Science.gov

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. Application of high spatial resolution airborne hyperspectral remote sensing data in thematic information extraction

    NASA Astrophysics Data System (ADS)

    Xu, Hong-gen; Ma, Hong-chao; Li, De-ren; Song, Yan

    2006-10-01

    The airborne hyperspectral remote sensing data, such as PHI, OMIS, has the virtues of high spatial and spectral resolution. Hence, from the view of target classification we can consider that it can provide the ability of discriminating targets more detailedly than other data. So it's important to extract thematic information and update database using this kind of data. Whereas, the hyperspectral data has abundant bands and high between-band correlation, the traditional classification methods such as maximum likelihood classifier (MLC) and spectral angle mapper (SAM) have performed poorly in thematic information extraction. For this reason, we present a new method for thematic information extraction with hyperspectral remote sensing data. We perform classification by means of combining the self-organizing map (SOM) neural network which is considered as full-pixel technique with linear spectral mixture analysis (LSMA) which is considered as mixed-pixel technique. The SOM neural network is improved from some aspects to classify the pure data and find the mixed data. And then the mixed data are unmixed and classified by LSMA. The result of experiment shows that we can have the better performance in thematic information extraction with PHI by this means.

  9. 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

  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. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. Design of an airborne Fourier transform visible hyperspectral imaging system for light aircraft environmental remote sensing

    NASA Astrophysics Data System (ADS)

    Otten, Leonard John, III; Butler, Eugene W.; Rafert, Bruce; Sellar, R. Glenn

    1995-06-01

    Kestrel Corporation and the Florida Institute of Technology have designed, and are now manufacturing, a Fourier transform visible hyperspectral imager system for use in a single engine light aircraft. The system is composed of a Sagnac-based interferometer optical subsystem, a data management system, and an aircraft attitude and current position sybsystem. The system is designed to have better than 5 nm spectral resolution at 450 nm, operates over the 440 nm to 1150 nm spectral band and has a 2D spatial resolution of 0.8 mrad. An internal calibration source is recorded with every frame of data to retain radiometric accuracy. The entire system fits into a Cessna 206 and uses a conventional downward looking view port located in the baggage compartment. During operation, data are collected at a rate of 15 Mbytes per second and stored direct to a disk array. Data storage has been sized to accommodate 56 minutes of observations. Designed for environmental mapping, this Fourier transform imager has uses in emergency response and military operations.

  1. 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.

  2. Airborne hyperspectral detection of small changes.

    PubMed

    Eismann, Michael T; Meola, Joseph; Stocker, Alan D; Beaven, Scott G; Schaum, Alan P

    2008-10-01

    Hyperspectral change detection offers a promising approach to detect objects and features of remotely sensed areas that are too difficult to find in single images, such as slight changes in land cover and the insertion, deletion, or movement of small objects, by exploiting subtle differences in the imagery over time. Methods for performing such change detection, however, must effectively maintain invariance to typically larger image-to-image changes in illumination and environmental conditions, as well as misregistration and viewing differences between image observations, while remaining sensitive to small differences in scene content. Previous research has established predictive algorithms to overcome such natural changes between images, and these approaches have recently been extended to deal with space-varying changes. The challenges to effective change detection, however, are often exacerbated in an airborne imaging geometry because of the limitations in control over flight conditions and geometry, and some of the recent change detection algorithms have not been demonstrated in an airborne setting. We describe the airborne implementation and relative performance of such methods. We specifically attempt to characterize the effects of spatial misregistration on change detection performance, the efficacy of class-conditional predictors in an airborne setting, and extensions to the change detection approach, including physically motivated shadow transition classifiers and matched change filtering based on in-scene atmospheric normalization. PMID:18830283

  3. 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...

  4. Operational multi-angle hyperspectral remote sensing for feature detection

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Brooks, Donald K.

    2013-10-01

    Remote sensing results of land and water surfaces from airborne and satellite platforms are dependent upon the illumination geometry and the sensor viewing geometry. Correction of pushbroom hyperspectral imagery can be achieved using bidirectional reflectance factors (BRF's) image features based upon their multi-angle hyperspectral signatures. Ground validation of features and targets utilize non-imaging sensors such as hemispherical goniometers. In this paper, a new linear translation based hyperspectral imaging goniometer system is described. Imagery and hyperspectral signatures obtained from a rotation stage platform and the new linear non-hemispherical goniometer system shows applications and a multi-angle correction approach for multi-angle hyperspectral pushbroom imagery corrections. Results are presented in a manner in order to describe how ground, vessel and airborne based multi-angle hyperspectral signatures can be applied to operational hyperspectral image acquisition by the calculation of hyperspectral anisotropic signature imagery. The results demonstrate the analysis framework from the systems to water and coastal vegetation for exploitation of surface and subsurface feature or target detection based using the multi-angle radiative transfer based BRF's. The hyperspectral pushbroom multi-angle analysis methodology forms a basis for future multi-sensor based multi-angle change detection algorithms.

  5. Mapping Waterhyacinth Infestations Using Airborne Hyperspectral Imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapp...

  6. 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...

  7. 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

  8. 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 ...

  9. Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

    NASA Astrophysics Data System (ADS)

    Sobrino, J. A.; Jiménez-Muñoz, J. C.; Zarco-Tejada, P. J.; Sepulcre-Cantó, G.; de Miguel, E.; Sòria, G.; Romaguera, M.; Julien, Y.; Cuenca, J.; Hidalgo, V.; Franch, B.; Mattar, C.; Morales, L.; Gillespie, A.; Sabol, D.; Balick, L.; Su, Z.; Jia, L.; Gieske, A.; Timmermans, W.; Olioso, A.; Nerry, F.; Guanter, L.; Moreno, J.; Shen, Q.

    2009-06-01

    The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.

  10. Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

    NASA Astrophysics Data System (ADS)

    Sobrino, J. A.; Jiménez-Muñoz, J. C.; Zarco-Tejada, P. J.; Sepulcre-Cantó, G.; de Miguel, E.; Sòria, G.; Romaguera, M.; Julien, Y.; Cuenca, J.; Hidalgo, V.; Franch, B.; Mattar, C.; Morales, L.; Gillespie, A.; Sabol, D.; Balick, L.; Su, Z.; Jia, L.; Gieske, A.; Timmermans, W.; Olioso, A.; Nerry, F.; Guanter, L.; Moreno, J.; Shen, Q.

    2009-11-01

    The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.

  11. Citrus greening detection using airborne hyperspectral and multispectral imaging techniques

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging can provide unique spectral signatures for diseased vegetation. Airborne multispectral and hyperspectral imaging can be used to detect potentially infected trees over a large area for rapid detection of infected zones. This paper proposes a method to detect the citrus greening...

  12. Citrus greening disease detection using airborne multispectral and hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imaging can provide unique spectral signatures for diseased vegetation. Airborne hyperspectral imaging can be used to detect potentially infected trees over a large area for rapid detection of infected zones. Ground inspection and management can be focused on these infected zones rath...

  13. 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

  14. 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.

  15. 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.

  16. Airborne Remote Sensing

    NASA Technical Reports Server (NTRS)

    1992-01-01

    NASA imaging technology has provided the basis for a commercial agricultural reconnaissance service. AG-RECON furnishes information from airborne sensors, aerial photographs and satellite and ground databases to farmers, foresters, geologists, etc. This service produces color "maps" of Earth conditions, which enable clients to detect crop color changes or temperature changes that may indicate fire damage or pest stress problems.

  17. Hyperspectral remote sensing and geospatial modeling for monitoring invasive plant species

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing is used to show the actual distribution of distinctive invasive weeds such as leafy spurge (Euphorbia esula L.), whereas landscape modeling can show the potential distribution over an area. Geographic information system data and hyperspectral imagery [NASA JPL’s Airborne Visible Infra...

  18. 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.

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

    PubMed

    William, David J; Rybicki, Nancy B; Lombana, Alfonso V; O'Brien, Tim M; Gomez, Richard 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 real-time 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. PMID:12620030

  20. 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.

  1. Evaluating Airborne Hyperspectral imagery for mapping waterhyacinth infestations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapp...

  2. 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.

  3. 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.

  4. Detection of European Corn Borer Infestation in Iowa Corn Plots using Spectral Vegetation Indices Derived from Airborne Hyperspectral Imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing technology was used to distinguish corn infested with European corn borers, Ostrinia nubilalis, from corn that was not infested. In 2004 and 2005, eleven spectral vegetation indices that emphasize foliar plant pigments were calculated using airborne hyperspectral imagery. Manual inocu...

  5. Estimating leaf area index and aboveground biomass of an invasive weed (yellow starthistle, Centaurea solstitalis L.) using airborne hyperspectral data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote sensed data was obtained via a Compact Airborne Spectrographic Imager (CASI) and used to estimate leaf area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (Centaurea solstitialis L.). In parallel, 34 ground-based field plots were used t...

  6. 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.

  7. 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).

  8. 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.

  9. Hyperspectral and multispectral sensors for remote sensing

    NASA Astrophysics Data System (ADS)

    Miller, James; Kullar, Sukhbir; Cochrane, David; O, Nixon; Lomako, Andrey; Draijer, Cees

    2010-11-01

    Remote Hyperspectral and Multispectral sensors have been developed using modern CCD and CMOS fabrication techniques combined with advanced dichroic filters. The resulting sensors are more cost effective while maintaining the high performance needed in remote sensing applications. A single device can contain multiple imaging areas tailored to different multispectral bandwidths in a highly cost effective and reliable package. This paper discusses a five band visible to near IR scanning sensor. By bonding advanced dichroic filters onto the cover glass and directly in the imaging path a highly efficient multispectral sensor is achieved. Up to 12,000 linear pixel arrays are possible1 with this advanced filter technology approach. Individual imaging areas on the device are designed to have unique pixel sizes and clocking to enable tailored imaging performance for the individual spectral bands. Individual elements are also based on high resolution Time Delay and Integration technology2,3 (TDI) to maximize sensitivity and throughput. Additionally for hyperspectral imagers, a split frame CCD design is discussed using high sensitivity back side illuminated (BSI) processes that can achieve high quantum efficiency. As these sensors are used in remote sensing applications, device robustness and radiation tolerance was required.

  10. 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.

  11. Airborne midwave and longwave infrared hyperspectral imaging of gases

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Duval, Marc; Farley, Vincent; Chamberland, Martin

    2014-05-01

    Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C2H4) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

  12. Airborne midwave and longwave infrared hyperspectral imaging of gases

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Duval, Marc; Farley, Vincent; Chamberland, Martin

    2014-11-01

    Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C2H4) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

  13. Airborne midwave and longwave infrared hyperspectral imaging of gases

    NASA Astrophysics Data System (ADS)

    Gagnon, Marc-André; Tremblay, Pierre; Savary, Simon; Duval, Marc; Farley, Vincent; Guyot, Éric; Chamberland, Martin

    2014-10-01

    Characterization of gas clouds are challenging situations to address due to the large and uneven distribution of these fast moving entities. Whether gas characterization is carried out for gas leaks surveys or environmental monitoring purposes, explosives and/or toxic chemicals are often involved. In such situations, airborne measurements present distinct advantages over ground based-techniques since large areas can be covered efficiently from a safe distance. In order to illustrate the potential of airborne thermal infrared hyperspectral imaging for gas cloud characterization, measurements were carried out above smokestacks and a ground-based gas release experiment. Quantitative airborne chemical images of carbon monoxide (CO) and ethylene (C2H4) were obtained from measurements carried out using a midwave (MWIR, 3-5 μm) and a longwave (LWIR, 8-12 μm) airborne infrared hyperspectral sensor respectively. Scattering effects were observed in the MWIR experiments on smokestacks as a result of water condensation upon rapid cool down of the hot emission gases. Airborne measurements were carried out using both mapping and targeting acquisition modes. The later provides unique time-dependent information such as the gas cloud direction and velocity.

  14. 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 ...

  15. Satellite Remote Sensing Hyperspectral Data Simulator

    NASA Astrophysics Data System (ADS)

    Liu, X.; Zhou, D. K.; Larar, A.; Li, H.; Wu, W.; Kizer, S.; Huang, X.

    2013-12-01

    Hyperspectral data from satellite remote sensors provide abundant information on atmospheric temperature, water, trace gases, clouds, and surface. Fast and accurate radiative transfer models are needed to perform and link GCM Observing System Simulation Experiment (OSSE) with remote sensing data. We have developed a Principal Component-based Radiative Transfer Model, which is capable of simulating Top of Atmosphere (TOA) radiance (or reflectance) from far-IR to UV-VIS spectral region. It is 3-4 orders of magnitude faster than line-by-line radiative transfer models. For example, it takes about 10 miliseconds to simulate one IASI spectrum with 8461 spectral channels in IR spectral region. It is about 900 times faster than MODTRAN fast model to simulate SCHIAMACHY data in solar spectral region. It has been validated using real satellite data such as AIRS, IASI, CrIS, and SCHIAMACHY satellite data and it can be easily applied to other satellite data.

  16. Airborne surveillance of water basins with hyperspectral FLS-LiDAR

    NASA Astrophysics Data System (ADS)

    Babichenko, S.; Alekseyev, V.; Lapimaa, J.; Lisin, A.; Poryvkina, L.; Shchemelyov, S.; Sobolev, I.; Vint, L.

    2010-10-01

    The airborne FLS-Lidars are based on the method of Laser Induced Fluorescence (LIF) and aimed at the analytical remote sensing of water objects. Scanning the laser beam across the flight trajectory and recording the comprehensive LIF spectrum with hyperspectral detector per every laser pulse provide detail maps of spectral properties of the water basins. A multi-tier model for integrated environmental assessment is applied for further analysis of this information to combine the benefits of "big-picture" capability of remote sensing techniques and GIS solutions with localized on-theground environmental data gathering. In this concept far looking satellite and airborne systems provide the highest tier information. The airborne data acquisition with FLS-Lidar is considered as the middle tier characterized by vast amount of LIF data with high spatial (less than 10 m) and spectral (less than 5 nm in UV/VIS spectral ranges) resolution. The lower tier is anchored with the geographical locations of important findings detected at the middle tier. Taken water samples are analyzed with fastscreening technology of Spectral Fluorescence Signatures (SFS) giving more analytical qualitative and quantitative results. And the base tier includes detail laboratory analysis of characteristic samples selected at the lower tier. Precisely geo-referenced LIF data of hyperspectral FLS-Lidar anchored to and calibrated by the ground SFS data allows detection of pollution incidents and mapping of environmental trends over vast water systems like coastal zone, lakes and rivers.r

  17. ASPIS, A Flexible Multispectral System for Airborne Remote Sensing Environmental Applications

    PubMed Central

    Papale, Dario; Belli, Claudio; Gioli, Beniamino; Miglietta, Franco; Ronchi, Cesare; Vaccari, Francesco Primo; Valentini, Riccardo

    2008-01-01

    Airborne multispectral and hyperspectral remote sensing is a powerful tool for environmental monitoring applications. In this paper we describe a new system (ASPIS) composed by a 4-CCD spectral sensor, a thermal IR camera and a laser altimeter that is mounted on a flexible Sky-Arrow airplane. A test application of the multispectral sensor to estimate durum wheat quality is also presented.

  18. Hyperspectral remote sensing for soil organic carbon mapping

    NASA Astrophysics Data System (ADS)

    Stevens, A.; van Wesemael, B.

    2009-04-01

    Satellite and airborne hyperspectral remote sensing is increasingly investigated as a fast and convenient tool to map soil properties. However, several research have pointed out the difficulty to obtain good calibration results over large areas due to spatial variation in soil types and surface soil conditions (moisture content, roughness, vegetation cover). These effects induce a spectral variability not directly related to the property studied and decrease the accuracy of predictions. A flight campaign was organized on 4-9th October 2007 using the AHS-160 airborne spectrometer to predict Soil Organic Carbon (SOC) in bare cropland soils in Grand-Duchy of Luxembourg. The study area consisted in a north-south transect of ~7 km width and ~60 km length and crossed 4 of the 5 agro-geological regions of Luxembourg, characterized by various soil types such as Cambisols, Luvisols, Arenosols and Calcisols. After collecting more than 300 soil samples of the soil surface, spectral data was related with SOC content using several standard multivariate calibration techniques (Partial Least Square Regression, Penalized-spline Regression, Support Vector Machine). It is shown that calibrations yield reasonably accurate predictions over large areas as long as secondary information (e.g. soil types, agro-pedological regions) are included in the models (Root Mean Square Error of Prediction: ~3 g C kg-1). Such calibration models could be applied to every soil pixel of the hyperspectral image to produce a SOC map of the area. However, predictions have been realized using statistical relationships based on a set of calibration randomly chosen from a set of samples collected during a field campaign, the rest being used for validation purposes. It means that the validation set is not completely independent from the calibration set. As a consequence, a true independent validation (over fields not covered by the calibration/validation sets) would probably give lower accuracies than the ones

  19. 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.

  20. 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.

  1. 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...

  2. Diffused Matrix Format: A New Storage and Processing Format for Airborne Hyperspectral Sensor Images

    PubMed Central

    Martínez, Pablo; Cristo, Alejandro; Koch, Magaly; Pérez, Rosa Mª.; Schmid, Thomas; Hernández, Luz M.

    2010-01-01

    At present, hyperspectral images are mainly obtained with airborne sensors that are subject to turbulences while the spectrometer is acquiring the data. Therefore, geometric corrections are required to produce spatially correct images for visual interpretation and change detection analysis. This paper analyzes the data acquisition process of airborne sensors. The main objective is to propose a new data format called Diffused Matrix Format (DMF) adapted to the sensor's characteristics including its spectral and spatial information. The second objective is to compare the accuracy of the quantitative maps derived by using the DMF data structure with those obtained from raster images based on traditional data structures. Results show that DMF processing is more accurate and straightforward than conventional image processing of remotely sensed data with the advantage that the DMF file structure requires less storage space than other data formats. In addition the data processing time does not increase when DMF is used. PMID:22399919

  3. 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

  4. Hyperspectral remote sensing algorithms for retrieving forest chlorophyll content

    NASA Astrophysics Data System (ADS)

    Zhang, Yongqin

    Quantitative estimates of forest chlorophyll content from hyperspectral remote sensing are of great use for terrestrial carbon cycle modeling and sustainable forest management. Open forest canopies present a big challenge for the separation of the effects from canopy structure and leaf optical properties, and thus the retrieval of biochemical parameters. Process-based algorithms were developed to estimate the chlorophyll content of broadleaves and needleleaves from hyperspectral measurements. Field experiments were conducted from 2003 to 2004 near Sudbury and Haliburton, Ontario, to collect canopy structural, leaf biophysical and biochemical data. Experiments show that optical properties and biochemical contents of broadleaves change with the growing season and canopy height. Needleleaves from different sites, age classes, and branch orientations demonstrate different visible optical properties in relation to their chlorophyll contents. A process-based radiative transfer model PROSPECT was modified to retrieve leaf chlorophyll content from measured leaf spectra. For broadleaves, leaf thickness was introduced to consider the seasonal and canopy-gradient variation in light absorption. The accuracy of chlorophyll retrieval is increased from 67% to 91%. For needleleaves, the effects of needleleaf width and thickness, and geometrical effects of leaf-holding devices on spectra measurements were taken into account. These modifications improve the accuracy of chlorophyll retrieval from 31% to 59%. Correct exposure for digital hemispherical photographs is crucial for estimating canopy structural parameters. A photographic exposure theory was tested for different forest types with various canopy closures and under different sky conditions. The exposure method improves the estimates of leaf area index by 40% in comparison with commonly used automatic exposure. The effects of canopy structure on optical remote sensing signals were investigated using the geometrical

  5. 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

  6. 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%.

  7. 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

  8. 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...

  9. 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...

  10. Spectral Unmixing of airborne hyperspectral imagery for mapping giant reed infestations

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spectral unmixing techniques applied to hyperspectral imagery were examined for mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems and riparian areas throughout the southern United States and northern Mexico. Airborne hyperspectral imagery with 102...

  11. Mapping of Soil Properties Using Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Dutta, D.; Goodwell, A. E.; Greenberg, J. A.; Kumar, P.; Darmody, R. G.; Garvey, J. E.; Jacobson, R. B.; Beretta, D. P.

    2013-12-01

    This study presents a novel framework for the quantification of surface soil properties over very large areas and at a very high spatial resolution using high resolution imaging spectroscopy from airborne sensors. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data collected by NASA immediately after the massive 2011 Mississippi floods at the Birds Point New Madrid (BPNM) floodway was used for quantitative prediction of soil surface textural properties such as percentages of sand, silt and clay and a suit of other chemical properties such as Mg, Al, Ca, K, Cu, P, Mn, Soil Organic Matter, S, B, Fe and Zn. The visible, near infrared and shortwave infrared region of the AVIRIS reflectance spectrum was used together with an automatic variable selection lasso algorithm to yield empirical models for the prediction of various soil properties using a set of field soil sample data which were analyzed in the laboratory for calibration of the models. The linear modeling framework was made rigorous by using an ensemble of bootstrapping techniques for selecting the set of predictors and determining the final model coefficients. The modeling results not only revealed the feasibility of quantifying the different properties using this approach but also showed that some of these properties can be predicted with high accuracy. The prediction models were further used for a pixel by pixel quantification of the soil properties resulting in maps showing the spatial extents over large areas of each of the properties in the entire BPNM floodway. The fine spatial resolution of 7.6m of these maps also revealed many interesting spatial patterns and correlations with the underlying topography immediately disturbed by a massive flooding event. This study employs hyperspectral remote sensing for the quantification of soil properties using AVIRIS data and examines the signatures of disasters such as floods on landscapes which has not been explored previously and paves the way for

  12. 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

  13. Combining hyperspectral imaging and Raman spectroscopy for remote chemical sensing

    NASA Astrophysics Data System (ADS)

    Ingram, John M.; Lo, Edsanter

    2008-04-01

    The Photonics Research Center at the United States Military Academy is conducting research to demonstrate the feasibility of combining hyperspectral imaging and Raman spectroscopy for remote chemical detection over a broad area of interest. One limitation of future trace detection systems is their ability to analyze large areas of view. Hyperspectral imaging provides a balance between fast spectral analysis and scanning area. Integration of a hyperspectral system capable of remote chemical detection will greatly enhance our soldiers' ability to see the battlefield to make threat related decisions. It can also queue the trace detection systems onto the correct interrogation area saving time and reconnaissance/surveillance resources. This research develops both the sensor design and the detection/discrimination algorithms. The one meter remote detection without background radiation is a simple proof of concept.

  14. Detection of Extreme Climate Event Impacts to Terrestrial Productivity From Airborne Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Desai, A. R.; DuBois, S.; Singh, A.; Serbin, S.; Goulden, M.; Baldocchi, D. D.; Oechel, W. C.; Kruger, E. L.; Townsend, P. A.

    2015-12-01

    Changes in drought frequency and intensity are likely to be some of the largest climate anomalies to influence the net productivity of ecosystems, especially in already water-limited regions. However, the physiological mechanisms that drive this response are limited by primarily infrequent and small-scale leaf-level measurements. Here, we integrated eddy covariance flux tower estimates of gross primary productivity (GPP) across an elevation-gradient in California with airborne imagery from the NASA HyspIRI Preparatory campaign to evaluate the potential of hyperspectral imagery to detect responses of GPP to prolonged drought. We observed a number of spectral features in the visible, infrared, and shortwave infrared regions that yielded stronger linkages than traditional broadband indices with space and time variation in GPP across a range of ecosystems in California experiencing water stress in the past three years. Further, partial least squares regression (PLSR) modeling offers the ability to generate predictive models of GPP from narrowband hyperspectral remote sensing that directly links plant chemistry and spectral properties to productivity, and could serve as a significant advance over broadband remote sensing of GPP anomalies.

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. [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. PMID:22097816

  6. New progress in study on vegetation models for hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Tong, Qingxi; Zhao, Yongchao; Zhang, Xia; Zhang, Bing; Zheng, Lanfen

    2001-02-01

    Some new vegetation models for hyperspectral remote sensing are provided in this paper. They are Derivative Spectral Model (DSM), Multi-temporal Index Image Cube Model (MIIC), Hybrid Decision Tree Model (HDT) and Correlation Simulating Analysis Model (CSAM). All models are developed and used to process the images acquired by Airborne Pushbroom Hyperspectral Imager (PHI) in Changzhou area, China, 1999. Some successful applications are provided and evaluated. The results show that DSM has the ability of eliminating the background interference of vegetation analysis. MIIC is a viable method for monitoring dynamic change of land cover and vegetation growth stages. HDT is effective in precise classification of rice land while CSAM provide a possibility and theoretical basis for crop identification, breed classification, and land information extraction especially for rice.

  7. An infrared hyperspectral sensor for remote sensing of gases in the atmosphere

    NASA Astrophysics Data System (ADS)

    Sabbah, Samer; Rusch, Peter; Gerhard, Jõrn-Hinnrich; Harig, Roland

    2010-10-01

    Remote sensing by infrared spectroscopy allows identification and quantification of atmospheric gases as well as airborne pollutants. Infrared hyperspectral sensors deliver high spectral and spatial resolution images of a scene. By analyzing the spectra, gas emissions, for example from industrial plants, chemical accidents, or ships can be identified and quantified from long distances. The image of the cloud can be used to pinpoint the source of the gas as well as to assess the dimension and the dispersion of the cloud. A hyperspectral sensor based on the method of Fourier-transform spectroscopy has been developed. A cube corner Michelson interferometer with large optical apertures has been designed specifically for the task. In addition, the system encompasses a cooled infrared focal plane array detector, a calibration source, and a video camera. The system is compact and field portable. Field measurements were conducted on ship exhausts. Gas clouds were successfully visualized and identified.

  8. 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

  9. Hyperspectral sounding: a revolutionary advance in atmospheric remote sensing

    NASA Astrophysics Data System (ADS)

    Smith, W. L., Sr.; Revercomb, Henry E.; Zhou, Daniel K.; Huang, Hung-Lung A.

    2005-01-01

    Hyperspectral remote sounding was introduced with the High spectral resolution Interferometer Sounder (HIS) that flew on the NASA ER-2 aircraft in the mid-1980s. The results from the HIS demonstrated that high vertical resolution sounding information could be achieved using quasi-continuous spectra of the atmosphere"s radiance to space. This has led to a series of research and operational satellite instruments designed to exploit the hyperspectral resolution sounding approach. The experimental versions, the ADEOS IMG (Interferometer for the Measurement of trace Gases) and the Aqua AIRS (Atmospheric InfraRed Sounder) have already been orbited. The IASI (Infrared Atmospheric Sounding Interferometer) and the CrIS (Cross-track Infrared Sounder) instruments are soon to be orbited on the METOP and the NPP/NPOESS operational series of polar orbiting satellites, respectively. Geostationary satellite hyperspectral resolution sounding instrumentation was initiated with the experimental GIFTS (Geostationary Imaging Fourier Transform Spectrometer) instrument whose development is providing risk reduction for the next generation of operational geostationary satellite instruments (e.g., the GOES-R Hyperspectral Environmental Suite, HES). This presentation traces the evolution of the hyperspectral resolution sounding program. Intercomparisons of the different satellite instrument approaches are discussed. Experimental results from the current aircraft and experimental satellite systems are presented to demonstrate the power of the hyperspectral resolution sounding technique.

  10. Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing.

    PubMed

    Zomer, R J; Trabucco, A; Ustin, S L

    2009-05-01

    Recent advances in remote sensing provide opportunities to map plant species and vegetation within wetlands at management relevant scales and resolutions. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space-based sensors that can document detailed information on the distribution of vegetation community types, and sometimes species. Development of spectral libraries of wetland species is a key component needed to facilitate advanced analytical techniques to monitor wetlands. Canopy and leaf spectra at five sites in California, Texas, and Mississippi were sampled to create a common spectral library for mapping wetlands from remotely sensed data. An extensive library of spectra (n=1336) for coastal wetland communities, across a range of bioclimatic, edaphic, and disturbance conditions were measured. The wetland spectral libraries were used to classify and delineate vegetation at a separate location, the Pacheco Creek wetland in the Sacramento Delta, California, using a PROBE-1 airborne hyperspectral data set (5m pixel resolution, 128 bands). This study discusses sampling and collection methodologies for building libraries, and illustrates the potential of advanced sensors to map wetland composition. The importance of developing comprehensive wetland spectral libraries, across diverse ecosystems is highlighted. In tandem with improved analytical tools these libraries provide a physical basis for interpretation that is less subject to conditions of specific data sets. To facilitate a global approach to the application of hyperspectral imagers to mapping wetlands, we suggest that criteria for and compilation of wetland spectral libraries should proceed today in anticipation of the wider availability and eventual space-based deployment of advanced hyperspectral high spatial resolution sensors. PMID:18395960

  11. 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.

  12. 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.

  13. 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

  14. Geometric correction of synchronous scanned Operational Modular Imaging Spectrometer II hyperspectral remote sensing images using spatial positioning data of an inertial navigation system

    NASA Astrophysics Data System (ADS)

    Zhou, Xiaohu; Neubauer, Franz; Zhao, Dong; Xu, Shichao

    2015-01-01

    The high-precision geometric correction of airborne hyperspectral remote sensing image processing was a hard nut to crack, and conventional methods of remote sensing image processing by selecting ground control points to correct the images are not suitable in the correction process of airborne hyperspectral image. The optical scanning system of an inertial measurement unit combined with differential global positioning system (IMU/DGPS) is introduced to correct the synchronous scanned Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing images. Posture parameters, which were synchronized with the OMIS II, were first obtained from the IMU/DGPS. Second, coordinate conversion and flight attitude parameters' calculations were conducted. Third, according to the imaging principle of OMIS II, mathematical correction was applied and the corrected image pixels were resampled. Then, better image processing results were achieved.

  15. 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

  16. [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. PMID:25532352

  17. 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.

  18. 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

  19. 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.

  20. First results of ground-based LWIR hyperspectral imaging remote gas detection

    NASA Astrophysics Data System (ADS)

    Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Wang, Hai-yang; Fu, Yan-peng; Liao, Ning-fang; Su, Jun-hong

    2014-11-01

    The new progress of ground-based long-wave infrared remote sensing is presented. The LWIR hyperspectral imaging by using the windowing spatial and temporal modulation Fourier spectroscopy, and the results of outdoor ether gas detection, verify the features of LWIR hyperspectral imaging remote sensing and technical approach. It provides a new technical means for ground-based gas remote sensing.

  1. Narrowband vegetation index performance using the AVIRIS hyperspectral remotely sensed data

    NASA Astrophysics Data System (ADS)

    Zhang, Lifu; Yan, Lei; Yang, Shaowen

    2006-10-01

    The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis, standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a pixel's reflectance. The later explanation is much more comprehensible than the former for the reason of additional supplementary pattern presence when necessary. The vegetation index based on the UPDM (VIUPD) is expressed as a linear sum of the pattern decomposition coefficients. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), and an enhanced vegetation index (EVI). This paper described the calculation of VIUPD, using AVIRIS airborne remotely sensed data. The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI.

  2. 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.

  3. 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...

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil hyperspectral reflectance imagery was obtained from an airborne imaging spectrometer (400 to 2450 nm with ~10 nm resolution, 2.5 m spatial resolution) flown over six tilled (bare soil) agricultural fields on the Eastern Shore of the Chesapeake Bay (Queen Anne’s county, MD). Surface soil samples...

  5. Applying linear spectral unmixing to airborne hyperspectral imagery for mapping crop yield variability.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from one grain sorghum field and a cotton field. A pair of plant and soil spect...

  6. Upgraded airborne scanner for commercial remote sensing

    NASA Astrophysics Data System (ADS)

    Chang, Sheng-Huei; Rubin, Tod D.

    1994-06-01

    Traditional commercial remote sensing has focused on the geologic market, with primary focus on mineral identification and mapping in the visible through short-wave infrared spectral regions (0.4 to 2.4 microns). Commercial remote sensing users now demand airborne scanning capabilities spanning the entire wavelength range from ultraviolet through thermal infrared (0.3 to 12 microns). This spectral range enables detection, identification, and mapping of objects and liquids on the earth's surface and gases in the air. Applications requiring this range of wavelengths include detection and mapping of oil spills, soil and water contamination, stressed vegetation, and renewable and non-renewable natural resources, and also change detection, natural hazard mitigation, emergency response, agricultural management, and urban planning. GER has designed and built a configurable scanner that acquires high resolution images in 63 selected wave bands in this broad wavelength range.

  7. 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.

  8. 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

  9. Canopy chlorophyll estimation with hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Gao, Jincheng

    In this research, proximal measurements of hyperspectral reflectance were used to develop models for estimating chlorophyll content in tallgrass prairie at leaf and canopy scales. Models were generated at the leaf scale and then extended to the canopy scale. Three chlorphyll estimation models were developed, one based on reflectance spectra and two derived from derivative transformations of the reflectance spectra. The triangle chlorophyll index (TCI) model was derived from the reflectance spectrum, whereas the first and second derivative indices (FDI and SDI) models were developed from the derivative transformed spectra. The three models were found to be well-correlated with the chlorophyll content measured with solvent extraction. The result indicated that the three models were effective for the leaf scale estimates of chlorophyll content. The three chlorophyll models developed at the leaf scale were further extended to the canopy scale and fine-scale images. The three models were found to be conditionally effective for estimating canopy chlorophyll content. The TCI model was more effective in dense vegetation, and the FDI and SDI models were better in sparser vegetation. This research suggests that the extension of chlorophyll models from the leaf scale to canopy scale is complex and affected not only by soil background, but also by canopy structure and components.

  10. A Synergistic Approach to Atmospheric Compensation of Neon's Airborne Hyperspectral Imagery Utilizing an Airborne Solar Spectral Irradiance Radiometer

    NASA Astrophysics Data System (ADS)

    Wright, L.; Karpowicz, B. M.; Kindel, B. C.; Schmidt, S.; Leisso, N.; Kampe, T. U.; Pilewskie, P.

    2014-12-01

    A wide variety of critical information regarding bioclimate, biodiversity, and biogeochemistry is embedded in airborne hyperspectral imagery. Most, if not all of the primary signal relies upon first deriving the surface reflectance of land cover and vegetation from measured hyperspectral radiance. This places stringent requirements on terrain, and atmospheric compensation algorithms to accurately derive surface reflectance properties. An observatory designed to measure bioclimate, biodiversity, and biogeochemistry variables from surface reflectance must take great care in developing an approach which chooses algorithms with the highest accuracy, along with providing those algorithms with data necessary to describe the physical mechanisms that affect the measured at sensor radiance. The Airborne Observation Platform (AOP) part of the National Ecological Observatory Network (NEON) is developing such an approach. NEON is a continental-scale ecological observation platform designed to collect and disseminate data to enable the understanding and forecasting of the impacts of climate change, land use change, and invasive species on ecology. The instrumentation package used by the AOP includes a visible and shortwave infrared hyperspectral imager, waveform LiDAR, and high resolution (RGB) digital camera. In addition to airborne measurements, ground-based CIMEL sun photometers will be used to help characterize atmospheric aerosol loading, and ground validation measurements with field spectrometers will be made at select NEON sites. While the core instrumentation package provides critical information to derive surface reflectance of land surfaces and vegetation, the addition of a Solar Spectral Irradiance Radiometer (SSIR) is being investigated as an additional source of data to help identify and characterize atmospheric aerosol, and cloud contributions contributions to the radiance measured by the hyperspectral imager. The addition of the SSIR provides the opportunity to

  11. Airborne multidimensional integrated remote sensing system

    NASA Astrophysics Data System (ADS)

    Xu, Weiming; Wang, Jianyu; Shu, Rong; He, Zhiping; Ma, Yanhua

    2006-12-01

    In this paper, we present a kind of airborne multidimensional integrated remote sensing system that consists of an imaging spectrometer, a three-line scanner, a laser ranger, a position & orientation subsystem and a stabilizer PAV30. The imaging spectrometer is composed of two sets of identical push-broom high spectral imager with a field of view of 22°, which provides a field of view of 42°. The spectral range of the imaging spectrometer is from 420nm to 900nm, and its spectral resolution is 5nm. The three-line scanner is composed of two pieces of panchromatic CCD and a RGB CCD with 20° stereo angle and 10cm GSD(Ground Sample Distance) with 1000m flying height. The laser ranger can provide height data of three points every other four scanning lines of the spectral imager and those three points are calibrated to match the corresponding pixels of the spectral imager. The post-processing attitude accuracy of POS/AV 510 used as the position & orientation subsystem, which is the aerial special exterior parameters measuring product of Canadian Applanix Corporation, is 0.005° combined with base station data. The airborne multidimensional integrated remote sensing system was implemented successfully, performed the first flying experiment on April, 2005, and obtained satisfying data.

  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. Hyperspectral Remote Sensing-Sensors and Applications

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral remote sensors have been traditionally used to map and monitor anthropogenic and environmental changes in the biosphere. While these sensors have proven robust for many applications, they often lack the spectral resolution necessary to differentiate characteristics of the Earth’s surfa...

  15. 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.

  16. Hyperspectral remote sensing of foliar nitrogen content.

    PubMed

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

    2013-01-15

    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. PMID:23213258

  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. 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

  19. 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

  20. [Inversion of vegetation canopy's chlorophyll content based on airborne hyperspectral image].

    PubMed

    Li, Ming-Ze; Zhao, Xiao-Hong; Liu, Yue; Lu, Wei; Dong, Shuai; Meng, Lu

    2013-01-01

    By using the airborne hyperspectral remote sensing data of Liangshui National Nature Reserve in Yichun of Heilongjiang Province, Northeast China, 15 spectral parameters including red edge area, triangular vegetation index, and normalized difference vegetation index, etc. were extracted, and in combining with 5 geographical parameters including slope, aspect, elevation, canopy density and total vegetation coverage, and by using SPAD-502, the vegetation canopy's relative chlorophyll content in the reserve were measured, with the correlations of the leaf spectral reflectivity, its first-order derivative and other deformations with the SPAD value analyzed. A prediction model for relative chlorophyll content was established by adopting the kernel-based partial least-squares regression, and a quantitative estimation of the vegetation canopy's relative chlorophyll content in the study area was carried out with the established model. The results showed that the model performed best when the sections were three and the principle components were ten. The co-efficient of determination of the model was R2 = 0.855, the mean absolute percent error was 9.6%, and the prediction precision was 89.7%. PMID:23718007

  1. 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

  2. Development of the second generation Hyperspectral Airborne Terrestrial Imager (HATI): HATI - 2500

    NASA Astrophysics Data System (ADS)

    Sandor-Leahy, S.; Thordarson, S.; Baldauf, B.; Figueroa, M.; Helmlinger, M.; Miller, H.; Reynolds, T.; Shepanski, J.

    2010-08-01

    Northrop Grumman Aerospace Systems (NGAS) has a long legacy developing and fielding hyperspectral sensors, including airborne and space based systems covering the visible through Long Wave Infrared (LWIR) wavelength ranges. Most recently NGAS has developed the Hyperspectral Airborne Terrestrial Instrument (HATI) family of hyperspectral sensors, which are compact airborne hyperspectral imagers designed to fly on a variety of platforms and be integrated with other sensors in NGAS's instrument suite. The current sensor under development is the HATI-2500, a full range Visible Near Infrared (VNIR) through Short Wave Infrared (SWIR) instrument covering the 0.4 - 2.5 micron wavelength range with high spectral resolution (3nm). The system includes a framing camera integrated with a GPS/INS to provide high-resolution multispectral imagery and precision geolocation. Its compact size and flexible acquisition parameters allow HATI-2500 to be integrated on a large variety of aerial platforms. This paper describes the HATI-2500 sensor and subsystems and its expected performance specifications.

  3. 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.

  4. 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.

  5. Probabilistic anomaly detector for remotely sensed hyperspectral data

    NASA Astrophysics Data System (ADS)

    Gao, Lianru; Guo, Qiandong; Plaza, Antonio; Li, Jun; Zhang, Bing

    2014-01-01

    Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors.

  6. 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.

  7. An algorithm for simultaneous inversion of aerosol properties and surface reflectance from airborne GeoTASO hyperspectral data

    NASA Astrophysics Data System (ADS)

    Hou, W.; Wang, J.; Xu, X.; Ding, S.; Han, D.; Leitch, J. W.; Delker, T.; Chen, G.

    2014-12-01

    This paper presents an inversion method to retrieve aerosol properties from the hyperspectral data collected by airborne GeoTASO (Geostationary Trance gas and Aerosol Sensor Optimization). Mounted on the NASA HU-25C aircraft, GeoTASO measures radiation in 1000 spectral bands from 415 nm to 696 nm, and is a prototype for the TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument. It flew over Houston during September 2013 and gathered several days' of airborne hyperspectral remote sensing data for our research. Our inversion method, which is based on the optimization theory and different from the traditional lookup table (LUT) retrieval technique, can simultaneously retrieve parameters of atmospheric aerosols such as the aerosol optical depth and other aerosol parameters, as well as the surface reflectance albedo. To provide constraints of hyperspectral surface reflectance in the inversion, we first conduct principal component analysis (PCA) using 46 reflectance spectra of various plants and vegetation to identify the most influential components. With the first six principal components and the corresponding calculated weight vector, the spectra could be reconstructed with an accuracy of 1%. UNL-VRTM (UNified Linearized Radiative Transfer Model) is employed for forward model calculation, and its outputs include not only the Stokes 4-vector elements, but also their sensitivities (Jacobians) with respect to the aerosol properties parameters and the principal components of surface spectral reflectance. The inversion is carried out with optimization algorithm L-BFGS-B (Large scale BFGS Bound constrained), and is conducted iteratively until the modeled spectral radiance fits with GeoTASO measurements. Finally, the retrieval results of aerosol optical depth and other aerosol parameters are compared against those retrieved by AEROENT and/or in situ measurements during the aircraft campaign.

  8. 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.

  9. Comparison of Airborne Multispectral and Hyperspectral Imagery for Yield Estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral and hyperspectral imagery is being used to monitor crop conditions and map yield variability. However, limited research has been conducted to compare the differences between these two types of imagery for assessing crop growth and yield. The objective of this study was to compare airbo...

  10. New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images

    NASA Astrophysics Data System (ADS)

    Xie, Huan; Luo, Xin; Xu, Xiong; Tong, Xiaohua; Jin, Yanmin; Pan, Haiyan; Zhou, Bingzhong

    2014-01-01

    Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error.

  11. Capabilities of Remote Sensing Hyperspectral Images for the Detection of Lead Contamination: a Review

    NASA Astrophysics Data System (ADS)

    Maliki, A. A.; Owens, G.; Bruce, D.

    2012-07-01

    Advances in remote sensing technologies are increasingly becoming more useful for resource, ecosystem and agricultural management applications to the extent that these techniques can now also be applied for monitoring of soil contamination and human health risk assessment. While, extensive previous studies have shown that Visible and Near Infrared Spectroscopy (VNIRS) in the spectral range 400-2500 nm can be used to quantify various soil constituents simultaneously, the direct determination of metal concentrations by remote sensing and reflectance spectroscopy is not as well examined as other soil parameters. The application of VNIRS, including laboratory hyperpectral measurements, field spectrometer measurements or image spectroscopy, generally achieves a good prediction of metal concentrations when compared to traditional wet chemical methods and has the advantage of being relatively less expensive and faster, allowing chemical assessment of contamination in close to real time. Furthermore, imaging spectroscopy can potentially provide significantly more samples over a larger spatial extent than traditional ground sampling methods. Thus the development of remote sensing techniques (field based and either airborne or satellite hyperspectral imaging) can support the monitoring and efficient mapping of metal contamination (in dust and soil) for environmental and health impact assessment. This review is concerned with the application of remote sensing and reflectance spectroscopy to the detection of heavy metals and discusses how current methods could be applied for the quantification of Pb contaminated soil surrounding mines and smelters.

  12. 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.

  13. Classification of the forest cover of Tver oblast using hyperspectral airborne imagery

    NASA Astrophysics Data System (ADS)

    Dmitriev, E. V.

    2014-12-01

    Recent research efforts have been focused on building a system of hyperspectral aerial sounding of forest vegetation on regional scales. The components of this system are developed using data obtained in the course of measurement campaigns in Tver forestry test sites. Hyperspectral airborne surveys are conducted using a Russian video spectrometer produced by the NPO Lepton company. The technique for recognizing ground-based objects is based on Bayesian classification principles with the feature space optimization. The choice of the most informative spectral channels is based on the step-up method. We propose an approach allowing the choice of channels to be more stable. We compare the classification of timber stands on the basis of hyperspectral imagery with ground-based data to demonstrate the consistency of the system developed.

  14. 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

  15. 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.

  16. 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

  17. 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

    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.

  18. 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.

  19. Measured performance of an airborne Fourier-transform hyperspectral imager

    NASA Astrophysics Data System (ADS)

    Otten, Leonard John, III; Meigs, Andrew D.; Sellar, R. Glenn; Rafert, Bruce

    1996-11-01

    A new hyperspectral imager has recently been developed by Kestrel Corporation for use in light aircraft platforms. The instrument provides 256 spectral channels with 87 cm-1 spectral bandwidth over the 450 nm to 1000 nm portion of the spectrum. Operated as a pushbroom imager, the FTVHSI has been shown to have a IFOV of 0.75 mrad, and a FOV of 0.23 rad. The sensor includes an internal spectral/radiometric calibration source, a self contained spectrally resolved downwelling sensor, and complete line of sight and GPS positioning information. The instrument is now operating from a Cessna TU-206 single engine aircraft.

  20. 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.

  1. 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.

  2. [Research on hyperspectral remote sensing in monitoring snow contamination concentration].

    PubMed

    Tang, Xu-guang; Liu, Dian-wei; Zhang, Bai; Du, Jia; Lei, Xiao-chun; Zeng, Li-hong; Wang, Yuan-dong; Song, Kai-shan

    2011-05-01

    Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well. PMID:21800591

  3. Hyperspectral remote sensing of water quality in Lake Atitlan, Guatemala

    NASA Astrophysics Data System (ADS)

    Flores Cordova, Africa Ixmucane

    Lake Atitlan in Guatemala is a vital source of drinking water. The deteriorating conditions of water quality in this lake threaten human and ecological health as well as the local and national economy. Given the sporadic and limited measurements available, it is impossible to determine the changing conditions of water quality. The goal of this thesis is to use Hyperion satellite images to measure water quality parameters in Lake Atitlan. For this purpose in situ measurements and satellite-derived reflectance data were analyzed to generate an algorithm that estimated Chlorophyll concentrations. This research provides for the first time a quantitative application of hyperspectral satellite remote sensing for water quality monitoring in Guatemala. This approach is readily transferable to other countries in Central America that face similar issues in the management of their water resources.

  4. 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.

  5. 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

  6. 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.

  7. Retrieval of cloud optical properties using airborne hyperspectral cameras during the VOCALS campaign.

    NASA Astrophysics Data System (ADS)

    Labrador, L.; Vaughan, G.

    2009-09-01

    A set of two hyperspectral imaging sensors have been used to analyze the optical properties of stratocumulus cloud off the coast of Northern Chile within the framework of the VAMOS Ocean Clouds Atmosphere Land Study (VOCALS) during September-October 2008. The SPECIM Aisa Eagle & Hawk are tandem pushbroom-type hyperspectral imagers scanning in the 400-970 and 970-2500 nm range, respectively. The instruments were mounted onboard the National Environmental Research Council's (NERC) Dornier DO-228 aircraft, based in Arica, northern Chile during the campaign. An area approximately 600 x 200 km was surveyed off the northern coast of Chile and a total of 14 science flights were carried out where hyperspectral data were successfully collected over the stratocumulus deck at altitudes varying between 10000 and 15000 ft. Cloud optical properties, such as cloud optical thickness, cloud effective radius and liquid water path can be retrieved which can then be compared with space-borne hyperspectral imagers' retrievals. Atmospheric corrections have been applied to enable the comparison between the different type of sensors and the analysis requires, amongst other, solving the back-scattering problems associated with off-nadir views. The high resolution, both spatial and temporal, of these airborne sensors makes them ideal to validate satellite retrievals of cloud optical properties.

  8. 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

  9. Estimating foliar nitrogen concentration with hyperspectral remote sensing image

    NASA Astrophysics Data System (ADS)

    Zhang, Xia; Zhang, Bing; Liu, Liangyun; Wang, Jihua

    2003-06-01

    The hyperspectral image used in this study was acquired by the airborne operative modular imaging spectrometer (OMIS) in Xiaotangshan area, Beijing, on April 26th, 2001. Accurate geometry correction and reflectance transformation was conducted on this image so that 43 image spectra were extracted to match with the canopy-level total nitrogen concentration (TN) of wheat precisely. By using methods of stepwise regression and spectrum feature analysis, characteristic bands and parameters were selected and developed for TN retrieval from the image spectra. Nitrogen distribution map was obtained by applying the best estimation equation to all wheat pixels. It turned out, the absorption depths and areas within spectral ranges 590-756nm,1096-1295nm and 1295-1642nm could be used to estimate TN. NDVI(NRCA1175.8,NRCA733.9) and NDVI(dr745,dr699.2) was the best estimator of TN (R2 = 0.8145 and 0.769 respectively). In addition, the value and distribution of TN map was quite consistent with the field measurements and growth status.

  10. Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography

    NASA Astrophysics Data System (ADS)

    Silvestri, Sonia; Marani, Marco; Marani, Alessandro

    The present paper deals with the relationship between vegetation patterns and salt marsh morphology in the Venice lagoon and with the use of remote sensing to infer salt marsh morphologic characteristics from vegetation mapping. Field measurements indicate that salt marsh vegetation species (halophytes) are reliable indicators of ground elevation and live within typical elevation ranges characterised by standard deviations of less than 5 cm. A model is then developed which uses vegetation as a morphological indicator of soil topography to estimate ground elevation from fractional cover values of each vegetation type. The use of data from an airborne remote hyperspectral sensor is presented as a means of discriminating between different salt marsh vegetation communities. Vegetation maps obtained from unmixing techniques have then been used to produce digital elevation maps (DEM) of salt marsh areas. The DEM based on halophytes cover estimates and extracted from high spatial and spectral resolution data allows a high estimation accuracy, with an error standard deviation of a few centimetres in the considered study area within the Venice lagoon. The accuracy and resolution attainable through this method are comparable and often superior to those obtained through state of the art laser altimetry.

  11. [Hyperspectral remote sensing estimation models for snow grain size].

    PubMed

    Wang, Jian-Geng; Feng, Xue-Zhi; Xiao, Peng-Feng; Liang, Ji; Zhang, Xue-Liang; Li, Hai-Xing; Li, Yun

    2013-01-01

    Snow grain size is a key parameter not only to affect the energy budget of the global or local region but also characterizing the status of snow vapor transport and temperature gradient. It is significant to monitor and estimate the snow grain size in large area for global or local climate change and water resource management. Recently, remote sensing technology has become a useful tool for snow grain size monitoring and estimating. In the present paper, the estimate models were built based on simulating the snow surface spectral reflectance curve in visible-infrared region and the sensitive bands and snow indices for snow grain size were selected. These models help estimate snow grain size by hyperspectral remote sensing. Through validating with ground true data, the results show that these models have higher explorative accuracy using 1 030, 1 260 nm and normalized difference snow index (460 and 1 030 nm). In addition, the correlation slopes of estimated and observed valves are 1.37, 0.61 and 0.62, respectively. R2 are 0.82, 0.86 and 0.93 and RMSE are 55.65, 50.83 and 35.91 microm, respectively. The result can provide a scientific basis for snow grain size monitoring and estimating. PMID:23586251

  12. 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.

  13. 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.

  14. 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.

  15. Airborne remote sensing of forest biomes

    NASA Technical Reports Server (NTRS)

    Sader, Steven A.

    1987-01-01

    Airborne sensor data of forest biomes obtained using an SAR, a laser profiler, an IR MSS, and a TM simulator are presented and examined. The SAR was utilized to investigate forest canopy structures in Mississippi and Costa Rica; the IR MSS measured forest canopy temperatures in Oregon and Puerto Rico; the TM simulator was employed in a tropical forest in Puerto Rico; and the laser profiler studied forest canopy characteristics in Costa Rica. The advantages and disadvantages of airborne systems are discussed. It is noted that the airborne sensors provide measurements applicable to forest monitoring programs.

  16. 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.

  17. 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.

  18. Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach

    NASA Astrophysics Data System (ADS)

    Mishra, Deepak R.; Narumalani, Sunil; Rundquist, Donald; Lawson, Merlin; Perk, R.

    2007-08-01

    Coral reefs and associated benthic habitats are heterogeneous in nature. A remote sensor designed to discriminate these environments requires a high number of narrow, properly placed bands which are not currently available in existing satellite sensors. Optical hyperspectral sensors mounted on aerial platforms seem to be appropriate for overcoming the lack of both high spectral and spatial resolution of satellite sensors. This research presents results of an innovative coral reef application by such a sensor. Using hyperspectral Airborne Imaging Spectroradiometer for Applications (AISA) Eagle data, the approach presented solves the confounding influence of water column attenuation on substrate reflectance on a per-pixel basis. The hyperspectral imagery was used in band ratio algorithms to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). The water column correction technique produced a bottom albedo image which revealed that the dark regions comprised of sea grasses and benthic algae had albedo values ≈15%, whereas sand- and coral-dominated areas had albedos >30% and ≈15-35%, respectively. The retrieved bottom albedo image was then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment.

  19. 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.

  20. 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...

  1. 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.

  2. 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.

  3. Airborne Multi-Angle Hyper-Spectral Measurements of White Caps on the Open Ocean

    NASA Astrophysics Data System (ADS)

    Laveigne, J.; Cairns, B.; Diner, D. J.

    2004-05-01

    The influence of whitecaps on the atmospheric correction of ocean color measurements is highly dependent on the spectral variation of albedo that is assumed for the whitecaps. Field measurements of breaking waves in the surf zone indicate a strong spectral variation in whitecap reflectance with the reflectance at 1650 nm nm decreasing by 95% relative to that at 440 nm. The cause of this spectral variation is thought to be the strong absorption by water at longer wavelengths that attenuates light reflected from submerged bubbles. Measurements made during an ocean cruise suggest that the magnitude of this decrease is typically less in the open ocean where the wave breaking is less violent and bubbles are not injected as deep into the water. Nonetheless, even in the open ocean, when whitecaps are large and bright similar decreases in reflectance from 440 nm to 860 nm to those observed in the surf zone are seen. Unfortunately, although measurements in the vicinity of 1600 and 2200 nm are important for remote sensing of aerosols and the atmospheric correction of ocean color measurements, the longest wavelength used for the open ocean measurements was 860 nm. Information about typical reflectance decreases from 440 nm to these longer wavelengths is therefore missing. One approach to remedying this absence of information about the spectral variation of white cap albedo across the solar spectrum is to use an airborne imaging spectrometer. However, a significant difficulty in using airborne, or ship-borne, instrumentation to measure the spectral albedo of whitecaps is the contamination of data by sun glitter. It is usually much more difficult than anticipated to filter data to reject glitter, even for ship-borne measurements with a television camera that provides a visual reference. This means that most data that is reported is obtained under overcast conditions. One approach to alleviating the problems caused by sun glitter is to using multi-angle remote sensing. If

  4. 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. PMID:26095901

  5. 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

  6. 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

  7. 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

  8. 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.

  9. The design and the development of a hyperspectral and multispectral airborne mapping system

    NASA Astrophysics Data System (ADS)

    Gorsevski, Pece V.; Gessler, Paul E.

    Flexible and cost-effective tools for rapid image acquisition and natural resource mapping are needed by land managers. This paper describes the hardware and software architecture of a low-cost system that can be deployed on a light aircraft for rapid data acquisition. The Hyperspectral and Multispectral Cameras for Airborne Mapping (HAMCAM) was designed and developed in the Geospatial Laboratory for Environmental Dynamics at the University of Idaho as a student-learning tool, and to enhance the existing curriculum currently offered. The system integrates a hyperspectral sensor with four multispectral cameras, an Inertial Navigation System (INS), a Wide Area Augmentation System (WAAS)-capable Global Positioning System (GPS), a data acquisition computer, and custom software for running the sensors in a variety of different modes. The outputs include very high resolution imagery obtained in four adjustable visible and near-infrared bands from the multispectral imager. The hyperspectral sensor acquires 240 spectral bands along 2.7 nm intervals within the 445-900 nm range. The INS provides aircraft pitch, roll and yaw information for rapid geo-registration of the imagery. This paper will discuss the challenges associated with the development of the system and the integration of components and software for implementation of this system for natural resource management applications. In addition, sample imagery acquired by the sensor will be presented.

  10. First Use of an Airborne Thermal Infrared Hyperspectral Scanner for Compositional Mapping

    NASA Technical Reports Server (NTRS)

    Kirkland, Laurel; Herr, Kenneth; Keim, Eric; Adams, Paul; Salisbury, John; Hackwell, John; Treiman, Allan

    2002-01-01

    In May 1999, the airborne thermal infrared hyperspectral imaging system, Spatially Enhanced Broadband Array Spectrograph System (SEBASS), was flown over Mon-non Mesa, NV, to provide the first test of such a system for geological mapping. Several types of carbonate deposits were identified using the 11.25 microns band. However, massive calcrete outcrops exhibited weak spectral contrast, which was confirmed by field and laboratory measurements. Because the weathered calcrete surface appeared relatively smooth in hand specimen, this weak spectral contrast was unexpected. Here we show that microscopic roughness not readily apparent to the eye has introduced both a cavity effect and volume scattering to reduce spectral contrast. The macroroughness of crevices and cobbles may also have a significant cavity effect. The diminished spectral contrast is important because it places higher signal-to-noise ratio (SNR) requirements for spectroscopic detection and identification. This effect should be factored into instrumentation planning and interpretations, especially interpretations without benefit of ground truth. SEBASS had the required high SNR and spectral resolution to allow us to demonstrate for the first time the ability of an airborne hyperspectral thermal infrared scanner to detect and identify spectrally subtle materials.

  11. 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.

  12. [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

  13. 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.

  14. 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

  15. 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.

  16. A methodology for luminance map retrieval using airborne hyperspectral and photogrammetric data

    NASA Astrophysics Data System (ADS)

    Pipia, Luca; Alamús, Ramon; Tardà, Anna; Pérez, Fernando; Palà, Vicenç; Corbera, Jordi

    2014-10-01

    This paper puts forward a methodology developed at the Institut Cartogràfic i Geològic de Catalunya (ICGC) to quantify upwelling light flux using hyperspectral and photogrammetric airborne data. The work was carried out in the frame of a demonstrative study requested by the municipality of Sant Cugat del Vallès, in the vicinity of Barcelona (Spain), and aimed to envisage a new approach to assess artificial lighting policies and actions as alternative to field campaigns. Hyperspectral and high resolution multispectral/panchromatic data were acquired simultaneously over urban areas. In order to avoid moon light contributions, data were acquired during the first days of new moon phase. Hyperspectral data were radiometrically calibrated. Then, National Center for Environmental Prediction (NCEP) atmospheric profiles were employed to estimate the actual Column Water Vapor (CWV) to be passed to ModTran5.0 for the atmospheric transmissivity τ calculation. At-the-ground radiance was finally integrated using the photopic sensitivity curve to generate a luminance map (cdm-2) of the flown area by mosaicking the different flight tracks. In an attempt to improve the spatial resolution and enhance the dynamic range of the luminance map, a sensor-fusion strategy was finally looked into. DMC Photogrammetric data acquired simultaneously to hyperspectral information were converted into at-the-ground radiance and upscaled to CASI spatial resolution. High-resolution (HR) luminance maps with enhanced dynamic range were finally generated by linearly fitting up-scaled DMC mosaics to the CASI-based luminance information. In the end, a preliminary assessment of the methodology is carried out using non-simultaneous in-situ measurements.

  17. Applying linear spectral unmixing to airborne hyperspectral imagery for mapping yield variability in grain sorghum and cotton fields

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study examined linear spectral unmixing techniques for mapping the variation in crop yield for precision agriculture. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery collected from a grain sorghum field and a cotton field. A pair...

  18. Remote Sensing Crop Leaf Area Index Using Unmanned Airborne Vehicles

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing with unmanned airborne vehicles (UAVs) has more potential for within-season crop management than conventional satellite imagery because: (1) pixels have very high resolution, (2) cloud cover would not prevent acquisition during critical periods of growth, and (3) quick delivery of inf...

  19. Development of an airborne remote sensing system for aerial applicators

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An airborne remote sensing system was developed and tested for recording aerial images of field crops, which were analyzed for variations of crop health or pest infestation. The multicomponent system consists of a multi-spectral camera system, a camera control system, and a radiometer for normalizi...

  20. 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.

  1. An update on SeaBED: a TesBED for validating subsurface aquatic hyperspectral remote sensing algorithms

    NASA Astrophysics Data System (ADS)

    Goodman, James A.; Vélez-Reyes, Miguel; Rosario-Torres, Samuel

    2008-10-01

    SeaBED is an integrated data set and testing infrastructure for researchers to validate subsurface aquatic remote sensing algorithms. The purpose behind developing SeaBED is to collect multiple levels of image, field, and laboratory data with which to validate physical models, inversion algorithms, feature extraction tools and classification methods for subsurface aquatic sensing using hyperspectral imaging. The focus of this testbed facility is a field site located on Enrique Reef in southwestern Puerto Rico. This field site, which includes a heterogeneous mixture of both coral reef and seagrass habitats, offers a well defined system for evaluating analysis techniques under natural environmental conditions. Data produced from the field site currently includes airborne, satellite, and field-level hyperspectral and multispectral images, in situ spectral signatures, and water bio-optical properties. This data provides a valuable combination of sensing imagery and fully characterized ground truth information for developing and validating remote sensing algorithms. A major accomplishment for SeaBED was the collection of high-resolution hyperspectral imagery and associated ground truth of the near shore reefs and coastal ecosystems in southwestern Puerto Rico in 2007. The mission included 1740 km2 of imagery acquired at 4 m spatial resolution, with 110 km2 enhanced coverage of four science areas at 1, 2, 4 and 8 m spatial resolutions to facilitate investigation of spatial scaling issues and the testing of subsurface unmixing algorithms. We present an overview of SeaBED and also describe particulars of the 2007 data collection campaign, including both image acquisition and field data collection efforts.

  2. 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

  3. 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.

  4. [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. PMID:24611400

  5. 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.

  6. 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.

  7. 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. ...

  8. 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

  9. Urban land-cover classification based on airborne hyperspectral data and field observation

    NASA Astrophysics Data System (ADS)

    Yamazaki, Fumio; Hara, Konomi; Liu, Wen

    2014-10-01

    Using a dataset from the 2013 IEEE data fusion contest, a basic study to classify urban land-cover was carried out. The spectral reflectance characteristics of surface materials were investigated from the airborne hyperspectral (HS) data acquired by CASI-1500 imager over Houston, Texas, USA. The HS data include 144 spectral bands in the visible to near-infrared (380 nm to 1050 nm) regions. A multispectral (MS) image acquired by WorldView-2 satellite was also introduced in order to compare it with the HS image. A field measurement in the Houston's test site was carried out using a handheld spectroradiometer by the present authors. The reflectance of surface materials obtained by the measurement was also compared with the pseudo-reflectance of the HS data and they showed good agreement. Finally a principal component analysis was conducted for the HS and MS data and the result was discussed.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. Best band selection of hyperspectral remote sensing image based on differential evolution algorithm

    NASA Astrophysics Data System (ADS)

    Cai, Z.; Li, Z.; Jiang, A.; Chen, X.

    2010-12-01

    The hyperspectral remote sensing makes use of spectrum resolution with the nano-scale collecting image data simultaneously in dozens or hundreds of narrow and adjacent spectral bands above the earth's surface. These hyperspectral remote sensors make it possible to derive a continuous spectrum line for each image pixel (or a special sort of material). It can acquire space information, radiated information and spectrum information of images synchronously, so that it has remarkable application value and extensive development prospect in many related fields. However, the hyperspectral remote sensing images' characteristics, such as hundreds of bands, high spectral resolution and large volumes of data, have induced many problems such as high ratio of redundant information, large-scale storage space query, long processing delay, the Hughes phenomenon and so on. The main approach to solve these problems is making dimensional reduction before the classification or visual interpretation with the hyperspectral image data. There are two main methods for dimensional reduction: feature abstraction and bands selection. Although the feature abstraction that can achieve the purpose of dimensional reduction, in the process of feature abstraction or non-linear changes in both linear transformation, it will cause the loss of the physical implication of the original image data and also make it hard to apply hyperspectral images to visual interpretation. In contrast, band selection method outperforms in terms of being more universal for application. The selected bands can not only be used as attributes (features) for classification but also synthesize RGB false color image for visual interpretation. Therefore, band selection of hyperspectral remote sensing images is an important dimensional reduction method. Here, we design a hyperspectral remote sensing image band selection algorithm based on differential evolution algorithm. Differential evolution is an evolutionary method based on

  15. 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

  16. Exploring the relationship between species discrimination and plant functional types with hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Roth, K. L.; Roberts, D. A.; Dennison, P. E.; Alonzo, M.

    2012-12-01

    Hyperspectral remote sensing data has been used extensively to map vegetation function and to classify plant functional types (PFTs) and species. Still, room remains to explore how these two exercises are related. Species-specific variations can hinder the broader applicability of models, and likewise, the role of functional differences in species discrimination has only recently been conceptually framed. The relationship between our ability to discriminate species with hyperspectral data and how species are grouped into plant functional types bears examination. Here we present an exploratory data analysis of this relationship using hyperspectral data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for approximately 56 plant species over five ecosystems. We address three main research questions: 1) How spectrally separable are species overall?; 2) Which wavelengths and functional indices/features best discriminate species and do these relate to functional differences?; and 3) What optical functional types appear to exist across species? Reflectance spectra from each site were extracted from areas of known species dominance, and a suite of vegetation indices and spectral feature parameters (e.g., red edge wavelength) were calculated. Reflectance data and index/feature data were used separately in analyses. Classification via Canonical Discriminant Analysis (CDA) was used to reduce data dimensionality and determine spectral separability across all species. The resulting kappa coefficient represents overall class separability, and the error matrix contains information on which pairs of species were more or less separable. The importance of individual variables to species discrimination was evaluated using the total structure coefficients for each function. These allowed us to identify the information a function carries that is useful for discrimination. We also calculated the potency index, a measure of the total contribution of each variable

  17. 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

  18. 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.

  19. 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...

  20. 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...

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. Analysis of Compressive Sensing for Hyperspectral Remote Sensing Applications

    NASA Astrophysics Data System (ADS)

    Busuioceanu, Maria

    Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spectral and spatial dimensions. This thesis utilizes a model of the Coded Aperture Snapshot Spectral Imager-Dual Disperser (CASSI-DD) to simulate CS measurements from traditionally sensed HyMap images. A novel reconstruction algorithm that combines spectral smoothing and spatial total variation (TV) is used to create high resolution hyperspectral imagery from the simulated CS measurements. This research examines the effect of the number of measurements, which corresponds to the percentage of physical data sampled, on the quality of simulated CS data as estimated through performance of spectral image processing algorithms. The effect of CS on the data cloud is explored through principal component analysis (PCA) and endmember extraction. The ultimate purpose of this thesis is to investigate the utility of the CS sensor model and reconstruction for various hyperspectral applications in order to identify the strengths and limitations of CS. While CS is shown to create useful imagery for visual analysis, the data cloud is altered and per-pixel spectral fidelity declines for CS reconstructions from only a small number of measurements. In some hyperspectral applications, many measurements are needed in order to obtain comparable results to traditionally sensed HSI, including atmospheric compensation and subpixel target detection. On the other hand, in hyperspectral applications where pixels must be dramatically altered in order to be misclassified, such as land classification or NDVI mapping, CS shows promise.

  6. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

    NASA Astrophysics Data System (ADS)

    Cho, Moses Azong; Skidmore, Andrew; Corsi, Fabio; van Wieren, Sipke E.; Sobhan, Istiak

    2007-12-01

    The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration ( n = 30) and test ( n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m -2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m -2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m -2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m -2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m -2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.

  7. Hyperspectral imaging for thermal analysis and remote gas sensing in the short wave infrared

    NASA Astrophysics Data System (ADS)

    Pisani, M.; Bianco, P.; Zucco, M.

    2012-07-01

    A novel hyperspectral imaging device based on Fourier transform analysis applied to a low finesse scanning Fabry-Pérot (F-P) interferometer has been demonstrated in the short wave infrared (SWIR) region. The technique allows the realization of a lightweight and compact instrument yet allowing much faster and/or better quality hyperspectral images with respect to classical instruments based on a dispersive means or on a tunable band-pass filter. The potentialities in spectroscopic applications like remote gas sensing are presented as well as accurate thermal imaging capabilities.

  8. 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

  9. An oil film information retrieval method overcoming the influence of sun glitter, based on AISA+ airborne hyper-spectral image

    NASA Astrophysics Data System (ADS)

    Zhan, Yuanzeng; Mao, Tianming; Gong, Fang; Wang, Difeng; Chen, Jianyu

    2010-10-01

    As an effective survey tool for oil spill detection, the airborne hyper-spectral sensor affords the potentiality for retrieving the quantitative information of oil slick which is useful for the cleanup of spilled oil. But many airborne hyper-spectral images are affected by sun glitter which distorts radiance values and spectral ratios used for oil slick detection. In 2005, there's an oil spill event leaking at oil drilling platform in The South China Sea, and an AISA+ airborne hyper-spectral image recorded this event will be selected for studying in this paper, which is affected by sun glitter terribly. Through a spectrum analysis of the oil and water samples, two features -- "spectral rotation" and "a pair of fixed points" can be found in spectral curves between crude oil film and water. Base on these features, an oil film information retrieval method which can overcome the influence of sun glitter is presented. Firstly, the radiance of the image is converted to normal apparent reflectance (NormAR). Then, based on the features of "spectral rotation" (used for distinguishing oil film and water) and "a pair of fixed points" (used for overcoming the effect of sun glitter), NormAR894/NormAR516 is selected as an indicator of oil film. Finally, by using a threshold combined with the technologies of image filter and mathematic morphology, the distribution and relative thickness of oil film are retrieved.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. (Cunninghamia lanceolata) caused by acid rain with hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Xie, Xiaozan; Jiang, Hong; Yu, Shuquan

    2009-10-01

    The purpose of this study is using hyperspectral data to detect the reflectance differences of Chinese fir (Cunninghamia lanceolata) which are sensitive to acidic stress and have been under different degrees of acid deposition stress for a long time. The hyperspectral reflectance for Chinese fir leaf is measured by Fieldspec Pro FR under three simulated acid rain levels (pH2.5, 4.0 and 5.6) during three years in order to monitor the response of leaf. The results indicated: (1) chlorophyll concentration of Chinese fir increased with the increasing of the simulated rain acidity in the late experimental period; (2) the 1st derivative values increased at the green edge (480-540nm) and red edge (680-760nm) with pH increasing; (3) the RVI550 and GNDVI values did differ significantly at pH2.5 and 5.6 treatment; (4) red edge position was found moving to longer wave bands with increasing rain acidity along with the experimental time; (5) there are significant differences vale at blue 510nm and 690nm wavelength between different treatments that can be used to be an useful parameters to distinguish the severity of acid deposition. The research also indicated that the hyperspectral parameters can be used to monitor the acid rain stress on trees.

  16. 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.

  17. [Independent component analysis for spectral unmixing in hyperspectral remote sensing image].

    PubMed

    Luo, Wen-Fei; Zhong, Liang; Zhang, Bing; Gao, Lian-Ru

    2010-06-01

    Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral un-mixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to un-mix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM. This embarrassment compromises ICA applicability to hyperspectral data. To overcome this problem, the present paper introduces a solution of minimization of total correlation of the components. Interestingly, with the minimization of total correlation of the components, the angle of the direction between each components is invariable. A Parallel oblique-ICA (Pob-ICA) algorithm is proposed to correct the angle of the searching direction between the components. Two novelties result from our proposed Pob-ICA algorithm. First, the algorithm completely satisfies the physical constraint conditions in LSMM and overcomes the limitation of statistical independency assumed by ICA. Second, the last component, which is missed in other existing ICA algorithms, can be estimated by our proposed algorithm. In experiments, Pob-ICA algorithm demonstrates excellent performance in the simulative and real hyperspectral images. PMID:20707164

  18. Location of the Rhine plume front by airborne remote sensing

    NASA Astrophysics Data System (ADS)

    Ruddick, K. G.; Lahousse, L.; Donnay, E.

    1994-04-01

    The aim of this study was to determine the feasibility of using airborne remote sensing to locate the Rhine plume front. Interest in fronts arises from the desire to predict the fate of pollutants and biological nutrients discharged from rivers into the open sea. Observations were made during flights over the Dutch coastal waters using a vertically-mounted video camera and a side-looking airborne radar (SLAR) designed for oil slick detection. Comparison of radar images with visual observations of the sea colour discontinuity and foam line establish that fronts can indeed be detected by SLAR because of high radar backscatter along the convergence line, where the fresh water jet impinges on saltier water. This provides a sound basis for future investigations using Synthetic Aperture Radar as mounted on ERS-1. An estimation of errors is given, identifying priorities for improvement of the technique. The accuracy achieved is considered sufficient for the validation of hydrodynamic models.

  19. 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.

    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

  20. 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

  1. 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.

  2. 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.

  3. Fusion of remotely sensed data from airborne and ground-based sensors for cotton regrowth study

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The study investigated the use of aerial multispectral imagery and ground-based hyperspectral data for the discrimination of different crop types and timely detection of cotton plants over large areas. Airborne multispectral imagery and ground-based spectral reflectance data were acquired at the sa...

  4. 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.

  5. 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

  6. Land surface emissivity retrieval from airborne hyperspectral scanner thermal infrared data over urban surfaces

    NASA Astrophysics Data System (ADS)

    Gao, C. X.; Qian, Y. G.; Wang, N.; Ma, L. L.; Jiang, X. G.

    2015-12-01

    Land surface emissivity (LSE) is a key parameter for characterizing the land surface, and is vital for a wide variety of surface-atmosphere studies. This paper retrieved LSEs of land surfaces over the city of Madrid, Spain from airborne hyperspectral scanner (AHS) thermal infrared data using temperature emissivity separation (TES) method. Six different kinds of urban surfaces: asphalt, bare soil, granite, pavement, shrub and grass pavement, were selected to evaluate the performance of the TES method in urban areas. The results demonstrate that the TES method can be successfully applied to retrieve LSEs in urban area. The six urban surfaces have similar curve shape of emissivity spectra, with the lowest emissivity in band 73, and highest in band 78; the LSE for bare soil varies significantly with spectra, approximately from 0.90 in band 72 to 0.98 in band 78, whereas the LSE for grass has the smallest spectral variation, approximately from 0.965 in band 72 to 0.974 in band 78, and the shrub presents higher LSE than other surfaces in bands 72, 73, 75-77, but a little lower in bands 78 and 79. Furthermore, it is worth noting that band 73 is suitable for discriminating different urban surfaces because large LSE differences exist in this channel for different urban surfaces.

  7. 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

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

    PubMed

    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

  9. 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., III; 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.

  10. 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

  11. 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.

  12. 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.

  13. Classifier Fusion of Hyperspectral and LIDAR Remote Sensing Data for Improvement of Land Cover Classifcation

    NASA Astrophysics Data System (ADS)

    Bigdeli, B.; Samadzadegan, F.; Reinartz, P.

    2013-09-01

    The interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for classification applications. This is because a combined use is supposed to improve the results of classification tasks compared to single-data use. This paper addressed using of combination of hyperspectral and Light Detection And Ranging (LIDAR) data in classification field. This paper presents a new method based on the definition of a Multiple Classifier System on Hyperspectral and LIDAR data. In the first step, the proposed method applied some feature extraction strategies on LIDAR data to produce more information in this data set. After that in second step, Support Vector Machine (SVM) applied as a supervised classification strategy on LIDAR data and hyperspectal data separately. In third and final step of proposed method, a classifier fusion method used to fuse the classification results on hypersepctral and LIDAR data. For comparative purposes, results of classifier fusion compared to the results of single SVM classifiers on Hyperspectral and LIDAR data. Finally, the results obtained by the proposed classifier fusion system approach leads to higher classification accuracies compared to the single classifiers on hyperspectral and LIDAR data.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. [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

  19. Airborne hyperspectral imaging for sensing phosphorus concentration in the Lake Okeechobee drainage basin

    NASA Astrophysics Data System (ADS)

    Bogrekci, Ismail; Lee, Won Suk; Jordan, Jonathan D.

    2005-05-01

    Eutrophication disturbs the ecological balance in the Lake Okeechobee due to high concentration of phosphorus emanated from the regions in the lake's drainage basin. Ability of measuring phosphorus (P) concentrations of water in the Lake Okeechobee itself is very important. Furthermore, monitoring P in its drainage basins is crucial in order to find the cause of P loading and contributing regions. Also, inexpensive real-time sensing capability for a large area in a short time would help scientist, government agents, and civilians to understand the causes, spot the high-risk areas, and develop management practices for restoring the natural equilibrium. In order to measure P concentrations in the Lake Okeechobee drainage basin, airborne hyperspectral images were taken from five representative target sites by deploying a modified queen air twin engine aircraft. Each flight line covered a swath of approximately 365 m wide. Spatial resolution was about 1 m. Spectral range covered was between 412.65 and 991.82 nm with an approximate of 5 nm spectral resolution. Ground truthing was conducted to collect soil and vegetation samples, GPS coordinates of each location, and reflectance measurement of each sample. On the ground, spectral reflectance was measured using a handheld spectrometer in 400-2500 nm. The samples were sent to a laboratory for chemical analysis. Also diffuse reflectance of the samples was measured in a laboratory setting using a spectrophotometer with an integrating sphere. Images were geocorrected and rectified to reduce geometric effect. Calibration of images was conducted to obtain actual reflectance of the target area. Score, SAM (Spectral Angle Mapping), SFF (Spectral Feature Fitting) were computed for spectral matching with image derived spectral library.

  20. 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.

  1. 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

  2. Airborne hyperspectral imaging for the detection of powdery mildew in wheat

    NASA Astrophysics Data System (ADS)

    Franke, Jonas; Mewes, Thorsten; Menz, Gunter

    2008-08-01

    Plant stresses, in particular fungal diseases, show a high variability in spatial and temporal dimension with respect to their impact on the host. Recent "Precision Agriculture"-techniques allow for a spatially and temporally adjusted pest control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stressdetection techniques such as random monitoring do not meet demands of such optimally placed management actions. The prerequisite is an accurate sensor-based detection of stress symptoms. The present study focuses on a remotely sensed detection of the fungal disease powdery mildew (Blumeria graminis) in wheat, Europe's main crop. In a field experiment, the potential of hyperspectral data for an early detection of stress symptoms was tested. A sophisticated endmember selection procedure was used and, additionally, a linear spectral mixture model was applied to a pixel spectrum with known characteristics, in order to derive an endmember representing 100% powdery mildew-infected wheat. Regression analyses of matched fraction estimates of this endmember and in-field-observed powdery mildew severities showed promising results (r=0.82 and r2=0.67).

  3. 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.

  4. Airborne imaging spectrometer - A new tool for remote sensing

    NASA Technical Reports Server (NTRS)

    Vane, G.; Goetz, A. F. H.; Wellman, J. B.

    1983-01-01

    The first of a new class of remote sensing instruments is described. The Airborne Imaging Spectrometer represents the first use of two-dimensional area arrays in a scientific application. The instrument images 32 cross-track pixels simultaneously, each in 128 spectral bands in the 1.2 to 2.4 micro region. The IFOV of the instrument is 1.9 mrad and the spectral sampling interval is 9.6 nanometers. Plans include upgrading the detector from the current 32 x 32 element HgCdTe CCD array to a 64 x 64 element array in 1984. Science and engineering data are currently being actively gathered with the instrument.

  5. Airborne imaging spectrometer - A new tool for remote sensing

    NASA Technical Reports Server (NTRS)

    Vane, G.; Goetz, A. F. H.; Wellman, J. B.

    1984-01-01

    The first of a new class of remote sensing instruments is described. The Airborne Imaging Spectrometer represents the first use of two-dimensional integrated infrared area arrays in a scientific application. The instrument images 32 cross-track pixels simultaneously, each in 128 spectral bands in the 1.2- to 2.4-micron region. The IFOV of the instrument is 1.9 mrad/pixel and the spectral sampling interval is 9.6 nm. Plans include upgrading the detector from the current 32 x 32 element HgCdTe CCD array to a 64 x 64 element array in 1985. Science and engineering data are currently being actively gathered with the instrument.

  6. 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.

  7. 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.

  8. 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

  9. 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,...

  10. Hyperspectral forest monitoring and imaging implications

    NASA Astrophysics Data System (ADS)

    Goodenough, David G.; Bannon, David

    2014-05-01

    The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68 Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of improved information products to land managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing

  11. 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

  12. Remote sensing for gas plume monitoring using state-of-the-art infrared hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Hinnrichs, Michele

    1999-02-01

    Under contract to the US Air Force and Navy, Pacific Advanced Technology has developed a very sensitive hyperspectral imaging infrared camera that can perform remote imaging spectro-radiometry. One of the most exciting applications for this technology is in the remote monitoring of gas plume emissions. Pacific Advanced Technology (PAT) currently has the technology available to detect and identify chemical species in gas plumes using a small light weight infrared camera the size of a camcorder. Using this technology as a remote sensor can give advanced warning of hazardous chemical vapors undetectable by the human eye as well as monitor the species concentrations in a gas plume from smoke stack and fugitive leaks. Some of the gas plumes that have been measured and species detected using an IMSS imaging spectrometer are refinery smoke stacks plumes with emission of CO2, CO, SO2, NOx. Low concentration vapor unseen by the human eye that has been imaged and measured is acetone vapor evaporating at room temperature. The PAT hyperspectral imaging sensor is called 'Image Multi-spectral Sensing or IMSS.' The IMSS instrument uses defractive optic technology and exploits the chromatic aberrations of such lenses. Using diffractive optics for both imaging and dispersion allows for a very low cost light weight robust imaging spectrometer. PAT has developed imaging spectrometers that span the spectral range from the visible, midwave infrared (3 to 5 microns) and longwave infrared (8 to 12 microns) with this technology. This paper will present the imaging spectral data that we have collected on various targets with our hyperspectral imaging instruments as will also describe the IMSS approach to imaging spectroscopy.

  13. 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.

  14. Study on Oil-Gas Reservoir Detecting Methods Using Hyperspectral Remote Sensing

    NASA Astrophysics Data System (ADS)

    Tian, Q.

    2012-07-01

    Oil-gas reservoir exploration using hyperspectral remote sensing, which based on the theory of hydrocarbon microseepage information and fine spectral response of target, is a new direction for the application of remote sensing technology. In this paper, Qaidam Basin and Liaodong Bay in China were selected as the study areas. Based on the hydrocarbon microseepage theory, the analysis of crude oil in soil in Qaidam Basin and spectral experiment of crude oil in sea water in Liaodong Bay, Hyperion hyperspectral remote sensing images were used to develop the method of oil-gas exploration. The results indicated that the area of oil-gas reservoir in Qaidam Basin could be delimited in two ways: the oil-gas reservoir can be obtained directly by the absorption bands near 1730nm in Hyperion image; and Linear Spectral Unmixing (LSU) and Spectral Angle Matching (SAM) of alteration mineral (e.g. kaolinite, illite) could be used to indirectly detect the target area in Qaidam Basin. In addition, combined with the optimal bands in the region of visible/near-infrared, SAM was used to extract the thin oil slick of microseepage in Liaodong Bay. Then the target area of oil-gas reservoir in Liaodong Bay can be delineated.

  15. 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.

  16. 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.

  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. 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

  19. 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

  20. AN ECOLOGICAL ASSESSMENT OF OPPORTUNISTIC PLANT SPECIES IN GREAT LAKES COASTAL WETLANDS USING AIRBORNE HYPERSPECTRAL DATE

    EPA Science Inventory

    Airbome hyperspectral data were used to detect dense patches of Phragmites australis, a native opportunist plant species, at the Pointe Mouillee coastal wetland complex (Wayne and Monroe Counties, Michigan). This study provides initial results from one of thirteen coastal wetland...

  1. 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...

  2. Investigation of Arctic mixed-phase clouds during VERDI and RACEPAC: Combining airborne remote sensing and in situ observations

    NASA Astrophysics Data System (ADS)

    Ehrlich, André; Wendisch, Manfred

    2015-04-01

    To improve our understanding of Arctic mixed-phase clouds in sea-ice covered areas the airborne research campaign Vertical distribution of ice in Arctic mixed-phase clouds (VERDI, April/May 2012) and the Radiation-Aerosol-Cloud Experiment in the Arctic Circle (RACEPAC, April/May 2014) were initiated by a collaboration of German and French research institutes. The aircraft operated by the Alfred Wegener Institute for Polar and Marine Research, Germany were based in Inuvik, Canada from where the research flights of in total 149 flight hours (62 h during VERDI, 87 h during RACEPAC) were able to cover a wide area above the Canadian Beaufort. The aim of both campaigns was to combine remote sensing and in-situ cloud, aerosol and trace gas measurements to investigate interactions between radiation, cloud and aerosol particles. Remote sensing instrumentation contained a backscatter lidar and spectral solar radiation measurements including a hyperspectral camera. In-situ sampling was highlighted by a suit of comprehensive cloud particle probes, aerosol particle counters and mass spectroscopy as well as trace gas detectors. While during VERDI remote sensing and in-situ measurements were performed by one aircraft (Polar 5) subsequently, for RACEPAC two identical aircraft (Polar 5 & 6, Basler BT-67) were coordinated at different altitudes to horizontally collocate both remote sensing and in-situ measurements. In this way not only the combined analysis of radiative and microphysical processes in the clouds can by studied more reliably, also remote sensing methods can be validated efficiently. Here we will illustrate the scientific strategy of both projects including instrumentation and flight patterns of the research flights. Beside flight missions dedicated to sample low level clouds by remote sensing and in situ probing, flights were also coordinated with satellite overpasses and ground based stations. Exemplary results will be highlighted.

  3. Algorithm for the retrieval of columnar water vapor from hyperspectral remotely sensed data.

    PubMed

    Barducci, Alessandro; Guzzi, Donatella; Marcoionni, Paolo; Pippi, Ivan

    2004-10-10

    A new algorithm for the retrieval of columnar water vapor content is presented. The proposed procedure computes the area of the H2O absorption centered about 940 nm to allow its integrated columnar abundance as well as its density at ground level to be assessed. The procedure utilizes the HITRAN 2000 database as the source of H2O cross-section spectra. Experimental results were derived from radiometrically calibrated hyperspectral images collected by the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor over the Cuprite mining district in Nevada. Numerical simulations based on the MODTRAN 4 radiative transfer code were also employed for investigating the algorithm's performance. An additional empirical H2O retrieval procedure was tested by use of data gathered by the VIRS-200 imaging spectrometer. PMID:15508614

  4. Advances in the Hyperspectral Thermal Emission Spectrometer (HyTES) and Application to the Remote Sensing of Fires and Trace Gases

    NASA Astrophysics Data System (ADS)

    Mihaly, J. M.; Johnson, W. R.; Hulley, G. C.; Hook, S. J.; Eng, B. T.

    2014-12-01

    The Hyperspectral Thermal Emission Spectrometer (HyTES) is an airborne imaging spectrometer developed by JPL and currently configured on the Twin Otter aircraft. The instrument utilizes 256 spectral channels between 7.5 and 12 micrometers in the Earth observing thermal infrared range of the electromagnetic spectrum and 512 spatial pixels cross-track. Given a 50 degree full angle field of view and the relatively low flight altitude of the Twin Otter aircraft, the instrument provides a wide swath with high spatial resolution (approximately 1.5 m at 1 km AGL). The available spatial and spectral resolution of HyTES represents a significant advance in airborne TIR remote sensing capability and considerable improvements to instrument performance have been made between the 2013 and 2014 science flights. The TIR wavelength range enables a wide range of remote sensing applications, including the detection of atmospheric trace gases (such as SO2, NH3, H2S, and N2O). The current performance, overall science objectives, and recent trace gas observations of the HyTES instrument will be presented. Results from a 2014 flight over a southern Utah wildfire will be discussed. Current work involving the miniaturization of the HyTES instrument for future deployment in the ER-2 high-altitude aircraft will also be presented.

  5. First Airborne Laser Remote Measurements of Atmospheric Carbon Dioxide

    NASA Astrophysics Data System (ADS)

    Browell, E. V.; Dobbs, M. E.; Dobler, J.; Kooi, S.; Choi, Y.; Harrison, F. W.; Moore, B.; Zaccheo, T. S.

    2008-12-01

    A unique, multi-frequency, single-beam, laser absorption spectrometer (LAS) that operates at 1.57 μm has been developed for a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A prototype of the space-based LAS system was developed by ITT, and it has been successfully flight tested in five airborne campaigns conducted in different geographic regions over the last three years. Flight tests were conducted over Oklahoma, Michigan, New Hampshire, and Virginia under a wide range of atmospheric conditions. Remote LAS measurements were compared to high-quality in situ measurements obtained from instrumentation on the same aircraft on spirals under the ground track of the LAS. LAS flights were conducted over a wide range of land and water reflectances and in the presence of scattered clouds. An extensive data set of CO2 measurements has been obtained for evaluating the LAS performance. LAS CO2 measurements with a signal-to-noise in excess of 250 were obtained for a 1-s average over land and for a 10-s average over water. Absolute comparisons of CO2 remote and in situ measurements showed agreement over a range of altitudes to better than 2 percent. LAS oxygen (O2) measurements, which are needed to convert LAS CO2 density measurements to CO2 mixing ratios (XCO2), have been made in the 1.26-μm region in horizontal ground-based experiments and in initial flight tests. Details of flight test campaigns and measured versus modeled results are presented in this paper.

  6. 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

  7. Remote sensing of gases by hyperspectral imaging: results of measurements in the Hamburg port area

    NASA Astrophysics Data System (ADS)

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

    2011-11-01

    Remote sensing by infrared spectroscopy allows detection and identification of hazardous clouds in the atmosphere from long distances. Previous work showed how imaging spectroscopy can be used to assess the location, the dimensions, and the dispersion of a potentially hazardous cloud. In this work an infrared hyperspectral imager based on a Michelson interferometer in combination with a focal plane array detector was deployed to measure gas emissions in the Hamburg port area. Emissions from ships, industrial sources as well as gases released intentionally were measured. Using algorithms for remote sensing by infrared spectroscopy it was possible to identify, visualize, and track the gas clouds in real time. The system proved to be robust in the field. It provided excellent spectra with low noise and high spatial resolution.

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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.

  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. 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

  15. 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

  16. 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...

  17. 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...

  18. 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...

  19. Hyperspectral Observations of Land Surfaces Using Ground-based, Airborne, and Satellite Sensors

    NASA Astrophysics Data System (ADS)

    Knuteson, R. O.; Best, F. A.; Revercomb, H. E.; Tobin, D. C.

    2006-12-01

    The University of Wisconsin-Madison Space Science and Engineering Center (UW-SSEC) has helped pioneer the use of high spectral resolution infrared spectrometers for application to atmospheric and surface remote sensing. This paper is focused on observations of land surface infrared emission from high spectral resolution measurements collected over the past 15 years using airborne, ground-based, and satellite platforms. The earliest data was collected by the High-resolution Interferometer Sounder (HIS), an instrument designed in the 1980s for operation on the NASA ER-2 high altitude aircraft. The HIS was replaced in the late 1990s by the Scanning-HIS instrument which has flown on the NASA ER-2, WB-57, DC-8, and Scaled Composites Proteus aircraft and continues to support field campaigns, such as those for EOS Terra, Aqua, and Aura validation. Since 1995 the UW-SSEC has fielded a ground-based Atmospheric Emitted Radiance Interferometer (AERI) in a research vehicle (the AERIBAGO) which has allowed for direct field measurements of land surface emission from a height of about 16 ft above the ground. Several ground-based and aircraft campaigns were conducted to survey the region surrounding the ARM Southern Great Plains site in north central Oklahoma. The ground- based AERIBAGO has also participated in surface emissivity campaigns in the Western U.S.. Since 2002, the NASA Atmospheric InfraRed Sounder (AIRS) has provided similar measurements from the Aqua platform in an afternoon sun-synchronous polar orbit. Ground-based and airborne observations are being used to validate the land surface products derived from the AIRS observations. These cal/val activities are in preparation for similar measurements anticipated from the operational Cross-track InfraRed Sounder (CrIS) on the NPOESS Preparatory Platform (NPP), expected to be launched in 2008. Moreover, high spectral infrared observations will soon be made by the Infrared Atmospheric Sounder Interferometer (IASI) on the

  20. Detection of spatio-temporal changes of Norway spruce forest stands in Ore Mountains using airborne hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Misurec, J.; Kopačková, V.; Lhotáková, Z.; Albrechtova, J.; Campbell, P. K. E.

    2015-12-01

    The Ore Mountains are an example of the region that suffered from severe environmental pollution caused by long-term coal mining and heavy industry leading to massive dieback of the local Norway spruce forests between the 1970's and 1990's. The situation became getting better at the end of 1990's after pollution loads significantly decreased. In 1998 and 2013, airborne hyperspectral data (with sensor ASAS and APEX, respectively) were used to study recovery of the originally damaged forest stands and compared them with those that have been less affected by environmental pollution. The field campaign (needle biochemical analysis, tree defoliation etc.) accompanied hyperspectral imagery acquisition. An analysis was conducted assessing a set of 16 vegetation indices providing complex information on foliage, biochemistry and canopy biophysics and structure. Five of them (NDVI, NDVI705, VOG1, MSR and TCARI/OSAVI) showing the best results were employed to study spatial gradients as well as temporal changes. The detected gradients are in accordance with ground truth data on representative trees. The obtained results indicate that the original significant differences between the damaged and undamaged stands have been generally levelled until 2013, although it is still possible to detect signs of the previous damages in several cases.

  1. 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

  2. Wheat growth modelling by a combination of a biophysical model approach and hyperspectral remote sensing data

    NASA Astrophysics Data System (ADS)

    Oppelt, Natascha M.

    2009-09-01

    The study presented here investigates the potential of improvement for a physically based model approach, when the static input data is enhanced by dynamic remote sensing information. The land surface model PROMET (Processes of Radiation, Mass and Energy Transfer) was generally applied, while the remote sensing input data was derived from hyperspectral data of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). The PROMET model, whose vegetation routine basically applies the Farquhar et al. photosynthesis approach, was set up to a field scale model run (10 x 10m) for a test acre tilled with wheat (Triticum aestivum L.) mapping the crop development of the season 2005. During the model run, information on the absorptive capacity of the leaves for two canopy layers (top, sunlit layer and bottom, shaded layer) was updated from remote sensing measurements, where angular CHRIS images were available. Control data were acquired through an intensive field campaign, which monitored the development of the stand throughout the vegetation period of the year 2005, also accompanying the satellite overflights. While the model without additional dynamic input data was able to reasonably reproduce the average development of the crop and yield, the spatial heterogeneity was severely underestimated. The combination of remote sensing information with the vegetation model led to a significant improvement of both the spatial heterogeneity of the crop development in the model and yield, which again entailed an overall improvement of the model results in comparison to measured reference data.

  3. A 868MHz-based wireless sensor network for ground truthing of soil moisture for a hyperspectral remote sensing campaign - design and preliminary results

    NASA Astrophysics Data System (ADS)

    Näthe, Paul; Becker, Rolf

    2014-05-01

    Soil moisture and plant available water are important environmental parameters that affect plant growth and crop yield. Hence, they are significant parameters for vegetation monitoring and precision agriculture. However, validation through ground-based soil moisture measurements is necessary for accessing soil moisture, plant canopy temperature, soil temperature and soil roughness with airborne hyperspectral imaging systems in a corresponding hyperspectral imaging campaign as a part of the INTERREG IV A-Project SMART INSPECTORS. At this point, commercially available sensors for matric potential, plant available water and volumetric water content are utilized for automated measurements with smart sensor nodes which are developed on the basis of open-source 868MHz radio modules, featuring a full-scale microcontroller unit that allows an autarkic operation of the sensor nodes on batteries in the field. The generated data from each of these sensor nodes is transferred wirelessly with an open-source protocol to a central node, the so-called "gateway". This gateway collects, interprets and buffers the sensor readings and, eventually, pushes the data-time series onto a server-based database. The entire data processing chain from the sensor reading to the final storage of data-time series on a server is realized with open-source hardware and software in such a way that the recorded data can be accessed from anywhere through the internet. It will be presented how this open-source based wireless sensor network is developed and specified for the application of ground truthing. In addition, the system's perspectives and potentials with respect to usability and applicability for vegetation monitoring and precision agriculture shall be pointed out. Regarding the corresponding hyperspectral imaging campaign, results from ground measurements will be discussed in terms of their contributing aspects to the remote sensing system. Finally, the significance of the wireless sensor

  4. 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

  5. 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.

  6. 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

  7. ESTIMATING WITHIN-FIELD VARIATIONS IN SOIL PROPERTIES FROM AIRBORNE HYPERSPECTRAL IMAGES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The ability of hyperspectral image (HSI) data to provide estimates of soil electrical conductivity (EC) and soil fertility levels without requiring extensive field data collection was investigated. Bare soil images were acquired using a prism grating pushbroom scanner in April 2000 and May 2001 for ...

  8. 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...

  9. Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Over the past two decades, hyperspectral (HS) imaging has provided remarkable performance in ground object classification and disease identification, due to its high spectral resolution. In this paper, a novel method named “extended spectral angle mapping (ESAM)” is proposed to detect citrus greenin...

  10. Prediction of senescent rangeland canopy structural attributes with airborne hyperspectral imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Canopy structural and chemical data are needed for senescent, mixed-grass prairie landscapes in autumn, yet models driven by image data are lacking for rangelands dominated by non-photosynthetically active vegetation (NPV). Here, we report how aerial hyperspectral imagery might be modeled to predic...

  11. Retrieval of chlorophyll a and suspended solid concentrations by hyperspectral remote sensing in Taihu Lake, China

    NASA Astrophysics Data System (ADS)

    Yang, Dingtian; Pan, Delu; Zhang, Xiaoyu; Zhang, Xiaofeng; He, Xianqiang; Li, Shujing

    2006-12-01

    Chlorophyll a (chl- a) and suspended solid concentrations are two frequently used water quality parameters for monitoring a lake. Traditional measurement of chl- a and suspended solids, requiring laborious laboratory work, which is often expensive and time consuming. Hyperspectral remote-sensing measurement provides a fast and easy tool for estimating water trophic status. In situ hyperspectral data on March 7 8, July 6 7, September 20 and December 7 8, 2004 and the corresponding water chemical data were used to regress the algorithm of water quality parameters. Results showed that the peak of water leaving radiance around 700 nm ( R 700) varied proportionally with chl- a concentration, and moved to infrared when algal bloom occurred. The reflectance ratio of R 702/ R 685 was well correlated with chl- a when water surface in no algal bloom case and the correlation coefficient was better if absorption of phycocyanin was considered. The reflectance ratio R 620/ R 531 was highly correlated to the concentration of suspended solids. The relationship between suspended solids and other band groups were also compared. Secchi disk depth could be calculated by non-linear correlation with suspended solids concentration.

  12. 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.

  13. [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

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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).

  19. 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.

  20. 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

  1. Recent advances in airborne terrestrial remote sensing with the NASA airborne visible/infrared imaging spectrometer (AVIRIS), airborne synthetic aperture radar (SAR), and thermal infrared multispectral scanner (TIMS)

    NASA Technical Reports Server (NTRS)

    Vane, Gregg; Evans, Diane L.; Kahle, Anne B.

    1989-01-01

    Significant progress in terrestrial remote sensing from the air has been made with three NASA-developed sensors that collectively cover the solar-reflected, thermal infrared, and microwave regions of the electromagnetic spectrum. These sensors are the airborne visible/infrared imaging spectrometer (AVIRIS), the thermal infrared mapping spectrometer (TIMS) and the airborne synthetic aperture radar (SAR), respectively. AVIRIS and SAR underwent extensive in-flight engineering testing in 1987 and 1988 and are scheduled to become operational in 1989. TIMS has been in operation for several years. These sensors are described.

  2. 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

  3. 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.

  4. 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

  5. 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.

  6. Fusion of remotely sensed data from airborne and ground-based sensors to enhance detection of cotton plants

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The study investigated the use of aerial multispectral imagery and ground-based hyperspectral data for the discrimination of different crop types and timely detection of cotton plants over large areas. Airborne multispectral imagery and ground-based spectral reflectance data were acquired at the sa...

  7. 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.

  8. 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.

  9. 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.

  10. 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

    2015-01-01

    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

  11. 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

  12. SPECTRAL PROPERTIES OF TREE CHOLLA AND YUCCA MEASURED BY AN AIRBORNE HYPERSPECTRAL SENSOR AND THE RESULTING MAXIMUM LIKELIHOOD CLASSIFICATION OF RANGELANDS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote sensing is an emerging technology with the potential to identify plant species, delineate vegetation and habitat characteristics, differentiate causes of vegetation stress, and characterize soil properties. This technology can be used in range management as a tool to map variou...

  13. Mapping of Foliar Content Using Radiative Transfer Modeling and Vis-Nir Hyperspectral Close-Range Remote-Sensing

    NASA Astrophysics Data System (ADS)

    Jay, S.; Bendoula, R.; Hadoux, X.; Gorretta, N.

    2015-08-01

    Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

  14. 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

  15. 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

  16. Comparative analysis of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Hyperspectral Thermal Emission Spectrometer (HyTES) longwave infrared (LWIR) hyperspectral data for geologic mapping

    NASA Astrophysics Data System (ADS)

    Kruse, Fred A.

    2015-05-01

    Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and spatially coincident Hyperspectral Thermal Emission Spectrometer (HyTES) data were used to map geology and alteration for a site in northern Death Valley, California and Nevada, USA. AVIRIS, with 224 bands at 10 nm spectral resolution over the range 0.4 - 2.5 μm at 3-meter spatial resolution were converted to reflectance using an atmospheric model. HyTES data with 256 bands at approximately 17 nm spectral resolution covering the 8 - 12 μm range at 4-meter spatial resolution were converted to emissivity using a longwave infrared (LWIR) radiative transfer atmospheric compensation model and a normalized temperature-emissivity separation approach. Key spectral endmembers were separately extracted for each wavelength region and identified, and the predominant material at each pixel was mapped for each range using Mixture-Tuned-Matched Filtering (MTMF), a partial unmixing approach. AVIRIS mapped iron oxides, clays, mica, and silicification (hydrothermal alteration); and the difference between calcite and dolomite. HyTES separated and mapped several igneous phases (not possible using AVIRIS), silicification, and validated separation of calcite from dolomite. Comparison of the material maps from the different modes, however, reveals complex overlap, indicating that multiple materials/processes exist in many areas. Combined and integrated analyses were performed to compare individual results and more completely characterize occurrences of multiple materials. Three approaches were used 1) integrated full-range analysis, 2) combined multimode classification, and 3) directed combined analysis in geologic context. Results illustrate that together, these two datasets provide an improved picture of the distribution of geologic units and subsequent alteration.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. Hyperspectral remote sensing of paddy crop using insitu measurement and clustering technique

    NASA Astrophysics Data System (ADS)

    Moharana, S.; Dutta, S.

    2014-11-01

    Rice Agriculture, mainly cultivated in South Asia regions, is being monitored for extracting crop parameter, crop area, crop growth profile, crop yield using both optical and microwave remote sensing. Hyperspectral data provide more detailed information of rice agriculture. The present study was carried out at the experimental station of the Regional Rainfed Low land Rice Research Station, Assam, India (26.1400° N, 91.7700° E) and the overall climate of the study area comes under Lower Brahmaputra Valley (LBV) Agro Climatic Zones. The hyperspectral measurements were made in the year 2009 from 72 plots that include eight rice varieties along with three different level of nitrogen treatments (50, 100, 150 kg/ha) covering rice transplanting to the crop harvesting period. With an emphasis to varieties, hyperspectral measurements were taken in the year 2014 from 24 plots having 24 rice genotypes with different crop developmental ages. All the measurements were performed using a spectroradiometer with a spectral range of 350-1050 nm under direct sunlight of a cloud free sky and stable condition of the atmosphere covering more than 95 % canopy. In this study, reflectance collected from canopy of rice were expressed in terms of waveforms. Furthermore, generated waveforms were analysed for all combinations of nitrogen applications and varieties. A hierarchical clustering technique was employed to classify these waveforms into different groups. By help of agglomerative clustering algorithm a few number of clusters were finalized for different rice varieties along with nitrogen treatments. By this clustering approach, observational error in spectroradiometer reflectance was also nullified. From this hierarchical clustering, appropriate spectral signature for rice canopy were identified and will help to create rice crop classification accurately and therefore have a prospect to make improved information on rice agriculture at both local and regional scales. From this

  2. [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. PMID:20210168

  3. Sea ice density estimation in the Bohai Sea using the hyperspectral remote sensing technology

    NASA Astrophysics Data System (ADS)

    Liu, Chengyu; Shao, Honglan; Xie, Feng; Wang, Jianyu

    2014-11-01

    Sea ice density is one of the significant physical properties of sea ice and the input parameters in the estimation of the engineering mechanical strength and aerodynamic drag coefficients; also it is an important indicator of the ice age. The sea ice in the Bohai Sea is a solid, liquid and gas-phase mixture composed of pure ice, brine pockets and bubbles, the density of which is mainly affected by the amount of brine pockets and bubbles. The more the contained brine pockets, the greater the sea ice density; the more the contained bubbles, the smaller the sea ice density. The reflectance spectrum in 350~2500 nm and density of sea ice of different thickness and ages were measured in the Liaodong Bay of the Bohai Sea during the glacial maximum in the winter of 2012-2013. According to the measured sea ice density and reflectance spectrum, the characteristic bands that can reflect the sea ice density variation were found, and the sea ice density spectrum index (SIDSI) of the sea ice in the Bohai Sea was constructed. The inversion model of sea ice density in the Bohai Sea which refers to the layer from surface to the depth of penetration by the light was proposed at last. The sea ice density in the Bohai Sea was estimated using the proposed model from Hyperion image which is a hyperspectral image. The results show that the error of the sea ice density inversion model is about 0.0004 g•cm-3. The sea ice density can be estimated through hyperspectral remote sensing images, which provide the data support to the related marine science research and application.

  4. 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.

  5. 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.

  6. 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.

  7. Classification of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps

    PubMed Central

    Doshi, Rushabh A.; King, Roger L.; Lawrence, Gary W.

    2010-01-01

    Rotylenchulus reniformis is one of the major nematode pests capable of reducing cotton yields by more than 60%, causing estimated losses that may exceed millions of dollars U.S. Therefore, early detection of nematode numbers is necessary to reduce these losses. This study investigates the feasibility of using remotely sensed hyperspectral data (reflectances) of cotton plants affected with different nematode population numbers with self-organizing maps (SOM) in correlating and classifying nematode population numbers extant in a plant's rhizosphere. The hyperspectral reflectances were classified into three classes based on R. renifomis population numbers present in plant's rhizosphere. Hyperspectral data (350-2500 nm) were also sub-divided into Visible, Red Edge + Near Infrared (NIR) and Mid-IR region to determine the sub-region most effective in spectrally classifying the nematode population numbers. Various combinations of different feature extraction and dimensionality reduction methods were applied in different regions to extract reduced sets of features. These features were then classified using a supervised-SOM classification method. Our results suggest that the overall classification accuracies, in general, for most methods in most regions (except visible region) varied from 60% to 80%, thereby, indicating a positive correlation between the nematode numbers present in plant's rhizosphere and the corresponding plant's hyperspectral signatures. Results showed that classification accuracies in the Mid-IR region were comparable to the accuracies obtained in other sub-regions. Finally, based on our findings, the use of remotely-sensed hyperspectral data with SOM could prove to be extremely time efficient in detecting nematode numbers present in the soil. PMID:22736855

  8. PROGRAM ASPECT - FOR REMOTE SENSING OF AIRBORNE PLUMES

    EPA Science Inventory

    The SAFEGUARD program is a multi-sensor program for the detection and imaging of chemical plumes and vapors. The system is composed of an airborne sensor suite including an infrared line scanner and a high-speed fourier transform infrared spectrometer. Both systems are integrat...

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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...

  15. A high-resolution airborne four-camera imaging system for agricultural remote sensing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper describes the design and testing of an airborne multispectral digital imaging system for remote sensing applications. The system consists of four high resolution charge coupled device (CCD) digital cameras and a ruggedized PC equipped with a frame grabber and image acquisition software. T...

  16. Remote Sensing of Leaf Area Index from Unmanned Airborne Vehicles (UAVs)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing with unmanned airborne vehicles (UAVs) has potential for rangeland management because: (1) pixels have very high spatial resolution, (2) cloud cover would not prevent acquisition during critical periods of plant growth, and (3) information is quickly delivered to the user. Winter whe...

  17. 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...

  18. Remote Sensing Crop Leaf Area Index Using Unmanned Airborne Vehicles (UAV's)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing with unmanned airborne vehicles (UAVs) has more potential for within-season crop management than conventional satellite imagery because: (1) pixels have very high resolution, (2) cloud cover would not prevent acquisition during critical periods of growth, and (3) quick delivery of inf...

  19. Remote Sensing Leaf Area Index of Winter Wheat from Unmanned Airborne Vehicles (UAVs)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing with unmanned airborne vehicles (UAVs) has more potential for within-season crop management than conventional satellite imagery because: (1) pixels have very high resolution, (2) cloud cover would not prevent acquisition during critical periods of growth, and (3) quick delivery of inf...

  20. 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.

  1. 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).

  2. 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.

  3. An analysis of the probability distribution of spectral angle and Euclidean distance in hyperspectral remote sensing using microspectroscopy

    NASA Astrophysics Data System (ADS)

    Resmini, Ronald G.; Deloye, Christopher J.; Allen, David W.

    2013-05-01

    decidedly non-Gaussian though the precise probability distribution is difficult to determine. Spectral angle values appear to be most closely related to beta distributed. The HSI microscopy method is described as are the results of the analyses applied to the data of the mineral fragments. The interpretation of the microspectroscopy data is considered within the ongoing investigation into determining how the spectral variability on the ~10 micrometer spatial scale relates to the spectral variability on larger scales such as those acquired by airborne remote sensing systems.

  4. 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

  5. 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

  6. Improved estimation of soil clay content by the fusion of remote hyperspectral and proximal geophysical sensing

    NASA Astrophysics Data System (ADS)

    Ciampalini, Andrea; André, Frédéric; Garfagnoli, Francesca; Grandjean, Gilles; Lambot, Sébastien; Chiarantini, Leandro; Moretti, Sandro

    2015-05-01

    Planning sustainable soil exploitation and land resource evaluation require up-to-date and accurate maps of soil properties. In that respect, geophysical techniques present particular interests given their non-invasiveness and their fast data acquisition capacity, which permit to characterize large areas with fine spatial and/or temporal resolutions. We investigated the relevancy of combining data from airborne hyperspectral (Hs), electromagnetic induction (EMI) and far-field ground-penetrating radar (GPR) for mapping soil properties, in particular soil clay content, at the field scale. Data from the three techniques were acquired at a test site in Mugello (Italy) characterized by relatively strong spatial variations of soil texture. Soil samples were collected for determining ground truth clay content. For the frequencies used in this study (200-650 MHz), the GPR surface reflection is mainly determined by soil dielectric permittivity, itself primarily influenced by soil moisture. In contrast, EMI is mostly sensitive to soil electrical conductivity, which integrates several soil properties including in particular soil moisture and clay content. Taking advantage of the complementary information provided by the two instruments, the GPR and EMI data were combined and correlated to local ground-truth clay content data to provide high-resolution clay content maps over the entire field area. Besides, a relationship was also observed between Hs data and clay content measurements, which permitted to produce a Hs-derived clay content map. EMI-GPR and Hs maps showed close spatial patterns and a relatively high correlation was observed between both clay content estimates, as well as between clay content estimates and ground-truth clay content measurements. Moreover, data fusion allowed constraining the EMI-GPR and Hs information and reduced the uncertainty of mapped clay content estimates. These results demonstrated great promise for integrated, digital soil mapping

  7. A Neural Network Approach For Volcanic Monitoring Of Sulpher Dioxide Using Hyperspectral Remote Sensed Data

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Carboni, Elisa; Don Granger, Roy; Del Frate, Fabio

    2013-12-01

    This paper describes an application of ANN for the simultaneous estimation of the columnar content and height of the SO2 plume from volcanic eruptions using hyperspectral remotely sensing data. ANN have been trained using all IASI channels between 1000-1200 and 1300-1410 cm-1, as inputs, and the corresponding values of SO2 amount and plume's height obtained using the Oxford retrieval scheme as outputs. As a case study we have chosen the Eyjafjallajökull volcano (Iceland), in particular the eruption took place during the months of April and May 2010, which had an enormous impact on the world economy. ANNs have been validated on some independent data sets belonging to the same eruption and also on IASI images of Grímsvötn eruption, occurred on May 2011. The results have provided values of RMSE between ANN outputs and targets always less than 20 DU for SO2 and 200 mb for height, so demonstrating the good performance in retrieval achieved by the ANN technique.

  8. Quality control of automated hyperspectral remote sensing measurements from a seaborne platform

    NASA Astrophysics Data System (ADS)

    Garaba, S. P.; Wernand, M. R.; Zielinski, O.

    2011-03-01

    In this study four data quality flags are presented for automated and unmanned above-water hyperspectral optical measurements collected underway in the North Sea, The Minch, Irish Sea and Celtic Sea in April/May 2009. Coincident to these optical measurements a DualDome D12 (Mobotix, Germany) camera system was used to capture sea surface and sky images. The first three flags are based on meteorological conditions, to select erroneous incoming solar irradiance (ES) taken during dusk, dawn, before significant incoming solar radiation could be detected or under rainfall. Furthermore, the relative azimuthal angle of the optical sensors to the sun is used to identify possible sunglint free sea surface zones. A total of 629 spectra remained after applying the meteorological masks (first three flags). Based on this dataset, a fourth flag for sunglint was generated by analysing and evaluating water leaving radiance (LW) and remote sensing reflectance (RRS) spectral behaviour in the presence and absence of sunglint salient in the simultaneously available sea surface images. Spectra conditions satisfying "mean LW (700-950 nm) < 2 mW m-2 nm-1 Sr-1" or alternatively "minimum RRS (700-950 nm) < 0.010 Sr-1", mask the most measurements affected by sunglint, providing efficient flagging of sunglint in automated quality control. It is confirmed that valid optical measurements can be performed 0° ≤ Φ ≤ 360° although 90° ≤ Φ ≤ 135° is recommended.

  9. [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

  10. 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

  11. Remote sensing of gases by hyperspectral imaging: system performance and measurements

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

    Remote gas detection and visualization provides vital information in scenarios involving gas leaks, environmental monitoring, chemical accidents or attacks. Imaging systems based on Fourier transform spectrometers with single detector elements have been applied for several years by emergency response forces for gas identification and quantification. In this work a hyperspectral imager employing a Michelson interferometer and an infrared focal plane array detector is characterized. The system provides spatially resolved spectral information about the measurement scene. The performance of the system is evaluated by laboratory measurements. Results of gas detection in the field are presented and discussed. The gas detection algorithm is based on a physical model for the measured radiance. In this model the atmosphere is divided into multiple homogenous layers of constant temperature. The signatures of the gases present in these layers are then compensated in the measured spectrum. No information about the signature of the background is required. Moreover an algorithm that combines spectral and spatial information is presented. This algorithm enhances the signal to noise ratio of the spectra and thus improves the detection limits. Using these algorithms it is possible to identify, visualize, and track gas clouds in real time.

  12. 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.

  13. Contextual classification of hyperspectral remote sensing images Application in vegetation monitoring

    NASA Astrophysics Data System (ADS)

    Thoonen, Guy

    The goal of this thesis is the study of strategies for including contextual information in the classification of hyperspectral remote sensing images. The objectives are twofold. The first objective is the development of new techniques for including contextual information. To this end, an important category of techniques, i.e. modelling the relationships between local pixel neighbourhoods as Markov Random Fields, is first considered. A strategy to extend the flexibility of this technique, by describing the classification problem at hand by an extended hierarchical tree, is introduced. The second technique under study, i.e. the state-of-the-art approach to extract contextual information in the form of attribute profiles, is extended to colour images. As a practical application, two images from the same scene, including a hyperspectral and a high spatial resolution colour image, are jointly classified by first extracting colour attribute profiles from the latter. In addition, a hybrid decision fusion approach is proposed to perform the classification. The third technique, developed in this work, is an approach for assessing the accuracy of contextual classification results, by introducing a new reference, and considering a new measure, based on the complexity of edges, i.e. transitions between classes. The second objective of this thesis is the application of contextual classification techniques to the essential problem of assessing the conservation status of Natura 2000 habitats. The main challenge is in the structural complexity of most of the habitats under study, since these habitats display a high degree of heterogeneity and, in addition, cannot be simply identified by the presence of a single or a few dominant species. In order to handle this complexity, a contextual framework has been developed to reduce the problem to a number of more manageable sub-problems. First, the list of habitats is translated to a hierarchical scheme that includes the most important

  14. Detection of a buoyant coastal wastewater discharge using airborne hyperspectral and infrared imagery

    NASA Astrophysics Data System (ADS)

    Marmorino, George O.; Smith, Geoffrey B.; Miller, W. D.; Bowles, Jeffrey H.

    2010-01-01

    Municipal wastewater discharged into the ocean through a submerged pipe, or outfall, can rise buoyantly to the sea surface, resulting in a near-field mixing zone and, in the presence of an ambient ocean current, an extended surface plume. In this paper, data from a CASI (Compact Airborne Spectrographic Imager) and an airborne infrared (IR) camera are shown to detect a municipal wastewater discharge off the southeast coast of Florida, U.S.A., through its elevated levels of chromophoric dissolved organic matter plus detrital material (CDOM) and cooler sea surface temperatures. CDOM levels within a ~15-m-diameter surface 'boil' are found to be about twice those in the ambient shelf water, and surface temperatures near the boil are lower by ~0.4°C, comparable to the vertical temperature difference across the ambient water column. The CASI and IR imagery show a nearly identically shaped buoyant plume, consistent with a fully surfacing discharge, but the IR data more accurately delineate the area of most rapid dilution as compared with previous in-situ measurements. The imagery also allows identification of ambient oceanographic processes that affect dispersion and transport in the far field. This includes an alongshore front, which limits offshore dispersion of the discharge, and shoreward-propagating nonlinear internal waves, which may be responsible for an enhanced onshore transport of the discharge.

  15. 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...

  16. 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...

  17. Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Vegetation indices derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each pixel of an image and have the potential for mapping crop yield variability. The objective ...

  18. 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.

  19. 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.

  20. 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.

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. Airborne remote sensing for geology and the environment: Present and future. Bulletin

    SciTech Connect

    Watson, K.; Knepper, D.H.

    1994-12-31

    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 report has arranged the six resulting working-group reports under two main headings: (1) Geologic Remote Sensing, for the reports on geologic mapping, mineral resoures, 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. A final section examines future advances and limitations in the field.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Hyperspectral Remote Sensing of Seasonally-Acquired Imported Fire Ant Mound Features (Hymenoptera: Formicidae) in Turfgrass

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Invasive mound-building imported fire ants (Solenopsis spp.) impact soil quality and turfgrass nutrient management in sod production, recreational, residential, and commercial settings. Ground-based hyperspectral studies focused on the seasonal monitoring of reflectance characteristics of imported f...

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. On the additional information content of hyperspectral remote sensing data for estimating ecosystem carbon dioxde and energy exchange

    NASA Astrophysics Data System (ADS)

    Wohlfahrt, Georg; Hammerle, Albin; Tomelleri, Enrico

    2015-04-01

    Radiation reflected back from an ecosystem carries a spectral signature resulting from the interaction of radiation with the vegetation canopy and the underlying soil and thus allows drawing conclusions about the structure and functioning of an ecosystem. When this information is linked to a model of the leaf CO2 exchange, the ecosystem-scale CO2 exchange can be simulated. A well-known and very simplistic example for this approach is the light-use efficiency (LUE) model proposed by Monteith which links the flux of absorbed photosynthetically active radiation times a LUE parameter, both of which may be estimated based on remote sensing data, to predict the ecosystem gross photosynthesis. Here we explore the ability of a more elaborate approach by using near-surface remote sensing of hyperspectral reflected radiation, eddy covariance CO2 and energy flux measurements and a coupled radiative transfer and soil-vegetation-atmosphere-transfer (SVAT) model. Our main objective is to understand to what degree the joint assimilation of hyperspectral reflected radiation and eddy covariance flux measurements into the model helps to better constrain model parameters. To this end we use the SCOPE model, a combination of the well-known PROSAIL model and a SVAT model, and the Bayesian inversion algorithm DREAM. In order to explicitly link reflectance in the visible light and the leaf CO2 exchange, a novel parameterisation of the maximum carboxylation capacity parameter (Vcmax) on the leaf a+b chlorophyll content parameter of PROSAIL is introduced. Results are discussed with respect to the additional information content the hyperspectral data yield for simulating canopy photosynthesis.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. Hyperspectral sensing of forests

    NASA Astrophysics Data System (ADS)

    Goodenough, David G.; Dyk, Andrew; Chen, Hao; Hobart, Geordie; Niemann, K. Olaf; Richardson, Ash

    2007-11-01

    Canada contains 10% of the world's forests covering an area of 418 million hectares. The sustainable management of these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of new and improved information products to resource managers to make more informed decisions. Research in this area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory, forest health, foliar biochemistry, biomass, and aboveground carbon than are currently available. This paper surveys recent methods and results in hyperspectral sensing of forests and describes space initiatives for hyperspectral sensing.

  2. 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.

  3. 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.

  4. Hyperspectral imaging Fourier transform spectrometers for astronomical and remote sensing observations

    NASA Astrophysics Data System (ADS)

    Rafert, J. Bruce; Sellar, R. Glenn; Holbert, Eirik; Blatt, Joel H.; Tyler, David W.; Durham, Susan E.; Newby, Harold D.

    1994-06-01

    The Florida Institue of Technology and the Phillips Laboratory have developed several advanced visible (0.4-0.8 micrometers ) imaging fourier transform spectrometer (IFTS) brassboards, which simultaneously acquire one spatial and one spectral dimension of the hyperspectral image cube. The initial versions of these instruments may be scanned across a scene or configured with a scan mirror to pick up the second spatial dimension of the image cube. The current visible hyperspectral imagers possess a combination of features, including (1) low to moderate spectral resolution for hundreds/thousands of spectral channels, (2) robust design, with no moving parts, (3) detector limited free spectral range, (4) detector-limited spatial and spectral resolution, and (5) field widened operation. The utility of this type of instrument reaches its logical conclusion however, with an instrument designed to acquire all three dimensions of the hyperspectral image cube (both spatial and one spectral) simultaneously. In this paper we present the (1) detailed optical system designs for the brassboard instruments, (2) the current data acquisition system, (3) data reduction and analysis techniques unique to hyperspectral sensor systems which operate with photometric accuracy, and (4) several methods to modify the basic instrument design to allow simultaneous acquistion of the entire hyperspectral image cube. The hyperspectral sensor systems which are being developed and whose utility is being pioneered by Florida Tech and the Phillips Laboratory are applicable to numerous DoD and civil applications including (1) space object identification, (2) radiometrically correct satellite image and spectral signature database observations, (3) simultaneous spactial/spectral observations of booster plumes for strategic and surrogate tactical missile signature identification, and (4) spatial/spectral visible and IR astronomical observations with photometric accuracy.

  5. A New Method to Retrieve the Data Requirements of the Remote Sensing Community – Exemplarily Demonstrated for Hyperspectral User Needs

    PubMed Central

    Nieke, Jens; Reusen, Ils

    2007-01-01

    User-driven requirements for remote sensing data are difficult to define, especially details on geometric, spectral and radiometric parameters. Even more difficult is a decent assessment of the required degrees of processing and corresponding data quality. It is therefore a real challenge to appropriately assess data costs and services to be provided. In 2006, the HYRESSA project was initiated within the framework 6 programme of the European Commission to analyze the user needs of the hyperspectral remote sensing community. Special focus was given to finding an answer to the key question, “What are the individual user requirements for hyperspectral imagery and its related data products?”. A Value-Benefit Analysis (VBA) was performed to retrieve user needs and address open items accordingly. The VBA is an established tool for systematic problem solving by supporting the possibility of comparing competing projects or solutions. It enables evaluation on the basis of a multidimensional objective model and can be augmented with expert's preferences. After undergoing a VBA, the scaling method (e.g., Law of Comparative Judgment) was applied for achieving the desired ranking judgments. The result, which is the relative value of projects with respect to a well-defined main objective, can therefore be produced analytically using a VBA. A multidimensional objective model adhering to VBA methodology was established. Thereafter, end users and experts were requested to fill out a Questionnaire of User Needs (QUN) at the highest level of detail - the value indicator level. The end user was additionally requested to report personal preferences for his particular research field. In the end, results from the experts' evaluation and results from a sensor data survey can be compared in order to understand user needs and the drawbacks of existing data products. The investigation – focusing on the needs of the hyperspectral user community in Europe – showed that a VBA is a

  6. 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.

  7. Comparation of Typical Wetlands Classification Accuracy in Yellow River Estuary Using Multi-Angle Proba CHRIS Hyperspectral Remote Sensing Images

    NASA Astrophysics Data System (ADS)

    Wang, Xiaopeng; Zhang, Jie; Ma, Yi; Ren, Guangbo

    2013-01-01

    In this paper, Multi-angle PROBA CHRIS hyperspectral remote sensing images were used to study on their imaging quality and the ability of classification of Typical Wetlands in Yellow River Estuary, by the cooperation of interpretation and automatic classification. Taking 5-angle (0°, ±36°, ±55°) CHRIS hyperspectral remote sensing images of mode 2 obtained in September 2006 as an example, this paper research results indicate that the 0° image has the best imaging quality, with the highest spatial resolution, the ±36° images come second, the ±55° images are last; 5 typical wetlands, such as reservoir, bulrush, watercourse, barren beach and swamp were selected as study objects, then a Support Vector Machine (SVM) algorithm is used to classify different-angle remote sensing images into these 5 typical wetlands using training samples in the same location, the results of classification were analyzed based on field survey data, which shows that (1) The classification accuracy differs along the viewing angle of images, the overall accuracy and Kappa factor of the 0° image is highest, and the -36° image is lowest. (2) The overall accuracy and Kappa factor of the positive-angle images is higher than which of minus-angle images. (3) The producer accuracy and user accuracy of swamp is the lowest among all 5 typical wetlands in all images. (4) The producer accuracy and user accuracy of reservoir, bulrush and barren beach are relatively stable in all 5-angle images, however, the accuracies of Watercourse and swamp are fluctuant in all 5-angle images, and highest in the 0° image.

  8. Estimating Yellow Starthistle (Centaurea solstitialis) Leaf Area Index and Aboveground Biomass with the Use of Hyperspectral Data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote-sensed data were obtained via a Compact Airborne Spectrographic Imager-II (CASI-II) and used to estimate leaf-area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (YST). In parallel, 34 ground-based field plots were used to measure abov...

  9. 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

  10. 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 ...

  11. 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

  12. 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.

  13. 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.

  14. Airborne remote spectrometry support to rescue personnel at Ground Zero after the World Trade Center attack on September 11, 2001

    NASA Astrophysics Data System (ADS)

    Simi, Christopher G.; Hill, Anthony B.; Kling, Henry; Zadnik, Jerome A.; Sviland, Marc D.; Williams, Mary M.; Lewis, Paul E.

    2002-11-01

    In order to assist Rescue and Recovery personnel after 11 September 2001, Night Vision and Electronic Sensors Directorate was requested to collect a variety of airborne electro-optic data of the WTC site. The immediate objective was to provide FDNY with geo-rectified high-resolution and solar reflective hyperspectral data to help map the debris-field. Later data collections included calibrated MWIR data. This thermal data provided accurate temperature profiles, which could be warped to the high-resolution data. This paper will describe the assets and software used to help provide the FDNY data products, which were incorporated into their GIS database.

  15. An airborne remote sensing platform of the Helsinki University of Technology

    SciTech Connect

    Nikulainen, M.; Hallikainen, M.; Kemppinen, M.; Tauriainen, S.

    1996-10-01

    In 1994 Helsinki University of Technology acquired a Short SC7 Skyvan turboprop aircraft to be modified to carry remote sensing instruments. As the aircraft is originally designed to carry heavy and space consuming cargo, a modification program was implemented to make the aircraft feasible for remote sensing operations. The twelve-month long modification program had three design objectives: flexibility, accessibility and cost efficiency. The aircraft interior and electrical system were modified. Furthermore, the aircraft is equipped with DGPS-navigation system, multi-channel radiometer system and side looking airborne radar. Future projects include installation of local area network, attitude GPS system, imaging spectrometer and 1.4 GHz radiometer. 6 refs., 5 figs., 1 tab.

  16. 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

  17. 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.

  18. Algorithms research of airborne long linear multi-elements whisk broom remote sensing image geometric correction

    NASA Astrophysics Data System (ADS)

    Xu, Bin; Ma, Yan-hua; Li, Sheng-hong

    2015-10-01

    Multi-Element scanning imaging is an imaging method that is conventionally used in space-born spectrometer. By multipixel scanning at the same time, increased exposure time can be achieved and the picture quality can be enhanced. But when this imaging method is applied in airborne remote sensing image systems, corresponding imaging model and correction algorithms must be built, because of the poor posture stability of airborne platform and different characteristics and requirements. This paper builds a geometric correction model of airborne long linear multi-element scanning imaging system by decomposing the process of imaging and also deduced related correction algorithms. The sampling moment of linear CCD can be treated as push broom imaging and a single pixel imaging during the whole whisk broom period can be treated as whisk broom imaging. Based on this kind of decomposition, col-linearity equation correction algorithm and a kind of new tangent correction algorithm are deduced. As shown in the simulation experiment result, combining with position and attitude date collected by the posture position measurement system, these algorithms can map pixel position from image coordinate to WGS84 coordinate with high precision. In addition, some error factors and correction accuracy are roughly analyzed.

  19. Kestrel's new FTVHSI instrument for hyperspectral remote sensing from light aircraft

    NASA Astrophysics Data System (ADS)

    Meigs, Andrew D.; Butler, Eugene W.; Jones, Bernard A.; Otten, Leonard John, III; Sellar, R. Glenn; Rafert, Bruce; O'Hair, John R.

    1996-12-01

    During the past year, Kestrel Corporation has designed and built a low cost Fourier transform visible hyperspectral imager (FTVHSI) for deployment in a light aircraft (Cessna TU-206). The instrument is an imaging spectrometer employing a Sagnac (triangle) interferometer, that operates over a range of 450 - 1050 nm with 256 spectral channels, and a 13 degree FOV with an 0.8 mrad pixel IFOV (450 spatial channels). To aid in the calibration of the instrument, calibration and downwelling signals are recorded with every frame. Installed with the optical instrument are attitude sensors and a scene camera. This auxiliary data allows us to place a hyperspectral slice to within less than 5 m of its true position (using selective availability 'on' and differential GPS). We have performed extensive testing and calibration studies, including data collection conducted synchronously with ground measurements at locations including a White Sands radiometric calibration site. This paper reports some of the calibration studies and their results.

  20. Hyperspectral remote sensing in mineral exploration: Ammonium-illite as a pathfinder for gold

    NASA Astrophysics Data System (ADS)

    Browning, David A.

    The presence of ammonium-illite on the Earth's surface has been correlated to known deposits via structures at Carlin-type gold deposits, suggesting its importance as a vector for gold ore. Additionally, ammonium-illite has often been proposed as a geochemical exploration tool due to its formation in hydrothermal systems. Very little work has focused on ammonium-illite as an exploration tool due to the costly, time-consuming, and often inaccurate methods of ammonium detection, such as wet chemical methods or X-Ray Diffraction. Short-wave infrared reflectance spectroscopy has the ability to detect even trace amounts of ammonium quickly and effectively. Two hyperspectral surveys were performed in Elko County, Nevada, USA. The hyperspectral images were processed to identify several clay anomalies, including ammonium-illite, on a regional exploration scale, while field trothing of an existing claim block showed a spatial relationship between ammonium-illite and gold soil anomalies.

  1. 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.

  2. 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.

  3. Satellite and airborne aerosol remote sensing in the presence of clouds

    NASA Astrophysics Data System (ADS)

    Redemann, Jens; Russell, Philip; Zhang, Qin; Livingston, John; Shinozuka, Yohei; Mattoo, Shana; Remer, Lorraine

    2010-05-01

    Our ability to assess aerosol effects on climate using remote sensing data depends on the discrimination between cloudy and cloud-free viewing elements. Aerosol microphysical and related radiative properties have been shown to vary rapidly in the immediate vicinity of clouds, a circumstance that further complicates the distinction of cloudy from cloud-free pixels and the assessment of direct and indirect aerosol effects on climate. In this paper we will discuss the utility of simultaneous airborne and satellite aerosol remote sensing and each method's caveats in the presence of clouds. In a few select case studies, we will show how MODIS aerosol retrievals vary as a function of distance from clouds and we will discuss which of the variations found in the MODIS aerosol data can be verified using airborne remote sensing observations. In a case study of aerosol measurements near cloud edges within a dissipating stratiform cloud deck near the California coast in March 2004, we find that the MODIS-derived visible AOD agrees well with the sunphotometer-derived measurements, but that the SWIR (1240-2130nm) AOD increases near cloud edges are of the order of 0.03 and as such three times as large as the sunphotometer-derived values. The implications for the recently discussed "bluing" of aerosols near cloud edges, i.e., a preferential apparent increase in the visible reflectances of clear-sky pixels due to 3-D radiative transfer effects in the vicinity of clouds, are discussed. From a compilation of MODIS validation studies using airborne sunphotometer measurements in a large number of field campaigns we show that the agreement between sunphotometer and MODIS derived aerosol properties varies only slightly with the satellite-derived cloud fraction. We show further how the comparison of MODIS AOD to AOD derived from the CALIPSO backscatter lidar shows a significant dependence on cloud fraction, suggesting that the current version CALIPSO and MODIS data sets can only be

  4. 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

  5. Experiment of monitoring thermal discharge drained from nuclear plant through airborne infrared remote sensing

    NASA Astrophysics Data System (ADS)

    Wang, Difeng; Pan, Delu; Li, Ning

    2009-07-01

    The State Development and Planning Commission has approved nuclear power projects with the total capacity of 23,000 MW. The plants will be built in Zhejiang, Jiangsu, Guangdong, Shandong, Liaoning and Fujian Province before 2020. However, along with the nuclear power policy of accelerated development in our country, the quantity of nuclear plants and machine sets increases quickly. As a result the environment influence of thermal discharge will be a problem that can't be slid over. So evaluation of the environment influence and engineering simulation must be performed before station design and construction. Further more real-time monitoring of water temperature need to be arranged after fulfillment, reflecting variety of water temperature in time and provided to related managing department. Which will help to ensure the operation of nuclear plant would not result in excess environment breakage. At the end of 2007, an airborne thermal discharge monitoring experiment has been carried out by making use of MAMS, a marine multi-spectral scanner equipped on the China Marine Surveillance Force airplane. And experimental subject was sea area near Qin Shan nuclear plant. This paper introduces the related specification and function of MAMS instrument, and decrypts design and process of the airborne remote sensing experiment. Experiment showed that applying MAMS to monitoring thermal discharge is viable. The remote sensing on a base of thermal infrared monitoring technique told us that thermal discharge of Qin Shan nuclear plant was controlled in a small scope, never breaching national water quality standard.

  6. 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.

  7. Miniaturized visible near-infrared hyperspectral imager for remote-sensing applications

    NASA Astrophysics Data System (ADS)

    Warren, Christopher P.; Even, Detlev; Pfister, William; Nakanishi, Keith; Velasco, Arleen; Breitwieser, David; Yee, Selwyn; Naungayan, Joseph

    2012-11-01

    A new approach for the design and fabrication of a miniaturized hyperspectral imager is described. A unique and compact instrument has been developed by taking advantage of light propagation within bonded solid blocks of optically transmitting glass. The resulting series of micro-hyperspectral imaging (microHSI™) spectrometer has been developed, patented, and built as a visible near-infrared (VNIR) hyperspectral sensor capable of operating in the 400- to 1000-nm wavelength range. The spectrometer employs a blazed, convex diffraction grating in Offner configuration embedded within the optical blocks for ruggedized operation. This, in combination with fast spectrometer operation at f/2.0, results in high optical throughput. The resulting microHSI™VNIR spectrometer weighs 0.54 kg, including foreoptics and camera, which results in a 2× decrease in spectrometer volume compared with current air-spaced Offner spectrometers. These instruments can accommodate custom, ruggedized foreoptics to adapt to a wide range of field-of-view requirements. These fast, telecentric foreoptics are chromatically corrected for wideband spectral applications. Results of field and laboratory testing of the microHSI™ spectrometers are presented and show that the sensor consistently meets technical performance predictions.

  8. 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

  9. 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

  10. 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.

  11. REMOTE SENSING AND GEOSPATIAL MODELING FOR MONITORING INVASIVE PLANT SPECIES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing is used to show the actual distribution of distinctive invasive weeds such as leafy spurge (Euphorbia esula L.), whereas landscape modeling can show the potential distribution over an area. Geographic information system data and hyperspectral imagery [NASA JPL’s Airborne Visible Infra...

  12. 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.

  13. 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.

  14. 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

  15. A survey of natural aggregate properties and characteristics important in remote sensing and airborne geophysics

    USGS Publications Warehouse

    Knepper, D.H., Jr.; 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.

  16. 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.

  17. 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.

  18. 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.

  19. Light weight airborne imaging spectrometer remote sensing system for mineral exploration in China

    NASA Astrophysics Data System (ADS)

    Wu, Taixia; Zhang, Lifu; Cen, Yi; Wang, Jinnian; Tong, Qingxi

    2014-05-01

    Imaging spectrometers provide the unique combination of both spatially contiguous spectra and spectrally contiguous images of the Earth's surface that allows spatial mapping of these minerals. One of the successful applications of imaging spectrometers remote sensing identified was geological mapping and mineral exploration. A Light weight Airborne Imaging Spectrometer System (LAISS) has been developed in China. The hardware of the compact LAISS include a VNIR imaging spectrometer, a SWIR imaging spectrometer, a high resolution camera and a position and attitude device. The weight of the system is less than 20kg. The VNIR imaging spectrometer measures incoming radiation in 344 contiguous spectral channels in the 400-1000 nm wavelength range with spectral resolution of better than 5 nm and creates images of 464 pixels for a line of targets with a nominal instantaneous field of view (IFOV) of ~1 mrad. The SWIR imaging spectrometer measures incoming radiation in the 1000-2500 nm wavelength range with spectral resolution of better than 10 nm with a nominal instantaneous field of view (IFOV) of ~2 mrad. The 400 to 2500nm spectral range provides abundant information about many important Earth-surface minerals. A ground mineral scan experiment and an UAV carried flying experiment has been done. The experiment results show the LAISS have achieved relative high performance levels in terms of signal to noise ratio and image quality. The potential applications for light weight airborne imaging spectrometer system in mineral exploration are tremendous.

  20. The airborne Laser Absorption Spectrometer - A new instrument of remote measurement of atmospheric trace gases

    NASA Technical Reports Server (NTRS)

    Shumate, M. S.; Menzies, R. T.

    1978-01-01

    The Laser Absorption Spectrometer is a portable instrument developed by JPL for remote measurement of trace gases from an aircraft platform. It contains two carbon dioxide lasers, two optical heterodyne receivers, appropriate optics to aim the lasers at the ground and detect the backscattered energy, and signal processing and recording electronics. Operating in the differential-absorption mode, it is possible to monitor one atmospheric gas at a time and record the data in real time. The system can presently measure ozone, ethylene, water vapor, and chlorofluoromethanes with high sensitivity. Airborne measurements were made in early 1977 from the NASA/JPL twin-engine Beechcraft and in May 1977 from the NASA Convair 990 during the ASSESS-II Shuttle Simulation Study. These flights resulted in measurements of ozone concentrations in the lower troposphere which were compared with ground-based values provided by the Air Pollution Control District. This paper describes the details of the instrument and results of the airborne measurements.

  1. 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.

  2. 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.

  3. 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

  4. 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).

  5. Remote Sensing of Snow-covered Sea Ice with Ultra-wideband Airborne Radars

    NASA Astrophysics Data System (ADS)

    Yan, S.; Gogineni, P. S.; Gomez-Garcia, D.; Leuschen, C.; Hale, R.; Rodriguez-Morales, F.; Paden, J. D.; Li, J.

    2015-12-01

    The extent and thickness of sea ice and snow play a critical role in the Earth's climate system. Both sea ice and snow have high albedo and control the heat exchange between the atmosphere and ocean and atmosphere and land. In terms of hydrology, the presence of sea ice and snow modulates the flow and the salinity of ocean water. This in turn can modify the weather patterns around the globe. Understanding the formation, coverage and the properties of sea ice and snow are important for both short-term and long-term climate modeling. The advancements in high-frequency electronics and digital signal processing enabled the development of ultra-wideband radars by the Center for Remote Sensing of Ice Sheets (CReSIS) for airborne measurements of snow and ice properties over large areas. CReSIS recently developed and deployed two ultra-wideband airborne radars, namely the Multichannel Coherent Radar Depth Sounder/Imager (MCoRDS/I) and the Snow Radar. The MCoRDS/I is designed to operate over the frequency range of 180-450 MHz for sounding land ice and imaging its ice-bed interface. We also took advantage of the deployment to explore the potential of UWB MCoRDS/I in sounding sea ice and collected data on flight lines flown as part of NASA Operation IceBridge mission during Spring 2015. Preliminary results show we sounded sea ice under favorable conditions. We will perform detailed processing and analysis of data over the next few months and we will compare results obtained are compared with existing altimetry-derived data products. The new snow radar, on the other hand, operating from 2 to 18 GHz, was deployed on the NRL Twin Otter aircraft in Barrow, AK. It was shown to have a vertical resolution of down to 1.5 cm which opens up the potential for thin snow measurement on both sea ice and land. Both of these new radars will be further optimized for future airborne missions to demonstrate their capabilities for sea ice and snow measurements. We will also show new technical

  6. 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

  7. 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).

  8. Use of field and airborne advanced remote sensing data for the characterisation of surface erosional stages in agricultural semi-arid soils (central Spain) at various scales

    NASA Astrophysics Data System (ADS)

    Milewski, Robert; Chabrillat, Sabine; Schmid, Thomas; Rodriguez, Manuel; Schuett, Brigitta

    2014-05-01

    The interest in the use of non-invasive remote sensing methods such as visible-near infrared reflectance spectroscopy for the remote determination of mineralogical composition in soils and planetary surfaces has been demonstrated since the 1970s with the development of databases in the laboratory of minerals spectra. Nowadays, quantitative soil spectroscopy has been shown to be a powerful tool for the identification and prediction of soil properties, and has been used in many soil science applications. With the upcoming launch of the next generation of hyperspectral satellite systems such as the German EnMAP (Environmental Mapping) satellite in 2017, new potential toward the quantitative analyses of chemical and physical soil attributes of the Earth's soil surface composition based on reflectance spectroscopy will be opened. In particular, in arid and semi-arid agricultural regions sensitive to soil erosion processes, the analyses of the spatial distribution of combined varying surface soil properties based on advanced hyperspectral methodology could be used to infer erosion and deposition stages in selected areas, although it was never thoroughly demonstrated. To fully utilize the potential of this technology for the assessment of surface soil erosional stages, new adapted approaches have to be developed, providing the context for this study. This research focuses on a semi-arid, agricultural area in Central Spain near Toledo and Madrid, in which airborne hyperspectral and LiDAR data have been obtained. The study area is under the influence of a Mediterranean climate with extended agricultural rainfed uses on mostly evolved soils. There, soil erosion features can be observed that are representative for areas throughout Southern Europe. Such erosion features are associated with different soil horizons and rock outcrops with contrasted physical and chemical characteristic. They are exposed at the surface as a consequence of human induced soil erosion which is

  9. 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.

  10. The research of a gyro-stabilized platform and POS application technology in airborne remote sensing

    NASA Astrophysics Data System (ADS)

    Xu, Jiang; Du, Qi

    2009-07-01

    The distortion of the collected images usually takes place since the attitude changes along with the flying aerocraft on airborne remote sensing. In order to get original images without distortion, it is necessary to use professional gyro-stabilized platform. In addition to this, another solution of correcting the original image distortion is to utilize later geometric rectification using position & orientation system ( POS ) data. The third way is to utilize medium-accuracy stabilized platform to control the distortion at a tolerant range, and then make use of the data obtained by high-solution posture measure system to correct the low-quality remote sensing images. The third way which takes advantage of both techniques is better than using only one of the two other ways. This paper introduces several kinds of structural forms of gyro-stabilized platforms, and POS acquiring instruments respectively. Then, the essay will make some analysis of their advantages and disadvantages, key technologies and the application experiment of the third method. After the analysis, the thesis discusses the design of the gyro-stabilized platform. The thesis provides crucial information not only for the application technology of gyro-stabilized platform and POS but also for future development.

  11. Airborne and satellite remote sensing of the mid-infrared water vapour continuum.

    PubMed

    Newman, Stuart M; Green, Paul D; Ptashnik, Igor V; Gardiner, Tom D; Coleman, Marc D; McPheat, Robert A; Smith, Kevin M

    2012-06-13

    Remote sensing of the atmosphere from space plays an increasingly important role in weather forecasting. Exploiting observations from the latest generation of weather satellites relies on an accurate knowledge of fundamental spectroscopy, including the water vapour continuum absorption. Field campaigns involving the Facility for Airborne Atmospheric Measurements research aircraft have collected a comprehensive dataset, comprising remotely sensed infrared radiance observations collocated with accurate measurements of the temperature and humidity structure of the atmosphere. These field measurements have been used to validate the strength of the infrared water vapour continuum in comparison with the latest laboratory measurements. The recent substantial changes to self-continuum coefficients in the widely used MT_CKD (Mlawer-Tobin-Clough-Kneizys-Davies) model between 2400 and 3200 cm(-1) are shown to be appropriate and in agreement with field measurements. Results for the foreign continuum in the 1300-2000 cm(-1) band suggest a weak temperature dependence that is not currently included in atmospheric models. A one-dimensional variational retrieval experiment is performed that shows a small positive benefit from using new laboratory-derived continuum coefficients for humidity retrievals. PMID:22547235

  12. 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. PMID:26281027

  13. Remote sensing of tropospheric gases and aerosols with airborne DIAL system

    NASA Technical Reports Server (NTRS)

    Browell, E. V.

    1983-01-01

    The multipurpose airborne DIAL system developed at NASA Langley Research Center is characterized, and the published results of tropospheric O3, H2O, and aerosol-backscatter remote-sensing experiments performed in 1980 and 1981 are summarized. The system comprises two tunable dye lasers pumped by frequency-doubled Nd:YAG lasers, dielectric-coated steering optics, a 36-cm-diameter Cassegrain receiver telescope, gateable photomultiplier tubes, and a minicomputer data-processing unit for real-time calculation of gas concentrations and backscattering profiles. The transmitted energy of the 100-microsec-separated dye-laser pulses is 40, 80, or 50 mJ/pulse at around 300, 600, or 720-nm wavelength, respectively. Good agreement was found between DIAL-remote-sensed and in-situ H2O and O3 profiles of the lower troposphere and O3 profiles of the tropopause region, and the usefulness of DIAL backscattering measurements in the study of boundary-layer and tropospheric dynamics is demonstrated. The feasibility of DIAL sensing of power-plant or urban plume SO2, of urban-area (or rural-area column-content) NO2, and of temperature and H2O (simultaneously using a third laser) has been suggested by simulation studies.

  14. 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.

  15. 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...

  16. Vegetation structure from quantitative fusion of hyperspectral optical and radar interferometric remote sensing

    NASA Technical Reports Server (NTRS)

    Asner, G. P.; Treuhaft, R. N.; Law, B. E.

    2000-01-01

    One of today's principle objecdtives of remote sensing is carbon accounting in the world's forests via biomass monitoring. Determining carbon sequestration by forest ecosystems requires understanding the carbon budgets of these ecosystems.

  17. 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.

  18. Airborne Visible / Infrared Imaging Spectrometer AVIS: Design, Characterization and Calibration

    PubMed Central

    Oppelt, Natascha; Mauser, Wolfram

    2007-01-01

    The Airborne Visible / Infrared imaging Spectrometer AVIS is a hyperspectral imager designed for environmental monitoring purposes. The sensor, which was constructed entirely from commercially available components, has been successfully deployed during several experiments between 1999 and 2007. We describe the instrument design and present the results of laboratory characterization and calibration of the system's second generation, AVIS-2, which is currently being operated. The processing of the data is described and examples of remote sensing reflectance data are presented.

  19. 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.

  20. 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.

  1. 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.

  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. 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.

  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. Hyperspectral remote sensing for estimating coastal water quality: case study on coast of Black Sea, Romania

    NASA Astrophysics Data System (ADS)

    Ghezehegn, S. G.; Steef, Peters; Hommersom, Annelies; Nils, De Reus; Culcea, Oana; Krommendijk, Bram

    2014-10-01

    The North-Western part of the Black Sea is highly affected by eutrophication due to nutrient and sediment load inflow from the Danube River, which is the second largest delta in Europe. To get a general spatial picture of the water quality of the Romanian coast, it is not only time consuming, but also hard to measure with traditional in situ sampling. To solve these issues, methods have been developed to use close range spectral measurements for accurate and cheap assessments in real-time for the concentrations of Chlorophyll-a, Total Suspended Matter and water transparency. This paper presents the applicability of a state-of-the-art hand-held hyper-spectral sensor and a simple water transparency indicator for monitoring water quality. The fieldwork was conducted during the summer of 2013 on the Romanian coast of the Black Sea. The same techniques are used to calculate these parameters from satellite images (MODIS). The validation results and potential applications of the instruments will be discussed.

  6. 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.

  7. A bio-optical approach to estimating chlorophyll-a concentration from hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Li, Linhai; Li, Lin; Song, Kaishan

    2010-08-01

    Eagle Creek Reservoir is one of three central Indiana reservoirs supplying drinking water for the residents of Indianapolis. The occurrence of blue-green algae blooms resulting from high nutrient input has been a major public concern so that estimation of chlorophyll-a concentration of this reservoir is significantly important for assessing the reservoir's water quality. Empirical and semi-empirical methods were used in our previous studies for estimating CHL. Due to limitations to empirical and semi-empirical methods, a bio-optical model is tested in this study. Field campaigns were carried out in Eagle Creek Reservoir in central Indiana, and water samples analyzed for water quality parameter concentrations and their inherent optical properties (IOPs). A bio-optical model parameterized with these derived IOPs is used to estimate CHL concentration through a matrix inversion of hyperspectral data, and its performance is compared with those for empirical and semi-empirical models. The result demonstrates that the bio-optical model results in a higher correlation than empirical and semi-empirical models do.

  8. 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.

  9. 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.

  10. Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models

    NASA Astrophysics Data System (ADS)

    Darvishzadeh, Roshanak; Atzberger, Clement; Skidmore, Andrew; Schlerf, Martin

    2011-11-01

    Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches ( R2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data ( R2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.

  11. 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

  12. The effectiveness and limitations of geometric and statistical spectral unmixing techniques for subpixel target detection in hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Yuen, Peter WT; Blagg, A.; Bishop, G.

    2005-10-01

    In the literature of spectral unmixing (SU), particularly for remote sensing applications, there are claims that both geometric and statistical techniques using independency as cost functions1-4, are very applicable for analysing hyperspectral imagery. These claims are vigorously examined and verified in this paper, using sets of simulated and real data. The objective is to study how effective these two SU approaches are with respected to the modality and independency of the source data. The data sets are carefully designed such that only one parameter is varied at a time. The 'goodness' of the unmixed result is judged by using the well-known Amari index (AI), together with a 3D visualisation of the deduced simplex in eigenvector space. A total of seven different algorithms, of which one is geometric and the others are statistically independent based have been studied. Two of the statistical algorithms use non-negative constraint of modelling errors (NMF & NNICA) as cost functions and the other four employ the independent component analysis (ICA) principle to minimise mutual information (MI) as the objective function. The result has shown that, the ICA based statistical technique is very effective to find the correct endmember (EM) even for the highly intermixed imagery, provided that the sources are completely independent. Modality of the data source is found to only have a second order impact on the unmixing capabilities of ICA based algorithms. All ICA based algorithms are seen to fail when the MI of sources are above 1, and the NMF type of algorithms are found even more sensitive to the dependency of sources. Typical independency of species found in the natural environment is in the range of 15-30. This indicates that, conventional statistical ICA and matrix factorisation (MF) techniques, are really not very suitable for the spectral unmixing of hyperspectral (HSI) data. Future work is proposed to investigate the idea of a dependent component clustering

  13. The Thermal Hyperspectral Imager (THI): an instrument for remote sensing of Earth's surface from a micro-satellite platform

    NASA Astrophysics Data System (ADS)

    Wright, R.; Lucey, P. G.; Horton, K. A.; Wood, M.; Garbeil, H.; Crites, S. T.

    2011-12-01

    The Thermal Hyperspectral Imager (THI) is a low cost, low mass, power efficient instrument designed to acquire hyperspectral remote sensing data in the long-wave infrared. The instrument has been designed to satisfy mass, volume, and power constraints necessary to allow for its accommodation in a 95 kg micro-satellite bus, designed by staff and students at the University of Hawai'i. THI acquires approximately 30 separate spectral bands in the 8 to 14 μm wavelength region, at 10 wavenumber resolution. Rather than using filtering or dispersion to generate the spectral information, THI uses an interferometric technique. Light from the scene is focused onto an uncooled microbolometer detector array through a stationary interferometer, causing the light incident at each detector at any instant in time to be phase shifted by an optical path difference which varies linearly across the array in the along-track dimension. As platform motion translates the detector array in the along-track direction at a rate of approximately one pixel per frame (the camera acquires data at 30 Hz) the radiance from each scene element can be sampled at each OPD, thus generating an interferogram. Spectral radiance as a function of wavelength is subsequently obtained for each scene element using standard Fourier transform techniques. Housed in a pressure vessel to shield COTS parts from the space environment, the total instrument has a mass of 15 kg. Peak power consumption, largely associated with the calibration procedure, is <90 W. From a nominal altitude of 550 km the resulting data would have a spatial resolution of approximately 300 m. Although an individual imaging event yields approximately 1 Gbit of raw uncompressed data, onboard processing (to convert the interferograms into a conventional spectral hypercube) can reduce this to tens of Mbits per scene. In this presentation we will describe a) the rationale for the project, b) the instrument design, and c) how the data are processed

  14. 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...

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. 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

  20. 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.

  1. Monitoring Ephemeral Streams Using Airborne Very High Resolution Multispectral Remote Sensing in Arid Environments

    NASA Astrophysics Data System (ADS)

    Hamada, Y.; O'Connor, B. L.

    2012-12-01

    Development in arid environments often results in the loss and degradation of the ephemeral streams that provide habitat and critical ecosystem functions such as water delivery, sediment transport, and groundwater recharge. Quantification of these ecosystem functions is challenging because of the episodic nature of runoff events in desert landscapes and the large spatial scale of watersheds that potentially can be impacted by large-scale development. Low-impact development guidelines and regulatory protection of ephemeral streams are often lacking due to the difficulty of accurately mapping and quantifying the critical functions of ephemeral streams at scales larger than individual reaches. Renewable energy development in arid regions has the potential to disturb ephemeral streams at the watershed scale, and it is necessary to develop environmental monitoring applications for ephemeral streams to help inform land management and regulatory actions aimed at protecting and mitigating for impacts related to large-scale land disturbances. This study focuses on developing remote sensing methodologies to identify and monitor impacts on ephemeral streams resulting from the land disturbance associated with utility-scale solar energy development in the desert southwest of the United States. Airborne very high resolution (VHR) multispectral imagery is used to produce stereoscopic, three-dimensional landscape models that can be used to (1) identify and map ephemeral stream channel networks, and (2) support analyses and models of hydrologic and sediment transport processes that pertain to the critical functionality of ephemeral streams. Spectral and statistical analyses are being developed to extract information about ephemeral channel location and extent, micro-topography, riparian vegetation, and soil moisture characteristics. This presentation will demonstrate initial results and provide a framework for future work associated with this project, for developing the necessary

  2. 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.

  3. A Nadir-adjusted Airborne Multi Spectral Imaging System (NAMSIS) for high-resolution remote sensing of carbon fluxes

    NASA Astrophysics Data System (ADS)

    Jiang, Z.; Scott, S.; Rahman, A. F.

    2012-12-01

    Satellite remote sensing is widely used in vegetation monitoring, water stress detection and carbon cycle modeling. However, image pixels from high temporal resolution satellite sensors (such as MODIS) have coarse spatial resolution, much larger than the canopies they are supposed to characterize. An alternative solution for on-demand high spatial resolution remote sensing is sensors onboard low-flying aircrafts. Airborne remote sensing has been traditionally used in crop management studies. In this presentation we demonstrate the application of a relatively low-cost airborne sensor system with customized spectral band combinations for studying forest carbon fluxes. Our team has developed an Inertia Measurement Unit (IMU) controlled automated system to detach aircraft movements (pitch and roll) and engine vibration from the six-band programmable imager, in order to maintain the sensor at nadir view at all times during the flight. Flight lines are configured by a GPS-controleld system to simulate MODIS pixels. A feature-based algorithm is used to automatically generate a mosaic of individual images along the flight lines. This algorithm eliminates the need to mosiac and georeference images manually. An empirical line method is used to calculate reflectance from the raw data. Images from this airborne system produce reflectance values that are comparable with MODIS reflectance product. These high spatial resolution (~0.5 m) images deliver detailed information about tree species and phenological conditions within each MODIS pixel, and thus permit a high resolution spatio-temporal assessment of forest carbon fluxes.

  4. 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.

  5. 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.

  6. 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.

  7. [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

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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...

  13. 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

  14. The NASA Airborne Earth Science Microwave Imaging Radiometer (AESMIR): A New Sensor for Earth Remote Sensing

    NASA Technical Reports Server (NTRS)

    Kim, Edward

    2003-01-01

    The Airborne Earth Science Microwave Imaging Radiometer (AESMIR) is a versatile new airborne imaging radiometer recently developed by NASA. The AESMIR design is unique in that it performs dual-polarized imaging at all standard passive microwave frequency bands (6-89 GHz) using only one sensor headscanner package, providing an efficient solution for Earth remote sensing applications (snow, soil moisture/land parameters, precipitation, ocean winds, sea surface temperature, water vapor, sea ice, etc.). The microwave radiometers themselves will incorporate state-of-the-art receivers, with particular attention given to instrument calibration for the best possible accuracy and sensitivity. The single-package design of AESMIR makes it compatible with high-altitude aircraft platforms such as the NASA ER-2s. The arbitrary 2-axis gimbal can perform conical and cross-track scanning, as well as fixed-beam staring. This compatibility with high-altitude platforms coupled with the flexible scanning configuration, opens up previously unavailable science opportunities for convection/precip/cloud science and co-flying with complementary instruments, as well as providing wider swath coverage for all science applications. By designing AESMIR to be compatible with these high-altitude platforms, we are also compatible with the NASA P-3, the NASA DC-8, C-130s and ground-based deployments. Thus AESMIR can provide low-, mid-, and high- altitude microwave imaging. Parallel filter banks allow AESMIR to simultaneously simulate the exact passbands of multiple satellite radiometers: SSM/I, TMI, AMSR, Windsat, SSMI/S, and the upcoming GPM/GMI and NPOESS/CMIS instruments --a unique capability among aircraft radiometers. An L-band option is also under development, again using the same scanner. With this option, simultaneous imaging from 1.4 to 89 GHz will be feasible. And, all receivers except the sounding channels will be configured for 4-Stokes polarimetric operation using high-speed digital

  15. 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

  16. Regional-scale mineral mapping using ASTER VNIR/SWIR data and validation of reflectance and mineral map products using airborne hyperspectral CASI/SASI data

    NASA Astrophysics Data System (ADS)

    Jing, Cui; Bokun, Yan; Runsheng, Wang; Feng, Tian; Yingjun, Zhao; Dechang, Liu; Suming, Yang; Wei, Shen

    2014-12-01

    ASTER data have been widely and successfully used in lithological mapping and mineral exploration for decades. The errors due to atmospheric water vapor and the characteristics of the photoelectric sensor could lead to the anomalous characteristics of band 5 and 9 in the ASTER standard reflectivity product. These anomalies could result in the spectroscopic misidentification of minerals. This study proposed a simple method of atmospheric correction for converting radiance-at-sensor to ground reflectance. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error using airborne hyperspectral CASI_SASI data. The ASTER VNIR/SWIR reflectance correction factor was derived to correct the spectral shape bias resulting from the radiometric calibration error. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, a band combination mapping method was proposed for identifying minerals more quickly and accurately. The results indicate that this method for atmospheric correction of ASTER data produces very good results in the arid and bare areas. It is still unknown whether the method is suitable for humid and rainy areas where atmospheric water vapor varies spatially more than in arid and bare areas. After applying the reflectance factor to the atmospheric-corrected ASTER L1B data, the mean error of all reflectance bands decreased from 0.0256 to 0.002, and the standard deviation decreased from 0.04251 to 0.0007. The errors of the 2/1, 5/6 and 9/8 band ratios decreased from 2.38%, 4.102%, and 4.28% to 1.26%, -0.162%, and 0.31%, respectively. The radiometric calibration error of the ASTER band 1-9 data can lead to the overestimation of kaolinite. A band index of 2/1 for retrieving Fe3+ cannot produce a reliable Fe3+ distribution map, and a new index should be developed.

  17. 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

  18. 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.

  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. Vineyard zonal management for grape quality assessment by combining airborne remote sensed imagery and soil sensors

    NASA Astrophysics Data System (