Since 1972, Landsat program has experienced six successful missions that have contributed to nearly 40 years record of Earth Observations for monitoring the land cover and change dynamics. The successful launch of the Landsat Data Continuity Mission (LDCM, now named Landsat 8) on February 11, 2013 continues the mission of collecting images of the Earth with an open (free) data policy. Landsat 8 carries two push broom sensors: the Operational Land Imager (OLI) will collect data for nine shortwave spectral bands over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band. The other instrument, the Thermal Infrared Sensor (TIRS) will collect image data for two thermal bands with a 100 m resolution over a 185 km swath. However, cloud and associated cloud shadows frequently obscure the detection of land surface and restrict the the analysis of change trends over time. This paper presents a new method to detect and remove cloud and cloud shadows using the Landsat 8 first Image data (WRS2: Path/Row =33/32, acquired on March 18, 2013). The method uses six bands for transformation to calculate intensity of cloud and cloud shadows from the nine spectral bands and was further removed. The method takes advantage of spectral information. The validation demonstrates that cloud and cloud shadows contaminated pixels were accurately detected with overall accuracies of 98 and 97%, respectively. However, for thick cloud and cloud shadows, the performance of this method was limited. With further development there is potential for this method using for atmospheric corrections to improve landscape change detection.
In this paper, a optimized algorithm to recognize and remove hazes and clouds from remotely sensed images of Landsat
MSS/TM/ETM+ over land has been proposed. This algorithm uses only the image feature to automatically recognize and
remove contamination of hazes and clouds which will prevent satellite image from assessing land surface variables.
The hazes and clouds can be detected on the base of the reflectance difference with the other regions, likes thermal
spectrum region. Based on both fourth tasseled cap parameter and a haze optimized transformation(HOT) as a measure
of haze/cloud spatial density for single Landsat MSS/TM/ETM+ image, haze and clouds can be quantitatively
recognized and removed. The performance of the proposed algorithm is demonstrated experimentally. This method can
be used for atmospheric corrections to improve landscape change detection.
Volcanic activity can present unpredictable disasters to city populations living within regions and for people traveling in
plane that intersect with ash-laden eruption clouds. Methods of monitoring volcanic activity include searching for
variations in the thermal anomaly, clouds resource and subsidence deformation from active volcano. Over any active
volcanoes, low spatial resolution satellite image are used to identify changes in eruptive activity, but are of insufficient
spatial resolution to map active volcanic features. The Landsat data can be used to identify the thermal characteristics of
a series of lava flows at Fuego volcano and Pacaya volcano, Guatemala. We use Landsat TM/ETM+ 7, 5, 4 (displayed in
red, green, and blue, respectively) false-color composite of the research region, acquired on 18 December 1989 and 23
January 2000 to indicate the volcano image features which appear halo structure with blue red and yellow. The
interpretation flag is obvious which indicate the difference temperature of volcano crater. Spatially varying haze emitted
by volcano activity is identified and removed based on Improved Haze Optimized Transform (HOT) which is a robust
haze assessing method. With improved spatial resolution in the thermal IR, we are able to map the bifurcation and
braiding of underground lava tubes. With higher spatial resolution panchromatic data, we are able to map lava flow
fields, trace very high temperature lava channels, and identify an accurate feature associated with a collapsed crater floor.
At both Fuego and Pacaya, we are able to use the thermal data to estimate temperature. We can monitor the dynamic
change of the two volcanoes using two difference date Landsat data.
The paper mainly discusses the oil and gas engineering techniques based on Digital Earth Platform (DEP), such as
exploration, development, gathering and transportation In the process of using oil and gas resources, Digital Earth
Platform can promote the engineering with the supported by spatial information. It also improves the scientificity,
accuracy, rationality of the oil and gas engineering techniques, greatly reduces cost and increases benefit.
Differential Interferometric Synthetic Aperture Radar (D-InSAR) is a new technology which is capable of detecting the
tiny ground deformation and extracting Digital Elevation Model (DEM). This paper introduces the basic principle of
D-InSAR. Using of two pass model and SRTM DEM, acquired Kunlun Mountains region surface deformation of Ms8.1
in 2001. The result provides an important reference for Qinghai-Tibet Railway Disaster Prevention.
This paper explains the basic points of earth science research, reviews the notion of digital earth and suggests the basic
concept of digital earth platform. Taking the theory of digital earth as an essence point and the framework of digital earth
platform as a main clue, we mainly discuss: the basic theories of digital earth platform, a comparatively flawless model
of earth science research framework based on the digital earth platform. The discussing issues involves the unified
coordinate projecting, data updating, data transferring, data processing and analyzing and data displaying and a series of
researches relating to energy sources, eco-system and disasters monitoring and forecasting with a combination of the
suggested model of earth science research framework.