Добірка наукової літератури з теми "Yongchuan Shi (Chongqing, China)"

Model Tao Shi ,Peng Zhang and Fang Cheng School of Chongqing University of Posts and Telecommunications, Chongqing 400065, China shitao12300@163.com Keywords: telecommunications network resources, resource model, object-oriented, virtualization technology, network management system Abstract. In order to manage and utilize telecommunication network resources more effectively, aiming at the shortage of the resource management of telecom network management system, through the study the concept of the shared information data (SID) model in the new generation of telecom operation support system (NGOSS) , according to the characteristics of the telecommunications network resources, telecommunications network resource model with object-oriented approach is proposed and designed. Meanwhile, Combined with telecommunication network business resource model to illustrate the mechanism of telecom network resource by users applications in network management system. Experiments have proved that resource model can provide powerful support for complex network management.

Due to the mountainous terrain in the urban areas of southwest China, there are a large number of barren slopes in the community unsuitable for construction. These areas, alongside other unusable space which is often cultivated by residents to create informal community vegetable gardens and fruit growing areas, have become a “gray area” for urban management. This paper attempts to study the characteristics of informal community growing, the composition of growers, the motivation for growing, and the satisfaction of residents in urban areas in mountainous southwest China to explore its relative value. The sample area for the study was Yongchuan, Chongqing, Southwest China. Through a field survey, a semantic differential questionnaire, and data analysis, we found that: (1) growers use traditional cultivation methods to grow diverse fruits and vegetables according to the size of the slope, and the scale is so large that it serves as a local food supply; (2) growers are mainly vulnerable groups who use the land for economic gain and green food acquisition; and (3) growers and non-growers are more satisfied with the food supply and economic benefits generated by cultivation, while they are dissatisfied with the environmental and social benefits and the planting process. Satisfaction also varies with age, occupation, income, education, household registration, and farming experience. Based on the findings, this paper presents recommendations for the future transformation and development of informal community cultivation in mountainous areas. The study has implications for the construction of community gardens and urban agriculture in the mountains.

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally.

Purpose: To evaluate the efficacy of sodium zirconium cyclosilicate (SZC) in combination with insulinand glucose infusion in managing hyperkalemia. Methods: A total of 126 patients, who were admitted with hyperkalemia (≥ 5 mmol/L) to the Yongchuan District Hospital of Traditional Chinese Medicine, Chongqing, China from January 2021 to December 2022, were retrospectively studied. Participants were divided into three groups based on different potassium-lowering regimens, viz, SZC group (40 patients), insulin with glucose (IG) group (38 patients) and SZC + IG group (48 patients). Changes in potassium levels, other serum electrolytes (magnesium, sodium, phosphate, calcium), alanine aminotransferase (ALT) and albumin before and after treatment were recorded. Adverse reactions during treatment were also recorded. Results: Post-treatment, the potassium levels in all three groups exhibited a significant reduction when compared to pre- treatment values (p < 0.05). The SZC + IG group showed the highest efficacy, with a significant reduction in blood potassium levels observed 4 h after administration, which was more pronounced compared to other groups. The SZC + IG group, maintained potassium ion concentration at a normal level for a longer duration and no serious adverse reactions were observed during treatment. Conclusion: Intervention with SZC + IG lowers blood potassium levels, maintains it within normal range, and is more effective than the individual use of SZC or IG. A combination of sodium zirconium cyclosilicate, insulin and glucose infusion for treating hyperkalemia will need to be investigated further in a large-scale multicenter study.

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

The oxytrichid species Pleurotricha curdsi (Shi et al., 2002) Gupta et al., 2003, isolated from a tributary of the Yangtze River in the Mudong district of Chongqing, southern China, was reinvestigated with emphasis on its morphology, morphogenesis and small-subunit (SSU) rDNA-based phylogeny. Compared with three previously described populations, the Mudong population of P. curdsi is characterized by its large body size, 170–295 × 65–110 μm in vivo, and by having a variable number of right marginal rows, either two or three. Likewise, the number of right marginal rows anlagen (RMA) is also variable, i.e. usually two, but sometimes several small extra anlagen that give rise to the formation of the third row, are present to the left of the RMAs. We posit that the Mudong population is an intermediate form between the three previously described populations. Phylogenetic analyses based on the SSU rDNA sequence data show that all populations of P. curdsi cluster with the type species of the genus, Pleurotricha lanceolata, in a clade nested within the Oxytrichidae.

Ratoon rice, an effective rice cultivation system, allows paddy rice to be harvested twice from the same stubble, playing an important role in ensuring food security and adapting to climate change with its unique growth characteristics. However, there is an absence of research related to remote-sensing monitoring of ratoon rice, and the presence of other rice cropping systems (e.g., double-season rice) with similar characteristics poses a hindrance to the accurate identification of ratoon rice. Furthermore, cloudy and rainy regions have limited available remote-sensing images, meaning that remote-sensing monitoring is limited. To address this issue, taking Yongchuan District, a typical cloud-prone region in Chongqing, China, as an example, this study proposed the construction of a time-series optical dataset using the Modified Neighborhood Similar Pixel Interpolator (MNSPI) method for cloud-removal interpolation and the Flexible Spatiotemporal DAta Fusion (FSDAF) model for fusing multi-source optical remote-sensing data, in combination with vegetation index features and phenological information to build a threshold model to map ratoon rice at high-resolution (10 m). The mapping performance of ratoon rice was evaluated using independent field samples to obtain the overall accuracy and kappa coefficient. The findings indicate that the combination of the MNSPI method and FSDAF model had a stable and effective performance, characterized by high correlation coefficient (r) values and low root mean square error (RMSE) values between the restored/predicted images and the true images. Notably, it was possible to effectively capture the distinct characteristics of ratoon rice in cloudy and rainy regions using the proposed threshold model. Specifically, the identified area of ratoon rice in the study region was 194.17 km2, which was close to the official data (158–180 km2), and the overall accuracy and kappa coefficient of ratoon rice identification result were 90.73% and 0.81, respectively. These results demonstrate that our proposed threshold model can effectively distinguish ratoon rice during vital phenological stages from other crop types, enrich the technical system of rice remote-sensing monitoring, and provide a reference for agricultural remote-sensing applications in cloudy and rainy regions.

The organizing Committee of AICT 2023 was proud to present the proceedings of the 2nd International Conference on Artificial Intelligence and Communication Technology (AICT 2023), which was held on May 30-31, 2023 in Chongqing, China. The aim as well as objective of this AICT is to present the latest research and results of scientists related to Artificial Intelligence, Communication Technology and related topics. This conference provides opportunities for the delegates to exchange new ideas face-to-face, to establish business or research relations as well as to find global partners for future collaborations. We hope that the conference results will lead to significant contributions to the knowledge in these up-to-date scientific fields. All full paper submissions to the AICT 2023 must be written in English, and will be sent to 2-4 reviewers and evaluated based on originality, technical or research content, correctness, relevance to conference, contributions, and readability. The full paper submissions will be chosen based on technical merit, interest, applicability, and how well they fit a coherent and balanced technical program. This AICT conference has received 93 manuscripts. And less than 50 articles were accepted by our reviewers and the press. By submitting a paper to this AICT conference, the authors agree to the review process and understand that papers undergo a peer-review process. Manuscripts will be reviewed by appropriately qualified experts in the field selected by the Conference Committee, who will give detailed comments and-if the submission gets accepted-the authors submit a revised version that takes into account this feedback. All papers are reviewed using a double-blind review process: authors declare their names and affiliations in the manuscript for the reviewers to see, but reviewers do not know each other's identities, nor do the authors receive information about who has reviewed their manuscript. The Committees of this 2nd AICT invest great efforts in reviewing the papers submitted to the conference and organizing the sessions to enable the participants to gain maximum benefit. Hopefully, all participants and other interested readers benefit scientifically from the proceedings and also find it stimulating in the process. With our warmest regards, Wei Zhong, Jun Shi, Herman Thompson Conference Organizing Committee

AbstractTo determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an ‘Input-Translate-Output’ three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing.

BackgroundThis study aimed to determine the molecular characteristics of carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates in a hospital in western Chongqing, southwestern China.MethodsA total of 127 unique CRKP isolates were collected from the Yongchuan Hospital of Chongqing Medical University, identified using a VITEK-2 compact system, and subjected to microbroth dilution to determine the minimal inhibitory concentration. Enterobacteriaceae intergenic repeat consensus polymerase chain reaction and multilocus sequence typing were used to analyze the homology among the isolates. Genetic information, including resistance and virulence genes, was assessed using polymerase chain reaction. The genomic features of the CRKP carrying gene blaKPC-2 were detected using whole-genome sequencing.ResultsST11 was the dominant sequence type in the homology comparison. The resistance rate to ceftazidime-avibactam in children was much higher than that in adults as was the detection rate of the resistance gene blaNDM (p &lt; 0.0001). Virulence genes such as mrkD (97.6%), uge (96.9%), kpn (96.9%), and fim-H (84.3%) had high detection rates. IncF (57.5%) was the major replicon plasmid detected, and sequencing showed that the CRKP063 genome contained two plasmids. The plasmid carrying blaKPC-2, which mediates carbapenem resistance, was located on the 359,625 base pair plasmid IncFII, together with virulence factors, plasmid replication protein (rep B), stabilizing protein (par A), and type IV secretion system (T4SS) proteins that mediate plasmid conjugation transfer.ConclusionOur study aids in understanding the prevalence of CRKP in this hospital and the significant differences between children and adults, thus providing new ideas for clinical empirical use of antibiotics.

So Lek Hang Lake.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (leaves 196-203).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.vii
Table of Content --- p.ix
List of Figures --- p.xiii
List of Tables --- p.xvi
Chapter CHAPTER 1. --- Introduction --- p.1
Chapter 1.1 --- Problem Statement --- p.1
Chapter 1.2 --- Research Objectives --- p.5
Chapter 1.3 --- Research Significance --- p.6
Chapter 1.4 --- Organization of the thesis --- p.7
Chapter 1.5 --- Definition of Urban Green Space --- p.9
Chapter CHAPTER 2. --- Literatu re Re view --- p.10
Chapter 2.1 --- Introduction --- p.10
Chapter 2.2 --- Urban Green Space --- p.10
Chapter 2.2.1 --- Classification of Urban Green Space --- p.11
Chapter 2.2.2 --- Configuration of Urban Green Space System --- p.12
Chapter 2.2.3 --- Different Approaches to Urban Green Space Study --- p.14
Chapter 2.3 --- Urban Green Space in China --- p.15
Chapter 2.3.1 --- General Problems --- p.16
Chapter 2.3.2 --- Increasing Awareness of Environment --- p.16
Chapter 2.3.3 --- Chinese Definition of Urban Green Space --- p.18
Chapter 2.4 --- Remote Sensing Techniques --- p.21
Chapter 2.4.1 --- Review of Image Classification Techniques --- p.21
Chapter 2.4.1.1 --- Conventional Classification Methods --- p.22
Chapter 2.4.1.2 --- Mixed Pixels Problem --- p.23
Chapter 2.4.1.3 --- Mixed Pixels，Effects on Conventional Classifiers --- p.25
Chapter 2.4.1.4 --- Alternative Solutions to Mixed Pixels Problems (Fuzzy Sets) --- p.26
Chapter 2.4.1.5 --- Problems Fuzzy Classifications are unable to solve --- p.28
Chapter 2.4.2 --- Object-oriented Classification Concept --- p.30
Chapter 2.4.2.1 --- Multiresolution Segmentation --- p.30
Chapter 2.4.2.2 --- Fuzzy Classification Procedure --- p.31
Chapter 2.4.2.3 --- Object-orien ted Approach to Image Processing --- p.32
Chapter 2.4.2.4 --- E cognition --- p.33
Chapter 2.4.2.5 --- Research about ecognition --- p.34
Chapter 2.5 --- Landscape Ecology --- p.35
Chapter 2.5.1 --- Basic Principles --- p.35
Chapter 2.5.2 --- Landscape Metrics --- p.36
Chapter 2.5.3 --- Application of Landscape Ecology in Landscape Analysis --- p.38
Chapter 2.6 --- Conclusion --- p.39
Chapter CHAPTER 3. --- Study Sites and Methodology --- p.41
Chapter 3.1 --- lntroduction --- p.41
Chapter 3.2 --- Study Area --- p.41
Chapter 3.2.1 --- Chongqing --- p.41
Chapter 3.2.1.1 --- Geography and geomorphology --- p.42
Chapter 3.2.1.2 --- Administration and governance --- p.42
Chapter 3.2.1.3 --- Environmental Quality --- p.43
Chapter 3.2.1.4 --- Governm ent Attempt to Improvement --- p.43
Chapter 3.2.2 --- Nanjing --- p.46
Chapter 3.2.2.1 --- Geography and Geomorphology --- p.46
Chapter 3.2.2.2 --- Administration and Governance --- p.46
Chapter 3.2.2.3 --- Landscape Planning of Nanjing --- p.47
Chapter 3.2.3 --- Comparison between Chongqing and Nanjing --- p.47
Chapter 3.2.3.1 --- Geographical setting --- p.49
Chapter 3.2.3.2 --- Population --- p.49
Chapter 3.2.3.3 --- Urbanization and Industrialization Levels --- p.51
Chapter 3.2.3.4 --- Variation in Landscape Quantity --- p.51
Chapter 3.2.3.5 --- Comparison from Satellite Images --- p.52
Chapter 3.3 --- Working procedures --- p.56
Chapter 3.3.1 --- Data --- p.56
Chapter 3.3.1.1 --- VNIR chann els --- p.58
Chapter 3.3.1.2 --- SWIR channels --- p.59
Chapter 3.3.1.3 --- Data Fusion --- p.59
Chapter 3.3.2 --- Designing Hierarchical Classification System --- p.60
Chapter 3.3.2.1 --- Chongqing --- p.60
Chapter 3.3.2.2 --- Nanjing --- p.61
Chapter 3.3.3 --- Object-oriented Classification --- p.62
Chapter 3.3.3.1 --- Introdu ction --- p.63
Chapter 3.3.3.2 --- Procedure of Object-oriented Classification --- p.65
Chapter 3.3.3.2.1 --- Analysis of Image Objects --- p.65
Chapter 3.3.3.2.2 --- Image Segmentation --- p.67
Chapter 3.3.3.2.3 --- Selection of Features and Data Conversion --- p.67
Chapter 3.3.3.2.4 --- Class-based Objects Sampling --- p.68
Chapter 3.3.3.2.5 --- Class-based Objects Analysis --- p.68
Chapter 3.3.3.2.6 --- Designing Object Level Hierarchy --- p.69
Chapter 3.3.3.2.7 --- Designing Class Hierarchy --- p.69
Chapter 3.3.3.2.8 --- Decision Tree Classification Structure --- p.69
Chapter 3.3.4 --- Comparison with other classification algorithms --- p.70
Chapter 3.4 --- Landscape Analyses --- p.71
Chapter 3.4.1 --- Selection of Landscape Metrics --- p.72
Chapter 3.4.2 --- Landscape Analysis for entire cities --- p.74
Chapter 3.4.3 --- Buffer Analysis --- p.74
Chapter 3.5 --- Conclusion --- p.77
Chapter CHAPTER 4. --- Results and Discussion I Variations of Image Object Signatures for Sampled Land Covers --- p.78
Chapter 4.1 --- Introduction --- p.78
Chapter 4.2 --- Chongqing --- p.79
Chapter 4.2.1 --- Spectral-shape ratio --- p.79
Chapter 4.2.1.1 --- Selection Criteria --- p.80
Chapter 4.2.1.2 --- Observations --- p.80
Chapter 4.2.2 --- Segmentation levels --- p.85
Chapter 4.2.2.1 --- Selection Criteria --- p.85
Chapter 4.2.2.2 --- Observations --- p.86
Chapter 4.2.3 --- Classifying Rules --- p.93
Chapter 4.2.3.1 --- Selection Criteria --- p.93
Chapter 4.2.3.2 --- Level 9 --- p.94
Chapter 4.2.3.3 --- Level 5 --- p.101
Chapter 4.2.3.4 --- Level 1 --- p.103
Chapter 4.3 --- Nanjing --- p.104
Chapter 4.3.1 --- Spectral-shape ratio --- p.104
Chapter 4.3.1.1 --- Selection Criteria --- p.105
Chapter 4.3.1.2 --- Observations --- p.105
Chapter 4.3.2 --- Segmentation Levels --- p.111
Chapter 4.3.2.1 --- Selection Criteria --- p.111
Chapter 4.3.2.2 --- Observations --- p.111
Chapter 4.3.3 --- Classifying Rules --- p.119
Chapter 4.3.3.1 --- Selection Criteria --- p.119
Chapter 4.3.3.2 --- Level 8 --- p.119
Chapter 4.3.3.3 --- Level 4 --- p.126
Chapter 4.3.3.4 --- Level 1 --- p.129
Chapter 4.4 --- Discussion --- p.131
Chapter CHAPTER 5. --- Results and Discussion II Image Classification --- p.134
Chapter 5.1 --- lntroduction --- p.134
Chapter 5.2 --- Chongqing --- p.135
Chapter 5.2.1 --- Class hierarchy --- p.135
Chapter 5.2.2 --- Description of the site --- p.136
Chapter 5.2.3 --- Classification of “lake´ح --- p.138
Chapter 5.2.4 --- "Classification of ""crops and grassland""" --- p.139
Chapter 5.2.5 --- Classification of “low density urban´ح --- p.140
Chapter 6.3.3 --- Classification Result --- p.142
Chapter 5.2.7 --- Error matrix --- p.144
Chapter 5.2.8 --- Class Proportion --- p.144
Chapter 5.2.9 --- Post-classification Aggregation --- p.147
Chapter 5.3 --- Nanjing --- p.149
Chapter 5.3.1 --- Class Hierarchy --- p.149
Chapter 5.3.2 --- Description of the site --- p.151
Chapter 5.3.3 --- Classification of lake --- p.151
Chapter 5.3.4 --- "Classification of ""crops and grassland II´ح" --- p.153
Chapter 5.3.5 --- "Classification of ""low density urban""" --- p.154
Chapter 5.3.6 --- Classification Result --- p.155
Chapter 5.3.7 --- Error Matrix --- p.156
Chapter 5.3.8 --- Class Proportion --- p.161
Chapter 5.3.9 --- Post-classification Aggregation --- p.161
Chapter 5.4 --- Discussion --- p.163
Chapter 5.4.1 --- Problems of object-oriented classification --- p.163
Chapter 5.4.2 --- Strengths of object-oriented classification --- p.165
Chapter 5.4.3 --- Transferability of classifying rules --- p.166
Chapter CHAPTER 6. --- "Results and Discussion HI Landscape Structure of ""Urban Green Space"", Chongqing and Nanjing" --- p.167
Chapter 6.1 --- Introduction --- p.167
Chapter 6.2 --- Chongqing --- p.167
Chapter 6.2.1 --- Landscape composition --- p.167
Chapter 6.2.2 --- Fragmentation --- p.169
Chapter 6.2.3 --- Contagion --- p.171
Chapter 6.2.4 --- Patch Shape Complexity --- p.171
Chapter 6.3 --- Nanjing --- p.173
Chapter 6.3.1 --- Landscape composition --- p.173
Chapter 6.3.2 --- Fragmentation --- p.175
Chapter 6.3.3 --- Contagion --- p.177
Chapter 6.3.4 --- Patch Shape Complexity --- p.178
Chapter 6.4 --- Discussion --- p.179
Chapter 6.4.1 --- Similarities --- p.179
Chapter 6.4.2 --- Differences --- p.182
Chapter CHAPTER 7. --- Conclusion --- p.186
Chapter 7.1 --- Summary on findings --- p.186
Chapter 7.1.1 --- Summary on image object analyses --- p.186
Chapter 7.1.2 --- Summary on object-oriented classification --- p.187
Chapter 7.1.3 --- Summary on landscape studies of ´ب´بurban green space´ح --- p.189
Chapter 7.2 --- Limitations of the research --- p.190
Chapter 7.2.1 --- Data preparation --- p.190
Chapter 7.2.2 --- Image classification --- p.191
Chapter 7.2.3 --- Landscape Analysis --- p.193
Chapter 7.3 --- Suggestions for further research --- p.194
Bibliography --- p.196
Appendix 1´ؤEquations of object features --- p.204
Appendix 2´ؤEquations for Landscape Metrics --- p.208
Appendix 3´ؤVariations of Object Features along Segmentation Levels in Chongqing --- p.216
Appendix 4´ؤVariations of Object Features along Segmentation Levels in Nanjing --- p.244
Appendix 5´ؤClassifying Rules --- p.277
Appendix 6´ؤVariations in Landscape Metrics along Buffers from City Center in Chongqing --- p.282
Appendix 7´ؤVariations in Landscape Metrics along Buffers from City Center in Nanjing --- p.290