CASI/SASI data covering the hongliugou hydrothermal metallic deposit, western altyn tagh mining districts, were used to map hydrothermal alteration minerals. The hyperspectral data were calibrated to apparent surface reflectance using the empirical line method and the method of radiative transfer model. The three methods of estimating apparent surface reflectance have been evaluated by using field spectral. Two spectral unmixing algorithms (MTMF and CEM) were applied to analyse imaging spectrometer data. The objective was to compare the performance of the two algorithms, in order to map surface hydrothermal alteration mineralogy in the hongliugou area. Altered rocks and faults in the district can be discriminated by use of mineral distribute images derived from CASI/SASI data.
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.
This paper aims to monitor desertification evolution of different stages and assess its factors using remote sensing (RS)
data and cellular automata (CA)-geographical information system (GIS) with an adaptive analytic hierarchy process (AHP) to derive weights of desertification factors. The study areas (114°E to 117°E and 39.5°to 42.2°N) are one of the important agro-pastoral transitional zone, located in Beijing and its neighboring areas, marginal desertified areas in North China. Desertification information including NDVI and desertification area were derived from the satellite images of 1987TM, 1996TM (with a resolution of 28.5), and 2006 CBERS-(with a resolution of 19.5 m) in study areas. The ancillary data in terms of meteorology, geology, 30m-DEM, hydrography can be statistical analyzed with GIS technology. A CA model based on the desertification factors with AHP-derived weights was built by AML program in ArcGIS workstation to assess the evolution of desertification in different stages (from 1987 to 1996, and from 1996 to 2006). The research results show that desertified areas was increased by 3.28% per year from 1987 to 1996, so was
0.51% per year from 1996 to 2006. Although the weights of desertification factors have some changes in different stages,
the main factors including climate, NDVI, and terrain did not change except the values in study areas.
KEYWORDS: Principal component analysis, Statistical analysis, Geographic information systems, Ecosystems, Data modeling, Analytical research, Databases, Minerals, Systems modeling, Roads
In this paper, a comprehensive study for the evaluation and simulation of regional carrying capacity has been presented. After constructing the modified operational framework for regional carrying capacity evaluation and the indictor system, the Bohai-Rim region in northern China is taken as an example to implement the theoretical system: (1) Database is constructed and the data preprocesses have been performed based on GIS; (2) the assimilative capacity, supportive capacity and loading have been evaluated by using principal components analysis (PCA) and ecological footprint method; (3) then, the carrying states of 1996~2003 have been evaluated by statistical analysis; (4) future carrying states of 2004~2020 are forecasted by using ANN-based CA model.
To get a sound method for mineral prediction in dense vegetation zones, this study applies RS and GIS technologies to predict mineral resources in Genma and Cangyuan of Yunnan, P.R.C., where mineralization is concentrative but little breakthrough is achieved in exploring mineral deposits resulting from dense vegetation covers. Methods on the geological application of RS in dense vegetation zones are developed in the study, and practically proven to be effective. Based on GIS, mineralization and alteration indicators for vegetation zones are formulated by applying the ETM RS multi-functional image processing techniques. Along with RS-based multivariate geological indicators, geological, geophysical and geochemical data are integrated and used to construct quantitative models for mineral resources prediction and assessment using Information Quantification Method. Based on the models, mineral deposits are digitally predicted, and accordingly information on deposit formation and control is effectively derived and optimized. The information is verified through all-around field surveys in the target areas, and satisfactory results are obtained. Hence, the techniques and methods in the study are worthy of extension.
According to the analysis of remotely sensed data and other multi-information of study area (its longitude: 113.5°-117° and latitude 39.5°-42°), the authors dynamically evolved desertification of Beijing and its neighboring areas, which the images were used in three different periods, 1987 TM, 1996 TM and the CB-1s CCD 4, 3, 2 bands of 2000, 4 orbits and 16 (CBERS-1) scenes (the resolution is 19.5m). By synthetically analysis, we obtained the area desertification distribute and evaluated space-time information. Based on the remote sensing interpretation and database of desertification multi-information efficiency distilled from geology, hydrography, anthropogeography, agrotype, vegetation and so on, we build up desertification dynamic simulation model with CA (Cellular Automata) theory and GIS (Geographic Information System) tool. Then utilizes this model to predict desertification development of Beijing and its neighboring areas. The result of the experiment proves that the model is effective to simulate desertification development in terms of macroscopic and microcosmic. The desertification of Beijing and its neighboring areas develop from west to the east, the southern edge is 72km to Beijing city where lies in the north of Jundu mountain crossing Yanshan with Taihang mountains, which prevents the development of desertification.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.