Gong Z.-N.,Capital Normal University |
Gong Z.-N.,Key Laboratory of 3D Information Acquisition and Application of Ministry of Education |
Gong Z.-N.,Key Laboratory of Resources Environment and GIS of Beijing Municipal |
Gong Z.-N.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 12 more authors.
Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | Year: 2014
Quantitative estimation of emerged plant water content with multi-spectral remote sensing technique is of great significance for emerged plant physiological status and growth trend monitoring. The hyperspectral reflectance of canopy of wetland typical emerged plant (reed and cattail) was measured by Field-Spec 3 wild high-spectrum radiometer. The leaf water content and leaf area index (LAI) of corresponding samples were also measured. First of all, the ground spectral data (reed and cattail) were resampled to simulate the spectral of WorldView-2 imagery, then the simple ratio vegetation index (SR) and normalized difference vegetation index (NDVI) were constructed with arbitrary two band combination from the simulated WorldView-2 spectra, respectively. The correlation between canopy water content (CWC) and vegetation index were analyzed. The estimation models were obtained by using regression and correlation analysis for different emerged plant community. In addition, the research result of ground data was applied to WorldView-2 high resolution multispectral imagery covering the study area, and the CWC of emerged plant community was estimation in spatial scale. The results show that the SR and NDVI constructed by the simulated WorldView-2 spectra had a good overall correlation with CWC. The SR(8, 3)reedwas selected as the optimal vegetation index to estimate the CWCreed, the best models are evaluated and validated as y=0.005x+0.003. The NDVI(8, 3)cattailwas selected as the optimal vegetation index to estimate the CWCcattail, the best models were evaluated and validated as y=2.461x2-0.313x+0.032. According to two K-fold cross validation examination, these estimation models have the satisfactory prediction accuracy. The prediction accuracy of CWCreedwas 87.42% and the prediction accuracy of CWCcattailwas 82.12%. Furthermore, based on the research result of ground data, we made use of WorldView-2 high resolution multispectral imagery to map the CWC of different emerged plant community. According to the examination of measured data, the estimation RMSE of CWCreedand CWCcattailfrom imagery were 0.0048 and 0.0052, respectively. The estimation accuracy were 83.56% and 80.31%, respectively. It was demonstrated that using WorldView-2 high resolution multispectral imagery to estimate the CWC of wetland emerged plant community has a high feasibility. ©, 2014, Science Press. All right reserved.
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.
Gong Z.N.,Capital Normal University |
Gong Z.N.,Key Laboratory of 3D Information Acquisition |
Gong Z.N.,Key Laboratory of Resources Environment and GIS of Beijing Municipal |
Gong Z.N.,Base of the State Laboratory of Urban Environmental Processes and Digital Modeling |
And 16 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2014
Chlorophyll can be an indicator in photosynthesis capacity and vegetation developmental stages,which is also one of important indicators to monitor health status of wetland vegetation growth. Hyperspectral remote sensing technology can provide a simple, effective and non-destructive data acquisition, which can offer processing method for quantifying diagnosis plant chlorophyll content as well. This study used the Fieldspec 3 spectrometer and a plant probe leaf clip spectral detector to guarantee the spectrum are detected in the same area of the leaf, it is also eliminating the background reflectance, spectral fluctuations caused by bending of the blade surface and the impact caused by leaf internal variability. This study determined the typical wetland plants leaf hyperspectral reflectance data at Wild Duck Lake, and at the same time the corresponding leaf chlorophyll content was measured using a spectrophotometer indoor. The relationship between chlorophyll content and the Trilateral parameters, as well as the ratio of spectral index model (SR) and normalized difference spectral index (ND) were established respectively using linear regression model., then 3-Fold Cross Validation(3K-CV) was used to test the accuracy of the estimation model. The results showed that most of the "trilateral" parameters were significantly correlated with plant leaf chlorophyll content; the maximum correlation coefficient reached 0.867. The correlation coefficient between ratio (SR) and normalized (ND) and chlorophyll content were high in general. Suitable band combinations were 550—700 nm,700—1400nm, 550—700 nm and 1600—1900 nm. The best indices with highest correlation with chlorophyll content were SR (calculated from bands 565 nm and 740 nm) and ND (calculated from bands 565 nm and 735 nm). And then by choosing the best correlation spectrum characteristic parameters based on the Trilateral parameters and ND model index, a plant chlorophyll estimation model was constructed. Among them, a chlorophyll content estimation model established by Red edge position (WP_r) of spectral characteristic parameters and ND (565nm, 735nm) spectral index achieved better test results, and R2 both reached above 0.8, the estimation model were y = 0.113x−78.74, y = 5.5762x + 4.4828. Using 3K-CV method for testing and validation, the prediction accuracies of both plant leaf chlorophyll content estimation models were 93. 9% and 90. 7%, respectively. The quantitative analysis of hyperspectral remote sensing technology shows a strong advantage in detecting vegetation weak spectral differences and provides an important theoretical basis and technical support for the practical application in the diagnosis of plant chlorophyll content. © 2014, Science Press. All rights reserved.
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.
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.
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.
Qu X.,Key Laboratory of Resources Environment and GIS of Beijing Municipal |
Qu X.,State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulationl |
Qu X.,Capital Normal University |
Duan F.,Key Laboratory of Resources Environment and GIS of Beijing Municipal |
And 8 more authors.
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | Year: 2015
It is unclear that what color descriptors best fits feature matching of aerial remote sensing Bayer real-color image. To analyze variation of imaging of the aerial image and algorithm characteristics of color descriptors, a theoretical analysis of invariant properties of color descriptors was presented. The effect and applicability of descriptors were verified and analyzed by three experimental methods which are evaluations of simulation data, different categories images and general applicability of task. The theoretical and experimental results show that RGBSIFT has best applicability for the feature matching of aerial Bayer real-color image. The usefulness of color descriptor is category-specific. ©, 2015, National University of Defense Technology. All right reserved.
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.