Microseepages is one way to identify the existence of oil and gas below the surface of the earth. Identification of microseepages could be done using remotesensing approaches. One of the remotesensingdata that can ...
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Microseepages is one way to identify the existence of oil and gas below the surface of the earth. Identification of microseepages could be done using remotesensing approaches. One of the remotesensingdata that can be used is Landsat 8. The purpose of this study is to map the potential of microseepages on the ground surface of West Tugu oil and gas field, North West Java Basin, Indonesia. The Landsat 8 dataprocessing were performed including radiometric and geometric corrections, and vegetation indices calculation. The vegetation indices calculated in this study are Normalized Differences Vegetation Index (NDVI), Enhanced Normalized Differences Vegetation Index (ENDVI) and Leaf Area Index (LAI). Based on the vegetation indices, we detected that physical condition of vegetation anomaly served as microseepages location. The results showed that microseepages is identified in the south to the east of the oil and gas field presented by vegetation anomaly. Field survey confirmed the possibility of microseepages is located at yellowish leaf vegetation, high spectral of the leaf at the visible wavelength and low magnetic susceptibility.
Geospatial Big data is currently received overwhelming attention and are on highlight globally and Google Earth Engine (GEE) is currently the hot pot platform to cater big dataprocessing for remotesensing and GIS. C...
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Geospatial Big data is currently received overwhelming attention and are on highlight globally and Google Earth Engine (GEE) is currently the hot pot platform to cater big dataprocessing for remotesensing and GIS. Currently few or no study regarding the usage of this platform to study land use/cover changes over years in Malaysia. The objective is to evaluate the feasibility of GEE as a free cloud-based platform by performing classification of Klang Valley area from Landsat composites of three different years (1988-2003-2018) using multiple Machine Learning Algorithms (MLA). The best classification results were then imported and further processed to quantify the changes over the years using commercial software. Although, the classification results are of high accuracy but CART shows the best accuracy with 94.71%, 97.72% and 96.57% in 1988, 2003 and 2018 in comparison with RF and SVM. Some misclassified pixels were encountered because the annual composited images were compiled without taken into considerations of crops phenological stages (paddy) which resulted to the misclassified agricultural land into urban and bare land. Hence, the selection and composition of data initially had to be structured and strategized prior to processing as they can affect the classification result and further analysis. Regardless, GEE has performed quite well and fast in term of time and processing complexity of multiple datasets with minimal human interaction and intervention. Generally, GEE has proven to be reliable in fulfilling the objectives of this study to evaluate the GEE feasibility by performing classification and quantifying the land use/cover of studied area and provide good base for further analysis using different platform.
In the processing of Ocean Color (OC) data from sensor data recorded by Visible Infrared Imaging Radiometer Suite (VIIRS) aboard JPSS-Suomi satellite, NASA Ocean Biology processing Group (OBPG) is deriving a continuou...
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ISBN:
(纸本)9780819497574
In the processing of Ocean Color (OC) data from sensor data recorded by Visible Infrared Imaging Radiometer Suite (VIIRS) aboard JPSS-Suomi satellite, NASA Ocean Biology processing Group (OBPG) is deriving a continuous temporal calibration based on the on-board calibration measurements for the visible bands, and then reprocessing the full mission to produce a continuously calibrated sensor data record (SDR) product. In addition, a vicarious calibration during SDR to OC Level-2 processing is applied. In the latest processing the vicarious calibration is derived from the Marine Optical Buoy (MOBY) data, whereas in the initial processing it was derived from a sea surface reflectance model and a climatology of chlorophyll-a concentration. Furthermore, NASA has recently reprocessed the OC data for the entire VIIRS mission with lunar-based temporal calibration and updated vicarious gains. On the other hand, in fulfilling the mission of the U. S. National Oceanic and atmospheric Administration (NOAA), the Interface dataprocessing Segment (IDPS) developed by Raytheon Intelligence and Information Systems, for the processing of the environmentaldata products from sensor data records, has gained beta status for evaluation. As these processing schemes continue to evolve, monitoring the validity and assessments of the related VIIRS ocean color products are necessary, especially for coastal waters, to evaluate the consistency of these processing and calibration schemes. The ocean color component of the Aerosol Robotic Network (AERONET-OC) has been designed to support long-term satellite ocean color investigations through cross-site measurements collected by autonomous multispectral radiometer systems deployed above water. As part of this network, the Long Island Sound Coastal Observatory (LISCO) near New York City and WaveCIS in the Gulf of Mexico expand those observational capabilities with continuous monitoring as well as (for the LISCO site) additional assessment of the hype
From 2002 to 2004, a satellite dataprocessing system for marine application had been built up in State Key Laboratory of Satellite Ocean Environment Dynamics (Second Institute of Oceanography, State Oceanic Administr...
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ISBN:
(纸本)9780819469076
From 2002 to 2004, a satellite dataprocessing system for marine application had been built up in State Key Laboratory of Satellite Ocean Environment Dynamics (Second Institute of Oceanography, State Oceanic Administration). The system received satellite data from TERRA, AQUA, NOAA-12/15/16/17/18, FY-1D and automatically generated Level3 products and Level4 products(products of single orbit and merged multi-orbits products) deriving from Level0 data, which is controlled by an operational control sub-system. Currently, the products created by this system play an important role in the marine environment monitoring, disaster monitoring and researches. Now a distribution platform has been developed on this foundation, namely WebGIS system for querying and browsing of oceanic remotesensingdata. This system is based upon large database system-Oracle. We made use of the space database engine of ArcSDE and other middleware to perform database operation in addition. J2EE frame was adopted as development model, and Oracle 9.2 DBMS as database background and server. Simply using standard browsers(such as IE6.0), users can visit and browse the public service information that provided by system, including browsing for oceanic remotesensingdata, and enlarge, contract, move, renew, traveling, further data inquiry, attribution search and data download etc. The system is still under test now. Founding of such a system will become an important distribution platform of Chinese satellite oceanic environment products of special topic and category (including Sea surface temperature, Concentration of chlorophyll, and so on), for the exaltation of satellite products' utilization and promoting the data share and the research of the oceanic remotesensing platform.
Regional Agency for environmental Protection of Campania (ARPAC) with the agreement entitled "Creation of a structure for the management and processing of remotesensingdata for environmental protection," c...
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atmospheric correction is the basis for quantitative analysis of satellite remotesensing images, such as monitoring land surface changes. However, precise atmospheric correction is still challenging. Landsat 8 is a s...
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ISBN:
(数字)9781510650046
ISBN:
(纸本)9781510650046;9781510650039
atmospheric correction is the basis for quantitative analysis of satellite remotesensing images, such as monitoring land surface changes. However, precise atmospheric correction is still challenging. Landsat 8 is a satellite used for surface monitoring launched by the United States National Aeronautics and Space Administration (NASA) in 2013, with good spatial resolution. Rich spectral information. In this paper, an improved dense dark vegetation(DDV)aerosol retrieval algorithm is developed, and the retrieved AOD map will be used to process the aerosol impact factor of remotesensing images. atmospheric correction is performed based on a lookup table generated by the 6SV model. Validation with the Land Surface Reflectance Code (LaSRC) algorithm produced atmospheric correction images, correction images of the paper algorithm showed a good agreement with high correlation(Correlation R exceeds 95%). Meanwhile, a reliable software prototype system for processingatmospheric correction on Landsat 8 OLI images was developed. This system is based on C++ language and can perform atmospheric correction automatically, low-latency, and accuracy. The data products corrected by the software prototype are helpful for the widespread application of remotesensingdata in emergency response, environmental monitoring, and national defense.
The NOAA GOES-R Advanced Baseline Imager (ABI) will have nearly the same capabilities as NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) to generate multi-wavelength retrievals of aerosol optical dept...
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As shown earlier by direct measurements, the spectrum of aerosol particles in the surface atmospheric layer is sharply changed before an earthquake. A lidar operating at two or three frequencies is capable to detect r...
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As shown earlier by direct measurements, the spectrum of aerosol particles in the surface atmospheric layer is sharply changed before an earthquake. A lidar operating at two or three frequencies is capable to detect regions with anomalous aerosol composition with the help of a simple dataprocessing algorithm. Such a lidar can be used as part of a global network on earthquake warning.
To derive the actual land surface information quantitatively, the atmospheric effects should be correctly removed. atmospheric effects dependent on aerosol particles, clouds and other atmosphere conditions. Aerosol pa...
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ISBN:
(纸本)3540340777
To derive the actual land surface information quantitatively, the atmospheric effects should be correctly removed. atmospheric effects dependent on aerosol particles, clouds and other atmosphere conditions. Aerosol parameters can be retrieved from the remotely sensed data. The retrieved aerosol characters can also be applied to environmental monitoring. To retrieval the aerosol optical thickness over land, many methods have been developed. The most popular one is the dark dense vegetation method. But it is confined to vegetation fields. The SYNTAM method can be used to retrieval aerosol optical thickness over land from MODIS data, no matter whether the land is dark or bright. In this paper, the SYNTAM method is applied to MODIS data for the retrieval of aerosol optical thickness over China. The retrieval process is complicated. And the EMS memory required is too large for a personal computing to run successfully. To solve this problem, the Grid environment is used. Our experiments were performed on the High-Throughput Spatial Information processing Prototype System based on Grid platform in Institute of remotesensing Applications, Chinese Academy of Sciences. The aerosol optical thickness retrieval process is described in this paper. And the detail data query, data pre-processing, job monitoring and post-processing is discussed. Moreover, test results are also reported in this paper.
Utilizing formats such as Geographic Information Systems (GIS) and remotesensingdata assessment, an attempt was made to identify possible releasers of effluent waste into the major coastal watershed regions pertaini...
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ISBN:
(纸本)9781424495665
Utilizing formats such as Geographic Information Systems (GIS) and remotesensingdata assessment, an attempt was made to identify possible releasers of effluent waste into the major coastal watershed regions pertaining to ongoing research conducted within monitored mussel watch sites. The categorization of possible contaminating locations was made available through the development of a series of dataset. This dataset was primarily derived from agencies such as the National Oceanic and atmospheric Administration (NOAA) Mussel Watch Program, the United States environmental Protection Agency (U. S. EPA), and the United States Geological Survey (USGS), as well as other state government databases. With the utilization of platforms such as the ESRI (R) ArcMap (TM) software, analysis and assessment of spatially referenced locations, via point, vector, and line data format were used to depict points and sites of interest within sampled locations and areas of Interest (AOI). Points and areas of interest (AOI) were also verified using remotesensing imagery in order to maintain spatial and spectral integrity within areas that were detected with known contaminant. As such, the contaminant Polybrominated Diphenyl Ethers (PBDEs) within observable mussel watch sites was cross checked with those assessed by the NOAA's Center for Coastal Monitoring and Assessment (CCMA), in order to clearly identify all the possible sources of the contaminant Polybrominated Diphenyl Ether (PBDE) within the sampled locations. With the use of this data, researchers are able to identify current as well future possible sources of Polybrominated Diphenyl Ether (PBDE) contaminant contributors within monitored mussel watch sites and other fragile coastal ecosystems.
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