Xinqi Zheng

The Land Gini Coefficient and Its Application for Land Use Structure Analysis in China

We introduce the Gini coefficient to assess the rationality of land use structure. The rapid transformation of land use in China provides a typical case for land use structure analysis. In this study, a land Gini coefficient (LGC) analysis tool was developed. The land use structure rationality was analyzed and evaluated based on statistical data for China between 1996 and 2008. The results show: (1)The LGC of three major land use types–farmland, built-up land and unused land–was smaller when the four economic districts were considered as assessment units instead of the provinces. Therefore, the LGC is spatially dependent; if the calculation unit expands, then the LGC decreases, and this relationship does not change with time. Additionally, land use activities in different provinces of a single district differed greatly. (2) At the national level, the LGC of the three main land use types indicated that during the 13 years analyzed, the farmland and unused land were evenly distributed across China. However, the built-up land distribution was relatively or absolutely unequal and highlights the rapid urbanization in China. (3) Trends in the distribution of the three major land use types are very different. At the national level, when using a district as the calculation unit, the LGC of the three main land use types increased, and their distribution became increasingly concentrated. However, when a province was used as the calculation unit, the LGC of the farmland increased, while the LGC of the built-up and unused land decreased. These findings indicate that the distribution of the farmland became increasingly concentrated, while the built-up land and unused land became increasingly uniform. (4) The LGC analysis method of land use structure based on geographic information systems (GIS) is flexible and convenient.

A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm.

Objective and effective image quality assessment (IQA) is directly related to the application of optical remote sensing images (ORSI). In this study, a new IQA method of standardizing the target object recognition rate (ORR) is presented to reflect quality. First, several quality degradation treatments with high-resolution ORSIs are implemented to model the ORSIs obtained in different imaging conditions; then, a machine learning algorithm is adopted for recognition experiments on a chosen target object to obtain ORRs; finally, a comparison with commonly used IQA indicators was performed to reveal their applicability and limitations. The results showed that the ORR of the original ORSI was calculated to be up to 81.95%, whereas the ORR ratios of the quality-degraded images to the original images were 65.52%, 64.58%, 71.21%, and 73.11%. The results show that these data can more accurately reflect the advantages and disadvantages of different images in object identification and information extraction when compared with conventional digital image assessment indexes. By recognizing the difference in image quality from the application effect perspective, using a machine learning algorithm to extract regional gray scale features of typical objects in the image for analysis, and quantitatively assessing quality of ORSI according to the difference, this method provides a new approach for objective ORSI assessment.

A WOE method for urban growth boundary delineation and its applications to land use planning

Abstract The aim of this study is to develop a new approach for delineating urban growth boundaries (UGBs) by applying the weight of evidence (WOE) method to land suitability assessments. Rapid urbanization is causing urban areas to encroach on agricultural land in China, posing a threat to national food security. Land use planning with clear delineation of UGBs is an effective method for controlling urban expansion. However, existing methods for delineating UGBs are typically complex or involve arbitrary decision-making. To address these drawbacks, we introduced the WOE method to develop a new UGB delineation approach, and applied this approach to a case study in the city of Jinan, China. This application achieved satisfactory accuracy; therefore, we concluded that the WOE method was an objective and effective approach to land use suitability assessments and UGB delineation. Land use planning could be benefitted considerably from the application of this method to land allocation and other planning decisions.

Design and Implementation of Spatial Data Mining System (M-SDM) based on MATLAB

Taking the design of data analyzing software for imaging brain function — SPM (Statistical Parameters Mapping) for reference, this study combined MATLAB, GIS and SDM organically, constructed an SDM system framework on MATLAB platform, integrated the major algorithms such as spatial association rule mining, spatial clustering analyzing, decision tree analyzing and so on, and applied the system in land-use spatial database, aiming at enhancing the efficiency of massive data processing and enlarging the application of MATLAB in spatial data mining, spatial vector data processing and other aspects.

Simulation of precise scale land use change based on the Markov-cellular automata model

The Markov-cellular automata is suitable to study complex spatial-temporal geographic system, especially for regional land use, and it has been an important tool and research focus for regional land use change modeling. Previous researchers focused on a few kind of land use type at the regional scale and the data resolution was cursory because land use maps were usually derived from TM image. Few researchers involved precise scale of land use change within a region. To solve this problem, we took the data of land-use survey as a data source maps that include detailed multiple land use types. The case study area was Changping District, which is a rapidly growing area of Beijing. We select the land use map of 2001 and 2005 which include the multiple land use types as data source to simulate the land use of 2012. The results of simulation show that simulation accuracy of multiple land use types is better than them of cursory scale land use types, although it takes a substantial amount of time to run. The statistical result derived from Morans I and fractal parameter indicates that simulation shows the high spatial stability. The simulation results showed that the number of cropland is keeping on decrease from 2005 to 2012 without the holistic sustainable development measures and severe land policy. This paper represents a good try to local land use change modeling as shown combined Markov chain analysis and cellular automata models. The simulated future land use changes have significant environmental and socioeconomic implications for sustainable region land detailed planning in the study area.

Simulating land use change by integrating landscape metrics into ANN-CA in a new way

Landscape metrics are measurements of landuse patterns and land-use change, but even so, have rarely been integrated into land-use change simulation models. This paper proposes a new artificial neural network-cellular automaton by integrating landscape metrics into the model. In this model, each cell acquires unique landscape metric values. The landscape metric values of each cell are actually the landscape metric values of land use type in its neighborhood, which takes the cell as center. The calculation of landscape metrics ensures that those of each cell can represent cellular spatial environmental characteristics. The model is used to simulate land use change in the Changping district of Beijing, China. Comparisons of the simulated land use map with the actual map show that the proposed model is effective for land use change simulation. The validation is further carried out by comparing the simulated land use map with that simulated by an artificial neural network-cellular automaton model, which has not been integrated with landscape metrics. Results indicate that the proposed model is more appropriate for simulating both quantity and spatial distribution of land use change in the study area.

Study on LUCC Based on Vector Date Source Using the CA_Markov Model: A Case Study of Changping District, Beijing, China

In the process of land use change modeling with CA-Markov model, data source is a crucial factor for the accuracy of the model. The existing research of CA_Markov usually utilized the remote sensing image as data source, and modeling outcome has been affected by many factors such as the remote-sensing imagery interpretation errors. Therefore, this article adopted the land use vector data of Changping District in 2001 and 2005, simulated land use pattern of Changping District in 2020 with CA-Markov model. Results showed that, the credibility was up to 95% by using vector data as data source.

Design and construction of spatial database for internal elements of urban based on TGIS

In recent years, during the construction of “digital urban”, urban geography research has also undergone tremendous changes, especially in the research of urban internal elements revolution. Meanwhile, computer technology, GIS, spatial information science and such technical applications have made this kind of research more effective, intelligent and precise; they also have provided the foundation for information mining with consideration of time factor. Urban internal elements have certain geometric features which involve massive spatial information and updating information. In view of this, construction of spatial database is basic work for digital urban construction. However, traditional spatial database targets less about historical characteristics, and spatio-temporal database costs too much time and vigor for construction. In order to solve these problems, this study, based on spatial-temporal GIS models, concentrated on the needs of urban geographic research under environment of digital urban, utilized object-oriented method to design spatial database and finally established the database and realized functions based on ArcSDE and Oracle. Results showed that this database can well satisfy the demands of urban geographic research, and it could be applied in many fields through function extension. Altogether, the system is more management-oriented, scalable and efficient; it is more convenient for data updating and information sharing.

Construction and Application of the Decision Tree Model for Agricultural Land Grading Based on MATLAB

Aiming at the insufficiencies of traditional agricultural land grading methods, this study discussed the process and technical route of agricultural land grading based on decision tree analysis method and GIS, constructed an agricultural land grading model based on MATLAB and decision tree C4.5 algorithm. Furthermore, We took Luanwan village of Pingyin county in China for the empirical study, selected seven indicators as the test attributes, predicted agricultural land grade on support of this model, and expressed the rules in the quantitative way. The results showed that agricultural land grading model based on decision tree which is coded in M-language of MATLAB doesn’t rely on the empirical knowledge. It has the ability of self-learning, and the gained rules are easy to be understood. Moreover, the high rate of accuracy will be able to meet the requirements of evaluation.

Simulating land use change by integrating ANN-CA model and landscape pattern indices

Artificial neural network–cellular automata model has been applied successfully in land use change simulation. However, it has rarely been integrated with landscape pattern indices (LPIs), the embodiment of the spatial heterogeneity of land use. This paper proposed to integrate LPIs as the parameters of artificial neural network–cellular automata model. Subsequent to a description of the principles and implementation of the model, a case study was presented in Changping district. In the case study, two LPIs, the landscape similarity index and patch density, along with 10 other spatial variables, were selected as the influencing factors of land use change. Based on land use maps in years 1988 and 1998, a land use map in 2008 was simulated by the proposed model. Comparing with the actual land use map in 2008 and the simulated result of artificial neural network–cellular automata model, the results showed that the proposed model is more applicable for simulating land use change in the study area; the limitation of this model is the spatial scale and calculation method of LPIs.