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Hu S.,Key Laboratory of 3D Information Acquisition and Application of Ministry of Education | Hu D.,Key Laboratory of Resources Environment and GIS of Beijing Municipal | Zhao W.,Capital Normal University
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2010

Vegetation is an important part of urban ecosystem; therefore timely access to vegetation coverage information is of great significance for monitoring urban ecological environment. Linear spectral mixture model (LSMM) was carried out for urban vegetation coverage extraction using medium-resolution Landsat TM remote sensing data. Meanwhile, the fuzzy c-means (FCM) method was chosen to extract vegetation coverage by improving the training sample selection method to obtain the end-member sample based on minimum noise transform (MNF), pixel purity index analysis (PPI), and N-dimensional visualization analysis. Finally, high-resolution SPOT5 remote sensing data extracted in two ways were used to carry out the accuracy test for vegetation coverage. The results showed that the correlation coefficients between the inspection data and LSMM-based and improved FCM-based data were 0. 8252 and 0. 9381, respectively. It indicated that the improved FCM-based method with higher accuracy can better eliminate the nonlinear effect of other elements. Source


Xu Y.,Capital Normal University | Xu Y.,Key Laboratory of Resources Environment and GIS of Beijing Municipal | Duan F.,Capital Normal University | Duan F.,Key Laboratory of Resources Environment and GIS of Beijing Municipal
International Conference on Geoinformatics | Year: 2013

Low-altitude aerial remote sensing platforms accessed reality multi-color images which had obvious characteristics and fitted for visual interpretation. These images were lacking of spectral information but rich in shape and texture information. But, the reality was that there was less study on the automatic extraction of ground information from aerial images. In this paper, UAV images were selected as test data. By combining the object oriented method and the multi-resolution segmentation, the paper selected some effective characteristics, constructed the rule sets and classify the image into water, shrub, farmland, road, and house. Then, the result was compared with which obtained by maximum likelihood classification method. The results showed that: With the object-oriented method, it could get higher accuracies and efficiencies for actual applications, the overall classification accuracies and Kappa coefficient are more than 85%. © 2013 IEEE. Source


Gao M.-L.,Key Laboratory of 3D Information Acquisition and Application of Ministry of Education | Gao M.-L.,Key Laboratory of Resources Environment and GIS of Beijing Municipal | Gao M.-L.,Capital Normal University | Zhao W.-J.,Key Laboratory of 3D Information Acquisition and Application of Ministry of Education | And 12 more authors.
Remote Sensing | Year: 2014

The availability of ZY-3 satellite data provides additional potential for surveying, mapping, and quantitative studies. Topographic correction, which eliminates the terrain effect caused by the topographic relief, is one of the fundamental steps in data preprocessing for quantitative analysis of vegetation. In this paper, we rectified ZY-3 satellite data using five commonly used topographic correction models and investigate their impact on the regression estimation of shrub forest leaf biomass obtained from sample plots in the study area. All the corrections were assessed by means of: (1) visual inspection (2) reduction of the standard deviation (SD) at different terrain slopes (3) correlation analysis of different correction results. Best results were obtained from the Minnaert+SCS correction, based on the non-Lambertian reflection assumption. Additional analysis showed that the coefficient correlation of the biomass fitting result was improved after the Minnaert+SCS correction, as well as the fitting precision. The R2 has increased by 0.113 to reach 0.869, while the SD (standard deviation) of the biomass dropped by 21.2%. Therefore, based on the facts, we conclude that in the region with large topographic relief, the topographical correction is essential to the estimation of the biomass. © 2014 by the authors; licensee MDPI, Basel, Switzerland. Source


Fan L.,Capital Normal University | Fan L.,Key Laboratory of 3D Information Acquisition and Application of Ministry of Education | Fan L.,Key Laboratory of Resources Environment and GIS of Beijing Municipal | Zhao W.-J.,Capital Normal University | And 9 more authors.
Jilin Daxue Xuebao (Diqiu Kexue Ban)/Journal of Jilin University (Earth Science Edition) | Year: 2012

Spectral feature is the physical basis of rock identification. In order to remove the rock spectral noise, the spectra including common 15 rock samples belonging to 10 rock types are pretreated by averaging, resampling, smoothing, and fitting the value of water vapor absorption band. The continuum removing methods are used to obtain absorption-band parameters of spectra. Among the rock samples, the mica slate's absorption feature is the most obvious. The normalized data studied by using R-mode principle factor method shows that the first principal factor axis represents the major absorption spectra of cations, anions and water vapor, and the second represents a small number of cation band. The characteristic spectral bands of the rock are 385-525 nm, 735-1365 nm, 1435-1785 nm, 1890-1952 nm and 1995-2310 nm. The physical meanings of these bands are also identified. The rock spectra are classified into four types by two-dimensional image analysis. From first type to last type, the spectra prove the gradually shallow absorption depth, the decreasing area, the gradually narrowing width and the increasing number of absorption peaks. Iron and pelitization alteration phenomenon are obvious. The classification results verified by cluster analysis are of better correspondence. A physical basis for rock classification and identification are provided by remote sensing technology. It is of significance to abstract effectively hyper spectral data and classify hyper spectra images. Source


Gong Z.,Capital Normal University | Gong Z.,Key Laboratory of 3D Information Acquisition and Application of Ministry of Education | Gong Z.,Key Laboratory of Resources Environment and GIS of Beijing Municipal | Gong Z.,Base of the State Laboratory of Urban Environmental Processes and Digital Modelling | And 8 more authors.
International Journal of Remote Sensing | Year: 2014

Vegetation abundance is a critical indicator for measuring the status of vegetation. It is also important for evaluating the eco-environment of wetland. In this article, linear spectral mixture analysis (LSMA) and fuzzy c-means (FCM) classification methods were applied to estimate vegetation abundance in Wild Duck Lake Wetland, one of the typical freshwater wetlands in North China, based on Landsat Thematic Mapper (TM) data acquired on 27 June 2011. Due to its effectiveness in characterizing vegetation activity and greenness, the normalized difference vegetation index (NDVI) was incorporated into the six reflective bands of the Landsat TM image to provide enough dimensionality to support the use of the a five-endmember LSMA model, which includes terrestrial plants, aquatic plants, high albedo, low albedo, and bare soil. Then, a fully constrained LSMA algorithm was performed to obtain vegetation abundance in our study area. An FCM classification algorithm was also used to generate vegetation abundance. Finally, both results were modified using the extracted water area of Wild Duck Lake Wetland, which was obtained with the combination of NDVI and normalized difference water index. The root mean square error (RMSE) and the coefficient of determination (R2) were calculated to assess the accuracy of vegetation abundance by using a WorldView-2 multispectral image. Validation showed that although there were slight differences between the vegetation abundance images, they shared similar spatial patterns of vegetation distribution: high vegetation abundance values in agricultural areas and riparian areas, moderate in grassland areas, and low in residential areas. The FCM classification generated an R2 of 0.791, while the LSMA yielded a result with an R2 of 0.672. Additionally, the RMSE also indicated that the FCM classification can obtain a much better result than LSMA: the former's RMSE is 0.091 and the latter is 0.172. The result suggests that the FCM classification based on the nonlinear assumption can handle mixed pixels more effectively than LSMA. © 2013 Taylor & Francis. Source

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