Yunyan Du

A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data

Parallel computing provides a promising solution to accelerate complicated spatial data processing, which has recently become increasingly computationally intense. Partitioning a big dataset into workload-balanced child data groups remains a challenge, particularly for unevenly distributed spatial data. This study proposed an algorithm based on the k-d tree method to tackle this challenge. The algorithm constructed trees based on the distribution variance of spatial data. The number of final sub-trees, unlike the conventional k-d tree method, is not always a power of two. Furthermore, the number of nodes on the left and right sub-trees is always no more than one to ensure a balanced workload. Experiments show that our algorithm is able to partition big datasets efficiently and evenly into equally sized child data groups. Speed-up ratios show that parallel interpolation can save up to 70% of the execution time of the consequential interpolation. A high efficiency of parallel computing was achieved when the datasets were divided into an optimal number of child data groups.

A representation framework for studying spatiotemporal changes and interactions of dynamic geographic phenomena

This research presented a framework to track and query spatiotemporal changes and interactions of dynamic geographic phenomena. The framework organized information of dynamic phenomena as a hierarchy of static structures, processes, and scenarios. Static structures of a dynamic phenomenon at its different evolution stages were described by its corepoint, footprint border, and composite border, which were extracted from time series remote sensing images. Time series static structures of a phenomenon were then grouped into processes to show its changes over space and time. Scenarios were used to describe a collection of interacting processes in space. We expanded the identity-based change (IBC) model by adding more primitives and operations to represent semantics of these changes and interactions. A geographic information system (GIS) database was built by integrating the expanded IBC model with our spatiotemporal framework. As demonstrated by a case study of ocean eddies in the South China Sea (SCS), query results of the behaviors and relationships of ocean eddies from the GIS database help us better understand their development and evolution, demonstrating the usefulness of this spatiotemporal framework. Spatial and semantic queries about a specific eddy from the database can further efficiently present its lifetime dynamic changes and all other eddies that interacted with it.

Integration of case-based reasoning and object-based image classification to classify SPOT images: a case study of aquaculture land use mapping in coastal areas of Guangdong province, China

We present a method to integrate case-based reasoning (CBR) with object-oriented image classification to classify SPOT images. Images were first segmented into discrete objects at multiple scales. CBR was then used to classify these objects by comparing their geometric shapes, spectral characteristics, and textural measurements with those of the past objects prepared from archived SPOT images and land use data. Once enough past objects were accumulated, this method was able to successfully classify image objects with promising results as demonstrated by a case study of aquaculture land use mapping in coastal areas of Guangdong province, China.

Automatic Identification of Oceanic Multieddy Structures From Satellite Altimeter Datasets

Very few of current eddy detection algorithms are capable of identifying multieddy structures resulted from interactions among eddies. In this study, we improve our previous hybrid detection (HD) algorithm by incorporating a new criterion to better identify multieddy structures from satellite altimeter data. The criterion defines an aspect ratio to determine if eddies have vortex overlaps and, as such, result in a composite structure of multiple eddies (a.k.a. multieddy structures). Compared with two previous studies on observed eddy-eddy interactions in eddy mergers from altimeter data, the improved HD algorithm not only successfully captures multieddy structures but also shows how eddies interact and evolve, including merging, splitting, and partial vorticity exchange. Tests of the improved HD algorithm on a series of sea-level anomaly maps in the South China Sea (SCS) from 1993 to 2012 show that single eddies, in contrast to eddies with composite structures, appear more concentrated in northwest of the Luzon and southeast of Vietnam. Tracking dual-eddy structures reveals several processes of eddy interactions in the SCS. The study demonstrates the potential value of the new HD algorithm in helping scientists to investigate characteristics of eddy-eddy interactions from satellite observations.

Mesoscale oceanic eddies in the South China Sea from 1992 to 2012: evolution processes and statistical analysis

Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea (SCS). However, very few studies attempt to examine eddies’ internal evolution processes. In this study, we reported a hybrid method to trace eddies’ propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies (DCEs) and the three long-lived anticyclonic eddies (ACEs) in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.

Comparison between CBR and CA methods for estimating land use change in Dongguan, China

Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research (Du et al. 2009), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.

Assimilation of field measured LAI into crop growth model based on SCE-UA optimization algorithm

Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation has been being a research focus in recent years. In this paper, the shuffled complex evolution (SCE-UA) global optimization algorithm was used to assimilate field measured LAI into EPIC model to simulate yield, sowing date and nitrogen fertilizer application amount of summer maize in Huanghuaihai Plain in China. The results showed that RMSE between simulated yield and field measured yield of summer maize was 0.84 t ha−1 and the R2 was only 0.033 without external data assimilation. While the performances of EPIC model of simulating yield, sowing date and nitrogen fertilizer application amount of summer maize was better through assimilating field measured LAI into the EPIC model. The RMSE of between simulated yield and field measured yield of summer maize was 0.60 t ha−1 and the R2 was 0.5301. The relative error between simulated sowing date and real sowing date of summer maize was 2.28%. On the simulation of nitrogen fertilizer application rate, the relative error was −6.00% compared with local statistical data. These above accuracy could meet the need of crop growth monitoring and yield estimation at regional scale. It proved that assimilating field measured LAI into crop growth model based on SCE-UA optimization algorithm to monitor crop growth and estimate crop yield was feasible.

Density-based clustering for data containing two types of points

When only one type of point is distributed in a region, clustered points can be seen as an anomaly. When two different types of points coexist in a region, they overlap at different places with various densities. In such cases, the meaning of a cluster of one type of point may be altered if points of the other type show different densities within the same cluster. If we consider the origins and destinations (OD) of taxicab trips, the clustering of both in the morning may indicate a transportation hub, whereas clustered origins and sparse destinations (a hot spot where taxis are in short supply) could suggest a densely populated residential area. This cannot be identified by previous clustering methods, so it is worthwhile studying a clustering method for two types of points. The concept of two-component clustering is first defined in this paper as a group containing two types of points, at least one of which exhibits clustering. We then propose a density-based method for identifying two-component clusters. The method is divided into four steps. The first estimates the clustering scale of the point data. The second transforms the point data into the 2D density domain, where the x and y axes represent the local density of each type of point around each point, respectively. The third determines the thresholds for extracting the clusters, and the fourth generates two-component clusters using a density-connectivity mechanism. The method is applied to taxicab trip data in Beijing. Three types of two-component clusters are identified: high-density origins and destinations, high-density origins and low-density destinations, and low-density origins and high-density destinations. The clustering results are verified by the spatial relationship between the cluster locations and their land-use types over different periods of the day.

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

A quantitative morphometric comparison of cockpit and doline karst landforms

This study presented a quantitative comparison of cockpit and doline karst by examining the numbers and characteristics of typical types of landform entities that are developed in Guilin (Guangxi, China), La Alianza (PR, USA), Avalton (KY, USA), and Oolitic (IN, USA). Five types of landform entities were defined: isolated hill (IH), clustered hills (CHs), isolated sinkhole (IS), clustered sinkholes (CSs), and clustered hills with sinkholes (CHSs). An algorithm was developed to automatically identify these types of landform entities by examining the contour lines on topographic maps of two cockpit karst areas (Guilin and La Alianza) and two doline karst areas (Oolitic and Avalton). Within each specific study area, the CHSs is the least developed type yet with a larger size and higher relief. The IH and IS entities are smaller in size, lower in relief, and outnumber their clustered counterparts. The total numbers of these types of entities are quite different in cockpit and doline karst areas. Doline karst is characterized by more negative (IS and CSs) than positive (IH and IHs) landforms and vice versa for cockpit karst. For example, the Guilin study area has 1192 positive landform entities in total, which occupy 9.81% of the total study area. It has only 622 negative landform entities occupying only 3.91% of the total study area. By contrast, the doline karst in Oolitic has 130 negative while only 10 positive landform entities. The positive and negative landforms in Oolitic occupy 12.68% and 2.61% of the total study area, respectively. Furthermore, average relief and slope of the landform entities are much higher and steeper in the cockpit karst than the doline karst areas. For instance, the average slope of CHs in Alvaton is 3.90 degrees while it is 19.78 degrees in La Alianza. The average relief of CSs is 4.07 m and 34.29 m in Oolitic and Guilin respectively. Such a difference within a specific area or between the cockpit and doline karst may reveal different controls on the development of karst landscape.