Fenzhen Su

Geoevent association rule discovery model based on rough set with marine fishery application

Vector-based association rule discovery models have been provided to look for spatial knowledge in terrestrial applications recently. Most of them just consider the attribution relationship of local space or the topological relationship between parcels or objects. And they are difficult to consider the temporal change, distance relationship or direction relationship. In this presentation, an geoevent association rule discovery model (GEARDM) is developed, which is based on the spatiotemporal grid and rough set, and the experiment in marine fishery application is provided in support of the model. In GEARDM, the continuous spatiotemporal process is discretized by fuzzy preknowledge as spatiotemporal assignment and will be assigned into a decision table. The mining algorithm based on rough set results from the decision table in the geoevent association rules, which shows what kind of pattern of spatiotemporal assignment of environmental factors determine the happen or attribution of the geoevents. After the mined rules being analyzed by knowledge, the refined knowledge is obtained and the spatiotemporal assignment will be renewed. Then the flow of GEARDM feeds back to the phase of discretization and iterates until the final rules are reasonable. These knowledge-based rules also can be used in the expert system to predict the happen or the scale of geoevent as the experiments show.

A visual circle based image registration algorithm for optical and SAR imagery

A visual circle based image registration algorithm for optical and SAR imagery is proposed in this paper. The visual circles with robustness to angle differences and flexible resolution build a bridge between the reference and sensed images with different resolutions and angles, and combine the spatial features and correlation measures together for the feature matching. Two universal correlation measures comprising normalized cross correlation coefficient (NCCC) and normalized mutual information (NMI) are adopted respectively for registration between Radarsat-2 and ASTER images. In order to testify the visual circles robustness to rotation, the visual squares and circles were constructed meanwhile and used to register the rotated sensed images with different angles respectively. The results proved that the total RMS error of the proposed algorithm was less than one pixel and the circle had higher robustness to angle differences and better performance than that of the square.

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

Sea surface temperature and purse net productivity in East China Sea

Temperature, as one of the main factors, is thought to play a dominant role in determining the spatial and temporal distribution of fish centers and fishing grounds. For many species of marine fish, research has provided much information about their spawning, feeding habitats and migrations; however, such information is always empirical. In order to investigate the relationship between sea surface temperature (SST) and purse net productivity in East China Sea (N24-N36, E118.5-E130), a time series of mean weekly SST images and corresponding purse net statistic productivity were analyzed in detail for the region from 1987-1997 using geographic information system (GIS) spatial analysis models and mapping techniques. Thereby, it is concluded that SST data has great correlation with purse net productivity and their relationship varied steadily in certain range over time and area.

Reconstruction of Satellite-Derived Sea Surface Temperature Data Based on an Improved DINEOF Algorithm

An improved data interpolating empirical orthogonal function (I-DINEOF) algorithm was proposed in this study. Compared with the ordinary DINEOF algorithm, in the I-DINEOF algorithm, the existing data are not necessary to be selected for cross-validation and the initial matrix is directly used for reconstruction. Instead of using single EOF to reconstruct the whole spatio-temporal matrix, the initial matrix is divided into several subareas and each subarea is reconstructed by the most suitable EOF. To validate the accuracy of the I-DINEOF algorithm, a real sea surface temperature (SST) data set and three synthetic data sets with different missing data percentage are reconstructed by using the DINEOF and I-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF algorithm, the I-DINEOF algorithm is less affected by the missing data and can significantly enhance the accuracy of reconstruction.

Application of a sea surface temperature front composite algorithm in the Bohai, Yellow, and East China Seas

The oceanic front is a narrow zone in which water properties change abruptly within a short distance. The sea surface temperature (SST) front is an important type of oceanic front, which plays a signifi cant role in many fi elds including fi sheries, the military, and industry. Satellite-derived SST images have been used widely for front detection, although these data are susceptible to infl uence by many objective factors such as clouds, which can cause missing data and a reduction in front detection accuracy. However, front detection in a single SST image cannot fully refl ect its temporal variability and therefore, the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time. In this paper, an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more eff ectively. Through experiments based on MODIS Terra and Aqua data, we verifi ed that fronts could be distinguished better by using the proposed algorithm. Additionally through its use, we analyzed the monthly variations of fronts in the Bohai, Yellow, and East China Seas, based on Terra data from 2000 to 2013.

A model of sea surface temperature front detection based on a threshold interval

A model (Bayesian oceanic front detection, BOFD) of sea surface temperature (SST) front detection in satellite- derived SST images based on a threshold interval is presented, to be used in different applications such as climatic and environmental studies or fisheries. The model first computes the SST gradient by using a Sobel algorithm template. On the basis of the gradient value, the threshold interval is determined by a gradient cumulative histogram. According to this threshold interval, front candidates can be acquired and prior probability and likelihood can be calculated. Whether or not the candidates are front points can be determined by using the Bayesian decision theory. The model is evaluated on the Advanced Very High-Resolution Radiometer images of part of the Kuroshio front region. Results are compared with those obtained by using several SST front detection methods proposed in the literature. This comparison shows that the BOFD not only suppresses noise and small-scale fronts, but also retains continuous fronts.

An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery

Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the trade-off between the spatial resolution and temporal frequency has limited their capacities in monitoring detailed spatio-temporal dynamics. Spatio-temporal fusion methods based on a linear model that considers the differences between fine- and coarse-spatial-resolution images as linear can effectively solve this trade-off problem, yet the existing linear fusion methods either regard the coefficients of the linear model as constants or have adopted regression methods to calculate the coefficients, both of which may introduce some errors in the fusion process. In this paper, we proposed an enhanced linear spatio-temporal fusion method (ELSTFM) to improve the data fusion accuracy. In the ELSTFM, it is not necessary to calculate the slope of the linear model, and the intercept, which can be deemed as the residual caused by systematic biases, is calculated based on spectral unmixing theory. Additionally, spectrally similar pixels in a given fine-spatial-resolution pixel’s neighborhood and their corresponding weights were used in the proposed method to mitigate block effects. Landsat-7/ETM+ and 8-day composite MODIS reflectance data covering two study sites with heterogeneous and homogenous landscapes were selected to validate the proposed method. Compared to three other typical spatio-temporal fusion methods visually and quantitatively, the predicted images obtained from ELSTFM could acquire better results for the two selected study sites. Furthermore, the resampling methods used to resample MODIS to the same spatial resolution of Landsat could slightly, but did not significantly influence the fusion accuracy, and the distributions of slopes of different bands for the two study sites could all be deemed as normal distributions with a mean value close to 1. The performance of ELSTFM depends on the accuracy of residual calculation at fine-resolution and large landscape changes may influence the fusion accuracy.

An enhanced spatial and temporal adaptive reflectance fusion model based on optimal window

In this paper, we introduce an enhanced spatial and temporal adaptive reflectance fusion model based on optimal sub-window size. The sub-window size can affect the accuracy of fusion and instead of using the fixed sub-window size in the original STARFM algorithm, the proposed algorithm uses the density of similar pixels to determine the optimal sub-window size. Compared with the original STARFM algorithm, the proposed algorithm can enhance the accuracy of fusion.

Regional hard coral distribution within geomorphic and reef flat ecological zones determined by satellite imagery of the Xisha Islands, South China Sea

Coral reefs in the Xisha Islands (also known as the Paracel Islands in English), South China Sea, have experienced dramatic declines in coral cover. However, the current regional scale hard coral distribution of geomorphic and ecological zones, essential for reefs management in the context of global warming and ocean acidification, is not well documented. We analyzed data from field surveys, Landsat-8 and GF-1 images to map the distribution of hard coral within geomorphic zones and reef flat ecological zones. In situ surveys conducted in June 2014 on nine reefs provided a complete picture of reef status with regard to live coral diversity, evenness of coral cover and reef health (live versus dead cover) for the Xisha Islands. Mean coral cover was 12.5% in 2014 and damaged reefs seemed to show signs of recovery. Coral cover in sheltered habitats such as lagoon patch reefs and biotic dense zones of reef flats was higher, but there were large regional differences and low diversity. In contrast, the more exposed reef slopes had high coral diversity, along with high and more equal distributions of coral cover. Mean hard coral cover of other zones was <10%. The total Xisha reef system was estimated to cover 1 060 km2, and the emergent reefs covered ~787 m2. Hard corals of emergent reefs were considered to cover 97 km2. The biotic dense zone of the reef flat was a very common zone on all simple atolls, especially the broader northern reef flats. The total cover of live and dead coral can reach above 70% in this zone, showing an equilibrium between live and dead coral as opposed to coral and algae. This information regarding the spatial distribution of hard coral can support and inform the management of Xisha reef ecosystems.