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Dr. Qiaozhen Guo
School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China;

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0 Land Use
0 Remote Sensing
0 Remote Sensing Applications
0 environment
0 Remote sensing of resources and environment

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Remote Sensing
Remote sensing of resources and environment
environment
Land Use

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Journal article
Published: 05 May 2021 in ISPRS International Journal of Geo-Information
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The highway is an important mode of transportation in the Qinghai–Tibet Plateau, and can be regarded as a major contributor to the high-quality and sustainable development of the Qinghai–Tibet Plateau. It is of great significance to explore its spatial distribution and characteristics for understanding the regional and geographical process. Although Qinghai–Tibet Plateau’s highway transportation infrastructure has been experiencing rapid development in recent years, there lacks a systematic examination of the whole Qinghai–Tibet Plateau from the perspective of supportive capacity for its socio-economic activities. This paper applies geospatial analysis methods, such as network analysis, spatial statistics, and weighted overlay, to model the highway transport dominance in the Qinghai–Tibet Plateau in 2015 at the county scale and reveals the basic characteristics of the highway transport dominance’s spatial pattern. The results are mainly of four aspects: 1) there is a significant difference between the east and west of the highway in the Qinghai–Tibet Plateau, showing an irregular circle structure of gradual attenuation from the east to west; 2) at the county scale, the highway transport dominance in the Qinghai–Tibet Plateau shows strong spatial autocorrelation and a certain extent of spatial heterogeneity, presenting a spatial distribution pattern of High–High and Low–Low clustering; 3) the urban locations of Lhasa, Xining and other center cities have obvious spatial constraints on the distribution of highway transport dominance and generally have a logarithmic decline trend; and 4) there are obvious differences in distribution among the three Urban Agglomerations in the Qinghai–Tibet Plateau. Due to the influence of traffic location, topography, construction of national trunk lines, and level of socio-economic development., the traffic conditions of Lan-Xi Urban Agglomeration and Lhasa Urban Agglomeration are better than Kashgar Urban Agglomeration. This study can be used to guide the optimization of the highway network structure and provide a macro decision-making reference for the planning and evaluation of major highway projects in the Qinghai–Tibet Plateau.

ACS Style

Zhiheng Wang; Hongkui Fan; Daikun Wang; Tao Xing; Dongchuan Wang; Qiaozhen Guo; Lina Xiu. Spatial Pattern of Highway Transport Dominance in Qinghai–Tibet Plateau at the County Scale. ISPRS International Journal of Geo-Information 2021, 10, 304 .

AMA Style

Zhiheng Wang, Hongkui Fan, Daikun Wang, Tao Xing, Dongchuan Wang, Qiaozhen Guo, Lina Xiu. Spatial Pattern of Highway Transport Dominance in Qinghai–Tibet Plateau at the County Scale. ISPRS International Journal of Geo-Information. 2021; 10 (5):304.

Chicago/Turabian Style

Zhiheng Wang; Hongkui Fan; Daikun Wang; Tao Xing; Dongchuan Wang; Qiaozhen Guo; Lina Xiu. 2021. "Spatial Pattern of Highway Transport Dominance in Qinghai–Tibet Plateau at the County Scale." ISPRS International Journal of Geo-Information 10, no. 5: 304.

Research article
Published: 01 March 2021 in Journal of the Indian Society of Remote Sensing
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Land use provides crucial data for earth science research and its accuracy has always been a hot topic. Various auxiliary data are used to improve the classification accuracy of land use. The digital elevation model (DEM) is one of the common auxiliary data since topography directly affects the surface landscape. However, few researches focus on the impact of the DEM on the classification accuracy of images. In this study, the terrain steepness index (TSI) was initially put forward to study the effect of the DEM on the classification accuracy of the land use/cover. Seven areas with different terrain were taken as the studied areas and the classification and regression tree was utilized to derive thematic land use/cover maps. It indicated that the DEM had an impact on classification accuracy in a certain TSI extent. The developed quadratic model accurately described the correlation between the DEM and the TSI. Based on the model, the DEM with 30 m resolution had a positive impact on the classification accuracy, when the TSI varies from 9.106 to 34.014. It was concluded that the TSI index for the DEM could be effectively used in the land use/cover classification.

ACS Style

Xiao Sang; Qiaozhen Guo; Xiaoxu Wu; Tongyao Xie; Chengwei He; Jinlong Zang; Yue Qiao; Huanhuan Wu; Yuchen Li. The Effect of DEM on the Land Use/Cover Classification Accuracy of Landsat OLI Images. Journal of the Indian Society of Remote Sensing 2021, 1 -12.

AMA Style

Xiao Sang, Qiaozhen Guo, Xiaoxu Wu, Tongyao Xie, Chengwei He, Jinlong Zang, Yue Qiao, Huanhuan Wu, Yuchen Li. The Effect of DEM on the Land Use/Cover Classification Accuracy of Landsat OLI Images. Journal of the Indian Society of Remote Sensing. 2021; ():1-12.

Chicago/Turabian Style

Xiao Sang; Qiaozhen Guo; Xiaoxu Wu; Tongyao Xie; Chengwei He; Jinlong Zang; Yue Qiao; Huanhuan Wu; Yuchen Li. 2021. "The Effect of DEM on the Land Use/Cover Classification Accuracy of Landsat OLI Images." Journal of the Indian Society of Remote Sensing , no. : 1-12.

Research article
Published: 10 November 2020 in International Journal of Remote Sensing
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The suitable thermal environment is a prerequisite for biological survival. In order to evaluate the change of land surface temperature (LST) and its effect on organisms, in this study, the Land satellite (Landsat) series data was used to invert the LST from 1988 to 2018 in Tianjin. The main surface features were extracted from the remotely sensed images using normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI). A multi-scale temperature grading method based on biological heat reaction was proposed to classify the LST. In terms of the suitability of the organism to temperature, the land surface temperature suitability (LSTS) model was established based on the niche suitability analysis method, and the suitability of organisms to temperature was evaluated based on the model. In the aspect of land area change, both the land surface temperature area (LSTA) model and the average relative contribution (ARC) were proposed to comprehensively evaluate the impact of the area change of surface features on LST. The results indicated that the temperature suitability of most organisms had decreased. The change of LST was related to the comprehensive change of surface features. In conclusion, the rise of LST had adverse effect on most organisms in Tianjin. Limiting the occupation of water area and cultivated land may inhibit the continuous rise in LST. Besides, the evaluation method novelty proposed in this study could provide new ideas to study the development trend of spatial objects in their environment thus would be applied widely.

ACS Style

Jinlong Zang; Qiaozhen Guo; Xiaoxu Wu; Xiao Sang; Huanhuan Wu; Yue Qiao. Integrated evaluation on multi-scale land surface temperature grading and bio-temperature suitability—a case study in Tianjin China. International Journal of Remote Sensing 2020, 42, 343 -366.

AMA Style

Jinlong Zang, Qiaozhen Guo, Xiaoxu Wu, Xiao Sang, Huanhuan Wu, Yue Qiao. Integrated evaluation on multi-scale land surface temperature grading and bio-temperature suitability—a case study in Tianjin China. International Journal of Remote Sensing. 2020; 42 (1):343-366.

Chicago/Turabian Style

Jinlong Zang; Qiaozhen Guo; Xiaoxu Wu; Xiao Sang; Huanhuan Wu; Yue Qiao. 2020. "Integrated evaluation on multi-scale land surface temperature grading and bio-temperature suitability—a case study in Tianjin China." International Journal of Remote Sensing 42, no. 1: 343-366.

Original article
Published: 05 December 2019 in Applied Water Science
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Surface water pollution is one of the serious environment pollution problems, posing threat to human and other creatures. Extraction, change detection and environment evaluation of surface water are prerequisite for water resource management. Undoubtedly, remote sensing data play an important role in these researches because of its large geographic coverage and high temporal frequency. In this study, the Tianjin Binhai New Area was chosen as the study area and the surface water extraction method Modified Normalized Difference Water Index (MNDWI) was used by combining with adaptive dynamic threshold to extract surface water and detect its change. Comparing with single-band threshold, model of multi-band spectral relationship, Iterative Self-organizing Data Analysis Technique Algorithm and MNDWI, MNDWI-based adaptive dynamic threshold method performed better, which considered the influence of background. Analysis on dynamic change of water showed the area of lake and river had increased and the area of seawater had decreased. Meanwhile, the correlation analysis between area change of surface water and impact factors indicated both climatic and anthropogenic factors made positive contribution to the present water environment situation. Finally, an improved model of surface water environment evaluation was established to evaluate water quality by combining genetic algorithm (GA) and backpropagation (BP) neural network model. And the test results proved that the optimized GA-BP neural network is better than the single BP neural network. The environment evaluation indicated that water quality of the Haihe River section in the study area was poor. Therefore, water environment protection should be strengthened in this area. Some suggestions on practical management were given accordingly.

ACS Style

Qiaozhen Guo; Xiaoxu Wu; Xiao Sang; Ying Fu; Yuchen Zang; Xuemei Gong. An integrated study on change detection and environment evaluation of surface water. Applied Water Science 2019, 10, 28 .

AMA Style

Qiaozhen Guo, Xiaoxu Wu, Xiao Sang, Ying Fu, Yuchen Zang, Xuemei Gong. An integrated study on change detection and environment evaluation of surface water. Applied Water Science. 2019; 10 (1):28.

Chicago/Turabian Style

Qiaozhen Guo; Xiaoxu Wu; Xiao Sang; Ying Fu; Yuchen Zang; Xuemei Gong. 2019. "An integrated study on change detection and environment evaluation of surface water." Applied Water Science 10, no. 1: 28.

Journal article
Published: 22 August 2019 in Scientific Reports
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Land use directly reflects degree of human development and utilization of land. Intensity analysis of land use is a quantitative method to analyze land use changes. In this paper, land use changes in Tianjin were studied using Thematic Mapper (TM) remote sensing images in 1995, 2000, 2005, 2010 and Operational Land Imager (OLI) remote sensing image in 2015. Land use information was extracted using decision tree classification method based on CART (Classification and Regression Trees) algorithm. This paper introduced land use intensity analysis to analyze its change intensity and stationarity, respectively at interval, category and transition levels. Based on the theory, new models were developed in the transition level to analyze land use change pattern. The analysis quantifies the contribution of a certain land categories to land use change during a specific time interval. The change of land use during 1995–2015 indicated that Tianjin experienced rapid urban development with the area of urban land increased by about 7.5%. This study provided a reference for the sustainable development of land use in Tianjin.

ACS Style

Xiao Sang; Qiaozhen Guo; Xiaoxu Wu; Ying Fu; Tongyao Xie; Chengwei He; Jinlong Zang. Intensity and Stationarity Analysis of Land Use Change Based on CART Algorithm. Scientific Reports 2019, 9, 1 -12.

AMA Style

Xiao Sang, Qiaozhen Guo, Xiaoxu Wu, Ying Fu, Tongyao Xie, Chengwei He, Jinlong Zang. Intensity and Stationarity Analysis of Land Use Change Based on CART Algorithm. Scientific Reports. 2019; 9 (1):1-12.

Chicago/Turabian Style

Xiao Sang; Qiaozhen Guo; Xiaoxu Wu; Ying Fu; Tongyao Xie; Chengwei He; Jinlong Zang. 2019. "Intensity and Stationarity Analysis of Land Use Change Based on CART Algorithm." Scientific Reports 9, no. 1: 1-12.

Articles
Published: 03 April 2018 in International Journal of Remote Sensing
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This article verified the error between inversed land surface temperature (LST) and measured LST and developed the modified model based on Landsat 8 remote-sensing data. First, a single-channel algorithm was used to invert the surface temperature using four Landsat 8 remote-sensing images and the LST of the 98 measured points were obtained meantime. Then, the modified model between inversed LST and measured LST was developed based on LST for the 74 measured points. Finally, the developed models were used to modify the inversion temperatures at other 24 measured points, and the mean absolute error (MAE) and mean square error (MSE) between the measured temperature and the inversed temperature before and after the modification were compared to verify the validity of the model. The results showed that the MAE and the MSE of temperature for the 24 measured points used for verification reduced by 0.26 and 0.20 K, respectively, after modification. The development of the modified model can provide an important reference for using Landsat 8 remote-sensing image to invert surface temperature in other regions.

ACS Style

Ying Fu; Qiaozhen Guo; Xiaoxu Wu; Chengwei He; Xiao Sang; Tongyao Xie. A modified model of surface temperature inversion based on Landsat 8 remote-sensing data and measured data. International Journal of Remote Sensing 2018, 39, 6170 -6181.

AMA Style

Ying Fu, Qiaozhen Guo, Xiaoxu Wu, Chengwei He, Xiao Sang, Tongyao Xie. A modified model of surface temperature inversion based on Landsat 8 remote-sensing data and measured data. International Journal of Remote Sensing. 2018; 39 (19):6170-6181.

Chicago/Turabian Style

Ying Fu; Qiaozhen Guo; Xiaoxu Wu; Chengwei He; Xiao Sang; Tongyao Xie. 2018. "A modified model of surface temperature inversion based on Landsat 8 remote-sensing data and measured data." International Journal of Remote Sensing 39, no. 19: 6170-6181.

Journal article
Published: 29 May 2017 in Sustainability
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Coastline change reflects the dynamics of natural processes and human activity, and influences the ecology and environment of the coastal strip. This study researched the change in coastline and sea area of the Bohai Sea, China, over a 30-year period using Landsat TM and OLI remote sensing data. The total change in coastline length, sea area, and the centroid of the sea surface were quantified. Variations in the coastline morphology were measured using four shape indexes: fractal dimension, compact ratio, circularity, and square degree. Equations describing fit of the shape index, coastline length, and marine area were built. Then the marine area 10 years later was predicted using the model that had the highest prediction accuracy. The results showed that the highest prediction accuracy for the coastline length was obtained using a compound function. When a cubic function was used to predict the compact ratio, then the highest prediction accuracy was obtained using this compact ratio and a quadratic function to predict sea area. This study can provide theoretical support for the coastal development planning and ecological environment protection around the Bohai Sea.

ACS Style

Ying Fu; Qiaozhen Guo; Xiaoxu Wu; Hui Fang; Yingyang Pan. Analysis and Prediction of Changes in Coastline Morphology in the Bohai Sea, China, Using Remote Sensing. Sustainability 2017, 9, 900 .

AMA Style

Ying Fu, Qiaozhen Guo, Xiaoxu Wu, Hui Fang, Yingyang Pan. Analysis and Prediction of Changes in Coastline Morphology in the Bohai Sea, China, Using Remote Sensing. Sustainability. 2017; 9 (6):900.

Chicago/Turabian Style

Ying Fu; Qiaozhen Guo; Xiaoxu Wu; Hui Fang; Yingyang Pan. 2017. "Analysis and Prediction of Changes in Coastline Morphology in the Bohai Sea, China, Using Remote Sensing." Sustainability 9, no. 6: 900.

Journal article
Published: 06 August 2016 in Sustainability
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The optical complexity of urban waters makes the remote retrieval of chlorophyll-a (Chl-a) concentration a challenging task. In this study, Chl-a concentration was retrieved using reflectance data of Landsat OLI images. Chl-a concentration in the Haihe River of China was obtained using mathematical regression analysis (MRA) and an artificial neural network (ANN). A regression model was built based on an analysis of the spectral reflectance and water quality sampling data. Remote sensing inversion results of Chl-a concentration were obtained and analyzed based on a verification of the algorithm and application of the models to the images. The analysis results revealed that the two models satisfactorily reproduced the temporal variation based on the input variables. In particular, the ANN model showed better performance than the MRA model, which was reflected in its higher accuracy in the validation. This study demonstrated that Landsat Operational Land Imager (OLI) images are suitable for remote sensing monitoring of water quality and that they can produce high-accuracy inversion results.

ACS Style

Qiaozhen Guo; Xiaoxu Wu; Qixuan Bing; Yingyang Pan; Zhiheng Wang; Ying Fu; Dongchuan Wang; Jianing Liu. Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China. Sustainability 2016, 8, 758 .

AMA Style

Qiaozhen Guo, Xiaoxu Wu, Qixuan Bing, Yingyang Pan, Zhiheng Wang, Ying Fu, Dongchuan Wang, Jianing Liu. Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China. Sustainability. 2016; 8 (8):758.

Chicago/Turabian Style

Qiaozhen Guo; Xiaoxu Wu; Qixuan Bing; Yingyang Pan; Zhiheng Wang; Ying Fu; Dongchuan Wang; Jianing Liu. 2016. "Study on Retrieval of Chlorophyll-a Concentration Based on Landsat OLI Imagery in the Haihe River, China." Sustainability 8, no. 8: 758.

Conference paper
Published: 13 January 2016 in Communications in Computer and Information Science
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Understanding of development law and trend for land use change can provide effective data and decision support for the sustainable development of the region. Taking Tianjin Urban as the study area, Landsat TM/OLI images were used. Based on RS and GIS, unsupervised classification and normalized indexes were combined to interpret images. Using single dynamic degree, comprehensive dynamic degree, transfer matrix, and choosing separating index, diversity index, evenness index, spatio-temporal change of land use was analyzed. Results showed that farmland area decreased dramatically. The area of residential land significantly increased. The farmland transformed mainly into the residential land, which showed that rapid urbanization took up a large amount of farmland. The separation degree of residential land reduced. The growth of residential land was more concentrated, and expanded outward from the city center gradually. The comprehensive dynamic degree, diversity index and evenness index of land use decreased.

ACS Style

Qiaozhen Guo; Lingchun Luo; Hongrui Zhao; Yingyang Pan; Qixuan Bing. Study on Spatio-Temporal Change of Land Use in Tianjin Urban Based on Remote Sensing Data. Communications in Computer and Information Science 2016, 228 -237.

AMA Style

Qiaozhen Guo, Lingchun Luo, Hongrui Zhao, Yingyang Pan, Qixuan Bing. Study on Spatio-Temporal Change of Land Use in Tianjin Urban Based on Remote Sensing Data. Communications in Computer and Information Science. 2016; ():228-237.

Chicago/Turabian Style

Qiaozhen Guo; Lingchun Luo; Hongrui Zhao; Yingyang Pan; Qixuan Bing. 2016. "Study on Spatio-Temporal Change of Land Use in Tianjin Urban Based on Remote Sensing Data." Communications in Computer and Information Science , no. : 228-237.

Journal article
Published: 17 December 2015 in Sustainability
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Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV) Model, and together with GIS (Geographical Information System) and AUC (Area Under Receiver Operating Characteristic Curve) test, 32 factor combinations were evaluated separately, and factor combination group with members Slope, Lithology, Drainage network, Annual precipitation, Faults, Road and Vegetation was selected as the optimal combination group with an accuracy of 95.0%. Based on this group, a landslide susceptibility zonation map was drawn, where the study area was reclassified into five classes, presenting an accurate description of different levels of landslide susceptibility, with 79.41% and 13.67% of the validating field survey landslides falling in the Very High and High zones, respectively, mainly distributed in the south and southeast of the catchment. It showed that MIV model can tackle the problem of “no data in subclass” well, generate the true information value and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area.

ACS Style

Qianqian Wang; Dongchuan Wang; Yong Huang; Zhiheng Wang; Lihui Zhang; Qiaozhen Guo; Wei Chen; Wengang Chen; Mengqin Sang. Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment. Sustainability 2015, 7, 16653 -16669.

AMA Style

Qianqian Wang, Dongchuan Wang, Yong Huang, Zhiheng Wang, Lihui Zhang, Qiaozhen Guo, Wei Chen, Wengang Chen, Mengqin Sang. Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment. Sustainability. 2015; 7 (12):16653-16669.

Chicago/Turabian Style

Qianqian Wang; Dongchuan Wang; Yong Huang; Zhiheng Wang; Lihui Zhang; Qiaozhen Guo; Wei Chen; Wengang Chen; Mengqin Sang. 2015. "Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment." Sustainability 7, no. 12: 16653-16669.