Enhancement and Application of WorldView-2 to Geological Interpretation

2014 ◽  
Vol 1010-1012 ◽  
pp. 1237-1242 ◽  
Author(s):  
Jia Jing Zhou ◽  
Shu Fang Tian ◽  
Na Wang ◽  
Xiao Hu
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Good Effect ◽  
Geological Interpretation ◽  
Spectral Bands ◽  
Texture Information ◽  
Kunlun Mountain ◽  
West Kunlun

By comparing WorlView-2 with other remote sensing data in the characteristics of spectral bands and spatial resolution, we found that all the eight bands of WorldView-2 are sensitive to lithology and helpful to distinguish them; besides, WorldView-2 provides a richer texture information with a high spatial resolution of 0.46m, which is also very important in geological interpretation of remote sensing. Therefore, WorldView-2 data has a strong advantage in geological applications. In the geological interpretation of the Kezile area in West Kunlun Mountain, different enhancement methods based on the spectrum, texture and geomorphology/vegetation were applyed to enhance the lithology information of WorldView-2 image, and it achieved a good effect. With the enhanced images of Kezile area, we subdivided the Jurassic, Cretaceous, Paleogene and Neogene into lithologies in detail, and completed the remote sensing geological interpretation map.

Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

2020 ◽  
Vol 40 (10) ◽  
pp. 1028001
Author(s):  
陈世涵 Chen Shihan ◽  
李玲 Li Ling ◽  
蒋弘凡 Jiang Hongfan ◽  
居伟杰 Ju Weijie ◽  
张曼玉 Zhang Manyu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Satellite Remote Sensing ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Environment Change ◽  
Sensing Data

scholarly journals Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data

2007 ◽  
Vol 29 (4) ◽  
pp. 1153-1167 ◽  
Author(s):  
C. Delenne ◽  
S. Durrieu ◽  
G. Rabatel ◽  
M. Deshayes ◽  
J. S. Bailly ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Very High Spatial Resolution ◽  
Very High

scholarly journals Damage Assessment of a Salt Dome in Jizan, Southwestern Saudi Arabia, Using High Spatial Resolution Remote Sensing Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Habes Ghrefat ◽  
Ahmed Hakami ◽  
Elkhedr Ibrahim ◽  
Saad Mogren ◽  
Saleh Qaysi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Spatial Resolution ◽  
Damage Assessment ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Salt Dome ◽  
Visual Interpretation ◽  
Sensing Data ◽  
Residential Neighborhood

The salt dome in Jizan, southwestern Saudi Arabia, has caused several problems related to underground dissolution, particularly in the old part of the city. Examples of these problems include surface collapse, building failure, fracturing, tilting, and road cracking. Analysis of the salt dome using X-ray diffraction (XRD) revealed the dominance of gypsum, anhydrite, and halite. This study evaluates the damage assessment using multitemporal high spatial resolution data of the GeoEye-1, and QuickBird-2 sensors. Change detection technique, textural analysis, and visual interpretation were applied to these data. Analysis of the data recorded before and after a particular damage event revealed that three neighborhoods located above the Jizan salt dome—Al-Ashaima, Shamiya, and Aljabal—were affected to the greatest extent. The entire residential neighborhood of Al-Ashaima was evacuated, and the buildings located in it were demolished. Several buildings in the Shamiya and Aljabal neighborhoods were also demolished. Therefore, high spatial remote sensing data are effective in assessing building damage and for anticipating future damage, thus benefiting decision making for the affected cities.

scholarly journals Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data

2021 ◽  
Author(s):  
David M. Atkinson ◽  
Paul Treitz
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ecosystem Respiration ◽  
Chlorophyll Concentration ◽  
Remote Sensing Data ◽  
Ecosystem Processes ◽  
Percent Cover ◽  
Vegetation Community ◽  
Arctic Environment

As a result of the warming observed at high latitudes, there is significant potential for the balance of ecosystem processes to change, i.e., the balance between carbon sequestration and respiration may be altered, giving rise to the release of soil carbon through elevated ecosystem respiration. Gross ecosystem productivity and ecosystem respiration vary in relation to the pattern of vegetation community type and associated biophysical traits (e.g., percent cover, biomass, chlorophyll concentration, etc.). In an arctic environment where vegetation is highly variable across the landscape, the use of high spatial resolution imagery can assist in discerning complex patterns of vegetation and biophysical variables. The research presented here examines the relationship between ecological and spectral variables in order to generate an ecologically meaningful vegetation classification from high spatial resolution remote sensing data. Our methodology integrates ordination and image classifications techniques for two non-overlapping Arctic sites across a 5° latitudinal gradient (approximately 70° to 75°N). Ordination techniques were applied to determine the arrangement of sample sites, in relation to environmental variables, followed by cluster analysis to create ecological classes. The derived classes were then used to classify high spatial resolution IKONOS multispectral data. The results demonstrate moderate levels of success. Classifications had overall accuracies between 69%–79% and Kappa values of 0.54–0.69. Vegetation classes were generally distinct at each site with the exception of sedge wetlands. Based on the results presented here, the combination of ecological and remote sensing techniques can produce classifications that have ecological meaning and are spectrally separable in an arctic environment. These classification schemes are critical for modeling ecosystem processes.

scholarly journals Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation

2019 ◽  
pp. 89-102
Author(s):  
V. V. Kozoderov ◽  
V. D. Egorov
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Spatial Resolution ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Automatic Data ◽  
Sensing Data ◽  
Satellite Sensor

Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

Sign in / Sign up

Export Citation Format

Share Document