Xuezhi Feng

A half‐century of changes in China's lakes: Global warming or human influence?

Lake size is sensitive to both climate change and human activities, and therefore serves as an excellent indicator to assess environmental changes. Using a large volume of various datasets, we provide a first complete picture of changes in Chinas lakes between 1960s-1980s and 2005-2006. Dramatic changes are found in both lake number and lake size; of these, 243 lakes vanished mainly in the northern provinces (and autonomous regions) and also in some southern provinces while 60 new lakes appeared mainly on the Tibetan Plateau and neighboring provinces. Limited evidence suggested that these geographically unbalanced changes might be associated primarily with climate change in North China and human activities in South China, yet targeted regional studies are required to confirm this preliminary observation. Citation: Ma, R., H. Duan, C. Hu, X. Feng, A. Li, W. Ju, J. Jiang, and G. Yang (2010), A half-century of changes in Chinas lakes: Global warming or human influence?, Geophys. Res. Lett., 37, L24106, doi:10.1029/2010GL045514.

Detecting China’s Urban Expansion Over the Past Three Decades Using Nighttime Light Data

China has experienced a rapid urban expansion over the past three decades because of its accelerated economic growth. In this study, we detected and analyzed the urban expansion of China during this period using multi-temporal Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data and multi-source Normalized Difference Vegetation Index (NDVI) data. First, an intercalibration was performed to improve the continuity and comparability of the nighttime light data from 1992 to 2010. The nighttime light and NDVI data were then subjected to a local support vector machine (SVM) based region-growing method to extract the urban areas from 1992 to 2010. The urban areas from 1981 to 1991 were identified using the areas in 1992 and NDVI data, based on the hypothesis that Chinas urban expansion continued during this period. Finally, the extracted time-series urban maps were validated with Landsat images. The proposed local SVM-based region-growing method performed better than a local thresholding method and a global SVM-based region-growing method according to visual and quantitative comparisons of the urban boundaries and areas. We also analyzed the expansion rates to understand the dynamics of the urban areas in China and in its seven economic regions. In particular, the urban expansion patterns were investigated in three typical urban agglomerations, i.e., Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. The proposed urban expansion direction, urban expansion intensity, and relative ratio of urban expansion demonstrated the regional variation among the three urban agglomerations.

An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation

Image segmentation is a critical step in the analysis of high-spatial-resolution remotely sensed imagery using object-based image analysis. The segmentation quality is extremely important to the subsequent analysis. This letter proposes an improved unsupervised method to evaluate the segmentation quality for remotely sensed imagery. The evaluation criteria take into account global intrasegment homogeneity and intersegment heterogeneity measures, which can be useful for the comparison of segmentation results produced by a single segmentation method. The proposed method is compared with other two mature unsupervised evaluation methods on two segmentation methods: region growing and mean shift. QuickBird images are used for the comparative study. The effectiveness of the proposed method is validated through comparing with the supervised evaluation method Rand Index and visual analysis.

Segmentation of multispectral high-resolution satellite imagery using log Gabor filters

Image segmentation has been recognized as a valuable approach that performs a region-based rather than a pixel-based analysis of high-resolution satellite imagery. A scheme for segmenting the multispectral IKONOS image based on frequency-domain filtering is presented. The frequency spectrum of typical landscape objects is analysed first. The spectrum curves are comparable in logarithmic coordinates rather than in Cartesian coordinates; therefore the Gabor filters are superseded by log Gabor filters to extract the multiscale texture features from panchromatic band. Edge features then are calculated from the pan-sharpened multispectral bands based on the vector field model. Finally, the texture-marked watershed segmentation algorithm is implemented and the segmentation accuracy is assessed. The experimental results show that the developed scheme generated an effective tool for automatic segmentation of multispectral high-resolution satellite imagery and suppressing the over-segmentation problem of watershed transform.

Image feature detection from phase congruency based on two-dimensional Hilbert transform

The theory of phase congruency is that features such as step edges, roofs, and deltas always reach the maximum phase of image harmonic components. We propose a modified algorithm of phase congruency to detect image features based on two-dimensional (2-D) discrete Hilbert transform. Windowing technique is introduced to locate image features in the algorithm. Local energy is obtained by convoluting original image with two operators of removing direct current (DC) component over current window and 2-D Hilbert transform, respectively. Then, local energy is divided with the sum of Fourier amplitude of current window to retrieve the value of phase congruency. Meanwhile, we add the DC component of current window on original image to the denominator of phase congruency model to reduce the noise. Finally, the proposed algorithm is compared with some existing algorithm in systematical way. The experimental results of images in Berkeley Segmentation Dataset (BSDS) and remotely sensed images show that this algorithm is readily to detect image features.

Urban heat island in summer of Nanjing based on TM data

When the area covered by buildings increases a lot, the heat island effect appears, that is the phenomenon that temperature of urban area is higher than suburb, and central area has higher temperature than surrounding areas. Heat island effect is the direct representation of worse urban environment. It is not a single and independent temperature index since it relates to a variety of factors. Since the 1970s, remote sensing technology has been applied to measure land surface temperature in the research on heat island effect. This paper takes Nanjing main city as the research area and chooses Landsat TM image in 2003 as the research data. The paper makes using of NDVI to extract the green land from Nanjing urban area. NDBI is also introduced in to extract the built-up land from the urban area. This paper then utilizes mono-window algorithm (created by Z. H Qin) to retrieve land surface temperature and brightness temperature. In order to understand the relationship among these special indices and learn their influence on heat island effect, .correlation analysis is done among land surface temperature, NDBI and NDVI. These correlation coefficients indicate that the influence of buildings on the heat island effect is positive; in contrast vegetation plays an important role in weakening the heat island effect. In order to further learn the influence of different types of underlying surface on the surface land temperature of Nanjing urban area, this paper applies supervised classification to the research area. According to the results, this paper classifies Nanjing urban area as four types of land: high density built-up land, low density built-up land, green land and water body. By analyzing the correlation between these two indices and different underlying surface temperature, the paper gets the correlation matrix and reaches an expected conclusion.

Segmentation on Multispectral Remote Sensing Image Using Watershed Transformation

In this paper, a new method for segmenting multispectral remote sensing image is proposed, which combines spectral properties of the pixels and their spatial properties. Spectral properties are studied by analyzing spectral angle of pixels while spatial properties are studied by morphological method. The spectral angle of each pixel is first computed by taken them as a vector with n-dimension. After an automatic selection of significant minima, an initial segmentation is achieved by applying watershed transformation to spectral angle map. To overcome the over-segmentation problem of watershed transformation, a region merging process using region adjacency graph (RAG) is employed to get the final segmentation result.

Cosegmentation for Object-Based Building Change Detection From High-Resolution Remotely Sensed Images

This paper presents a cosegmentation-based method for building change detection from multitemporal high-resolution (HR) remotely sensed images, providing a new solution to object-based change detection (OBCD). First, the magnitude of a difference image is calculated to represent the change feature. Next, cosegmentation is performed via graph-based energy minimization by combining the change feature with image features at each phase, directly resulting in foreground as multitemporal changed objects and background as unchanged area. Finally, the spatial correspondence between changed objects is established through overlay analysis. Cosegmentation provides a separate and associated, rather than a separate and independent, multitemporal image segmentation method for OBCD, which has two advantages: 1) both the image and change features are used to produce foreground segments as changed objects, which can take full advantage of multitemporal information and produce two spatially corresponded change detection maps by the association of the change feature, having the ability to reveal the thematic, geometric, and numeric changes of objects and 2) the background in the cosegmentation result represents the unchanged area, which naturally avoids the problem of matching inconsistent unchanged objects caused by the separate and independent multitemporal segmentation strategy. Experimental results on five HR datasets verify the effectiveness of the proposed method and the comparisons with the state-of-the-art OBCD methods further show its superiority.

Comparison of Snow Indexes in Estimating Snow Cover Fraction in a Mountainous Area in Northwestern China

The normalized difference snow index (NDSI), a key part of the snow-mapping algorithm for extracting snow information from remotely sensed imageries, has been frequently employed in acquiring snow cover extent in the past decades. A linear regression analysis has been frequently used in estimating snow cover fraction (SCF) on a subpixel basis by developing a statistical relationship between NDSI and SCF. In this letter, a comparison of NDSI, ratio snow index (RSI), and difference snow index (DSI) in estimating SCF is carried out in mountainous area of northwestern China. Three kinds of fitting methods, i.e., linear fitting, logarithmic curve fitting, and exponential curve fitting, are employed in this comparison for the purpose of finding out a best statistical fitting relationship between SCF and snow index. The results show that RSI and DSI are both good substitutes for NDSI in SCF estimation due to high R-squares, up to 0.83, of fitted lines between snow indexes and SCF, respectively. Furthermore, exponential fitting is considered to be of the highest robustness in the three fitting methods of interest for all the snow indexes studied.

Evaluation of the surface reflectance retrieval on the satellite data

Electromagnetic radiance acquired by sensors is distorted mainly by atmospheric absorbing and scattering. Atmospheric correction is required for quantitatively analysis of remote sensing information. Radiation transfer model based atmospheric correction usually needs some atmospheric parameters to be chosen and estimated reasonably in advance when atmospheric observation data is lacked. In our work, a radiometric calibration was applied on the satellite data using revised coefficients at first. Then several parameters were determined for the correction process, taking into account the earths surface and atmospheric properties of the study area. Moreover, the atmospheric correction was implemented using 6S code and the surface reflectance was retrieved. Lastly, the influence of atmospheric correction on spectral response characteristics of different land covers was discussed in respects of the spectral response curve, NDVI and the classification process, respectively. The results showed that the reflectance of all land covers decreases evidently in three visible bands, but increases in the near-infrared and shortwave infrared bands after atmospheric correction. NDVI of land covers also increases obviously after atmospheric influence was removed, and NDVI derived from the surface reflectance is the highest comparing to that from the original digital number and the top of atmosphere reflectance. The accuracy of the supervised classification is improved greatly, which is up to 87.23%, after the atmospheric effect is corrected. Methods of the parameter determination can be used for reference in similar studies.