sensor and techniques can complement existing ground-based measurement of LAI. Spatially explicit measurements of LAI extracted from hyperspectral remotely sensed data are component necessary for simulation of ecological variables and processes. This paper firstly summarized LAI retrieval method based on different level hyperspectral remote sensing platform (i. e., airborne, satelliteborne and ground-based); and secondly different kinds of retrieval model were summed up both at home and abroad in recent years by using hyperspectral remote sensing data; and finally the direction of future development of LAI remote sensing inversion was analyzed.

  7. MTU-Kestrel airborne hyperspectral imaging campaigns of the Lake Superior ecosystem

    NASA Astrophysics Data System (ADS)

    Rafert, J. Bruce; Slough, William J.; Rohde, Charles A.; Pilant, Andrew; Otten, Leonard J.; Meigs, Andrew D.; Jones, Al; Butler, Eugene W.

    1999-10-01

    The clear waters of Lake Superior constitute the heart of one of the most significant fresh water ecosystems in the world. Lake Superior is the world's largest lake by surface area (82,100 km2) holding approximately 10% of the earth's freshwater (12,230 km3) that is not locked into glaciers or ice caps. Although Superior is arguably the most significant fresh water ecosystem on earth, questions relating to the lake and its watershed remain unanswered, including the effects of human habitation, exploitation, and economic potential of the region. There is a great diversity of scientific disciplines with a common interest in remote sensing of the Lake Superior ecosystem which have the need for data at all spatial, spectral, and temporal scales-from scales supplied by satellites, ships or aircraft at low spatial, spectral or temporal resolution, to a requirement for synoptic high resolution spatial (approximately 1 meter)/spectral (1 - 10 nm) data. During May and August of 1998, two week-long data collection campaigns were performed using the Kestrel airborne visible hyperspectral imager to acquire hyperspectral data of a broad taxonomy of ecologically significant targets, including forests, urban areas, lakeshore zones and rivers, mining industry tailing basins, and the Lake itself. We will describe the Kestrel airborne hyperspectral sensor, the collection and data reduction methodology, and flight imagery from both campaigns.

  8. Exploration of geothermal systems using hyperspectral thermal infrared remote sensing

    NASA Astrophysics Data System (ADS)

    Reath, Kevin A.; Ramsey, Michael S.

    2013-09-01

    Visible near infrared (VNIR), short-wave infrared (SWIR), and thermal infrared (TIR) remote sensing has long been used for geothermal exploration. Specific focus on the TIR region (8-12 μm) has resulted in major-rock-forming mineral classes being identified and their areal percentages to be more easily mapped due in part to the linear mixing behavior of TIR emission. To understand the mineral compositional and thermal distribution of active geothermal surfaces systems, hyperspectral TIR data from the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) airborne sensor were acquired over the Salton Sea, CA geothermal fields by The Aerospace Corporation on March 26, 2009 and April 6, 2010. SEBASS collects 128 wavelength channels at ~ 1 m spatial resolution. Such high resolution data are rarely available for this type of scientific analysis and enabled the identification of rare mineral assemblages associated with the geothermally-active areas. One surface unit with a unique spectrum, believed to be a magnesium sulfate of unknown hydration state, was identified for the first time in the SEBASS data. The abundance and distribution of this mineral varied between 2009 and 2010 likely due to the precipitation conditions. Data obtained by the SEBASS sensor were also regressed to the 32 channel spectral resolution of the Mineral and Gas Identifier (MAGI) airborne sensor in order to test sensitivity limits. At this lower spectral resolution, all surface minerals were still effectively identified and therefore validated data at MAGI resolution are still very effective for accurate surface compositional mapping. A similar approach used at active geothermal areas in other semi-arid regions around the world has the potential to better characterize transient mineralogy, identify "indicators minerals", understand the influence of surface and ground water, and ultimately to locate new geothermal targets for future exploration. Furthermore, new Mineral and Gas

  9. Hyperspectral Imagery Data for Remote Sensing

    NASA Technical Reports Server (NTRS)

    Garegnani, Jerry; Gualtney, Lawrence

    1999-01-01

    In order for remotely sensed data to be useful in a practical application for agriculture, an information product must be made available to the land management decision maker within 24 to 48 hours of data acquisition. Hyperspectral imagery data is proving useful in differentiation of plant species potentially allowing identification of non-healthy areas and pest infestations within crop fields that may require the farm managers attention. Currently however, extracting the needed site-specific feature information from the vast spectral content of large hyperspectral image files is a labor intensive and time consuming task prohibiting the necessary fast turnaround from raw data to final product. We illustrate the methods, techniques and technologies necessary to produce field-level information products from imagery and other related spatial data that are useful to the farm manager for specific decisions that must be made throughout the growing season. We also propose to demonstrate the cost effectiveness of an integrated system, from acquisition to final product distribution, to utilize imagery for decisions on a working farm in conjunction with a commercial agricultural services company and their crop scouts. The demonstration farm is Chesapeake Farms, a 3000 acre research farm in Chestertown, Maryland on the Eastern Shore and is owned by the DuPont Corporation.

  10. Mapping invasive weeds using airborne hyperspectral imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Invasive plant species present a serious problem to the natural environment and have adverse ecological and economic impacts on both terrestrial and aquatic ecosystems they invade. This article provides a brief overview on the use of remote sensing for mapping invasive plant species in both terrestr...

  11. Hyperspectral low altitude flashtube illuminator system for visible and near-infrared remote sensing

    SciTech Connect

    Kalshoven, J.E.

    1996-10-01

    A high energy flashtube is integrated with an airborne spectrometer system for hyperspectral remote sensing of the Earth`s surface. The system, called AVIS (Airborne Vegetation Index Sensor) and currently mounted on a NASA helicopter, is flown at a nominal altitude of 500 feet (150 m). The flashtube is a two joule Xenon lamp pulsed at a 2 Hz rate. The transmitting optics give a 15 x 35 mrad beam output. The receiver is a grating spectrometer with a 512 element CCD linear array which provides a high resolution output of the backscattered visible and infrared spectrum. The system is currently used for forest canopy studies. 5 figs., 2 tabs.

  12. Alteration mineral mapping and metallogenic prediction using CASI/SASI airborne hyperspectral data in Mingshujing area of Gansu Province, NW China

    NASA Astrophysics Data System (ADS)

    Sun, Yu; Zhao, Yingjun; Qin, Kai; Tian, Feng

    2016-04-01

    Hyperspectral remote sensing is a frontier of remote sensing. Due to its advantage of integrated image with spectrum, it can realize objects identification, superior to objects classification of multispectral remote sensing. Taken the Mingshujing area in Gansu Province of China as an example, this study extracted the alteration minerals and thus to do metallogenic prediction using CASI/SASI airborne hyperspectral data. The Mingshujing area, located in Liuyuan region of Gansu Province, is dominated by middle Variscan granites and Indosinian granites, with well developed EW- and NE-trending faults. In July 2012, our project team obtained the CASI/SASI hyperspectral data of Liuyuan region by aerial flight. The CASI hyperspectral data have 32 bands and the SASI hyperspectral data have 88 bands, with spectral resolution of 15nm for both. The hyperspectral raw data were first preprocessed, including radiometric correction and geometric correction. We then conducted atmospheric correction using empirical line method based on synchronously measured ground spectra to obtain hyperspectral reflectance data. Spectral dimension of hyperspectral data was reduced by the minimum noise fraction transformation method, and then purity pixels were selected. After these steps, image endmember spectra were obtained. We used the endmember spectrum election method based on expert knowledge to analyze the image endmember spectra. Then, the mixture tuned matched filter (MTMF) mapping method was used to extract mineral information, including limonite, Al-rich sericite, Al-poor sericite and chlorite. Finally, the distribution of minerals in the Mingshujing area was mapped. According to the distribution of limonite and Al-rich sericite mapped by CASI/SASI hyperspectral data, we delineated five gold prospecting areas, and further conducted field verification in these areas. It is shown that there are significant gold mineralized anomalies in surface in the Baixianishan and Xitan prospecting

  13. Airborne thermography or infrared remote sensing.

    PubMed

    Goillot, C C

    1975-01-01

    Airborne thermography is part of the more general remote sensing activity. The instruments suitable for image display are infrared line scanners. A great deal of interest has developed during the past 10 years in airborne thermal remote sensing and many applications are in progress. Infrared scanners on board a satellite are used for observation of cloud cover; airborne infrared scanners are used for forest fire detection, heat budget of soils, detecting insect attack, diseases, air pollution damage, water stress, salinity stress on vegetation, only to cite some main applications relevant to agronomy. Using this system it has become possible to get a 'picture' of our thermal environment.

  14. Multi- and hyperspectral remote sensing of tropical marine benthic habitats

    NASA Astrophysics Data System (ADS)

    Mishra, Deepak R.

    Tropical marine benthic habitats such as coral reef and associated environments are severely endangered because of the environmental degradation coupled with hurricanes, El Nino events, coastal pollution and runoff, tourism, and economic development. To monitor and protect this diverse environment it is important to not only develop baseline maps depicting their spatial distribution but also to document their changing conditions over time. Remote sensing offers an important means of delineating and monitoring coral reef ecosystems. Over the last twenty years the scientific community has been investigating the use and potential of remote sensing techniques to determine the conditions of the coral reefs by analyzing their spectral characteristics from space. One of the problems in monitoring coral reefs from space is the effect of the water column on the remotely sensed signal. When light penetrates water its intensity decreases exponentially with increasing depth. This process, known as water column attenuation, exerts a profound effect on remotely sensed data collected over water bodies. The approach presented in this research focuses on the development of semi-analytical models that resolves the confounding influence water column attenuation on substrate reflectance to characterize benthic habitats from high resolution remotely sensed imagery on a per-pixel basis. High spatial resolution satellite and airborne imagery were used as inputs in the models to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). These parameters were subsequently used in various bio-optical algorithms to deduce bottom albedo and then to classify the benthos, generating a detailed map of benthic habitats. IKONOS and QuickBird multispectral satellite data and AISA Eagle hyperspectral airborne data were used in this research for benthic habitat mapping along the north shore of Roatan Island, Honduras. The AISA Eagle classification was

  15. Evaluating the feasibility of multitemporal hyperspectral remote sensing for monitoring bioremediation

    NASA Astrophysics Data System (ADS)

    Noomen, Marleen; Hakkarainen, Annika; van der Meijde, Mark; van der Werff, Harald

    2015-02-01

    In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (user's) accuracy was 71%.

  16. Hyperspectral Geobotanical Remote Sensing for CO2 Storage Monitoring

    SciTech Connect

    Pickles, W; Cover, W

    2004-05-14

    This project's goal is to develop remote sensing methods for early detection and spatial mapping, over whole regions simultaneously, of any surface areas under which there are significant CO2 leaks from deep underground storage formations. If large amounts of CO2 gas percolated up from a storage formation below to within plant root depth of the surface, the CO2 soil concentrations near the surface would become elevated and would affect individual plants and their local plant ecologies. Excessive soil CO2 concentrations are observed to significantly affect local plant and animal ecologies in our geothermal exploration, remote sensing research program at Mammoth Mountain CA USA. We also know from our geothermal exploration remote sensing programs, that we can map subtle hidden faults by spatial signatures of altered minerals and of plant species and health distributions. Mapping hidden faults is important because in our experience these highly localized (one to several centimeters) spatial pathways are good candidates for potentially significant CO2 leaks from deep underground formations. The detection and discrimination method we are developing uses primarily airborne hyperspectral, high spatial (3 meter) with 128 band wavelength resolution, visible and near infrared reflected light imagery. We also are using the newly available ''Quickbird'' satellite imagery that has high spatial resolution (0.6 meter for panchromatic images, 2.4 meters for multispectral). We have a commercial provider, HyVista Corp of Sydney Australia, of airborne hyperspectral imagery acquisitions and very relevant image data post processing, so that eventually the ongoing surveillance of CO2 storage fields can be contracted for commercially. In this project we have imaged the Rangely Colorado Oil field and surrounding areas with an airborne hyperspectral visible and near infrared reflected light sensor. The images were analyzed by several methods using the suite of tools available in the ENVI

  17. Evaluating airborne hyperspectral imagery for mapping saltcedar infestations in west Texas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Rio Grande of west Texas contains by far the largest infestation of saltcedar (Tamarix spp.) in Texas. The objective of this study was to evaluate airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Hyperspectral imagery with 102 usable band...

  18. Using airborne hyperspectral imagery for mapping saltcedar infestations in west Texas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Rio Grande of west Texas contains, by far, the largest infestation of saltcedar (Tamarix spp.) in Texas. The objective of this study was to evaluate airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Hyperspectral imagery with 102 usable ba...

  19. Shallow water substrate mapping using hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Fearns, P. R. C.; Klonowski, W.; Babcock, R. C.; England, P.; Phillips, J.

    2011-08-01

    During April 2004 the airborne hyperspectral sensor, HyMap, collected data over a shallow coastal region of Western Australia. These data were processed by inversion of a semi-analytical shallow water optical model to classify the substrate. Inputs to the optical model include water column constituent specific inherent optical properties (SIOPs), view and illumination geometry, surface condition (based on wind speed) and normalised reflectance spectra of substrate types. A sub-scene of the HyMap data covering approximately 4 km 2 was processed such that each 3×3 m 2 pixel was classed as sand, seagrass, brown algae or various mixtures of these three components. Coincident video data were collected and used to estimate substrate types. We present comparisons of the habitat classifications determined by these two methods and show that the percentage validation of the remotely sensed habitat map may be optimised by selection of appropriate optical model parameters. The optical model was able to retrieve classes for approximately 80% of all pixels in the scene, with validation percentages of approximately 50% for sand and seagrass classification, and 90% for brown algae classification. The semi-analytical model inversion approach to classification can be expected to be applied to any shallow water region where substrate reflectance spectra and SIOPs are known or can be inferred.

  20. Detection of single graves by airborne hyperspectral imaging.

    PubMed

    Leblanc, G; Kalacska, M; Soffer, R

    2014-12-01

    Airborne hyperspectral imaging (HSI) was assessed as a potential tool to locate single grave sites. While airborne HSI has shown to be useful to locate mass graves, it is expected the location of single graves would be an order of magnitude more difficult due to the smaller size and reduced mass of the targets. Two clearings were evaluated (through a blind test) as potential sites for containing at least one set of buried remains. At no time prior to submitting the locations of the potential burial sites from the HSI were the actual locations of the sites released or shared with anyone from the analysis team. The two HSI sensors onboard the aircraft span the range of 408-2524nm. A range of indicators that exploit the narrow spectral and spatial resolutions of the two complimentary HSI sensors onboard the aircraft were calculated. Based on the co-occurrence of anomalous pixels within the expected range of the indicators three potential areas conforming to our underlying assumptions of the expected spectral responses (and spatial area) were determined. After submission of the predicted burial locations it was revealed that two of the targets were located within GPS error (10m) of the true burial locations. Furthermore, due to the history of the TPOF site for burial work, investigation of the third target is being considered in the near future. The results clearly demonstrate promise for hyperspectral imaging to aid in the detection of buried remains, however further work is required before these results can justifiably be used in routine scenarios. PMID:25447169

  1. Detection of single graves by airborne hyperspectral imaging.

    PubMed

    Leblanc, G; Kalacska, M; Soffer, R

    2014-12-01

    Airborne hyperspectral imaging (HSI) was assessed as a potential tool to locate single grave sites. While airborne HSI has shown to be useful to locate mass graves, it is expected the location of single graves would be an order of magnitude more difficult due to the smaller size and reduced mass of the targets. Two clearings were evaluated (through a blind test) as potential sites for containing at least one set of buried remains. At no time prior to submitting the locations of the potential burial sites from the HSI were the actual locations of the sites released or shared with anyone from the analysis team. The two HSI sensors onboard the aircraft span the range of 408-2524nm. A range of indicators that exploit the narrow spectral and spatial resolutions of the two complimentary HSI sensors onboard the aircraft were calculated. Based on the co-occurrence of anomalous pixels within the expected range of the indicators three potential areas conforming to our underlying assumptions of the expected spectral responses (and spatial area) were determined. After submission of the predicted burial locations it was revealed that two of the targets were located within GPS error (10m) of the true burial locations. Furthermore, due to the history of the TPOF site for burial work, investigation of the third target is being considered in the near future. The results clearly demonstrate promise for hyperspectral imaging to aid in the detection of buried remains, however further work is required before these results can justifiably be used in routine scenarios.

  2. Incorporating multiresolution analysis with multiclassifiers and decision fusion for hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    West, Terrance R.

    The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods' sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies

  3. Second International Airborne Remote Sensing Conference and Exhibition

    NASA Technical Reports Server (NTRS)

    1996-01-01

    cloud cover analysis, Quadantid meteor shower studies, extra-solar far infrared ionic structure lines measurement, Cape Kennedy launch support, and studies in air pollution, The Products and Services Exhibit showcased new sensor and image processing technologies, aircraft data collection services, unmanned vehicle technology, platform equipment, turn-key services, software a workstations, GPS services, publications, and processing and integration systems by 58 exhibitors. The participation of aircraft users and crews provided unique dialogue between those who plan data collection a operate the remote sensing technology, and those who supply the data processing and integration equipment. Research results using hyperspectral imagery, radar and optical sensors, lidar, digital aerial photography, a integrated systems were presented. Major research and development programs and campaigns we reviewed, including CNR's LARA Project and European Space Agency's 1991-1995 Airborne Campaign. The pre-conference short courses addressed airborne video, photogrammetry, hyperspectral data analysis, digital orthophotography, imagery and GIS integration, IFSAR, GPS, and spectrometer calibration.

  4. Airborne infrared hyperspectral imager for intelligence, surveillance and reconnaissance applications

    NASA Astrophysics Data System (ADS)

    Lagueux, Philippe; Puckrin, Eldon; Turcotte, Caroline S.; Gagnon, Marc-André; Bastedo, John; Farley, Vincent; Chamberland, Martin

    2012-09-01

    Persistent surveillance and collection of airborne intelligence, surveillance and reconnaissance information is critical in today's warfare against terrorism. High resolution imagery in visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, leading to additional tools for detection and identification of targets, based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 μm spectral range. The Hyper-Cam has been recently used for the first time in two compact airborne platforms: a bellymounted gyro-stabilized platform and a gyro-stabilized gimbal ball. Both platforms are described in this paper, and successful results of high-altitude detection and identification of targets, including industrial plumes, and chemical spills are presented.

  5. Airborne infrared hyperspectral imager for intelligence, surveillance, and reconnaissance applications

    NASA Astrophysics Data System (ADS)

    Puckrin, Eldon; Turcotte, Caroline S.; Gagnon, Marc-André; Bastedo, John; Farley, Vincent; Chamberland, Martin

    2012-06-01

    Persistent surveillance and collection of airborne intelligence, surveillance and reconnaissance information is critical in today's warfare against terrorism. High resolution imagery in visible and infrared bands provides valuable detection capabilities based on target shapes and temperatures. However, the spectral resolution provided by a hyperspectral imager adds a spectral dimension to the measurements, leading to additional tools for detection and identification of targets, based on their spectral signature. The Telops Hyper-Cam sensor is an interferometer-based imaging system that enables the spatial and spectral analysis of targets using a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides datacubes of up to 320×256 pixels at spectral resolutions as fine as 0.25 cm-1. The LWIR version covers the 8.0 to 11.8 μm spectral range. The Hyper-Cam has been recently used for the first time in two compact airborne platforms: a belly-mounted gyro-stabilized platform and a gyro-stabilized gimbal ball. Both platforms are described in this paper, and successful results of high-altitude detection and identification of targets, including industrial plumes, and chemical spills are presented.

  6. Extracting Roof Parameters and Heat Bridges Over the City of Oldenburg from Hyperspectral, Thermal, and Airborne Laser Scanning Data

    NASA Astrophysics Data System (ADS)

    Bannehr, L.; Luhmann, Th.; Piechel, J.; Roelfs, T.; Schmidt, An.

    2011-09-01

    Remote sensing methods are used to obtain different kinds of information about the state of the environment. Within the cooperative research project HiReSens, funded by the German BMBF, a hyperspectral scanner, an airborne laser scanner, a thermal camera, and a RGB-camera are employed on a small aircraft to determine roof material parameters and heat bridges of house tops over the city Oldenburg, Lower Saxony. HiReSens aims to combine various geometrical highly resolved data in order to achieve relevant evidence about the state of the city buildings. Thermal data are used to obtain the energy distribution of single buildings. The use of hyperspectral data yields information about material consistence of roofs. From airborne laser scanning data (ALS) digital surface models are inferred. They build the basis to locate the best orientations for solar panels of the city buildings. The combination of the different data sets offers the opportunity to capitalize synergies between differently working systems. Central goals are the development of tools for the collection of heat bridges by means of thermal data, spectral collection of roofs parameters on basis of hyperspectral data as well as 3D-capture of buildings from airborne lasers scanner data. Collecting, analyzing and merging of the data are not trivial especially not when the resolution and accuracy is aimed in the domain of a few decimetre. The results achieved need to be regarded as preliminary. Further investigations are still required to prove the accuracy in detail.

  7. Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data

    NASA Astrophysics Data System (ADS)

    Cui, Jing; Yan, Bokun; Dong, Xinfeng; Zhang, Shimin; Zhang, Jingfa; Tian, Feng; Wang, Runsheng

    2015-08-01

    Thermal infrared remote sensing (8-12 μm) (TIR) has great potential for geologic remote sensing studies. TIR has been successfully used for terrestrial and planetary geologic studies to map surface materials. However, the complexity of the physics and the lack of hyperspectral data make the studies under-investigated. A new generation of commercial hyperspectral infrared sensors, known as Thermal Airborne Spectrographic Imager (TASI), was used for image analysis and mineral mapping in this study. In this paper, a combined method integrating normalized emissivity method (NEM), ratio algorithm (RATIO) and maximum-minimum apparent emissivity difference (MMD), being applied in multispectral data, has been modified and used to determine whether this method is suitable for retrieving emissivity from TASI hyperspectral data. MODTRAN 4 has been used for the atmospheric correction. The retrieved emissivity spectra matched well with the field measured spectra except for bands 1, 2, and 32. Quartz, calcite, diopside/hedenbergite, hornblende and microcline have been mapped by the emissivity image. Mineral mapping results agree with the dominant minerals identified by laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Both of the results indicated that the atmospheric correction method and the combined temperature-emissivitiy method are suitable for TASI image. Carbonate skarnization was first found in the study area by the spatial extent of diopside. Chemical analyses of the skarn samples determined that the Au content was 0.32-1.74 g/t, with an average Au content of 0.73 g/t. This information provides an important resource for prospecting for skarn type gold deposits. It is also suggested that TASI is suitable for prospect and deposit scale exploration.

  8. NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

    NASA Technical Reports Server (NTRS)

    Cook, Bruce D.; Corp, Lawrence A.; Nelson, Ross F.; Middleton, Elizabeth M.; Morton, Douglas C.; McCorkel, Joel T.; Masek, Jeffrey G.; Ranson, Kenneth J.; Ly, Vuong; Montesano, Paul M.

    2013-01-01

    The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard's LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (approximately 1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT's data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA's Data and Information policy.

  9. Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas

    NASA Astrophysics Data System (ADS)

    Kozoderov, Vladimir; Kondranin, Timofei; Dmitriev, Egor; Kamentsev, Vladimir

    2015-06-01

    Pattern recognition problem is outlined in the context of textural and spectral analysis of remote sensing imagery processing. Main attention is paid to Bayesian classifier that can be used to realize the processing procedures based on parallel machine-learning algorithms and high-productive computers. We consider the maximum of the posterior probability principle and the formalism of Markov random fields for the neighborhood description of the pixels for the related classes of objects with the emphasis on forests of different species and ages. The energy category of the selected classes serves to account for the likelihood measure between the registered radiances and the theoretical distribution functions approximating remotely sensed data. Optimization procedures are undertaken to solve the pattern recognition problem of the texture description for the forest classes together with finding thin nuances of their spectral distribution in the feature space. As a result, possible redundancy of the channels for imaging spectrometer due to their correlations is removed. Difficulties are revealed due to different sampling data while separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images. Such separation of pixels for the forest classes is maintained to enhance the recognition accuracy, but learning ensembles of data need to be agreed for these categories of pixels. We present some results of the Bayesian classifier applicability for recognizing airborne hyperspectral images using the relevant improvements in separating such pixels for the forest classes on a test area of the 4 × 10 km size encompassed by 13 airborne tracks, each forming the images by 500 pixels across the track and from 10,000 to 14,000 pixels along the track. The spatial resolution of each image is near to 1 m from the altitude near to 2 km above the ground level. The results of the hyperspectral imagery

  10. Advances in Unmixing of Hyperspectral Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Burazerovic, Dzevdet

    Remote sensing technology has advanced tremendously in recent decades. An important driver for this development has been the offering of wide spatial and temporal coverage by space- and airborne platforms, as well as the ever-improving capability of their sensors to record images with high spatial and spectral resolution. A modality that produces a bulk of data for remote sensing is hyperspectral imaging. This modality records the reflected solar radiation in contiguous and often numerous spectral bands, thereby extending the standard photography by enabling to treat each pixel individually as a spectrum discernible for each class of materials. One limitation of such imaging, where the spatial and spectral resolutions are inherently traded against each other, is the occurrence of mixed pixels and spectral mixing. The unraveling of spectral mixtures has been widely studied as spectral unmixing, where two main aspects are of interest: the estimation of the constituent spectra, and of their fractions or abundances, in the mixture. The work described in the thesis regards spectral unmixing from two objectives: advancement of methodology and introduction of unmixing in new applications. The first part, specifically, is concerned with the development of data-driven methods for spectral unmixing that can mitigate the dependency on physical parameters and models, and reduce high computational complexity due to the typical use of optimization techniques. A concrete realization consists of several algorithms that reformulate the known geometrical framework of spectral unmixing by introducing linear and nonlinear distance-based and analytical formulations. The second part introduces or elaborates spectral unmixing for detection of the atmospheric adjacency-effect and the estimation of quality of inland and coastal waters. The presented unmixing-based approaches in this context have been validated through theoretical and empirical comparison using available datasets and

  11. Airborne hyperspectral and LiDAR data integration for weed detection

    NASA Astrophysics Data System (ADS)

    Tamás, János; Lehoczky, Éva; Fehér, János; Fórián, Tünde; Nagy, Attila; Bozsik, Éva; Gálya, Bernadett; Riczu, Péter

    2014-05-01

    Agriculture uses 70% of global available fresh water. However, ca. 50-70% of water used by cultivated plants, the rest of water transpirated by the weeds. Thus, to define the distribution of weeds is very important in precision agriculture and horticulture as well. To survey weeds on larger fields by traditional methods is often time consuming. Remote sensing instruments are useful to detect weeds in larger area. In our investigation a 3D airborne laser scanner (RIEGL LMS-Q680i) was used in agricultural field near Sopron to scouting weeds. Beside the airborne LiDAR, hyperspectral imaging system (AISA DUAL) and air photos helped to investigate weed coverage. The LiDAR survey was carried out at early April, 2012, before sprouting of cultivated plants. Thus, there could be detected emerging of weeds and direction of cultivation. However airborne LiDAR system was ideal to detect weeds, identification of weeds at species level was infeasible. Higher point density LiDAR - Terrestrial laser scanning - systems are appropriate to distinguish weed species. Based on the results, laser scanner is an effective tool to scouting of weeds. Appropriate weed detection and mapping systems could contribute to elaborate water and herbicide saving management technique. This publication was supported by the OTKA project K 105789.

  12. [Advances in researches on hyperspectral remote sensing forestry information-extracting technology].

    PubMed

    Wu, Jian; Peng, Dao-Li

    2011-09-01

    The hyperspectral remote sensing technology has become one of the leading technologies in forestry remote sensing domain. In the present review paper, the advances in researches on hyperspectral remote sensing technology in forestry information extraction both at home and abroad were reviewed, and the five main research aspects including the hyperspectral classification and recognition of forest tree species, the hyperspectral inversion and extraction of forest ecological physical parameters, the hyperspectral monitoring and diagnosis of forest nutrient element, the forest crown density information extraction and the hyperspectral monitoring of forest disasters were summarized. The unresolved problems of hyperspectral technology in the forestry remote sensing applications were pointed out and the possible ways to solve these problems were expounded. Finally, the application prospect of hyperspectral remote sensing technology in forestry was analyzed.

  13. Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

    NASA Technical Reports Server (NTRS)

    Cetin, Haluk

    1999-01-01

    The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and

  14. High Resolution Airborne Laser Scanning and Hyperspectral Imaging with a Small Uav Platform

    NASA Astrophysics Data System (ADS)

    Gallay, Michal; Eck, Christoph; Zgraggen, Carlo; Kaňuk, Ján; Dvorný, Eduard

    2016-06-01

    The capabilities of unmanned airborne systems (UAS) have become diverse with the recent development of lightweight remote sensing instruments. In this paper, we demonstrate our custom integration of the state-of-the-art technologies within an unmanned aerial platform capable of high-resolution and high-accuracy laser scanning, hyperspectral imaging, and photographic imaging. The technological solution comprises the latest development of a completely autonomous, unmanned helicopter by Aeroscout, the Scout B1-100 UAV helicopter. The helicopter is powered by a gasoline two-stroke engine and it allows for integrating 18 kg of a customized payload unit. The whole system is modular providing flexibility of payload options, which comprises the main advantage of the UAS. The UAS integrates two kinds of payloads which can be altered. Both payloads integrate a GPS/IMU with a dual GPS antenna configuration provided by OXTS for accurate navigation and position measurements during the data acquisition. The first payload comprises a VUX-1 laser scanner by RIEGL and a Sony A6000 E-Mount photo camera. The second payload for hyperspectral scanning integrates a push-broom imager AISA KESTREL 10 by SPECIM. The UAS was designed for research of various aspects of landscape dynamics (landslides, erosion, flooding, or phenology) in high spectral and spatial resolution.

  15. Method for Retrieving Leaf Biochemical Parameters from Hyperspectral Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Chen, J. M.; Li, Y.; Zhang, Y.

    2002-05-01

    Retrieving biophysical and biochemical parameters is one of the most important and promising applications of hyperspectral remote sensing and will contribute greatly to the understanding of terrestrial ecosystems. We developed a mathematical inversion method for deriving leaf chlorophyll and water contents, which are potentially very useful as inputs to photosynthesis and carbon cycle models. From hyperspectral data cubes, global absorption coefficients for these parameters have been derived from two leaf spectral models named LIBERTY and PROSPECT. Taking into account the various factors affecting the coefficients such as the cellular structure of leaves and concentrations of other substances (lignin, protein, etc.), we have successfully retrieved chlorophyll and water concentrations of leaves. The inversion model is tested by comparing these parameters used in the forward calculations of leaf spectra and the same parameters retrieved from these forward-calculated spectra. Excellent agreement is found between the inverse and forward results, the difference being generally less than 1%. Atmospherically corrected airborne hyperspectral CASI (Compact Airborne Spectrographic Imager) images over a boreal forest are chosen for further model test. Through the model inversion, the data cube with 72 spectral bands and 405x2852 pixels is used to produce maps of the chlorophyll and water concentrations for the forest. The influence of canopy architecture on the inversion results is yet to be investigated using a geometrical optical model.

  16. Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

    PubMed

    Varga, Timothy A; Asner, Gregory P

    2008-04-01

    Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread.

  17. Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park.

    PubMed

    Varga, Timothy A; Asner, Gregory P

    2008-04-01

    Alien invasive grasses threaten to transform Hawaiian ecosystems through the alteration of ecosystem dynamics, especially the creation or intensification of a fire cycle. Across sub-montane ecosystems of Hawaii Volcanoes National Park on Hawaii Island, we quantified fine fuels and fire spread potential of invasive grasses using a combination of airborne hyperspectral and light detection and ranging (LiDAR) measurements. Across a gradient from forest to savanna to shrubland, automated mixture analysis of hyperspectral data provided spatially explicit fractional cover estimates of photosynthetic vegetation, non-photosynthetic vegetation, and bare substrate and shade. Small-footprint LiDAR provided measurements of vegetation height along this gradient of ecosystems. Through the fusion of hyperspectral and LiDAR data, a new fire fuel index (FFI) was developed to model the three-dimensional volume of grass fuels. Regionally, savanna ecosystems had the highest volumes of fire fuels, averaging 20% across the ecosystem and frequently filling all of the three-dimensional space represented by each image pixel. The forest and shrubland ecosystems had lower FFI values, averaging 4.4% and 8.4%, respectively. The results indicate that the fusion of hyperspectral and LiDAR remote sensing can provide unique information on the three-dimensional properties of ecosystems, their flammability, and the potential for fire spread. PMID:18488621

  18. Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

    USGS Publications Warehouse

    Tian, Y.Q.; Yu, Q.; Zimmerman, M.J.; Flint, S.; Waldron, M.C.

    2010-01-01

    This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water-quality standards. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near-infrared (NIR)-Green Angle Index (NGAI) and normalized water absorption depth (DH), at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral-derived NDVI. The IKONOS-based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the DH. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High-resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. Interpretation of biophysical parameters derived from high-resolution satellite or airborne imagery should prove to be a

  19. [Analysis of related factors of slope plant hyperspectral remote sensing].

    PubMed

    Sun, Wei-Qi; Zhao, Yun-Sheng; Tu, Lin-Ling

    2014-09-01

    In the present paper, the slope gradient, aspect, detection zenith angle and plant types were analyzed. In order to strengthen the theoretical discussion, the research was under laboratory condition, and modeled uniform slope for slope plant. Through experiments we found that these factors indeed have influence on plant hyperspectral remote sensing. When choosing slope gradient as the variate, the blade reflection first increases and then decreases as the slope gradient changes from 0° to 36°; When keeping other factors constant, and only detection zenith angle increasing from 0° to 60°, the spectral characteristic of slope plants do not change significantly in visible light band, but decreases gradually in near infrared band; With only slope aspect changing, when the dome meets the light direction, the blade reflectance gets maximum, and when the dome meets the backlit direction, the blade reflectance gets minimum, furthermore, setting the line of vertical intersection of incidence plane and the dome as an axis, the reflectance on the axis's both sides shows symmetric distribution; In addition, spectral curves of different plant types have a lot differences between each other, which means that the plant types also affect hyperspectral remote sensing results of slope plants. This research breaks through the limitations of the traditional vertical remote sensing data collection and uses the multi-angle and hyperspectral information to analyze spectral characteristics of slope plants. So this research has theoretical significance to the development of quantitative remote sensing, and has application value to the plant remote sensing monitoring.

  20. Optimization of spectral bands for hyperspectral remote sensing of forest vegetation

    NASA Astrophysics Data System (ADS)

    Dmitriev, Egor V.; Kozoderov, Vladimir V.

    2013-10-01

    Optimization principles of accounting for the most informative spectral channels in hyperspectral remote sensing data processing serve to enhance the efficiency of the employed high-productive computers. The problem of pattern recognition of the remotely sensed land surface objects with the accent on the forests is outlined from the point of view of the spectral channels optimization on the processed hyperspectral images. The relevant computational procedures are tested using the images obtained by the produced in Russia hyperspectral camera that was installed on a gyro-stabilized platform to conduct the airborne flight campaigns. The Bayesian classifier is used for the pattern recognition of the forests with different tree species and age. The probabilistically optimal algorithm constructed on the basis of the maximum likelihood principle is described to minimize the probability of misclassification given by this classifier. The classification error is the major category to estimate the accuracy of the applied algorithm by the known holdout cross-validation method. Details of the related techniques are presented. Results are shown of selecting the spectral channels of the camera while processing the images having in mind radiometric distortions that diminish the classification accuracy. The spectral channels are selected of the obtained subclasses extracted from the proposed validation techniques and the confusion matrices are constructed that characterize the age composition of the classified pine species as well as the broad age-class recognition for the pine and birch species with the fully illuminated parts of their crowns.

  1. Optimal band selection in hyperspectral remote sensing of aquatic benthic features: a wavelet filter window approach

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R., Jr.

    2006-09-01

    This paper describes a wavelet based approach to derivative spectroscopy. The approach is utilized to select, through optimization, optimal channels or bands to use as derivative based remote sensing algorithms. The approach is applied to airborne and modeled or synthetic reflectance signatures of environmental media and features or objects within such media, such as benthic submerged vegetation canopies. The technique can also applied to selected pixels identified within a hyperspectral image cube obtained from an board an airborne, ground based, or subsurface mobile imaging system. This wavelet based image processing technique is an extremely fast numerical method to conduct higher order derivative spectroscopy which includes nonlinear filter windows. Essentially, the wavelet filter scans a measured or synthetic signature in an automated sequential manner in order to develop a library of filtered spectra. The library is utilized in real time to select the optimal channels for direct algorithm application. The unique wavelet based derivative filtering technique makes us of a translating, and dilating derivative spectroscopy signal processing (TDDS-SP (R)) approach based upon remote sensing science and radiative transfer processes unlike other signal processing techniques applied to hyperspectral signatures.

  2. [Research on airborne hyperspectral identification of red tide organism dominant species based on SVM].

    PubMed

    Ma, Yi; Zhang, Jie; Cui, Ting-wei

    2006-12-01

    Airborne hyperspectral identification of red tide organism dominant species can provide technique for distinguishing red tide and its toxin, and provide support for scaling the disaster. Based on support vector machine(SVM), the present paper provides an identification model of red tide dominant species. Utilizing this model, the authors accomplished three identification experiments with the hyperspectral data obtained on 16th July, and 19th and 25th August, 2001. It is shown from the identification results that the model has a high precision and is not restricted by high dimension of the hyperspectral data.

  3. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment

    NASA Astrophysics Data System (ADS)

    Koch, Barbara

    2010-11-01

    This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.

  4. Relating Hyperspectral Airborne Data to Ground Measurements in a Complex and Discontinuous Canopy

    NASA Astrophysics Data System (ADS)

    Calleja, Javier F.; Hellmann, Christine; Mendiguren, Gorka; Punalekar, Suvarna; Peón, Juanjo; MacArthur, Alasdair; Alonso, Luis

    2015-12-01

    The work described in this paper is aimed at validating hyperspectral airborne reflectance data collected during the Regional Experiments For Land-atmosphere EXchanges (REFLEX) campaign. Ground reflectance data measured in a vineyard were compared with airborne reflectance data. A sampling strategy and subsequent ground data processing had to be devised so as to capture a representative spectral sample of this complex crop. A linear model between airborne and ground data was tried and statistically tested. Results reveal a sound correspondence between ground and airborne reflectance data (R2 > 0.97), validating the atmospheric correction of the latter.

  5. Estimating Leaf Water Potential of Giant Sequoia Trees from Airborne Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Francis, E. J.; Asner, G. P.

    2015-12-01

    Recent drought-induced forest dieback events have motivated research on the mechanisms of tree survival and mortality during drought. Leaf water potential, a measure of the force exerted by the evaporation of water from the leaf surface, is an indicator of plant water stress and can help predict tree mortality in response to drought. Scientists have traditionally measured water potentials on a tree-by-tree basis, but have not been able to produce maps of tree water potential at the scale of a whole forest, leaving forest managers unaware of forest drought stress patterns and their ecosystem-level consequences. Imaging spectroscopy, a technique for remote measurement of chemical properties, has been used to successfully estimate leaf water potentials in wheat and maize crops and pinyon-pine and juniper trees, but these estimates have never been scaled to the canopy level. We used hyperspectral reflectance data collected by the Carnegie Airborne Observatory (CAO) to map leaf water potentials of giant sequoia trees (Sequoiadendron giganteum) in an 800-hectare grove in Sequoia National Park. During the current severe drought in California, we measured predawn and midday leaf water potentials of 48 giant sequoia trees, using the pressure bomb method on treetop foliage samples collected with tree-climbing techniques. The CAO collected hyperspectral reflectance data at 1-meter resolution from the same grove within 1-2 weeks of the tree-level measurements. A partial least squares regression was used to correlate reflectance data extracted from the 48 focal trees with their water potentials, producing a model that predicts water potential of giant sequoia trees. Results show that giant sequoia trees can be mapped in the imagery with a classification accuracy of 0.94, and we predicted the water potential of the mapped trees to assess 1) similarities and differences between a leaf water potential map and a canopy water content map produced from airborne hyperspectral data, 2

  6. Land cover mapping in Latvia using hyperspectral airborne and simulated Sentinel-2 data

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Filipovs, Jevgenijs; Brauns, Agris; Taskovs, Juris; Erins, Gatis

    2016-08-01

    Land cover mapping in Latvia is performed as part of the Corine Land Cover (CLC) initiative every six years. The advantage of CLC is the creation of a standardized nomenclature and mapping protocol comparable across all European countries, thereby making it a valuable information source at the European level. However, low spatial resolution and accuracy, infrequent updates and expensive manual production has limited its use at the national level. As of now, there is no remote sensing based high resolution land cover and land use services designed specifically for Latvia which would account for the country's natural and land use specifics and end-user interests. The European Space Agency launched the Sentinel-2 satellite in 2015 aiming to provide continuity of free high resolution multispectral satellite data thereby presenting an opportunity to develop and adapted land cover and land use algorithm which accounts for national enduser needs. In this study, land cover mapping scheme according to national end-user needs was developed and tested in two pilot territories (Cesis and Burtnieki). Hyperspectral airborne data covering spectral range 400-2500 nm was acquired in summer 2015 using Airborne Surveillance and Environmental Monitoring System (ARSENAL). The gathered data was tested for land cover classification of seven general classes (urban/artificial, bare, forest, shrubland, agricultural/grassland, wetlands, water) and sub-classes specific for Latvia as well as simulation of Sentinel-2 satellite data. Hyperspectral data sets consist of 122 spectral bands in visible to near infrared spectral range (356-950 nm) and 100 bands in short wave infrared (950-2500 nm). Classification of land cover was tested separately for each sensor data and fused cross-sensor data. The best overall classification accuracy 84.2% and satisfactory classification accuracy (more than 80%) for 9 of 13 classes was obtained using Support Vector Machine (SVM) classifier with 109 band

  7. Assessment of chlorophyll-a concentration in the Gulf of Riga using hyperspectral airborne and simulated Sentinel-3 OLCI data

    NASA Astrophysics Data System (ADS)

    Jakovels, Dainis; Brauns, Agris; Filipovs, Jevgenijs; Taskovs, Juris; Fedorovicha, Dagnija; Paavel, Birgot; Ligi, Martin; Kutser, Tiit

    2016-08-01

    Remote sensing has proved to be an accurate and reliable tool in clear water environments like oceans or the Mediterranean Sea. However, the current algorithms and methods usually fail on optically complex waters like coastal and inland waters. The whole Baltic Sea can be considered as optically complex coastal waters. Remote assessment of water quality parameters (eg., chlorophyll-a concentration) is of interest for monitoring of marine environment, but hasn't been used as a routine approach in Latvia. In this study, two simultaneous hyperspectral airborne data and in situ measurement campaigns were performed in the Gulf of Riga near the River Daugava mouth in summer 2015 to simulate Sentinel-3 data and test existing algorithms for retrieval of Level 2 Water products. Comparison of historical data showed poor overall correlation between in situ measurements and MERIS chlorophyll-a data products. Better correlation between spectral chl-a data products and in situ water sampling measurements was achieved during simultaneous airborne and field campaign resulting in R2 up to 0.94 for field spectral data, R2 of 0.78 for airborne data. Test of all two band ratio combinations showed that R2 could be improved from 0.63 to 0.94 for hyperspectral airborne data choosing 712 and 728 nm bands instead of 709 and 666 nm, and R2 could be improved from 0.61 to 0.83 for simulated Sentinel-3 OLCI data choosing Oa10 and Oa8 bands instead of Oa11 and Oa8. Repeated campaigns are planned during spring and summer blooms 2016 in the Gulf of Riga to get larger data set for validation and evaluate repeatability. The main challenges remain to acquire as good data as possible within rapidly changing environment and often cloudy weather conditions.

  8. Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Citrus greening or Huanglongbing (HLB) is a devastating disease spread in many citrus groves since first found in 2005 in Florida. Multispectral (MS) and hyperspectral (HS) airborne images of citrus groves in Florida were taken to detect citrus greening infected trees in 2007 and 2010. Ground truthi...

  9. Concept of an advanced hyperspectral remote sensing system for pipeline monitoring

    NASA Astrophysics Data System (ADS)

    Keskin, Göksu; Teutsch, Caroline D.; Lenz, Andreas; Middelmann, Wolfgang

    2015-10-01

    Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.

  10. Use of field reflectance data for crop mapping using airborne hyperspectral image

    NASA Astrophysics Data System (ADS)

    Nidamanuri, Rama Rao; Zbell, Bernd

    2011-09-01

    Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question "what is the prospect of using independent reference reflectance spectra for image classification", while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of "non-existence of characteristic reflectance spectral signatures for vegetation", results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.

  11. VNIR-SWIR-TIR hyperspectral airborne campaign for soil and sediment mapping in semi-arid south african environments

    NASA Astrophysics Data System (ADS)

    Milewski, Robert; Chabrillat, Sabine; Eisele, Andreas

    2016-04-01

    Airborne hyperspectral remote sensing techniques has been proven to offer efficient procedures for soil and sediment mineralogical mapping in arid areas on larger scales. Optical methods based on traditional remote sensing windows using the solar reflective spectral wavelength range from the visible-near infrared (VNIR: 0.4-1.1 μm) to the short-wave infrared region (SWIR: 1.1-2.5 μm) allow mapping of common soil properties such as iron oxides, textural characteristics and organic carbon. However, soil mapping in semi-arid environments using VNIR-SWIR is currently limited due to specific spectral characteristics. Challenges appear in such environments due to the common presence of sandy soils (coarse textured) which grain size distribution is driven by the dominant mineral, quartz (SiO2), and which lacks any distinctive Si-O bond related spectral features within the VNIR-SWIR. Furthermore, another challenge is represented by the common presence of other specific spectral features due to different salts (gypsum, halite) or coatings of different forms (cyanobacteria, iron-oxides and/or -oxyhydroxides) for which few studies exists or that oft prevent detection of any other potential spectral feature of e.g. soil organics. In this context, more methodological developments are needed to overcome current limitations of hyperspectral remote sensing for arid areas, and to extent its scope using the thermal infrared (TIR) wavelength region within the atmospheric window between 8 and 14 μm (longwave infrared). In 2015 an extensive VNIR-SWIR-TIR airborne hyperspectral dataset consisting of HySpex-VNIR, HySpex-SWIR (NEO) and Hyper-Cam (TELOPS) data has been acquired in various Namibian and South African landscapes part of the Dimap/GFZ campaign in the frame of the BMBF-SPACES Geoarchive project. Research goals are 1) to demonstrate the capabilities to extract information from such a dataset and 2) to demonstrate the potential of advanced hyperspectral remote sensing

  12. Support Vector Machines for Hyperspectral Remote Sensing Classification

    NASA Technical Reports Server (NTRS)

    Gualtieri, J. Anthony; Cromp, R. F.

    1998-01-01

    The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

  13. Hyper-spectral remote sensing of global CO2

    NASA Astrophysics Data System (ADS)

    Wang, Ding Yi

    2016-07-01

    Monitoring of greenhouse gas CO2 on a basis of global scale, high precision, and real time has great significance for the understanding CO2 sources and sinks, as well as global climate change. In order to meet the urgent needs, several research projects are ongoing in China and in the world for retrieving CO2 from satellite-based hyper-spectral observations. In this talk, the projects are briefly introduced, the theory of atmospheric near-infrared remote sensing is discussed, and a forward model and inversion software system for near-infrared hyper-spectral measurements of CO2 is outlined. The validation of the software package against GOST-FTS observation are performed, and their relative error is less than 1.0%. Future cross-validation between the Chinese satellite and other observations is suggested.

  14. Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land.

    PubMed

    Bassani, Cristiana; Cavalli, Rosa Maria; Pignatti, Stefano

    2010-01-01

    Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550 nm (τ(550)) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ(550) with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ(550) and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ(550) retrieved by Module A (r(2) = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r(2) ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness. PMID:22163558

  15. Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land

    PubMed Central

    Bassani, Cristiana; Cavalli, Rosa Maria; Pignatti, Stefano

    2010-01-01

    Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550nm (τ550) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ550 with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ550 and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ550 retrieved by Module A (r2 = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r2 ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness. PMID:22163558

  16. Hyperspectral Remote Sensing of Foliar Nitrogen Content

    NASA Technical Reports Server (NTRS)

    Knyazikhin, Yuri; Schull, Mitchell A.; Stenberg, Pauline; Moettus, Matti; Rautiainen, Miina; Yang, Yan; Marshak, Alexander; Carmona, Pedro Latorre; Kaufmann, Robert K.; Lewis, Philip; Disney, Mathias I.; Vanderbilt, Vern; Davis, Anthony B.; Baret, Frederic; Jacquemoud, Stephane; Lyapustin, Alexei; Myneni, Ranga B.

    2013-01-01

    A strong positive correlation between vegetation canopy bidirectional reflectance factor (BRF) in the near infrared (NIR) spectral region and foliar mass-based nitrogen concentration (%N) has been reported in some temperate and boreal forests. This relationship, if true, would indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data. We report, however, that the previously reported correlation is an artifact - it is a consequence of variations in canopy structure, rather than of %N. The data underlying this relationship were collected at sites with varying proportions of foliar nitrogen-poor needleleaf and nitrogen-rich broadleaf species, whose canopy structure differs considerably. When the BRF data are corrected for canopy-structure effects, the residual reflectance variations are negatively related to %N at all wavelengths in the interval 423-855 nm. This suggests that the observed positive correlation between BRF and %N conveys no information about %N. We find that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710-790 nm provide critical information for correction of structural influences. Our analysis also suggests that surface characteristics of leaves impact remote sensing of its internal constituents. This further decreases the ability to remotely sense canopy foliar nitrogen. Finally, the analysis presented here is generic to the problem of remote sensing of leaf-tissue constituents and is therefore not a specific critique of articles espousing remote sensing of foliar %N.

  17. Multi- and hyperspectral geologic remote sensing: A review

    NASA Astrophysics Data System (ADS)

    van der Meer, Freek D.; van der Werff, Harald M. A.; van Ruitenbeek, Frank J. A.; Hecker, Chris A.; Bakker, Wim H.; Noomen, Marleen F.; van der Meijde, Mark; Carranza, E. John M.; Smeth, J. Boudewijn de; Woldai, Tsehaie

    2012-02-01

    Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region allowed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc.). The step toward quantitative and validated (subpixel) surface mineralogic mapping was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectra and to unravel mixed pixel spectra to pure endmember spectra to derive subpixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) data continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly

  18. Analysis of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing monitoring key technology in coastal wetland

    NASA Astrophysics Data System (ADS)

    Ma, Yi; Zhang, Jie; Zhang, Jingyu

    2016-01-01

    The coastal wetland, a transitional zone between terrestrial ecosystems and marine ecosystems, is the type of great value to ecosystem services. For the recent 3 decades, area of the coastal wetland is decreasing and the ecological function is gradually degraded with the rapid development of economy, which restricts the sustainable development of economy and society in the coastal areas of China in turn. It is a major demand of the national reality to carry out the monitoring of coastal wetlands, to master the distribution and dynamic change. UAV, namely unmanned aerial vehicle, is a new platform for remote sensing. Compared with the traditional satellite and manned aerial remote sensing, it has the advantage of flexible implementation, no cloud cover, strong initiative and low cost. Image-spectrum merging is one character of high spectral remote sensing. At the same time of imaging, the spectral curve of each pixel is obtained, which is suitable for quantitative remote sensing, fine classification and target detection. Aimed at the frontier and hotspot of remote sensing monitoring technology, and faced the demand of the coastal wetland monitoring, this paper used UAV and the new remote sensor of high spectral imaging instrument to carry out the analysis of the key technologies of monitoring coastal wetlands by UAV on the basis of the current situation in overseas and domestic and the analysis of developing trend. According to the characteristic of airborne hyperspectral data on UAV, that is "three high and one many", the key technology research that should develop are promoted as follows: 1) the atmosphere correction of the UAV hyperspectral in coastal wetlands under the circumstance of complex underlying surface and variable geometry, 2) the best observation scale and scale transformation method of the UAV platform while monitoring the coastal wetland features, 3) the classification and detection method of typical features with high precision from multi scale

  19. Can Hyperspectral Remote Sensing Detect Species Specific Biochemicals ?

    NASA Astrophysics Data System (ADS)

    Vanderbilt, V. C.; Daughtry, C. S.

    2011-12-01

    Discrimination of a few plants scattered among many plants is a goal common to detection of agricultural weeds, invasive plant species and illegal Cannabis clandestinely grown outdoors, the subject of this research. Remote sensing technology provides an automated, computer based, land cover classification capability that holds promise for improving upon the existing approaches to Cannabis detection. In this research, we investigated whether hyperspectral reflectance of recently harvested, fully turgid Cannabis leaves and buds depends upon the concentration of the psychoactive ingredient Tetrahydrocannabinol (THC) that, if present at sufficient concentration, presumably would allow species-specific identification of Cannabis.

  20. Geological applications of machine learning on hyperspectral remote sensing data

    NASA Astrophysics Data System (ADS)

    Tse, C. H.; Li, Yi-liang; Lam, Edmund Y.

    2015-02-01

    The CRISM imaging spectrometer orbiting Mars has been producing a vast amount of data in the visible to infrared wavelengths in the form of hyperspectral data cubes. These data, compared with those obtained from previous remote sensing techniques, yield an unprecedented level of detailed spectral resolution in additional to an ever increasing level of spatial information. A major challenge brought about by the data is the burden of processing and interpreting these datasets and extract the relevant information from it. This research aims at approaching the challenge by exploring machine learning methods especially unsupervised learning to achieve cluster density estimation and classification, and ultimately devising an efficient means leading to identification of minerals. A set of software tools have been constructed by Python to access and experiment with CRISM hyperspectral cubes selected from two specific Mars locations. A machine learning pipeline is proposed and unsupervised learning methods were implemented onto pre-processed datasets. The resulting data clusters are compared with the published ASTER spectral library and browse data products from the Planetary Data System (PDS). The result demonstrated that this approach is capable of processing the huge amount of hyperspectral data and potentially providing guidance to scientists for more detailed studies.

  1. VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

    SciTech Connect

    Serrato, M.; Jungho, I.; Jensen, J.; Jensen, R.; Gladden, J.; Waugh, J.

    2012-01-17

    Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.

  2. Winter wheat growth spatial variation monitoring through hyperspectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Song, Xiaoyu; Li, Ting; Wang, Jihua; Gu, Xiaohe; Xu, Xingang

    2015-10-01

    This work aims at quantifying the winter wheat growth spatial heterogeneity captured by hyperspectral airborne images. The field experiment was conducted in 2001 and 2002 and airborne hyperspectral remote-sensing data was acquired at noon on 11 April 2001 using an operational modular imaging spectrometer (OMIS). Totally 12 winter fields which covered by both dense and sparse winter wheat canopies were selected to analysis the winter wheat growth heterogeneity. The experimental semi-variograms for bands covered from invisible to mid-infrared were computed for each field then the theoretical models were be fitted with least squares algorithm for spherical model, exponential model. The optimization model was selected after evaluated by R-square. Three key terms in each model, the sill, the range, and nugget variance were then calculated from the models. The study results show that the sill, range and nugget for same field wheat were varied with the wavelength from blue to mid infrared bands. Although wheat growth in different fields showed different spatial heterogeneity, they all showed an obvious sill pattern. The minimum of mean range value was 7.52 m for mid-infrared bands while the maximum value was 91.71 m for visible bands. The minimum of mean sill value ranged from 1.46 for visible bands to 39.76 for NIR bands, the minimum of mean nugget value ranged from 0.06 for visible bands to5.45 for mid-infrared bands. This study indicate that remote sensing image is important for crop growth spatial heterogeneity study. But it is necessary to explore the effect of different wavelength of image data on crop growth semi-variogram estimation and find out which band data could be used to estimate crop semi-variogram reliably.

  3. Sunglint effects on the characterization of optically active substances in high spatial resolution airborne hyperspectral images

    NASA Astrophysics Data System (ADS)

    Streher, A. S.; Faria Barbosa, C. Clemente; Soares Galvão, L.; Goodman, J. A.; Silva, T. S.

    2013-05-01

    Sunglint, also known as the specular reflection of light from water surfaces, is a component of sensor-received radiance that represents a confounding factor on the characterization of water bodies by remote sensing. In airborne remote sensing images, the effect of sunglint can be minimized by optimizing the flight paths, directing the sensor towards or away from the Sun, and by keeping solar zenith angles between 30° and 60°. However, these guidelines cannot always be applied, often due to the irregular spatial pattern of lakes, estuaries and coastlines. The present study assessed the impact of sunglint on the relationship between the optically active substances (OAS) concentration, in optically complex waters, and the spectral information provided by an airborne high spatial resolution hyperspectral sensor (SpecTIR). The Ibitinga reservoir, located in southeastern Brazil (state of São Paulo), was selected as the study area because of its meandering shape. As a result, there is demanding constant changes in data acquisition geometry to achieve complete coverage, therefore not allowing sunglint conditions to be minimized during image acquisition. Field data collection was carried out on October 23 and 24, 2011. During these two days, 15 water stations along the reservoir were sampled, concurrently with the SpecTIR image acquisition in 357 bands (398-2455 nm) and at 3 m spatial resolution. Chlorophyll, pheophytin, total suspended solids, organic and inorganic suspended solids and colored dissolved matter were determined in laboratory. The images were corrected for the atmospheric effects using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm and then geometrically corrected. In order to evaluate the sunglint effects on the OAS characterization, the images were corrected for such effects using the deglint algorithm from Goodman et al. (2008). The SpecTIR 662-nm band reflectance was selected to be correlated to the OAS due to

  4. An algorithm for hyperspectral remote sensing of aerosols: theoretical framework, information content analysis and application to GEO-TASO

    NASA Astrophysics Data System (ADS)

    Hou, W.; Wang, J.; Xu, X.; Leitch, J. W.; Delker, T.; Chen, G.

    2015-12-01

    This paper includes a series of studies that aim to develop a hyperspectral remote sensing technique for retrieving aerosol properties from a newly developed instrument GEO-TASO (Geostationary Trance gas and Aerosol Sensor Optimization) that measures the radiation at 0.4-0.7 wavelengths at spectral resolution of 0.02 nm. GEOS-TASO instrument is a prototype instrument of TEMPO (Tropospheric Emissions: Monitoring of Pollution), which will be launched in 2022 to measure aerosols, O3, and other trace gases from a geostationary orbit over the N-America. The theoretical framework of optimized inversion algorithm and the information content analysis such as degree of freedom for signal (DFS) will be discussed for hyperspectral remote sensing in visible bands, as well as the application to GEO-TASO, which has mounted on the NASA HU-25C aircraft and gathered several days' of airborne hyperspectral data for our studies. Based on the optimization theory and different from the traditional lookup table (LUT) retrieval technique, our inversion method intends to retrieve the aerosol parameters and surface reflectance simultaneously, in which UNL-VRTM (UNified Linearized Radiative Transfer Model) is employed for forward model and Jacobians calculation, meanwhile, principal component analysis (PCA) is used to constrain the hyperspectral surface reflectance.The information content analysis provides the theoretical analysis guidance about what kind of aerosol parameters could be retrieved from GeoTASO hyperspectral remote sensing to the practical inversion study. Besides, the inversion conducted iteratively until the modeled spectral radiance fits with GeoTASO measurements by a Quasi-Newton method called L-BFGS-B (Large scale BFGS Bound constrained). Finally, the retrieval results of aerosol optical depth and other aerosol parameters are compared against those retrieved by AEROENT and/or in situ measurements such as DISCOVER-AQ during the aircraft campaign.

  5. Subtropical Forest Biomass Estimation Using Airborne LiDAR and Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Pang, Yong; Li, Zengyuan

    2016-06-01

    Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF's (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

  6. Application of multivariate curve resolution alternating least squares (MCR-ALS) to remote sensing hyperspectral imaging.

    PubMed

    Zhang, Xin; Tauler, Romà

    2013-01-31

    The application of the MCR-ALS method is demonstrated on two simulated remote sensing spectroscopic images and on one experimental reference remote sensing spectroscopic image obtained by the Airborn Visible/Infrared Imaging Spectrometer (AVIRIS). By application of MCR-ALS, the spectra signatures of the pure constituents present in the image and their concentration distribution at a pixel level are estimated. Results obtained by MCR-ALS are compared to those obtained by other methods frequently used in the remote sensing spectroscopic imaging field like VCA and MVSA. In the case of the analysis of the experimental data set, the resolved pure spectra signatures were compared to reference spectra from USGS library for their identification. In all cases, results were also evaluated for the presence of rotational ambiguities using the MCR-BANDS method. The obtained results confirmed that the MCR-ALS method can be successfully used for remote sensing hyperspectral image resolution purposes. However, the amount of rotation ambiguity still present in the solutions obtained by this and other resolution methods (like VCA or MVSA) can still be large and it should be evaluated with care, trying to reduce its effects by selecting the more appropriate constraints. Only in this way it is possible to increase the reliability of the solutions provided by these methods and decrease the uncertainties associated to their use.

  7. Mapping tree health using airborne full-waveform laser scans and hyperspectral imagery: a case study for floodplain eucalypt forest

    NASA Astrophysics Data System (ADS)

    Shendryk, I.; Tulbure, M. G.; Broich, M.

    2014-12-01

    Barmah-Millewa Forest (BMF), the largest River Red Gum forest in the world, located in south-eastern Australia is suffering from severe dieback, thus diminishing its ecological and economical value. Previous research showed that dieback is a good predictor of the forest health and stressed the need for BMF health mapping and change monitoring. In this respect, airborne laser scanning and hyperspectral imaging offer extensive spatial and spectral coverage of measurements and represent an ideal tool for forest health mapping at individual tree scale. The aim of this project is to quantify the health of individual, structurally complex floodplain eucalypt trees by integrating airborne hyperspectral imagery, full-waveform laser scans and field measurements. An aerial survey, conducted in May 2014, was designed to provide a representative sample of BMF tree health. The positioning of 17 flight lines aimed to capture the heterogeneity of the forest health and flood frequency. Preliminary analysis of the aerial remote sensing data with regards to chlorophyll concentrations, dieback levels and canopy densities allowed us to target our field campaign (conducted in June 2014). Field measurements included accurate position measurements, LAI, visual assessment, spectral measurement and mensuration of individual trees in 30 m2 plots. For detection of individual tree trunks from airborne laser scans we used a novel approach based on Euclidean distance clustering, taking advantage of the intensity and pulse width difference between woody and leaf tree compartments. The detected trunks were used to seed a minimum cut algorithm for tree crown delineation. In situ measurements confirmed the high structural diversity of the forest and allowed the calibration of the tree detection algorithm. An overall accuracy of the tree detection of 54% and 67% was achieved for trees with circumference over 40 cm and over 100 cm respectively. As a further step, 3D point clusters representing

  8. Detection and identification of toxic air pollutants using airborne LWIR hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Williams, David J.; Feldman, Barry L.; Williams, Tim J.; Pilant, Drew; Lucey, Paul G.; Worthy, L. D.

    2005-01-01

    Airborne longwave infrared (LWIR) hyperspectral imagery was utilized to detect and identify gaseous chemical release plumes at sites in southern Texas. The Airborne Hyperspectral Imager (AHI), developed by the University of Hawai"i, was flown over a petrochemical facility and a confined animal feeding operation on a modified DC-3 during April, 2004. Data collected by the AHI system was successfully used to detect and identify numerous plumes at both sites. Preliminary results indicate the presence of benzene and ammonia and several other organic compounds. Emissions were identified using regression analysis on atmospherically compensated data. Data validation was conducted using facility emission inventories. This technology has great promise for monitoring and inventorying facility emissions, and may be used as means to assist ground inspection teams to focus on actual fugitive emission points.

  9. How Cities Breathe: Ground-Referenced, Airborne Hyperspectral Imaging Precursor Measurements To Space-Based Monitoring

    NASA Technical Reports Server (NTRS)

    Leifer, Ira; Tratt, David; Quattrochi, Dale; Bovensmann, Heinrich; Gerilowski, Konstantin; Buchwitz, Michael; Burrows, John

    2013-01-01

    Methane's (CH4) large global warming potential (Shindell et al., 2012) and likely increasing future emissions due to global warming feedbacks emphasize its importance to anthropogenic greenhouse warming (IPCC, 2007). Furthermore, CH4 regulation has far greater near-term climate change mitigation potential versus carbon dioxide CO2, the other major anthropogenic Greenhouse Gas (GHG) (Shindell et al., 2009). Uncertainties in CH4 budgets arise from the poor state of knowledge of CH4 sources - in part from a lack of sufficiently accurate assessments of the temporal and spatial emissions and controlling factors of highly variable anthropogenic and natural CH4 surface fluxes (IPCC, 2007) and the lack of global-scale (satellite) data at sufficiently high spatial resolution to resolve sources. Many important methane (and other trace gases) sources arise from urban and mega-urban landscapes where anthropogenic activities are centered - most of humanity lives in urban areas. Studying these complex landscape tapestries is challenged by a wide and varied range of activities at small spatial scale, and difficulty in obtaining up-to-date landuse data in the developed world - a key desire of policy makers towards development of effective regulations. In the developing world, challenges are multiplied with additional political access challenges. As high spatial resolution satellite and airborne data has become available, activity mapping applications have blossomed - i.e., Google maps; however, tap a minute fraction of remote sensing capabilities due to limited (three band) spectral information. Next generation approaches that incorporate high spatial resolution hyperspectral and ultraspectral data will allow detangling of the highly heterogeneous usage megacity patterns by providing diagnostic identification of chemical composition from solids (refs) to gases (refs). To properly enable these next generation technologies for megacity include atmospheric radiative transfer modeling

  10. Airborne measurements in the infrared using FTIR-based imaging hyperspectral sensors

    NASA Astrophysics Data System (ADS)

    Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Farley, V.; Lagueux, P.; Marcotte, F.; Chamberland, M.

    2009-05-01

    Hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. Push-broom dispersive sensors have been typically used for airborne hyperspectral mapping. However, extending the spectral range towards the mid-wave and long-wave infrared brings performance limitations due to the self emission of the sensor itself. The Fourier-transform spectrometer technology has been extensively used in the infrared spectral range due to its high transmittance as well as throughput and multiplex advantages, thereby reducing the sensor self-emission problem. Telops has developed the Hyper-Cam, a rugged and compact infrared hyperspectral imager. The Hyper-Cam is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The Hyper-Cam has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. A special pointing module was designed to compensate for airplane attitude and forward motion. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. The first airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in

  11. Airborne measurements in the infrared using FTIR-based imaging hyperspectral sensors

    NASA Astrophysics Data System (ADS)

    Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Lagueux, P.; Farley, V.; Marcotte, F.; Chamberland, M.

    2009-09-01

    Hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. Push-broom dispersive sensors have been typically used for airborne hyperspectral mapping. However, extending the spectral range towards the mid-wave and long-wave infrared brings performance limitations due to the self emission of the sensor itself. The Fourier-transform spectrometer technology has been extensively used in the infrared spectral range due to its high transmittance as well as throughput and multiplex advantages, thereby reducing the sensor self-emission problem. Telops has developed the Hyper-Cam, a rugged and compact infrared hyperspectral imager. The Hyper-Cam is based on the Fourier-transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. It provides passive signature measurement capability, with up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1. The Hyper-Cam has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. A special pointing module was designed to compensate for airplane attitude and forward motion. To our knowledge, the Hyper-Cam is the first commercial airborne hyperspectral imaging sensor based on Fourier-transform infrared technology. The first airborne measurements and some preliminary performance criteria for the Hyper-Cam are presented in

  12. Detecting and discriminating petroleum and petroleum products from water on terrestrial backgrounds with hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Allen, C. Scott

    Petroleum and petroleum product spills are frequent and as both Hurricane Katrina and the Deepwater Horizon accident demonstrated, they can be catastrophic. A prominent portion of the response is mapping the extent to which oil has reached both shoreline and inland areas. Yet, petroleum and water--when present on common substrates such as sand, concrete, and vegetation--are often difficult to distinguish in panchromatic and multispectral imagery. This research demonstrates how hyperspectral remote sensing, also known as imaging spectroscopy, provides petroleum detection and discrimination from water on terrestrial backgrounds. Utilizing spectral libraries, it also performs material identification and successfully discriminates some petroleum products from one another as a means of further classification and mapping spill extent. To achieve these goals, this effort collected spectral signatures of four crude oils and five refined petroleum products on ten common terrestrial substrates and compared them to water on the same backgrounds over a period of 1-90 days, depending on liquid volatility. The result is the first publicly available spectral library for petroleum and petroleum products on terrestrial substrates in the reflective portion of the electromagnetic spectrum (400-2500 nm) for use in petroleum spill detection and response. It also establishes a baseline for the use of imaging spectroscopy as a technique for confident, accurate petroleum detection in the terrestrial environment. Using common material identification algorithms, the spectra were successfully applied to airborne hyperspectral data from the Hurricane Katrina disaster in 2005 as a proof-of-concept for discriminating petroleum from water.

  13. Hyperspectral remote sensing of vegetation and agricultural crops: knowledge gain and knowledge gap after 40 years of research

    USGS Publications Warehouse

    Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo; Edited by Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

    2011-01-01

    The focus of this chapter was to summarize the advances made over last 40+ years, as reported in various chapters of this book, in understanding, modeling, and mapping terrestrial vegetation using hyperspectral remote sensing (or imaging spectroscopy) using sensors that are ground-based, truck-mounted, airborne, and spaceborne. As we have seen in various chapters of this book and synthesized in this chapter, the advances made include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracies (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (b), (e) ability to access stress resulting from causes such as management practices, pests and disease, water deficit or excess; , and (f) establishing more sensitive wavebands and indices to detect plant water\\moisture content. The advent of spaceborne hyperspectral sensors (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming NASA’s HyspIRI) and numerous methods and techniques espoused in this book to overcome Hughes phenomenon or data redundancy when handling large volumes of hyperspectral data have generated tremendous interest in advancing our hyperspectral applications knowledge base over larger spatial extent such as region, nation, continent, and globe.

  14. Detection of abandoned mines/caves using airborne LWIR hyperspectral data

    NASA Astrophysics Data System (ADS)

    Shen, Sylvia S.; Roettiger, Kurt A.

    2012-09-01

    The detection of underground structures, both natural and man-made, continues to be an important requirement in both the military/intelligence and civil communities. There are estimates that as many as 70,000 abandoned mines/caves exist across the nation. These mines represent significant hazards to public health and safety, and they are of concern to Government agencies at the local, state, and federal levels. NASA is interested in the detection of caves on Mars and the Moon in anticipation of future manned space missions. And, the military/ intelligence community is interested in detecting caves, mines, and other underground structures that may be used to conceal the production of weapons of mass destruction or to harbor insurgents or other persons of interest by the terrorists. Locating these mines/caves scattered over millions of square miles is an enormous task, and limited resources necessitate the development of an efficient and effective broad area search strategy using remote sensing technologies. This paper describes an internally-funded research project of The Aerospace Corporation (Aerospace) to assess the feasibility of using airborne hyperspectral data to detect abandoned cave/mine entrances in a broad-area search application. In this research, we have demonstrated the potential utility of using thermal contrast between the cave/mine entrance and the ambient environment as a discriminatory signature. We have also demonstrated the use of a water vapor absorption line at12.55 μm and a quartz absorption feature at 9.25 μm as discriminatory signatures. Further work is required to assess the broader applicability of these signatures.

  15. Analysis of Coincident HICO and Airborne Hyperspectral Images Over Lake Erie Western Basin HABs

    NASA Astrophysics Data System (ADS)

    Cline, M., Jr.; Becker, R.; Lekki, J.; Bridgeman, T. B.; Tokars, R. P.; Anderson, R. C.

    2015-12-01

    Harmful algal blooms (HABs) produce waterborne toxins that pose a significant threat to people, livestock, and wildlife. 40 million people in both Canada and the U.S. depend on Great Lakes water. In the summer of 2014, in the Lake Erie Western Basin, an HAB of the cyanobacteria Microsystis was so severe that a water-use ban was in effect for the greater Toledo area, Ohio. This shut off the water supply to over 400,000 people from a single water intake. We investigated bloom intensity, composition, and spatial variability by comparing hyperspectral data from NASA's HICO, multispectral data from MODIS spaceborne imagers and NASA GRC's HSI imagers to on-lake ASD radiometer measurements using in situ water quality testing as ground reference data, all acquired on a single day during the bloom in 2014. HICO imagery acquired on Aug 15, 2014 was spatially georeferenced and atmospherically corrected using empirical line method utilizing on-lake ASD spectra. HSI imagery were processed in a similar way. Cyanobacteria Index (CI) images were created from processed images using the Wynne (2010) algorithm, previously used for MODIS and MERIS imagery. This algorithm-generated CI images provide reliable results for both ground level (R²=0.7784), and satellite imagery (R²=0.7794) for seven sampling points in Lake Erie's western basin. Spatial variability in the bloom was high, and was not completely characterized by the lower spatial resolution MODIS data. The ability to robustly atmospherically correct and generate useful CI maps from airborne and satellite sensors can provide a time- and cost-effective method for HABs analysis. Timely processing of these high spatial and spectral resolution remote sensing data can aid in management of water intake resources.

  16. Radiometric sensitivity contrast metrics for hyperspectral remote sensors

    NASA Astrophysics Data System (ADS)

    Silny, John F.; Zellinger, Lou

    2014-09-01

    This paper discusses the calculation, interpretation, and implications of various radiometric sensitivity metrics for Earth-observing hyperspectral imaging (HSI) sensors. The most commonly used sensor performance metric is signal-to-noise ratio (SNR), from which additional noise equivalent quantities can be computed, including: noise equivalent spectral radiance (NESR), noise equivalent delta reflectance (NEΔρ), noise equivalent delta emittance (NEΔƐ), and noise equivalent delta temperature (NEΔT). For hyperspectral sensors, these metrics are typically calculated from an at-aperture radiance (typically generated by MODTRAN) that includes both target radiance and non-target (atmosphere and background) radiance. Unfortunately, these calculations treat the entire at-aperture radiance as the desired signal, even when the target radiance is only a fraction of the total (such as when sensing through a long or optically dense atmospheric path). To overcome this limitation, an augmented set of metrics based on contrast signal-to-noise ratio (CNSR) is developed, including their noise equivalent counterparts (CNESR, CNEΔρ, CNEΔƐ, and CNEΔT). These contrast metrics better quantify sensor performance in an operational environment that includes remote sensing through the atmosphere.

  17. Retrieving Atmospheric Profiles Data in the Presence of Clouds from Hyperspectral Remote Sensing Data

    NASA Technical Reports Server (NTRS)

    Liu, Xu; Larar, Allen M.; Zhou, Daniel K.; Kizer, Susan H.; Wu, Wan; Barnet, Christopher; Divakarla, Murty; Guo, Guang; Blackwell, Bill; Smith, William L.; Yang, Ping; Gu, Degui

    2011-01-01

    Different methods for retrieving atmospheric profiles in the presence of clouds from hyperspectral satellite remote sensing data will be described. We will present results from the JPSS cloud-clearing algorithm and NASA Langley cloud retrieval algorithm.

  18. Airborne measurements in the longwave infrared using an imaging hyperspectral sensor

    NASA Astrophysics Data System (ADS)

    Allard, Jean-Pierre; Chamberland, Martin; Farley, Vincent; Marcotte, Frédérick; Rolland, Matthias; Vallières, Alexandre; Villemaire, André

    2008-07-01

    Emerging applications in Defense and Security require sensors with state-of-the-art sensitivity and capabilities. Among these sensors, the imaging spectrometer is an instrument yielding a large amount of rich information about the measured scene. Standoff detection, identification and quantification of chemicals in the gaseous state is one important application. Analysis of the surface emissivity as a means to classify ground properties and usage is another one. Imaging spectrometers have unmatched capabilities to meet the requirements of these applications. Telops has developed the FIRST, a LWIR hyperspectral imager. The FIRST is based on the Fourier Transform technology yielding high spectral resolution and enabling high accuracy radiometric calibration. The FIRST, a man portable sensor, provides datacubes of up to 320×256 pixels at 0.35mrad spatial resolution over the 8-12 μm spectral range at spectral resolutions of up to 0.25cm-1. The FIRST has been used in several field campaigns, including the demonstration of standoff chemical agent detection [http://dx.doi.org/10.1117/12.788027.1]. More recently, an airborne system integrating the FIRST has been developed to provide airborne hyperspectral measurement capabilities. The airborne system and its capabilities are presented in this paper. The FIRST sensor modularity enables operation in various configurations such as tripod-mounted and airborne. In the airborne configuration, the FIRST can be operated in push-broom mode, or in staring mode with image motion compensation. This paper focuses on the airborne operation of the FIRST sensor.

  19. Hyperspectral remote sensing techniques for early detection of plant diseases

    NASA Astrophysics Data System (ADS)

    Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas

    Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications in Earth observation. Nowadays spectral remote sensing techniques allow presymptomatic monitoring of changes in the physiological state of plants with high spectral resolution. Hyperspectral leaf reflectance and chlorophyll fluorescence proved to be highly suitable for identification of growth anomalies of cultural plants that result from the environmental changes and different stress factors. Hyperspectral technologies can find place in many scientific areas, as well as for monitoring of plants status and functioning to help in making timely management decisions. This research aimed to detect a presence of viral infection in young pepper plants (Capsicum annuum L.) caused by Cucumber Mosaic Virus (CMV) by using hyperspectral reflectance and fluorescence data and to assess the effect of some growth regulators on the development of the disease. In Bulgaria CMV is one of the widest spread pathogens, causing the biggest economical losses in crop vegetable production. Leaf spectral reflectance and fluorescence data were collected by a portable fibre-optics spectrometer in the spectral ranges 450÷850 nm and 600-900 nm. Greenhouse experiment with pepper plants of two cultivars, Sivria (sensitive to CMV) and Ostrion (resistant to CMV) were used. The plants were divided into six groups. The first group consisted of healthy (control) plants. At growth stage 4-6 expanded leaf, the second group was inoculated with CMV. The other four groups were treated with growth regulators: Spermine, MEIA (beta-monomethyl ester of itaconic acid), ВТН (benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester) and Phytoxin. On the next day, the pepper plants of these four groups were inoculated with CMV. The viral concentrations in the plants were determined by the serological method DAS-ELISA. Statistical, first derivative and cluster analysis were applied and several vegetation indices were

  20. Ecohydrological Characterization of a Floodplain Mire by Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Batelaan, O.; Verbeiren, B.; Hung, L. Q.

    2010-12-01

    For the emerging field of ecohydrology it is essential to be able to estimate spatially distributed water and energy balances in different types of ecosystems. This information is a prerequisite for predicting the occurrence of vegetation species in dependence of site specific conditions. In this contribution the results of a field and airborne imaging spectroscopy campaign in the Doode Bemde floodplain mire (Belgium) are presented. Vegetation is characterized and water and energy balance components are estimated on a scale, which allows the discrimination of local wetness and vegetation heterogeneity in relation to differences in soil and vegetation condition. Among the common vegetation indices, the red-edge index proved to separate the phreatophytic vegetation types and showed to be linearly correlated with the maximum groundwater depth of the vegetation types. Since evapotranspiration is the largest component of the water and energy fluxes, we combine high resolution thermal airborne sensor data (ATM) with hyperspectral CASI imagery to enable derivation of ecological relevant observation of evapotranspiration at a resolution of 1-10 m. The evapotranspiration is simulated with the Surface Energy Balance Algorithm for Land (SEBAL). The estimated evapotranspiration values are statistically related to measured soil moisture conditions via the evaporative fraction. The evaporative fraction shows a non-linear relationship with soil moisture, and its level is dependent on the general wetness of the area. It appears feasible to map spatially detailed soil moisture on basis of estimation of the evaporative fraction. The results contribute to an increased understanding of the ecohydrological functioning of the study area.

  1. Multispectral, hyperspectral, and LiDAR remote sensing and geographic information fusion for improved earthquake response

    NASA Astrophysics Data System (ADS)

    Kruse, F. A.; Kim, A. M.; Runyon, S. C.; Carlisle, Sarah C.; Clasen, C. C.; Esterline, C. H.; Jalobeanu, A.; Metcalf, J. P.; Basgall, P. L.; Trask, D. M.; Olsen, R. C.

    2014-06-01

    The Naval Postgraduate School (NPS) Remote Sensing Center (RSC) and research partners have completed a remote sensing pilot project in support of California post-earthquake-event emergency response. The project goals were to dovetail emergency management requirements with remote sensing capabilities to develop prototype map products for improved earthquake response. NPS coordinated with emergency management services and first responders to compile information about essential elements of information (EEI) requirements. A wide variety of remote sensing datasets including multispectral imagery (MSI), hyperspectral imagery (HSI), and LiDAR were assembled by NPS for the purpose of building imagery baseline data; and to demonstrate the use of remote sensing to derive ground surface information for use in planning, conducting, and monitoring post-earthquake emergency response. Worldview-2 data were converted to reflectance, orthorectified, and mosaicked for most of Monterey County; CA. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired at two spatial resolutions were atmospherically corrected and analyzed in conjunction with the MSI data. LiDAR data at point densities from 1.4 pts/m2 to over 40 points/ m2 were analyzed to determine digital surface models. The multimodal data were then used to develop change detection approaches and products and other supporting information. Analysis results from these data along with other geographic information were used to identify and generate multi-tiered products tied to the level of post-event communications infrastructure (internet access + cell, cell only, no internet/cell). Technology transfer of these capabilities to local and state emergency response organizations gives emergency responders new tools in support of post-disaster operational scenarios.

  2. Crop stress detection and classification using hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Irby, Jon Trenton

    crop stresses utilizing hyperspectral remote sensing. Key words: crop stress, herbicide drift, remote sensing

  3. An analysis task comparison of uncorrected vs. geo-registered airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Sun, Yihang; Kerekes, John

    2015-05-01

    Geo-registration is the task of assigning geospatial coordinates to the pixels of an image and placing them in a geographic coordinate system. However, the process of geo-registration can impair the quality of the image. This paper studies this topic by applying a comparison methodology to uncorrected and geo-registered airborne hyperspectral images obtained from the RIT SHARE 2012 data set. The uncorrected image was analyzed directly as collected by the sensor without being treated, while the geo-registered image was corrected using the nearest neighbor resampling approach. A comparison of performance was done for the analysis tasks of spectral unmixing and subpixel target detection, which can represent a measure of utility. The comparison demonstrates that the geo-registration process can affect the utility of hyperspectral imagery to a limited extent.

  4. Assessing Wheat Yellow Rust Disease through Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Krishna, G.; Sahoo, R. N.; Pargal, S.; Gupta, V. K.; Sinha, P.; Bhagat, S.; Saharan, M. S.; Singh, R.; Chattopadhyay, C.

    2014-12-01

    The potential of hyperspectral reflectance data was explored to assess severity of yellow rust disease (Biotroph Pucciniastriiformis) of winter wheat in the present study. The hyperspectral remote sensing data was collected for winter wheat (Triticum aestivum L.) cropat different levels of disease infestation using field spectroradiometer over the spectral range of 350 to 2500 nm. The partial least squares (PLS) and multiple linear (MLR) regression techniques were used to identify suitable bands and develop spectral models for assessing severity of yellow rust disease in winter wheat crop. The PLS model based on the full spectral range and n = 36, yielded a coefficient of determination (R2) of 0.96, a standard error of cross validation (SECV) of 12.74 and a root mean square error of cross validation (RMSECV) of 12.41. The validation analysis of this PLS model yielded r2 as 0.93 with a SEP (Standard Error of Prediction) of 7.80 and a RMSEP (Root Mean Square Error of prediction) of 7.46. The loading weights of latent variables from PLS model were used to identify sensitive wavelengths. To assess their suitability multiple linear regression (MLR) model was applied on these wavelengths which resulted in a MLR model with three identified wavelength bands (428 nm, 672 nm and 1399 nm). MLR model yielded acceptable results in the form of r2 as 0.89 for calibration and 0.90 for validation with SEP of 3.90 and RMSEP of 3.70. The result showed that the developed model had a great potential for precise delineation and detection of yellow rust disease in winter wheat crop.

  5. Hyperspectral remote sensing of crop leaf chlorophyll content using reflectance simulation model and field data in open canopies

    NASA Astrophysics Data System (ADS)

    Jiao, Quanjun; Wu, Yanhong; Liu, Liangyun; Zhang, Bing

    2015-04-01

    Leaf chlorophyll content -a and -b content (Cab) is an indicator for crop nutrition status and photosynthetic capacity. Remote sensing of Cab plays an important role in crop growth monitoring, pest and disease diagnosis, and crop yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels when crop canopies are not closed. This study analyzes the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL reflectance model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well accepted approach of the combination of TCARI and OSAVI. A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm-2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure. This study, therefore, offers a feasible technique that might be applied to crop chlorophyll content estimation using large-scale remote sensing data.

  6. Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data

    PubMed Central

    Vaglio Laurin, Gaia; Chan, Jonathan Cheung-Wai; Chen, Qi; Lindsell, Jeremy A.; Coomes, David A.; Guerriero, Leila; Frate, Fabio Del; Miglietta, Franco; Valentini, Riccardo

    2014-01-01

    Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m2 in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m2 resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R2 = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales. PMID:24937407

  7. Extraction, modelling, and use of linear features for restitution of airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Lee, Changno; Bethel, James S.

    This paper presents an approach for the restitution of airborne hyperspectral imagery with linear features. The approach consisted of semi-automatic line extraction and mathematical modelling of the linear features. First, the line was approximately determined manually and refined using dynamic programming. The extracted lines could then be used as control data with the ground information of the lines, or as constraints with simple assumption for the ground information of the line. The experimental results are presented numerically in tables of RMS residuals of check points as well as visually in ortho-rectified images.

  8. Airborne Remote Sensing for Earth Science Applications

    NASA Technical Reports Server (NTRS)

    Aubrey, Andrew

    2013-01-01

    Topics covered include: Passive Remote Sensing Methods, Imaging Spectroscopy Approach, Remote Measurement via Spectral Fitting, Imaging Spectroscopy Mapping Wetland Dominants 2010 LA (AVIRIS), Deepwater Horizon Response I, Deepwater Horizon Response II, AVIRIS Ocean Color Studies.

  9. A potential hyperspectral remote sensing imager for water quality measurements

    NASA Astrophysics Data System (ADS)

    Zur, Yoav; Braun, Ofer; Stavitsky, David; Blasberger, Avigdor

    2003-04-01

    Utilization of Pan Chromatic and Multi Spectral Remote Sensing Imagery is wide spreading and becoming an established business for commercial suppliers of such imagery like ISI and others. Some emerging technologies are being used to generate Hyper-Spectral imagery (HSI) by aircraft as well as other platforms. The commercialization of such technology for Remote Sensing from space is still questionable and depends upon several parameters including maturity, cost, market reception and many others. HSI can be used in a variety of applications in agriculture, urban mapping, geology and others. One outstanding potential usage of HSI is for water quality monitoring, a subject studied in this paper. Water quality monitoring is becoming a major area of interest in HSI due to the increase in water demand around the globe. The ability to monitor water quality in real time having both spatial and temporal resolution is one of the advantages of Remote Sensing. This ability is not limited only for measurements of oceans and inland water, but can be applied for drinking and irrigation water reservoirs as well. HSI in the UV-VNIR has the ability to measure a wide range of constituents that define water quality. Among the constituents that can be measured are the pigment concentration of various algae, chlorophyll a and c, carotenoids and phycocyanin, thus enabling to define the algal phyla. Other parameters that can be measured are TSS (Total Suspended Solids), turbidity, BOD (Biological Oxygen Demand), hydrocarbons, oxygen demand. The study specifies the properties of such a space borne device that results from the spectral signatures and the absorption bands of the constituents in question. Other parameters considered are the repetition of measurements, the spatial aspects of the sensor and the SNR of the sensor in question.

  10. COUPLING HYPERSPECTRAL REMOTE SENSING WITH FIELD SPECTROMETRY TO MONITOR INLAND WATER QUALITY PARAMETERS

    EPA Science Inventory

    Visible to near-infrared, airborne hyperspectral data were successfully used to estimate water quality parameters such as chlorophyll a, turbidity and total phosphorus from the Great Miami River, Ohio. During the summer of 1999, spectral data were collected with a hand-held fiel...

  11. Hyperspectral Remote Sensing Techniques in Predicting Phycocyanin Concentrations in Cyanobacteria: A Comprehensive Study

    NASA Astrophysics Data System (ADS)

    Mishra, S.; Mishra, D. R.; Schluchter, W. M.

    2009-12-01

    The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. Our results also show that the widely used 654 nm reflectance peak in existing algorithms is highly sensitive to changes in chlorophyll-a concentration and offers poor PC predictive ability. We present a novel spectral band ratio algorithm that is least sensitive to the presence of chlorophyll. The newly developed band ratio model showed promising results by yielding low root mean squared error (RMSE, 15,260 cells mL-1) and significantly low relative root mean squared error (RMS, 101%) as compared to the existing band ratio algorithms. Natural logarithmic transformation of the new model yielded the lowest RMSE (13,885 cells mL-1) and a high coefficient of determination (0.95) between measured and predicted PC concentration. We also show that the new algorithm is species independent and accurately retrieves PC concentration in the presence of varying amount of chlorophyll-a in the system. Band setting of the model confirms that it can be used for retrieval of PC using hyperspectral sensors such as Hyperion as well as data acquired by other airborne sensors. Figure (A, B, C) Percent reflectance spectra of Synechocystis PCC 6803 from Exp I, II, III respectively. (D) Percent reflectance spectra of Anabaena from Exp IV. Data collected from these experiments were included in the evaluation of existing PC predictive models and the calibration and validation of the new spectral band ratio model.

  12. Model for the Interpretation of Hyperspectral Remote-Sensing Reflectance

    NASA Technical Reports Server (NTRS)

    Lee, Zhongping; Carder, Kendall L.; Hawes, Steve K.; Steward, Robert G.; Peacock, Thomas G.; Davis, Curtiss O.

    1994-01-01

    Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/cu m and gelbstoff absorption at 440 nm from 0.02-0.4/m. Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.

  13. Mapping beech ( Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Cho, Moses Azong; Skidmore, Andrew K.; Sobhan, Istiak

    2009-06-01

    Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest ( Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI ( i, j) = ( Ri - Rj)/( Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration ( n = 33) and test ( n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756-820 nm) and the water absorption feature centred at 1200 nm (1172-1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH

  14. Urban forest ecosystem analysis using fused airborne hyperspectral and lidar data

    NASA Astrophysics Data System (ADS)

    Alonzo, Mike Gerard

    Urban trees are strategically important in a city's effort to mitigate their carbon footprint, heat island effects, air pollution, and stormwater runoff. Currently, the most common method for quantifying urban forest structure and ecosystem function is through field plot sampling. However, taking intensive structural measurements on private properties throughout a city is difficult, and the outputs from sample inventories are not spatially explicit. The overarching goal of this dissertation is to develop methods for mapping urban forest structure and function using fused hyperspectral imagery and waveform lidar data at the individual tree crown scale. Urban forest ecosystem services estimated using the USDA Forest Service's i-Tree Eco (formerly UFORE) model are based largely on tree species and leaf area index (LAI). Accordingly, tree species were mapped in my Santa Barbara, California study area for 29 species comprising >80% of canopy. Crown-scale discriminant analysis methods were introduced for fusing Airborne Visible Infrared Imaging Spectrometry (AVIRIS) data with a suite of lidar structural metrics (e.g., tree height, crown porosity) to maximize classification accuracy in a complex environment. AVIRIS imagery was critical to achieving an overall species-level accuracy of 83.4% while lidar data was most useful for improving the discrimination of small and morphologically unique species. LAI was estimated at both the field-plot scale using laser penetration metrics and at the crown scale using allometry. Agreement of the former with photographic estimates of gap fraction and the latter with allometric estimates based on field measurements was examined. Results indicate that lidar may be used reasonably to measure LAI in an urban environment lacking in continuous canopy and characterized by high species diversity. Finally, urban ecosystem services such as carbon storage and building energy-use modification were analyzed through combination of aforementioned

  15. Field-Based and Airborne Hyperspectral Imaging for Applied Research in the State of Alaska

    NASA Astrophysics Data System (ADS)

    Prakash, A.; Buchhorn, M.; Cristobal, J.; Kokaly, R. F.; Graham, P. R.; Waigl, C. F.; Hampton, D. L.; Werdon, M.; Guldager, N.; Bertram, M.; Stuefer, M.

    2015-12-01

    Hyperspectral imagery acquired using Hyspex VNIR-1800 and SWIR-384 camera systems have provided unique information on terrestrial and aquatic biogeochemical parameters, and diagnostic mineral properties in exposed outcrops in selected sites in the state of Alaska. The Hyspex system was configured for in-situ and field scanning by attaching it to a gimbal-mounted rotational stage on a robust tripod. Scans of vertical faces of vegetation and rock outcrops were made close to the campus of the University of Alaska Fairbanks, in an abandoned mine near Fairbanks, and on exposures of Orange Hill in Wrangell-St. Elias National Park. Atmospherically corrected integrated VNIR_SWIR spectra were extracted which helped to study varying nitrogen content in the vegetation, and helped to distinguish the various micas. Processed imagery helped to pull out carbonates, clays, sulfates, and alteration-related minerals. The same instrument was also mounted in airborne configuration on two different aircrafts, a DeHavilland Beaver and a Found Bush Hawk. Test flights were flown over urban and wilderness areas that presented a variety of landcover types. Processed imagery shows promise in mapping man-made surfaces, phytoplankton, and dissolved materials in inland water bodies. Sample data and products are available on the University of Alaska Fairbanks Hyperspectral Imaging Laboratory (HyLab) website at http://hyperspectral.alaska.edu.

  16. Great Lakes Hyperspectral Water Quality Instrument Suite for Airborne Monitoring of Algal Blooms

    NASA Technical Reports Server (NTRS)

    Lekki, John; Leshkevich, George; Nguyen, Quang-Viet; Flatico, Joseph; Prokop, Norman; Kojima, Jun; Anderson, Robert; Demers, James; Krasowski, Michael

    2007-01-01

    NASA Glenn Research Center and NOAA Great Lakes Environmental Research Lab are collaborating to utilize an airborne hyperspectral imaging sensor suite to monitor Harmful Algal Blooms (HABs) in the western basin of Lake Erie. The HABs are very dynamic events as they form, spread and then disappear within a 4 to 8 week time period in late summer. They are a concern for human health, fish and wildlife because they can contain blue green toxic algae. Because of this toxicity there is a need for the blooms to be continually monitored. This situation is well suited for aircraft based monitoring because the blooms are a very dynamic event and they can spread over a large area. High resolution satellite data is not suitable by itself because it will not give the temporal resolution due to the infrequent overpasses of the quickly changing blooms. A custom designed hyperspectral imager and a point spectrometer mounted on aT 34 aircraft have been used to obtain data on an algal bloom that formed in the western basin of Lake Erie during September 2006. The sensor suite and operations will be described and preliminary hyperspectral data of this event will be presented

  17. Chlorophyll content retrieval from hyperspectral remote sensing imagery.

    PubMed

    Yang, Xiguang; Yu, Ying; Fan, Wenyi

    2015-07-01

    Chlorophyll content is the essential parameter in the photosynthetic process determining leaf spectral variation in visible bands. Therefore, the accurate estimation of the forest canopy chlorophyll content is a significant foundation in assessing forest growth and stress affected by diseases. Hyperspectral remote sensing with high spatial resolution can be used for estimating chlorophyll content. In this study, the chlorophyll content was retrieved step by step using Hyperion imagery. Firstly, the spectral curve of the leaf was analyzed, 25 spectral characteristic parameters were identified through the correlation coefficient matrix, and a leaf chlorophyll content inversion model was established using a stepwise regression method. Secondly, the pixel reflectance was converted into leaf reflectance by a geometrical-optical model (4-scale). The three most important parameters of reflectance conversion, including the multiple scattering factor (M 0 ), and the probability of viewing the sunlit tree crown (P T ) and the background (P G ), were estimated by leaf area index (LAI), respectively. The results indicated that M 0 , P T , and P G could be described as a logarithmic function of LAI, with all R (2) values above 0.9. Finally, leaf chlorophyll content was retrieved with RMSE = 7.3574 μg/cm(2), and canopy chlorophyll content per unit ground surface area was estimated based on leaf chlorophyll content and LAI. Chlorophyll content mapping can be useful for the assessment of forest growth stage and diseases.

  18. Exploiting hyperspectral sounders for volcanic ash remote sensing

    NASA Astrophysics Data System (ADS)

    Western, Luke; Watson, Matthew; Francis, Peter

    2016-04-01

    Assumptions are made when retrieving properties of volcanic ash clouds using passive infrared satellite remote sensing. Assumptions in the retrieval method lead to larger uncertainties in the retrieved volcanic ash cloud properties. It is a general desire to reduce these uncertainties by removing some of the assumptions that must be made. Hyperspectral sounders provide the spectral capabilities to explore many of the physical parameters that describe volcanic ash clouds - the question is, which parameters is it possible to retrieve? We show that using the Infrared Atmospheric Sounding Interferometer (IASI) it is possible to retrieve the mass column loading and cloud top pressure of a volcanic ash cloud, together with the effective radius and spread of the ash particle size distribution, as well as the cloud top pressure of any underlying water cloud using an optimal estimation technique. We discuss the capabilities and shortcomings of the method. The consideration of an underlying water cloud is of importance for improving retrievals, and we place a particular focus on how well the particle size distribution can be described. More specifically, we investigate the viability of using either a lognormal or a gamma distribution to describe the distribution of ash particles, and we show that it is possible to retrieve information about the spread of a lognormal distribution of particles, whereas it is not for a gamma distribution. Some preliminary conclusions on the size distribution of volcanic ash are presented.

  19. Applications of airborne remote sensing in atmospheric sciences research

    NASA Technical Reports Server (NTRS)

    Serafin, R. J.; Szejwach, G.; Phillips, B. B.

    1984-01-01

    This paper explores the potential for airborne remote sensing for atmospheric sciences research. Passive and active techniques from the microwave to visible bands are discussed. It is concluded that technology has progressed sufficiently in several areas that the time is right to develop and operate new remote sensing instruments for use by the community of atmospheric scientists as general purpose tools. Promising candidates include Doppler radar and lidar, infrared short range radiometry, and microwave radiometry.

  20. Airborne infrared-hyperspectral mapping for detection of gaseous and solid targets

    NASA Astrophysics Data System (ADS)

    Puckrin, E.; Turcotte, C. S.; Lahaie, P.; Dubé, D.; Farley, V.; Lagueux, P.; Marcotte, F.; Chamberland, M.

    2010-04-01

    Airborne hyperspectral ground mapping is being used in an ever-increasing extent for numerous applications in the military, geology and environmental fields. The different regions of the electromagnetic spectrum help produce information of differing nature. The visible, near-infrared and short-wave infrared radiation (400 nm to 2.5 μm) has been mostly used to analyze reflected solar light, while the mid-wave (3 to 5 μm) and long-wave (8 to 12 μm or thermal) infrared senses the self-emission of molecules directly, enabling the acquisition of data during night time. The Telops Hyper-Cam is a rugged and compact infrared hyperspectral imager based on the Fourier-transform technology. It has been used on the ground in several field campaigns, including the demonstration of standoff chemical agent detection. More recently, the Hyper-Cam has been integrated into an airplane to provide airborne measurement capabilities. The technology offers fine spectral resolution (up to 0.25 cm-1) and high accuracy radiometric calibration (better than 1 degree Celsius). Furthermore, the spectral resolution, spatial resolution, swath width, integration time and sensitivity are all flexible parameters that can be selected and optimized to best address the specific objectives of each mission. The system performance and a few measurements have been presented in previous publications. This paper focuses on analyzing additional measurements in which detection of fertilizer and Freon gas has been demonstrated.

  1. Testing different classification methods in airborne hyperspectral imagery processing.

    PubMed

    Kozoderov, Vladimir V; Dmitriev, Egor V

    2016-05-16

    To enhance the efficiency of machine-learning algorithms of optical remote sensing imagery processing, optimization techniques are evolved of the land surface objects pattern recognition. Different methods of supervised classification are considered for these purposes, including the metrical classifier operating with Euclidean distance between any points of the multi-dimensional feature space given by registered spectra, the K-nearest neighbors classifier based on a majority vote for neighboring pixels of the recognized objects, the Bayesian classifier of statistical decision making, the Support Vector Machine classifier dealing with stable solutions of the mini-max optimization problem and their different modifications. We describe the related techniques applied for selected test regions to compare the listed classifiers. PMID:27409968

  2. Retrieval of Topsoil Properties of Vegetation-Covered Terrain Using Airborne Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Liu, Lanfa; Buchroithner, Manfred

    2016-04-01

    Soil spectroscopy is a promising technique for topsoil analysis, and has been successfully utilized in the laboratory. When it is applied from airborne platforms, the presence of vegetation significantly affects imaging spectroscopy or hyperspectral imaging when retrieving topsoil properties. A Forced Invariance Approach has been proved to be able to effectively suppress the vegetation signal in mixed pixels. However, the approach is still mainly limited to lithological mapping. In this paper, we attempted to apply it to the retrieval of topsoil properties (soil moisture and soil salinity at depths 4 cm and 10 cm) using airborne hyperspectral data. The corresponding ground truth data was obtained from an eco-hydrological wireless sensing network in the Zhangye Oasis in the middle stream of the Heihe River Basin, China. The General Linear Model with Logit Link Function was adopted to model the relationships between measured soil properties and the spectra. The vegetation suppression result demonstrates that the spectral response curves of hyperspectral image pixels are flattened and the shapes are rather similar to the soil endmenber spectrum. From the modelling results it can be seen that the Forced Invariance Approach is more effective for soil moisture than for soil salinity at depth 10 cm, as the salt content is comparatively lower than the water content in soil, and the corresponding spectral response is weaker. This approach did not work for soil at a depth of 4 cm. The reason for this is that surface soil is significantly influenced by exterior factors like irrigation and wind, and landscape fragmentation and cultivation activities also contribute to the high spatial heterogeneity of the surface soil properties.

  3. [Progress in inversion of vegetation nitrogen concentration by hyperspectral remote sensing].

    PubMed

    Wang, Li-Wen; Wei, Ya-Xing

    2013-10-01

    Nitrogen is the necessary element in life activity of vegetation, which takes important function in biosynthesis of protein, nucleic acid, chlorophyll, and enzyme etc, and plays a key role in vegetation photosynthesis. The technology about inversion of vegetation nitrogen concentration by hyperspectral remote sensing has been the research hotspot since the 70s of last century. With the development of hyperspectral remote sensing technology in recent years, the advantage of spectral bands subdivision in a certain spectral region provides the powerful technology measure for correlative spectral characteristic research on vegetation nitrogen. In the present paper, combined with the newest research production about monitoring vegetation nitrogen concentration by hyperspectral remote sensing published in main geography science literature in recent several years, the principle and correlated problem about monitoring vegetation nitrogen concentration by hyperspectral remote sensing were introduced. From four aspects including vegetation nitrogen spectral index, vegetation nitrogen content inversion based on chlorophyll index, regression model, and eliminating influence factors to inversion of vegetation nitrogen concentration, main technology methods about inversion of vegetation nitrogen concentration by hyperspectral remote sensing were detailedly introduced. Correlative research conclusions were summarized and analyzed, and research development trend was discussed.

  4. [Hyperspectral Remote Sensing Estimation Models for Pasture Quality].

    PubMed

    Ma, Wei-wei; Gong, Cai-lan; Hu, Yong; Wei, Yong-lin; Li, Long; Liu, Feng-yi; Meng, Peng

    2015-10-01

    that the first derivatives or the wavelet coefficients of hyperspectral reflectance in visible and near-infrared regions can be used for pasture quality estimation, and that it will provide a basis for real-time prediction of pasture quality using remote sensing techniques. PMID:26904831

  5. Airborne remote sensing for Deepwater Horizon oil spill emergency response

    NASA Astrophysics Data System (ADS)

    Kroutil, Robert T.; Shen, Sylvia S.; Lewis, Paul E.; Miller, David P.; Cardarelli, John; Thomas, Mark; Curry, Timothy; Kudaraskus, Paul

    2010-08-01

    On April 28, 2010, the Environmental Protection Agency's (EPA) Airborne Spectral Photometric Environmental Collection Technology (ASPECT) aircraft was deployed to Gulfport, Mississippi to provide airborne remotely sensed air monitoring and situational awareness data and products in response to the Deepwater Horizon oil rig disaster. The ASPECT aircraft was released from service on August 9, 2010 after having flown over 75 missions that included over 250 hours of flight operation. ASPECT's initial mission responsibility was to provide air quality monitoring (i.e., identification of vapor species) during various oil burning operations. The ASPECT airborne wide-area infrared remote sensing spectral data was used to evaluate the hazard potential of vapors being produced from open water oil burns near the Deepwater Horizon rig site. Other significant remote sensing data products and innovations included the development of an advanced capability to correctly identify, locate, characterize, and quantify surface oil that could reach beaches and wetland areas. This advanced identification product provided the Incident Command an improved capability to locate surface oil in order to improve the effectiveness of oil skimmer vessel recovery efforts directed by the US Coast Guard. This paper discusses the application of infrared spectroscopy and multispectral infrared imagery to address significant issues associated with this national crisis. More specifically, this paper addresses the airborne remote sensing capabilities, technology, and data analysis products developed specifically to optimize the resources and capabilities of the Deepwater Horizon Incident Command structure personnel and their remediation efforts.

  6. Remote sensing using an airborne biosensor

    SciTech Connect

    Ligler, F.S.; Anderson, G.P.; Davidson, P.T.; Stenger, D.A.; Ives, J.T.; King, K.D.; Page, G.; Whelan, J.P.

    1998-08-15

    There is no current method for remote identification of aerosolized bacteria. In particular, such a capability is required to warn of a biological warfare attack prior to human exposure. A fiber optic biosensor, capable of running four simultaneous immunoassays, was integrated with an automated fluidics unit, a cyclone-type air sampler, a radio transceiver, and batteries on a small, remotely piloted airplane capable of carrying a 4.5-kg payload. The biosensor system was able to collect aerosolized bacteria in flight, identify them, and transmit the data to the operator on the ground. The results demonstrate the feasibility of integrating a biosensor into a portable, remotely operated system for environmental analysis.

  7. Remote Sensing of Vegetation Senescence and Stress using Derivative Spectroscopy Applied to Hyperspectral Images

    NASA Astrophysics Data System (ADS)

    Cipar, J. J.

    2012-12-01

    It is well established that senescence and stress affect the shape of the optical reflectance spectrum of vegetation. A prime example is the shift of the red edge inflection point (REIP) to lower wavelength as senescence or stress increases. The red edge refers to the sharp rise in vegetation reflectance between the chlorophyll well in the red (670-680 nm) and the near infrared plateau (~790-1350 nm). The REIP wavelength shift, however, can be subtle and not easily detected with hyperspectral imagers. I explore the use of derivative spectroscopy to enhance the features in the reflectance spectrum. Conventional analysis focuses on the wavelength position of the REIP as a measure of stress. In this paper, I examine the shape of the entire derivative spectrum to characterize the transition from healthy to senescent deciduous vegetation over the summer to autumn transition. While this transition occurs naturally, it causes changes in the reflectance spectrum similar to those changes due to stress such as drought or soil contamination. The experiment (carried out in southern New England) consisted of clipping leaves from maple and oak trees every two to three days from early September through late November and measuring the optical reflectance in the laboratory using an Analytical Spectral Devices (ASD) Field Spectrometer. Reflectance spectra were measured for stacks of leaves using different numbers of leaves in the stack and different backgrounds. The primary data set was measured using four-leaf stacks on a flat black background. The time series of derivative spectra clearly show the shift in the red edge inflection point as a function of date, as expected. In addition, the overall shape of the derivative spectra changes significantly as leaf senescence proceeds. The utility of derivative spectroscopy lay in whether it can be used with remote sensing data recorded by hyperspectral sensors such the NASA-JPL AVIRIS instrument. The lower spectral sampling of current

  8. Airborne Hyperspectral Infrared Imaging Survey of the Southern San Andreas Fault

    NASA Astrophysics Data System (ADS)

    Lynch, D. K.; Tratt, D. M.; Buckland, K. N.; Johnson, P. D.

    2014-12-01

    The San Andreas Fault (SAF) between Desert Hot Springs and Bombay Beach has been surveyed with Mako, an airborne hyperspectral imager operating across the wavelength range 7.6-13.2 μm in the thermal-infrared (TIR) spectral region. The data were acquired with a 4-km swath width centered on the SAF, and many tectonic features are recorded in the imagery. Spectral analysis using diagnostic features of minerals can identify rocks, soils and vegetation. Mako imagery can also locate rupture zones and measure slip distances. Designed and built by The Aerospace Corporation, the innovative and highly capable airborne imaging spectrometer used for this work enables low-noise performance (NEΔT ≲ 0.1 K @ 10 μm) at small pixel IFOV (0.55 mrad) and high frame rates, making possible an area-coverage rate of 20 km2 per minute with 2-m ground resolution from 12,500 ft (3.8 km) above-ground altitude. Since its commissioning in 2010, Mako has been used in numerous studies involving other earthquake fault systems (Hector Mine, S. Bristol Mts.), mapping of surface geology, geothermal sources (fumaroles near the Salton Sea), urban surveys, and the detection, quantification, and tracking of natural and anthropogenic gaseous emission plumes. Mako is available for airborne field studies and new applications are of particular interest. It can be flown at any altitude below 20,000 ft to achieve the desired GSD.

  9. [Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].

    PubMed

    Tan, Chang-Wei; Zhou, Qing-Bo; Qi, La; Zhuang, Heng-Yang

    2008-06-01

    The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem < sheath < spike < leaf. The spectral reflectance at visible light bands was leaf < spike < sheath < stem, but that at near-infrared bands was in adverse. The linear and exponential models with the raw hyperspectral reflectance at 796.7 nm and the first derivative hyperspectral reflectance at 738.4 nm as independent variables could better diagnose rice plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.

  10. The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area

    NASA Astrophysics Data System (ADS)

    Richter, Ronny; Reu, Björn; Wirth, Christian; Doktor, Daniel; Vohland, Michael

    2016-10-01

    The success of remote sensing approaches to assess tree species diversity in a heterogeneously mixed forest stand depends on the availability of both appropriate data and suitable classification algorithms. To separate the high number of in total ten broadleaf tree species in a small structured floodplain forest, the Leipzig Riverside Forest, we introduce a majority based classification approach for Discriminant Analysis based on Partial Least Squares (PLS-DA), which was tested against Random Forest (RF) and Support Vector Machines (SVM). The classifier performance was tested on different sets of airborne hyperspectral image data (AISA DUAL) that were acquired on single dates in August and September and also stacked to a composite product. Shadowed gaps and shadowed crown parts were eliminated via spectral mixture analysis (SMA) prior to the pixel-based classification. Training and validation sets were defined spectrally with the conditioned Latin hypercube method as a stratified random sampling procedure. In the validation, PLS-DA consistently outperformed the RF and SVM approaches on all datasets. The additional use of spectral variable selection (CARS, "competitive adaptive reweighted sampling") combined with PLS-DA further improved classification accuracies. Up to 78.4% overall accuracy was achieved for the stacked dataset. The image recorded in August provided slightly higher accuracies than the September image, regardless of the applied classifier.

  11. [Application of hyper-spectral remote sensing technology in environmental protection].

    PubMed

    Zhao, Shao-Hua; Zhang, Feng; Wang, Qiao; Yao, Yun-Jun; Wang, Zhong-Ting; You, Dai-An

    2013-12-01

    Hyper-spectral remote sensing (RS) technology has been widely used in environmental protection. The present work introduces its recent application in the RS monitoring of pollution gas, green-house gas, algal bloom, water quality of catch water environment, safety of drinking water sources, biodiversity, vegetation classification, soil pollution, and so on. Finally, issues such as scarce hyper-spectral satellites, the limits of data processing and information extract are related. Some proposals are also presented, including developing subsequent satellites of HJ-1 satellite with differential optical absorption spectroscopy, greenhouse gas spectroscopy and hyper-spectral imager, strengthening the study of hyper-spectral data processing and information extraction, and promoting the construction of environmental application system.

  12. High spatial resolution imaging of methane and other trace gases with the airborne Hyperspectral Thermal Emission Spectrometer (HyTES)

    NASA Astrophysics Data System (ADS)

    Hulley, Glynn C.; Duren, Riley M.; Hopkins, Francesca M.; Hook, Simon J.; Vance, Nick; Guillevic, Pierre; Johnson, William R.; Eng, Bjorn T.; Mihaly, Jonathan M.; Jovanovic, Veljko M.; Chazanoff, Seth L.; Staniszewski, Zak K.; Kuai, Le; Worden, John; Frankenberg, Christian; Rivera, Gerardo; Aubrey, Andrew D.; Miller, Charles E.; Malakar, Nabin K.; Sánchez Tomás, Juan M.; Holmes, Kendall T.

    2016-06-01

    Currently large uncertainties exist associated with the attribution and quantification of fugitive emissions of criteria pollutants and greenhouse gases such as methane across large regions and key economic sectors. In this study, data from the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) have been used to develop robust and reliable techniques for the detection and wide-area mapping of emission plumes of methane and other atmospheric trace gas species over challenging and diverse environmental conditions with high spatial resolution that permits direct attribution to sources. HyTES is a pushbroom imaging spectrometer with high spectral resolution (256 bands from 7.5 to 12 µm), wide swath (1-2 km), and high spatial resolution (˜ 2 m at 1 km altitude) that incorporates new thermal infrared (TIR) remote sensing technologies. In this study we introduce a hybrid clutter matched filter (CMF) and plume dilation algorithm applied to HyTES observations to efficiently detect and characterize the spatial structures of individual plumes of CH4, H2S, NH3, NO2, and SO2 emitters. The sensitivity and field of regard of HyTES allows rapid and frequent airborne surveys of large areas including facilities not readily accessible from the surface. The HyTES CMF algorithm produces plume intensity images of methane and other gases from strong emission sources. The combination of high spatial resolution and multi-species imaging capability provides source attribution in complex environments. The CMF-based detection of strong emission sources over large areas is a fast and powerful tool needed to focus on more computationally intensive retrieval algorithms to quantify emissions with error estimates, and is useful for expediting mitigation efforts and addressing critical science questions.

  13. Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots

    EPA Science Inventory

    Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. ...

  14. Multivariate curve resolution for the analysis of remotely sensed thermal infrared hyperspectral images.

    SciTech Connect

    Haaland, David Michael; Stork, Christopher Lyle; Keenan, Michael Robert

    2004-07-01

    While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.

  15. Real-time progressive hyperspectral remote sensing detection methods for crop pest and diseases

    NASA Astrophysics Data System (ADS)

    Wu, Taixia; Zhang, Lifu; Peng, Bo; Zhang, Hongming; Chen, Zhengfu; Gao, Min

    2016-05-01

    Crop pests and diseases is one of major agricultural disasters, which have caused heavy losses in agricultural production each year. Hyperspectral remote sensing technology is one of the most advanced and effective method for monitoring crop pests and diseases. However, Hyperspectral facing serial problems such as low degree of automation of data processing and poor timeliness of information extraction. It resulting we cannot respond quickly to crop pests and diseases in a critical period, and missed the best time for quantitative spraying control on a fixed point. In this study, we take the crop pests and diseases as research point and breakthrough, using a self-development line scanning VNIR field imaging spectrometer. Take the advantage of the progressive obtain image characteristics of the push-broom hyperspectral remote sensor, a synchronous real-time progressive hyperspectral algorithms and models will development. Namely, the object's information will get row by row just after the data obtained. It will greatly improve operating time and efficiency under the same detection accuracy. This may solve the poor timeliness problem when we using hyperspectral remote sensing for crop pests and diseases detection. Furthermore, this method will provide a common way for time-sensitive industrial applications, such as environment, disaster. It may providing methods and technical reserves for the development of real-time detection satellite technology.

  16. Radiometric calibration of multiple Earth observation sensors using airborne hyperspectral data at the Newell County rangeland test site

    NASA Astrophysics Data System (ADS)

    Teillet, Phil M.; Fedosejevs, Gunar; Gauthier, Robert P.; Shin, Raymond T.; O'Neill, Norman T.; Thome, Kurtis J.; Biggar, Stuart F.; Ripley, Herb T.; Meygret, Aime

    1999-09-01

    A single data set of spatially extensive hyperspectral imagery is used to carry out vicarious calibrations for multiple Earth observation sensors. Results are presented based on a data acquisition campaign at the newell County rangeland test site in Alberta in October 1998, which included ground-based measurements, satellite imagery, and airborne casi hyperspectral data. This paper present new calibration monitoring obtained for NOAA-14 AVHRR, OrbView-2 SeaWiFS, SPOT-4 VGT, Landsat-5 TM, and SPOT-2 HRV.

  17. Design and modeling of spectral-thermal unmixing targets for airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Clare, Phil

    2006-05-01

    Techniques to determine the proportions of constituent materials within a single pixel spectrum are well documented in the reflective (0.4-2.5μm) domain. The same capability is also desirable for the thermal (7-14μm) domain, but is complicated by the thermal contributions to the measured spectral radiance. Atmospheric compensation schemes for the thermal domain have been described along with methods for estimating the spectral emissivity from a spectral radiance measurement and hence the next stage to be tackled is the unmixing of thermal spectral signatures. In order to pursue this goal it is necessary to collect data of well-calibrated targets which will expose the limits of the available techniques and enable more robust methods to be designed. This paper describes the design of a set of ground targets for an airborne hyperspectral imager, which will test the effectiveness of available methods. The set of targets include panels to explore a number of difficult scenarios such as isothermal (different materials at identical temperature), isochromal (identical materials, but at differing temperatures), thermal adjacency and thermal point sources. Practical fabrication issues for heated targets and selection of appropriate materials are described. Mathematical modelling of the experiments has enabled prediction of at-sensor measured radiances which are used to assess the design parameters. Finally, a number of useful lessons learned during the fielding of these actual targets are presented to assist those planning future trials of thermal hyperspectral sensors.

  18. Performance of Three Reflectance Calibration Methods for Airborne Hyperspectral Spectrometer Data

    PubMed Central

    Miura, Tomoaki; Huete, Alfredo R.

    2009-01-01

    In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The “reflectance mode (RM)” method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The “linear-interpolation (LI)” method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and post-flight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The “continuous panel (CP)” method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accurate reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 ± 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies

  19. Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data.

    PubMed

    Miura, Tomoaki; Huete, Alfredo R

    2009-01-01

    In this study, the performances and accuracies of three methods for converting airborne hyperspectral spectrometer data to reflectance factors were characterized and compared. The "reflectance mode (RM)" method, which calibrates a spectrometer against a white reference panel prior to mounting on an aircraft, resulted in spectral reflectance retrievals that were biased and distorted. The magnitudes of these bias errors and distortions varied significantly, depending on time of day and length of the flight campaign. The "linear-interpolation (LI)" method, which converts airborne spectrometer data by taking a ratio of linearly-interpolated reference values from the preflight and post-flight reference panel readings, resulted in precise, but inaccurate reflectance retrievals. These reflectance spectra were not distorted, but were subject to bias errors of varying magnitudes dependent on the flight duration length. The "continuous panel (CP)" method uses a multi-band radiometer to obtain continuous measurements over a reference panel throughout the flight campaign, in order to adjust the magnitudes of the linear-interpolated reference values from the preflight and post-flight reference panel readings. Airborne hyperspectral reflectance retrievals obtained using this method were found to be the most accurate and reliable reflectance calibration method. The performances of the CP method in retrieving accurate reflectance factors were consistent throughout time of day and for various flight durations. Based on the dataset analyzed in this study, the uncertainty of the CP method has been estimated to be 0.0025 ± 0.0005 reflectance units for the wavelength regions not affected by atmospheric absorptions. The RM method can produce reasonable results only for a very short-term flight (e.g., < 15 minutes) conducted around a local solar noon. The flight duration should be kept shorter than 30 minutes for the LI method to produce results with reasonable accuracies. An important

  20. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Zhang, Minghua; Qin, Zhihao; Liu, Xue; Ustin, Susan L.

    2003-11-01

    Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4-2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7-1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for

  1. Applications of multi-season hyperspectral remote sensing for acid mine water characterization and mapping of secondary iron minerals associated with acid mine drainage

    NASA Astrophysics Data System (ADS)

    Davies, Gwendolyn E.

    Acid mine drainage (AMD) resulting from the oxidation of sulfides in mine waste is a major environmental issue facing the mining industry today. Open pit mines, tailings ponds, ore stockpiles, and waste rock dumps can all be significant sources of pollution, primarily heavy metals. These large mining-induced footprints are often located across vast geographic expanses and are difficult to access. With the continuing advancement of imaging satellites, remote sensing may provide a useful monitoring tool for pit lake water quality and the rapid assessment of abandoned mine sites. This study explored the applications of laboratory spectroscopy and multi-season hyperspectral remote sensing for environmental monitoring of mine waste environments. Laboratory spectral experiments were first performed on acid mine waters and synthetic ferric iron solutions to identify and isolate the unique spectral properties of mine waters. These spectral characterizations were then applied to airborne hyperspectral imagery for identification of poor water quality in AMD ponds at the Leviathan Mine Superfund site, CA. Finally, imagery varying in temporal and spatial resolutions were used to identify changes in mineralogy over weathering overburden piles and on dry AMD pond liner surfaces at the Leviathan Mine. Results show the utility of hyperspectral remote sensing for monitoring a diverse range of surfaces associated with AMD.

  2. An airborne remote sensing system for urban air quality

    NASA Technical Reports Server (NTRS)

    Duncan, L. J.; Friedman, E. J.; Keitz, E. L.; Ward, E. A.

    1974-01-01

    Several NASA sponsored remote sensors and possible airborne platforms were evaluated. Outputs of dispersion models for SO2 and CO pollution in the Washington, D.C. area were used with ground station data to establish the expected performance and limitations of the remote sensors. Aircraft/sensor support requirements are discussed. A method of optimum flight plan determination was made. Cost trade offs were performed. Conclusions about the implementation of various instrument packages as parts of a comprehensive air quality monitoring system in Washington are presented.

  3. Hyperspectral remote sensing for water quality applications in Guatemala

    NASA Astrophysics Data System (ADS)

    Flores Cordova, A. I.; Christopher, S. A.; Irwin, D.

    2013-12-01

    Water quality measurements are relevant to control and prevent the pollution of surface water essential for human use. Previous studies have used standard methods of water sampling to estimate water quality parameters. Nevertheless those methods are extremely expensive and time-consuming and do not provide information for an entire water body. Hence it is important to implement techniques that allow for the monitoring of water quality parameters in a timely and cost-effective manner, and remote sensing represents a feasible alternative. This study focuses on the largest algal bloom affecting Lake Atitlan, located in Guatemala, by using the hyperspectral sensor Hyperion on board the EO-1 satellite. This algal bloom had a life span that extended for a little more than a month and had a maximum coverage of approximately 40% of the lake's 137 square kilometer surface. This algal bloom occurred at the end of the year 2009, with November being the most critical month. Different satellite sensors were used to monitor the extent of the algal bloom, including Landsat Enhanced Thematic Mapper Plus (ETM+), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Advanced Land Imager (ALI). However, Hyperion images were used to distinguish the characteristics of the vegetation populating the algal bloom. Hyperion satellite images provided a more complete spectral profile of the algal bloom affecting the lake due to its high spectral resolution characteristics. This enabled the identification of unique peaks of reflectance and absorption features of the spectral signature obtained from the algal bloom. The algal bloom was formed mainly by the cyanobacteria Lyngbya robusta. Hyperion satellite images were used to characterize the algal bloom and the unique pigments of cyanobacteria such as phycocyanin. Atmospheric correction was critical to obtain the pure reflectance of the algal bloom and differentiate the spectral features unique to the cyanobacteria

  4. Remote sensing of shorelines using data fusion of hyperspectral and multispectral imagery acquired from mobile and fixed platforms

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Frystacky, Heather

    2012-06-01

    An optimized data fusion methodology is presented and makes use of airborne and vessel mounted hyperspectral and multispectral imagery acquired at littoral zones in Florida and the northern Gulf of Mexico. The results demonstrate the use of hyperspectral-multispectral data fusion anomaly detection along shorelines and in surface and subsurface waters. Hyperspectral imagery utilized in the data fusion analysis was collected using a 64-1024 channel, 1376 pixel swath width; temperature stabilized sensing system; an integrated inertial motion unit; and differential GPS. The imaging system is calibrated using dual 18 inch calibration spheres, spectral line sources, and custom line targets. Simultaneously collected multispectral three band imagery used in the data fusion analysis was derived either a 12 inch focal length large format camera using 9 inch high speed AGFA color negative film, a 12.3 megapixel digital camera or dual high speed full definition video cameras. Pushbroom sensor imagery is corrected using Kalman filtering and smoothing in order to correct images for airborne platform motions or motions of a small vessel. Custom software developed for the hyperspectral system and the optimized data fusion process allows for post processing using atmospherically corrected and georeferenced reflectance imagery. The optimized data fusion approach allows for detecting spectral anomalies in the resolution enhanced data cubes. Spectral-spatial anomaly detection is demonstrated using simulated embedded targets in actual imagery. The approach allows one to utilize spectral signature anomalies to identify features and targets that would otherwise not be possible. The optimized data fusion techniques and software has been developed in order to perform sensitivity analysis of the synthetic images in order to optimize the singular value decomposition model building process and the 2-D Butterworth cutoff frequency selection process, using the concept of user defined "feature

  5. Actual evapotranspiration estimation by means of airborne and satellite remote sensing data

    NASA Astrophysics Data System (ADS)

    Ciraolo, Giuseppe; D'Urso, Guido; Minacapilli, Mario

    2006-09-01

    During the last the two decades, the scientific community developed detailed mathematical models for simulating land surface energy fluxes and crop evapotranspiration rates by means of a energy balance approach. These models can be applied in large areas and with a spatial distributed approach using surface brightness temperature and some ancillary data retrieved from satellite/airborne remote sensed imagery. In this paper a district scale application in combination with multispectral (LandaSat 7 TM data) and hyperspectral airborne MIVIS data has been carried out to test the potentialities of two different energy balance models to estimate evapotranspiration fluxes from a set of typical Mediterranean crops (wine, olive, citrus). The impact of different spatial and radiometric resolutions of MIVIS (3m x 3m) and LandSat (60m x 60m) on models-derived fluxes has been investigated to understand the roles and the main conceptual differences between the two models which respectively use a "single-layer" (SEBAL) and a "two-layer" (TS) schematisation.

  6. Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA+ hyperspectral imagery and image transformation and classificatio...

  7. Mapping Black Mangrove Along the South Texas Gulf Coast Using AISA+ Hyperspectral Imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne hyperspectral imagery and image compression and classification techniq...

  8. Enhancement of Capabilities in Hyperspectral and Radar Remote Sensing for Environmental Assessment and Monitoring

    NASA Technical Reports Server (NTRS)

    Hepner, George F.

    1999-01-01

    The University of Utah, Department of Geography has developed a research and instructional program in satellite remote sensing and image processing. The University requested funds for the purchase of software licenses, mass storage for massive hyperspectral imager data sets, upgrades for the central data server to handle the additional storage capacity, a spectroradiometer for field data collection. These purchases have been made. This equipment will support research in one of the newest and most rapidly expanding areas of remote sensing.

  9. [Spectral curve shape feature-based hyperspectral remote sensing image retrieval].

    PubMed

    Li, Fei; Zhou, Cheng-Hu; Chen, Rong-Guo

    2008-11-01

    With the rapid development of technology of sensors and data transmission, using all kinds of airplane sensors and satellite sensors, the authors can get different voluminous remote sensing image data of earth. Those voluminous remote sensing image data bring problems of data storage and management. It is becoming increasingly necessary to retrieve some information the authors need from those voluminous image data. Image retrieval was proposed by CHANG firstly in 1980 and can be regarded as expansion of traditional information retrieval. Oriented to the demands of efficient retrieval for voluminous remote sensing image, and considering that there are many bands in hyperspectral remote sensing image, the authors first analyzed image distance function and similarity measure in image retrieval. The most crucial issues in retrieval are spectral features extraction and similarity measure. In the present paper, the authors used classical Douglas-Peucker algorithm (hereinafter referred to DP algorithm) for curve simplification to extract shape features of spectral curve, in order to speed up hyperspectral remote sensing image retrieval. And the authors proposed a new method of spectral curve and remote sensing image retrieval, called Douglas-Peucker Spectral Retrieval algorithm (hereinafter referred to DPSR algorithm). Spectral shape features were used in image retrieval. DPSR used features of spectral curve, reduced the computation amount, realized match and retrieval efficiently, and is suitable for spectral curve retrieval in hyperspectral remote sensing image. The authors selected four ground features (grass, apple garden, grape garden and pond) in OMISI hyperspectral remote sensing image to compute similarity measure results, in order to test the effect of DPSR algorithm. Compared with traditional analysis method such as spectral angle match (SAM) and spectral information divergence (SID), DPSR can maintain high precision of results with less amount of computation

  10. Hyperspectral remote sensing for monitoring species-specific drought impacts in southern California

    NASA Astrophysics Data System (ADS)

    Coates, Austin Reece

    A drought persisting since the winter of 2011-2012 has resulted in severe impacts on shrublands and forests in southern California, USA. Effects of drought on vegetation include leaf wilting, leaf abscission, and potential plant mortality. These impacts vary across plant species, depending on differences in species' adaptations to drought, rooting depth, and edaphic factors. During 2013 and 2014, Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data were acquired seasonally over the Santa Ynez Mountains and Santa Ynez Valley north of Santa Barbara, California. To determine the impacts of drought on individual plant species, spectral mixture analysis was used to model a relative green vegetation fraction (RGVF) for each image date in 2013 and 2014. A July 2011 AVIRIS image acquired during the last nondrought year was used to determine a reference green vegetation (GV) endmember for each pixel. For each image date in 2013 and 2014, a three-endmember model using the 2011 pixel spectrum as GV, a lab nonphotosynthetic vegetation (NPV) spectrum, and a photometric shade spectrum was applied. The resulting RGVF provided a change in green vegetation cover relative to 2011. Reference polygons collected for 14 plant species and land cover classes were used to extract the RGVF values from each date. The deeply rooted tree species and tree species found in mesic areas appeared to be the least affected by the drought, whereas the evergreen chaparral showed the most extreme signs of distress. Coastal sage scrub had large seasonal variability; however, each year, it returned to an RGVF value only slightly below the previous year. By binning all the RGVF values together, a general decreasing trend was observed from the spring of 2013 to the fall of 2014. This study intends to lay the groundwork for future research in the area of multitemporal, hyperspectral remote sensing. With proposed plans for a hyperspectral sensor in space (HyspIRI), this type of research will prove to

  11. Can the normalized soil moisture index improve the prediction of soil organic carbon based on hyperspectral remote sensing data?

    NASA Astrophysics Data System (ADS)

    van Wesemael, Bas; Nocita, Marco

    2016-04-01

    One of the problems for mapping of soil organic carbon (SOC) at large-scale based on visible - near and short wave infrared (VIS-NIR-SWIR) remote sensing techniques is the spatial variation of topsoil moisture when the images are collected. Soil moisture is certainly an aspect causing biased SOC estimations, due to the problems in discriminating reflectance differences due to either variations in organic matter or soil moisture, or their combination. In addition, the difficult validation procedures make the accurate estimation of soil moisture from optical airborne a major challenge. After all, the first millimeters of the soil surface reflect the signal to the airborne sensor and show a large spatial, vertical and temporal variation in soil moisture. Hence, the difficulty of assessing the soil moisture of this thin layer at the same moment of the flight. The creation of a soil moisture proxy, directly retrievable from the hyperspectral data is a priority to improve the large-scale prediction of SOC. This paper aims to verify if the application of the normalized soil moisture index (NSMI) to Airborne Prima Experiment (APEX) hyperspectral images could improve the prediction of SOC. The study area was located in the loam region of Wallonia, Belgium. About 40 samples were collected from bare fields covered by the flight lines, and analyzed in the laboratory. Soil spectra, corresponding to the sample locations, were extracted from the images. Once the NSMI was calculated for the bare fields' pixels, spatial patterns, presumably related to within field soil moisture variations, were revealed. SOC prediction models, built using raw and pre-treated spectra, were generated from either the full dataset (general model), or pixels belonging to one of the two classes of NSMI values (NSMI models). The best result, with a RMSE after validation of 1.24 g C kg-1, was achieved with a NSMI model, compared to the best general model, characterized by a RMSE of 2.11 g C kg-1. These

  12. Hyperspectral Remote Sensing of Vegetation:Knowledge Gain and Knowledge Gap after 40 years of research

    NASA Astrophysics Data System (ADS)

    Thenkabail, P. S.; Huete, A. R.

    2012-12-01

    This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher: Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advancements in processing large volumes of hyperspectral data have generated tremendous interest in expanding the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband spectral data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) the ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reduced uncertainty in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) the ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing wavebands and indices with greater sensitivity for analyzing vegetation characteristics. Current state of knowledge on hyperspectral narrowbands (HNBs) for agricultural and vegetation studies inferred from the Book entitled hyperspectral remote sensing of vegetation by Thenkabail et al., 2011. Six study areas of the World for which we have extensive data from field spectroradiometers for 8 major world crops (wheat, corn, rice, barley, soybeans, pulses, and cotton). Approx. 10,500 such data points will be used in crop modeling and in building spectral libraries.

  13. Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data.

    PubMed

    Moses, Wesley J; Gitelson, Anatoly A; Perk, Richard L; Gurlin, Daniela; Rundquist, Donald C; Leavitt, Bryan C; Barrow, Tadd M; Brakhage, Paul

    2012-03-15

    Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophyll-a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll-a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll-a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll-a concentration, with a root mean square error of 5.54 mg m(-3) for chlorophyll-a concentrations in the range 2.27-81.17 mg m(-3). PMID:22209281

  14. ARE AIRBORNE CONTAMINANTS A RISK FACTOR TO AQUATIC ECOSYSTEMS IN REMOTE WESTERN NATIONAL PARKS (USA)

    EPA Science Inventory

    The Western Airborne Contaminants Assessment Project (WACAP) was initiated in 2002 by the National Park Service to determine if airborne contaminants were having an impact on remote western ecosystems. Multiple sample media (snow, water, sediment, fish and terrestrial vegetation...

  15. Mapping tree health using airborne laser scans and hyperspectral imagery: a case study for a floodplain eucalypt forest

    NASA Astrophysics Data System (ADS)

    Shendryk, Iurii; Tulbure, Mirela; Broich, Mark; McGrath, Andrew; Alexandrov, Sergey; Keith, David

    2016-04-01

    Airborne laser scanning (ALS) and hyperspectral imaging (HSI) are two complementary remote sensing technologies that provide comprehensive structural and spectral characteristics of forests over large areas. In this study we developed two algorithms: one for individual tree delineation utilizing ALS and the other utilizing ALS and HSI to characterize health of delineated trees in a structurally complex floodplain eucalypt forest. We conducted experiments in the largest eucalypt, river red gum forest in the world, located in the south-east of Australia that experienced severe dieback over the past six decades. For detection of individual trees from ALS we developed a novel bottom-up approach based on Euclidean distance clustering to detect tree trunks and random walks segmentation to further delineate tree crowns. Overall, our algorithm was able to detect 67% of tree trunks with diameter larger than 13 cm. We assessed the accuracy of tree delineations in terms of crown height and width, with correct delineation of 68% of tree crowns. The increase in ALS point density from ~12 to ~24 points/m2 resulted in tree trunk detection and crown delineation increase of 11% and 13%, respectively. Trees with incorrectly delineated crowns were generally attributed to areas with high tree density along water courses. The accurate delineation of trees allowed us to classify the health of this forest using machine learning and field-measured tree crown dieback and transparency ratios, which were good predictors of tree health in this forest. ALS and HSI derived indices were used as predictor variables to train and test object-oriented random forest classifier. Returned pulse width, intensity and density related ALS indices were the most important predictors in the tree health classifications. At the forest level in terms of tree crown dieback, 77% of trees were classified as healthy, 14% as declining and 9% as dying or dead with 81% mapping accuracy. Similarly, in terms of tree

  16. [Remote sensing of atmospheric trace gas by airborne passive FTIR].

    PubMed

    Gao, Min-quang; Liu, Wen-qing; Zhang, Tian-shu; Liu, Jian-guo; Lu, Yi-huai; Wang, Ya-ping; Xu, Liang; Zhu, Jun; Chen, Jun

    2006-12-01

    The present article describes the details of aviatic measurement for remote sensing trace gases in atmosphere under various surface backgrounds with airborne passive FTIR. The passive down viewing and remote sensing technique used in the experiment is discussed. The method of acquiring atmospheric trace gases infrared characteristic spectra in complicated background and the algorithm of concentration retrieval are discussed. The concentrations of CO and N2O of boundary-layer atmosphere in experimental region below 1000 m are analyzed quantitatively. This measurement technique and the data analysis method, which does not require a previously measured background spectrum, allow fast and mobile remote detection and identification of atmosphere trace gas in large area, and also can be used for urgent monitoring of pollution accidental breakout.

  17. Comparison between laboratory and airborne BRDF measurements for remote sensing

    NASA Astrophysics Data System (ADS)

    Georgiev, Georgi T.; Gatebe, Charles K.; Butler, James J.; King, Michael D.

    2006-08-01

    Samples from soil and leaf litter were obtained at a site located in the savanna biome of South Africa (Skukuza; 25.0°S, 31.5°E) and their bidirectional reflectance distribution functions (BRDF) were measured using the out-of-plane scatterometer located in the National Aeronautics and Space Administration's (NASA's) Goddard Space Flight Center (GSFC) Diffuser Calibration Facility (DCaF). BRDF was measured using P and S incident polarized light over a range of incident and scatter angles. A monochromator-based broadband light source was used in the ultraviolet (uv) and visible (vis) spectral ranges. The diffuse scattered light was collected using an uv-enhanced silicon photodiode detector with output fed to a computer-controlled lock-in amplifier. Typical measurement uncertainties of the reported laboratory BRDF measurements are found to be less than 1% (k=1). These laboratory results were compared with airborne measurements of BRDF from NASA's Cloud Absorption Radiometer (CAR) instrument over the same general site where the samples were obtained. This study presents preliminary results of the comparison between these laboratory and airborne BRDF measurements and identifies areas for future laboratory and airborne BRDF measurements. This paper presents initial results in a study to try to understand BRDF measurements from laboratory, airborne, and satellite measurements in an attempt to improve the consistency of remote sensing models.

  18. Estimation of aerosol type from airborne hyperspectral data: a new technique designed for industrial plume characterization

    NASA Astrophysics Data System (ADS)

    Deschamps, A.; Marion, R.; Foucher, P.-Y.; Briottet, X.

    2012-11-01

    The determination of the aerosol type in a plume from remotely sensed data without any a priori knowledge is a challenging task. If several methods have already been developed to characterize the aerosols from multi or hyperspectral data, they are not suited for industrial particles, which have specific physical and optical properties, changing quickly and in a complex way with the distance from the source emission. From radiative transfer equations, we have developed an algorithm, based on a Look-Up Table approach, enabling the determination of the type of this kind of particles from hyperspectral data. It consists in the selection of pixels pairs, located at the transitions between two kinds of grounds (or between an illuminated and a shadow area), then in the comparison between normalized estimated Aerosol Optical Thicknesses (AOTs) and pre-calculated AOTs. The application of this algorithm to simulated data leads to encouraging results: the selection of only six pixels pairs allows the algorithm to differentiate aerosols emitted by a metallurgical plant from biomass burning particles, urban aerosols and particles from an oil depot explosion, regardless the size and the aerosol concentration. The algorithm performances are better for a relatively high AOT but the single scattering approximation does not enable the characterization of thick plumes (AOT above 2.0). However, the choice of transitions (type of grounds) does not seem to significantly affect the results.

  19. Hyperspectral remote sensing study of harmful algal blooms in the Chesapeake Bay

    NASA Astrophysics Data System (ADS)

    Nie, Yixiang

    Recent development of hyperspectral remote sensing provides capability to identify and classify harmful algal blooms beyond the estimation of chlorophyll concentrations. This study uses hyperspectral data to extract spectral signatures, classify algal blooms, and map the spatial distribution of the algal blooms in the upper Chesapeake Bay. Furthermore, water quality parameters from ground stations have been used together with remote sensing data to provide better understanding of the formation and transformation of the life cycle of harmful algal blooms, and the cause of their outbreaks in the upper Chesapeake Bay. The present results show a strong and significant positive correlation between chlorophyll concentrations and total organic nitrogen concentrations. This relation suggests that total organic nitrogen played an important role in triggering the harmful algal blooms in the upper Chesapeake Bay in this study. This study establishes an integrated approach which combines hyperspectral imaging with multispectral ocean color remote sensing data and traditional water quality monitoring system in the study of harmful algal blooms in small water bodies such as the Chesapeake Bay. Presently, remote sensing is well integrated into the research community, but is less commonly used by resource managers. This dissertation couples remote sensing technologies with specific monitoring programs. The present results will help natural resource managers, local authorities, and the public to utilize an integrated approach in order to better understand, evaluate, preserve, and restore the health of the Chesapeake Bay waters and habitats.

  20. Water Quality Measurements from Hyperspectral Remote Sensing: The Case of the River Ganga

    NASA Astrophysics Data System (ADS)

    Baruch, A.; Carbonneau, P.; Sinha, R.; Scott, S.

    2014-12-01

    Water pollution is a major challenge in large river systems such as the Ganga (i.e. Ganges). With a population of 400 million, widespread agriculture and a heavy industrial base, this river basin is facing multiple stressors and as a result, is now notorious for poor water quality. One of the key issues in addressing this problem remains basic water quality monitoring with systematic and reliable methods. Currently, water quality datasets in the River Ganga are highly fragmented and inadequate for most investigations. Given the sub-continental scale of the system, remote sensing could offer a plausible solution if capable of producing holistic assessments of water quality with a standardised methodology. Specifically, the development of hyperspectral remote sensing, capable of detecting very small changes in incident radiation, offers the potential to mimic laboratory spectroscopy and thus identify the chemicals polluting a body of water, and perhaps, even measure their concentration. However, the use of hyperspectral remote sensing in order to measure water quality is not yet established and remains a very challenging problem. In this study, laboratory experiments, ancillary field data and hyperspectral imagery from the Hyperion sensor were used to explore the feasibility of using remote sensing to detect chromium pollution in the River Ganga. The laboratory experiments demonstrated that field spectroscopy was indeed capable of detecting chromium in concentrations that can currently be found in the Ganga. Furthermore, the analysis of the Hyperion images of the River Ganga shows some promising results which suggest that chromium compounds can be detected using hyperspectral satellite imagery. However, the results confirm that measuring water quality from spaceborne hyperspectral imagery is extremely challenging and further research is required to improve the confidence of these results and refine this methodology.

  1. Research on ground-based LWIR hyperspectral imaging remote gas detection

    NASA Astrophysics Data System (ADS)

    Yang, Zhixiong; Yu, Chunchao; Zheng, Weijian; Lei, Zhenggang; Yan, Min; Yuan, Xiaochun; Zhang, Peizhong

    2015-10-01

    The new progress of ground-based long-wave infrared remote sensing is presented, which describes the windowing spatial and temporal modulation Fourier spectroscopy imaging in details. The prototype forms the interference fringes based on the corner-cube of spatial modulation of Michelson interferometer, using cooled long-wave infrared photovoltaic staring FPA (focal plane array) detector. The LWIR hyperspectral imaging is achieved by the process of collection, reorganization, correction, apodization, FFT etc. from data cube. Noise equivalent sensor response (NESR), which is the sensitivity index of CHIPED-1 LWIR hyperspectral imaging prototype, can reach 5.6×10-8W/(cm-1.sr.cm2) at single sampling. Hyperspectral imaging is used in the field of organic gas VOC infrared detection. Relative to wide band infrared imaging, it has some advantages. Such as, it has high sensitivity, the strong anti-interference ability, identify the variety, and so on.

  2. Thermal Hyperspectral Remote Sensing for Plant Species and Stress Detection

    NASA Astrophysics Data System (ADS)

    Schlerf, M.; Rock, G.; Ullah, S.; Gerhards, M.; Udelhoven, T.; Skidmore, A. K.

    2014-12-01

    Thermal infrared (TIR) spectroscopy offers a novel opportunity for measuring emissivity spectra of natural surfaces. Emissivity spectra are not directly measured, they first have to be retrieved from the raw measurements. Once retrieved, the spectra can be used, for example, to discriminate plant species or to detect plant stress. Knowledge of plant species distribution is essential for the sustainable management of ecosystems. Remote sensing of plant species has so far mostly been limited to data in the visible and near-infrared where, however, different species often reveal similar reflectance curves. Da Luz and Crowley showed in a recent paper that in the TIR plants indeed have distinct spectral features. Also with a certain species, subtle changes of emissivity in certain wavebands may occur, when biochemical compounds change due to osmotic adjustment induced by water stress. Here we show, that i) emissive imaging spectroscopy allows for reliable and accurate retrieval of plant emissivity spectra, ii) emissivity spectra are well suited to discriminate plant species, iii) a reduction in stomatal conductance (caused by stress) changes the thermal infrared signal. For 13 plant species in the laboratory and for 8 plant species in a field setup emissivity spectra were retrieved. A comparison shows, that for most species the shapes of the emissivity curves agree quite well, but that clear offsets between the two types of spectra exist. Discrimination analysis revealed that based on the lab spectra, 13 species could be distinguished with an average overall classification accuracy of 92% using the 6 best spectral bands. For the field spectra (8 species), a similar high OAA of 89% was achieved. Species discrimination is likely to be possible due to variations in the composition of the superficial epidermal layer of plant leaves and in internal chemical concentrations producing unique emissivity features. However, to date, which spectral feature is responsible for which

  3. SYSIPHE system: a state of the art airborne hyperspectral imaging system: initial results from the first airborne campaign

    NASA Astrophysics Data System (ADS)

    Rousset-Rouviere, Laurent; Coudrain, Christophe; Fabre, Sophie; Poutier, Laurent; Løke, Trond; Fridman, Andrei; Blaaberg, Søren; Baarstad, Ivar; Skauli, Torbjorn; Mocoeur, Isabelle

    2014-10-01

    SYSIPHE is an airborne hyperspectral imaging system, result of a cooperation between France (Onera and DGA) and Norway (NEO and FFI). It is a unique system by its spatial sampling -0.5m with a 500m swath at a ground height of 2000m- combined with its wide spectral coverage -from 0.4μm to 11.5μm in the atmospheric transmission bands. Its infrared component, named SIELETERS, consists in two high étendue imaging static Fourier transform spectrometers, one for the midwave infrared and one for the longwave infrared. These two imaging spectrometers are closely similar in design, since both are made of a Michelson interferometer, a refractive imaging system, and a large IRFPA (1016x440 pixels). Moreover, both are cryogenically cooled and mounted on their own stabilization platform which allows the line of sight to be controlled and recorded. These data are useful to reconstruct and to georeference the spectral image from the raw interferometric images. The visible and shortwave infrared component, named Hyspex ODIN-1024, consists of two spectrographs for VNIR and SWIR based on transmissive gratings. These share a common fore-optics and a common slit, to ensure perfect registration between the VNIR and the SWIR images. The spectral resolution varies from 5nm in the visible to 6nm in the shortwave infrared. In addition, the STAD, the post processing and archiving system, is developed to provide spectral reflectance and temperature products (SRT products) from calibrated georeferenced and inter-band registered spectral images at the sensor level acquired and pre-processed by SIELETERS and Hyspex ODIN-1024 systems.

  4. Parallel implementation of linear and nonlinear spectral unmixing of remotely sensed hyperspectral images

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio; Plaza, Javier

    2011-11-01

    Hyperspectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It addresses the (possibly) mixed nature of pixels collected by instruments for Earth observation, which are due to several phenomena including limited spatial resolution, presence of mixing effects at different scales, etc. Spectral unmixing involves the separation of a mixed pixel spectrum into its pure component spectra (called endmembers) and the estimation of the proportion (abundance) of endmember in the pixel. Two models have been widely used in the literature in order to address the mixture problem in hyperspectral data. The linear model assumes that the endmember substances are sitting side-by-side within the field of view of the imaging instrument. On the other hand, the nonlinear mixture model assumes nonlinear interactions between endmember substances. Both techniques can be computationally expensive, in particular, for high-dimensional hyperspectral data sets. In this paper, we develop and compare parallel implementations of linear and nonlinear unmixing techniques for remotely sensed hyperspectral data. For the linear model, we adopt a parallel unsupervised processing chain made up of two steps: i) identification of pure spectral materials or endmembers, and ii) estimation of the abundance of each endmember in each pixel of the scene. For the nonlinear model, we adopt a supervised procedure based on the training of a parallel multi-layer perceptron neural network using intelligently selected training samples also derived in parallel fashion. The compared techniques are experimentally validated using hyperspectral data collected at different altitudes over a so-called Dehesa (semi-arid environment) in Extremadura, Spain, and evaluated in terms of computational performance using high performance computing systems such as commodity Beowulf clusters.

  5. Modular airborne remote sampling and sensing system (MARSSS)

    SciTech Connect

    Woods, R.O.

    1982-04-01

    Sandia is developing a modular airborne instrumentation system for the Environmental Protection Agency. This system will allow flexibility in the choice of instruments by standardizing mountings, power supplies and sampling modes. The objective is to make it possible to perform aerial surveys from chartered aircraft that have not been adapted in a more than superficial manner. It will also allow the experimenter to tailor his choice of instruments to the specific problem. Since the equipment will have a stand-alone capability, it can be applied to other problems such as long-term unattended use at remote locations or in toxic or otherwise hazardous environments.

  6. Hyperspectral remote sensing tools for quantifying plant litter and invasive species in arid ecosystems

    USGS Publications Warehouse

    Nagler, Pamela L.; Sridhar, B.B. Maruthi; Olsson, Aaryn Dyami; Glenn, Edward P.; van Leeuwen, Willem J.D.; Thenkabail, Prasad S.; Huete, Alfredo; Lyon, John G.

    2012-01-01

    Green vegetation can be distinguished using visible and infrared multi-band and hyperspectral remote sensing methods. The problem has been in identifying and distinguishing the non-photosynthetically active radiation (PAR) landscape components, such as litter and soils, and from green vegetation. Additionally, distinguishing different species of green vegetation is challenging using the relatively few bands available on most satellite sensors. This chapter focuses on hyperspectral remote sensing characteristics that aim to distinguish between green vegetation, soil, and litter (or senescent vegetation). Quantifying litter by remote sensing methods is important in constructing carbon budgets of natural and agricultural ecosystems. Distinguishing between plant types is important in tracking the spread of invasive species. Green leaves of different species usually have similar spectra, making it difficult to distinguish between species. However, in this chapter we show that phenological differences between species can be used to detect some invasive species by their distinct patterns of greening and dormancy over an annual cycle based on hyperspectral data. Both applications require methods to quantify the non-green cellulosic fractions of plant tissues by remote sensing even in the presence of soil and green plant cover. We explore these methods and offer three case studies. The first concerns distinguishing surface litter from soil using the Cellulose Absorption Index (CAI), as applied to no-till farming practices where plant litter is left on the soil after harvest. The second involves using different band combinations to distinguish invasive saltcedar from agricultural and native riparian plants on the Lower Colorado River. The third illustrates the use of the CAI and NDVI in time-series analyses to distinguish between invasive buffelgrass and native plants in a desert environment in Arizona. Together the results show how hyperspectral imagery can be applied to

  7. Geometric correction of airborne remote sensing data: An operational procedure to geocode MIVIS data

    SciTech Connect

    Avanzi, G.; Bianchi, R.; Cavalli, R.M.

    1996-11-01

    Study to develop a software methodology to geocode MIVIS hyperspectral images collected by the CNR LARA Project. Gol of the study is to integrate the airborne Position and Attitude System with the image data to obtain geoceded images at a medium-small scale (1: 15000 - 1: 10000). 4 refs., 4 figs.

  8. Hyperspectral remote sensing exploration of carbonatite - an example from Epembe, Kunene region, Namibia

    NASA Astrophysics Data System (ADS)

    Zimmermann, Robert; Brandmeier, Melanie; Andreani, Louis; Gloaguen, Richard

    2015-04-01

    Remote sensing data can provide valuable information about ore deposits and their alteration zones at surface level. High spectral and spatial resolution of the data is essential for detailed mapping of mineral abundances and related structures. Carbonatites are well known for hosting economic enrichments in REE, Ta, Nb and P (Jones et al. 2013). These make them a preferential target for exploration for those critical elements. In this study we show how combining geomorphic, textural and spectral data improves classification result. We selected a site with a well-known occurrence in northern Namibia: the Epembe dyke. For analysis LANDSAT 8, SRTM and airborne hyperspectral (HyMap) data were chosen. The overlapping data allows a multi-scale and multi-resolution approach. Results from data analysis were validated during fieldwork in 2014. Data was corrected for atmospherical and geometrical effects. Image classification, mineral mapping and tectonic geomorphology allow a refinement of the geological map by lithological mapping in a second step. Detailed mineral abundance maps were computed using spectral unmixing techniques. These techniques are well suited to map abundances of carbonate minerals, but not to discriminate the carbonatite itself from surrounding rocks with similar spectral signatures. Thus, geometric indices were calculated using tectonic geomorphology and textures. For this purpose the TecDEM-toolbox (SHAHZAD & GLOAGUEN 2011) was applied to the SRTM-data for geomorphic analysis. Textural indices (e.g. uniformity, entropy, angular second moment) were derived from HyMap and SRTM by a grey-level co-occurrence matrix (CLAUSI 2002). The carbonatite in the study area is ridge-forming and shows a narrow linear feature in the textural bands. Spectral and geometric information were combined using kohonen Self-Organizing Maps (SOM) for unsupervised clustering. The resulting class spectra were visually compared and interpreted. Classes with similar signatures

  9. Using airborne hyperspectral data to characterize the surface pH and mineralogy of pyrite mine tailings

    NASA Astrophysics Data System (ADS)

    Zabcic, N.; Rivard, B.; Ong, C.; Mueller, A.

    2014-10-01

    Acid mine drainage (AMD) is a key concern of the mining industry due to its impact on the quality of water and soils surrounding mine waste deposits. Acid mine drainage derives from the oxidation of metal sulphides, e.g. pyrite (FeS2), exposed to oxygen and water. The leachate acidity is capable of releasing heavy metals contained in the mining waste rock, which can affect water quality and lead to metal enrichment in sediments and potentially resulting in ecosystem degradation. Predicting tailings leachate pH is key to the management of sulfide-bearing mine wastes and is an emerging remote sensing application with limited studies having been realized. Such a capability would supplement traditional methods (i.e. ground surveys) that are challenging to implement due to the extent and large volume of mine waste. This study reports regional scale tailings mineral maps generated from airborne hyperspectral information of the Sotiel-Migollas complex in Spain and pinpoints sources of AMD. The extraction of spectral endmembers from imagery revealed twenty six endmembers for tailings material that represent mostly mineral mixtures. From these, eleven spectral groups were defined, each encompassing minor variations in mineral mixtures. The mineral maps resulting from the use of these endmembers for the detailed investigation of four tailings serve as indicators of the metal, sulphate, and pH levels of the AMD solution at the time of mineral precipitation. Predicted mineralogy was assessed using spectra from samples collected in the field and associated X-ray diffraction measurements. We also discuss the relative merits of the minerals maps of this study and soil leachate pH maps that we previously reported for the same locality using the same airborne data. The pH maps tend to provide predictions consistent with the mineralogy predicted from the mineral maps and the field and laboratory evidence. The pH maps offer information on the pH conditions of the tailings thus giving

  10. Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

    NASA Astrophysics Data System (ADS)

    Peerbhay, Kabir Yunus; Mutanga, Onisimo; Ismail, Riyad

    2013-05-01

    Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393-900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user's and producer's accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user's and producer's accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393-723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.

  11. Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

    USGS Publications Warehouse

    Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

    2011-01-01

    Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

  12. Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data

    PubMed Central

    Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R.

    2015-01-01

    Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds. PMID:26378538

  13. GPU implementation of target and anomaly detection algorithms for remotely sensed hyperspectral image analysis

    NASA Astrophysics Data System (ADS)

    Paz, Abel; Plaza, Antonio

    2010-08-01

    Automatic target and anomaly detection are considered very important tasks for hyperspectral data exploitation. These techniques are now routinely applied in many application domains, including defence and intelligence, public safety, precision agriculture, geology, or forestry. Many of these applications require timely responses for swift decisions which depend upon high computing performance of algorithm analysis. However, with the recent explosion in the amount and dimensionality of hyperspectral imagery, this problem calls for the incorporation of parallel computing techniques. In the past, clusters of computers have offered an attractive solution for fast anomaly and target detection in hyperspectral data sets already transmitted to Earth. However, these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power integrated components are essential to reduce mission payload and obtain analysis results in (near) real-time, i.e., at the same time as the data is collected by the sensor. An exciting new development in the field of commodity computing is the emergence of commodity graphics processing units (GPUs), which can now bridge the gap towards on-board processing of remotely sensed hyperspectral data. In this paper, we describe several new GPU-based implementations of target and anomaly detection algorithms for hyperspectral data exploitation. The parallel algorithms are implemented on latest-generation Tesla C1060 GPU architectures, and quantitatively evaluated using hyperspectral data collected by NASA's AVIRIS system over the World Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in the WTC complex.

  14. Research on Ground-Based LWIR Hyperspectral Imaging Remote Gas Detection.

    PubMed

    Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Yang, Zhi-xiong; Wang, Hai-yangi; Fu, Yan-peng; Li, Xun-niu; Liao, Ning-fang; Su, Jun-hong

    2016-02-01

    The new progress of ground-based long-wave infrared remote sensing is presented, which describes the windowing spatial and temporal modulation Fourier spectroscopy imaging in details. The prototype forms the interference fringes based on the corner-cube of spatial modulation of Michelson interferometer, using cooled long-wave infrared photovoltaic staring FPA (focal plane array) detector. The LWIR hyperspectral imaging is achieved by the process of collection, reorganization, correction, apodization, FFT etc. from data cube. Noise equivalent spectral radiance (NESR), which is the sensitivity index of CHIPED-1 LWIR hyperspectral imaging prototype, can reach 5.6 x 10⁻⁸ W · (cm⁻¹ · sr · cm²)⁻¹ at single sampling. The data is the same as commercial temporal modulation hyperspectral imaging spectrometer. It can prove the advantage of this technique. This technique still has space to be improved. For instance, spectral response range of CHIPED-1 LWIR hyperspectral imaging prototype can reach 11. 5 µm by testing the transmission curve of polypropylene film. In this article, choosing the results of outdoor high-rise and diethyl ether gas experiment as an example, the authors research on the detecting method of 2D distribution chemical gas VOC by infrared hyperspectral imaging. There is no observed diethyl ether gas from the infrared spectral slice of the same wave number in complicated background and low concentration. By doing the difference spectrum, the authors can see the space distribution of diethyl ether gas clearly. Hyperspectral imaging is used in the field of organic gas VOC infrared detection. Relative to wide band infrared imaging, it has some advantages. Such as, it has high sensitivity, the strong anti-interference ability, identify the variety, and so on.

  15. Research on Ground-Based LWIR Hyperspectral Imaging Remote Gas Detection.

    PubMed

    Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Yang, Zhi-xiong; Wang, Hai-yangi; Fu, Yan-peng; Li, Xun-niu; Liao, Ning-fang; Su, Jun-hong

    2016-02-01

    The new progress of ground-based long-wave infrared remote sensing is presented, which describes the windowing spatial and temporal modulation Fourier spectroscopy imaging in details. The prototype forms the interference fringes based on the corner-cube of spatial modulation of Michelson interferometer, using cooled long-wave infrared photovoltaic staring FPA (focal plane array) detector. The LWIR hyperspectral imaging is achieved by the process of collection, reorganization, correction, apodization, FFT etc. from data cube. Noise equivalent spectral radiance (NESR), which is the sensitivity index of CHIPED-1 LWIR hyperspectral imaging prototype, can reach 5.6 x 10⁻⁸ W · (cm⁻¹ · sr · cm²)⁻¹ at single sampling. The data is the same as commercial temporal modulation hyperspectral imaging spectrometer. It can prove the advantage of this technique. This technique still has space to be improved. For instance, spectral response range of CHIPED-1 LWIR hyperspectral imaging prototype can reach 11. 5 µm by testing the transmission curve of polypropylene film. In this article, choosing the results of outdoor high-rise and diethyl ether gas experiment as an example, the authors research on the detecting method of 2D distribution chemical gas VOC by infrared hyperspectral imaging. There is no observed diethyl ether gas from the infrared spectral slice of the same wave number in complicated background and low concentration. By doing the difference spectrum, the authors can see the space distribution of diethyl ether gas clearly. Hyperspectral imaging is used in the field of organic gas VOC infrared detection. Relative to wide band infrared imaging, it has some advantages. Such as, it has high sensitivity, the strong anti-interference ability, identify the variety, and so on. PMID:27209776

  16. Hyperspectral remote sensing data maps minerals in Afghanistan

    NASA Astrophysics Data System (ADS)

    King, Trude V. V.; Kokaly, Raymond F.; Hoefen, Todd M.; Johnson, Michaela R.

    2012-08-01

    Although Afghanistan has abundant mineral resources, including gold, silver, copper, rare earth elements, uranium, tin, iron ore, mercury, lead-zinc, bauxite, and industrial minerals, most have not been successfully developed or explored using modern methods. The U.S. Geological Survey (USGS) with cooperation from the Afghan Geological Survey (AGS) and support from the Department of Defense's Task Force for Business and Stability Operations (TFBSO) has used new imaging spectroscopy surface material maps to help refine the geologic signatures of known but poorly understood mineral deposits and identify previously unrecognized mineral occurrences. To help assess the potential mineral deposit types, the high-resolution hyperspectral data were analyzed to detect the presence of selected minerals that may be indicative of past mineralization processes. This legacy data set is providing tangible support for economic decisions by both the government of Afghanistan and other public and private sector parties interested in the development of the nation's natural resources.

  17. Generation of remotely sensed reference data using low altitude, high spatial resolution hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Williams, McKay D.; van Aardt, Jan; Kerekes, John P.

    2016-05-01

    Exploitation of imaging spectroscopy (hyperspectral) data using classification and spectral unmixing algorithms is a major research area in remote sensing, with reference data required to assess algorithm performance. However, we are limited by our inability to generate rapid, accurate, and consistent reference data, thus making quantitative algorithm analysis difficult. As a result, many investigators present either limited quantitative results, use synthetic imagery, or provide qualitative results using real imagery. Existing reference data typically classify large swaths of imagery pixel-by-pixel, per cover type. While this type of mapping provides a first order understanding of scene composition, it is not detailed enough to include complexities such as mixed pixels, intra-end-member variability, and scene anomalies. The creation of more detailed ground reference data based on field work, on the other hand, is complicated by the spatial scale of common hyperspectral data sets. This research presents a solution to this challenge via classification of low altitude, high spatial resolution (1m GSD) National Ecological Observatory Network (NEON) hyperspectral imagery, on a pixel-by-pixel basis, to produce sub-pixel reference data for high altitude, lower spatial resolution (15m GSD) AVIRIS imagery. This classification is performed using traditional classification techniques, augmented by (0.3m GSD) NEON RGB data. This paper provides a methodology for generating large scale, sub-pixel reference data for AVIRIS imagery using NEON imagery. It also addresses challenges related to the fusion of multiple remote sensing modalities (e.g., different sensors, sensor look angles, spatial registration, varying scene illumination, etc.). A new algorithm for spatial registration of hyperspectral imagery with disparate resolutions is presented. Several versions of reference data results are compared to each other and to direct spectral unmixing of AVIRIS data. Initial results are

  18. Airborne LiDAR and hyperspectral mapping of snow depth and albedo in the Upper Colorado River Basin, Colorado, USA by the NASA JPL Airborne Snow Observatory

    NASA Astrophysics Data System (ADS)

    Deems, J. S.; Painter, T. H.

    2014-12-01

    Operational hydrologic simulation and forecasting in snowmelt-dominated watersheds currently relies on indices of snow accumulation and melt from measurements at a small number of point locations or geographically-limited manual surveys. These data sources cannot adequately characterize the spatial distribution of snow depth/water equivalent, which is the primary determinant of snowpack volume and runoff rates. The NASA JPL Airborne Snow Observatory's airborne laser scanning system maps snow depth at high spatial and temporal resolutions, and is paired with a hyperspectral imager to provide an unprecedented snowpack monitoring capability and enabling a new operational paradigm. We present the initial results from this new application of multi-temporal LiDAR and hyperspectral mapping. During the snowmelt seasons of 2013 and 2014, the ASO mapped snow depth and albedo in the Uncompahgre River Basin in Colorado's Upper Colorado River Basin on a nominally monthly basis. These products enable an assessment and comparison of spatial snow accumulation and melt processes in two years with very different snowmelt hydrographs.

  19. Remote sensing capabilities of the GEO-CAPE airborne simulator

    NASA Astrophysics Data System (ADS)

    Kowalewski, Matthew G.; Janz, Scott J.

    2014-09-01

    The Geostationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was designed and built at the NASA Goddard Space Flight Center (GSFC) as a technology demonstration instrument for the atmospheric science study group of GEO-CAPE and potential validation instrument for NASA's Tropospheric Emissions: Monitoring Pollution (TEMPO) mission. GCAS was designed to make high altitude remote sensing observations of tropospheric and boundary layer pollutants, coastal and ocean water leaving radiances, and visible imagery for cloud and surface information. The instrument has participated in one flight campaign in Houston, TX as part of the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) in September 2013. An overview of the instrument's design, characterization, and preliminary slant column retrievals of nitrogen dioxide (NO2) and ozone (O3) during the DISCOVER-AQ campaign will be provided in this paper.

  20. Multiresolution processing for fractal analysis of airborne remotely sensed data

    NASA Technical Reports Server (NTRS)

    Jaggi, S.; Quattrochi, D.; Lam, N.

    1992-01-01

    Images acquired by NASA's Calibrated Airborne Multispectral Scanner are used to compute the fractal dimension as a function of spatial resolution. Three methods are used to determine the fractal dimension: Shelberg's (1982, 1983) line-divider method, the variogram method, and the triangular prism method. A description of these methods and the result of applying these methods to a remotely-sensed image is also presented. The scanner data was acquired over western Puerto Rico in January, 1990 over land and water. The aim is to study impacts of man-induced changes on land that affect sedimentation into the near-shore environment. The data were obtained over the same area at three different pixel sizes: 10 m, 20 m, and 30 m.

  1. Airborne remote sensing of coal waste and acid mine drainage

    SciTech Connect

    Kim, K.E.; Lee, T.S.

    1996-07-01

    High resolution airborne remote sensing data, spatial resolution of 2m X 2m, were used to study the stream quality degradation due to the coal mines in Taebaek city, one of the major coalfields in Korea. In order to circumvent the severe topographic effect and small scale of the water stream, principal components with the least variances were utilized. They showed the subtle details in the image that were obscured by higher contrast due to the topographic effect. Through maximum likelihood classification of those components, yellowboy and mine waste could be effectively identified. Areas affected by acid mine drainage and mine waste could be also located by identifying areas of dead or dying vegetation using vegetation index map.

  2. Regional prediction of soil organic carbon content over croplands using airborne hyperspectral data

    NASA Astrophysics Data System (ADS)

    Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefebvre, Josias; Chehdi, Kacem

    2015-04-01

    This study was carried out in the framework of the Prostock-Gessol3 and the BASC-SOCSENSIT projects, dedicated to the spatial monitoring of the effects of exogenous organic matter land application on soil organic carbon storage. It aims at identifying the potential of airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks which were georeferenced. Tracks were atmospherically corrected using a set of 22 synchronous field spectra of both bare soils, black and white targets and impervious surfaces. Atmospherically corrected track tiles were mosaicked at a 2 m-resolution resulting in a 66 Gb image. A SPOT4 satellite image was acquired the same day in the framework of the SPOT4-Take Five program of the French Space Agency (CNES) which provided it with atmospheric correction. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then NDVI calculation and thresholding enabled to map agricultural fields with bare soil. All 18 sampled sites known to be bare at this very date were correctly included in this map. A total of 85 sites sampled in 2013 or in the 3 previous years were identified as bare by means of this map. Predictions were made from the mosaic spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples. The use of the total sample including 27 sites under cloud shadows led to non-significant results. Considering 43 sites outside cloud shadows only, median

  3. Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Pullanagari, R. R.; Kereszturi, Gábor; Yule, I. J.

    2016-07-01

    On-farm assessment of mixed pasture nutrient concentrations is important for animal production and pasture management. Hyperspectral imaging is recognized as a potential tool to quantify the nutrient content of vegetation. However, it is a great challenge to estimate macro and micro nutrients in heterogeneous mixed pastures. In this study, canopy reflectance data was measured by using a high resolution airborne visible-to-shortwave infrared (Vis-SWIR) imaging spectrometer measuring in the wavelength region 380-2500 nm to predict nutrient concentrations, nitrogen (N) phosphorus (P), potassium (K), sulfur (S), zinc (Zn), sodium (Na), manganese (Mn) copper (Cu) and magnesium (Mg) in heterogeneous mixed pastures across a sheep and beef farm in hill country, within New Zealand. Prediction models were developed using four different methods which are included partial least squares regression (PLSR), kernel PLSR, support vector regression (SVR), random forest regression (RFR) algorithms and their performance compared using the test data. The results from the study revealed that RFR produced highest accuracy (0.55 ⩽ R2CV ⩽ 0.78; 6.68% ⩽ nRMSECV ⩽ 26.47%) compared to all other algorithms for the majority of nutrients (N, P, K, Zn, Na, Cu and Mg) described, and the remaining nutrients (S and Mn) were predicted with high accuracy (0.68 ⩽ R2CV ⩽ 0.86; 13.00% ⩽ nRMSECV ⩽ 14.64%) using SVR. The best training models were used to extrapolate over the whole farm with the purpose of predicting those pasture nutrients and expressed through pixel based spatial maps. These spatially registered nutrient maps demonstrate the range and geographical location of often large differences in pasture nutrient values which are normally not measured and therefore not included in decision making when considering more effective ways to utilized pasture.

  4. [Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data].

    PubMed

    Tang, Jian-min; Liao, Qin-hong; Liu, Yi-qing; Yang, Gui-jun; Feng, Hai-kuanr; Wang, Ji-hua

    2015-05-01

    The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0. 52, 0. 45 (p<0. 01) , respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0. 77(p<0. 000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy. PMID:26415459

  5. Hyperspectral Remote Sensing of Atmosphere and Surface Properties

    NASA Technical Reports Server (NTRS)

    Liu, Xu; Zhou, Daniel K.; Larar, Allen M.; Yang, Ping

    2011-01-01

    Atmospheric Infrared Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), and Cross-track Infrared Sounder (CrIS) are all hyper-spectral satellite sensors with thousands of spectral channels. Top of atmospheric radiance spectra measured by these sensors contain high information content on atmospheric, cloud, and surface properties. Exploring high information content contained in these high spectral resolution spectra is a challenging task due to computation e ort involved in modeling thousands of spectral channels. Usually, only very small fractions (4{10 percent) of the available channels are included in physical retrieval systems or numerical weather forecast (NWP) satellite data assimilations. We will describe a method of simultaneously retrieving atmospheric temperature, moisture, cloud, and surface properties using all available spectral channels without sacrificing computational speed. The essence of the method is to convert channel radiance spectra into super-channels by an Empirical Orthogonal Function (EOF) transformation. Because the EOFs are orthogonal to each other, about 100 super-channels are adequate to capture the information content of the radiance spectra. A Principal Component-based Radiative Transfer Model (PCRTM) developed at NASA Langley Research Center is used to calculate both the super-channel magnitudes and derivatives with respect to atmospheric profiles and other properties. There is no need to perform EOF transformations to convert super channels back to spectral space at each iteration step for a one-dimensional variational retrieval or a NWP data assimilation system. The PCRTM forward model is also capable of calculating radiative contributions due to multiple-layer clouds. The multiple scattering effects of the clouds are efficiently parameterized. A physical retrieval algorithm then performs an inversion of atmospheric, cloud, and surface properties in super channel domain directly therefore both reducing the

  6. [Hyperspectral remote sensing image classification based on radical basis function neural network].

    PubMed

    Tan, Kun; Du, Pei-jun

    2008-09-01

    Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.

  7. Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Vidhya, R.; Vijayasekaran, D.; Ahamed Farook, M.; Jai, S.; Rohini, M.; Sinduja, A.

    2014-11-01

    Mangrove ecosystem plays a crucial role in costal conservation and provides livelihood supports to humans. It is seriously affected by the various climatic and anthropogenic induced changes. The continuous monitoring is imperative to protect this fragile ecosystem. In this study, the mangrove area and health status has been extracted from Hyperspectral remote sensing data (EO- 1Hyperion) using support vector machine classification (SVM). The principal component transformation (PCT) technique is used to perform the band reduction in Hyperspectral data. The soil adjusted vegetation Indices (SAVI) were used as additional parameters. The mangroves are classified into three classes degraded, healthy and sparse. The SVM classification is generated overall accuracy of 73 % and kappa of 0.62. The classification results were compared with the results of spectral angle mapper classification (SAM). The SAVI also included in SVM classification and the accuracy found to be improved to 82 %. The sparse and degraded mangrove classes were well separated. The results indicate that the mapping of mangrove health is accurate when the machine learning classifier like SVM combined with different indices derived from hyperspectral remote sensing data.

  8. Claycap anomaly detection using hyperspectral remote sensing and lidargrammetric techniques

    NASA Astrophysics Data System (ADS)

    Garcia Quijano, Maria Jose

    Clay capped waste sites are a common method to dispose of the more than 40 million tons of hazardous waste produced in the United States every year (EPA, 2003). Due to the potential threat that hazardous waste poses, it is essential to monitor closely the performance of these facilities. Development of a monitoring system that exploits spectral and topographic changes over hazardous waste sites is presented. Spectral anomaly detection is based upon the observed changes in absolute reflectance and spectral derivatives in centipede grass (Eremochloa ophiuroides) under different irrigation levels. The spectral features that provide the best separability among irrigation levels were identified using Stepwise Discriminant Analyses. The Red Edge Position was selected as a suitable discriminant variable to compare the performance of a global and a local anomaly detection algorithm using a DAIS 3715 hyperspectral image. Topographical anomaly detection is assessed by evaluating the vertical accuracy of two LIDAR datasets acquired from two different altitudes (700 m and 1,200 m AGL) over a clay-capped hazardous site at the Savannah River National Laboratory, SC using the same Optech ALTM 2050 and Cessna 337 platform. Additionally, a quantitative comparison is performed to determine the effect that decreasing platform altitude and increasing posting density have on the vertical accuracy of the LIDAR data collected.

  9. Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

    NASA Astrophysics Data System (ADS)

    Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas; Petrov, Nikolay; Stoev, Antoniy

    Remote sensing technologies have advanced significantly at last decade and have improved the capability to gather information about Earth’s resources and environment. They have many applications in Earth observation, such as mapping and updating land-use and cover, weather forecasting, biodiversity determination, etc. Hyperspectral remote sensing offers unique opportunities in the environmental monitoring and sustainable use of natural resources. Remote sensing sensors on space-based platforms, aircrafts, or on ground, are capable of providing detailed spectral, spatial and temporal information on terrestrial ecosystems. Ground-based sensors are used to record detailed information about the land surface and to create a data base for better characterizing the objects which are being imaged by the other sensors. In this paper some applications of two hyperspectral remote sensing techniques, leaf reflectance and chlorophyll fluorescence, for monitoring and assessment of the effects of adverse environmental conditions on plant ecosystems are presented. The effect of stress factors such as enhanced UV-radiation, acid rain, salinity, viral infections applied to some young plants (potato, pea, tobacco) and trees (plums, apples, paulownia) as well as of some growth regulators were investigated. Hyperspectral reflectance and fluorescence data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (450-850 nm and 600-900 nm), respectively. The differences between the reflectance data of healthy (control) and injured (stressed) plants were assessed by means of statistical (Student’s t-criterion), first derivative, and cluster analysis and calculation of some vegetation indices in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm). Fluorescence spectra were analyzed at five characteristic wavelengths located at the

  10. Research on segmentation method of hyperspectral remote sensing images based on probabilistic neural networks

    NASA Astrophysics Data System (ADS)

    Liu, Gang; Liu, Xingjian

    2008-12-01

    Image segmentation is essential for information extraction from remote sensing image, but it remains the lacks of a general mathematical theory, object merging for poor object boundary localization, dealing with object fragmentation and sensitivity of current procedures to noise. This paper focuses on hyper-spectral image segmentation using probabilistic neural networks (PNN). The methodology, implementation and optimization of a PNN are studied, and a constructed PNN is applied to segment hyper-spectral image. The experience demonstrates main advantage of a PNN that it has quick training and learning, gives a measurement of confidence associated with an output, and has the ability to process large data set. It is concluded that the PNN is superior in image segmentation and the obtained results are satisfied.

  11. Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments

    SciTech Connect

    Im, Jungho; Jensen, John R.; Coleman, Mark; Nelson, Eric

    2009-08-01

    Abstract - Hyperspectral remote sensing research was conducted to document the biophysical and biochemical characteristics of controlled forest plots subjected to various nutrient and irrigation treatments. The experimental plots were located on the Savannah River Site near Aiken, SC. AISA hyperspectral imagery were analysed using three approaches, including: (1) normalized difference vegetation index based simple linear regression (NSLR), (2) partial least squares regression (PLSR) and (3) machine-learning regression trees (MLRT) to predict the biophysical and biochemical characteristics of the crops (leaf area index, stem biomass and five leaf nutrients concentrations). The calibration and cross-validation results were compared between the three techniques. The PLSR approach generally resulted in good predictive performance. The MLRT approach appeared to be a useful method to predict characteristics in a complex environment (i.e. many tree species and numerous fertilization and/or irrigation treatments) due to its powerful adaptability.

  12. Evaluation of multi-scale hyperspectral reflectance and emittance image data for remote mineral mapping in northeastern Death Valley National Park, California and Oasis Valley, Nevada

    NASA Astrophysics Data System (ADS)

    Aslett, Zan

    This dissertation focuses upon the analyses of hyperspectral reflectance and thermal emission image data to remotely detect and map surficial mineralogy in an arid environment in southern Nevada and southeastern California. It includes four manuscripts prepared for submission to peer-reviewed journals, which are presented as single chapters. The research involves the use of longwave-infrared (LWIR) hyper- and multi-spectral measurements made from ground, aerial, and spaceborne perspectives of sedimentary and meta-sedimentary geologic units in northeastern Death Valley National Park, California and both shortwave-infrared (SWIR) and LWIR hyperspectral measurements in an area of diverse Paleozoic and Tertiary geology in Oasis Valley, Nevada. In Chapter 1, a brief overview of the dissertation is provided, including background on reflected and thermal-infrared mineral spectroscopy; remote sensing; the impacts of spatial and spectral resolution upon the ability to detect, identify, and map minerals using remote sensing image data; and the use of combined reflectance and emittance image data to better map minerals. In Chapter 2, ground-based SEBASS LWIR hyperspectral image data is analyzed in order to determine the utility of very high resolution remotely-sensed emittance measurements to delineate late-Proterozoic and Paleozoic sedimentary lithologies of an outcrop at Hell's Gate, Death Valley. In Chapter 3, airborne SEBASS image data over Boundary Canyon are analyzed in conjunction with moderate-scale geologic maps and laboratory measurements to map minerals associated with sedimentary and meta-sedimentary rocks and important in recognizing a detachment fault structure, as well as metamorphic facies. In Chapter 4, ground-based and aerial SEBASS, aerial MASTER, and spaceborne ASTER emittance measurements are compared over two study sites to determine what repercussions viewing perspective and spatial, spectral, and radiometric resolutions have upon remote identification

  13. Can hyperspectral remote sensing detect species specific biochemicals?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Discrimination of a few plants scattered among many plants is a goal common to detection of agricultural weeds and invasive species. Detection of clandestinely grown Cannabis sativa L. is in many ways a special case of weed detection. Remote sensing technology provides an automated, computer based,...

  14. Airborne retrieval of cirrus cloud optical and microphysical properties using Airborne Remote Earth Sensing System 5.1-5.3 and 3.7-μm channel data

    NASA Astrophysics Data System (ADS)

    Ou, S. C.; Liou, K. N.; Yang, P.; Rolland, P.; Caudill, T. R.; Lisowski, J.; Morrison, B.

    1998-09-01

    We present an airborne retrieval algorithm to infer cirrus cloud temperature, optical depth, and mean effective sizes using the Airborne Remote Earth Sensing System (ARES) hyperspectral spectrometer data for the 5.1-5.3 and 3.7 μm channels. The algorithm, development and the selection of the channels are based on the principle and parameterization of radiative transfer involving cirrus clouds and the associated atmospheric and surface properties. It has been applied to a selected case of the ARES data collected over the western Boston area on September 16, 1995. Validation of the retrieved parameters was carried out using the collocated and coincident ground-based 8.6-mm radar data and ice crystal size distribution measurements obtained from the 2D-P probe on board the high-altitude reconnaissance platform (HARP). We show that the retrieved cirrus cloud temperature, mean effective ice crystal size, and optical depth match closely with those derived from the observations.

  15. Developing a Scalable Remote Sampling Design for the NEON Airborne Observation Platform (AOP)

    NASA Astrophysics Data System (ADS)

    Musinsky, J.; Wasser, L. A.; Kampe, T. U.; Leisso, N.; Krause, K.; Petroy, S. B.; Cawse-Nicholson, K.; van Aardt, J. A.; Serbin, S.

    2013-12-01

    The National Ecological Observatory Network (NEON) airborne observation platform (AOP) will collect co-registered high-resolution hyperspectral imagery, discrete and waveform LiDAR, and high-resolution digital photography for more than 60 terrestrial and 23 aquatic sites spread across the continental United States, Puerto Rico, Alaska and Hawaii on an annual basis over the next 30 years. These data, to be made freely available to the public, will facilitate the scaling of field-based biological, physical and chemical measurements to regional and continental scales, enabling a better understanding of the relationships between climate variability and change, land use change and invasive species, and their ecological consequences in areas not directly sampled by the NEON facilities. However, successful up-scaling of in situ measurements requires a flight sampling design that captures environmental heterogeneity and diversity (i.e., ecological and topographic gradients), is sensitive to temporal system variation (e.g., phenology), and can respond to major disturbance events. Alignment of airborne campaigns - composed of two payloads for nominal science acquisitions and one payload for PI-driven rapid-response campaigns -- with other ground, airborne (e.g., AVIRIS) and satellite (e.g., Landsat, MODIS) collections will further facilitate scaling between sensors and data sources of varying spatial and spectral resolution and extent. This presentation will discuss the approach, challenges and future goals associated with the development of NEON AOP's sampling design, using examples from the 2013 nominal flight campaigns in the Central Plains (NEON Domain 10) and the Pacific Southwest (Domain 17), and the rapid response flight campaign of the High Park Fire site outside of Fort Collins, CO. Determination of the specific flight coverage areas for each campaign involved analysis of the landscape scale ecological, geophysical and bioclimatic attributes and trends most closely

  16. Discrimination of plant stress caused by oil pollution and waterlogging using hyperspectral and thermal remote sensing

    NASA Astrophysics Data System (ADS)

    Emengini, Ebele Josephine; Blackburn, George Alan; Theobald, Julian Charles

    2013-01-01

    Remote sensing of plant stress holds promise for detecting environmental pollution by oil. However, in oil-rich delta regions, waterlogging is a frequent source of plant stress that has similar physiological effects to oil pollution. This study investigated the capabilities of remote sensing for discriminating between these two sources of plant stress. Bean plants were subjected to oil pollution, waterlogging, and combined oil and waterlogging treatments. Canopy physiological, hyperspectral, and thermal measurements were taken every two to three days after treatment to follow the stress responses. For plants treated with oil, spectral and thermal responses were evident six days before symptoms could be observed visually. In waterlogged plants, only spectral responses were observed, but these were present up to eight days before visual symptoms. A narrowband reflectance ratio was efficient in detecting stress caused by oil and waterlogging. Canopy temperature and a thermal index were good indicators of oil and combined oil and waterlogging stress, but insensitive to waterlogging alone. Hence, this study provides evidence that combined hyperspectral and thermal remote sensing of vegetation has potential for monitoring oil pollution in environments that are also subjected to waterlogging.

  17. Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data - ValCalHyp airborne hyperspectral campaign under the EUFAR framework

    NASA Astrophysics Data System (ADS)

    Brook, A.; Ben Dor, E.

    2014-09-01

    A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

  18. Efficient Method for Analyzing Hyperspectral Remote Sensing Data

    NASA Technical Reports Server (NTRS)

    Liu, Xu; Zhou, Daniel K.; Lrar, Allen M.; Smith, William L., Jr.

    2009-01-01

    The current and next generation satellite infrared sensors such as AIRS, IASI, CrIS, and CLARREO all have one thing in common: large number of spectral channels. In order to fully utilize the information content of these sensors, a large number of Radiative Transfer (RT) calculations through the inhomogeneous atmosphere are needed. It is also computationally intensive to invert atmospheric and surface properties using all the channels. Usually, only subsets of channels are used to perform physical inversions for atmospheric profiles. We will explore ways to speed up RT calculations and inversions and test the algorithms using data from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. We will describe a retrieval algorithm using a Principal Component-based Radiative Transfer Model (PCRTM) for generating atmospheric temperature/moisture/ozone profiles and surface properties. The retrieval algorithm performs both the radiative transfer calculations and inversions in the Principal Component (PC) domain. The inversion algorithm is based on a non-linear Levenberg- Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the ultraspectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a sub-set of the channels. We will also use data collected during the Joint Airborne IASI Validation Experiment (JAIVEx) field campaign to validate the algorithm and IASI retrievals.

  19. Remote sensing of gases by hyperspectral imaging: algorithms and results of field measurements

    NASA Astrophysics Data System (ADS)

    Sabbah, Samer; Rusch, Peter; Eichmann, Jens; Gerhard, Jörn-Hinnrich; Harig, Roland

    2012-09-01

    Remote gas detection and visualization provides vital information in scenarios involving chemical accidents, terrorist attacks or gas leaks. Previous work showed how imaging infrared spectroscopy can be used to assess the location, the dimensions, and the dispersion of a potentially hazardous cloud. In this work the latest developments of an infrared hyperspectral imager based on a Michelson interferometer in combination with a focal plane array detector are presented. The performance of the system is evaluated by laboratory measurements. The system was deployed in field measurements to identify industrial gas emissions. Excellent results were obtained by successfully identifying released gases from relatively long distances.

  20. Adaptive feature extraction techniques for subpixel target detections in hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Yuen, Peter W. T.; Bishop, Gary J.

    2004-12-01

    Most target detection algorithms employed in hyperspectral remote sensing rely on a measurable difference between the spectral signature of the target and background. Matched filter techniques which utilise a set of library spectra as filter for target detection are often found to be unsatisfactory because of material variability and atmospheric effects in the field data. The aim of this paper is to report an algorithm which extracts features directly from the scene to act as matched filters for target detection. Methods based upon spectral unmixing using geometric simplex volume maximisation (SVM) and independent component analysis (ICA) were employed to generate features of the scene. Target and background like features are then differentiated, and automatically selected, from the endmember set of the unmixed result according to their statistics. Anomalies are then detected from the selected endmember set and their corresponding spectral characteristics are subsequently extracted from the scene, serving as a bank of matched filters for detection. This method, given the acronym SAFED, has a number of advantages for target detection, compared to previous techniques which use orthogonal subspace of the background feature. This paper reports the detection capability of this new technique by using an example simulated hyperspectral scene. Similar results using hyperspectral military data show high detection accuracy with negligible false alarms. Further potential applications of this technique for false alarm rate (FAR) reduction via multiple approach fusion (MAF), and, as a means for thresholding the anomaly detection technique, are outlined.

  1. From HYSOMA to ENSOMAP - A new open source tool for quantitative soil properties mapping based on hyperspectral imagery from airborne to spaceborne applications

    NASA Astrophysics Data System (ADS)

    Chabrillat, Sabine; Guillaso, Stephane; Rabe, Andreas; Foerster, Saskia; Guanter, Luis

    2016-04-01

    Soil spectroscopy from the visible-near infrared to the short wave infrared has been shown to be a proven method for the quantitative prediction of key soil surface properties in the laboratory, field, and up to airborne studies for exposed soils in appropriate surface conditions. With the upcoming launch of the next generation of spaceborne hyperspectral sensors within the next 3 to 5 years (EnMAP, HISUI, PRISMA, SHALOM), a great potential for the global mapping and monitoring of soil properties is appearing. This potential can be achieved only if adequate software tools are available, as shown by the increasing demand for the availability/accessibility of hyperspectral soil products from the geoscience community that have neither the capacity nor the expertise to deliver these soil products. In this context, recently many international efforts were tuned toward the development of robust and easy-to-access soil algorithms to allow non-remote sensing experts to obtain geoscience information based on non-expensive software packages where repeatability of the results is an important prerequisite. In particular, several algorithms for geological and mineral mapping were recently released such as the U.S. Geological Survey Processing Routines in IDL for Spectroscopic Measurements (PRISM) software, or the GFZ EnMAP Geological Mapper. For quantitative soil mapping and monitoring, the HYSOMA (Hyperspectral Soil Mapper) software interface was developed at GFZ under the EUFAR (www.eufar.net) and the EnMAP (www.enmap.org) programs. HYSOMA was specifically oriented toward digital soil mapping applications and has been distributed since 2012 for free as IDL plug-ins under the IDL-virtual machine at www.gfz-potsdam.de/hysoma under a close source license. The HYSOMA interface focuses on fully automatic generation of semi-quantitative soil maps such as soil moisture, soil organic matter, iron oxide, clay content, and carbonate content. With more than 100 users around the world

  2. Development of an airborne remote sensing system for crop pest management: System integration and verification

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing along with Global Positioning Systems, Geographic Information Systems, and variable rate technology has been developed, which scientists can implement to help farmers maximize the economic and environmental benefits of crop pest management through precision agriculture. Airborne remo...

  3. An algorithm for hyperspectral remote sensing of aerosols: 1. Development of theoretical framework

    NASA Astrophysics Data System (ADS)

    Hou, Weizhen; Wang, Jun; Xu, Xiaoguang; Reid, Jeffrey S.; Han, Dong

    2016-07-01

    This paper describes the first part of a series of investigations to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from a newly developed hyperspectral instrument, the GEOstationary Trace gas and Aerosol Sensor Optimization (GEO-TASO), by taking full advantage of available hyperspectral measurement information in the visible bands. We describe the theoretical framework of an inversion algorithm for the hyperspectral remote sensing of the aerosol optical properties, in which major principal components (PCs) for surface reflectance is assumed known, and the spectrally dependent aerosol refractive indices are assumed to follow a power-law approximation with four unknown parameters (two for real and two for imaginary part of refractive index). New capabilities for computing the Jacobians of four Stokes parameters of reflected solar radiation at the top of the atmosphere with respect to these unknown aerosol parameters and the weighting coefficients for each PC of surface reflectance are added into the UNified Linearized Vector Radiative Transfer Model (UNL-VRTM), which in turn facilitates the optimization in the inversion process. Theoretical derivations of the formulas for these new capabilities are provided, and the analytical solutions of Jacobians are validated against the finite-difference calculations with relative error less than 0.2%. Finally, self-consistency check of the inversion algorithm is conducted for the idealized green-vegetation and rangeland surfaces that were spectrally characterized by the U.S. Geological Survey digital spectral library. It shows that the first six PCs can yield the reconstruction of spectral surface reflectance with errors less than 1%. Assuming that aerosol properties can be accurately characterized, the inversion yields a retrieval of hyperspectral surface reflectance with an uncertainty of 2% (and root-mean-square error of less than 0.003), which suggests self-consistency in the

  4. Optical component of the European Airborne Remote Sensing Capabilities (EARSEC)

    NASA Astrophysics Data System (ADS)

    Carrere, Veronique; Oertel, Dieter; Verdebout, Jean; Maracci, G.; Schmuck, Guido; Sieber, Alois J.

    1995-06-01

    The European Ariborne Remote Sensing Capabilities (EARSEC) is a program of the Commission of the European Union in coordination with the European Space Agency. Its goal is to establish an independent European state-of-the-art capability in remote sensing for a wide range of applications. The core instrument of the 'Optical' component of EARSEC is an Imaging Spectrometer (the Digital Airborne Imaging Spectrometer 7915 or DIAS 7915) built by Geophysical & Environmental Research Corporation (GER) and operated by DLR, Oberpfaffenhofen, in collaboration with JRC. The 79 channel high resolution Imaging Spectrometer (IS) covers the 0.4 to 12.3 micrometers wavelength range with a spectral resolution varying from 16 to 2000 nm. Operated from a Dornier 228 aircraft, the spatial resolution can vary between 3 and 15 m. The instrument is calibrated and improved at DLR and should be operational in 1995 for campaigns over Europe. The 'Optical' component of EARSEC also includes ground facilities, mainly an electro-optical (EO) processor developed for JRC by Earth Observation Sciences Limited in the United Kingdom for DAIS 7915 data processing. This processor will generate four levels of products, from simple ingestion, to calibration into physical units of radiance, to geolocation and geocoding. Collaborations are foreseen with European groups operating other advanced optical sensors such as the Italian LARA project, operating the MIVIS (IS comparable to the DAIS), and DLR, operating the ROSIS (developed for marine applications, covering the 430-850 nm region). The EO processor could be adapted in the future to handle other IS data for a more universal use.

  5. Remote sensing of forest damage in the Czech Republic using hyperspectral methods

    NASA Astrophysics Data System (ADS)

    Entcheva, Petya K.

    The current study assesses the potential of hyperspectral data for monitoring the initial stages of damage in Norway spruce forests characterized by subtle changes in foliar chlorophyll and chemistry. Both field and airborne high spectral resolution reflectance measurements were obtained for selected study sites in the Krusne hory, Czech Republic. High spectral resolution airborne canopy data and field foliar samples were acquired simultaneously in August 1998 for a total of 51 study sites within the Krusne hory. The sites were selected to represent a full range of damage conditions in even-aged Norway spruce (Picea abies (L.) Karst) stands located between 820--920m elevation. Reflectance, foliar pigments, nitrogen and chemical constituents were determined for first-, second- and third-year needles. A strong correlation to damage was established for the foliar chemistry. A significant increase in polar compounds (such as tannins, sugars and starch) and a reduced needle lignification occurs with increasing damage. Foliar chemical constituents appear to be effective indicators of long-term environmental conditions. The strong relationship between damage level and polar compounds suggests high potential for use of these constituents as bio-indicators of stress. Both field and airborne high spectral resolution data separate the initial forest damage classes. Based on field reflectance measurements for third-year needles, derivative indices from the red edge region were most strongly correlated to damage level, followed by indices ratioing damage-sensitive and damage-insensitive bands and a parameter describing the fit of an Inverted Gaussian curve. Red/red edge spectral data from the Airborne Solid State Array Spectrometer (ASAS) had the highest potential for separation of initial levels of damage, which corresponds with the region suggested as most sensitive to damage as seen in conducting the field reflectance measurements. Both optical and derivative indices

  6. Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data.

    PubMed

    Huang, Jingfeng; Wei, Chen; Zhang, Yao; Blackburn, George Alan; Wang, Xiuzhen; Wei, Chuanwen; Wang, Jing

    2015-01-01

    Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550-560nm) and red edge (680-750nm) regions; chlorophyll b on the red, (630-660nm), red edge (670-710nm) and the near-infrared (800-810nm); carotenoids on the 500-580nm region; and anthocyanins on the green (550-560nm), red edge (700-710nm) and near-infrared (780-790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a.

  7. Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data.

    PubMed

    Huang, Jingfeng; Wei, Chen; Zhang, Yao; Blackburn, George Alan; Wang, Xiuzhen; Wei, Chuanwen; Wang, Jing

    2015-01-01

    Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550-560nm) and red edge (680-750nm) regions; chlorophyll b on the red, (630-660nm), red edge (670-710nm) and the near-infrared (800-810nm); carotenoids on the 500-580nm region; and anthocyanins on the green (550-560nm), red edge (700-710nm) and near-infrared (780-790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a. PMID:26356842

  8. Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data

    PubMed Central

    Huang, Jingfeng; Wei, Chen; Zhang, Yao; Blackburn, George Alan; Wang, Xiuzhen; Wei, Chuanwen; Wang, Jing

    2015-01-01

    Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550–560nm) and red edge (680–750nm) regions; chlorophyll b on the red, (630–660nm), red edge (670–710nm) and the near-infrared (800–810nm); carotenoids on the 500–580nm region; and anthocyanins on the green (550–560nm), red edge (700–710nm) and near-infrared (780–790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a. PMID:26356842

  9. Prediction of soil stability and erosion in semiarid regions using numerical hydrological model (MCAT) and airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Brook, Anna; Wittenberg, Lea

    2015-04-01

    promising models is the MCAT, which is a MATLAB library of visual and numerical analysis tools for the evaluation of hydrological and environmental models. The model applied in this paper presents an innovative infrastructural system for predicting soil stability and erosion impacts. This integrated model is applicable to mixed areas with spatially varying soil properties, landscape, and land-cover characteristics. Data from a semiarid site in southern Israel was used to evaluate the model and analyze fundamental erosion mechanisms. The findings estimate the sensitivity of the suggested model to the physical parameters and encourage the use of hyperspectral remote sensing imagery (HSI). The proposed model is integrated according to the following stages: 1. The soil texture, aggregation, soil moisture estimated via airborne HSI data, including soil surface clay and calcium carbonate erosions; 2. The mechanical stability of soil assessed via pedo-transfer function corresponding to load dependent changes in soil physical properties due to pre-compression stress (set of equations study shear strength parameters take into account soil texture, aggregation, soil moisture and ecological soil variables); 3. The precipitation-related runoff model program (RMP) satisfactorily reproduces the observed seasonal mean and variation of surface runoff for the current climate simulation; 4. The Monte Carlo Analysis Toolbox (MCAT), a library of visual and numerical analysis tools for the evaluation of hydrological and environmental models, is proposed as a tool for integrate all the approaches to an applicable model. The presented model overcomes the limitations of existing modeling methods by integrating physical data produced via HSI and yet stays generic in terms of space and time independency.

  10. Automatic Extraction of Optimal Endmembers from Airborne Hyperspectral Imagery Using Iterative Error Analysis (IEA) and Spectral Discrimination Measurements

    PubMed Central

    Song, Ahram; Chang, Anjin; Choi, Jaewan; Choi, Seokkeun; Kim, Yongil

    2015-01-01

    Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic extraction of appropriate endmembers without a priori information have focused on N-FINDR. Although there are many different versions of N-FINDR algorithms, computational complexity issues still remain and these algorithms cannot consider the case where spectrally mixed materials are extracted as final endmembers. A sequential endmember extraction-based algorithm may be more effective when the number of endmembers to be extracted is unknown. In this study, we propose a simple but accurate method to automatically determine the optimal endmembers using such a method. The proposed method consists of three steps for determining the proper number of endmembers and for removing endmembers that are repeated or contain mixed signatures using the Root Mean Square Error (RMSE) images obtained from Iterative Error Analysis (IEA) and spectral discrimination measurements. A synthetic hyperpsectral image and two different airborne images such as Airborne Imaging Spectrometer for Application (AISA) and Compact Airborne Spectrographic Imager (CASI) data were tested using the proposed method, and our experimental results indicate that the final endmember set contained all of the distinct signatures without redundant endmembers and errors from mixed materials. PMID:25625907

  11. Hyperspectral remote sensing of Cyanobacteria: successes and challenges

    NASA Astrophysics Data System (ADS)

    Mishra, Deepak R.; Mishra, Sachidananda; Narumalani, Sunil

    2014-11-01

    Cyanobacterial harmful algal blooms (CHABs) is a major water quality issue in surface water bodies because of its scum and bad odor forming and toxin producing abilities. Terminations of blooms also cause oxygen depletion leading to hypoxia and widespread fish kills. Therefore, continuous monitoring of CHABs in recreational water bodies and surface drinking water sources is highly required for their early detection and subsequent issuance of a health warning and reducing the economic loss. We present a comparative study between a modified quasi-analytical algorithm (QAA) and a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. An extensive dataset, consisting of radiometric measurements, absorption measurements of phytoplankton, organic matter, detritus, and pigment concentration, was used to optimize the algorithms. The QAA algorithm isolates the PC signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves PC concentration in the water bodies through bio-optical inversion. Validation of the QAA algorithm, using an independent dataset, produced a mean relative error (MRE) of 34%. For the PC3 algorithm, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating chlorophyll-a (chla) absorption at 620 nm to 665 nm enables PC3 to compensate for the confounding effect of chl-a and considerably increases the accuracy of the PC prediction algorithm. The MRE of prediction for PC3 was 27%. Moreover, PC3 eliminates the nonlinear sensitivity issue of PC algorithms at high range.

  12. Essential Biodiversity Variables (EBV) and Plant Functional Traits (PFT) from Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Skidmore, A. K.

    2013-12-01

    Through the development of variables (EBVs), policy and scientific bodies such as IPBES and GEOSS seek consensus around which essential biodiversity variables could form the basis of a global monitoring program for biodiversity. It is argued that essential climate variables (ECVs) can be calculated directly or indirectly from remotely sensed data. However a number of the proposed essential biodiversity variables essential biodiversity variables are challenging to derive from remote sensing. In this presentation, the derivation of plant functional traits (PFTs) using hyperspectral remote sensing is explored. The plant functional traits are then examined as a proxy for a number of the proposed essential biodiversity variables. For example, suitable plant functional traits that may be used as proxies for essential biodiversity variables include ecosystem extent, species occurrence, cover (biomass, LAI, plant height) and leaf nitrogen content. The accurate derivation of plant functional traits from hyperspectral remote sensing using empirical as well as radiatve transfer models is described at a local scale. Radiative transfer models explain the transfer and interaction of radiation inside vegetation canopies based on physical laws, offering an explicit connection between biophysical and biochemical variables and canopy reflectance. However, specificity to local conditions limits the applicability of physical and empirical models to other regions - in other words the generalization of physical models to larger extents require information to constrain the parameter range. The generalization of physical models is a problem particularly where plant species heterogeneity limits accuracy. An emerging approach to generate essential biodiversity variables at a global level is to upscale empirical models. A possible solution to the problem of transferability and upscaling of both empirical and physical model approaches for essential biodiversity variables is to add data driven

  13. Detecting plant metabolic responses induced by ground shock using hyperspectral remote sensing and physiological contact measurements

    SciTech Connect

    Pickles, W.L.; Cater, G.A.

    1996-12-03

    A series of field experiments were done to determine if ground shock could have induced physiological responses in plants and if the level of the response could be observed. The observation techniques were remote sensing techniques and direct contact physiological measurements developed by Carter for detecting pre-visual plant stress. The remote sensing technique was similar to that used by Pickles to detect what appeared to be ground shock induced plant stress above the 1993 Non Proliferation Experiment`s underground chemical explosion. The experiment was designed to provide direct plant physiological measurements and remote sensing ratio images and from the same plants at the same time. The simultaneous direct and remote sensing measurements were done to establish a ground truth dataset to compare to the results of the hyperspectral remote sensing measurements. In addition, the experiment was designed to include data on what was thought to be the most probable interfering effect, dehydration. The experimental design included investigating the relative magnitude of the shock induced stress effects compared to dehydration effects.

  14. Multi- and hyperspectral remote-sensing retrieval of floodplain-forest aboveground biomass using machine learning

    NASA Astrophysics Data System (ADS)

    Filippi, A. M.; Guneralp, I.; Randall, J.

    2014-12-01

    Forests within dynamic floodplain landscapes, such as meandering-river landscapes, are composed of uneven-aged trees and entail high spatial variability, which results from intersecting hydrological, fluvial, and ecological processes. Floodplain forests are an important carbon sink relative to other terrestrial ecosystems and thus serve a critical role in the global carbon cycle. Accurate, quantitative aboveground biomass (AGB) retrieval within floodplain forests is urgently needed for improved carbon-pool estimates in such areas and enhanced process understanding of river-floodplain biomorphodynamics. We perform remote AGB retrieval for a meander-bend bottomland hardwood forest, based on utilization of stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), and Cubist algorithms and multi- and hyperspectral image-based data sets. For multispectral experiments, we use 30-m and 10-m image bands (Landsat 7 ETM+ and SPOT 5, respectively) and ancillary input vectors; for hyperspectral-based experiments, we use 30-m Hyperion bands and other input variables. Results indicate that for both the multispectral and hyperspectral experimental trials, SGB- and MARS-derived AGB are significantly more accurate than Cubist estimates. (Cubist is used for U.S. national-scale forest biomass mapping.) For the multispectral results, across all data-experiments and algorithms, at 10-m spatial resolution, SGB gives the most accurate estimates (RMSE = 22.49 tonnes/ha; coefficient of determination (R2) = 0.96) when geomorphometric data are also included. For 30-m multispectral data trials, MARS performs the best (RMSE = 29.2 tonnes/ha; R2 = 0.94) when image-derived data are also incorporated. For the hyperspectral experiments, the most accurate MARS- and SGB-based retrievals have R2 of 0.97 and 0.95, respectively; the most accurate Cubist AGB retrieval has R2 of 0.85. MARS and SGB AGB are not significantly different though for the hyperspectral experiments. The

  15. CLASSIFICATION OF HIGH SPATIAL RESOLUTION, HYPERSPECTRAL REMOTE SENSING IMAGERY OF THE LITTLE MIAMI RIVER WATERSHED IN SOUTHWEST OHIO, USA (FINAL)

    EPA Science Inventory

    The document and associated land use/land cover (LULC) coverage, entitled Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA, is the result of a collaborative effort among an interdisci...

  16. THE USE OF HYPERSPECTRAL REMOTE SENSING FOR THE DEVELOPMENT OF OPTICAL WATER QUALITY INDICATORS IN THE OHIO RIVER BASIN

    EPA Science Inventory

    Hyperspectral remote sensing for the assessment of inland water quality can be used in enhancing the capabilities of resource managers to monitor water bodies in a timely and cost-effective manner. The key factor in assessing the accuracy of water quality assessments based on re...

  17. Implementation of Hyperspectral Techniques in the Remote Detection of Imported Fire Ants Mounds (Hymenoptera: Formicidae) in Cultivated Turfgrass

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Safe, expedient, and cost-effective treatments of imported fire ant (IFA) infestations require technological developments that exploit the use of remotely-sensed contrasting features to detect cryptic mounds in heavily-managed turfgrass. Ground-based implementation of hyperspectral techniques in the...

  18. Aerosol, Cloud and Trace Gas Observations Derived from Airborne Hyperspectral Radiance and Direct Beam Measurements in Recent Field Campaigns

    NASA Technical Reports Server (NTRS)

    Redemann, J.; Flynn, C. J.; Shinozuka, Y.; Kacenelenbogen, M.; Segal-Rosenheimer, M.; LeBlanc, S.; Russell, P. B.; Livingston, J. M.; Schmid, B.; Dunagan, S. E.; Johnson, R. R.

    2014-01-01

    The AERONET (AErosol RObotic NETwork) ground-based suite of sunphotometers provides measurements of spectral aerosol optical depth (AOD), precipitable water and spectral sky radiance, which can be inverted to retrieve aerosol microphysical properties that are critical to assessments of aerosol-climate interactions. Because of data quality criteria and sampling constraints, there are significant limitations to the temporal and spatial coverage of AERONET data and their representativeness for global aerosol conditions. The 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) instrument, jointly developed by NASA Ames and PNNL with NASA Goddard collaboration, combines airborne sun tracking and AERONET-like sky scanning with spectroscopic detection. Being an airborne instrument, 4STAR has the potential to fill gaps in the AERONET data set. Dunagan et al. [2013] present results establishing the performance of the instrument, along with calibration, engineering flight test, and preliminary scientific field data. The 4STAR instrument operated successfully in the SEAC4RS [Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys] experiment in Aug./Sep. 2013 aboard the NASA DC-8 and in the DoE [Department of Energy]-sponsored TCAP [Two Column Aerosol Project, July 2012 & Feb. 2013] experiment aboard the DoE G-1 aircraft (Shinozuka et al., 2013), and acquired a wealth of data in support of mission objectives on all SEAC4RS and TCAP research flights. 4STAR provided direct beam measurements of hyperspectral AOD, columnar trace gas retrievals (H2O, O3, NO2; Segal-Rosenheimer et al., 2014), and the first ever airborne hyperspectral sky radiance scans, which can be inverted to yield the same products as AERONET ground-based observations. In addition, 4STAR measured zenith radiances underneath cloud decks for retrievals of cloud optical depth and effective diameter. In this presentation, we provide an overview of the new

  19. Mapping Weathering and Alteration Minerals in the Comstock and Geiger Grade Areas using Visible to Thermal Infrared Airborne Remote Sensing Data

    NASA Technical Reports Server (NTRS)

    Vaughan, Greg R.; Calvin, Wendy M.

    2005-01-01

    To support research into both precious metal exploration and environmental site characterization a combination of high spatial/spectral resolution airborne visible, near infrared, short wave infrared (VNIR/SWIR) and thermal infrared (TIR) image data were acquired to remotely map hydrothermal alteration minerals around the Geiger Grade and Comstock alteration regions, and map the mineral by-products of weathered mine dumps in Virginia City. Remote sensing data from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), SpecTIR Corporation's airborne hyperspectral imager (HyperSpecTIR), the MODIS-ASTER airborne simulator (MASTER), and the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) were acquired and processed into mineral maps based on the unique spectral signatures of image pixels. VNIR/SWIR and TIR field spectrometer data were collected for both calibration and validation of the remote data sets, and field sampling, laboratory spectral analyses and XRD analyses were made to corroborate the surface mineralogy identified by spectroscopy. The resulting mineral maps show the spatial distribution of several important alteration minerals around each study area including alunite, quartz, pyrophyllite, kaolinite, montmorillonite/muscovite, and chlorite. In the Comstock region the mineral maps show acid-sulfate alteration, widespread propylitic alteration and extensive faulting that offsets the acid-sulfate areas, in contrast to the larger, dominantly acid-sulfate alteration exposed along Geiger Grade. Also, different mineral zones within the intense acid-sulfate areas were mapped. In the Virginia City historic mining district the important weathering minerals mapped include hematite, goethite, jarosite and hydrous sulfate minerals (hexahydrite, alunogen and gypsum) located on mine dumps. Sulfate minerals indicate acidic water forming in the mine dump environment. While there is not an immediate threat to the community, there are clearly sources of

  20. The Relationship Between Fossil and Dairy Greenhouse Gas Emissions and Complex Urban Land-Use Patterns by In Situ and Remote Sensing Data from Surface Mobile, Airborne, and Satellite Instruments

    NASA Astrophysics Data System (ADS)

    Leifer, I.; Melton, C.; Tratt, D. M.; Kuze, A.; Buckland, K. N.; Butz, A.; Deguchi, A.; Eastwood, M. L.; Fischer, M. L.; Frash, J.; Fladeland, M. M.; Gore, W.; Iraci, L. T.; Johnson, P. D.; Kataoka, F.; Kolyer, R.; Leen, J. B.; Quattrochi, D. A.; Shiomi, K.; Suto, H.; Tanaka, T.; Thompson, D. R.; Yates, E. L.; Van Damme, M.; Yokota, T.

    2015-12-01

    The GOSAT-COMEX-IASI Experiment (Greenhouse gases Observing SATellite-CO2and Methane EXperiment) demonstrated a novel approach to airborne-surface mobile in situ data fusion for interpretation and validation of satellite and airborne remote sensing data of greenhouse gases and direct calculation of flux. Key data were collected for the Chino Dairy in the Los Angeles Basin, California and for the Kern River Oil Fields adjacent to Bakersfield, California. In situ surface and remote sensing greenhouse gas and ammonia observations were compared with IASI and GOSAT retreivals, while hyperspectral imaging data from the AVIRIS, AVIRIS NG, and Mako airborne sensors were analyzed to relate emissions and land use. Figure - platforms participating in the experiment. TANSO-FTS aboard the Ibuki satellite (GOSAT) provided targeted pixels to measure column greenhouse gases. AMOG is the AutoMObile Gas Surveyor which supports a suite of meteorology and in situ trace gas sensors for mobile high speed measurement. AVIRIS, the Airborne Visual InfraRed Imaging Spectrometer aboard the NASA ER-2 airplane collected hyperspectral imaging data at 20 m resolution from 60,000 ft. Mako is a thermal infrared imaging spectrometer that was flown on the Twin Otter International. AJAX is a fighter jet outfitted for science sporting meteorology and greenhouse gas sensors. RAMVan is an upward looking FTIR for measuring column methane and ammonia and other trace gases.

  1. Comparison of airborne multispectral and hyperspectral imagery for estimating grain sorghum yield

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare the differences between these two types of imagery for assessing crop growth and yields. The objective of this study was to compare airb...

  2. Hyperspectral remote sensing for mineral mapping of structural related mineralizations around Mount Isa, Queensland, Australia

    NASA Astrophysics Data System (ADS)

    Jakob, Sandra; Salati, Sanaz; Gloaguen, Richard

    2015-04-01

    Alone or combined with other remote sensing data, hyperspectral mineral mapping can be used to investigate mineralizations and deposits via alteration minerals. Their kind, abundance and spatial distribution can deliver important statements about the occurrence and formation of mineralizations and their relation to structural features. The high spectral and spatial resolution of HyMap data exceeds multispectral data distinctly and makes the recognition of even smaller geological structures possible. The spectral unmixing of single endmembers can be used for the accurate mapping of specific materials or minerals. The support of hyperspectral imaging by spectral data gathered in the field and the analysis of the composition of rock samples can help to determine endmembers and to identify absorption features. This study demonstrates the possibilities and limitations of remote sensing, especially hyperspectral data, for mineral mapping purposes, using the example of the Mount Isa Inlier. This geological area is situated in Northern Queensland, Australia, and is known for its considerable ore deposits and consequent mining of predominantly copper, zinc, lead, silver and gold. Beside hyperspectral HyMap data, multispectral Landsat 8 and SRTM digital elevation data were analyzed. A three-week field study in 2014 supported the investigations. After preprocessing and vegetation masking the data were analyzed using Spectral Feature Fitting (SFF) and Mixture Tuned Matched Filtering (MTMF) for alteration mineral mapping. The outcomes were combined with results from decorrelation stretch, band ratioing, topographic indices and automated lineament analysis. Additional information was provided by field spectrometer measurements and the XRF and XRD analysis of rock samples. Throughout the study, mineral mapping using remote sensing data, especially hyperspectral data, turned out to deliver high qualitative results when it is supported by additional information. In situ

  3. Biological control and hyperspectral remote sensing of leafy spurge (Euphorbia esula L.) an exotic plant species in North America

    NASA Astrophysics Data System (ADS)

    Parker Williams, Amy Elizabeth

    Leafy spurge (Euphorbia esula L.) is an adventive, invasive, plant species in North America. Aphthona lacertosa and A. nigriscutis are flea beetles (Coleoptera: Chrysomelidae) introduced for biological control of leafy spurge in North America. This research directly addresses the need for quantitative assessment of biological control and for regional scale mapping of leafy spurge infestations. This research had three main objectives. The first was to document the establishment and impact on leafy spurge populations of introduced Aphthona flea beetles. In 1998, 3,000 beetles of each species (6,000 total per site) were released on 76 of 101 monitoring sites in Wyoming. Flea beetle abundance, leafy spurge canopy cover, and flea beetle impact area were measured in 1999 and 2000. After two years, Aphthona releases resulted in significant reductions in leafy spurge canopy cover (from 49% to 6%) with suppression of leafy spurge averaging 285 m2. Flea beetles were effective in controlling leafy spurge regardless of the site characteristics (vegetation type, topographical position, soil type, and aspect) and initial leafy spurge canopy cover. The second objective was to develop methods for detecting and estimating leafy spurge abundance from remotely sensed data. Ground spectrometer data demonstrated that leafy spurge was spectrally distinct due to its conspicuous yellow-green bracts. Mixture Tuned Matched Filtering was used to estimate leafy spurge canopy cover and map leafy spurge distribution from Airborne Visible/Infrared Imaging Spectrometer imagery acquired in 1999. Overall performance of MTMF for estimating percent cover of leafy spurge for all sites was good (r2 = 0.69) with better performance in prairie areas (r 2 = 0.79) and poorer performance occurring on wooded sites (r 2 = 0.57). The third objective was to assess the accuracy of using remotely sensed data for mapping leafy spurge in various habitat types. Leafy spurge was mapped with an overall accuracy ranging

  4. [Study on the modeling of earth-atmosphere coupling over rugged scenes for hyperspectral remote sensing].

    PubMed

    Zhao, Hui-Jie; Jiang, Cheng; Jia, Guo-Rui

    2014-01-01

    Adjacency effects may introduce errors in the quantitative applications of hyperspectral remote sensing, of which the significant item is the earth-atmosphere coupling radiance. However, the surrounding relief and shadow induce strong changes in hyperspectral images acquired from rugged terrain, which is not accurate to describe the spectral characteristics. Furthermore, the radiative coupling process between the earth and the atmosphere is more complex over the rugged scenes. In order to meet the requirements of real-time processing in data simulation, an equivalent reflectance of background was developed by taking into account the topography and the geometry between surroundings and targets based on the radiative transfer process. The contributions of the coupling to the signal at sensor level were then evaluated. This approach was integrated to the sensor-level radiance simulation model and then validated through simulating a set of actual radiance data. The results show that the visual effect of simulated images is consistent with that of observed images. It was also shown that the spectral similarity is improved over rugged scenes. In addition, the model precision is maintained at the same level over flat scenes. PMID:24783559

  5. Novel Hyperspectral Sun Photometer for Satellite Remote Sensing Data Radiometeic Calibration and Atmospheric Aerosol Studies

    NASA Technical Reports Server (NTRS)

    Pagnutti, Mary; Ryan, Robert E.; Holekamp, Kara; Harrington, Gary; Frisbie, Troy

    2006-01-01

    A simple and cost-effective, hyperspectral sun photometer for radiometric vicarious remote sensing system calibration, air quality monitoring, and potentially in-situ planetary climatological studies, was developed. The device was constructed solely from off the shelf components and was designed to be easily deployable for support of short-term verification and validation data collects. This sun photometer not only provides the same data products as existing multi-band sun photometers but also the potential of hyperspectral optical depth and diffuse-to-global products. As compared to traditional sun photometers, this device requires a simpler setup, less data acquisition time and allows for a more direct calibration approach. Fielding this instrument has also enabled Stennis Space Center (SSC) Applied Sciences Directorate personnel to cross-calibrate existing sun photometers. This innovative research will position SSC personnel to perform air quality assessments in support of the NASA Applied Sciences Program's National Applications program element as well as to develop techniques to evaluate aerosols in a Martian or other planetary atmosphere.

  6. Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Yanfei; Wang, Xinyu; Zhao, Lin; Feng, Ruyi; Zhang, Liangpei; Xu, Yanyan

    2016-09-01

    Recently, many blind source separation (BSS)-based techniques have been applied to hyperspectral unmixing. In this paper, a new blind spectral unmixing method based on sparse component analysis (BSUSCA) is proposed to solve the problem of highly mixed data. The BSUSCA algorithm consists of an alternative scheme based on two-block alternating optimization, by which we can simultaneously obtain the endmember signatures and their corresponding fractional abundances. According to the spatial distribution of the endmembers, the sparse properties of the fractional abundances are considered in the proposed algorithm. A sparse component analysis (SCA)-based mixing matrix estimation method is applied to update the endmember signatures, and the abundance estimation problem is solved by the alternating direction method of multipliers (ADMM). SCA is utilized for the unmixing due to its various advantages, including the unique solution and robust modeling assumption. The robustness of the proposed algorithm is verified through simulated experimental study. The experimental results using both simulated data and real hyperspectral remote sensing images confirm the high efficiency and precision of the proposed algorithm.

  7. [Study on the modeling of earth-atmosphere coupling over rugged scenes for hyperspectral remote sensing].

    PubMed

    Zhao, Hui-Jie; Jiang, Cheng; Jia, Guo-Rui

    2014-01-01

    Adjacency effects may introduce errors in the quantitative applications of hyperspectral remote sensing, of which the significant item is the earth-atmosphere coupling radiance. However, the surrounding relief and shadow induce strong changes in hyperspectral images acquired from rugged terrain, which is not accurate to describe the spectral characteristics. Furthermore, the radiative coupling process between the earth and the atmosphere is more complex over the rugged scenes. In order to meet the requirements of real-time processing in data simulation, an equivalent reflectance of background was developed by taking into account the topography and the geometry between surroundings and targets based on the radiative transfer process. The contributions of the coupling to the signal at sensor level were then evaluated. This approach was integrated to the sensor-level radiance simulation model and then validated through simulating a set of actual radiance data. The results show that the visual effect of simulated images is consistent with that of observed images. It was also shown that the spectral similarity is improved over rugged scenes. In addition, the model precision is maintained at the same level over flat scenes.

  8. The PNNL Quantitative IR Database for Infrared Remote Sensing and Hyperspectral Imaging

    SciTech Connect

    Sharpe, Steven W.; Sams, Robert L.; Johnson, Timothy J.

    2002-10-01

    Pacific Northwest National Laboratory (PNNL) is presently compiling a quantitative and high spectral resolution (0.10 cm-1) set of infrared reference data that are specifically designed for atmospheric monitoring, remote sensing, and hyperspectral imaging. The final list of target compounds will contain nearly 500 gas-phase species, whereby each species is reported as a composite reference spectrum at 25?C, with most species also having reference data for 5?C and 50?C. Each composite spectrum is a quantitative Beer's law fit to typically 10 or more individual infrared measurements, whereby each spetrum corresponds to a different pressure of the gas that has been pressurized to 760 Torr with N2 so as to emulate atmospheric pressure broadening. Details of the data acquisition protocol and spectral analysis are discussed.

  9. Enhancements of target detection using atmospheric correction preprocessing techniques in hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Yuen, Peter W. T.; Bishop, Gary J.

    2004-12-01

    This paper reports the result of a study on how atmospheric correction techniques (ACT) enhance target detection in hyperspectral remote sensing, using different sets of real data. Based on the data employed in this study, it has been shown that ACT can reduce the masking effect of the atmosphere and effectively improving spectral contrast. By using the standard Kmeans cluster based unsupervised classifier, it has been shown that the accuracy of the classification obtained from the atmospheric corrected data is almost an order of magnitude better than that achieved using the radiance data. This enhancement is entirely due to the improved separability of the classes in the atmospherically corrected data. Moreover, it has been found that intrinsic information concerning the nature of the imaged surface can be retrieved from the atmospherically corrected data. This has been done to within an error of 5% by using a model based atmospheric correction package ATCOR.

  10. Best practices in passive remote sensing VNIR hyperspectral system hardware calibrations

    USGS Publications Warehouse

    Jablonski, Joseph; Durell, Christopher; Slonecker, Terry; Wong, Kwok; Simon, Blair; Eichelberger, Andrew; Osterberg, Jacob

    2016-01-01

    Hyperspectral imaging (HSI) is an exciting and rapidly expanding area of instruments and technology in passive remote sensing. Due to quickly changing applications, the instruments are evolving to suit new uses and there is a need for consistent definition, testing, characterization and calibration. This paper seeks to outline a broad prescription and recommendations for basic specification, testing and characterization that must be done on Visible Near Infra-Red grating-based sensors in order to provide calibrated absolute output and performance or at least relative performance that will suit the user’s task. The primary goal of this paper is to provide awareness of the issues with performance of this technology and make recommendations towards standards and protocols that could be used for further efforts in emerging procedures for national laboratory and standards groups.

  11. Improved technique for retrieval of forest parameters from hyperspectral remote sensing data.

    PubMed

    Kozoderov, Vladimir V; Dmitriev, Egor V; Sokolov, Anton A

    2015-11-30

    This paper describes an approach of machine-learning pattern recognition procedures for the land surface objects using their spectral and textural features on remotely sensed hyperspectral images together with the biological parameters retrieval for the recognized classes of forests. Modified Bayesian classifier is used to improve the related procedures in spatial and spectral domains. Direct and inverse problems of atmospheric optics are solved based on modeling results of the projective cover and density of the forest canopy for the selected classes of forests of different species and ages. Applying the proposed techniques to process images of high spectral and spatial resolution, we have detected object classes including forests within their contours on a particular image and can retrieve the phytomass amount of leaves/needles as well as the relevant total biomass amount for the forest canopy. PMID:26698785

  12. Novel Hyperspectral Sun Photometer for Satellite Remote Sensing Data Radiometric Calibration and Atmospheric Aerosol Studies

    NASA Technical Reports Server (NTRS)

    Pagnutti, Mary; Ryan, Robert E.; Holekamp, Kara; Harrington, Gary; Frisbie, Troy

    2006-01-01

    A simple and cost-effective, hyperspectral sun photometer for radiometric vicarious remote sensing system calibration, air quality monitoring, and potentially in-situ planetary climatological studies, was developed. The device was constructed solely from off the shelf components and was designed to be easily deployable for support of short-term verification and validation data collects. This sun photometer not only provides the same data products as existing multi-band sun photometers, this device requires a simpler setup, less data acquisition time and allows for a more direct calibration approach. Fielding this instrument has also enabled Stennis Space Center (SSC) Applied Sciences Directorate personnel to cross calibrate existing sun photometers. This innovative research will position SSC personnel to perform air quality assessments in support of the NASA Applied Sciences Program's National Applications program element as well as to develop techniques to evaluate aerosols in a Martian or other planetary atmosphere.

  13. Best practices in passive remote sensing VNIR hyperspectral system hardware calibrations

    NASA Astrophysics Data System (ADS)

    Jablonski, Joseph; Durell, Christopher; Slonecker, Terrence; Wong, Kwok; Simon, Blair; Eichelberger, Andrew; Osterberg, Jacob

    2016-05-01

    Hyperspectral imaging (HSI) is an exciting and rapidly expanding area of instruments and technology in passive remote sensing. Due to quickly changing applications, the instruments are evolving to suit new uses and there is a need for consistent definition, testing, characterization and calibration. This paper seeks to outline a broad prescription and recommendations for basic specification, testing and characterization that must be done on Visible Near Infra-Red grating-based sensors in order to provide calibrated absolute output and performance or at least relative performance that will suit the user's task. The primary goal of this paper is to provide awareness of the issues with performance of this technology and make recommendations towards standards and protocols that could be used for further efforts in emerging procedures for national laboratory and standards groups.

  14. UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

    SciTech Connect

    Nancy F. Glenn; Jessica J. Mitchell; Matthew O. Anderson; Ryan C. Hruska

    2012-06-01

    UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).

  15. AIRBORNE, OPTICAL REMOTE SENSNG OF METHANE AND ETHANE FOR NATURAL GAS PIPELINE LEAK DETECTION

    SciTech Connect

    Jerry Myers

    2005-04-15

    Ophir Corporation was awarded a contract by the U. S. Department of Energy, National Energy Technology Laboratory under the Project Title ''Airborne, Optical Remote Sensing of Methane and Ethane for Natural Gas Pipeline Leak Detection'' on October 14, 2002. The scope of the work involved designing and developing an airborne, optical remote sensor capable of sensing methane and, if possible, ethane for the detection of natural gas pipeline leaks. Flight testing using a custom dual wavelength, high power fiber amplifier was initiated in February 2005. Ophir successfully demonstrated the airborne system, showing that it was capable of discerning small amounts of methane from a simulated pipeline leak. Leak rates as low as 150 standard cubic feet per hour (scf/h) were detected by the airborne sensor.

  16. G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

    NASA Technical Reports Server (NTRS)

    Cook, Bruce; Corp, Lawrence; Nelson, Ross; Morton, Douglas; Ranson, Kenneth J.; Masek, Jeffrey; Middleton, Elizabeth

    2012-01-01

    Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation

  17. Spatial-Spectral Classification Based on the Unsupervised Convolutional Sparse Auto-Encoder for Hyperspectral Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Han, Xiaobing; Zhong, Yanfei; Zhang, Liangpei

    2016-06-01

    Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets - Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

  18. Nitrogen contents of rice panicle and paddy by hyperspectral remote sensing.

    PubMed

    Tang, Yan-Lin; Huang, Jing-Feng; Cai, Shao-Hong; Wang, Ren-Chao

    2007-12-15

    The nitrogen content or crude protein content in rice grains is one of the important indices to evaluate the nutrition and taste quality of rice. Normal determination of their contents by chemical methods is highly expensive and time consuming. The hyperspectral reflectances of the canopy, flag leaf and panicle of 5 rice varieties are measured by a ASD FieldSpec Pro FR in field under 3 nitrogen support levels in maturing process. The nitrogen contents of stems, leaves, flag leaves, panicles and rice paddy and their crude protein contents are determined. The correlation among them is analyzed. The panicles nitrogen contents (%) are very significantly correlate not only to that of stems, leaves and flag leaves and chlorophyll contents (mg g(-1)) of flag leaves at milking and maturing stages, but also to the spectral reflectance rho(lambda), the first derivative spectra D(lambda) and RVI of canopy, flag leaf and panicle itself. The nitrogen contents (%) of rice paddy are very significantly correlative to that of stems and leaves and the spectral reflectance rho(lambda), the first derivative spectra D(lambda) and RVI of canopy at some wave bands at booting, milking and maturing stages. For the squared multiple correlation coefficients (R2) of estimating the nitrogen contents of panicle and paddy by canopies spectra, we find R2 > 0.80 at milking stage, R2 > 0.75 at maturing stage, but for the estimation of panicle by the spectra of flag leaf and panicle itself, we have R2 > 0.65. It indicates that it can be feasible for estimating the contents of nitrogen and crude protein in rice grains by hyperspectral remote sensing. It provide basis for monitoring rice quality by remote sensing. PMID:19093505

  19. Hyperspectral remote sensing of boreal forest tree diversity at multiple scales

    NASA Astrophysics Data System (ADS)

    DeLancey, Evan R.

    This research compared the variability/diversity of spectral information captured with spectrometers at the airborne, field, and leaf level to tree species diversity. Airborne measurements were made over the North Saskatchewan River Valley while field and leaf measurements were done with synthetic tree plots on the roof of the Biological Sciences building, University of Alberta. Measures of optical diversity (spectral variables), such as the standard deviation in vegetation indices, principal components, and slope analysis, showed significant correlation to species diversity indices. The strongest correlations (R 2: ODI#3 = 0.90, ODI#6 = 0.86) were achieved with linear models using three to five spectral variables, called Optical Diversity Indices (ODIs). Experimental methods found that this correlation was based primarily on variation in leaf optical properties. Additionally, rough canopies increased optical diversity and greater spectral range improved correlations slightly. These findings can help design operational methods for remote assessment of biodiversity based on optical diversity.

  20. Remotely Measured Terrestrial Chlorophyll Fluorescence Using Airborne G-LiHT and APFS Sensors

    NASA Astrophysics Data System (ADS)

    Cook, W. B.; Yee, J. H.; Corp, L. A.; Cook, B. D.; Huemmrich, K. F.

    2014-12-01

    In September 2014 the Goddard Lidar, Hyperspectral and Thermal (G-LiHT) and the APL/JHU Airborne Plant Fluorescence Sensor (APFS) were flown together on a NASA Langley King Air over vegetated targets in North Carolina and Virginia. The instruments provided high spatial and spectral resolution data in the visible and near infrared, down-welling irradiance, elevation maps, and thermal imagery. Ground validation data was also collected concurrently. Here we report the results of these measurements and show the feasibility of using these types of instruments for collection the fluorescence and other information essential for ecological and carbon cycle studies.

  1. Remote detection of forest structure in the White Mountains of New Hampshire: An integration of waveform lidar and hyperspectral remote sensing data

    NASA Astrophysics Data System (ADS)

    Anderson, Jeanne Elizabeth

    The capability of waveform lidar, used singly and through integration with high-resolution spectral data, to describe and predict various aspects of the structure of a northern temperate forest is explored. Waveform lidar imagery was acquired in 1999 and 2003 over Bartlett Experimental Forest in the White Mountains of central New Hampshire using NASA's airborne Laser Vegetation Imaging Sensor (LVIS). High-resolution spectral imagery from 1997 and 2003 was likewise acquired using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). USDA Forest Service Northeastern Research Station (USFS NERS) 2001-2003 inventory data was used to define basal area, above-ground biomass, quadratic mean stem diameter and proportional species abundances within each of over 400 plots. Field plots scaled to LVIS footprints were also established. At the smallest scale, metrics derived from single LVIS footprints were strongly correlated with coincident forest measurements. At the larger scale of USFS NERS plots, strong correlations encompassing the full variability of the Forest Service data could not be established. Restrictions set by species composition and land-use, however, significantly improved both the descriptive and predictive power of the regression analyses. Higher amplitude values of 1999 LUIS ground return metrics obtained within two years of the January 1998 ice storm, were found to provide a spatial record of higher levels of canopy damage within older, unmanaged forest tracts. Subjected to repeated disturbance of intermediate severity over the time frame of decades, these particular tracts, predominately found on southeastern aspects, simultaneously support by levels of sugar maple abundance and low levels of sugar maple coarse woody debris. LVIS height metrics were used here to establish a statistical relationship with coarse woody debris data. The integration of waveform lidar with hyperspectral data did enhance the ability to remotely describe a number of

  2. Airborne Demonstration of FPGA Implementation of Fast Lossless Hyperspectral Data Compression System

    NASA Technical Reports Server (NTRS)

    Keymeulen, D.; Aranki, N.; Bakhshi, A.; Luong, H.; Sartures, C.; Dolman, D.

    2014-01-01

    Efficient on-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. The technique also improves signature extraction, object recognition and feature classification capabilities by providing exact reconstructed data on constrained downlink resources. At JPL a novel, adaptive and predictive technique for lossless compression of hyperspectral data was recently developed. This technique uses an adaptive filtering method and achieves a combination of low complexity and compression effectiveness that far exceeds state-of-the-art techniques currently in use. The JPL-developed 'Fast Lossless' algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. It is of low computational complexity and thus well-suited for implementation in hardware.

  3. Mapping Ungulate Habitats in Yellowstone National Park with Airborne Hyperspectral Data

    NASA Technical Reports Server (NTRS)

    Terrie, Gregory; Warner, Amanda; Spruce, Joseph

    2001-01-01

    Mapping vegetation habitats of ungulates (e.g., bison, elk, and deer) is critical to the development of efficient wildlife management and monitoring practices in Yellowstone National Park. Image endmembers were chosen using the ENVI minimum noise fraction, pixel purity index, N-dimensional visualizer approach. The spectral angle mapper algorithm was used to classify the image. This process was applied to low altitude AVIRIS and Probe-1 hyperspectral imagery of the Lamar River/Soda Butte Creek confluence to map several ungulate habitats (e.g., grasses, sedge, sage, aspen, willow, and cottonwood. The results are being compared to field measurements and large-scale color infrared aerial photography to assess mapping accuracy. The use of AVIRIS and Probe-1 data enabled the examination of hyperspectral data collected at different spatial and spectral resolutions.

  4. Development of a Cost-Effective Airborne Remote Sensing System for Coastal Monitoring.

    PubMed

    Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael

    2015-09-30

    Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge.

  5. Development of a Cost-Effective Airborne Remote Sensing System for Coastal Monitoring

    PubMed Central

    Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael

    2015-01-01

    Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge. PMID:26437413

  6. Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

    PubMed

    Allison, Robert S; Johnston, Joshua M; Craig, Gregory; Jennings, Sion

    2016-08-18

    For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

  7. Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring

    PubMed Central

    Allison, Robert S.; Johnston, Joshua M.; Craig, Gregory; Jennings, Sion

    2016-01-01

    For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context. PMID:27548174

  8. Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

    PubMed

    Allison, Robert S; Johnston, Joshua M; Craig, Gregory; Jennings, Sion

    2016-01-01

    For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context. PMID:27548174

  9. [Application of hyperspectral remote sensing in research on ecological boundary in north farming-pasturing transition in China].

    PubMed

    Wang, Hong-Mei; Wang, Kun; Xie, Ying-Zhong

    2009-06-01

    Studies of ecological boundaries are important and have become a rapidly evolving part of contemporary ecology. The ecotones are dynamic and play several functional roles in ecosystem dynamics, and the changes in their locations can be used as an indicator of environment changes, and for these reasons, ecotones have recently become a focus of investigation of landscape ecology and global climate change. As the interest in ecotone increases, there is an increased need for formal techniques to detect it. Hence, to better study and understand the functional roles and dynamics of ecotones in ecosystem, we need quantitative methods to characterize them. In the semi-arid region of northern China, there exists a farming-pasturing transition resulting from grassland reclamation and deforestation. With the fragmentation of grassland landscape, the structure and function of the grassland ecosystem are changing. Given this perspective; new-image processing approaches are needed to focus on transition themselves. Hyperspectral remote sensing data, compared with wide-band remote sensing data, has the advantage of high spectral resolution. Hyperspectral remote sensing can be used to visualize transitional zones and to detect ecotone based on surface properties (e. g. vegetation, soil type, and soil moisture etc). In this paper, the methods of hyperspectral remote sensing information processing, spectral analysis and its application in detecting the vegetation classifications, vegetation growth state, estimating the canopy biochemical characteristics, soil moisture, soil organic matter etc are reviewed in detail. Finally the paper involves further application of hyperspectral remote sensing information in research on local climate in ecological boundary in north farming-pasturing transition in China.

  10. Long-term agroecosystem research in the central Mississippi river basin: hyperspectral remote sensing of reservoir water quality.

    PubMed

    Sudduth, Kenneth A; Jang, Gab-Sue; Lerch, Robert N; Sadler, E John

    2015-01-01

    In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters-chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH, all measured water quality parameters were strongly related ( ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.

  11. Long-term agroecosystem research in the central Mississippi river basin: hyperspectral remote sensing of reservoir water quality.

    PubMed

    Sudduth, Kenneth A; Jang, Gab-Sue; Lerch, Robert N; Sadler, E John

    2015-01-01

    In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters-chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH, all measured water quality parameters were strongly related ( ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality. PMID:25602322

  12. Remote monitoring of soil moisture using airborne microwave radiometers

    NASA Technical Reports Server (NTRS)

    Kroll, C. L.

    1973-01-01

    The current status of microwave radiometry is provided. The fundamentals of the microwave radiometer are reviewed with particular reference to airborne operations, and the interpretative procedures normally used for the modeling of the apparent temperature are presented. Airborne microwave radiometer measurements were made over selected flight lines in Chickasha, Oklahoma and Weslaco, Texas. Extensive ground measurements of soil moisture were made in support of the aircraft mission over the two locations. In addition, laboratory determination of the complex permittivities of soil samples taken from the flight lines were made with varying moisture contents. The data were analyzed to determine the degree of correlation between measured apparent temperatures and soil moisture content.

  13. Airborne remote sensing of photosynthetic light use efficiency and carbon uptake along an Arctic transect in Finland

    NASA Astrophysics Data System (ADS)

    Atherton, J.; Hill, T. C.; Prieto-Blanco, A.; Wade, T.; Clement, R.; Moncrieff, J.; Williams, M. D.; Disney, M.; Nichol, C. J.

    2009-12-01

    It is critical to understand the dynamics of ecosystem carbon uptake through seasonal changes and in response to environmental drivers. In this study we utilised aircraft based remote sensing and CO2/H2O flux monitoring systems to quantify changes in photosynthesis along an Arctic transect. The University of Edinburgh's (UK) research aircraft (a Diamond HK 36 TTC-ECO Dimona) was deployed in the Arctic during summer 2008 to carry out a series of transect-flights over a birch-mire mosaic site near Kevo, Finland as part of the Arctic Biosphere Atmosphere Coupling at Multiple Scales (ABACUS) project. The aircraft is equipped with automated dual field-of-view (hyperspectral) radiometers and CO2/H2O flux and meteorological instrumentation. Vegetation indices known to be related to photosynthetic light use efficiency (LUE), including the well established Photochemical Reflectance Index (PRI) and Solar-induced Fluorescence (SiF) as well as the Normalized Difference Vegetation Index (NDVI) were calculated from the spectral data and matched in space to the CO2 flux measurements. We explored spatial relationships between NDVI and CO2 flux, LUE (CO2 flux / Absorbed Photosynthetically Active Radiation) and PRI and finally SiF (calculated using the Fraunhofer infilling method) and relevant environmental drivers. Our results highlight the unique ability of an airborne platform to quantify ecosystem physiology across a landscape and demonstrate how such measurements can bridge the spatial gap between ground and satellite-based observations.

  14. Hyperspectral imaging utility for transportation systems

    NASA Astrophysics Data System (ADS)

    Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver

    2015-03-01

    The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique spectral signature that offers new opportunities for informed decision-making in transportation systems development, operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest new potential applications of hyperspectral imaging in transportation asset management, network performance evaluation, and risk assessments to enable effective and objective decision- and policy-making.

  15. SpecBase - A Virtual Research Environment for Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Klump, Jens; Haubrock, Sören-Nils

    2010-05-01

    Remote sensing methods are able to quantify the electromagnetic reflections or emissions of the Earth's surface materials and artefacts. The properties of these materials affect the reflectance, absorption or transmission of electromagnetic radiation at specific wavelengths. As a consequence, each material generates characteristic electromagnetic spectra that can be quantitatively analysed. For research purposes, the compilation and comparison of so-called reference spectra as well as the management of spectra from laboratory and field studies is essential. The workflows underlying the creation of reference spectra share characteristics with many other research fields in earth sciences in that they have many elements in common but sometimes require ad-hoc changes. In practice, the process that leads to the creation of a reference spectrum is often only poorly documented and the resulting data are accessible to only a small group of users. The increasing application of hyperspectral remote sensing techniques calls for a better dissemination and documentation of available reference spectra. The aim of SpecBase is to offer researchers in hyperspectral remote sensing a virtual research environment (VRE) for the creation, documentation and dissemination of reference spectra. A core component is a data repository that allows versioning and roll-back of intermediate products in the workflow leading to new reference spectra. Once a reference spectrum is ready for publication it can be published and made citeable by assigning a Digital Object Identifier (DOI ®) through the DataCite service for publication of scientific data. The repository is supplemented by web 2.0 tools (user blog, wiki) to support the documentation, discussion and review of the newly created reference spectra. Important parts of the SpecBase project are the evaluation of the available web 2.0 tools and the analysis of the workflows leading to the creation of new reference spectra. Through the evaluation

  16. Hyperspectral remote sensing of evaporate minerals and associated sediments in Lake Magadi area, Kenya

    NASA Astrophysics Data System (ADS)

    Kodikara, Gayantha R. L.; Woldai, Tsehaie; van Ruitenbeek, Frank J. A.; Kuria, Zack; van der Meer, Freek; Shepherd, Keith D.; van Hummel, G. J.

    2012-02-01

    Pleistocene to present evaporitic lacustrine sediments in Lake Magadi, East African Rift Valley, Kenya were studied and mapped using spectral remote sensing methods. This approach incorporated surface mineral mapping using space-borne hyperspectral Hyperion imagery together with laboratory analysis, including visible, near-infrared diffuse reflectance spectroscopy (VNIR) measurements and X-ray diffraction for selected rock and soil samples of the study area. The spectral signatures of Magadiite and Kenyaite, which have not been previously reported, were established and the spectral signatures of trona, chert series, volcanic tuff and the High Magadi bed were also analyzed. Image processing techniques, MNF (Minimum Noise Fraction) and MTMF (Mixture Tuned Matched Filtering) using a stratified approach (image analysis with and without the lake area), were used to enhance the mapping of evaporates. High Magadi beds, chert series and volcanic tuff were identified from the Hyperion image with an overall mapping accuracy of 84.3%. Even though, the spatial distribution of evaporites and sediments in Lake Magadi area change in response to climate variations, the mineralogy of this area has not been mapped recently. The results of this study shows the usefulness of the hypersspectral remote sensing to map the surface geology of this kind of environment and to locate promising sites for industrial open-pit trona mining in a qualitative and quantitative manner.

  17. The Hico Image Processing System: A Web-Accessible Hyperspectral Remote Sensing Toolbox

    NASA Astrophysics Data System (ADS)

    Harris, A. T., III; Goodman, J.; Justice, B.

    2014-12-01

    As the quantity of Earth-observation data increases, the use-case for hosting analytical tools in geospatial data centers becomes increasingly attractive. To address this need, HySpeed Computing and Exelis VIS have developed the HICO Image Processing System, a prototype cloud computing system that provides online, on-demand, scalable remote sensing image processing capabilities. The system provides a mechanism for delivering sophisticated image processing analytics and data visualization tools into the hands of a global user community, who will only need a browser and internet connection to perform analysis. Functionality of the HICO Image Processing System is demonstrated using imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), an imaging spectrometer located on the International Space Station (ISS) that is optimized for acquisition of aquatic targets. Example applications include a collection of coastal remote sensing algorithms that are directed at deriving critical information on water and habitat characteristics of our vulnerable coastal environment. The project leverages the ENVI Services Engine as the framework for all image processing tasks, and can readily accommodate the rapid integration of new algorithms, datasets and processing tools.

  18. The Earth Climate Hyperspectral Observatory: Advances in Cloud and Aerosol Remote Sensing

    NASA Astrophysics Data System (ADS)

    Pilewskie, Peter; Schmidt, Sebastian; Coddington, Odele; Kopp, Greg

    2015-04-01

    Future satellite missions to monitor global change require the establishment of high-accuracy spectrally resolved benchmark data records of reflected shortwave radiation for trend detection and attribution. Not surprisingly, these same attributes also provide substantial improvements in the retrieval of microphysical and optical properties of clouds and aerosols over current discrete-band observations. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission, currently in pre-formulation, defines a set of fundamental direct observations of spectrally resolved reflected shortwave and emitted longwave radiation, and GNSS radio occultation in order to detect climate trends and to test and improve climate prediction models. The Earth Climate Hyperspectral Observatory (ECHO), a proposed pathfinder mission to CLARREO, focuses on measuring spectrally resolved Earth-reflected shortwave radiation over a spectral range that comprised approximately 95% of the solar radiative energy incident at the top-of-atmosphere. This paper will report on the ECHO requirements specifically directed at objectives related to cloud and aerosol remote sensing, and more generally, characterizing the physical parameters responsible for the observed spectral and temporal variability in a benchmark data record. These objectives are centered on targeted remote sensing and data assimilation analyses to derive the dominant contributors to the observed spectral, temporal, and spatial perturbations in the reflected shortwave signal. Specific improvements in the retrieval of cloud and aerosol properties due to increased spectral coverage, spectral resolution, and radiometric accuracy will be discussed.

  19. [Hyperspectral remote sensing of total suspended matter concentrations in Lake Taihu based on water optical classification].

    PubMed

    Zhou, Xiao-Yu; Sun, De-Yong; Li, Yun-Mei; Li, Jun-Sheng; Gong, Shao-Qi

    2013-07-01

    Four field investigations were carried out in the Taihu Lake for collecting in situ observation data in Nov. 2008, Apr. 2009, May and Aug. 2010. On the basis of water optical classification, different retrieval algorithms were developed, specific for different types of waters. Based on the preformance of each model comparion in each type of waters, the optimal models obtained were (1) the band ratio model for Type 1 water; (2) the semi-analysis algorithm model 2 for Type 2 water; (3) the first-order differential model for Type 3 water. Meanwhile, an optimal retrieval model was also established using the same collection of calibration data. Some comparisons were done between the developed models for the classified and non-classified waters. The comparison results showed that the models for the classified waters had better performances than that for the non-classified water, in both the retrieval accuracy and the model stability. Then, analyses of the optical classification leading to the accuracy decrease of the semi-analysis algorithm model were processed. Finally, the results of this study in hyperspectral remote sensing date showed a great application potential by analysis. The findings of this study are significant for promoting the development of water color remote sensing for optically complex turbid inland waters.

  20. [Disease index inversion of wheat stripe rust on different wheat varieties with hyperspectral remote sensing].

    PubMed

    Guo, Jie-Bin; Huang, Chong; Wang, Hai-Guang; Sun, Zhen-Yu; Ma, Zhan-Hong

    2009-12-01

    It is becoming more and more important to use mixed wheat varieties to control wheat stripe rust. Different wheat varieties were planted in field and stripe rust was caused by artificial inoculation. Disease index (DI) was assessed and the canopy reflection data of wheat canopy were obtained by ASD FieldSpec HandHeld FR(325-1 075 nm) made by ASD Company. The correlation analysis between DI and spectral data (reflectance and the first derivative) was conducted, and the estimation models between DI and reflection data (reflectance at 690 and 850 nm, SDr, NDVI and RVI) were built using linear regression method. The results showed that different combinations of wheat varieties had the similar variation at different disease index. DI has positive correlation with reflectance of wheat canopy in visible region, and has significant negative correlation in the near infrared region. DI has stable negative correlation with the first derivative in the region of 700-760 nm and with big fluctuation in other regions. The correlation was compared between DI and hyperspectral derivative index, and SDr has the best correlation with DI. DI estimation models were built based on the canopy reflectance at 690 and 850 nm, SDr, NDVI and RVI. The determinant coefficient of the models is between 0.588 and 0.855, 0.669 and 0.911, 0.534 and 0.773, and 0.587 and 0.751, respectively, and all the models were fit well. The results indicated that DI of wheat stripe rust could be inverted using hyperspectral remote sensing technique and that the inversion effect was hardly influenced by the different combinations of wheat varieties.

  1. Airborne Remote Sensing of River Flow and Morphology

    NASA Astrophysics Data System (ADS)

    Zuckerman, S.; Anderson, S. P.; McLean, J.; Redford, R.

    2014-12-01

    River morphology, surface slope and flow are some of the fundamental measurements required for surface water monitoring and hydrodynamic research. This paper describes a method of combining bathymetric lidar with space-time processing of mid-wave infrared (MWIR) imagery to simultaneously measure bathymetry, currents and surface slope from an airborne platform. In May 2014, Areté installed a Pushbroom Imaging Lidar for Littoral Surveillance (PILLS) and a FLIR SC8000 MWIR imaging system sampling at 2 Hz in a small twin-engine aircraft. Data was collected over the lower Colorado River between Picacho Park and Parker. PILLS is a compact bathymetric lidar based on streak-tube sensor technology. It provides channel and bank topography and water surface elevation at 1 meter horizontal scales and 25 cm vertical accuracy. Surface currents are derived from the MWIR imagery by tracking surface features using a cross correlation algorithm. This approach enables the retrieval of currents along extended reaches at the forward speed of the aircraft with spatial resolutions down to 5 m with accuracy better than 10 cm/s. The fused airborne data captures current and depth variability on scales of meters over 10's of kilometers collected in just a few minutes. The airborne MWIR current retrievals are combined with the bathymetric lidar data to calculate river discharge which is then compared with real-time streamflow stations. The results highlight the potential for improving our understanding of complex river environments with simultaneous collections from multiple airborne sensors.

  2. Airborne Dial Remote Sensing of the Arctic Ozone Layer

    NASA Technical Reports Server (NTRS)

    Wirth, Martin; Renger, Wolfgang; Ehret, Gerhard

    1992-01-01

    A combined ozone and aerosol LIDAR was developed at the Institute of Physics of the Atmosphere at the DLR in Oberpfaffenhofen. It is an airborne version, that, based on the DIAL-principle, permits the recording of two-dimensional ozone profiles. This presentation will focus on the ozone-part; the aerosol subsection will be treated later.

  3. Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

    NASA Astrophysics Data System (ADS)

    Thompson, D. R.; Leifer, I.; Bovensmann, H.; Eastwood, M.; Fladeland, M.; Frankenberg, C.; Gerilowski, K.; Green, R. O.; Kratwurst, S.; Krings, T.; Luna, B.; Thorpe, A. K.

    2015-06-01

    Localized anthropogenic sources of atmospheric CH4 are highly uncertain and temporally variable. Airborne remote measurement is an effective method to detect and quantify these emissions. In a campaign context, the science yield can be dramatically increased by real-time retrievals that allow operators to coordinate multiple measurements of the most active areas. This can improve science outcomes for both single- and multiple-platform missions. We describe a case study of the NASA/ESA CO2 and Methane Experiment (COMEX) campaign in California during June and August/September 2014. COMEX was a multi-platform campaign to measure CH4 plumes released from anthropogenic sources including oil and gas infrastructure. We discuss principles for real-time spectral signature detection and measurement, and report performance on the NASA Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). AVIRIS-NG successfully detected CH4 plumes in real-time at Gb s-1 data rates, characterizing fugitive releases in concert with other in situ and remote instruments. The teams used these real-time CH4 detections to coordinate measurements across multiple platforms, including airborne in situ, airborne non-imaging remote sensing, and ground-based in situ instruments. To our knowledge this is the first reported use of real-time trace gas signature detection in an airborne science campaign, and presages many future applications.

  4. Oil and gas reservoir exploration based on hyperspectral remote sensing and super-low-frequency electromagnetic detection

    NASA Astrophysics Data System (ADS)

    Qin, Qiming; Zhang, Zili; Chen, Li; Wang, Nan; Zhang, Chengye

    2016-01-01

    This paper proposes a method that combined hyperspectral remote sensing with super-low-frequency (SLF) electromagnetic detection to extract oil and gas reservoir characteristics from surface to underground, for the purpose of determining oil and gas exploration target regions. The study area in Xinjiang Karamay oil-gas field, China, was investigated. First, a Hyperion dataset was used to extract altered minerals (montmorillonite, chlorite, and siderite), which were comparatively verified by field survey and spectral measurement. Second, the SLF electromagnetic datasets were then acquired where the altered minerals were distributed. An inverse distance weighting method was utilized to acquire two-dimensional profiles of the electrical feature distribution of different formations on the subsurface. Finally, existing geological data, field work, and the results derived from Hyperion images and SLF electromagnetic datasets were comprehensively analyzed to confirm the oil and gas exploration target region. The results of both hyperspectral remote sensing and SLF electromagnetic detection had a good consistency with the geological materials in this study. This paper demonstrates that the combination of hyperspectral remote sensing and SLF electromagnetic detection is suitable for the early exploration of oil and gas reservoirs, which is characterized by low exploration costs, large exploration areas, and a high working efficiency.

  5. Validation of Satellite Ammonia Retrievals using Airborne Hyperspectral Thermal-Infrared Spectrometry

    NASA Astrophysics Data System (ADS)

    Tratt, D. M.; Hall, J. L.; Chang, C. S.; Qian, J.; Clarisse, L.; Van Damme, M.; Clerbaux, C.; Hurtmans, D.; Coheur, P.

    2011-12-01

    We demonstrate the utility of a new airborne sensor with the requisite spatial, spectral, and radiometric resolution to characterize "point" sources of ammonia emission. Flights were conducted over California's San Joaquin Valley, which is a region of intensive agriculture and animal husbandry that has been identified as one of the single largest sources of atmospheric free ammonia worldwide. Airborne data acquisition operations were coordinated with daytime overpasses of the Infrared Atmospheric Sounding Interferometer (IASI) aboard the European Space Agency's MetOp-A platform. IASI is capable of measuring total columns of ammonia and the primary purpose of this investigation was to compare and validate the IASI ammonia product against high-spatial-resolution airborne retrievals acquired contemporaneously over the same footprint. The ~12-km pixel size of the IASI satellite measurements cannot resolve the local-scale variability of ammonia abundance and consequently cannot characterize the often compact source emissions. The nominal 2-m pixel size of the airborne data revealed variability of ammonia concentration at several different scales within the IASI footprint. At this pixel size, well-defined plumes issuing from individual dairy facilities could be imaged and their dispersion characteristics resolved. Retrieved ammonia concentrations in excess of 50 ppb were inferred for some of the strongest discrete plumes.

  6. Data products of NASA Goddard's LiDAR, hyperspectral, and thermal airborne imager (G-LiHT)

    NASA Astrophysics Data System (ADS)

    Corp, Lawrence A.; Cook, Bruce D.; McCorkel, Joel; Middleton, Elizabeth M.

    2015-06-01

    Scientists in the Biospheric Sciences Laboratory at NASA's Goddard Space Flight Center have undertaken a unique instrument fusion effort for an airborne package that integrates commercial off the shelf LiDAR, Hyperspectral, and Thermal components. G-LiHT is a compact, lightweight and portable system that can be used on a wide range of airborne platforms to support a number of NASA Earth Science research projects and space-based missions. G-LiHT permits simultaneous and complementary measurements of surface reflectance, vegetation structure, and temperature, which provide an analytical framework for the development of new algorithms for mapping plant species composition, plant functional types, biodiversity, biomass, carbon stocks, and plant growth. G-LiHT and its supporting database are designed to give scientists open access to the data that are needed to understand the relationship between ecosystem form and function and to stimulate the advancement of synergistic algorithms. This system will enhance our ability to design new missions and produce data products related to biodiversity and climate change. G-LiHT has been operational since 2011 and has been used to collect data for a number of NASA and USFS sponsored studies, including NASA's Carbon Monitoring System (CMS) and the American ICESat/GLAS Assessment of Carbon (AMIGA-Carb). These acquisitions target a broad diversity of forest communities and ecoregions across the United States and Mexico. Here, we will discuss the components of G-LiHT, their calibration and performance characteristics, operational implementation, and data processing workflows. We will also provide examples of higher level data products that are currently available.

  7. Region-based geometric active contour for classification using hyperspectral remote sensing images

    NASA Astrophysics Data System (ADS)

    Yan, Lin

    2011-12-01

    The high spectral resolution of hyperspectral imaging (HSI) systems greatly enhances the capabilities of discrimination, identification and quantification of objects of different materials from remote sensing images, but they also bring challenges to the processing and analysis of HSI data. One issue is the high computation cost and the curse of dimensionality associated with the high dimensions of HSI data. A second issue is how to effectively utilize the information including spectral and spatial information embedded in HSI data. Geometric Active Contour (GAC) is a widely used image segmentation method that utilizes the geometric information of objects within images. One category of GAC models, the region-based GAC models (RGAC), have good potential for remote sensing image processing because they use both spectral and geometry information in images are robust to initial contour placement. These models have been introduced to target extractions and classifications on remote sensing images. However, there are some restrictions on the applications of the RGAC models on remote sensing. First, the heavy involvement of iterative contour evolutions makes GAC applications time-consuming and inconvenient to use. Second, the current RGAC models must be based on a certain distance metric and the performance of RGAC classifiers are restricted by the performance of the employed distance metrics. According to the key features of the RGAC models analyzed in this dissertation, a classification framework is developed for remote sensing image classifications using the RGAC models. This framework allows the RGAC models to be combined with conventional pixel-based classifiers to promote them to spectral-spatial classifiers and also greatly reduces the iterations of contour evolutions. An extended Chan-Vese (ECV) model is proposed that is able to incorporate the widely used distance metrics in remote sensing image processing. A new type of RGAC model, the edge-oriented RGAC model

  8. Remote sensing for non-renewable resources - Satellite and airborne multiband scanners for mineral exploration

    NASA Technical Reports Server (NTRS)

    Goetz, Alexander F. H.

    1986-01-01

    The application of remote sensing techniques to mineral exploration involves the use of both spatial (morphological) as well as spectral information. This paper is directed toward a discussion of the uses of spectral image information and emphasizes the newest airborne and spaceborne sensor developments involving imaging spectrometers.

  9. Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this study, six extrapolation methods have been compared for their ability to estimate daily crop evapotranspiration (ETd) from instantaneous latent heat flux estimates derived from digital airborne multispectral remote sensing imagery. Data used in this study were collected during an experiment...

  10. An airborne multispectral imaging system based on two consumer-grade cameras for agricultural remote sensing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper describes the design and evaluation of an airborne multispectral imaging system based on two identical consumer-grade cameras for agricultural remote sensing. The cameras are equipped with a full-frame complementary metal oxide semiconductor (CMOS) sensor with 5616 × 3744 pixels. One came...

  11. Husbandry Trace Gas Emissions from a Dairy Complex By Mobile in Situ and Airborne and Spaceborne Remote Sensing: A Comex Campaign Focus

    NASA Astrophysics Data System (ADS)

    Leifer, I.; Tratt, D. M.; Bovensmann, H.; Buckland, K. N.; Burrows, J. P.; Frash, J.; Gerilowski, K.; Iraci, L. T.; Johnson, P. D.; Kolyer, R.; Krautwurst, S.; Krings, T.; Leen, J. B.; Hu, C.; Melton, C.; Vigil, S. A.; Yates, E. L.; Zhang, M.

    2014-12-01

    Recent field study reviews on the greenhouse gas methane (CH4) found significant underestimation from fossil fuel industry and husbandry. The 2014 COMEX campaign seeks to develop methods to derive CH4 and carbon dioxide (CO2) from remote sensing data by combining hyperspectral imaging (HSI) and non-imaging spectroscopy (NIS) with in situ airborne and surface data. COMEX leverages synergies between high spatial resolution HSI column abundance maps and moderate spectral/spatial resolution NIS. Airborne husbandry data were collected for the Chino dairy complex (East Los Angeles Basin) by NIS-MAMAP, HSI-Mako thermal-infrared (TIR); AVIRIS NG shortwave IR (SWIR), with in situ surface mobile-AMOG Surveyor (AutoMObile greenhouse Gas)-and airborne in situ from a Twin Otter and the AlphaJet. AMOG Surveyor uses in situ Integrated Cavity Off Axis Spectroscopy (OA-ICOS) to measure CH4, CO2, H2O, H2S and NH3 at 5-10 Hz, 2D winds, and thermal anomaly in an adapted commuter car. OA-ICOS provides high precision and accuracy with excellent stability. NH3 and CH4 emissions were correlated at dairy size-scales but not sub-dairy scales in surface and Mako data, showing fine-scale structure and large variations between the numerous dairies in the complex (herd ~200,000-250,000) embedded in an urban setting. Emissions hotspots were consistent between surface and airborne surveys. In June, surface and MAMAP data showed a weak overall plume, while surface and Mako data showed a stronger plume in late (hotter) July. Multiple surface plume transects using NH3 fingerprinting showed East and then NE advection out of the LA Basin consistent with airborne data. Long-term trends were investigated in satellite data. This study shows the value of synergistically combined NH3 and CH4 remote sensing data to the task of CH4 source attribution using airborne and space-based remote sensing (IASI for NH3) and top of atmosphere sensitivity calculations for Sentinel V and Carbon Sat (CH4).

  12. Development of Hyperspectral Remote Sensing Capability For the Early Detection and Monitoring of Harmful Algal Blooms (HABs) in the Great Lakes

    NASA Technical Reports Server (NTRS)

    Lekki, John; Anderson, Robert; Nguyen, Quang-Viet; Demers, James; Leshkevich, George; Flatico, Joseph; Kojima, Jun

    2013-01-01

    Hyperspectral imagers have significant capability for detecting and classifying waterborne constituents. One particularly appropriate application of such instruments in the Great Lakes is to detect and monitor the development of potentially Harmful Algal Blooms (HABs). Two generations of small hyperspectral imagers have been built and tested for aircraft based monitoring of harmful algal blooms. In this paper a discussion of the two instruments as well as field studies conducted using these instruments will be presented. During the second field study, in situ reflectance data was obtained from the Research Vessel Lake Guardian in conjunction with reflectance data obtained with the hyperspectral imager from overflights of the same locations. A comparison of these two data sets shows that the airborne hyperspectral imager closely matches measurements obtained from instruments on the lake surface and thus positively supports its utilization for detecting and monitoring HABs.

  13. Use of hyperspectral remote sensing for detection and monitoring of chemical and biological agents: a survey

    NASA Astrophysics Data System (ADS)

    Gomez, Richard B.; Dasgupta, Swarvanu

    2004-12-01

    This paper surveys the potential use of hyperspectral imaging technology for standoff detection of chemical and biological agents in terrorism defense applications. In particular it focuses on the uses of hyperspectral imaging technology to detect and monitor chemical and biological attacks. In so doing it examines current technologies, their advantages and disadvantages, and investigates the possible role of hyperspectral imaging for homeland security applications. The study also addresses and provides applicable solutions for several of the potential challenges that currently create barriers to the full use of hyperspectral technology in the standoff detection of likely available chemical and biological agents.

  14. Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing

    PubMed Central

    Ma, Dan; Liu, Jun; Huang, Junyi; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

    2016-01-01

    Hyperspectral images possess properties such as rich spectral information, narrow bandwidth, and large numbers of bands. Finding effective methods to retrieve land features from an image by using similarity assessment indices with specific spectral characteristics is an important research question. This paper reports a novel hyperspectral image similarity assessment index based on spectral curve patterns and a reflection-absorption index. First, some spectral reflection-absorption features are extracted to restrict the subsequent curve simplification. Then, the improved Douglas-Peucker algorithm is employed to simplify all spectral curves without setting the thresholds. Finally, the simplified curves with the feature points are matched, and the similarities among the spectral curves are calculated using the matched points. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral image datasets are then selected to test the effect of the proposed index. The practical experiments indicate that the proposed index can achieve higher precision and fewer points than the traditional spectral information divergence and spectral angle match. PMID:26821030

  15. Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing.

    PubMed

    Ma, Dan; Liu, Jun; Huang, Junyi; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

    2016-01-01

    Hyperspectral images possess properties such as rich spectral information, narrow bandwidth, and large numbers of bands. Finding effective methods to retrieve land features from an image by using similarity assessment indices with specific spectral characteristics is an important research question. This paper reports a novel hyperspectral image similarity assessment index based on spectral curve patterns and a reflection-absorption index. First, some spectral reflection-absorption features are extracted to restrict the subsequent curve simplification. Then, the improved Douglas-Peucker algorithm is employed to simplify all spectral curves without setting the thresholds. Finally, the simplified curves with the feature points are matched, and the similarities among the spectral curves are calculated using the matched points. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral image datasets are then selected to test the effect of the proposed index. The practical experiments indicate that the proposed index can achieve higher precision and fewer points than the traditional spectral information divergence and spectral angle match. PMID:26821030

  16. Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing.

    PubMed

    Ma, Dan; Liu, Jun; Huang, Junyi; Li, Huali; Liu, Ping; Chen, Huijuan; Qian, Jing

    2016-01-01

    Hyperspectral images possess properties such as rich spectral information, narrow bandwidth, and large numbers of bands. Finding effective methods to retrieve land features from an image by using similarity assessment indices with specific spectral characteristics is an important research question. This paper reports a novel hyperspectral image similarity assessment index based on spectral curve patterns and a reflection-absorption index. First, some spectral reflection-absorption features are extracted to restrict the subsequent curve simplification. Then, the improved Douglas-Peucker algorithm is employed to simplify all spectral curves without setting the thresholds. Finally, the simplified curves with the feature points are matched, and the similarities among the spectral curves are calculated using the matched points. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral image datasets are then selected to test the effect of the proposed index. The practical experiments indicate that the proposed index can achieve higher precision and fewer points than the traditional spectral information divergence and spectral angle match.

  17. Stray light effects in above-water remote-sensing reflectance from hyperspectral radiometers.

    PubMed

    Talone, Marco; Zibordi, Giuseppe; Ansko, Ilmar; Banks, Andrew Clive; Kuusk, Joel

    2016-05-20

    Stray light perturbations are unwanted distortions of the measured spectrum due to the nonideal performance of optical radiometers. Because of this, stray light characterization and correction is essential when accurate radiometric measurements are a necessity. In agreement with such a need, this study focused on stray light correction of hyperspectral radiometers widely applied for above-water measurements to determine the remote-sensing reflectance (RRS). Stray light of sample radiometers was experimentally characterized and a correction algorithm was developed and applied to field measurements performed in the Mediterranean Sea. Results indicate that mean stray light corrections are appreciable, with values generally varying from -1% to +1% in the 400-700 nm spectral region for downward irradiance and sky radiance, and from -1% to +4% for total radiance from the sea. Mean corrections for data products such as RRS exhibit values that depend on water type varying between -0.5% and +1% in the blue-green spectral region, with peaks up to 9% in the red in eutrophic waters. The possibility of using one common stray light correction matrix for the analyzed class of radiometers was also investigated. Results centered on RRS support such a feasibility at the expense of an increment of the uncertainty typically well below 0.5% in the blue-green and up to 1% in the red, assuming sensors are based on spectrographs from the same production batch.

  18. [Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data].

    PubMed

    Yao, Yuan; Ding, Jian-Li; Kelimul, Ardak; Zhang, Fang; Lei, Lei

    2013-07-01

    In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different saliniza tion monitoring models. The authors' conclusion is that with R2 = 0.799 3 (p < 0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2 = 0.587 4, p < 0 01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization.

  19. [Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data].

    PubMed

    Yao, Yuan; Ding, Jian-Li; Kelimul, Ardak; Zhang, Fang; Lei, Lei

    2013-07-01

    In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different saliniza tion monitoring models. The authors' conclusion is that with R2 = 0.799 3 (p < 0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2 = 0.587 4, p < 0 01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization. PMID:24059201

  20. Stray light effects in above-water remote-sensing reflectance from hyperspectral radiometers.

    PubMed

    Talone, Marco; Zibordi, Giuseppe; Ansko, Ilmar; Banks, Andrew Clive; Kuusk, Joel

    2016-05-20

    Stray light perturbations are unwanted distortions of the measured spectrum due to the nonideal performance of optical radiometers. Because of this, stray light characterization and correction is essential when accurate radiometric measurements are a necessity. In agreement with such a need, this study focused on stray light correction of hyperspectral radiometers widely applied for above-water measurements to determine the remote-sensing reflectance (RRS). Stray light of sample radiometers was experimentally characterized and a correction algorithm was developed and applied to field measurements performed in the Mediterranean Sea. Results indicate that mean stray light corrections are appreciable, with values generally varying from -1% to +1% in the 400-700 nm spectral region for downward irradiance and sky radiance, and from -1% to +4% for total radiance from the sea. Mean corrections for data products such as RRS exhibit values that depend on water type varying between -0.5% and +1% in the blue-green spectral region, with peaks up to 9% in the red in eutrophic waters. The possibility of using one common stray light correction matrix for the analyzed class of radiometers was also investigated. Results centered on RRS support such a feasibility at the expense of an increment of the uncertainty typically well below 0.5% in the blue-green and up to 1% in the red, assuming sensors are based on spectrographs from the same production batch. PMID:27411122

  1. Progress in the development of airborne remote sensing instrumentation for the National Ecological Observatory Network

    NASA Astrophysics Data System (ADS)

    Kampe, Thomas U.; McCorkel, Joel; Hamlin, Louise; Green, Robert O.; Krause, Keith S.; Johnson, Brian R.

    2011-09-01

    The National Ecological Observatory Network (NEON) is a planned facility of the National Science Foundation with the mission to enable understanding and forecasting of the impacts of climate change, land use change and invasive species on continental-scale ecology. Airborne remote sensing plays a critical role by providing measurements at the scale of individual shrubs and larger plants over hundreds of square kilometers. The NEON Airborne Observation Platform is designed to bridge scales from organism and stand scales, as captured by plot and tower observations, to the scale of satellite based remote sensing. Fused airborne spectroscopy and waveform LiDAR is used to quantify vegetation composition and structure. Panchromatic photography at better than 30 cm resolution will retrieve fine-scale information on land use, roads, impervious surfaces, and built structures. NEON will build three airborne systems to allow for regular coverage of NEON sites and the capacity to respond to investigator requests for specific projects. The system design achieves a balance between performance and development cost and risk, taking full advantage of existing commercial airborne LiDAR and camera components. To reduce risk during NEON construction, an imaging spectrometer design verification unit is being developed at the Jet Propulsion Laboratory to demonstrate that operational and performance requirements can be met. As part of this effort, NEON is also focusing on science algorithm development, computing hardware prototyping and early airborne test flights with similar technologies. This paper presents an overview of the development status of the NEON airborne instrumentation in the context of the NEON mission.

  2. Estimating Crop Residue Distribution Using Airborne and Satellite Remote Sensing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop residue management and reduced tillage are commonly accepted best management practices that improve soil quality through the sequestration of soil organic carbon. A major goal of this study was to evaluate remote sensing data for rapid quantification of conservation tillage at the field and wa...

  3. Airborne remote sensing to detect greenbug stress to wheat

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Vegetation indices calculated from the quantity of reflected electromagnetic radiation have been used to quantify levels of stress to plants. Greenbugs cause stress to wheat plants and therefore multi-spectral remote sensing may be useful for detecting greenbug infested wheat fields. The objective...

  4. Aerosol Remote Sensing Applications for Airborne Multiangle, Multispectral Shortwave Radiometers

    NASA Astrophysics Data System (ADS)

    von Bismarck, Jonas; Ruhtz, Thomas; Starace, Marco; Hollstein, André; Preusker, René; Fischer, Jürgen

    2010-05-01

    Aerosol particles have an important impact on the surface net radiation budget by direct scattering and absorption (direct aerosol effect) of solar radiation, and also by influencing cloud formation processes (semi-direct and indirect aerosol effects). To study the former, a number of multispectral sky- and sunphotometers have been developed at the Institute for Space Sciences of the Free University of Berlin in the past two decades. The latest operational developments were the multispectral aureole- and sunphotometer FUBISS-ASA2, the zenith radiometer FUBISS-ZENITH, and the nadir polarimeter AMSSP-EM, all designed for a flexible use on moving platforms like aircraft or ships. Currently the multiangle, multispectral radiometer URMS/AMSSP (Universal Radiation Measurement System/ Airborne Multispectral Sunphotometer and Polarimeter) is under construction for a Wing-Pod of the high altitude research aircraft HALO operated by DLR. The system is expected to have its first mission on HALO in 2011. The algorithms for the retrieval of aerosol and trace gas properties from the recorded multidirectional, multispectral radiation measurements allow more than deriving standard products, as for instance the aerosol optical depth and the Angstrom exponent. The radiation measured in the solar aureole contains information about the aerosol phasefunction and therefore allows conclusions about the particle type. Furthermore, airborne instrument operation allows vertically resolved measurements. An inversion algorithm, based on radiative transfer simulations and additionally including measured vertical zenith-radiance profiles, allows conclusions about the aerosol single scattering albedo and the relative soot fraction in aerosol layers. Ozone column retrieval is performed evaluating measurements from pixels in the Chappuis absorption band. A retrieval algorithm to derive the water-vapor column from the sunphotometer measurements is currently under development. Of the various airborne

  5. Airborne Remote Sensing (ARS) for Agricultural Research and Commercialization Applications

    NASA Technical Reports Server (NTRS)

    Narayanan, Ram; Bowen, Brent D.; Nickerson, Jocelyn S.

    2002-01-01

    Tremendous advances in remote sensing technology and computing power over the last few decades are now providing scientists with the opportunity to investigate, measure, and model environmental patterns and processes with increasing confidence. Such advances are being pursued by the Nebraska Remote Sensing Facility, which consists of approximately 30 faculty members and is very competitive with other institutions in the depth of the work that is accomplished. The development of this facility targeted at applications, commercialization, and education programs in the area of precision agriculture provides a unique opportunity. This critical area is within the scope of NASA goals and objectives of NASA s Applications, Technology Transfer, Commercialization, and Education Division and the Earth Science Enterprise. This innovative integration of Aerospace (Aeronautics) Technology Enterprise applications with other NASA enterprises serves as a model of cross-enterprise transfer of science with specific commercial applications.

  6. Hyperspectral laboratory and airborne measurements as tools for local mapping of swelling soils in Orléans area (France)

    NASA Astrophysics Data System (ADS)

    Grandjean, Gilles; Dufrechou, Gregory; Hohmann, Audrey

    2013-04-01

    Swelling soils contain clay minerals that change volume with water content and cause extensive and expensive damage on infrastructures. Based on spatial distribution of infrastructure damages and existing geological maps, the Bureau de Recherches Géologiques et Minières (BRGM, the French Geological Survey) published in 2010 a 1:50 000 swelling hazard map of France. This map indexes the territory to low, intermediate, or high swell susceptibility, but does not display smallest and isolated clays lithologies. At local scale, identification of clay minerals and characterization of swell potential of soils using conventional soil analysis (DRX, chemical, and geotechnical analysis) are slow, expensive, and does not permit integrated measurements. Shortwave infrared (SWIR: 1100-2500 nm) spectral domains are characterized by significant spectral absorption bands that provide an underused tool for estimate the swell potential of soils. Reflectance spectroscopy, using an ASD Fieldspec Pro spectrometer, permits a rapid and less expensive measurement of soil reflectance spectra in the field and laboratory. In order to produce high precision map of expansive soils, the BRGM aims to optimize laboratory reflectance spectroscopy for mapping swelling soils. Geotechnical use of laboratory reflectance spectroscopy for local characterization of swell potential of soils could be assessable from an economical point of view. A new high resolution airborne hyperspectral survey (covering ca. 280 km², 380 channels ranging from 400 to 2500 nm) located at the W of Orléans (Loiret, France) will also be combined with field and laboratory measurements to detect and map swelling soils.

  7. Airborne remote sensing for geology and the environment; present and future

    USGS Publications Warehouse

    Watson, Ken; Knepper, Daniel H.

    1994-01-01

    In 1988, a group of leading experts from government, academia, and industry attended a workshop on airborne remote sensing sponsored by the U.S. Geological Survey (USGS) and hosted by the Branch of Geophysics. The purpose of the workshop was to examine the scientific rationale for airborne remote sensing in support of government earth science in the next decade. This report has arranged the six resulting working-group reports under two main headings: (1) Geologic Remote Sensing, for the reports on geologic mapping, mineral resources, and fossil fuels and geothermal resources; and (2) Environmental Remote Sensing, for the reports on environmental geology, geologic hazards, and water resources. The intent of the workshop was to provide an evaluation of demonstrated capabilities, their direct extensions, and possible future applications, and this was the organizational format used for the geologic remote sensing reports. The working groups in environmental remote sensing chose to present their reports in a somewhat modified version of this format. A final section examines future advances and limitations in the field. There is a large, complex, and often bewildering array of remote sensing data available. Early remote sensing studies were based on data collected from airborne platforms. Much of that technology was later extended to satellites. The original 80-m-resolution Landsat Multispectral Scanner System (MSS) has now been largely superseded by the 30-m-resolution Thematic Mapper (TM) system that has additional spectral channels. The French satellite SPOT provides higher spatial resolution for channels equivalent to MSS. Low-resolution (1 km) data are available from the National Oceanographic and Atmospheric Administration's AVHRR system, which acquires reflectance and day and night thermal data daily. Several experimental satellites have acquired limited data, and there are extensive plans for future satellites including those of Japan (JERS), Europe (ESA), Canada

  8. Hyperspectral remote sensing techniques applied to the noninvasive investigation of mural paintings: a feasibility study carried out on a wall painting by Beato Angelico in Florence

    NASA Astrophysics Data System (ADS)

    Cucci, Costanza; Picollo, Marcello; Chiarantini, Leandro; Sereni, Barbara

    2015-06-01

    Nowadays hyperspectral imaging is a well-established methodology for the non-invasive diagnostics of polychrome surfaces, and is increasingly utilized in museums and conservation laboratories for documentation purposes and in support of restoration procedures. However, so far the applications of hyperspectral imaging have been mainly limited to easel paintings or paper-based artifacts. Indeed, specifically designed hyperspectral imagers, are usually used for applications in museum context. These devices work at short-distances from the targets and cover limited size surfaces. Instead, almost still unexplored remain the applications of hyperspectral imaging to the investigations of frescoes and large size mural paintings. For this type of artworks a remote sensing approach, based on sensors capable of acquiring hyperspectral data from distances of the order of tens of meters, is needed. This paper illustrates an application of hyperspectral remote sensing to an important wall-painting by Beato Angelico, located in the San Marco Museum in Florence. Measurements were carried out using a re-adapted version of the Galileo Avionica Multisensor Hyperspectral System (SIM-GA), an avionic hyperspectral imager originally designed for applications from mobile platforms. This system operates in the 400-2500 nm range with over 700 channels, thus guaranteeing acquisition of high resolution hyperspectral data exploitable for materials identification and mapping. In the present application, the SIM-GA device was mounted on a static scanning platform for ground-based applications. The preliminary results obtained on the Angelico's wall-painting are discussed, with highlights on the main technical issues addressed to optimize the SIM-GA system for new applications on cultural assets.

  9. Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

    NASA Astrophysics Data System (ADS)

    Thompson, D. R.; Leifer, I.; Bovensmann, H.; Eastwood, M.; Fladeland, M.; Frankenberg, C.; Gerilowski, K.; Green, R. O.; Kratwurst, S.; Krings, T.; Luna, B.; Thorpe, A. K.

    2015-10-01

    Localized anthropogenic sources of atmospheric CH4 are highly uncertain and temporally variable. Airborne remote measurement is an effective method to detect and quantify these emissions. In a campaign context, the science yield can be dramatically increased by real-time retrievals that allow operators to coordinate multiple measurements of the most active areas. This can improve science outcomes for both single- and multiple-platform missions. We describe a case study of the NASA/ESA CO2 and MEthane eXperiment (COMEX) campaign in California during June and August/September 2014. COMEX was a multi-platform campaign to measure CH4 plumes released from anthropogenic sources including oil and gas infrastructure. We discuss principles for real-time spectral signature detection and measurement, and report performance on the NASA Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). AVIRIS-NG successfully detected CH4 plumes in real-time at Gb s-1 data rates, characterizing fugitive releases in concert with other in situ and remote instruments. The teams used these real-time CH4 detections to coordinate measurements across multiple platforms, including airborne in situ, airborne non-imaging remote sensing, and ground-based in situ instruments. To our knowledge this is the first reported use of real-time trace-gas signature detection in an airborne science campaign, and presages many future applications. Post-analysis demonstrates matched filter methods providing noise-equivalent (1σ) detection sensitivity for 1.0 % CH4 column enhancements equal to 141 ppm m.

  10. Changing scale: from site thorough landscape to taskscape within airborne remote sensing perspective

    NASA Astrophysics Data System (ADS)

    Kostyrko, Mikołaj; RÄ czkowski, Włodzimierz; Ruciński, Dominik

    2016-08-01

    In consequence of a long tradition, archaeologists focus on individual sites and features and not landscape itself. We propose to perceive the landscape as a taskscapes, a space where tasks are performed, by that its own identity is created. Airborne remote sensing methods establish a possibility of studies on a larger scale of and to perceive places as context for landscapes and vice versa. On the other hand we would like to draw attention to identification of paleoenvironment features in the context of past landscapes. Although it is not always possible to determine the relationship between these element and traces of past human activities, we must be aware that in the past they had and influence on human behavior. In this paper will address the question: how much do airborne remote sensing data through the ability to change the scale of our perspective upon archaeological sites and their local landscapes alter or enrich interpretation of the context of past human activities.

  11. Multipurpose hyperspectral imaging system

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A hyperspectral imaging system of high spectral and spatial resolution that incorporates several innovative features has been developed to incorporate a focal plane scanner (U.S. Patent 6,166,373). This feature enables the system to be used for both airborne/spaceborne and laboratory hyperspectral i...

  12. Airborne remote sensors applied to engineering geology and civil works design investigations

    NASA Technical Reports Server (NTRS)

    Gelnett, R. H.

    1975-01-01

    The usefulness of various airborne remote sensing systems in the detection and identification of regional and specific geologic structural features that may affect the design and location of engineering structures on major civil works projects is evaluated. The Butler Valley Dam and Blue Lake Project in northern California was selected as a demonstration site. Findings derived from the interpretation of various kinds of imagery used are given.

  13. A New Airborne Lidar for Remote Sensing of Canopy Fluorescence and Vertical Profile

    NASA Astrophysics Data System (ADS)

    Ounis, A.; Bach, J.; Mahjoub, A.; Daumard, F.; Moya, I.; Goulas, Y.

    2016-06-01

    We report the development of a new lidar system for airborne remote sensing of chlorophyll fluorescence (ChlF) and vertical profile of canopies. By combining laserinduced fluorescence (LIF), sun-induced fluorescence (SIF) and canopy height distribution, the new instrument will low the simultaneous assessment of gross primary production (GPP), photosynthesis efficiency and above ground carbon stocks. Technical issues of the lidar development are discussed and expected performances are presented.

  14. Flight Tests of the DELICAT Airborne LIDAR System for Remote Clear Air Turbulence Detection

    NASA Astrophysics Data System (ADS)

    Vrancken, Patrick; Wirth, Martin; Ehret, Gerhard; Witschas, Benjamin; Veerman, Henk; Tump, Robert; Barny, Hervé; Rondeau, Philippe; Dolfi-Bouteyre, Agnès; Lombard, Laurent

    2016-06-01

    An important aeronautics application of lidar is the airborne remote detection of Clear Air Turbulence which cannot be performed with onboard radar. We report on a DLR-developed lidar system for the remote detection of such turbulent areas in the flight path of an aircraft. The lidar, consisting of a high-power UV laser transmitter and a direct detection system, was installed on a Dutch research aircraft. Flight tests executed in 2013 demonstrated the performance of the lidar system to detect local subtle variations in the molecular backscatter coefficient indicating the turbulence some 10 to 15 km ahead.

  15. Hyperspectral imaging system for UAV

    NASA Astrophysics Data System (ADS)

    Zhang, Da; Zheng, Yuquan

    2015-10-01

    Hyperspectral imaging system for Unmanned Aerial Vehicle (UAV) is proposed under airborne remote sensing application background. By the application of Offner convex spherical grating spectral imaging system and using large area array detector push-broom imaging, hyperspectral imaging system with the indicators of 0.4μm to 1.0μm spectral range, 120 spectral bands, 5nm spectral resolution and 1m ground sampling interval (flight altitude 5km) is developed and completed. The Offner convex grating spectral imaging system is selected to achieve non-spectral line bending and colorless distortion design results. The diffraction efficiency is 15%-30% in the range of 0.4μm to 1.0μm wavelength. The system performances are tested by taking spectral and radiometric calibration methods in the laboratory. Based on monochromatic collimated light method for spectral performance parameters calibration of hyperspectral optical remote sensor, the analysis results of spectral calibration data show that the calibration test repeatability is less than 0.2 nm within one hour. The spectral scaling results show that the average spectral resolution of hyperspectral optical remote sensor is 4.94 nm, and the spatial dimension of the high-spectral optical remote sensor spectral resolution is less than 5 nm, the average of the typical spectral bandwidth is about 6 nm, the system average signal-to-noise ratio (SNR) is up to 43dB under typical operating conditions. Finally the system functionalities and performance indicators are verified by the aviation flight tests, which it's equipped on UAV. The actual image quality is good, and the spectral position is stable.

  16. An improved radiance simulation for hyperspectral infrared remote sensing of Asian dust

    NASA Astrophysics Data System (ADS)

    Han, Hyo-Jin; Sohn, Byung-Ju; Huang, Hung-Lung; Weisz, Elisabeth; Saunders, Roger; Takamura, Tamio

    2012-05-01

    The fast Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV) (Version 9.3) model was used for simulating the effect of East Asian dust on top of atmosphere radiances. The size distribution of Asian dust was retrieved from nine years of sky radiometer measurements at Dunhunag located in the east of Taklimakan desert of China. The default surface emissivity in RTTOV was replaced by the geographically and monthly varying data from University of Wisconsin (UW)/Cooperative Institute for Meteorological Satellite Studies (CIMSS) infrared surface spectral emissivities. For a given size distribution and surface emissivity, the effects of three refractive indices of Optical Properties of Aerosols and Clouds (OPAC) mineral aerosol, dust-like aerosol by Volz, and High Resolution Transmission (HITRAN) quartz were examined. Results indicate that the specification of surface emissivity using geographically and monthly varying UW/CIMSS data significantly improved the performance of the simulation of AIRS brightness temperature (TB) difference (BTD) between window channels, in comparison to the results from the use of default emissivity value of 0.98 in the RTTOV model, i.e., increase of the correlation coefficient from 0.1 to 0.83 for BTD between 8.9 μm and 11 μm, and from 0.31 to 0.61 for BTD between 3.8 μm and 11 μm. On the other hand, the use of Asian dust size distributions contributed to a general reduction of radiance biases over dust-sensitive window bands. A further improvement of the TB simulations has been made by considering the Volz refractive index, suggesting that hyperspectral infrared remote sensing of Asian dust can be improved using the proper optical properties of the dust and surface emissivity.

  17. Quantitative retrieving of soil organic matter using field spectrometer and hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Zhuo, Luo; Liu, Yaolin; Chen, Jie; Hu, Changji; Wu, Jian

    2008-12-01

    As the important component of soil, soil organic matter not only provides every nutrient element for crop, but also has determinant effect for forming of soil structure and melioration the soil physical character. Mapping and dating soil organic matter is of great importance in soil use and evaluation. In this study we examine the feasibility of soil organic matter content by using Hyperspectrally reflective remote sensing methodology. This technique was tested in Xiaochang County located in Hubei province. The soil reflectance properties of samples were measured in the laboratory by ASD field spectrometer. The correlation analysis related with organic matter content was processed from three factors: the spectral reflectance parameter ((lgρ)', ρ/ ρ450-750 and (1/lgρ623)'/ (1/lgρ564)'). The results show that the correlation coefficients of r values were: organic matter identification index (ρ/ ρ450-750) > logarithmic first derivative of reflectivity ((lgρ)') > organic matter mix identification index ((1/lgρ623)'/(1/lgρ564)'). Knowing these correlations we were able to use the best prominence correlation of organic matter identification index of 1850nm wavelength as the variable regression to build up statistical regression analysis. We used five model types (Linear Function, Logarithmic Function, Quadratic Function, Power Function and Exponential Function) to forecast the soil organic matter content Hyperion model. The accuracy assessment (R2= 0.8484) by relating forecasted organic matter values with Quadratic Function regression showed that the model is reliable and significantly correlative with known stabilization processes throughout the study area. The quantitative methodology developed in this study for refutations soil organic matter content can be adapted to other regions throughout the world.

  18. Using Hyperspectral Aircraft Remote Sensing to Support Ecosystems Services Research in New England Lakes and Ponds

    NASA Astrophysics Data System (ADS)

    Keith, D. J.; Milstead, B.; Walker, H.; Worthy, D.; Szykman, J.; Wusk, M.; Kagey, L.; Howell, C.; Snook, H.; Drueke, C.

    2010-12-01

    Northeastern lakes and ponds provide important ecosystem services to New England residents and visitors. These include the provisioning of abundant, clean water for consumption, agriculture, and industry as well as cultural services (recreation, aesthetics, and wilderness experiences) which enhance local economies and quality of life. Less understood, but equally important, are the roles that these lakes play in protecting all life through supportive services such as nutrient cycling. Nitrogen and phosphorus have a direct impact on the condition of fresh water lakes. Excesses of these nutrients can lead to eutrophication, toxic cyanobacteria blooms, decreased biodiversity, and loss of ecosystem function leading to a reduction in the availability and delivery of ecosystem services. In this study, we examined how variations in lake nutrient concentrations and phytoplankton pigment concentrations correlated with changes in the potential to provide cultural ecosystem services. Using a NASA Cessna 206 aircraft, hyperspectral data were collected during late summer 2009 from 55 lakes in New Hampshire, Massachusetts, Connecticut, and Rhode Island over a 2 day period. From the spectral data, algorithms were created which estimated concentrations of chlorophyll a, phycocyanin, and colored dissolved organic matter. The remotely sensed estimates were supplemented by in situ chlorophyll a, total nitrogen, total phosphorus and lake color data from 43 lakes sampled by field crews from the New England states. The purpose of this research is to understand how variations in lake nutrient concentrations and phytoplankton pigment concentrations correlate with changes in availability of cultural ecosystem services in the surveyed lakes. This dataset will be combined with information from the EPA National Lake Survey (2007), the EPA New England Lakes and Ponds Survey (2008) and the USGS SPARROW model to explore the association between lake condition and the provisioning of ecosystem

  19. AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPELINE LEAK DETECTION

    SciTech Connect

    Jerry Myers

    2003-11-12

    Ophir Corporation was awarded a contract by the U. S. Department of Energy, National Energy Technology Laboratory under the Project Title ''Airborne, Optical Remote Sensing of Methane and Ethane for Natural Gas Pipeline Leak Detection'' on October 14, 2002. This second six-month technical report summarizes the progress made towards defining, designing, and developing the hardware and software segments of the airborne, optical remote methane and ethane sensor. The most challenging task to date has been to identify a vendor capable of designing and developing a light source with the appropriate output wavelength and power. This report will document the work that has been done to identify design requirements, and potential vendors for the light source. Significant progress has also been made in characterizing the amount of light return available from a remote target at various distances from the light source. A great deal of time has been spent conducting laboratory and long-optical path target reflectance measurements. This is important since it helps to establish the overall optical output requirements for the sensor. It also reduces the relative uncertainty and risk associated with developing a custom light source. The data gathered from the optical path testing has been translated to the airborne transceiver design in such areas as: fiber coupling, optical detector selection, gas filters, and software analysis. Ophir will next, summarize the design progress of the transceiver hardware and software development. Finally, Ophir will discuss remaining project issues that may impact the success of the project.

  20. Hyperspectral Imaging of Forest Resources: The Malaysian Experience

    NASA Astrophysics Data System (ADS)

    Mohd Hasmadi, I.; Kamaruzaman, J.

    2008-08-01

    Remote sensing using satellite and aircraft images are well established technology. Remote sensing application of hyperspectral imaging, however, is relatively new to Malaysian forestry. Through a wide range of wavelengths hyperspectral data are precisely capable to capture narrow bands of spectra. Airborne sensors typically offer greatly enhanced spatial and spectral resolution over their satellite counterparts, and able to control experimental design closely during image acquisition. The first study using hyperspectral imaging for forest inventory in Malaysia were conducted by Professor Hj. Kamaruzaman from the Faculty of Forestry, Universiti Putra Malaysia in 2002 using the AISA sensor manufactured by Specim Ltd, Finland. The main objective has been to develop methods that are directly suited for practical tropical forestry application at the high level of accuracy. Forest inventory and tree classification including development of single spectral signatures have been the most important interest at the current practices. Experiences from the studies showed that retrieval of timber volume and tree discrimination using this system is well and some or rather is better than other remote sensing methods. This article reviews the research and application of airborne hyperspectral remote sensing for forest survey and assessment in Malaysia.

  1. Assessment of Superflux relative to remote sensing. [airborne remote sensing of the Chesapeake Bay plume and shelf regions

    NASA Technical Reports Server (NTRS)

    Campbell, J. W.

    1981-01-01

    The state-of-the-art advancements in remote sensor technology due to the Superflux program are examined. Three major individual sensor technologies benefitted from the program: laser fluorosensors, optical-range scanners, and passive microwave sensors. Under Superflux, convincing evidence was obtained that the airborne oceanographic lidar fluorosensor can map chlorophyll, i.e., is linear, over a wide range from less than 0.5 to 5.0 mg/cu m. The lidar oceanographic probe dual-excitation concept for addressing phytoplankton color group composition was also demonstrated convincingly. Algorithm development, real time capabilities, and multisensor integration are also addressed.

  2. Semi-Blind Source Separation for Estimation of Clay Content Over Semi-Vegetated Areas, from Vnir/swir Hyperspectral Airborne Data

    NASA Astrophysics Data System (ADS)

    Ouerghemmi, W.; Gomez, C.; Nacer, S.; Lagacherie, P.

    2015-08-01

    The applicability of Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery for soil property mapping decreases when surfaces are partially covered by vegetation. The objective of this research was to develop and evaluate a methodology based on the "double-extraction" technique, for clay content estimation over semi-vegetated surfaces using VNIR/SWIR hyperspectral airborne data. The "double-extraction" technique initially proposed by Ouerghemmi et al. (2011) consists of 1) an extraction of a soil reflectance spectrum ssoil from semi-vegetated spectra using a Blind Source Separation technique, and 2) an extraction of clay content from the soil reflectance spectrum ssoil, using a multivariate regression method. In this paper, the Source Separation approach is Semi-Blind thanks to the integration of field knowledge in Source Separation model. And the multivariate regression method is a partial least squares regression (PLSR) model. This study employed VNIR/SWIR HyMap airborne data acquired in a French Mediterranean region over an area of 24 km2. Our results showed that our methodology based on the "double-extraction" technique is accurate for clay content estimation when applied to pixels under a specific Cellulose Absorption Index threshold. Finally the clay content can be estimated over around 70% of the semi-vegetated pixels of our study area, which may offer an extension of soil properties mapping, at the moment restricted to bare soils.

  3. Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region

    PubMed Central

    Thorpe, Andrew K.; Thompson, David R.; Hulley, Glynn; Kort, Eric Adam; Vance, Nick; Borchardt, Jakob; Krings, Thomas; Gerilowski, Konstantin; Sweeney, Colm; Conley, Stephen; Bue, Brian D.; Aubrey, Andrew D.; Hook, Simon; Green, Robert O.

    2016-01-01

    Methane (CH4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ∼ 2 kg/h to 5 kg/h through ∼ 5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign. PMID:27528660

  4. Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region.

    PubMed

    Frankenberg, Christian; Thorpe, Andrew K; Thompson, David R; Hulley, Glynn; Kort, Eric Adam; Vance, Nick; Borchardt, Jakob; Krings, Thomas; Gerilowski, Konstantin; Sweeney, Colm; Conley, Stephen; Bue, Brian D; Aubrey, Andrew D; Hook, Simon; Green, Robert O

    2016-08-30

    Methane (CH4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit [Formula: see text] 2 kg/h to 5 kg/h through [Formula: see text] 5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571-6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign. PMID:27528660

  5. Detection of stress factors in crop and weed species using hyperspectral remote sensing reflectance

    NASA Astrophysics Data System (ADS)

    Henry, William Brien

    The primary objective of this work was to determine if stress factors such as moisture stress or herbicide injury stress limit the ability to distinguish between weeds and crops using remotely sensed data. Additional objectives included using hyperspectral reflectance data to measure moisture content within a species, and to measure crop injury in response to drift rates of non-selective herbicides. Moisture stress did not reduce the ability to discriminate between species. Regardless of analysis technique, the trend was that as moisture stress increased, so too did the ability to distinguish between species. Signature amplitudes (SA) of the top 5 bands, discrete wavelet transforms (DWT), and multiple indices were promising analysis techniques. Discriminant models created from one year's data set and validated on additional data sets provided, on average, approximately 80% accurate classification among weeds and crop. This suggests that these models are relatively robust and could potentially be used across environmental conditions in field scenarios. Distinguishing between leaves grown at high-moisture stress and no-stress was met with limited success, primarily because there was substantial variation among samples within the treatments. Leaf water potential (LWP) was measured, and these were classified into three categories using indices. Classification accuracies were as high as 68%. The 10 bands most highly correlated to LWP were selected; however, there were no obvious trends or patterns in these top 10 bands with respect to time, species or moisture level, suggesting that LWP is an elusive parameter to quantify spectrally. In order to address herbicide injury stress and its impact on species discrimination, discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an

  6. Exploring the Potential of Hyperspectral Remote Sensing for Assessing Nitrate Distributions in Surface Water: In-Situ Experiments

    NASA Astrophysics Data System (ADS)

    Zhu, J.; Pai, H.; Butler, C. A.; Guo, Q.; Harmon, T.

    2011-12-01

    Nitrate is a key aquatic nitrogen species and an important indicator of anthropogenic impacts on water quality. Regulatory agencies through the world mandate nitrate monitoring in freshwater and coastal marine settings over large spatial scales. While remote sensing techniques are clearly advantageous in this context, and have proven useful in monitoring other water quality properties (e.g., chlorophyll, organic matter, suspended solids), researchers have thus far been unable to extend this approach to smaller molecules, such as nitrate . Here, we undertook a controlled tank experiment indoors (using artificial light) and outdoors (natural light) to characterize and compare the relationship between nitrate concentrations and reflectance of shallow waters. Nitrate standards ranging from 0 to 100 ppm (as NO3) were tested in a matrix of de-ionized water. Reflectances were recorded using a spectroradiometer (ASD FieldSpec), and tank bottom and depth effect were characterized and parameterized using Remote Cosine Receptor (RCR). Resulting reflectance spectra, specifically in and around a small peak at roughly 404 nm, correlated well with the lower range of nitrate concentrations (1-30 ppm, R2 = 0.84), but poorly at greater concentrations. The reason for this inconsistent behavior is not yet clear and the subject of further investigations. The hyperspectral reflectance study is currently being pilot-tested on the Lower Merced River in Central California, an agricultural watershed where nitrate levels vary seasonally from roughly 0.5 to 10 ppm. Synoptic surveys using in-situ UV-visible spectroscopy are providing longitudinal nitrate maps for comparison with hyperspectral reflectance spectra collected along the river. The hyperspectral system will be tested on several reaches of the river during low flow conditions in Fall 2011. Currently, the system is undergoing further testing with nitrate, dissolved organic matter, chlorophyll-a and turbidity standards in preparation

  7. Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants

    NASA Astrophysics Data System (ADS)

    Atherton, Daniel

    Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p < 0.0001) between disease rating group means. In the majority of the experiments, comparisons of diseased plants with healthy plants using Fisher's LSD revealed more heavily diseased plants were significantly different from healthy plants. PLS analysis demonstrated the feasibility of detecting early blight infected plants, finding four optimal factors for raw spectra with the predictor variation explained ranging from 93.4% to 94.6% and the response variation explained ranging from 42.7% to 64.7%. Cluster analysis successfully distinguished healthy plants from all diseased plants except for the most mildly diseased plants, showing clustering analysis was an effective method for detection of early blight. Analysis of the reflectance spectra using the simple ratio (SR) and the normalized difference vegetative index (NDVI) was effective at differentiating all diseased plants from healthy plants, except for the

  8. Application of Multispectral and Hyperspectral Remote Sensing For Detection of Freshwater Harmful Algal Blooms

    NASA Astrophysics Data System (ADS)

    Kudela, R. M.; Accorsi, E.; Austerberry, D.; Palacios, S. L.

    2013-12-01

    Freshwater Cyanobacterial Harmful algal blooms (CHABs) represent a pressing and apparently increasing threat to both human and environmental health. In California, toxin producing blooms of several species, including Aphanizomenon, Microcystis, Lyngbya, and Anabaena are common; toxins from these blooms have been linked to impaired drinking water, domestic and wild animal deaths, and increasing evidence for toxin transfer to coastal marine environments, including the death of several California sea otters, a threatened marine species. California scientists and managers are under increasing pressure to identify and mitigate these potentially toxic blooms, but point-source measurements and grab samples have been less than effective. There is increasing awareness that these toxic events are both spatially widespread and ephememeral, leading to the need for better monitoring methods applicable to large spatial and temporal scales. Based on monitoring in several California water bodies, it appears that Aphanizomenon blooms frequently precede dangerous levels of toxins from Microcystis. We are exploring new detection methods for identifying CHABs and potentially distinguishing between blooms of the harmful cyanobacteria Aphanizomenon and Microcystis using remote sensing reflectance from a variety of airborne and satellite sensors. We suggest that Aphanizomenon blooms could potentially be used as an early warning of more highly toxic subsequent blooms, and that these methods, combined with better toxin monitoring, can lead to improved understanding and prediction of CHABs by pinpointing problematic watersheds.

  9. Separating vegetation and soil temperature using airborne multiangular remote sensing image data

    NASA Astrophysics Data System (ADS)

    Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

    2012-07-01

    Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

  10. Landmine detection using passive hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    McFee, John E.; Anger, Cliff; Achal, Steve; Ivanco, Tyler

    2007-04-01

    Airborne hyperspectral imaging has been studied since the late 1980s as a tool to detect minefields for military countermine operations and for level I clearance for humanitarian demining. Hyperspectral imaging employed on unmanned ground vehicles may also be used to augment or replace broadband imagers to detect individual mines. This paper will discuss the ability of different optical wavebands - the visible/near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) - to detect surface-laid and buried mines. The phenomenology that determines performance in the different bands is discussed. Hyperspectral imagers have usually been designed and built for general purpose remote sensing applications and often do not meet the requirements of mine detection. The DRDC mine detection research program has sponsored the development by Itres Research of VNIR, SWIR and TIR instruments specifically intended for mine detection. The requirements for such imagers are described, as well as the instruments. Some results of mine detection experiments are presented. To date, reliable day time detection of surface-laid mines in non-real-time, independent of solar angle, time of day and season has been demonstrated in the VNIR and SWIR. Real-time analysis, necessary for military applications, has been demonstrated from low speed ground vehicles and recently from airborne platforms. Reliable, repeatable detection of buried mines has yet to be demonstrated, although a recently completed TIR hyperspectral imager will soon be tested for such a capability.

  11. Introduction to an airborne remote sensing system equipped onboard the Chinese marine surveillance plane

    NASA Astrophysics Data System (ADS)

    Gong, Fang; Wang, Difeng; Pan, Delu; Hao, Zengzhou

    2008-10-01

    The airborne remote sensing system onboard the Chinese Marine Surveillance Plane have three scanners including marine airborne multi-spectrum scanner(MAMS), airborne hyper spectral system(AISA+) and optical-electric platform(MOP) currently. MAMS is developed by Shanghai Institute of Technology and Physics CAS with 11 bands from ultraviolet to infrared and mainly used for inversion of oceanic main factors and pollution information, like chlorophyll, sea surface temperature, red tide, etc. The AISA+ made by Finnish Specim company is a push broom system, consist of a high spectrum scanner head, a miniature GPS/INS sensor and data collecting PC. It is a kind of aviation imaging spectrometer and has the ability of ground target imaging and measuring target spectrum characteristic. The MOP mainly supports for object watching, recording and track. It mainly includes 3 equipments: digital CCD with Sony-DXC390, CANON EOS film camera and digital camera Sony F717. This paper mainly introduces these three remote sensing instruments as well as the ground processing information system, involving the system's hardware and software design, related algorithm research, etc.

  12. AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPLINE LEAK DETECTION

    SciTech Connect

    Jerry Myers

    2004-05-12

    Ophir Corporation was awarded a contract by the U. S. Department of Energy, National Energy Technology Laboratory under the Project Title ''Airborne, Optical Remote Sensing of Methane and Ethane for Natural Gas Pipeline Leak Detection'' on October 14, 2002. The third six-month technical report contains a summary of the progress made towards finalizing the design and assembling the airborne, remote methane and ethane sensor. The vendor has been chosen and is on contract to develop the light source with the appropriate linewidth and spectral shape to best utilize the Ophir gas correlation software. Ophir has expanded upon the target reflectance testing begun in the previous performance period by replacing the experimental receiving optics with the proposed airborne large aperture telescope, which is theoretically capable of capturing many times more signal return. The data gathered from these tests has shown the importance of optimizing the fiber optic receiving fiber to the receiving optic and has helped Ophir to optimize the design of the gas cells and narrowband optical filters. Finally, Ophir will discuss remaining project issues that may impact the success of the project.

  13. Hyperspectral remote sensing of the responses of vegetation ecosystems to physical and biological changes of the environment

    NASA Astrophysics Data System (ADS)

    Krezhova, Dora; Krezhov, Kiril; Maneva, Svetla; Moskova, Irina; Petrov, Nikolay

    2016-07-01

    Hyperspectral remote sensing technique, based on reflectance measurements acquired in a high number of contiguous spectral bands in the visible and near infrared spectral ranges, was used to detect the influence of some environmental changes to vegetation ecosystems. Adverse physical and biological conditions give rise to morphological, physiological, and biochemical changes in the plants that affect the manner in which they interact with the light. All green vegetation species have unique spectral features, mainly because of the chlorophyll and carotenoid, and other pigments, and water content. Because spectral reflectance is a function of the illumination conditions, tissue optical properties and biochemical content of the plants it may be used to collect information on several important biophysical parameters such as color and the spectral signature of features, vegetation chlorophyll absorption characteristics, vegetation moisture content, etc. Remotely sensed data collected by means of a portable fiber-optics spectrometer in the spectral range 350-1100 nm were used to extract information on the influence of some environmental changes. Stress factors such as enhanced UV-radiation, salinity, viral infections, were applied to some young plants species (potato, tomato, plums). The test data were subjected to different digital image processing techniques. This included statistical (Student's t-criterion), first derivative and cluster analyses and some vegetation indices. Statistical analyses were carried out in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (680-720 nm) and near infrared (720-780 nm). The strong relationship, which was found between the results from the remote sensing technique and some biochemical and serological analyses (stress markers, DAS-ELISA), indicates the importance of hyperspectral reflectance data for conducting, easily and without damage, rapid assessments of plant biophysical

  14. The Geologic Remote Sensing Field Experiment (GRSFE): The first geology multisensor airborne campaign

    NASA Technical Reports Server (NTRS)

    Evans, Diane L.; Arvidson, Raymond E.

    1991-01-01

    The primary objective of the Geologic Remote Sensing Field Experiment (GRSFE) is to acquire relevant data for geological sites that can be used to test models for extraction of surface property information from remote sensing data for earth, Mars and Venus in support of the Earth Observing System (EOS), Mars Observer, and Magellan, respectively. Over forty scientists from eight universities and three NASA centers are participating in GRSFE which is co-sponsored by the NASA Planetary Geology and Geophysics Program and the NASA Geology Program. Highlights of the airborne campaign included the first simultaneous acquisition of Airborne Visible and Infrared Imaging Spectrometer (AVRIS) and Thermal Infrared Multispectral Scanner (TIMS) data on September 29, 1989, and acquisition of Advanced Solid-State Array Spectroradiometer (ASAS), Polarimetric Synthetic Aperture Radar (AIRSAR), and Airborne Terrain Laser Altimeter System (ATLAS) data all within three months of each other. The sites covered were Lunar Crater Volcanic Field and Fish Lake Valley in Nevada; and Cima Volcanic Field, Death Valley, and Ubehebe Crater in California. Coincident field measurements included meteorological and atmospheric measurements, visible/near-infrared and thermal spectra, and characterization of geology and vegetation cover. The GRSFE airborne and field data will be reduced to a suite of standard products and submitted, along with appropriate documentation, to the Planetary Data System (PDS) and the Pilot Land Data System (PLDS). These data will be used for a variety of investigations including paleoclimatic studies in the arid southwestern United States, and analysis of Magellan data. GRSFE data will also be used to support Mars Observer Laser Altimeter (MOLA) and Mars Rover Sample Return (MRSR) simulation studies.

  15. Colorimetric Detection of an Airborne Remote Photocatalytic Reaction Using a Stratified Ag Nanoparticle Sheet.

    PubMed

    Degawa, Ryo; Wang, Pangpang; Tanaka, Daisuke; Park, Susie; Sakai, Nobuyuki; Tatsuma, Tetsu; Okamoto, Koichi; Tamada, Kaoru

    2016-08-16

    Photocatalysts are practically used for decomposition of harmful and fouling organic compounds. Among the photocatalytic reactions, remote oxidation via airborne species is a relatively slow process, so that a sensitive technique for its detection has been awaiting. Here, we investigated an airborne remote photocatalytic reaction of a TiO2 photocatalyst modified with Pt nanoparticles as co-catalysts via the color change caused by a decomposition of a multilayered silver nanoparticle sheet. The silver nanoparticle sheet fabricated by the Langmuir-Schaefer method on a gold substrate exhibits a unique multicolor depending upon the number of layers. The color originates from multiple light trapping in the stratified sheets that has a metamaterial characteristic along with an intra- and interlayer coupling of localized surface plasmon resonance (LSPR). The stepwise decomposition of the sheets was confirmed by the colorimetric data, which exhibited not only a monotonic decrease but also a maximized absorption of light when the film thickness reached the optimal thickness for light trapping or when the oxidation of the Ag core started. Scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and surface plasmon resonance (SPR) spectroscopy data provided a complete view of the decomposition process of this inorganic-organic nanocomposite film, and simulation by the transfer-matrix method explained a simultaneous plasmonic response rationally. The influence of the humidity and gas flow rate on the airborne remote photocatalytic reaction kinetics was examined by this colorimetric detection method, and it suggests that H2O in air plays an essential role in the reaction. PMID:27445001

  16. The first aerosol indirect effect quantified through airborne remote sensing during VOCALS-REx

    NASA Astrophysics Data System (ADS)

    Painemal, D.; Zuidema, P.

    2013-01-01

    The first aerosol indirect effect (1AIE) is investigated using a combination of in situ and remotely-sensed aircraft (NCAR C-130) observations acquired during VOCALS-REx over the southeast Pacific stratocumulus cloud regime. Satellite analyses have previously identified a high albedo susceptibitility to changes in cloud microphysics and aerosols over this region. The 1AIE was broken down into the product of two independently-estimated terms: the cloud aerosol interaction metric ACIτ =dlnτ/dlnNa|LWP , and the relative albedo (A) susceptibility SR-τ =dA/3dlnτ|LWP, with τ and Na denoting retrieved cloud optical thickness and in situ aerosol concentration respectively and calculated for fixed intervals of liquid water path (LWP). ACIτ was estimated by combining in situ Na sampled below the cloud, with τ and LWP derived from, respectively, simultaneous upward-looking broadband irradiance and narrow field-of-view millimeter-wave radiometer measurements, collected at 1 Hz during four eight-hour daytime flights by the C-130 aircraft. ACIτ values were typically large, close to the physical upper limit (0.33), with a modest increase with LWP. The high ACIτ values slightly exceed values reported from many previous in situ airborne studies in pristine marine stratocumulus and reflect the imposition of a LWP constraint and simultaneity of aerosol and cloud measurements. SR-τ increased with LWP and τ, reached a maximum SR-τ (0.086) for LWP (τ) of 58 g m-2 (~14), and decreased slightly thereafter. The 1AIE thus increased with LWP and is comparable to a radiative forcing of -3.2- -3.8 W m-2 for a 10% increase in Na, exceeding previously-reported global-range values. The aircraft-derived values are consistent with satellite estimates derived from instantaneous, collocated Clouds and the Earth's Radiant Energy System (CERES) albedo and MOderate resolution Imaging Spectroradiometer (MODIS)-retrieved droplet number concentrations at 50 km resolution. The consistency of

  17. Ground-based Hyperspectral Remote Sensing for Mapping Rock Alterations and Lithologies: Case Studies from Semail Ophiolite, Oman and Rush Springs Sandstone, Oklahoma

    NASA Astrophysics Data System (ADS)

    Sun, L.; Khan, S.; Hauser, D. L.; Glennie, C. L.; Snyder, C.; Okyay, U.

    2014-12-01

    This study used ground-based hyperspectral remote sensing data to map rock alterations and lithologies at Semail Ophiolite, Oman, as well as hydrocarbon-induced rock alterations at Cement, Oklahoma. The Samail Ophiolite exposed the largest, least-deformed, and the most-studied ophiolite in the world. Hydrocarbon seepages at Cement, Oklahoma brought hydrocarbons to the Rush Springs sandstones at surface, and generated rock alterations including bleaching of red beds, and carbonate cementation. Surficial expressions of rock alterations and different lithofacies are distinct from adjacent rocks, and can be detected by remote sensing techniques. Hyperspectral remote sensing acquires light intensity for hundreds of bands in a continuous electromagnetic spectrum from visible light to short-wave infrared radiation, and holds potential to characterize rocks with great precision. Ground-based hyperspectral study could scan the objects at close ranges thus provide very fine spatial resolutions (millimeters to centimeters). This study mapped all the major iconic outcrops of Semail ophiolite including pillow lava, sheeted dykes, layered gabbros, and peridotites. This study also identified surficial rock alterations induced by hydrocarbons at Cement, Oklahoma. Reddish-brown Rush Spring sandstones are bleached to pink, yellow, and gray colors; pore spaces in the sandstones have been filled with carbonate cementation. Laboratory spectroscopy was used to assist with mineral identification and classification in hyperspectral data. Terrestrial laser scanning (TLS) was used to provide high-accuracy spatial references. Principal component analysis, minimum noise fraction, spectral angle mapper, and band ratios are used in image processing. Combining lithological, remote sensing and geochemical data, this study built a model for petroleum seepage and related rock alterations, and provided a workflow for employing ground-based hyperspectral remote sensing techniques in petrological

  18. Utilizing hyperspectral and hyperspatial remote sensing to track invasive species in BARC wetland ecosystems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Wetland vegetation is a critical component to the function of and ecological services provided by wetland ecosystems. Two non-native invasive species threaten wetland ecosystems in the Mid Atlantic region, Phragmites australis (giant reed) and Lythrum salicaria (purple loosestrife). Hyperspectral ...

  19. Linking goniometer measurements to hyperspectral and multisensor imagery for retrieval of beach properties and coastal characterization

    NASA Astrophysics Data System (ADS)

    Bachmann, Charles M.; Gray, Deric; Abelev, Andrei; Philpot, William; Montes, Marcos J.; Fusina, Robert; Musser, Joseph; Li, Rong-Rong; Vermillion, Michael; Smith, Geoffrey; Korwan, Daniel; Snow, Charlotte; Miller, W. David; Gardner, Joan; Sletten, Mark; Georgiev, Georgi; Truitt, Barry; Killmon, Marcus; Sellars, Jon; Woolard, Jason; Parrish, Christopher; Schwarzscild, Art

    2012-06-01

    In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements. Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500), topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR'11). Focus areas for VCR'11 were properties of beaches and tidal flats and barrier island vegetation and, in the water column, shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density, moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007 related to beach properties and shallow water bathymetry. A priority for VCR'11 was to collect and model relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery, as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR'11.

  20. Potential of hyperspectral remote sensing on estimating foliar chemistry and predicting the quality of tea (Camellia sinensis)

    NASA Astrophysics Data System (ADS)

    Bian, Meng; Skidmore, Andrew K.; Ni, Dejiang; de Leeuw, Jan; Schlerf, Martin; Liu, Yanfang; Fei, Teng

    2008-12-01

    In this study, we monitored the quality of fresh tea leaves as raw materials of tea products by hyperspectral technology, as a way to explore the potential of hyperspectral remote sensing to detect the taste-related chemical components with low concentration in living plants. At leaf scale, empirical models have been established to find the relationships between quality-related chemicals in fresh tea leaves and foliar spectral data. Tea polyphenols (TP) and amino acid (AA) and water-soluble protein (SP) are three target chemicals in this paper. Near infrared spectroscopy (NIRS) was also been applied to estimate these chemicals for dried and ground leaves in laboratory. They are compared in terms of retrieval precision. Two main methodologies have been employed for modelling: (a) two bands normalized ratio index (NRI), (b) partial least squares (PLS) regression. The PLS method was performed using the original and transformed spectra: mean centred spectra, standard first derivative and standard normal variate (SNV) transformed spectra. The results demonstrated that the biochemical parameters related to the quality of tea can be estimated with satisfactory accuracy both at dried powder and fresh leaf scales.

  1. Compact Hyperspectral Imaging System (cosi) for Small Remotely Piloted Aircraft Systems (rpas) - System Overview and First Performance Evaluation Results

    NASA Astrophysics Data System (ADS)

    Sima, A. A.; Baeck, P.; Nuyts, D.; Delalieux, S.; Livens, S.; Blommaert, J.; Delauré, B.; Boonen, M.

    2016-06-01

    This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute for Technological Research (VITO, Belgium) and suitable for remotely piloted aircraft systems. A hyperspectral dataset captured from a multirotor platform over a strawberry field is presented and explored in order to assess spectral bands co-registration quality. Thanks to application of line based interference filters deposited directly on the detector wafer the COSI camera is compact and lightweight (total mass of 500g), and captures 72 narrow (FWHM: 5nm to 10 nm) bands in the spectral range of 600-900 nm. Covering the region of red edge (680 nm to 730 nm) allows for deriving plant chlorophyll content, biomass and hydric status indicators, making the camera suitable for agriculture purposes. Additionally to the orthorectified hypercube digital terrain model can be derived enabling various analyses requiring object height, e.g. plant height in vegetation growth monitoring. Geometric data quality assessment proves that the COSI camera and the dedicated data processing chain are capable to deliver very high resolution data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results reported in similar studies for an alternative system based on the Fabry-Pérot interferometer.

  2. Interpretation of Observations of Trans-Spectral Phenomena Acquired Using Hyperspectral Sensors Aboard a Remotely Operated Vehicle in Exuma Sound

    NASA Technical Reports Server (NTRS)

    Costello, D.; Carder, Kendall L.; Ivey, J.; English, D.

    2001-01-01

    Hyper-spectral (512-channel) optical data acquired during a relatively deep (102m) dive of our ROSEBUD Remotely Operated Vehicle (ROV) in the clear waters of Exuma Sound, Bahamas provided the opportunity to investigate the trans-spectral shift of photonic energy (inelastic scattering) as a function of water depth. Results show a convolution of several spectral processes (e.g. absorption, scattering) involving water molecules, dissolved material and particulates as well as trans-spectral (inelastic) processes involving fluorescence by water molecules (Raman), dissolved material and chlorophyll. The spectral signatures of these convolved causes and effects allow deconvolution with a hyperspectral approach. Intrinsic to the convolution was the ability to position the vehicle at depths where Raman fluorescence dominated at red wavelengths. Results show that the calculated Raman absorption coefficients are generally consistent with historical values (i.e. 0.9 x 10(sup)-4 at 525 nm excitation) and that an angstrom exponent of 5 is more appropriate than the often cited value of 4.

  3. Using TerraSAR-X and hyperspectral airborne data to monitor surface deformation and physical properties of the Barrow permafrost landscape, Alask

    NASA Astrophysics Data System (ADS)

    Haghshenas-Haghighi, M.; Motagh, M.; Heim, B.; Sachs, T.; Kohnert, K.; Streletskiy, D. A.

    2014-12-01

    In this study, we assess seasonal subsidence/heaving due to thawing/freezing of the permafrost in Barrow (71.3 N, 156.5 W) at the northernmost point of Alaska. The topographic relief in this area is low. Thick Permafrost underlies the entire area, with large ice volumes in its upper layer. With a large collection of field measurements during the past decades at the Barrow Environmental Observatory (BEO), it is an ideal site for permafrost investigation. There are long term systematic geocryological investigations within the Global Terrestrial Network (GTN-P) of the Circumpolar Active Layer Monitoring (CALM) programme. We use 28 TerraSAR-X images, acquired between December 2012 and December 2013 and analyze them using the Small BAseline Subset (SBAS) technique to extract time-series of ground surface deformation. We also analyze hyperspectral images acquired by the airborne AISA sensor over Barrow area, within the AIRMETH2013 programme, to assess physical characteristics such as vegetation biomass and density, surface moisture, and water bodies. Finally, we combine the information derived from both InSAR and hyperspectral analysis, with field measurements to investigate the link between physical characteristics of the permafrost and surface displacement.

  4. Evaluation of the spatial distribution, in percent coverage of the oil spilled during the Trecate blow-out, based on the analysis of airborne hyperspectral MIVIS data

    SciTech Connect

    Bianchi, R.; Cavalli, R.M.; Marino, C.M.

    1996-07-01

    On February 1994 a large area close to the Trecate town has been interested by an oil blow-out from an AGIP rig located within the Ticino Regional Park. One month later an airborne survey has been carried out in the framework of the CNR Lara Project, by utilizing the Daedalus AA5000 MIVIS spectrometer with 102 channels from Visible to Thermal Infrared. Different authors stress, for oil slicks discrimination, the utility of laser and microwaves based techniques, but the high spatial and spectral MIVIS resolutions can improve the detection of the relative coverage by spilled oil. This task has been performed by applying hyperspectral unmixing methods to the MIVIS calibrated data, obtaining an oil fractional image with respect to other chosen end-members. The analysis has shown a good agreement between the results of the unconstrained unmixing technique applied to MIVIS data and the ground truths, offering a tool useful to quantify in a synoptic overview the effects of oil spills over land, by relating the ppm of oil with the oil hyperspectral information gathered by MIVIS.

  5. The GEISA Spectroscopic Database as a Tool for Hyperspectral Earth' Tropospheric Remote Sensing Applications

    NASA Astrophysics Data System (ADS)

    Jacquinet-Husson, Nicole; Crépeau, Laurent; Capelle, Virginie; Scott, Noëlle; Armante, Raymond; Chédin, Alain

    2010-05-01

    Remote sensing of the terrestrial atmosphere has advanced significantly in recent years, and this has placed greater demands on the compilations in terms of accuracy, additional species, and spectral coverage. The successful performances of the new generation of hyperspectral Earth' atmospheric sounders like AIRS (Atmospheric Infrared Sounder -http://www-airs.jpl.nasa.gov/), in the USA, and IASI (Infrared Atmospheric Sounding Interferometer -http://earth-sciences.cnes.fr/IASI/) in Europe, which have a better vertical resolution and accuracy, compared to the previous satellite infrared vertical sounders, depend ultimately on the accuracy to which the spectroscopic parameters of the optically active gases are known, since they constitute an essential input to the forward radiative transfer models that are used to interpret their observations. In this context, the GEISA (1) (Gestion et Etude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information) computer-accessible database, initiated in 1976, is continuously developed and maintained at LMD (Laboratoire de Météorologie Dynamique, France). The updated 2009 edition of GEISA (GEISA-09)is a system comprising three independent sub-databases devoted respectively to: line transition parameters, infrared and ultraviolet/visible absorption cross-sections, microphysical and optical properties of atmospheric aerosols. In this edition, the contents of which will be summarized, 50 molecules are involved in the line transition parameters sub-database, including 111 isotopes, for a total of 3,807,997 entries, in the spectral range from 10-6 to 35,877.031 cm-1. Currently, GEISA is involved in activities related to the assessment of the capabilities of IASI through the GEISA/IASI database derived from GEISA (2). Since the Metop (http://www.eumetsat.int) launch (October 19th 2006), GEISA/IASI is the reference spectroscopic database for the validation of the level-1 IASI data

  6. Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

    NASA Astrophysics Data System (ADS)

    Zabalza, Jaime; Ren, Jinchang; Yang, Mingqiang; Zhang, Yi; Wang, Jun; Marshall, Stephen; Han, Junwei

    2014-07-01

    As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.

  7. AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPELINE LEAK DETECTION

    SciTech Connect

    Jerry Myers

    2003-05-13

    Ophir Corporation was awarded a contract by the U. S. Department of Energy, National Energy Technology Laboratory under the Project Title ''Airborne, Optical Remote Sensing of Methane and Ethane for Natural Gas Pipeline Leak Detection'' on October 14, 2002. This six-month technical report summarizes the progress for each of the proposed tasks, discusses project concerns, and outlines near-term goals. Ophir has completed a data survey of two major natural gas pipeline companies on the design requirements for an airborne, optical remote sensor. The results of this survey are disclosed in this report. A substantial amount of time was spent on modeling the expected optical signal at the receiver at different absorption wavelengths, and determining the impact of noise sources such as solar background, signal shot noise, and electronic noise on methane and ethane gas detection. Based upon the signal to noise modeling and industry input, Ophir finalized the design requirements for the airborne sensor, and released the critical sensor light source design requirements to qualified vendors. Responses from the vendors indicated that the light source was not commercially available, and will require a research and development effort to produce. Three vendors have responded positively with proposed design solutions. Ophir has decided to conduct short path optical laboratory experiments to verify the existence of methane and absorption at the specified wavelength, prior to proceeding with the light source selection. Techniques to eliminate common mode noise were also evaluated during the laboratory tests. Finally, Ophir has included a summary of the potential concerns for project success and has established future goals.

  8. Remote Sensing of Wind Fields and Aerosol Distribution with Airborne Scanning Doppler Lidar

    NASA Technical Reports Server (NTRS)

    Rothermel, Jeffry; Cutten, Dean R.; Johnson, Steven C.; Jazembski, Maurice; Arnold, James E. (Technical Monitor)

    2001-01-01

    The coherent Doppler laser radar (lidar), when operated from an airborne platform, is a unique tool for the study of atmospheric and surface processes and features. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are typically at a disadvantage. The atmospheric lidar remote sensing groups of several US institutions, led by Marshall Space Flight Center, have developed an airborne coherent Doppler lidar capable of mapping the wind field and aerosol structure in three dimensions. The instrument consists of an eye-safe approx. 1 Joule/pulse lidar transceiver, telescope, scanner, inertial measurement unit, and flight computer system to orchestrate all subsystem functions and tasks. The scanner is capable of directing the expanded lidar beam in a variety of ways, in order to extract vertically-resolved wind fields. Horizontal resolution is approx. 1 km; vertical resolution is even finer. Winds are obtained by measuring backscattered, Doppler-shifted laser radiation from naturally-occurring aerosol particles (of order 1 micron diameter). Measurement coverage depends on aerosol spatial distribution and composition. Velocity accuracy has been verified to be approx. 1 meter per second. A variety of applications have been demonstrated during the three flight campaigns conducted during 1995-1998. Examples will be shown during the presentation. In 1995, boundary layer winds over the ocean were mapped with unprecedented resolution. In 1996, unique measurements were made of. flow over the complex terrain of the Aleutian Islands; interaction of the marine boundary layer jet with the California coastal mountain range; a weak dry line in Texas - New Mexico; the angular dependence of sea surface scattering; and in-flight radiometric calibration using the surface of White Sands National Monument. In 1998, the first measurements of eyewall and boundary layer winds within a

  9. Remote Sensing of Wind Fields and Aerosol Distributions with Airborne Scanning Doppler Lidar

    NASA Technical Reports Server (NTRS)

    Rothermel, Jeffry; Cutten, Dean R.; Goodman, H. Michael (Technical Monitor)

    2000-01-01

    The coherent Doppler lidar, when operated from an airborne platform, offers a unique measurement capability for study of atmospheric and surface processes and feature. This is especially true for scientific objectives requiring measurements in optically-clear air, where other remote sensing technologies such as Doppler radar are at a disadvantage in terms of spatial resolution and coverage. The atmospheric lidar remote sensing groups of several US institutions, led by Marshall Space Flight Center, have developed an airborne coherent Doppler lidar capable of mapping the wind field and aerosol structure in three dimensions. The instrument consists of about a 1 Joule/pulse (eyesafe) lidar transceiver, telescope, scanner, inertial measurement unit, and operations control system to orchestrate all subsystem functions and tasks. The scanner is capable of directing the expanded lidar beam in a variety of ways, in order to extract vertically resolved wind fields. Horizontal resolution is about 1 km; vertical resolution is even finer. Winds are obtained by measuring backscattered, Doppler-shifted laser radiation from naturally-occurring aerosol particles (on an order of 1 micron in diameter). Measurement coverage depends on aerosol spatial distribution and concentration. Velocity accuracy has been verified to be about 1 m/s. A variety of applications has been demonstrated during the three flight campaigns conducted during 1995-1998. Examples will be shown during the presentation. In 1995, boundary layer winds over the ocean were mapped with unprecedented resolution. In 1996, unique measurements were made of flow over the complex terrain of the Aleutian Islands; interaction of the marine boundary layer jet with the California coastal mountain range; a weak dry line in Texas - New Mexico; an upper tropospheric jet stream; the angular dependence of sea surface scattering; and in-flight radiometric calibration using the surface of White Sands National Monument. In 1998, the

  10. Mapping and Assessing Surface Morphology of Holocene Lava Field in Krafla (NE Iceland) Using Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Aufaristama, M.; Höskuldsson, A.; Jónsdóttir, I.; Ólafsdóttir, R.

    2016-01-01

    Iceland is well known for its volcanic activity due to its location on the spreading Mid Atlantic Ridge and one of the earth's hot spot. In the past 1000 years there were about 200 eruptions occurring in Iceland, meaning volcanic eruptions occurred every four to five years, on average. Iceland currently has 30 active volcano systems, distributed evenly throughout the so- called Neovolcanic Zone. One of these volcanic systems is the Krafla central volcano, which is located in the northern Iceland at latitude 65°42'53'' N and longitude 16°43'40'' W. Krafla has produced two volcanic events in historic times: 1724-1729 (Myvatn Fires) and 1975-1984 (Krafla Fires). The Krafla Fires began in December 1975 and lasted until September 1984. This event covered about 36-km2 surrounding area with lava, having a total volume of 0.25-0.3 km3. Previous studies of lava surface morphology at Krafla focused on an open channel area by remote sensing are essential as a complementary tool to the previous investigations and to extend the area of mapping. Using Spectral Angle Mapper (SAM) classification approach by selecting spectral reflectance end members, this study has successfully produced a detailed map of the surface morphology in Krafla lava field EO-1 Hyperion (Hyperspectral) satellite images. The overall accuracy of lava morphology map is 61.33% (EO-1 Hyperion). These results show that hyperspectral remote sensing is an acceptable alternative to field mapping and assessing the lava surface morphology in the Krafla lava field. In order to get validation of the satellite image's spectral reflectance, in-situ measurements of the lava field's spectral reflectance using ASD FieldSpec3 is essential.

  11. Application of Hyperspectral Remote Sensing Techniques to Evaluate Water Quality in Turbid Coastal Waters of South Carolina.

    NASA Astrophysics Data System (ADS)

    Ali, K. A.; Ryan, K.

    2014-12-01

    Coastal and inland waters represent a diverse set of resources that support natural habitat and provide valuable ecosystem services to the human population. Conventional techniques to monitor water quality using in situ sensors and laboratory analysis of water samples can be very time- and cost-intensive. Alternatively, remote sensing techniques offer better spatial coverage and temporal resolution to accurately characterize the dynamic and unique water quality parameters. Existing remote sensing ocean color products, such as the water quality proxy chlorophyll-a, are based on ocean derived bio-optical models that are primarily calibrated in Case 1 type waters. These traditional models fail to work when applied in turbid (Case 2 type), coastal waters due to spectral interference from other associated color producing agents such as colored dissolved organic matter and suspended sediments. In this work, we introduce a novel technique for the predictive modeling of chlorophyll-a using a multivariate-based approach applied to in situ hyperspectral radiometric data collected from the coastal waters of Long Bay, South Carolina. This method uses a partial least-squares regression model to identify prominent wavelengths that are more sensitive to chlorophyll-a relative to other associated color-producing agents. The new model was able to explain 80% of the observed chlorophyll-a variability in Long Bay with RMSE = 2.03 μg/L. This approach capitalizes on the spectral advantage gained from current and future hyperspectral sensors, thus providing a more robust predicting model. This enhanced mode of water quality monitoring in marine environments will provide insight to point-sources and problem areas that may contribute to a decline in water quality. The utility of this tool is in its versatility to a diverse set of coastal waters and its use by coastal and fisheries managers with regard to recreation, regulation, economic and public health purposes.

  12. Hyperspectral remote sensing image classification based on combined SVM and LDA

    NASA Astrophysics Data System (ADS)

    Zhang, Chunsen; Zheng, Yiwei

    2014-11-01

    This paper presents a novel method for hyperspectral image classification based on the minimum noise fraction (MNF) and an approach combining support vector machine (SVM) and linear discriminant analysis (LDA). A new SVM/LDA algorithm is used for the classification. First, we use MNF method to reduce the dimension and extract features of the image, and then use the SVM/LDA algorithm to transform the extracted features. Next, we train the result of transformation, optimize the parameters through cross-validation and grid search method, then get a optimal hyperspectral image classifier. Finally, we use this classifier to complete classification. In order to verify the proposed method, the AVIRIS Indian Pines image was used. The experimental results show that the proposed method can solve the contradiction between the small amount of samples and high dimension, improve classification accuracy compared to the classical SVM method.

  13. Predicting species cover of marine macrophyte and invertebrate species combining hyperspectral remote sensing, machine learning and regression techniques.

    PubMed

    Kotta, Jonne; Kutser, Tiit; Teeveer, Karolin; Vahtmäe, Ele; Pärnoja, Merli

    2014-01-01

    In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.

  14. Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing, Machine Learning and Regression Techniques

    PubMed Central

    Kotta, Jonne; Kutser, Tiit; Teeveer, Karolin; Vahtmäe, Ele; Pärnoja, Merli

    2013-01-01

    In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems. PMID:23755113

  15. Fractal Characterization of Hyperspectral Imagery

    NASA Technical Reports Server (NTRS)

    Qiu, Hon-Iie; Lam, Nina Siu-Ngan; Quattrochi, Dale A.; Gamon, John A.

    1999-01-01

    Two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral images selected from the Los Angeles area, one representing urban and the other, rural, were used to examine their spatial complexity across their entire spectrum of the remote sensing data. Using the ICAMS (Image Characterization And Modeling System) software, we computed the fractal dimension values via the isarithm and triangular prism methods for all 224 bands in the two AVIRIS scenes. The resultant fractal dimensions reflect changes in image complexity across the spectral range of the hyperspectral images. Both the isarithm and triangular prism methods detect unusually high D values on the spectral bands that fall within the atmospheric absorption and scattering zones where signature to noise ratios are low. Fractal dimensions for the urban area resulted in higher values than for the rural landscape, and the differences between the resulting D values are more distinct in the visible bands. The triangular prism method is sensitive to a few random speckles in the images, leading to a lower dimensionality. On the contrary, the isarithm method will ignore the speckles and focus on the major variation dominating the surface, thus resulting in a higher dimension. It is seen where the fractal curves plotted for the entire bandwidth range of the hyperspectral images could be used to distinguish landscape types as well as for screening noisy bands.

  16. Utilizing hyperspectral and multispectral remote sensing and geographic information systems to identify and differentiate weed and crop species

    NASA Astrophysics Data System (ADS)

    Barber, Lon Thomas

    2004-12-01

    Weed species are not evenly distributed across a field; thus, if remote sensing imagery could be utilized as a tool for locating and identifying these patches, herbicides could be applied according to species and spatial distribution. In order to utilize site-specific technology to apply herbicides, the spatial variability of weed populations within a field must be known. Research was conducted to determine if remote sensing could be utilized as a tool for identifying and separating weed species from cotton and corn. Additionally geographic information systems and herbicide decision aids were tested to determine if weed population mapping could result in accurate site-specific and multiple product herbicide applications. Species separation based on hyperspectral data was successful in differentiating cotton and corn from weed species. By identifying the best spectral bands and utilizing vegetation indices, species classification ranged from 67 to 99% for cotton, 47 to 98% for corn and 95 to 98% for weed species. Species identification increased with data accumulation later in the growing season due to increased leaf area and biomass. Results indicate that weed and crop separation is possible when the best spectral bands are identified and utilized in conjunction with vegetation indices. Multispectral imagery was also tested for species identification and was found to produce lower classification accuracies than hyperspectral data. Cotton and corn were classified 50 to 100%; however, weed species classification was poor (<50%) with multispectral imagery. Poor classification was observed because only 4 regions of the electromagnetic spectrum were utilized. Multispectral identification would likely improve if the best spectral bands identified in the hyperspectral research were utilized in sensors for aerial imagery. Weed species and density information was mapped utilizing a geographic information system. Site-specific and broadcast herbicide applications were made

  17. A survey of natural aggregate properties and characteristics important in remote sensing and airborne geophysics

    USGS Publications Warehouse

    Knepper, D.H.; Langer, W.H.; Miller, S.

    1995-01-01

    Natural aggregate is vital to the construction industry. Although natural aggregate is a high volume/low value commodity that is abundant, new sources are becoming increasingly difficult to find and develop because of rigid industry specifications, political considerations, development and transportation costs, and environmental concerns. There are two primary sources of natural aggregate: (1) exposed or near-surface bedrock that can be crushed, and (2) deposits of sand and gravel. Remote sensing and airborne geophysics detect surface and near-surface phenomena, and may be useful for detecting and mapping potential aggregate sources; however, before a methodology for applying these techniques can be developed, it is necessary to understand the type, distribution, physical properties, and characteristics of natural aggregate deposits. The distribution of potential aggregate sources is closely tied to local geologic history. Conventional exploration for natural aggregate deposits has been largely a ground-based operation, although aerial photographs and topographic maps have been extensively used to target possible deposits. Today, the exploration process also considers factors such as the availability of the land, space and water supply for processing, political and environmental factors, and distance from the market; exploration and planning cannot be separated. There are many physical properties and characteristics by which to judge aggregate material for specific applications; most of these properties and characteristics pertain only to individual aggregate particles. The application of remote sensing and airborne geophysical measurements to detecting and mapping potential aggregate sources, however, is based on intrinsic bulk physical properties and extrinsic characteristics of the deposits that can be directly measured, mathematically derived from measurement, or interpreted with remote sensing and geophysical data. ?? 1995 Oxford UniversityPress.

  18. Geometric and Reflectance Signature Characterization of Complex Canopies Using Hyperspectral Stereoscopic Images from Uav and Terrestrial Platforms

    NASA Astrophysics Data System (ADS)

    Honkavaara, E.; Hakala, T.; Nevalainen, O.; Viljanen, N.; Rosnell, T.; Khoramshahi, E.; Näsi, R.; Oliveira, R.; Tommaselli, A.

    2016-06-01

    Light-weight hyperspectral frame cameras represent novel developments in remote sensing technology. With frame camera technology, when capturing images with stereoscopic overlaps, it is possible to derive 3D hyperspectral reflectance information and 3D geometric data of targets of interest, which enables detailed geometric and radiometric characterization of the object. These technologies are expected to provide efficient tools in various environmental remote sensing applications, such as canopy classification, canopy stress analysis, precision agriculture, and urban material classification. Furthermore, these data sets enable advanced quantitative, physical based retrieval of biophysical and biochemical parameters by model inversion technologies. Objective of this investigation was to study the aspects of capturing hyperspectral reflectance data from unmanned airborne vehicle (UAV) and terrestrial platform with novel hyperspectral frame cameras in complex, forested environment.

  19. Uas Based Tree Species Identification Using the Novel FPI Based Hyperspectral Cameras in Visible, NIR and SWIR Spectral Ranges

    NASA Astrophysics Data System (ADS)

    Näsi, R.; Honkavaara, E.; Tuominen, S.; Saari, H.; Pölönen, I.; Hakala, T.; Viljanen, N.; Soukkamäki, J.; Näkki, I.; Ojanen, H.; Reinikainen, J.

    2016-06-01

    Unmanned airborne systems (UAS) based remote sensing offers flexible tool for environmental monitoring. Novel lightweight Fabry-Perot interferometer (FPI) based, frame format, hyperspectral imaging in the spectral range from 400 to 1600 nm was used for identifying different species of trees in a forest area. To the best of the authors' knowledge, this was the first research where stereoscopic, hyperspectral VIS, NIR, SWIR data is collected for tree species identification using UAS. The first results of the analysis based on fusion of two FPI-based hyperspectral imagers and RGB camera showed that the novel FPI hyperspectral technology provided accurate geometric, radiometric and spectral information in a forested scene and is operational for environmental remote sensing applications.

  20. Application of Hyperspectral Methods in Hydrothermal Mineral System Studies

    NASA Astrophysics Data System (ADS)

    Laukamp, Carsten; Cudahy, Thomas; Gessner, Klaus; Haest, Maarten; Cacetta, Mike; Rodger, Andrew; Jones, Mal; Thomas, Matilda

    2010-05-01

    Hyperspectral infrared reflectance spectra are used to identify abundances and compositional differences of mineral groups and single mineral phases. 3D mineral maps are derived from surface (airborne and satellite sensed) and sub-surface (drill core) mineralogical data and integrated with geological, geochemical and geophysical datasets, enabling a quantitative mineral systems analysis. The Western Australian Centre of Excellence for 3D Mineral Mapping is working on a variety of mineral deposits to showcase the emerging applications of hyperspectral techniques in mineral system studies. Applied remote sensing technologies comprise hyperspectral airborne surveys (HyMap) covering 126 bands in the visible and shortwave infrared, as well as satellite-based multispectral surveys (ASTER) featuring 14 bands from the visible to thermal infrared. Drill cores were scanned with CSIRO's HyLoggingTM systems, which allow a fast acquisition of mineralogical data in cm-spacing and thereby providing statistically significant datasets. Building on procedures developed for public Australian geosurvey data releases for north Queensland, Broken Hill and Kalgoorlie (http://c3dmm.csiro.au), the ultimate goal is to develop sensor-independent scalars based on the position, depth and shape of selected absorption features in the visible-near (VNIR), shortwave (SWIR) and thermal infrared (TIR), which can be applied to a wide range of mineral deposit types. In the Rocklea Dome Channel Iron Ore deposits of the Pilbara (Western Australia) for example, hyperspectral drill core data were processed into 3D mineral maps to delineate major ore zones by identifying various ore types and possible contaminants. Vitreous (silica-rich) iron ore was successfully separated from ochreous goethitic ore, with both of them requiring different metallurgical processing. The silicified vitreous iron ore as well as outlined carbonate-rich zones are presumably related to overprinting groundwater effects. The

  1. The first aerosol indirect effect quantified through airborne remote sensing during VOCALS-REx

    NASA Astrophysics Data System (ADS)

    Painemal, D.; Zuidema, P.

    2012-09-01

    The first aerosol indirect effect (1AIE) is investigated using a combination of in situ and remotely-sensed aircraft (NCAR C-130) observations acquired during VOCALS-REx over the Southeast Pacific stratocumulus cloud regime. Satellite analyses have previously identified a high albedo susceptibitility to changes in cloud microphysics and aerosols over this region. The 1AIE was broken down into the product of two independently-estimated terms: the cloud aerosol interaction metric ACIτ =dln τ/dln Na|LWP, and the relative albedo (A) susceptibility SR-τ = dA/3dln τ|LWP, with τ and Na denoting retrieved cloud optical thickness and in-situ aerosol concentration, respectively and calculated for fixed intervals of liquid water path (LWP). ACIτ was estimated by combining in-situ Na sampled below the cloud, with τ and LWP derived from, respectively, simultaneous upward-looking broadband irradiance and narrow field-of-view millimeter-wave radiometer measurements, collected at 1 Hz during four eight-hour daytime flights by the C-130 aircraft. ACIτ values were typically large, close to the physical upper limit (0.33), increasing with LWP. The high ACIτ values were in agreement with other in-situ airborne studies in pristine marine stratocumulus and reflect the imposition of a LWP constraint and simultaneity of aerosol and cloud measurements. SR-τ increased with LWP and τ, reached a maximum SR-τ (0.086) for LWP (τ) of 58 g m-2 (13-14), decreasing slightly thereafter. The net first aerosol indirect effect thus increased over the LWP range of 30-80 g m-2. These values were consistent with satellite estimates derived from instantaneous, collocated CERES albedo and MODIS-retrieved droplet number concentrations at 50 km resolution. The consistency of the airborne and satellite estimates (for airborne remotely sensed Nd < 1100 cm-3), despite their independent approaches, differences in observational scales, and retrieval assumptions, is hypothesized to reflect the robust

  2. Visibility through the gaseous smoke in airborne remote sensing using a DSLR camera

    NASA Astrophysics Data System (ADS)

    Chabok, Mirahmad; Millington, Andrew; Hacker, Jorg M.; McGrath, Andrew J.

    2016-08-01

    Visibility and clarity of remotely sensed images acquired by consumer grade DSLR cameras, mounted on an unmanned aerial vehicle or a manned aircraft, are critical factors in obtaining accurate and detailed information from any area of interest. The presence of substantial haze, fog or gaseous smoke particles; caused, for example, by an active bushfire at the time of data capture, will dramatically reduce image visibility and quality. Although most modern hyperspectral imaging sensors are capable of capturing a large number of narrow range bands of the shortwave and thermal infrared spectral range, which have the potential to penetrate smoke and haze, the resulting images do not contain sufficient spatial detail to enable locating important objects or assist search and rescue or similar applications which require high resolution information. We introduce a new method for penetrating gaseous smoke without compromising spatial resolution using a single modified DSLR camera in conjunction with image processing techniques which effectively improves the visibility of objects in the captured images. This is achieved by modifying a DSLR camera and adding a custom optical filter to enable it to capture wavelengths from 480-1200nm (R, G and Near Infrared) instead of the standard RGB bands (400-700nm). With this modified camera mounted on an aircraft, images were acquired over an area polluted by gaseous smoke from an active bushfire. Processed data using our proposed method shows significant visibility improvements compared with other existing solutions.

  3. Remote sensing of spruce budworm defoliation using EO-1 Hyperion hyperspectral data: an example in Quebec, Canada

    NASA Astrophysics Data System (ADS)

    Huang, Z.; Zhang, Y.

    2016-04-01

    Each year, the spruce budworm (SBW) causes severe, widespread damage to spruces and fir in east coast Canada. Early estimation of the defoliation can provide crucial support to mitigate the socio-economic impact on vulnerable forests. Remote sensing techniques are suitable to investigate the affected regions that usually consist of large and inaccessible forestry areas. Using satellite images, surface reflectance values at two or more wavelengths are combined to generate vegetation indices (VIs), revealing a relative abundance of features of interest. Forest health analysis based on VIs is considered as one of the primary information sources for monitoring vegetation conditions. Especially the spectral resolution of Hyperion hyperspectral satellite imagery used in this study allows for a detailed examination of the red to near-infrared portion of the spectrum to identify areas of stressed vegetation. Several narrow-band vegetation indices are used to indicate the overall amount and quality of photosynthetic material and moisture content in vegetation. By integrating the information from VIs that focus on different aspects of overall health and vigour in forested areas, the study aims at detecting defoliated condition in a forested region in the Province of Quebec, Canada. In June and August of 2014 two Hyperion images were acquired by NASA's EO-1 satellite for this study. Changes in vegetation health and vigour are observed and quantitatively compared using the multi-temporal remote sensing images. The experimental results suggest that the VI- based forest health analysis is effective in estimating SBW defoliation in the study area.

  4. Estimation of trace vapor concentration-pathlength in plumes for remote sensing applications from hyperspectral images

    SciTech Connect

    Gallagher, Neal B.; Sheen, David M.; Shaver, Jeremy M.; Wise, Barry M.; Schultz, John F.

    2003-09-30

    Hyperspectral images in the long wave-infrared can be used for quantification of analytes in stack plumes. One approach uses eigenvectors of the off-plume covariance to develop models of the background that are employed in quantification. In this paper, it is shown that end members can be used in a similar way with the added advantage that the end members provide a simple approach to employ non-negativity constraints. A novel approach to end member extraction is used to extract from 14 to 53 factors from synthetic hyperspectral images. It is shown that the eigenvector and end member methods yield similar quantification performance and, as was seen previously, quantification error depends on net analyte signal. Mismatch between the temperature of the spectra used in the estimator and the actual plume temperature was also studied. A simple model used spectra from three different temperatures to interpolate to an “observed” spectrum at the plume temperature. Using synthetic images, it is shown that temperature mismatch generally results in increases in quantification error. However, in some cases it caused an off-set of the model bias that resulted in apparent decreases in quantification error.

  5. Parallel hyperspectral compressive sensing method on GPU

    NASA Astrophysics Data System (ADS)

    Bernabé, Sergio; Martín, Gabriel; Nascimento, José M. P.

    2015-10-01

    Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

  6. Mapping Geology and Vegetation using Hyperspectral Data in Antarctica: Current Challenges, New Solutions and Looking to the Future

    NASA Astrophysics Data System (ADS)

    Black, M.; Riley, T. R.; Fleming, A. H.; Ferrier, G.; Fretwell, P.; Casanovas, P.

    2015-12-01

    Antarctica is a unique and geographically remote environment. Traditional field campaigns investigating geology and vegetation in the region encounter numerous challenges including the harsh polar climate, the invasive nature of the work, steep topography and high infrastructure costs. Additionally, such field campaigns are often limited in terms of spatial and temporal resolution, and particularly, the topographical challenges presented in the Antarctic mean that many areas remain inaccessible. Remote Sensing, particularly hyperspectral imaging, may provide a solution to overcome the difficulties associated with field based mapping in the Antarctic. Planned satellite launches, such as EnMAP and HyspIRI, if successful, will yield large-scale, repeated hyperspectral imagery of Antarctica. Hyperspectral imagery has proven mapping capabilities and can yield greater information than can be attained using multispectral data. As a precursor to future satellite imagery, we utilise hyperspectral imagery from the first known airborne hyperspectral survey carried out in the Antarctic by the British Antarctic Survey and partners in 2011. Multiple imaging spectrometers were simultaneously deployed covering the visible, shortwave and thermal infrared regions of the electromagnetic spectrum. Additional data was generated during a field campaign deploying multiple ground spectrometers covering the same wavelengths as the airborne imagers. We utilise this imagery to assess the current challenges and propose some new solutions for mapping vegetation and geology, which may be directly applicable to future satellite hyperspectral imagery in the Antarctic.

  7. Hyperspectral analysis of columbia spotted frog habitat

    USGS Publications Warehouse

    Shive, J.P.; Pilliod, D.S.; Peterson, C.R.

    2010-01-01

    Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96 (44 wetlands) correctly classified with the 2002 data and 89 (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74 (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72 (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats. ?? 2010 The Wildlife Society.

  8. A fluorescence LIDAR sensor for hyper-spectral time-resolved remote sensing and mapping.

    PubMed

    Palombi, Lorenzo; Alderighi, Daniele; Cecchi, Giovanna; Raimondi, Valentina; Toci, Guido; Lognoli, David

    2013-06-17

    In this work we present a LIDAR sensor devised for the acquisition of time resolved laser induced fluorescence spectra. The gating time for the acquisition of the fluorescence spectra can be sequentially delayed in order to achieve fluorescence data that are resolved both in the spectral and temporal domains. The sensor can provide sub-nanometric spectral resolution and nanosecond time resolution. The sensor has also imaging capabilities by means of a computer-controlled motorized steering mirror featuring a biaxial angular scanning with 200 μradiant angular resolution. The measurement can be repeated for each point of a geometric grid in order to collect a hyper-spectral time-resolved map of an extended target.

  9. A fluorescence LIDAR sensor for hyper-spectral time-resolved remote sensing and mapping.

    PubMed

    Palombi, Lorenzo; Alderighi, Daniele; Cecchi, Giovanna; Raimondi, Valentina; Toci, Guido; Lognoli, David

    2013-06-17

    In this work we present a LIDAR sensor devised for the acquisition of time resolved laser induced fluorescence spectra. The gating time for the acquisition of the fluorescence spectra can be sequentially delayed in order to achieve fluorescence data that are resolved both in the spectral and temporal domains. The sensor can provide sub-nanometric spectral resolution and nanosecond time resolution. The sensor has also imaging capabilities by means of a computer-controlled motorized steering mirror featuring a biaxial angular scanning with 200 μradiant angular resolution. The measurement can be repeated for each point of a geometric grid in order to collect a hyper-spectral time-resolved map of an extended target. PMID:23787661

  10. Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation)

    USGS Publications Warehouse

    Marshall, Michael T.; Thenkabail, Prasad S.; Biggs, Trent; Post, Kirk

    2016-01-01

    Evapotranspiration (ET) is an important component of micro- and macro-scale climatic processes. In agriculture, estimates of ET are frequently used to monitor droughts, schedule irrigation, and assess crop water productivity over large areas. Currently, in situ measurements of ET are difficult to scale up for regional applications, so remote sensing technology has been increasingly used to estimate crop ET. Ratio-based vegetation indices retrieved from optical remote sensing, like the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index, and Enhanced Vegetation Index are critical components of these models, particularly for the partitioning of ET into transpiration and soil evaporation. These indices have their limitations, however, and can induce large model bias and error. In this study, micrometeorological and spectroradiometric data collected over two growing seasons in cotton, maize, and rice fields in the Central Valley of California were used to identify spectral wavelengths from 428 to 2295 nm that produced the highest correlation to and lowest error with ET, transpiration, and soil evaporation. The analysis was performed with hyperspectral narrowbands (HNBs) at 10 nm intervals and multispectral broadbands (MSBBs) commonly retrieved by Earth observation platforms. The study revealed that (1) HNB indices consistently explained more variability in ET (ΔR2 = 0.12), transpiration (ΔR2 = 0.17), and soil evaporation (ΔR2 = 0.14) than MSBB indices; (2) the relationship between transpiration using the ratio-based index most commonly used for ET modeling, NDVI, was strong (R2 = 0.51), but the hyperspectral equivalent was superior (R2 = 0.68); and (3) soil evaporation was not estimated well using ratio-based indices from the literature (highest R2 = 0.37), but could be after further evaluation, using ratio-based indices centered on 743 and 953 nm (R2 = 0.72) or 428 and 1518 nm (R2 = 0.69).

  11. Airborne Thermal Remote Sensing for Estimation of Groundwater Discharge to a River.

    PubMed

    Liu, Chuankun; Liu, Jie; Hu, Yue; Wang, Heshun; Zheng, Chunmiao

    2016-05-01

    Traditional methods for studying surface water and groundwater interactions have usually been limited to point measurements, such as geochemical sampling and seepage measurement. A new methodology is presented for quantifying groundwater discharge to a river, by using river surface temperature data obtained from airborne thermal infrared remote sensing technology. The Hot Spot Analysis toolkit in ArcGIS was used to calculate the percentage of groundwater discharge to a river relative to the total flow of the river. This methodology was evaluated in the midstream of the Heihe River in the arid and semiarid northwest China. The results show that the percentage of groundwater discharge relative to the total streamflow was as high as 28%, which is in good agreement with the results from previous geochemical studies. The data analysis methodology used in this study is based on the assumption that the river water is fully mixed except in the areas of extremely low flow velocity, which could lead to underestimation of the amount of groundwater discharge. Despite this limitation, this remote sensing-based approach provides an efficient means of quantifying the surface water and groundwater interactions on a regional scale.

  12. High-precision geometric correction of airborne remote sensing revisited: the multiquadric interpolation

    NASA Astrophysics Data System (ADS)

    Ehlers, Manfred; Fogel, David N.

    1994-12-01

    For a geographic analysis of multispectral scanner data from aircraft and their integration in spatial databases and geographic integration systems (GIS), geometric registration/rectification of the scanner imagery is required as a first step. Usually, one has to rely on global mapping functions such as polynomial equations as provided by most commercial image processing systems. These techniques have been proven to be very effective and accurate for satellite images. However, there are a umber of shortcomings when this method is applied to aircraft data. We see the multiquadric interpolation method as a promising alternative. The multiquadric function was first developed for the interpolation of irregular surfaces. It could be modified, however, to be used for image correction of remotely sensed data. In this form, it is particularly suited for the rectification of remote sensing images of large scale and locally varying geometric distortions. The multiquadric interpolation method yields a perfect fit at the used control points (CPs). With this, it is necessary to withhold independent test points that can be used for accuracy assessment. Within the registration/rectification process, all CPs contribute to the geometric warping of any given pixel in the image. Their effects, however, are weighted inversely to the distances between CPs and the current pixel location. The paper presents the multiquadric interpolation techniques and demonstrates successful application with airborne scanner data.

  13. An Airborne A-Band Spectrometer for Remote Sensing Of Aerosol and Cloud Optical Properties

    NASA Technical Reports Server (NTRS)

    Pitts, Michael; Hostetler, Chris; Poole, Lamont; Holden, Carl; Rault, Didier

    2000-01-01

    Atmospheric remote sensing with the O2 A-band has a relatively long history, but most of these studies were attempting to estimate surface pressure or cloud-top pressure. Recent conceptual studies have demonstrated the potential of spaceborne high spectral resolution O2 A-band spectrometers for retrieval of aerosol and cloud optical properties. The physical rationale of this new approach is that information on the scattering properties of the atmosphere is embedded in the detailed line structure of the O2 A-band reflected radiance spectrum. The key to extracting this information is to measure the radiance spectrum at very high spectral resolution. Instrument performance requirement studies indicate that, in addition to high spectral resolution, the successful retrieval of aerosol and cloud properties from A-band radiance spectra will also require high radiometric accuracy, instrument stability, and high signal-to-noise measurements. To experimentally assess the capabilities of this promising new remote sensing application, the NASA Langley Research Center is developing an airborne high spectral resolution A-band spectrometer. The spectrometer uses a plane holographic grating with a folded Littrow geometry to achieve high spectral resolution (0.5 cm-1) and low stray light in a compact package. This instrument will be flown in a series of field campaigns beginning in 2001 to evaluate the overall feasibility of this new technique. Results from these campaigns should be particularly valuable for future spaceborne applications of A-band spectrometers for aerosol and cloud retrievals.

  14. Simple models for complex natural surfaces - A strategy for the hyperspectral era of remote sensing

    NASA Technical Reports Server (NTRS)

    Adams, John B.; Smith, Milton O.; Gillespie, Alan R.

    1989-01-01

    A two-step strategy for analyzing multispectral images is described. In the first step, the analyst decomposes the signal from each pixel (as expressed by the radiance or reflectance values in each channel) into components that are contributed by spectrally distinct materials on the ground, and those that are due to atmospheric effects, instrumental effects, and other factors, such as illumination. In the second step, the isolated signals from the materials on the ground are selectively edited, and recombined to form various unit maps that are interpretable within the framework of field units. The approach has been tested on multispectral images of a variety of natural land surfaces ranging from hyperarid deserts to tropical rain forests. Data were analyzed from Landsat MSS (multispectral scanner) and TM (Thematic Mapper), the airborne NS001 TM simulator, Viking Lander and Orbiter, AIS, and AVRIS (Airborne Visible and Infrared Imaging Spectrometer).

  15. Artificial intelligence analysis of hyperspectral remote sensing data for management of water, weed, and nitrogen stresses in corn fields

    NASA Astrophysics Data System (ADS)

    Waheed, Tahir

    This study investigated the possibility of using ground-based remotely sensed hyperspectral observations with a special emphasis on detection of water, weed and nitrogen stresses contributing towards in-season decision support for precision crop management (PCM). A three factor split-split-plot experiment, with four randomized blocks as replicates, was established during the growing seasons of 2003 and 2004. Corn (Zea mays L.) hybrid DKC42-22 was grown because this hybrid is a good performer on light soils in Quebec. There were twelve 12 x 12m plots in a block (one replication per treatment per block) and the total number of plots was 48. Water stress was the main factor in the experiment. A drip irrigation system was laid out and each block was split into irrigated and non-irrigated halves. The second main factor of the experiment was weeds with two levels i.e. full weed control and no weed control. Weed treatments were assigned randomly by further splitting the irrigated and non-irrigated sub-blocks into two halves. Each of the weed treatments was furthermore split into three equal sub-sub-plots for nitrogen treatments (third factor of the experiment). Nitrogen was applied at three levels i.e. 50, 150 and 250 kg N ha-1 (Quebec norm is between 120-160 kg N ha-1). The hyperspectral data were recorded (spectral resolution = 1 nm) mid-day (between 1000 and 1400 hours) with a FieldSpec FR spectroradiometer over a spectral range of 400-2500 run at three growth stages namely: early growth, tasseling and full maturity, in each of the growing season. There are two major original contributions in this thesis: First is the development of a hyperspectral data analysis procedure for separating visible (400-700 nm), near-infrared (700-1300 nm) and mid-infrared (1300-2500 nm) regions of the spectrum for use in discriminant analysis procedure. In addition, of all the spectral band-widths analyzed, seven waveband-aggregates were identified using STEPDISC procedure, which were the

  16. Ground-based hyperspectral remote sensing for weed management in crop production

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Agricultural remote sensing has been developed and applied in monitoring soil, crop growth, weed infestation, insects, diseases, and water status in farm fields to provide data and information to guide agricultural management practices. Precision agriculture has been implemented through prescription...

  17. Scanning infrared remote sensing system for identification, visualization, and quantification of airborne pollutants

    NASA Astrophysics Data System (ADS)

    Harig, Roland; Matz, Gerhard; Rusch, Peter

    2002-02-01

    Remote sensing by Fourier-transform infrared (FTIR) spectrometry allows detection, identification, and quantification of airborne pollutants. In the case of leaks in pipelines or leaks in chemical plants, chemical accidents, terrorism, or war, hazardous compounds are often released into the atmosphere. Various Fourier-transform infrared spectrometers have been developed for the remote detection and identification of hazardous clouds. However, for the localization of a leak and a complete assessment of the situation in the case of the release of a hazardous cloud, information about the position and the size of a cloud is essential. Therefore, an imaging passive remote sensing system comprised of an interferometer (Bruker OPAG 22), a data acquisition, processing, and control system with a digital signal processor (FTIR DSP), an azimuth-elevation-scanning mirror, a video system with a DSP, and a personal computer has been developed. The FTIR DSP system controls the scanning mirror, collects the interferograms, and performs the Fourier transformation. The spectra are transferred to a personal computer and analyzed by a real-time identification algorithm that does not require background spectra for the analysis. The results are visualized by a video image, overlaid by false color images. For each target compound of a spectral library, images of the coefficient of correlation, the signal to noise ratio, the brightness temperature of the background, the difference between the temperature of the ambient air and the brightness temperature of the background, and the noise equivalent column density are produced. The column densities of all directions in which a target compound has been identified may be retrieved by a nonlinear least squares fitting algorithm and an additional false color image is displayed. The system has a high selectivity, low noise equivalent spectral radiance, and it allows identification, visualization, and quantification of pollutant clouds.

  18. Retrieval Lesson Learned from NAST-I Hyperspectral Data

    NASA Technical Reports Server (NTRS)

    Zhou, Daniel K.; Smith, William L.; Liu, Xu; Larar, Allen M.; Mango, Stephen A.

    2007-01-01

    The retrieval lesson learned is important to many current and future hyperspectral remote sensors. Validated retrieval algorithms demonstrate the advancement of hyperspectral remote sensing capabilities to be achieved with current and future satellite instruments.

  19. Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

    NASA Astrophysics Data System (ADS)

    Marwaha, Richa; Kumar, Anil; Kumar, Arumugam Senthil

    2015-01-01

    Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

  20. An Open Source Software and Web-GIS Based Platform for Airborne SAR Remote Sensing Data Management, Distribution and Sharing

    NASA Astrophysics Data System (ADS)

    Changyong, Dou; Huadong, Guo; Chunming, Han; Ming, Liu

    2014-03-01

    With more and more Earth observation data available to the community, how to manage and sharing these valuable remote sensing datasets is becoming an urgent issue to be solved. The web based Geographical Information Systems (GIS) technology provides a convenient way for the users in different locations to share and make use of the same dataset. In order to efficiently use the airborne Synthetic Aperture Radar (SAR) remote sensing data acquired in the Airborne Remote Sensing Center of the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), a Web-GIS based platform for airborne SAR data management, distribution and sharing was designed and developed. The major features of the system include map based navigation search interface, full resolution imagery shown overlaid the map, and all the software adopted in the platform are Open Source Software (OSS). The functions of the platform include browsing the imagery on the map navigation based interface, ordering and downloading data online, image dataset and user management, etc. At present, the system is under testing in RADI and will come to regular operation soon.

  1. Characterizing the marsh dieback spectral response at the plant and canopy level with hyperspectral and temporal remote sensing data

    USGS Publications Warehouse

    Ramsey, E.; Rangoonwala, A.

    2008-01-01

    We describe newly developed remote sensing tools to map the localized occurrences and regional distribution of the marsh dieback in coastal Louisiana (Fig. 1). As a final goal of our research and development, we identified what spectral features accompanied the onset of dieback and could be directly linked to the optical signal measured at the satellite. In order to accomplish our research goal, we carried out two interlinked objectives. First, we determined the spectral features within the hyperspectral spectra of the impacted plant that could be linked to the spectral return. This was accomplished by measuring the differences in leaf optical properties of impacted and non impacted marsh plants in such a way that the measured differences could be linked to the dieback onset and progression. The spectral analyses were constrained to selected wavelengths (bands of reflectance data) historically associated with changes in leaf composition and structure caused by changes in the plant biophysical environment. Second, we determined what changes in the canopy reflectance (canopy signal sensed at the satellite) could be linked to dieback onset and progression. Third, we transformed a suite of six Landsat Thematic Mapper images collected before, during, and in the final stages of dieback to maps of dieback occurrences. ??2008 IEEE.

  2. Fusion of hyperspectral remote sensing data for near real-time monitoring of microcystin distribution in Lake Erie

    NASA Astrophysics Data System (ADS)

    Vannah, Benjamin; Chang, Ni-Bin

    2013-09-01

    Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophic zones. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. Microcystis has a unique advantage over its competition as a result of the invasive zebra mussel population that filters algae out of the water column except for the toxic Microcystis. The toxin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This presentation demonstrates the prototype of a near real-time early warning system using Integrated Data Fusion techniques with the aid of both hyperspectral remote sensing data to determine spatiotemporal microcystin concentrations. The temporal resolution of MODIS is fused with the higher spatial and spectral resolution of MERIS to create synthetic images on a daily basis. As a demonstration, the spatiotemporal distributions of microcystin within western Lake Erie are reconstructed using the band data from the fused products and applied machine-learning techniques. Analysis of the results through statistical indices confirmed that the this type of algorithm has better potential to accurately estimating microcystin concentrations in the lake, which is better than current two band models and other computational intelligence models.

  3. Mapping Alteration Caused by Hydrocarbon Microseepages in Patrick Draw area Southwest Wyoming Using Image Spectroscopy and Hyperspectral Remote Sensing

    SciTech Connect

    Shuhab D. Khan

    2008-06-21

    Detection of underlying reservoir accumulations using remote sensing techniques had its inception with the identification of macroseeps. However, today we find ourselves relying on the detection of more subtle characteristics associated with petroleum reservoirs, such as microseeps. Microseepages are the result of vertical movement of light hydrocarbons from the reservoir to the surface through networks of fractures, faults, and bedding planes that provide permeable routes within the overlying rock. Microseepages express themselves at the surface in an array of alterations and anomalies, such as chemical or mineralogical changes in overlying soils and sediments. Using NASA's Hyperion hyperspectral imaging sensors, this project has developed spectral and geochemical ground truthing techniques to identify and map alterations caused by hydrocarbon microseepages and to determine their relationships to the underlying geology in the Patrick Draw area of Southwest Wyoming. Training the classification of satellite imagery with spectral inputs of samples collected over previously defined areas of hydrocarbon microseepage resulted in the successful identification of an anomalous zone. Geochemical characteristics of samples that defined this anomalous zone were then compared to the remaining non-anomalous samples using XRD, ICP, spectroscopy and carbon isotope techniques.

  4. Cybernetic group method of data handling (GMDH) statistical learning for hyperspectral remote sensing inverse problems in coastal ocean optics

    NASA Astrophysics Data System (ADS)

    Filippi, Anthony Matthew

    For complex systems, sufficient a priori knowledge is often lacking about the mathematical or empirical relationship between cause and effect or between inputs and outputs of a given system. Automated machine learning may offer a useful solution in such cases. Coastal marine optical environments represent such a case, as the optical remote sensing inverse problem remains largely unsolved. A self-organizing, cybernetic mathematical modeling approach known as the group method of data handling (GMDH), a type of statistical learning network (SLN), was used to generate explicit spectral inversion models for optically shallow coastal waters. Optically shallow water light fields represent a particularly difficult challenge in oceanographic remote sensing. Several algorithm-input data treatment combinations were utilized in multiple experiments to automatically generate inverse solutions for various inherent optical property (IOP), bottom optical property (BOP), constituent concentration, and bottom depth estimations. The objective was to identify the optimal remote-sensing reflectance Rrs(lambda) inversion algorithm. The GMDH also has the potential of inductive discovery of physical hydro-optical laws. Simulated data were used to develop generalized, quasi-universal relationships. The Hydrolight numerical forward model, based on radiative transfer theory, was used to compute simulated above-water remote-sensing reflectance Rrs(lambda) psuedodata, matching the spectral channels and resolution of the experimental Naval Research Laboratory Ocean PHILLS (Portable Hyperspectral Imager for Low-Light Spectroscopy) sensor. The input-output pairs were for GMDH and artificial neural network (ANN) model development, the latter of which was used as a baseline, or control, algorithm. Both types of models were applied to in situ and aircraft data. Also, in situ spectroradiometer-derived Rrs(lambda) were used as input to an optimization-based inversion procedure. Target variables

  5. Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots.

    PubMed

    Carroll, Matthew W; Glaser, John A; Hellmich, Richard L; Hunt, Thomas E; Sappington, Thomas W; Calvin, Dennis; Copenhaver, Ken; Fridgen, John

    2008-10-01

    Eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery and evaluated in 2004 and 2005 for their ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hübner) neonate larvae. Manual inoculations were timed to simulate infestation of corn, Zea mays L., by first and second flights of adult O. nubilalis. The ability of spectral vegetation indices to detect O. nubilalis-inoculated plots improved as the growing season progressed, with multiple spectral vegetation indices able to identify infested plots in late August and early September. Our findings also indicate that for detecting O. nubilalis-related plant stress in corn, spectral vegetation indices targeting carotenoid and anthocyanin pigments are not as effective as those targeting chlorophyll. Analysis of image data suggests that feeding and stem boring by O. nubilalis larvae may increase the rate of plant senescence causing detectable differences in plant biomass and vigor when compared with control plots. Further, we identified an approximate time frame of 5-6 wk postinoculation, when spectral differences of manually inoculated "second" generation O. nubilalis plots seem to peak. PMID:18950044

  6. Thermal Infrared Airborne Hyperspectral Detection of Fumarolic Ammonia Venting on the Calipatria Fault in the Salton Sea Geothermal Field, Imperial County, California

    NASA Astrophysics Data System (ADS)

    Lynch, D. K.; Tratt, D. M.; Buckland, K. N.; Hall, J. L.; Kasper, B. P.; Martino, M. G.; Ortega, L. J.; Westberg, K. R.; Young, S. J.; Johnson, P. D.

    2009-12-01

    An airborne hyperspectral imaging survey was conducted along the Calipatria Fault in the vicinity of the Salton Sea in Southern California. In addition to strong thermal hotspots associated with active fumaroles along the fault, a number of discrete and distributed sources of ammonia were detected. Mullet Island, some recently exposed areas of sea floor, and a shallow-water fumarolic geothermal vent all indicated ammonia emissions, presumed to originate from the eutrophic reduction of nitrate fertilizer in agricultural runoff and the decay (oxidation) of organic matter, probably algae. All emission sources detected lay along the putative Calipatria Fault, one of a number of en echelon faults in the Brawley Seismic Zone that is part of the northern-most spreading center of the East Pacific Rise. The techniques developed during this field experiment suggest a potential methodology for monitoring certain of the toxic episodes that are a known source of mass aquatic fauna kills within the Salton Sea ecosystem. The imagery was acquired at ~0.05 micron spectral resolution across the 7.6-13.5 micron thermal-infrared spectral region with a ground sample distance of approximately 1 m using the SEBASS (Spatially Enhanced Broadband Array Spectrograph System) sensor.

  7. SSUSI-Lite: a far-ultraviolet hyper-spectral imager for space weather remote sensing

    NASA Astrophysics Data System (ADS)

    Ogorzalek, Bernard; Osterman, Steven; Carlsson, Uno; Grey, Matthew; Hicks, John; Hourani, Ramsey; Kerem, Samuel; Marcotte, Kathryn; Parker, Charles; Paxton, Larry J.

    2015-09-01

    SSUSI-Lite is a far-ultraviolet (115-180nm) hyperspectral imager for monitoring space weather. The SSUSI and GUVI sensors, its predecessors, have demonstrated their value as space weather monitors. SSUSI-Lite is a refresh of the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) design that has flown on the Defense Meteorological Satellite Program (DMSP) spacecraft F16 through F19. The refresh updates the 25-year-old design and insures that the next generation of SSUSI/GUVI sensors can be accommodated on any number of potential platforms. SSUSI-Lite maintains the same optical layout as SSUSI, includes updates to key functional elements, and reduces the sensor volume, mass, and power requirements. SSUSI-Lite contains an improved scanner design that results in precise mirror pointing and allows for variable scan profiles. The detector electronics have been redesigned to employ all digital pulse processing. The largest decrease in volume, mass, and power has been obtained by consolidating all control and power electronics into one data processing unit.

  8. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

    SciTech Connect

    Zhao, Kaiguang; Valle, Denis; Popescu, Sorin; Zhang, Xuesong; Malick, Bani

    2013-05-15

    Model specification remains challenging in spectroscopy of plant biochemistry, as exemplified by the availability of various spectral indices or band combinations for estimating the same biochemical. This lack of consensus in model choice across applications argues for a paradigm shift in hyperspectral methods to address model uncertainty and misspecification. We demonstrated one such method using Bayesian model averaging (BMA), which performs variable/band selection and quantifies the relative merits of many candidate models to synthesize a weighted average model with improved predictive performances. The utility of BMA was examined using a portfolio of 27 foliage spectral–chemical datasets representing over 80 species across the globe to estimate multiple biochemical properties, including nitrogen, hydrogen, carbon, cellulose, lignin, chlorophyll (a or b), carotenoid, polar and nonpolar extractives, leaf mass per area, and equivalent water thickness. We also compared BMA with partial least squares (PLS) and stepwise multiple regression (SMR). Results showed that all the biochemicals except carotenoid were accurately estimated from hyerspectral data with R2 values > 0.80.

  9. Remote gas plume sensing and imaging with NASA's Hyperspectral Thermal Emission Spectrometer (HyTES).

    NASA Astrophysics Data System (ADS)

    Johnson, William R.; Hulley, Glynn; Hook, Simon J.

    2014-05-01

    The hyperspectral thermal emission spectrometer was developed under NASA's instrument incubator program and has now completed three deployments. The scan head uses a state-of-the-art Dyson spectrometer cooled to 100K coupled to a quantum well infrared photodetector array held at 40K. The combination allows for 256 spectral channels between 7.5μm and 12μm with 512 cross track spatial pixels. Spectral features for many interesting gases fall within the instrument passband. We first review the pre-flight calibration and validation process for HyTES using a suite of instrumentation. This includes a smile measurement at two wavelengths (8.18μm and 10.6μm) as well as a concentration determination using large aperture gas cells. We then show positive gas plume detection at ranges >1000m for various cases: Ammonia gas detection from Salton Sea fumaroles, Methane detection from staged releases points in Wyoming as well as naturally occurring methane hot spots off the coast of Santa Barbara.

  10. Mapping the distribution of materials in hyperspectral data using the USGS Material Identification and Characterization Algorithm (MICA)

    USGS Publications Warehouse

    Kokaly, R.F.; King, T.V.V.; Hoefen, T.M.

    2011-01-01

    Identifying materials by measuring and analyzing their reflectance spectra has been an important method in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow scientists to detect materials and map their distributions across the landscape. With new satellite-borne hyperspectral sensors planned for the future, for example, HYSPIRI (HYPerspectral InfraRed Imager), robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral-feature based analysis of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described in this paper. The core concepts and calculations of MICA are presented. A MICA command file has been developed and applied to map minerals in the full-country coverage of the 2007 Afghanistan HyMap hyperspectral data. ?? 2011 IEEE.

  11. Hyperspectral remote sensing estimation of crop residue cover: Soil mineralogy, surface conditions, and their effects

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Conservation tillage practices can enhance soil organic carbon content (SOC), improve soil structure, and reduce erosion. However, direct assessment of tillage practice for monitoring SOC change over large regions is difficult. Remote sensing of crop residue cover (CRC) can help assess tillage pra...

  12. a Comparison of LIDAR Reflectance and Radiometrically Calibrated Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Roncat, A.; Briese, C.; Pfeifer, N.

    2016-06-01

    In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a "single-wavelength reflectometer" to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems.

  13. Supporting relief efforts of the 2010 Haitian earthquake using an airborne multimodal remote sensing platform

    NASA Astrophysics Data System (ADS)

    Faulring, Jason W.; McKeown, Donald M.; van Aardt, Jan; Casterline, May V.; Bartlett, Brent D.; Raqueno, Nina

    2011-06-01

    The small island nation of Haiti was devastated in early 2010 following a massive 7.0 earthquake that brought about widespread destruction of infrastructure, many deaths and large-scale displacement of the population in the nation's major cities. The World Bank and ImageCat, Inc tasked the Rochester Institute of Technology's (RIT) Wildfire Airborne Sensor Platform (WASP) to gather a multi-spectral and multi-modal assessment of the disaster over a seven-day period to be used for relief and reconstruction efforts. Traditionally, private sector aerial remote sensing platforms work on processing and product delivery timelines measured in days, a scenario that has the potential to reduce the value of the data in time-sensitive situations such as those found in responding to a disaster. This paper will describe the methodologies and practices used by RIT to deliver an open set of products typically within a twenty-four hour period from when they were initially collected. Response to the Haiti disaster can be broken down into four major sections: 1) data collection and logistics, 2) transmission of raw data from a remote location to a central processing and dissemination location, 3) rapid image processing of a massive amount of raw data, and 4) dissemination of processed data to global organizations utilizing it to provide the maximum benefit. Each section required it's own major effort to ensure the success of the overall mission. A discussion of each section will be provided along with an analysis of methods that could be implemented in future exercises to increase efficiency and effectiveness.

  14. ALTIUS: a spaceborne AOTF-based UV-VIS-NIR hyperspectral imager for atmospheric remote sensing

    NASA Astrophysics Data System (ADS)

    Dekemper, Emmanuel; Fussen, Didier; Van Opstal, Bert; Vanhamel, Jurgen; Pieroux, Didier; Vanhellemont, Filip; Mateshvili, Nina; Franssens, Ghislain; Voloshinov, Vitaly; Janssen, Christof; Elandaloussi, Hadj

    2014-10-01

    Since the recent losses of several atmospheric instruments with good vertical sampling capabilities (SAGE II, SAGE III, GOMOS, SCIAMACHY,. . . ), the scientific community is left with very few sounders delivering concentration pro les of key atmospheric species for understanding atmospheric processes and monitoring the radiative balance of the Earth. The situation is so critical that at the horizon 2020, less than five such instruments will be on duty (most probably only 2 or 3), whereas their number topped at more than 15 in the years 2000. In parallel, recent inter-comparison exercises among the climate chemistry models (CCM) and instrument datasets have shown large differences in vertical distribution of constituents (SPARC CCMVal and Data Initiative), stressing the need for more vertically-resolved and accurate data at all latitudes. In this frame, the Belgian Institute for Space Aeronomy (IASB-BIRA) proposed a gap-filler small mission called ALTIUS (Atmospheric Limb Tracker for the Investigation of the Upcoming Stratosphere), which is currently in preliminary design phase (phase B according to ESA standards). Taking advantage of the good performances of the PROBA platform (PRoject for On-Board Autonomy) in terms of pointing precision and accuracy, on-board processing ressources, and agility, the ALTIUS concept relies on a hyperspectral imager observing limb-scattered radiance and solar/stellar occultations every orbit. The objective is twofold: the imaging feature allows to better assess the tangent height of the sounded air masses (through easier star tracker information validation by scene details recognition), while its spectral capabilities will be good enough to exploit the characteristic signatures of many molecular absorption cross-sections (O3, NO2, CH4, H2O, aerosols,...). The payload will be divided in three independent optical channels, associated to separated spectral ranges (UV: 250- 450 nm, VIS: 440-800 nm, NIR: 900-1800 nm). This approach also

  15. Optical design of high resolution and large format CCD airborne remote sensing camera on unmanned aerial vehicle

    NASA Astrophysics Data System (ADS)

    Qian, Yixian; Cheng, Xiaowei; Shao, Jie

    2010-11-01

    Unmanned aerial vehicle remote sensing (UAVRS) is lower in cost, flexible on task arrangement and automatic and intelligent in application, it has been used widely for mapping, surveillance, reconnaissance and city planning. Airborne remote sensing missions require sensors with both high resolution and large fields of view, large format CCD digital airborne imaging systems are now a reality. A refractive system was designed to meet the requirements with the help of code V software, It has a focal length of 150mm, F number of 5.6, waveband of 0.45~0.7um, and field of view reaches 20°. It is shown that the value of modulation transfer function is higher than 0.5 at 55lp/mm, distortion is less than 0.1%, image quality reaches the diffraction limit. The system with large format CCD and wide field can satisfy the demand of the wide ground overlay area and high resolution. The optical system with simpler structure, smaller size and lighter weight, can be used in airborne remote sensing.

  16. Combined multispectral/hyperspectral remote sensing of tropospheric aerosols for quantification of their direct radiative effect

    NASA Astrophysics Data System (ADS)

    McGarragh, Gregory R.

    Scattering and absorption of solar radiation by aerosols in the atmosphere has a direct radiative effect on the climate of the Earth. Unfortunately, according to the IPCC the uncertainties in aerosol properties and their effect on the climate system represent one of the largest uncertainties in climate change research. Related to aerosols, one of the largest uncertainties is the fraction of the incident radiation that is scattered rather than absorbed, or their single scattering albedo. In fact, differences in single scattering albedo have a significant impact on the magnitude of the cooling effect of aerosols (opposite to that of greenhouse gasses) which can even have a warming effect for strongly absorbing aerosols. Satellites provide a unique opportunity to measure aerosol properties on a global scale. Traditional approaches use multispectral measurements of intensity at a single view angle to retrieve at most two aerosol parameters over land but it is being realized that more detail is required for accurate quantification of the direct effect of aerosols, in particular its anthropogenic component, and therefore more measurement information is required. One approach to more advanced measurements is to use not only intensity measurements but also polarimetric measurements and to use multiple view angles. In this work we explore another alternative: the use of hyperspectral measurements in molecular absorption bands. Our study can be divided into three stages the first of which is the development of a fast radiative transfer model for rapid simulation of measurements. Our approach is matrix operator based and uses the Pade approximation for the matrix exponential to evaluate the homogeneous solution. It is shown that the method is two to four times faster than the standard and efficient discrete ordinate technique and is accurate to the 6th decimal place. The second part of our study forms the core and is divided into two chapters the first of which is a rigorous

  17. Combined multispectral/hyperspectral remote sensing of tropospheric aerosols for quantification of their direct radiative effect

    NASA Astrophysics Data System (ADS)

    McGarragh, Gregory R.

    Scattering and absorption of solar radiation by aerosols in the atmosphere has a direct radiative effect on the climate of the Earth. Unfortunately, according to the IPCC the uncertainties in aerosol properties and their effect on the climate system represent one of the largest uncertainties in climate change research. Related to aerosols, one of the largest uncertainties is the fraction of the incident radiation that is scattered rather than absorbed, or their single scattering albedo. In fact, differences in single scattering albedo have a significant impact on the magnitude of the cooling effect of aerosols (opposite to that of greenhouse gasses) which can even have a warming effect for strongly absorbing aerosols. Satellites provide a unique opportunity to measure aerosol properties on a global scale. Traditional approaches use multispectral measurements of intensity at a single view angle to retrieve at most two aerosol parameters over land but it is being realized that more detail is required for accurate quantification of the direct effect of aerosols, in particular its anthropogenic component, and therefore more measurement information is required. One approach to more advanced measurements is to use not only intensity measurements but also polarimetric measurements and to use multiple view angles. In this work we explore another alternative: the use of hyperspectral measurements in molecular absorption bands. Our study can be divided into three stages the first of which is the development of a fast radiative transfer model for rapid simulation of measurements. Our approach is matrix operator based and uses the Pade approximation for the matrix exponential to evaluate the homogeneous solution. It is shown that the method is two to four times faster than the standard and efficient discrete ordinate technique and is accurate to the 6th decimal place. The second part of our study forms the core and is divided into two chapters the first of which is a rigorous

  18. Remote detection of water stress in orchard canopies using MODIS/ASTER airborne simulator (MASTER) data

    NASA Astrophysics Data System (ADS)

    Cheng, Tao; Riaño, David; Koltunov, Alexander; Whiting, Michael L.; Ustin, Susan L.

    2011-09-01

    Vegetation canopy water content (CWC) is an important parameter for monitoring natural and agricultural ecosystems. Previous studies focused on the observation of annual or monthly variations in CWC but lacked temporal details to study vegetation physiological activities within a diurnal cycle. This study provides an evaluation of detecting vegetation diurnal water stress using airborne data acquired with the MASTER instrument. Concurrent with the morning and afternoon acquisitions of MASTER data, an extensive field campaign was conducted over almond and pistachio orchards in southern San Joaquin Valley of California to collect CWC measurements. Statistical analysis of the field measurements indicated a significant decrease of CWC from morning to afternoon. Field measured CWC was linearly correlated to the normalized difference infrared index (NDII) calculated with atmospherically corrected MASTER reflectance data using either FLAASH or empirical line (EL). Our regression analysis demonstrated that both atmospheric corrections led to a root mean square error (RMSE) of approximately 0.035 kg/m2 for the estimation of CWC (R2=0.42 for FLAASH images and R2=0.45 for EL images). Remote detection of the subtle decline in CWC awaits an improved prediction of CWC. Diurnal CWC maps revealed the spatial patterns of vegetation water status in response to variations in irrigation treatment.

  19. Assessing stream temperature variations in the Pacific Northwest using airborne thermal infrared remote sensing

    NASA Astrophysics Data System (ADS)

    Tan, J.; Cherkauer, K. A.

    2010-12-01

    Stream temperature is an important indicator of water quality, and a significant concern for endangered cold-water fish species in the Pacific Northwest. Thermal-infrared (TIR) remote sensing allows for the observation of water temperatures in entire river systems in a relatively short space of time, as opposed to more traditional point-based in situ observing methods that can capture only localized water conditions. Point measurements can therefore miss important spatial patterns associated with various factors including exposure to solar radiation, urbanization, changes to riparian zone vegetation, and the presence of groundwater returns and springs. In this paper, we analyze moderate resolution TIR imagery collected from an airborne platform for the Green River in Washington State. Five-meter MODIS/ASTER (MASTER) imagery along the main channel of the Green River was acquired in multiple straight line passes with image overlaps occurring at time intervals of between 3 and 30 minutes on August 25 and 27, 2001. Overlaps of two adjacent images provide a detailed comparison of how stream temperature changes over relatively short time scales, while image captured from different days help identify persistent localized temperature differences. Trees and shrubs in the riparian zone increases shading of the stream and reduces along-stream increases in temperature compared to stream reaches with reduced shading, such as urban areas. Longitudinal profiles of stream temperature from upstream to downstream show that other factors, such as sandbars and cold-water seeps, also contribute to along-stream temperature variations.

  20. Operational considerations for the application of remotely sensed forest data from LANDSAT or other airborne platforms

    NASA Technical Reports Server (NTRS)

    Baker, G. R.; Fethe, T. P.

    1975-01-01

    Research in the application of remotely sensed data from LANDSAT or other airborne platforms to the efficient management of a large timber based forest industry was divided into three phases: (1) establishment of a photo/ground sample correlation, (2) investigation of techniques for multi-spectral digital analysis, and (3) development of a semi-automated multi-level sampling system. To properly verify results, three distinct test areas were selected: (1) Jacksonville Mill Region, Lower Coastal Plain, Flatwoods, (2) Pensacola Mill Region, Middle Coastal Plain, and (3) Mississippi Mill Region, Middle Coastal Plain. The following conclusions were reached: (1) the probability of establishing an information base suitable for management requirements through a photo/ground double sampling procedure, alleviating the ground sampling effort, is encouraging, (2) known classification techniques must be investigated to ascertain the level of precision possible in separating the many densities involved, and (3) the multi-level approach must be related to an information system that is executable and feasible.

  1. Remote tree species identification in a diverse tropical forest using airborne imaging spectroscopy

    NASA Astrophysics Data System (ADS)

    Baldeck, C.; Asner, G. P.; Kellner, J. R.; Martin, R.; Anderson, C.; Knapp, D. E.

    2013-12-01

    Plant species identification and mapping based on remotely-sensed spectral signatures is a challenging task with the potential to contribute enormously to ecological studies. This task is especially difficult in highly diverse ecosystems such as tropical forests, and for these ecosystems it may be more strategic to direct efforts to identifying crowns of a focal species. We used imaging spectrometer data collected by the Carnegie Airborne Observatory over Barro Colorado Island, Panama, to develop classification models for the identification of tree crowns belonging to selected focal species. We explored alternative methods for detecting crowns of focal species, which included binary, one-class, and biased support vector machines (SVM). Best performance was given by binary and biased SVM, with poor performance observed for one-class SVM. Binary and biased SVM were able to identify crowns of focal species with classification sensitivity and specificity of 87-91% and 89-94%, respectively. The main tradeoff between binary and biased SVM is that construction of binary SVM requires a far greater amount of training data while biased SVM is more difficult to parameterize. Our results show that with sufficient training data, focal species can be mapped with a high degree of accuracy, in terms of both sensitivity and specificity, in this diverse tropical forest.

  2. High Spatial Resolution Airborne Multispectral Thermal Infrared Remote Sensing Data for Analysis of Urban Landscape Characteristics

    NASA Technical Reports Server (NTRS)

    Quattrochi, Dale A.; Luvall, Jeffrey C.; Estes, Maurice G., Jr.; Arnold, James E. (Technical Monitor)

    2000-01-01

    We have used airborne multispectral thermal infrared (TIR) remote sensing data collected at a high spatial resolution (i.e., 10m) over several cities in the United States to study thermal energy characteristics of the urban landscape. These TIR data provide a unique opportunity to quantify thermal responses from discrete surfaces typical of the urban landscape and to identify both the spatial arrangement and patterns of thermal processes across the city. The information obtained from these data is critical to understanding how urban surfaces drive or force development of the Urban Heat Island (UHI) effect, which exists as a dome of elevated air temperatures that presides over cities in contrast to surrounding non-urbanized areas. The UHI is most pronounced in the summertime where urban surfaces, such as rooftops and pavement, store solar radiation throughout the day, and release this stored energy slowly after sunset creating air temperatures over the city that are in excess of 2-4'C warmer in contrast with non-urban or rural air temperatures. The UHI can also exist as a daytime phenomenon with surface temperatures in downtown areas of cities exceeding 38'C. The implications of the UHI are significant, particularly as an additive source of thermal energy input that exacerbates the overall production of ground level ozone over cities. We have used the Airborne Thermal and Land Applications Sensor (ATLAS), flown onboard a Lear 23 jet aircraft from the NASA Stennis Space Center, to acquire high spatial resolution multispectral TIR data (i.e., 6 bandwidths between 8.2-12.2 (um) over Huntsville, Alabama, Atlanta, Georgia, Baton Rouge, Louisiana, Salt Lake City, Utah, and Sacramento, California. These TIR data have been used to produce maps and other products, showing the spatial distribution of heating and cooling patterns over these cities to better understand how the morphology of the urban landscape affects development of the UHI. In turn, these data have been used

  3. Adaptive Multi-Objective Sub-Pixel Mapping Framework Based on Memetic Algorithm for Hyperspectral Remote Sensing Imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Y.; Zhang, L.

    2012-07-01

    Sub-pixel mapping technique can specify the location of each class within the pixels based on the assumption of spatial dependence. Traditional sub-pixel mapping algorithms only consider the spatial dependence at the pixel level. The spatial dependence of each sub-pixel is ignored and sub-pixel spatial relation is lost. In this paper, a novel multi-objective sub-pixel mapping framework based on memetic algorithm, namely MSMF, is proposed. In MSMF, the sub-pixel mapping is transformed to a multi-objective optimization problem, which maximizing the spatial dependence index (SDI) and Moran's I, synchronously. Memetic algorithm is utilized to solve the multi-objective problem, which combines global search strategies with local search heuristics. In this framework, the sub-pixel mapping problem can be solved using different evolutionary algorithms and local algorithms. In this paper, memetic algorithm based on clonal selection algorithm (CSA) and random swapping as an example is designed and applied simultaneously in the proposed MSMF. In MSMF, CSA inherits the biologic properties of human immune systems, i.e. clone, mutation, memory, to search the possible sub-pixel mapping solution in the global space. After the exploration based on CSA, the local search based on random swapping is employed to dynamically decide which neighbourhood should be selected to stress exploitation in each generation. In addition, a solution set is used in MSMF to hold and update the obtained non-dominated solutions for multi-objective problem. Experimental results demonstrate that the proposed approach outperform traditional sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of hyperspectral remote sensing imagery.

  4. [Retrieval of leaf net photosynthetic rate of moso bamboo forests using hyperspectral remote sen-sing based on wavelet transform].

    PubMed

    Sun, Shao-bo; Du, Hua-qiangl; Li, Ping-heng; Zhou, Guo-mo; Xu, Xiao-juni; Gao, Guo-long; Li, Xue-jian

    2016-01-01

    This study focused on retrieval of net photosynthetic rate (Pn) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of Pn retrieved by the ideal high frequency wavelet vegetation index ( VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVIw), simple ratio vegetation index of wavelet (SRw) and difference vegetation index of wavelet (Dw) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with Pn, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between Pn estimated by multivariate linear model constructed by the ideal wavelet VI and the measured Pn, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the Pn of moso bamboo more in detail, and the overall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of Pn of moso bamboo forest using hyper spectral remotely sensed data. PMID:27228592

  5. Research Implementation and Quality Assurance Project Plan: An Evaluation of Hyperspectral Remote Sensing Technologies for the Detection of Fugitive Contamination at Selected Superfund Hazardous Waste Sites

    USGS Publications Warehouse

    Slonecker, E. Terrence; Fisher, Gary B.

    2009-01-01

    This project is a research collaboration between the U.S. Environmental Protection Agency (EPA) Office of Inspector General (OIG) and the U.S. Geological Survey (USGS) Eastern Geographic Science Center (EGSC), for the purpose of evaluating the utility of hyperspectral remote sensing technology for post-closure monitoring of residual contamination at delisted and closed hazardous waste sites as defined under the Comprehensive Environmental Response Compensation and Liability Act [CERCLA (also known as 'Superfund')] of 1980 and the Superfund Amendments and Reauthorization Act (SARA) of 1986.

  6. Expected Improvements in the Quantitative Remote Sensing of Optically Complex Waters with the Use of an Optically Fast Hyperspectral Spectrometer—A Modeling Study

    PubMed Central

    Moses, Wesley J.; Bowles, Jeffrey H.; Corson, Michael R.

    2015-01-01

    Using simulated data, we investigated the effect of noise in a spaceborne hyperspectral sensor on the accuracy of the atmospheric correction of at-sensor radiances and the consequent uncertainties in retrieved water quality parameters. Specifically, we investigated the improvement expected as the F-number of the sensor is changed from 3.5, which is the smallest among existing operational spaceborne hyperspectral sensors, to 1.0, which is foreseeable in the near future. With the change in F-number, the uncertainties in the atmospherically corrected reflectance decreased by more than 90% across the visible-near-infrared spectrum, the number of pixels with negative reflectance (caused by over-correction) decreased to almost one-third, and the uncertainties in the retrieved water quality parameters decreased by more than 50% and up to 92%. The analysis was based on the sensor model of the Hyperspectral Imager for the Coastal Ocean (HICO) but using a 30-m spatial resolution instead of HICO’s 96 m. Atmospheric correction was performed using Tafkaa. Water quality parameters were retrieved using a numerical method and a semi-analytical algorithm. The results emphasize the effect of sensor noise on water quality parameter retrieval and the need for sensors with high Signal-to-Noise Ratio for quantitative remote sensing of optically complex waters. PMID:25781507

  7. Expected improvements in the quantitative remote sensing of optically complex waters with the use of an optically fast hyperspectral spectrometer-a modeling study.

    PubMed

    Moses, Wesley J; Bowles, Jeffrey H; Corson, Michael R

    2015-01-01

    Using simulated data, we investigated the effect of noise in a spaceborne hyperspectral sensor on the accuracy of the atmospheric correction of at-sensor radiances and the consequent uncertainties in retrieved water quality parameters. Specifically, we investigated the improvement expected as the F-number of the sensor is changed from 3.5, which is the smallest among existing operational spaceborne hyperspectral sensors, to 1.0, which is foreseeable in the near future. With the change in F-number, the uncertainties in the atmospherically corrected reflectance decreased by more than 90% across the visible-near-infrared spectrum, the number of pixels with negative reflectance (caused by over-correction) decreased to almost one-third, and the uncertainties in the retrieved water quality parameters decreased by more than 50% and up to 92%. The analysis was based on the sensor model of the Hyperspectral Imager for the Coastal Ocean (HICO) but using a 30-m spatial resolution instead of HICO's 96 m. Atmospheric correction was performed using Tafkaa. Water quality parameters were retrieved using a numerical method and a semi-analytical algorithm. The results emphasize the effect of sensor noise on water quality parameter retrieval and the need for sensors with high Signal-to-Noise Ratio for quantitative remote sensing of optically complex waters. PMID:25781507

  8. Application of hyperspectral infrared analysis of hydrothermal alteration on Earth and Mars.

    PubMed

    Thomas, Matilda; Walter, Malcolm R

    2002-01-01

    An integrated analysis of both airborne and field short-wave infrared hyperspectral measurements was used in conjunction with conventional field mapping techniques to map hydrothermal alteration in the central portion of the Mount Painter Inlier in the Flinders Ranges, South Australia. The airborne hyperspectral data show the spatial distribution of spectrally distinct minerals occurring as primary minerals and as weathering and alteration products. Field spectral measurements, taken with a portable infrared mineral analyzer spectrometer and supported by thin-section analyses, were used to verify the mineral maps and enhance the level of information obtainable from the airborne data. Hydrothermal alteration zones were identified and mapped separately from the background weathering signals. A main zone of alteration, coinciding with the Paralana Fault zone, was recognized, and found to contain kaolinite, muscovite, biotite, and K-feldspar. A small spectral variation associated with a ring-like feature around Mount Painter was tentatively determined to be halloysite and interpreted to represent a separate hydrothermal fluid and fluid source, and probably a separate system. The older parts of the alteration system are tentatively dated as Permo-Carboniferous. The remote sensing of alteration at Mount Painter confirms that hyperspectral imaging techniques can produce accurate mineralogical maps with significant details that can be used to identify and map hydrothermal activity. Application of hyperspectral surveys such as that conducted at Mount Painter would be likely to provide similar detail about putative hydrothermal deposits on Mars.

  9. Application of hyperspectral infrared analysis of hydrothermal alteration on Earth and Mars.

    PubMed

    Thomas, Matilda; Walter, Malcolm R

    2002-01-01

    An integrated analysis of both airborne and field short-wave infrared hyperspectral measurements was used in conjunction with conventional field mapping techniques to map hydrothermal alteration in the central portion of the Mount Painter Inlier in the Flinders Ranges, South Australia. The airborne hyperspectral data show the spatial distribution of spectrally distinct minerals occurring as primary minerals and as weathering and alteration products. Field spectral measurements, taken with a portable infrared mineral analyzer spectrometer and supported by thin-section analyses, were used to verify the mineral maps and enhance the level of information obtainable from the airborne data. Hydrothermal alteration zones were identified and mapped separately from the background weathering signals. A main zone of alteration, coinciding with the Paralana Fault zone, was recognized, and found to contain kaolinite, muscovite, biotite, and K-feldspar. A small spectral variation associated with a ring-like feature around Mount Painter was tentatively determined to be halloysite and interpreted to represent a separate hydrothermal fluid and fluid source, and probably a separate system. The older parts of the alteration system are tentatively dated as Permo-Carboniferous. The remote sensing of alteration at Mount Painter confirms that hyperspectral imaging techniques can produce accurate mineralogical maps with significant details that can be used to identify and map hydrothermal activity. Application of hyperspectral surveys such as that conducted at Mount Painter would be likely to provide similar detail about putative hydrothermal deposits on Mars. PMID:12530243

  10. Watershed Airborne Telemetry Experimental Research (WATER): An Remote Sensing Experiment in a Typical Arid Region Inland River Basin of China

    NASA Astrophysics Data System (ADS)

    Li, X.; Wang, J.; Ma, M.; Liu, Q.; Hu, Z.; Liu, Q.; Che, T.; Su, P.; Jin, R.; Wang, W.

    2007-12-01

    Among the many land surface experiments have been carried out so far, arid and cold regions were paid little attentions. The land surface observations in arid and cold regions, both remotely sensed and in situ, need to be strengthened for a better understanding of hydrological and ecological processes at different scales. The Watershed Airborne Telemetry Experimental Research (WATER) is a simultaneous air-borne, satellite- borne, and ground-based remote sensing experiment conducted in the Heihe Basin, the second largest inland river basin in the northwest arid regions of China. The WATER is aiming at the research on water cycles, eco- hydrological and other land surface processes in catchment-scale. Data sets with high-resolution and spatiotemporal consistency will be generated based on this experiment. An integrated watershed model and a catchment-scale land/hydrological data assimilation system is proposed to be developed. The mission of WATER is to improve the observability, understanding, and predictability of hydrological and related ecological processes at catchmental scale, accumulate basic data for the development of watershed science and promote the applicability of quantitative remote sensing in watershed science studies. The objectives of the experiment will be (1) Observing major components of water cycle in three experiment areas, i.e., cold region, forest, and arid region hydrology experiment areas, by carrying out a simultaneous air-borne, satellite-borne, and ground-based experiment. (2) Developing the scaling method using airborne high-resolution remote sensing data and intensive in situ observations, and improving remote sensing retrieval models and algorithms of water cycle variables and corresponding ecological and other land variables/parameters. (3) Developing a catchment-scale land data assimilation system, which is capable of merging multi-source and multi-scale remote sensing data to generate high resolution and spatiotemporal consistent

  11. Remote Sensing of Selected Water-Quality Indicators with the Hyperspectral Imager for the Coastal Ocean (HICO) Sensor

    EPA Science Inventory

    The Hyperspectral Imager for the Coastal Ocean (HICO) offers the coastal environmental monitoring community an unprecedented opportunity to observe changes in coastal and estuarine water quality across a range of spatial scales not feasible with traditional field-based monitoring...

  12. Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra

    NASA Astrophysics Data System (ADS)

    Vaudour, E.; Gilliot, J. M.; Bel, L.; Lefevre, J.; Chehdi, K.

    2016-07-01

    This study aimed at identifying the potential of Vis-NIR airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soil types comprised haplic luvisols, calcaric cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks. Tracks were atmospherically corrected then mosaicked at a 2 m-resolution using a set of 24 synchronous field spectra of bare soils, black and white targets and impervious surfaces. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then calculation and thresholding of NDVI from an atmospherically corrected SPOT image acquired the same day enabled to map agricultural fields with bare soil. A total of 101 sites sampled either in 2013 or in the 3 previous years and in 2015 were identified as bare by means of this map. Predictions were made from the mosaic AISA spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples, considering 74 sites outside cloud shadows only, and different sampling strategies for selecting calibration samples. Validation root-mean-square errors (RMSE) were comprised between 3.73 and 4.49 g Kg-1 and were ∼4 g Kg-1 in median. The most performing models in terms of coefficient of determination (R2) and Residual Prediction Deviation (RPD) values were the calibration models derived either from Kennard-Stone or conditioned Latin Hypercube sampling on smoothed spectra. The most generalizable model leading to lowest RMSE value of 3.73 g Kg-1 at the regional scale and 1.44 g Kg-1 at the within-field scale and low bias was the cross-validated leave

  13. Assessing the capabilities of hyperspectral remote sensing to map oil films on waters

    NASA Astrophysics Data System (ADS)

    Liu, Bingxin; Li, Ying; Zhu, Xueyuan

    2014-11-01

    The harm of oil spills has caused extensive public concern. Remote sensing technology has become one of the most effective means of monitoring oil spill. However, how to evaluate the information extraction capabilities of various sensors and choose the most effective one has become an important issue. The current evaluation of sensors to detect oil films was mainly using in-situ measured spectra as a reference to determine the favorable band, but ignoring the effects of environmental noise and spectral response function. To understand the precision and accuracy of environment variables acquired from remote sensing, it is important to evaluate the target detection sensitivity of the entire sensor-air-target system corresponding to the change of reflectivity. The measurement data associated with the evaluation is environmental noise equivalent reflectance difference (NEΔRE ), which depends on the instrument signal to noise ratio(SNR) and other image data noise (such as atmospheric variables, scattered sky light scattering and direct sunlight, etc.). Hyperion remote sensing data is taken as an example for evaluation of its oil spill detection capabilities with the prerequisite that the impact of the spatial resolution is ignored. In order to evaluate the sensor's sensitivity of the film of water, the reflectance spectral data of light diesel and crude oil film were used. To obtain Hyperion reflectance data, we used FLAASH to do the atmospheric correction. The spectral response functions of Hyperion sensor was used for filtering the measured reflectance of the oil films to the theoretic spectral response. Then, these spectral response spectra were normalized to NEΔRE, according to which, the sensitivity of the sensor in oil film detecting could be evaluated. For crude oil, the range for Hyperion sensor to identify the film is within the wavelength from 518nm to 610nm (Band 17 to Band 26 of Hyperion sensors), within which the thin film and thick film can also be

  14. Use of airborne hyperspectral imagery to investigate the influence of soil nitrogen supplies and variable-rate fertilization to winter wheat growth

    NASA Astrophysics Data System (ADS)

    Song, Xiaoyu; Yan, Guangjian; Wang, Jihua; Liu, Liangyun; Xue, Xuzhang; Li, Cunjun; Huang, Wenjiang

    2007-10-01

    Advanced technology in airborne detection of crop growth can help optimize the strategies of fertilization, and help maximize the grain output by adjusting field inputs. In this study, Push-broom Hyperspectral Image sensor (PHI) was used to investigate the influence of soil nitrogen supplied and variable-rate fertilization to the growth of winter wheat. The objective was to determine to what extent the reflectance obtained in the 80 visible and near-infrared (NIR) wavebands (from 410nm to 832nm) might be related to differences of variance of soil nitrogen and variable-rate fertilization. Management plots were arranged at Beijing Precision Farming Experimental Station. Three flights were made during the wheat growing season. Several field experiments, including the crop sampling, soil sampling and variable-rate fertilization were carried out in the field. Data were analyzed for each flight and each band separately. Some spectrum indices were derived from PHI images and statistical correlation analysis were carried out among the spectrum indices and soil nitrogen, variable-rate fertilization amount. In addition, the spectrum indices difference between elongation stage and grain filling stage are calculated and the correlation analysis was also carried out. The analysis results indicated that the reflectance of winter wheat is significantly influenced at certain wavelength by the soil nitrogen and the variable-rate fertilization. The soil nitrogen effect was detectable in all the three flights. Differences in response due to soil nitrogen variance were most evident at spectrum indices, such as dλ red, INFLEX, Green/Red, NIRness, DVI and RDVI. Furthermore, analysis results also indicated that the variable fertilization can reduce the growth difference of winter wheat caused by spatial distribution difference of soil nitrogen.

  15. [Hyperspectral remote sensing of chlorophyll a concentrations in the Lake Taihu, based on water optical classification].

    PubMed

    Sun, De-Yong; Zhou, Xiao-Yu; Li, Yun-Mei; Chen, Xiao-Ling; Huang, Chang-Chun; Gong, Shao-Qi

    2013-08-01

    Four field investigations into the Lake Taihu were carried out for collecting in situ observed data in Nov., 2006, Nov., 2008, May and Aug. , 2010. On the basis of water optical classification, different retrieval algorithms were developed, specifically for different types of waters. The obtained optimal models were (1) the four-band model for Type 1 water; (2) the first-order differential model for Type 2 and Type 3 waters. Meanwhile, an optimal retrieval model was also established using the same aggregated calibration data. Some comparisons were done between the developed models for the classified and non-classified waters. The compared results showed that models for the classified waters had better performances than that for the non-classified water, in both retrieval accuracy and model stability. The findings of this study are significant for promoting the development of water color remote sensing for optically complex turbid inland waters.

  16. Use of airborne remote sensing to detect riverside Brassica rapa to aid in risk assessment of transgenic crops

    NASA Astrophysics Data System (ADS)

    Elliott, Luisa M.; Mason, David C.; Allainguillaume, Joel; Wilkinson, Mike J.

    2009-11-01

    High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modeling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.

  17. Vineyard zonal management for grape quality assessment by combining airborne remote sensed imagery and soil sensors

    NASA Astrophysics Data System (ADS)

    Bonilla, I.; Martínez De Toda, F.; Martínez-Casasnovas, J. A.

    2014-10-01

    Vineyard variability within the fields is well known by grape growers, producing different plant responses and fruit characteristics. Many technologies have been developed in last recent decades in order to assess this spatial variability, including remote sensing and soil sensors. In this paper we study the possibility of creating a stable classification system that better provides useful information for the grower, especially in terms of grape batch quality sorting. The work was carried out during 4 years in a rain-fed Tempranillo vineyard located in Rioja (Spain). NDVI was extracted from airborne imagery, and soil conductivity (EC) data was acquired by an EM38 sensor. Fifty-four vines were sampled at véraison for vegetative parameters and before harvest for yield and grape analysis. An Isocluster unsupervised classification in two classes was performed in 5 different ways, combining NDVI maps individually, collectively and combined with EC. The target vines were assigned in different zones depending on the clustering combination. Analysis of variance was performed in order to verify the ability of the combinations to provide the most accurate information. All combinations showed a similar behaviour concerning vegetative parameters. Yield parameters classify better by the EC-based clustering, whilst maturity grape parameters seemed to give more accuracy by combining all NDVIs and EC. Quality grape parameters (anthocyanins and phenolics), presented similar results for all combinations except for the NDVI map of the individual year, where the results were poorer. This results reveal that stable parameters (EC or/and NDVI all-together) clustering outcomes in better information for a vineyard zonal management strategy.

  18. Using High-Resolution Airborne Remote Sensing to Study Aerosol Near Clouds

    NASA Technical Reports Server (NTRS)

    Levy, Robert; Munchak, Leigh; Mattoo, Shana; Marshak, Alexander; Wilcox, Eric; Gao, Lan; Yorks, John; Platnick, Steven

    2016-01-01

    The horizontal space in between clear and cloudy air is very complex. This so-called twilight zone includes activated aerosols that are not quite clouds, thin cloud fragments that are not easily observable, and dying clouds that have not quite disappeared. This is a huge challenge for satellite remote sensing, specifically for retrieval of aerosol properties. Identifying what is cloud versus what is not cloud is critically important for attributing radiative effects and forcings to aerosols. At the same time, the radiative interactions between clouds and the surrounding media (molecules, surface and aerosols themselves) will contaminate retrieval of aerosol properties, even in clear skies. Most studies on aerosol cloud interactions are relevant to moderate resolution imagery (e.g. 500 m) from sensors such as MODIS. Since standard aerosol retrieval algorithms tend to keep a distance (e.g. 1 km) from the nearest detected cloud, it is impossible to evaluate what happens closer to the cloud. During Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS), the NASA ER-2 flew with the enhanced MODIS Airborne Simulator (eMAS), providing MODIS-like spectral observations at high (50 m) spatial resolution. We have applied MODIS-like aerosol retrieval for the eMAS data, providing new detail to characterization of aerosol near clouds. Interpretation and evaluation of these eMAS aerosol retrievals is aided by independent MODIS-like cloud retrievals, as well as profiles from the co-flying Cloud Physics Lidar (CPL). Understanding aerosolcloud retrieval at high resolution will lead to better characterization and interpretation of long-term, global products from lower resolution (e.g.MODIS) satellite retrievals.

  19. Icepod: A modular approach to the development of an airborne remote sensing and data acquisition platform

    NASA Astrophysics Data System (ADS)

    Frearson, N.; Bell, R. E.; Tinto, K. J.; Zappa, C. J.

    2013-12-01

    The New York Air National Guard [NYANG] provides regular airborne support to the National Science Foundation [NSF] moving science parties and their equipment onto and around the ice-sheets in both polar regions during the respective summer seasons. Icepod has been developed to utilize this readily available resource, providing the aircraft with a modular external pod attached to the rear-paratrooper door on either side of the NYANG's ski-equipped LC-130s. The pod is divided into five separate bays each approximately a 2ft cube within which can be mounted an array of remote sensors. Power, heating, sensor control and data management services are provided to each bay. An Ethernet network is used to transfer commands and data packets between the individual sensors and data acquisition system located inside the aircraft. Data for each sensor is stored on ruggedized and removable hard-drives that can be taken off the aircraft at the end of a flight for further analysis. In its current configuration the pod is equipped for the remote sensing of ice sheets and their margins and the bay's contain two radar systems, radar antennas, a vibration isolated optics bay including a scanning laser, Infra-red camera and high-definition visible wave camera. Sensor data is geo-referenced using GNSS and orientation sensors located inside the pod. A Pyrometer provides the downward looking IR Camera with the current sky temperature. In January 2013, the Icepod system was flight certified at the Stratton air base in Schenectady, New York. The system deployed to Greenland in April and July 2013 to test the instrumentation suite over ice and its ease of deployment with the NYANG. Icepod can be operated in two modes, a traditional dedicated science flight mode and a piggy-back mode. In piggy-back mode science parties and their cargo are delivered to their destinations with Icepod installed but stowed. Once they have been delivered the Icepod is deployed and measurements can be taken on the

  20. Challenges in collecting hyperspectral imagery of coastal waters using Unmanned Aerial Vehicles (UAVs)

    NASA Astrophysics Data System (ADS)

    English, D. C.; Herwitz, S.; Hu, C.; Carlson, P. R., Jr.; Muller-Karger, F. E.; Yates, K. K.; Ramsewak, D.

    2013-12-01

    Airborne multi-band remote sensing is an important tool for many aquatic applications; and the increased spectral information from hyperspectral sensors may increase the utility of coastal surveys. Recent technological advances allow Unmanned Aerial Vehicles (UAVs) to be used as alternatives or complements to manned aircraft or in situ observing platforms, and promise significant advantages for field studies. These include the ability to conduct programmed flight plans, prolonged and coordinated surveys, and agile flight operations under difficult conditions such as measurements made at low altitudes. Hyperspectral imagery collected from UAVs should allow the increased differentiation of water column or shallow benthic communities at relatively small spatial scales. However, the analysis of hyperspectral imagery from airborne platforms over shallow coastal waters differs from that used for terrestrial or oligotrophic ocean color imagery, and the operational constraints and considerations for the collection of such imagery from autonomous platforms also differ from terrestrial surveys using manned aircraft. Multispectral and hyperspectral imagery of shallow seagrass and coral environments in the Florida Keys were collected with various sensor systems mounted on manned and unmanned aircrafts in May 2012, October 2012, and May 2013. The imaging systems deployed on UAVs included NovaSol's Selectable Hyperspectral Airborne Remote-sensing Kit (SHARK), a Tetracam multispectral imaging system, and the Sunflower hyperspectal imager from Galileo Group, Inc. The UAVs carrying these systems were Xtreme Aerial Concepts' Vision-II Rotorcraft UAV, MLB Company's Bat-4 UAV, and NASA's SIERRA UAV, respectively. Additionally, the Galileo Group's manned aircraft also surveyed the areas with their AISA Eagle hyperspectral imaging system. For both manned and autonomous flights, cloud cover and sun glint (solar and viewing angles) were dominant constraints on retrieval of quantitatively

  1. Accumulating pyramid spatial-spectral collaborative coding divergence for hyperspectral anomaly detection

    NASA Astrophysics Data System (ADS)

    Sun, Hao; Zou, Huanxin; Zhou, Shilin

    2016-03-01

    Detection of anomalous targets of various sizes in hyperspectral data has received a lot of attention in reconnaissance and surveillance applications. Many anomaly detectors have been proposed in literature. However, current methods are susceptible to anomalies in the processing window range and often make critical assumptions about the distribution of the background data. Motivated by the fact that anomaly pixels are often distinctive from their local background, in this letter, we proposed a novel hyperspectral anomaly detection framework for real-time remote sensing applications. The proposed framework consists of four major components, sparse feature learning, pyramid grid window selection, joint spatial-spectral collaborative coding and multi-level divergence fusion. It exploits the collaborative representation difference in the feature space to locate potential anomalies and is totally unsupervised without any prior assumptions. Experimental results on airborne recorded hyperspectral data demonstrate that the proposed methods adaptive to anomalies in a large range of sizes and is well suited for parallel processing.

  2. Airborne Sunphotometer Studies of Aerosol Properties and Effects, Including Closure Among Satellite, Suborbital Remote, and In situ Measurements

    NASA Technical Reports Server (NTRS)

    Russlee, Philip B.; Schmid, B.; Redemann, J.; Livingston, J. M.; Bergstrom, R. W.; Ramirez, S. A.; Hipskind, R. Stephen (Technical Monitor)

    2001-01-01

    Airborne sunphotometry has been used to measure aerosols from North America, Europe, and Africa in coordination with satellite and in situ measurements in TARFOX (1996), ACE-2 (1997), PRIDE (2000), and SAFARI 2000. Similar coordinated measurements of Asian aerosols are being conducted this spring in ACE-Asia and are planned for North American aerosols this summer in CLAMS. This paper summarizes the approaches used, key results, and implications for aerosol properties and effects, such as single scattering albedo and regional radiative forcing. The approaches exploit the three-dimensional mobility of airborne sunphotometry to access satellite scenes over diverse surfaces (including open ocean with and without sunglint) and to match exactly the atmospheric layers sampled by airborne in situ measurements and other radiometers. These measurements permit tests of the consistency, or closure, among such diverse measurements as aerosol size-resolved chemical composition; number or mass concentration; light extinction, absorption, and scattering (total, hemispheric back and 180 deg.); and radiative fluxes. In this way the airborne sunphotometer measurements provide a key link between satellite and in situ measurements that helps to understand any discrepancies that are found. These comparisons have led to several characteristic results. Typically these include: (1) Better agreement among different types of remote measurements than between remote and in situ measurements. (2) More extinction derived from transmission measurements than from in situ measurements. (3) Larger aerosol absorption inferred from flux radiometry than from in situ measurements. Aerosol intensive properties derived from these closure studies have been combined with satellite-retrieved fields of optical depth to produce fields of regional radiative forcing. We show results for the North Atlantic derived from AVHRR optical depths and aerosol intensive properties from TARFOX and ACE-2. Companion papers

  3. COMET: a planned airborne mission to simultaneously measure CO2 and CH4 columns using airborne remote sensing and in-situ techniques

    NASA Astrophysics Data System (ADS)

    Fix, A.; Amediek, A.; Büdenbender, C.; Ehret, G.; Wirth, M.; Quatrevalet, M.; Rapp, M.; Gerilowski, K.; Bovensmann, H.; Gerbig, C.; Pfeilsticker, K.; Zöger, M.; Giez, A.

    2013-12-01

    To better predict future trends in the cycles of the most important anthropogenic greenhouse gases, CO2 and CH4, there is a need to measure and understand their distribution and variation on various scales. To address these requirements it is envisaged to deploy a suite of state-of-the-art airborne instruments that will be capable to simultaneously measure the column averaged dry-air mixing ratios (XGHG) of both greenhouse gases along the flight path. As the measurement platform serves the research aircraft HALO, a modified Gulfstream G550, operated by DLR. This activity is dubbed CoMet (CO2 and Methane Mission). The instrument package of CoMet will consist of active and passive remote sensors as well as in-situ instruments to complement the column measurements by highly-resolved profile information. As an active remote sensing instrument CHARM-F, the integrated-path differential absorption lidar currently under development at DLR, will provide both, XCO2 and XCH4, below flight altitude. The lidar instrument will be complemented by MAMAP which is a NIR/SWIR absorption spectrometer developed by University of Bremen and which is also capable to derive XCH4 and XCO2. As an additional passive instrument, mini-DOAS operated by University of Heidelberg will contribute with additional context information about the investigated air masses. In order to compare the remote sensing instruments with integrated profile information, in-situ instrumentation is indispensable. The in-situ package will therefore comprise wavelength-scanned Cavity-Ring-Down Spectroscopy (CRDS) for the detection of CO2, CH4, CO and H2O and a flask sampler for collection of atmospheric samples and subsequent laboratory analysis. Furthermore, the BAsic HALO Measurement And Sensor System (BAHAMAS) will provide an accurate set of meteorological and aircraft state parameters for each scientific flight. Within the frame of the first CoMet mission scheduled for the 2015 timeframe it is planned to concentrate

  4. On the retrieval of water-related canopy biochemistry from airborne hyperspectral data and its comparison to MODIS spectral response

    NASA Astrophysics Data System (ADS)

    Casas Planes; Riaño, D.; Ustin, S.; Dennison, P. E.; Salas, J.

    2013-12-01

    Quantification of states and rates of water content in vegetation is critical in plant ecology. This work aims to assess the performance of a wide range of methodologies for the retrieval of vegetation biochemical and biophysical properties related to water, including: (i) foliar water content (FWC, cm), (ii) canopy water content (CWC, cm), (iii) fuel moisture content (FMC) and several interrelated variables: (iv) leaf mass per area (LMA, g/cm2), (v) foliar biomass (FB, g/m2), and (vi) leaf area index (LAI, m2/m2). Methods are applied to Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected over Stanford University's Jasper Ridge Biological Preserve, California, USA, and derived Moderate Resolution Imaging Spectrometer (MODIS)-like data, within a multitemporal frame and stratified by cover type (i.e. grassland, shrubland and forest). Assessed methods are: (i) spectral fitting techniques applied to AVIRIS data, ii) the use of standard and recently designed indices, iii) AVIRIS PROSAIL and MODIS CWC PROSAIL inversion; and iv) the estimation of best band combination indices calibrated with the experimental dataset. This work shows how CWC retrieved from spectral fitting techniques proved relatively inaccurate. RTM simulations were significantly improved with the incorporation of a soil spectrum particularly in the case of grasslands and only for LAI in forests. Spectral indices provided higher accuracy; however, the most accurate index differed by variable and by cover types. Empirical calibration of indices improved the retrievals significantly in the case of FMC, LMA and FB using bands in the longer wavelength SWIR region.

  5. Content-based hyperspectral image retrieval using spectral unmixing

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio J.

    2011-11-01

    The purpose of content-based image retrieval (CBIR) is to retrieve, from real data stored in a database, information that is relevant to a query. A major challenge for the development of efficient CBIR systems in the context of hyperspectral remote sensing applications is how to deal with the extremely large volumes of data produced by current Earth-observing (EO) imaging spectrometers. The data resulting from EO campaigns often comprises many Gigabytes per flight. When multiple instruments or timelines are combined, this leads to the collection of massive amounts of data coming from heterogeneous sources, and these data sets need to be effectively stored, managed, shared and retrieved. Furthermore, the growth in size and number of hyperspectral data archives demands more sophisticated search capabilities to allow users to locate and reuse data acquired in the past. In this paper we develop a new strategy to effectively retrieve hyperspectral image data sets using spectral unmixing concepts. Spectral unmixing is a very important task for hyperspectral data exploitation since the spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. In this work, we use the information provided by spectral unmixing (i.e. the spectral endmembers and their corresponding abundances in the scene) as effective meta-data to develop a new CBIR system that can assist users in the task of efficiently searching hyperspectral image instances in large data repositories. The proposed approach is validated using a collection of 154 hyperspectral data sets (comprising seven full flightlines) gathered by NASA using the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the World Trade Center (WTC) area in New York City during the last two weeks of September, 2001, only a few days after the terrorist attacks that

  6. Hyperspectral sensors and the conservation of monumental buildings

    NASA Astrophysics Data System (ADS)

    Camaiti, Mara; Benvenuti, Marco; Chiarantini, Leandro; Costagliola, Pilar; Moretti, Sandro; Paba, Francesca; Pecchioni, Elena; Vettori, Silvia

    2010-05-01

    The continuous control of the conservation state of outdoor materials is a good practice for timely planning conservative interventions and therefore to preserve historical buildings. The monitoring of surfaces composition, in order to characterize compounds of neo-formation and deposition, by traditional diagnostic campaigns, although gives accurate results, is a long and expensive method, and often micro-destructive analyses are required. On the other hand, hyperspectral analysis in the visible and near infrared (VNIR) region is a very common technique for determining the characteristics and properties of soils, air, and water in consideration of its capability to give information in a rapid, simultaneous and not-destructive way. VNIR Hypespectral analysis, which discriminate materials on the basis of their different patterns of absorption at specific wavelengths, are in fact successfully used for identifying minerals and rocks (1), as well as for detecting soil properties including moisture, organic content and salinity (2). Among the existing VNIR techniques (Laboratory Spectroscopy - LS, Portable Spectroscopy - PS and Imaging Spectroscopy - IS), PS and IS can play a crucial role in the characterization of components of exposed stone surfaces. In particular, the Imaging Spectroscopic (remote sensing), which uses sensors placed both on land or airborne, may contribute to the monitoring of large areas in consideration of its ability to produce large areal maps at relatively low costs. In this presentation the application of hyperspectral instruments (mainly PS and IS, not applied before in the field of monumental building diagnostic) to quantify the degradation of carbonate surfaces will be discussed. In particular, considering gypsum as the precursor symptom of damage, many factors which may affect the estimation of gypsum content on the surface will be taken into consideration. Two hyperspectral sensors will be considered: 1) A portable radiometer (ASD

  7. Hyperspectral sensors and the conservation of monumental buildings

    NASA Astrophysics Data System (ADS)

    Camaiti, Mara; Benvenuti, Marco; Chiarantini, Leandro; Costagliola, Pilar; Moretti, Sandro; Paba, Francesca; Pecchioni, Elena; Vettori, Silvia

    2010-05-01

    The continuous control of the conservation state of outdoor materials is a good practice for timely planning conservative interventions and therefore to preserve historical buildings. The monitoring of surfaces composition, in order to characterize compounds of neo-formation and deposition, by traditional diagnostic campaigns, although gives accurate results, is a long and expensive method, and often micro-destructive analyses are required. On the other hand, hyperspectral analysis in the visible and near infrared (VNIR) region is a very common technique for determining the characteristics and properties of soils, air, and water in consideration of its capability to give information in a rapid, simultaneous and not-destructive way. VNIR Hypespectral analysis, which discriminate materials on the basis of their different patterns of absorption at specific wavelengths, are in fact successfully used for identifying minerals and rocks (1), as well as for detecting soil properties including moisture, organic content and salinity (2). Among the existing VNIR techniques (Laboratory Spectroscopy - LS, Portable Spectroscopy - PS and Imaging Spectroscopy - IS), PS and IS can play a crucial role in the characterization of components of exposed stone surfaces. In particular, the Imaging Spectroscopic (remote sensing), which uses sensors placed both on land or airborne, may contribute to the monitoring of large areas in consideration of its ability to produce large areal maps at relatively low costs. In this presentation the application of hyperspectral instruments (mainly PS and IS, not applied before in the field of monumental building diagnostic) to quantify the degradation of carbonate surfaces will be discussed. In particular, considering gypsum as the precursor symptom of damage, many factors which may affect the estimation of gypsum content on the surface will be taken into consideration. Two hyperspectral sensors will be considered: 1) A portable radiometer (ASD

  8. On the modeling of hyperspectral remote-sensing reflectance of high-sediment-load waters in the visible to shortwave-infrared domain.

    PubMed

    Lee, Zhongping; Shang, Shaoling; Lin, Gong; Chen, Jun; Doxaran, David

    2016-03-01

    We evaluated three key components in modeling hyperspectral remote-sensing reflectance in the visible to shortwave-infrared (Vis-SWIR) domain of high-sediment-load (HSL) waters, which are the relationship between remote-sensing reflectance (R(rs)) and inherent optical properties (IOPs), the absorption coefficient spectrum of pure water (a(w)) in the IR-SWIR region, and the spectral variation of sediment absorption coefficient (a(sed)). Results from this study indicate that it is necessary to use a more generalized R(rs)-IOP model to describe the spectral variation of R(rs) of HSL waters from Vis to SWIR; otherwise it may result in a spectrally distorted R(rs) spectrum if a constant model parameter is used. For hyperspectral a(w) in the IR-SWIR domain, the values reported in Kou et al. (1993) provided a much better match with the spectral variation of R(rs) in this spectral range compared to that of Segelstein (1981). For a(sed) spectrum, an empirical a(sed) spectral shape derived from sample measurements is found working much better than the traditional exponential-decay function of wavelength in modeling the spectral variation of R(rs) in the visible domain. These results would improve our understanding of the spectral signatures of R(rs) of HSL waters in the Vis-SWIR domain and subsequently improve the retrieval of IOPs from ocean color remote sensing, which could further help the estimation of sediment loading of such waters. Limitations in estimating chlorophyll concentration in such waters are also discussed.

  9. Remote Sensing of Chlorophyll Fluorescence by the Airborne Plant Fluorescence Sensor (APFS)

    NASA Astrophysics Data System (ADS)

    Yee, J. H.; Boldt, J.; Cook, W. B.; Morgan, F., II; Demajistre, R.; Cook, B. D.; Corp, L. A.

    2014-12-01

    Solar-induced chlorophyll fluorescence (ChlF) by terrestrial vegetation is linked closely to photosynthetic efficiency that can be exploited to monitor the plant health status and to assess the terrestrial carbon budget from space. The weak, broad continuum ChlF signal can be detected from the amount of fill-in of strong O2 absorption lines or Fraunhofer lines in the reflected solar spectral radiation. The Johns Hopkins University, Applied Physics Laboratory (JHU/APL) Airborne Plant Fluorescence Sensor (APFS) is designed and constructed specifically for airborne and groundbased ChlF measurements using the line fill-in ChlF measurement technique. In this paper, we will present the design of this triple etalon Fabry-Perot imaging instrument and the results of its vegetation fluorescence measurements obtained from the ground in the laboratory and from a NASA Langley King Air during our 2014 airborne campaign over vegetated targets in North Carolina and Virginia.

  10. Hyper-spectral Atmospheric Sounding. Appendixes 1

    NASA Technical Reports Server (NTRS)

    Smith, W. L.; Zhou, D. K.; Revercomb, H. E.; Huang, H. L.; Antonelli, P.; Mango, S. A.

    2002-01-01

    The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) is the first hyper-spectral remote sounding system to be orbited aboard a geosynchronous satellite. The GETS is designed to obtain revolutionary observations of the four dimensional atmospheric temperature, moisture, and wind structure as well as the distribution of the atmospheric trace gases, CO and O3. Although GIFTS will not be orbited until 2006-2008, a glimpse at the its measurement capabilities has been obtained by analyzing data from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Test-bed-Interferometer (NAST-I) and Aqua satellite Atmospheric Infrared Sounder (AIRS). In this paper we review the GIFTS experiment and empirically assess measurement expectations based on meteorological profiles retrieved from the NAST aircraft and Aqua satellite AIRS spectral radiances.

  11. Remote sensing and airborne geophysics in the assessment of natural aggregate resources

    USGS Publications Warehouse

    Knepper, D.H.; Langer, W.H.; Miller, S.H.

    1994-01-01

    Natural aggregate made from crushed stone and deposits of sand and gravel is a vital element of the construction industry in the United States. Although natural aggregate is a high volume/low value commodity that is relatively abundant, new sources of aggregate are becoming increasingly difficult to find and develop because of rigid industry specifications, political considerations, development and transporation costs, and environmental concerns, especially in urban growth centers where much of the aggregate is used. As the demand for natural aggregate increases in response to urban growth and the repair and expansion of the national infrastructure, new sources of natural aggregate will be required. The USGS has recognized the necessity of developing the capability to assess the potential for natural aggregate sources on Federal lands; at present, no methodology exists for systematically describing and evaluating potential sources of natural aggregate. Because remote sensing and airborne geophysics can detect surface and nearsurface phenomena, these tools may useful for detecting and mapping potential sources of natural aggregate; however, before a methodology for applying these tools can be developed, it is necessary to understand the type, distribution, physical properties, and characteristics of natural aggregate deposits, as well as the problems that will be encountered in assessing their potential value. There are two primary sources of natural aggregate: (1) exposed or near-surface igneous, metamorphic, and sedimentary bedrock that can be crushed, and (2) deposits of sand and gravel that may be used directly or crushed and sized to meet specifications. In any particular area, the availability of bedrock suitable for crushing is a function of the geologic history of the area - the processes that formed, deformed, eroded and exposed the bedrock. Deposits of sand and gravel are primarily surficial deposits formed by the erosion, transportation by water and ice

  12. Informed Source Separation of Atmospheric and Surface Signal Contributions in Shortwave Hyperspectral Imagery using Non-negative Matrix Factorization

    NASA Astrophysics Data System (ADS)

    Wright, L.; Coddington, O.; Pilewskie, P.

    2015-12-01

    Current challenges in Earth remote sensing require improved instrument spectral resolution, spectral coverage, and radiometric accuracy. Hyperspectral instruments, deployed on both aircraft and spacecraft, are a growing class of Earth observing sensors designed to meet these challenges. They collect large amounts of spectral data, allowing thorough characterization of both atmospheric and surface properties. The higher accuracy and increased spectral and spatial resolutions of new imagers require new numerical approaches for processing imagery and separating surface and atmospheric signals. One potential approach is source separation, which allows us to determine the underlying physical causes of observed changes. Improved signal separation will allow hyperspectral instruments to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. In this work, we investigate a Non-negative Matrix Factorization (NMF) method for the separation of atmospheric and land surface signal sources. NMF offers marked benefits over other commonly employed techniques, including non-negativity, which avoids physically impossible results, and adaptability, which allows the method to be tailored to hyperspectral source separation. We adapt our NMF algorithm to distinguish between contributions from different physically distinct sources by introducing constraints on spectral and spatial variability and by using library spectra to inform separation. We evaluate our NMF algorithm with simulated hyperspectral images as well as hyperspectral imagery from several instruments including, the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), NASA Hyperspectral Imager for the Coastal Ocean (HICO) and National Ecological Observatory Network (NEON) Imaging Spectrometer.

  13. Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above-surface hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Zhu, Weining; Yu, Qian; Tian, Yong Q.; Chen, Robert F.; Gardner, G. Bernard

    2011-02-01

    A method for the inversion of hyperspectral remote sensing was developed to determine the absorption coefficient for chromophoric dissolved organic matter (CDOM) in the Mississippi and Atchafalaya river plume regions and the northern Gulf of Mexico, where water types vary from Case 1 to turbid Case 2. Above-surface hyperspectral remote sensing data were measured by a ship-mounted spectroradiometer and then used to estimate CDOM. Simultaneously, water absorption and attenuation coefficients, CDOM and chlorophyll fluorescence, turbidities, and other related water properties were also measured at very high resolution (0.5-2 m) using in situ, underwater, and flow-through (shipboard, pumped) optical sensors. We separate ag, the absorption coefficient a of CDOM, from adg (a of CDOM and nonalgal particles) based on two absorption-backscattering relationships. The first is between ad (a of nonalgal particles) and bbp (total particulate backscattering coefficient), and the second is between ap (a of total particles) and bbp. These two relationships are referred as ad-based and ap-based methods, respectively. Consequently, based on Lee's quasi-analytical algorithm (QAA), we developed the so-called Extended Quasi-Analytical Algorithm (QAA-E) to decompose adg, using both ad-based and ap-based methods. The absorption-backscattering relationships and the QAA-E were tested using synthetic and in situ data from the International Ocean-Colour Coordinating Group (IOCCG) as well as our own field data. The results indicate the ad-based method is relatively better than the ap-based method. The accuracy of CDOM estimation is significantly improved by separating ag from adg (R2 = 0.81 and 0.65 for synthetic and in situ data, respectively). The sensitivities of the newly introduced coefficients were also analyzed to ensure QAA-E is robust.

  14. Converting Snow Depth to SWE: The Fusion of Simulated Data with Remote Sensing Retrievals and the Airborne Snow Observatory

    NASA Astrophysics Data System (ADS)

    Bormann, K.; Marks, D. G.; Painter, T. H.; Hedrick, A. R.; Deems, J. S.

    2015-12-01

    Snow cover monitoring has greatly benefited from remote sensing technology but, despite their critical importance, spatially distributed measurements of snow water equivalent (SWE) in mountain terrain remain elusive. Current methods of monitoring SWE rely on point measurements and are insufficient for distributed snow science and effective management of water resources. Many studies have shown that the spatial variability in SWE is largely controlled by the spatial variability in snow depth. JPL's Airborne Snow Observatory mission (ASO) combines LiDAR and spectrometer instruments to retrieve accurate and very high-resolution snow depth measurements at the watershed scale, along with other products such as snow albedo. To make best use of these high-resolution snow depths, spatially distributed snow density data are required to leverage SWE from the measured snow depths. Snow density is a spatially and temporally variable property that cannot yet be reliably extracted from remote sensing techniques, and is difficult to extrapolate to basin scales. However, some physically based snow models have shown skill in simulating bulk snow densities and therefore provide a pathway for snow depth to SWE conversion. Leveraging model ability where remote sensing options are non-existent, ASO employs a physically based snow model (iSnobal) to resolve distributed snow density dynamics across the basin. After an adjustment scheme guided by in-situ data, these density estimates are used to derive the elusive spatial distribution of SWE from the observed snow depth distributions from ASO. In this study, we describe how the process of fusing model data with remote sensing retrievals is undertaken in the context of ASO along with estimates of uncertainty in the final SWE volume products. This work will likely be of interest to those working in snow hydrology, water resource management and the broader remote sensing community.

  15. Airborne Particles: What We Have Learned About Their Role in Climate from Remote Sensing, and Prospects for Future Advances

    NASA Technical Reports Server (NTRS)

    Kahn, Ralph A.

    2013-01-01

    Desert dust, wildfire smoke, volcanic ash, biogenic and urban pollution particles, all affect the regional-scale climate of Earth in places and at times; some have global-scale impacts on the column radiation balance, cloud properties, atmospheric stability structure, and circulation patterns. Remote sensing has played a central role in identifying the sources and transports of airborne particles, mapping their three-dimensional distribution and variability, quantifying their amount, and constraining aerosol air mass type. The measurements obtained from remote sensing have strengths and limitations, and their value for characterizing Earths environment is enhanced immensely when they are combined with direct, in situ observations, and used to constrain aerosol transport and climate models. A similar approach has been taken to study the role particles play in determining the climate of Mars, though based on far fewer observations. This presentation will focus what we have learned from remote sensing about the impacts aerosol have on Earths climate; a few points about how aerosols affect the climate of Mars will also be introduced, in the context of how we might assess aerosol-climate impacts more generally on other worlds.

  16. High altitude airborne remote sensing mission using the advanced microwave precipitation radiometer (AMPR)

    NASA Technical Reports Server (NTRS)

    Galliano, J.; Platt, R. H.; Spencer, Roy; Hood, Robbie

    1991-01-01

    The advanced microwave precipitation radiometer (AMPR) is an airborne multichannel imaging radiometer used to better understand how the earth's climate structure works. Airborne data results from the October 1990 Florida thunderstorm mission in Jacksonville, FL, are described. AMPR data on atmospheric precipitation in mesoscale storms were retrieved at 10.7, 19.35, 37.1, and 85.5 GHz onboard the ER-2 aircraft at an altitude of 20 km. AMPR's three higher-frequency data channels were selected to operate at the same frequencies as the spaceborne special sensor microwave/imager (SSM/I) presently in orbit. AMPR uses two antennas to receive the four frequencies: the lowest frequency channel uses a 9.7-in aperture lens antennas, while the three higher-frequency channels share a separate 5.3-in aperture lens antenna. The radiometer's temperature resolution performance is summarized.

  17. Mining in subarctic Canada: airborne PM2.5 metal concentrations in two remote First Nations communities.

    PubMed

    Liberda, Eric N; Tsuji, Leonard J S; Peltier, Richard E

    2015-11-01

    Airborne particulate matter arising from upwind mining activities is a concern for First Nations communities in the western James Bay region of Ontario, Canada. Aerosol chemical components were collected in 2011 from two communities in northern Ontario. The chemical and mass concentration data of particulate matter collected during this study shows a significant difference in PM2.5 in Attawapiskat compared to Fort Albany. Elemental profiles indicate enhanced levels of some tracers thought to arise from mining activities, such as, K, Ni, and crustal materials. Both communities are remote and isolated from urban and industrial pollution sources, however, Attawapiskat First Nation has significantly enhanced levels of particulate matter, and it is likely that some of this arises from upwind mining activities. PMID:26255141

  18. Mining in subarctic Canada: airborne PM2.5 metal concentrations in two remote First Nations communities.

    PubMed

    Liberda, Eric N; Tsuji, Leonard J S; Peltier, Richard E

    2015-11-01

    Airborne particulate matter arising from upwind mining activities is a concern for First Nations communities in the western James Bay region of Ontario, Canada. Aerosol chemical components were collected in 2011 from two communities in northern Ontario. The chemical and mass concentration data of particulate matter collected during this study shows a significant difference in PM2.5 in Attawapiskat compared to Fort Albany. Elemental profiles indicate enhanced levels of some tracers thought to arise from mining activities, such as, K, Ni, and crustal materials. Both communities are remote and isolated from urban and industrial pollution sources, however, Attawapiskat First Nation has significantly enhanced levels of particulate matter, and it is likely that some of this arises from upwind mining activities.

  19. Airborne multispectral remote sensing data to estimate several oenological parameters in vineyard production. A case study of application of remote sensing data to precision viticulture in central Italy.

    NASA Astrophysics Data System (ADS)

    Tramontana, Gianluca; Girard, Filippo; Belli, Claudio; Comandini, Maria Cristina; Pietromarchi, Paolo; Tiberi, Domenico; Papale, Dario

    2010-05-01

    It is widely recognized that environmental differences within the vineyard, with respect to soils, microclimate, and topography, can influence grape characteristics and crop yields. Besides, the central Italy landscape is characterized by a high level of fragmentation and heterogeneity It requires stringent Remote sensing technical features in terms of spectral, geometric and temporal resolution to aimed at supporting applications for precision viticulture. In response to the needs of the Italian grape and wine industry for an evaluation of precision viticulture technologies, the DISAFRI (University of Tuscia) and the Agricultural Research Council - Oenological research unit (ENC-CRA) jointly carried out an experimental study during the year 2008. The study was carried out on 2 areas located in the town of Velletri, near Rome; for each area, two varieties (red and white grape) were studied: Nero d'Avola and Sauvignon blanc in first area , Merlot and Sauvignon blanc in second. Remote sensing data were acquired in different periods using a low cost multisensor Airborne remote sensing platform developed by DISAFRI (ASPIS-2 Advanced Spectroscopic Imager System). ASPIS-2, an evolution of the ASPIS sensor (Papale et al 2008, Sensors), is a multispectral sensor based on 4 CCD and 3 interferential filters per CCD. The filters are user selectable during the flight and in this way Aspis is able to acquire data in 12 bands in the visible and near infrared regions with a bandwidth of 10 or 20 nm. To the purposes of this study 7 spectral band were acquired and 15 vegetation indices calculated. During the ripeness period several vegetative and oenochemical parameters were monitored. Anova test shown that several oenochemical variables, such as sugars, total acidity, polyphenols and anthocyanins differ according to the variety taken into consideration. In order to evaluate the time autocorrelation of several oenological parameters value, a simple linear regression between

  20. Alignment of Hyperspectral Imagery and Full-Waveform LIDAR Data for Visualisation and Classification Purposes

    NASA Astrophysics Data System (ADS)

    Miltiadou, M.; Warren, M. A.; Grant, M.; Brown, M.

    2015-04-01

    The overarching aim of this paper is to enhance the visualisations and classifications of airborne remote sensing data for remote forest surveys. A new open source tool is presented for aligning hyperspectral and full-waveform LiDAR data. The tool produces coloured polygon representations of the scanned areas and aligned metrics from both datasets. Using data provided by NERC ARSF, tree coverage maps are generated and projected into the polygons. The 3D polygon meshes show well-separated structures and are suitable for direct rendering with commodity 3D-accelerated hardware allowing smooth visualisation. The intensity profile of each wave sample is accumulated into a 3D discrete density volume building a 3D representation of the scanned area. The 3D volume is then polygonised using the Marching Cubes algorithm. Further, three user-defined bands from the hyperspectral images are projected into the polygon mesh as RGB colours. Regarding the classifications of full-waveform LiDAR data, previous work used extraction of point clouds while this paper introduces a new approach of deriving information from the 3D volume representation and the hyperspectral data. We generate aligned metrics of multiple resolutions, including the standard deviation of the hyperspectral bands and width of the reflected waveform derived from the volume. Tree coverage maps are then generated using a Bayesian probabilistic model and due to the combination of the data, higher accuracy classification results are expected.

  1. Airborne multi-spectral remote sensing with ground truth for areawide pest management

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Scientists and researchers have been developing, integrating, and evaluating multiple strategies and technologies into a systems approach for management of field crop insect pests. Remote sensing along with Global Positioning Systems, Geographic Information Systems, and variable rate technology are...

  2. Airborne multispectral remote sensing with ground truth for areawide pest management

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Scientists and engineers in areawide pest management programs have been developing, integrating, and evaluating multiple strategies and technologies into a systems approach for management of field crop insect pests. Remote sensing along with global positioning systems, geographic information system...

  3. Remote sensing of phytoplankton density and diversity in Narragansett Bay using an airborne fluorosensor

    NASA Technical Reports Server (NTRS)

    Farmer, F. H.; Brown, C. A., Jr.; Jarrett, O., Jr.; Campbell, J. W.; Staton, W. L.

    1979-01-01

    An aircraft-borne remote system is presented that utilizes narrow-band light from multiple dye lasers to excite selected algae photopigments and then measures the resultant flourescence emitted from chlorophyll a at 685 nm. Tests were conducted with both pure and mixed cultures of marine algae from a series of field tests taken from piers and bridges of Narragansett Bay, and a prototype remote fluorosensor was flown over the Bay during the 1978 winter-spring diatom bloom. Remote fluorescence obtained at hover points over sea-truth stations showed correlations with in situ fluorescence, total chlorophyll a, and cell count. It was concluded that the ratio of remote fluorescence to direct chlorophyll a concentration was less variable than expected, and the distribution of total chlorophyll a between two major photoplankton color groups showed three distinct areas, within the Bay, of green and golden-brown species.

  4. CNR LARA project, Italy: Airborne laboratory for environmental research

    NASA Technical Reports Server (NTRS)

    Bianchi, R.; Cavalli, R. M.; Fiumi, L.; Marino, C. M.; Pignatti, S.

    1995-01-01

    The increasing interest for the environmental problems and the study of the impact on the environment due to antropic activity produced an enhancement of remote sensing applications. The Italian National Research Council (CNR) established a new laboratory for airborne hyperspectral imaging, the LARA Project (Laboratorio Aero per Ricerche Ambientali - Airborne Laboratory for Environmental Research), equipping its airborne laboratory, a CASA-212, mainly with the Daedalus AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) instrument. MIVIS's channels, spectral bandwidths, and locations are chosen to meet the needs of scientific research for advanced applications of remote sensing data. MIVIS can make significant contributions to solving problems in many diverse areas such as geologic exploration, land use studies, mineralogy, agricultural crop studies, energy loss analysis, pollution assessment, volcanology, forest fire management and others. The broad spectral range and the many discrete narrow channels of MIVIS provide a fine quantization of spectral information that permits accurate definition of absorption features from a variety of materials, allowing the extraction of chemical and physical information of our environment. The availability of such a hyperspectral imager, that will operate mainly in the Mediterranean area, at the present represents a unique opportunity for those who are involved in environmental studies and land-management to collect systematically large-scale and high spectral-spatial resolution data of this part of the world. Nevertheless, MIVIS deployments will touch other parts of the world, where a major interest from the international scientific community is present.

  5. The Greenland ice sheet perennial firn aquifer: characteristics, extent and evolution obtained from airborne remote sensing

    NASA Astrophysics Data System (ADS)

    Miège, C.; Forster, R. R.; Koenig, L.; Brucker, L.; Box, J. E.; Burgess, E. W.

    2013-12-01

    The presence of a perennial firn aquifer (PFA) was identified April 2011, in the southeast part of the Greenland ice sheet, from firn-core drilling, surface- and airborne-radar. The PFA is a component of the ice sheet hydrology and corresponds to a liquid water saturated firn aquifer, which persists over the winter without freezing. The average depth of the top of the aquifer is ~20 m below the surface, and is guided by surface topography, following surface undulations, similar to an unconfined aquifer observed in other groundwater aquifer systems. We use a combination of 400 MHz ground-based radar and the 600 to 900 MHz Accumulation Radar on board NASA's airborne Operation IceBridge (OIB) to identify and map PFA extent and evolution between 2011 and 2013. Here, we present an ice-sheet wide mapping of the PFA, including the 2013 field campaign with detailed ground-based radar grids near the firn core site drilled in April 2013 (PFA-13, 66.18°N, 39.04°W and 1563 m). At the PFA-13 location, OIB Accumulation Radar and ground-based radar data were acquired along the same track within two weeks in both 2011 and 2013, offering a unique comparison dataset. This dataset is used to analyze the three year (2011-2013) evolution of PFA top depth, i.e. stored meltwater volume, in areas where radar transects are repeated from one year to the next. This evolution suggests possible horizontal flow of this stored meltwater toward the ice-sheet margins but must be confirmed by further field investigations. In addition, we derive surface slope from latest digital elevation model available for Southeast Greenland and use this slope as parameter to interpolate the PFA top in the area between ground radar transects and airborne radar flight lines. This slope interpolation would aim to improve PFA water volume/extent estimations for areas without airborne radar coverage. The fate of this stored meltwater is currently unknown, even if flow is suggested and drainage into nearby crevasses

  6. Clusters versus FPGAs for spectral mixture analysis-based lossy hyperspectral data compression

    NASA Astrophysics Data System (ADS)

    Plaza, Antonio J.

    2008-08-01

    The increasing number of airborne and satellite platforms that incorporate hyperspectral imaging spectrometers has soon created the need for efficient storage, transmission and data compression methodologies. In particular, hyperspectral data compression is expected to play a crucial role in many remote sensing applications. Many efforts have been devoted to designing and developing lossless and lossy algorithms for hyperspectral imagery. However, most available lossy compression approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop a simple lossy compression technique which relies on the concept of spectral unmixing, one of the most popular approaches to deal with mixed pixels and subpixel targets in hyperspectral analysis. The proposed method uses a two-stage approach in which the purest spectral signatures (also called endmembers) are first extracted from the input data, and then used to express mixed pixels as linear combinations of endmembers. Analytical and experimental results are presented in the context of a real application, using hyperspectral data collected by NASA's Jet Propulsion Laboratory over the World Trade Center area in New York City, right after the terrorist attacks of September 11th. These data are used in this work to evaluate the impact of compression using different methods on spectral signature quality for accurate detection of hot spot fires. Two parallel implementations are developed for the proposed lossy compression algorithm: a multiprocessor implementation tested on Thunderhead, a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center, and a hardware implementation developed on a Xilinx Virtex-II FPGA device. Combined, these parts offer a thoughtful perspective on the potential and emerging challenges of incorporating parallel

  7. Estimation of the spectral diffuse attenuation coefficient of downwelling irradiance in inland and coastal waters from hyperspectral remote sensing data: Validation with experimental data

    NASA Astrophysics Data System (ADS)

    Simon, Arthi; Shanmugam, Palanisamy

    2016-07-01

    A semi-analytical model is developed for estimating the spectral diffuse attenuation coefficient of downwelling irradiance (Kd(λ)) in inland and coastal waters. The model works as a function of the inherent optical properties (absorption and backscattering), depth, and solar zenith angle. Results of this model are validated using a large number of in-situ measurements of Kd(λ) in clear oceanic, turbid coastal and productive lagoon waters. To further evaluate its relative performance, Kd(λ) values obtained from this model are compared with results from three existing models. Validation results show that the present model is a better descriptor of Kd(λ) and shows an overall better performance compared to the existing models. The applicability of the present model is further tested on two Hyperspectral Imager for the Coastal Ocean (HICO) remote sensing images acquired simultaneously with our field measurements. The Kd(λ) spectra derived from HICO imageries have good agreement with measured data with the mean relative percent error of less than 12% which are well within the benchmark for a validated uncertainty of ±35% endorsed for the remote sensing products in oceanic waters. The model offers potential advantages for predicting changes in spectral and vertical Kd values in a wide variety of waters within inland and coastal environments.

  8. Hyperspectral Technique for Detecting Soil Parameters

    NASA Astrophysics Data System (ADS)

    Garfagnoli, F.; Ciampalini, A.; Moretti, S.; Chiarantini, L.

    2011-12-01

    In satellite and airborne remote sensing, hyperspectral technique has become a very powerful tool, due to the possibility of rapidly realizing chemical/mineralogical maps of the studied areas. Many studies are trying to customize the algorithms to identify several geo-physical soil properties. The specific objective of this study is to investigate those soil characteristics, such as clay mineral content, influencing degradation processes (soil erosion and shallow landslides), by means of correlation analysis, in order to examine the possibility of predicting the selected property using high-resolution reflectance spectra and images. The study area is located in the Mugello basin, about 30 km north of Firenze (Tuscany, Italy). Agriculturally suitable terrains are assigned mainly to annual crops, marginally to olive groves, vineyards and orchards. Soils mostly belong to Regosols and Cambisols orders. An ASD FieldSpec spectroradiometer was used to obtain reflectance spectra from about 80 dried, crushed and sieved samples under controlled laboratory conditions. Samples were collected simultaneously with the flight of SIM.GA hyperspectral camera from Selex Galileo, over an area of about 5 km2 and their positions were recorded with a differential GPS. The quantitative determination of clay minerals content was performed by means of XRD and Rietveld refinement. Different chemometric techniques were preliminarily tested to correlate mineralogical records with reflectance data. A one component partial least squares regression model yielded a preliminary R2 value of 0.65. A slightly better result was achieved by plotting the absorption peak depth at 2210 versus total clay content (band-depth analysis). The complete SIM.GA hyperspectral geocoded row dataset, with an approximate pixel resolution of 0.6 m (VNIR) and 1.2 m (SWIR), was firstly transformed into at sensor radiance values, by applying calibration coefficients and parameters from laboratory measurements to non

  9. An Integrated Data Acquisition / User Request/ Processing / Delivery System for Airborne Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Chapman, B.; Chu, A.; Tung, W.

    2003-12-01

    Airborne science data has historically played an important role in the development of the scientific underpinnings for spaceborne missions. When the science community determines the need for new types of spaceborne measurements, airborne campaigns are often crucial in risk mitigation for these future missions. However, full exploitation of the acquired data may be difficult due to its experimental and transitory nature. Externally to the project, most problematic (in particular, for those not involved in requesting the data acquisitions) may be the difficulty in searching for, requesting, and receiving the data, or even knowing the data exist. This can result in a rather small, insular community of users for these data sets. Internally, the difficulty for the project is in maintaining a robust processing and archival system during periods of changing mission priorities and evolving technologies. The NASA/JPL Airborne Synthetic Aperture Radar (AIRSAR) has acquired data for a large and varied community of scientists and engineers for 15 years. AIRSAR is presently supporting current NASA Earth Science Enterprise experiments, such as the Soil Moisture EXperiment (SMEX) and the Cold Land Processes experiment (CLPX), as well as experiments conducted as many as 10 years ago. During that time, it's processing, data ordering, and data delivery system has undergone evolutionary change as the cost and capability of resources has improved. AIRSAR now has a fully integrated data acquisition/user request/processing/delivery system through which most components of the data fulfillment process communicate via shared information within a database. The integration of these functions has reduced errors and increased throughput of processed data to customers.

  10. Design of an Airborne Portable Remote Imaging Spectrometer (PRISM) for the Coastal Ocean

    NASA Technical Reports Server (NTRS)

    Mouroulis, P.; vanGorp, B.; Green, R. O.; Cohen, D.; Wilson, D.; Randall, D.; Rodriguez, J.; Polanco, O.; Dierssen, H.; Balasubramanian, K.; Vargas, R.; Hein, R.; Sobel, H.; Eastwood, M.

    2010-01-01

    PRISM is a pushbroom imaging spectrometer currently under development at the Jet Propulsion Laboratory, intended to address the needs of airborne coastal ocean science research. We describe here the instrument design and the technologies that enable it to achieve its distinguishing characteristics. PRISM covers the 350-1050 nm range with a 3.1 nm sampling and a 33(deg) field of view. The design provides for high signal to noise ratio, high uniformity of response, and low polarization sensitivity. The complete instrument also incorporates two additional wavelength bands at 1240 and 1610 nm in a spot radiometer configuration to aid with atmospheric correction.

  11. Using Hyperspectral Imagery to Identify Turfgrass Stresses

    NASA Technical Reports Server (NTRS)

    Hutto, Kendall; Shaw, David

    2008-01-01

    The use of a form of remote sensing to aid in the management of large turfgrass fields (e.g. golf courses) has been proposed. A turfgrass field of interest would be surveyed in sunlight by use of an airborne hyperspectral imaging system, then the raw observational data would be preprocessed into hyperspectral reflectance image data. These data would be further processed to identify turfgrass stresses, to determine the spatial distributions of those stresses, and to generate maps showing the spatial distributions. Until now, chemicals and water have often been applied, variously, (1) indiscriminately to an entire turfgrass field without regard to localization of specific stresses or (2) to visible and possibly localized signs of stress for example, browning, damage from traffic, or conspicuous growth of weeds. Indiscriminate application is uneconomical and environmentally unsound; the amounts of water and chemicals consumed could be insufficient in some areas and excessive in most areas, and excess chemicals can leak into the environment. In cases in which developing stresses do not show visible signs at first, it could be more economical and effective to take corrective action before visible signs appear. By enabling early identification of specific stresses and their locations, the proposed method would provide guidance for planning more effective, more economical, and more environmentally sound turfgrass-management practices, including application of chemicals and water, aeration, and mowing. The underlying concept of using hyperspectral imagery to generate stress maps as guides to efficient management of vegetation in large fields is not new; it has been applied in the growth of crops to be harvested. What is new here is the effort to develop an algorithm that processes hyperspectral reflectance data into spectral indices specific to stresses in turfgrass. The development effort has included a study in which small turfgrass plots that were, variously, healthy or

  12. Using Airborne Remote Sensing to Increase Situational Awareness in Civil Protection and Humanitarian Relief - the Importance of User Involvement

    NASA Astrophysics Data System (ADS)

    Römer, H.; Kiefl, R.; Henkel, F.; Wenxi, C.; Nippold, R.; Kurz, F.; Kippnich, U.

    2016-06-01

    Enhancing situational awareness in real-time (RT) civil protection and emergency response scenarios requires the development of comprehensive monitoring concepts combining classical remote sensing disciplines with geospatial information science. In the VABENE++ project of the German Aerospace Center (DLR) monitoring tools are being developed by which innovative data acquisition approaches are combined with information extraction as well as the generation and dissemination of information products to a specific user. DLR's 3K and 4k camera system which allow for a RT acquisition and pre-processing of high resolution aerial imagery are applied in two application examples conducted with end users: a civil protection exercise with humanitarian relief organisations and a large open-air music festival in cooperation with a festival organising company. This study discusses how airborne remote sensing can significantly contribute to both, situational assessment and awareness, focussing on the downstream processes required for extracting information from imagery and for visualising and disseminating imagery in combination with other geospatial information. Valuable user feedback and impetus for further developments has been obtained from both applications, referring to innovations in thematic image analysis (supporting festival site management) and product dissemination (editable web services). Thus, this study emphasises the important role of user involvement in application-related research, i.e. by aligning it closer to user's requirements.

  13. Potential of Airborne Imaging Spectroscopy at Czechglobe

    NASA Astrophysics Data System (ADS)

    Hanuš, J.; Fabiánek, T.; Fajmon, L.

    2016-06-01

    Ecosystems, their services, structures and functions are affected by complex environmental processes, which are both natural and human-induced and globally changing. In order to understand how ecosystems behave in globally changing environment, it is important to monitor the current status of ecosystems and their structural and functional changes in time and space. An essential tool allowing monitoring of ecosystems is remote sensing (RS). Many ecosystems variables are being translated into a spectral response recorded by RS instruments. It is however important to understand the complexity and synergies of the key ecosystem variables influencing the reflected signal. This can be achieved by analysing high resolution RS data from multiple sources acquired simultaneously from the same platform. Such a system has been recently built at CzechGlobe - Global Change Research Institute (The Czech Academy of Sciences). CzechGlobe has been significantly extending its research infrastructure in the last years, which allows advanced monitoring of ecosystem changes at hierarchical levels spanning from molecules to entire ecosystems. One of the CzechGlobe components is a laboratory of imaging spectroscopy. The laboratory is now operating a new platform for advanced remote sensing observations called FLIS (Flying Laboratory of Imaging Spectroscopy). FLIS consists of an airborne carrier equipped with passive RS systems. The core instrument of FLIS is a hyperspectral imaging system provided by Itres Ltd. The hyperspectral system consists of three spectroradiometers (CASI 1500, SASI 600 and TASI 600) that cover the reflective spectral range from 380 to 2450 nm, as well as the thermal range from 8 to 11.5 μm. The airborne platform is prepared for mounting of full-waveform laser scanner Riegl-Q780 as well, however a laser scanner is not a permanent part of FLIS. In 2014 the installation of the hyperspectral scanners was completed and the first flights were carried out with all

  14. Hyperspectral target detection using manifold learning and multiple target spectra

    DOE PAGES

    Ziemann, Amanda K.; Theiler, James; Messinger, David W.

    2016-03-31

    Imagery collected from satellites and airborne platforms provides an important tool for remotely analyzing the content of a scene. In particular, the ability to remotely detect a specific material within a scene is of critical importance in nonproliferation and other applications. The sensor systems that process hyperspectral images collect the high-dimensional spectral information necessary to perform these detection analyses. For a d-dimensional hyperspectral image, however, where d is the number of spectral bands, it is common for the data to inherently occupy an m-dimensional space with m << d. In the remote sensing community, this has led to recent interestmore » in the use of manifold learning, which seeks to characterize the embedded lower-dimensional, nonlinear manifold that the data discretely approximate. The research presented in this paper focuses on a graph theory and manifold learning approach to target detection, using an adaptive version of locally linear embedding that is biased to separate target pixels from background pixels. Finally, this approach incorporates multiple target signatures for a particular material, accounting for the spectral variability that is often present within a solid material of interest.« less

  15. On-orbit characterization of hyperspectral imagers

    NASA Astrophysics Data System (ADS)

    McCorkel, Joel

    Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne- and satellite-based sensors. Often, ground-truth measurements at these tests sites are not always successful due to weather and funding availability. Therefore, RSG has also employed automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor. This dissertation presents a method for determining the radiometric calibration of a hyperspectral imager using multispectral imagery. The work relies on a multispectral sensor, Moderate-resolution Imaging Spectroradiometer (MODIS), as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. A method to predict hyperspectral surface reflectance using a combination of MODIS data and spectral shape information is developed and applied for the characterization of Hyperion. Spectral shape information is based on RSG's historical in situ data for the Railroad Valley test site and spectral library data for the Libyan test site. Average atmospheric parameters, also based on historical measurements, are used in reflectance prediction and transfer to space. Results of several cross-calibration scenarios that differ in image acquisition coincidence, test site, and reference sensor are found for the characterization of Hyperion. These are compared with results from the reflectance-based approach of vicarious calibration, a well-documented method developed by the RSG that serves as a baseline for calibration performance for the cross-calibration method developed here. Cross-calibration provides results that are within 2% of those of reflectance-based results in most spectral regions. Larger disagreements exist

  16. Road Asphalt Pavements Analyzed by Airborne Thermal Remote Sensing: Preliminary Results of the Venice Highway

    PubMed Central

    Pascucci, Simone; Bassani, Cristiana; Palombo, Angelo; Poscolieri, Maurizio; Cavalli, Rosa

    2008-01-01

    This paper describes a fast procedure for evaluating asphalt pavement surface defects using airborne emissivity data. To develop this procedure, we used airborne multispectral emissivity data covering an urban test area close to Venice (Italy).For this study, we first identify and select the roads' asphalt pavements on Multispectral Infrared Visible Imaging Spectrometer (MIVIS) imagery using a segmentation procedure. Next, since in asphalt pavements the surface defects are strictly related to the decrease of oily components that cause an increase of the abundance of surfacing limestone, the diagnostic absorption emissivity peak at 11.2μm of the limestone was used for retrieving from MIVIS emissivity data the areas exhibiting defects on asphalt pavements surface.The results showed that MIVIS emissivity allows establishing a threshold that points out those asphalt road sites on which a check for a maintenance intervention is required. Therefore, this technique can supply local government authorities an efficient, rapid and repeatable road mapping procedure providing the location of the asphalt pavements to be checked.

  17. Evaluation of the airborne imaging spectrometer for remote sensing of forest stand conditions

    NASA Technical Reports Server (NTRS)

    Olson, Charles E., Jr.

    1986-01-01

    Five pairs of plots were established in forest stands with one of each pair trenched and covered to prevent precipitation from reaching the tree roots. High winds and falling limbs destroyed the covers on three of the plots. The two remaining plots were in a red pine plantation and in a natural stand of sugar maple. Trees in both plots developed levels of moisture stress more than nine bars higher than control trees on the dates of overflights with the Airborne Imaging Spectrometer (AIS) and the Collins' Airborne Spectroradiometer (CAS). Hemispherical reflectance from stressed and control trees was measured with a Beckman DK2A spectrophotometer. On the day of the AIS overflight, stressed maple foliage was less reflective than the control from 1000 to 1300 nm, but more reflective at wavelengths longer than 1300 nm. Pine foliage was less reflective than the control from 1000 to 1600 nm, but the difference was small at wavelengths longer than 1350 nm. AIS data collected showed brightness values for both maple and pine to be lower than for the controls from 1000 to 1300 nm. CAS data were used to determine the gain in species identification accuracy obtainable with high spectral resolution data.

  18. DETECTION AND IDENTIFICATION OF TOXIC AIR POLLUTANTS USING FIELD PORTABLE AND AIRBORNE REMOTE IMAGING SYSTEMS

    EPA Science Inventory

    Remote sensing technologies are a class of instrument and sensor systems that include laser imageries, imaging spectrometers, and visible to thermal infrared cameras. These systems have been successfully used for gas phase chemical compound identification in a variety of field e...

  19. Multispectral Imaging Systems for Airborne Remote Sensing to Support Agricultural Production Management

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing has shown promise as a tool for managing agricultural application and production. Earth-observing satellite systems have an advantage for large-scale analysis at regional levels but are limited in spatial resolution. High-resolution satellite systems have been available in recent year...

  20. The use of hyperspectral data to estimate soil properties

    NASA Astrophysics Data System (ADS)

    Wu, Yong

    Recently there has been a significant increase in the amount of satellite and airborne remotely sensed data (e.g., multi- and hyperspectral) available for use in engineering. The use of these types of data for the assessment of soil properties requires the identification and quantitative estimate of key parameters from soil spectral measurement. This study focuses on soil water content and soil composition. Spectral response of soils with different water content was studied. Artificial neural networks were developed to predict water content of soils from their spectral response. The predicted water contents are in good agreement with the actual water contents. The study could serve as an initial step for developing in-situ water content determination techniques. Spectral un-mixing was also discussed in this study. The N-FINDER algorithm was adapted to find the end-members in a hyperspectral data set. The linear mixture model and the Orthogonal Subspace Projection (OSP) algorithm were used to calculate the fractions of end-members. It is shown that the linear model and OSP can give a fairly good estimate of the end-member abundances. A hyperspectral imaging software tool was used to analyze an AVIRIS data set following a proposed processing routine. The abundance maps of end-members were generated. It is expected that the spectrally determined soil information can be used along with other soil engineering correlations to help engineers characterize a site.