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Measuring the regionally coordinated development degree quantitively at an urban agglomeration scale is vital for regional sustainable development. To date, existing studies mainly utilized statistical data to analyze coordinated development degrees between different subsystems, which failed to measure the development gap of subsystems between cities. This study integrated remote sensing and statistical data to evaluate the development degree from six subsystems. The coordinated index (CI) and coordinated development index (CDI) were then promoted to assess the coordinated degree and coordinated development degree. The main findings were: (1) The coordinated development degree of Jing-Jin-Ji (JJJ) had increased from 0.4616 in 2000 to 0.6099 in 2015, with the corresponding grade improvement from “moderate” to “good”; (2) JJJ and six subsystems’ development degree showed an increasing trend. JJJ’s whole development degree had improved from 0.34 to 0.52, and the grade had changed from “fair” to “moderate”; (3) The coordinated degree of JJJ displayed a “V” shape. However, the coordinated degree was lower in 2015 than in 2000.
Jianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015. Sustainability 2021, 13, 4075 .
AMA StyleJianwan Ji, Shixin Wang, Yi Zhou, Wenliang Liu, Litao Wang. Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015. Sustainability. 2021; 13 (7):4075.
Chicago/Turabian StyleJianwan Ji; Shixin Wang; Yi Zhou; Wenliang Liu; Litao Wang. 2021. "Spatiotemporal Change and Coordinated Development Analysis of “Population-Society-Economy-Resource-Ecology-Environment” in the Jing-Jin-Ji Urban Agglomeration from 2000 to 2015." Sustainability 13, no. 7: 4075.
Landslide dam, always triggered by the strong earthquake and heavy rain, is a common natural disaster around the world. In this study, a coupled model was built by combining DB-IWHR model and the two-dimensional hydrodynamic model to simulate the landslide dam flood discharge. We mapped the maximum Baige landslide dam flood inundated area based on Gaofen-1 imagery, and then simulated the process of Baige landslide dam flood discharge using this coupled model. It was proved that, with 80.05% F values, the coupled model was suitable to simulate the process of landslide dam flood discharge. Lastly, multiple scenarios were simulated respectively by setting varying width and depth of spillway. The results of scenarios 1–4 indicated that spillway width presented low sensibility to the peak flow in spillway and the time of its arrival, and similarly to the water depth at river cross-section and the inundated area. Water depth at river cross-section and the inundated area decreased as spillway width increased. Even if spillway width varied at 10 m interval, the average variation of water depth was less than 1.82 m and the variation of inundated area was less than 2.85%. However, the results of scenarios 5–8 indicated that spillway depth was sensitive to the peak flow in spillway and its arrival time, and also to water depth at river cross-section and the inundated area. Water depth at river cross-section and the inundated area increased first and then started to drop with spillway depth kept decreasing. When spillway depth varied at only 2 m interval, the average variation of water depth at river cross-section basically exceeded 2 m and the variation of inundated area was more than 2.85%.
Hongjie Wang; Yi Zhou; Shixin Wang; Futao Wang. Coupled model constructed to simulate the landslide dam flood discharge: a case study of Baige landslide dam, Jinsha River. Frontiers of Earth Science 2020, 14, 63 -76.
AMA StyleHongjie Wang, Yi Zhou, Shixin Wang, Futao Wang. Coupled model constructed to simulate the landslide dam flood discharge: a case study of Baige landslide dam, Jinsha River. Frontiers of Earth Science. 2020; 14 (1):63-76.
Chicago/Turabian StyleHongjie Wang; Yi Zhou; Shixin Wang; Futao Wang. 2020. "Coupled model constructed to simulate the landslide dam flood discharge: a case study of Baige landslide dam, Jinsha River." Frontiers of Earth Science 14, no. 1: 63-76.
As the backbone and arteries of a comprehensive transportation network, highways play an important role in improving people’s living standards and promoting economic growth. However, globally, there is limited quantifiable data evaluating the highway traffic state, characteristics, and performance. From the 1960s to the present, remote sensing has been regarded as the most effective technology for long-term and large-scale monitoring of surface information. However, how to reflect the dynamic “flow” information of traffic with a static remote sensing image has always been a difficult problem that is hard to solve in the field. This study aims to construct a method of evaluating highway traffic prosperity using nighttime remote sensing. First, based on nighttime light data that indicate social and economic activities, a highway-oriented method was proposed to extract highway nighttime light data from 2015 annual nighttime light data of the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (SNPP-VIIRS). Subsequently, Pearson correlation analysis was used to fit the relationship between freeway traffic flow volume and freeway nighttime light at the provincial level. The results showed that Pearson Correlation Coefficient of freeway nighttime light and freeway traffic flow volume for coach and truck are 0.905 and 0.731, respectively, which are higher than between freeway traffic flow volume for coach and truck and total nighttime light (0.593 and 0.516, respectively). A new index—Highway Nighttime Traffic Prosperity Index (HNTPI)—was proposed to evaluate highway traffic across China. The results showed that HNTPI has a strong correspondence with socio-economic parameters. The Pearson Correlation Coefficient of HNTPI and gross domestic product (GDP) per capita, consumption per capita, and population are 0.772, 0.895, and 0.968, respectively. There is a huge spatial heterogeneity in China nighttime traffic, the prosperity degree of highway traffic in developed coastal areas is obviously higher than that inland. The national general highway is the most prosperous highway at night and the national general highway nighttime prosperity of Shanghai reached 22.34%. This research provides basic data for the long-term monitoring and evaluation of regional traffic operation at night and research on the correlation between regional highway construction and the economy.
Ying Chang; Shixin Wang; Yi Zhou; Litao Wang; Futao Wang. A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing. Remote Sensing 2019, 12, 102 .
AMA StyleYing Chang, Shixin Wang, Yi Zhou, Litao Wang, Futao Wang. A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing. Remote Sensing. 2019; 12 (1):102.
Chicago/Turabian StyleYing Chang; Shixin Wang; Yi Zhou; Litao Wang; Futao Wang. 2019. "A Novel Method of Evaluating Highway Traffic Prosperity Based on Nighttime Light Remote Sensing." Remote Sensing 12, no. 1: 102.
The quality of “non-landslide” negative samples may result in unreasonable prediction results for machine learning (ML) models. The aim of this study is to improve the performance of ML models by perfecting the quality of “non-landslide” samples in landslide susceptibility modelling so as to produce more reliable susceptibility maps. The middle and lower reaches of Jinsha River basin (MLRJB) were chosen as the study area, and the elevation, slope aspect, curvature, lithology, distance to faults, slope of slope, slope of aspect, precipitation, land use, and NDVI were considered as predisposing factors for landslide susceptibility mapping. Firstly, three “non-landslide” samples are randomly selected from the low-slope area, landslide-free area and very low susceptibility area based on fractal theory (FT) model generation, and then three sample scenarios are constructed with 4445 landslide positive samples. Next, the performance of cross-application of three sample scenarios in the support vector machines (SVM) and naïve Bayes (NB) models are compared and evaluated based on the statistical indicators such as accuracy, recall, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). The evaluation results show that the “non-landslide” negative samples generated on the basis of FT model are more reasonable and that the hybrid method supported by FT and ML models exhibits the highest prediction efficiency, around 94% overall accuracy produced by scenario-FT, followed by scenario-SS (87%) and scenario-RS (65%). Finally, with the validation of landslide and unstable slopes data, the landslide susceptibility map produced by the hybrid method composed of FT model and the SVM model is the ultimate output product for landslide prevention.
Qiao Hu; Yi Zhou; Shixing Wang; Futao Wang. Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin. Geomorphology 2019, 351, 106975 .
AMA StyleQiao Hu, Yi Zhou, Shixing Wang, Futao Wang. Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin. Geomorphology. 2019; 351 ():106975.
Chicago/Turabian StyleQiao Hu; Yi Zhou; Shixing Wang; Futao Wang. 2019. "Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin." Geomorphology 351, no. : 106975.
Litao Wang; Shixin Wang; Yi Zhou; Jinfeng Zhu; Jiazhen Zhang; Yanfang Hou; Wenliang Liu. Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China. Global Ecology and Conservation 2019, 21, 1 .
AMA StyleLitao Wang, Shixin Wang, Yi Zhou, Jinfeng Zhu, Jiazhen Zhang, Yanfang Hou, Wenliang Liu. Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China. Global Ecology and Conservation. 2019; 21 ():1.
Chicago/Turabian StyleLitao Wang; Shixin Wang; Yi Zhou; Jinfeng Zhu; Jiazhen Zhang; Yanfang Hou; Wenliang Liu. 2019. "Landscape pattern variation, protection measures, and land use/land cover changes in drinking water source protection areas: A case study in Danjiangkou Reservoir, China." Global Ecology and Conservation 21, no. : 1.
The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples.
Qiao Hu; Yi Zhou; Shixing Wang; Futao Wang; Hongjie Wang. Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China. Remote Sensing 2019, 11, 2530 .
AMA StyleQiao Hu, Yi Zhou, Shixing Wang, Futao Wang, Hongjie Wang. Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China. Remote Sensing. 2019; 11 (21):2530.
Chicago/Turabian StyleQiao Hu; Yi Zhou; Shixing Wang; Futao Wang; Hongjie Wang. 2019. "Improving the Accuracy of Landslide Detection in “Off-site” Area by Machine Learning Model Portability Comparison: A Case Study of Jiuzhaigou Earthquake, China." Remote Sensing 11, no. 21: 2530.
This study presents an analysis of the atmospheric column nitrogen dioxide (NO2) over China during 2008-2017. Measurements of NO2 columns obtained from the Ozone Monitoring Instrument (OMI) are used to investigate the temporal and spatial dynamics of NO2. Temporal and spatial distributions of NO2 obtained from OMI over China from 2008 to 2017 are presented, and annual changes and trends in the seasonal cycle are shown. The annual mean NO2 columns are found to decrease 13% from 2008 to 2017. NO2 shows significant cyclical seasonal characteristics over China, with maximum values in winter and minimum in summer. In addition, the spatial distribution is unbalanced, the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region, the Yangtze River Delta region and the Pearl River Delta region are found to be highly polluted areas, the NO2 columns in these regions have been declining by 17.6%, 24.5% and 16.7% during 2008-2017 respectively. Yearly variations in the NO2 columns over the four major economic plates are prominent, and there is a maximum in the eastern region and a minimum in the western region during the past decade, these four regions show a downward trend in recent years. Three areas and six groups are important pollution regions, and the NO2 columns over these regions also show decrease trend.
Yanfang Hou; Litao Wang; Yi Zhou; Shixin Wang; Wenliang Liu; Jinfeng Zhu. Analysis of the tropospheric column nitrogen dioxide over China based on satellite observations during 2008–2017. Atmospheric Pollution Research 2018, 10, 651 -655.
AMA StyleYanfang Hou, Litao Wang, Yi Zhou, Shixin Wang, Wenliang Liu, Jinfeng Zhu. Analysis of the tropospheric column nitrogen dioxide over China based on satellite observations during 2008–2017. Atmospheric Pollution Research. 2018; 10 (2):651-655.
Chicago/Turabian StyleYanfang Hou; Litao Wang; Yi Zhou; Shixin Wang; Wenliang Liu; Jinfeng Zhu. 2018. "Analysis of the tropospheric column nitrogen dioxide over China based on satellite observations during 2008–2017." Atmospheric Pollution Research 10, no. 2: 651-655.
With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.
Yi Zhou; Rui Zhang; Shixin Wang; Futao Wang. Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors 2018, 18, 2013 .
AMA StyleYi Zhou, Rui Zhang, Shixin Wang, Futao Wang. Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors. 2018; 18 (7):2013.
Chicago/Turabian StyleYi Zhou; Rui Zhang; Shixin Wang; Futao Wang. 2018. "Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy." Sensors 18, no. 7: 2013.
In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB), random trees (RT), and support vector machine (SVM). For pixel-based classification, we utilized the maximum likelihood classifier (MLC). Specifically, we explored the influence of repeated sampling on classification results with different training sample sizes. We found that (1) except the overall accuracy of NB, those of the other four classifiers increased as the training sample size increased; (2) repeated sampling had a significant effect on classification accuracy, especially for the DT and NB classifiers; and (3) SVM achieved the best classification accuracy. In addition, the performance of the object-based classifiers was superior to that of the pixel-based classifier. The results of this study can provide guidance on the training sample size and classifier selection.
Ming Shang; Shi-Xin Wang; Yi Zhou; Cong Du. Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery. Journal of the Indian Society of Remote Sensing 2018, 46, 1333 -1340.
AMA StyleMing Shang, Shi-Xin Wang, Yi Zhou, Cong Du. Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery. Journal of the Indian Society of Remote Sensing. 2018; 46 (9):1333-1340.
Chicago/Turabian StyleMing Shang; Shi-Xin Wang; Yi Zhou; Cong Du. 2018. "Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery." Journal of the Indian Society of Remote Sensing 46, no. 9: 1333-1340.
The Chinese government has promulgated a de-capacity policy for economic growth and environmental sustainability, especially for the iron and steel industry. With these policies, this study aimed to monitor the economic activities and evaluate the production conditions of an iron and steel factory based on satellites via Landsat-8 Thermal Infrared Sensor (TIRS) data and high-resolution images from January 2013 to October 2017, and propel next economic adjustment and environmental protection. Our methods included the construction of a heat island intensity index for an iron and steel factory (ISHII), a heat island radio index for an iron and steel factory (ISHRI) and a dense classifying approach to monitor the spatiotemporal changes of the internal heat field of an iron and steel factory. Additionally, we used GF-2 and Google Earth images to identify the main production area, detect facility changes to a factory that alters its heat field and verify the accuracy of thermal analysis in a specific time span. Finally, these methods were used together to evaluate economic activity. Based on five iron and steel factories in the Beijing-Tianjin-Hebei region, when the ISHII curve is higher than the seasonal changes in a time series, production is normal; otherwise, there is a shut-down or cut-back. In the spatial pattern analyses, the ISHRI is large in normal production and decreases when cut-back or shut-down occurs. The density classifying images and high-resolution images give powerful evidence to the above-mentioned results. Finally, three types of economic activities of normal production, shut-down or cut-back were monitored for these samples. The study provides a new perspective and method for monitoring the economic activity of an iron and steel factory and provides supports for sustainable development in China.
Yi Zhou; Fei Zhao; Shixin Wang; Wenliang Liu; Litao Wang. A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites. Sustainability 2018, 10, 1935 .
AMA StyleYi Zhou, Fei Zhao, Shixin Wang, Wenliang Liu, Litao Wang. A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites. Sustainability. 2018; 10 (6):1935.
Chicago/Turabian StyleYi Zhou; Fei Zhao; Shixin Wang; Wenliang Liu; Litao Wang. 2018. "A Method for Monitoring Iron and Steel Factory Economic Activity Based on Satellites." Sustainability 10, no. 6: 1935.
Identifying urban built-up area boundaries is critical to urban data statistics, size measurement, and spatial control. However, previous methods of extracting urban built-up area boundaries based on low-resolution remote-sensing data are frequently constrained by data accuracy. In this paper, a new method for extracting urban built-up area boundaries using high-resolution remote sensing images based on scale effects is proposed. Firstly, we generate a number of different levels of edge-multiplied hexagonal vector grids. Secondly, the impervious surface densities are calculated based on the hexagonal vector grids with the longest edge. Then, the hexagonal grids with higher impervious surface densities are extracted as the built-up area of the first level. Thirdly, we gradually reduce the spatial scale of the hexagonal vector grid and repeat the extraction process based on the extracted built-up area in the previous step. Eventually, we obtain the urban built-up area boundary at the smallest scale. Plausibility checks indicate that the suggested method not only guarantees the spatial continuity of the resultant urban built-up area boundary, but also highlights the prevailing orientation of urban expansion. The extracted Beijing built-up area boundary can serve as a reference in decision-making for space planning and land-use control.
Yi Zhou; Mingguang Tu; Shixin Wang; Wenliang Liu. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS International Journal of Geo-Information 2018, 7, 135 .
AMA StyleYi Zhou, Mingguang Tu, Shixin Wang, Wenliang Liu. A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS International Journal of Geo-Information. 2018; 7 (4):135.
Chicago/Turabian StyleYi Zhou; Mingguang Tu; Shixin Wang; Wenliang Liu. 2018. "A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect." ISPRS International Journal of Geo-Information 7, no. 4: 135.
Atmospheric sulfur dioxide (SO 2 ) is a major air pollutant and has the most negative effect on atmospheric chemistry. In this study, measurements of SO 2 column concentration obtained from an ozone monitoring instrument (OMI) are used to investigate the temporal and spatial dynamics of SO 2 ....
Yanfang Hou; Litao Wang; Yi Zhou; Shixin Wang; Feng Wang. Analysis of the Sulfur Dioxide Column Concentration over Jing-Jin-Ji, China, Based on Satellite Observations during the Past Decade. Polish Journal of Environmental Studies 2018, 27, 1551 -1557.
AMA StyleYanfang Hou, Litao Wang, Yi Zhou, Shixin Wang, Feng Wang. Analysis of the Sulfur Dioxide Column Concentration over Jing-Jin-Ji, China, Based on Satellite Observations during the Past Decade. Polish Journal of Environmental Studies. 2018; 27 (4):1551-1557.
Chicago/Turabian StyleYanfang Hou; Litao Wang; Yi Zhou; Shixin Wang; Feng Wang. 2018. "Analysis of the Sulfur Dioxide Column Concentration over Jing-Jin-Ji, China, Based on Satellite Observations during the Past Decade." Polish Journal of Environmental Studies 27, no. 4: 1551-1557.
Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models.
Litao Wang; Shixin Wang; Yi Zhou; Wenliang Liu; Yanfang Hou; Jinfeng Zhu; Futao Wang. Mapping population density in China between 1990 and 2010 using remote sensing. Remote Sensing of Environment 2018, 210, 269 -281.
AMA StyleLitao Wang, Shixin Wang, Yi Zhou, Wenliang Liu, Yanfang Hou, Jinfeng Zhu, Futao Wang. Mapping population density in China between 1990 and 2010 using remote sensing. Remote Sensing of Environment. 2018; 210 ():269-281.
Chicago/Turabian StyleLitao Wang; Shixin Wang; Yi Zhou; Wenliang Liu; Yanfang Hou; Jinfeng Zhu; Futao Wang. 2018. "Mapping population density in China between 1990 and 2010 using remote sensing." Remote Sensing of Environment 210, no. : 269-281.
Estimates of regional net primary productivity (NPP) are useful in modeling regional and global carbon cycles, especially in karst areas. This work developed a new method to study NPP characteristics and changes in Chongqing, a typical karst area. To estimate NPP accurately, the model which integrated an ecosystem process model (CEVSA) with a light use efficiency model (GLOPEM) called GLOPEM-CEVSA was applied. The fraction of photosynthetically active radiation (fPAR) was derived from remote sensing data inversion based on moderate resolution imaging spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values, which were simulated by the model, matched the observed data well. The values of other relevant NPP models, as well as the MOD17A3 NPP products (NPP MOD17), were compared. In terms of spatial distribution, NPP decreased from northeast to southwest in the Chongqing region. The annual average NPP in the study area was approximately 534 gC/m2a (Std. = 175.53) from 2001 to 2011, with obvious seasonal variation characteristics. The NPP from April to October accounted for 80.1% of the annual NPP, while that from June to August accounted for 43.2%. NPP changed with the fraction of absorbed PAR, and NPP was also significantly correlated to precipitation and temperature at monthly temporal scales, and showed stronger sensitivity to interannual variation in temperature.
Rui Zhang; Yi Zhou; Hongxia Luo; Futao Wang; Shixin Wang. Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area. Remote Sensing 2017, 9, 477 .
AMA StyleRui Zhang, Yi Zhou, Hongxia Luo, Futao Wang, Shixin Wang. Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area. Remote Sensing. 2017; 9 (5):477.
Chicago/Turabian StyleRui Zhang; Yi Zhou; Hongxia Luo; Futao Wang; Shixin Wang. 2017. "Estimation and Analysis of Spatiotemporal Dynamics of the Net Primary Productivity Integrating Efficiency Model with Process Model in Karst Area." Remote Sensing 9, no. 5: 477.
Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B1–4) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B1–4, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B1–4, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features.
Yi Zhou; Chenxi Lin; Shixin Wang; Wenliang Liu; Ye Tian. Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data. Remote Sensing 2016, 8, 969 .
AMA StyleYi Zhou, Chenxi Lin, Shixin Wang, Wenliang Liu, Ye Tian. Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data. Remote Sensing. 2016; 8 (11):969.
Chicago/Turabian StyleYi Zhou; Chenxi Lin; Shixin Wang; Wenliang Liu; Ye Tian. 2016. "Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data." Remote Sensing 8, no. 11: 969.
On 16 March 2014, the State Council of China launched its first urbanization planning initiative dubbed “National New Urbanization Planning (2014–2020)” (NNUP). NNUP put forward 20 urban agglomerations and a sustainable development approach aiming to transform traditional Chinese urbanization to sustainable new urbanization. This study quantitatively evaluates the level of sustainability of the present new urbanization process in 20 Chinese urban agglomerations and provides some positive suggestions for the achievement of sustainable new urbanization. A three-level index system which is based on six fundamental elements in a city and a Full Permutation Polygon Synthetic Indicator evaluation method are adopted. The results show that China is undergoing a new urbanization process with a low level of sustainability and there are many problems remaining from traditional urbanization processes. There exists a polarized phenomenon in the urbanization of 20 urban agglomerations. Based on their own development patterns, the 20 urban agglomerations can be divided into seven categories. Every category has its own development characteristics. The analyses also show that waste of water resources, abuse of land resources, and air pollution are three big problems that are closely linked to traditional Chinese urbanization processes. To achieve sustainable new urbanization in China, four relevant suggestions and comments have been provided.
Cong Xu; Shixin Wang; Yi Zhou; Litao Wang; Wenliang Liu. A Comprehensive Quantitative Evaluation of New Sustainable Urbanization Level in 20 Chinese Urban Agglomerations. Sustainability 2016, 8, 91 .
AMA StyleCong Xu, Shixin Wang, Yi Zhou, Litao Wang, Wenliang Liu. A Comprehensive Quantitative Evaluation of New Sustainable Urbanization Level in 20 Chinese Urban Agglomerations. Sustainability. 2016; 8 (2):91.
Chicago/Turabian StyleCong Xu; Shixin Wang; Yi Zhou; Litao Wang; Wenliang Liu. 2016. "A Comprehensive Quantitative Evaluation of New Sustainable Urbanization Level in 20 Chinese Urban Agglomerations." Sustainability 8, no. 2: 91.
The accurate extraction of burned area is important for biomass burning monitoring and loss evaluation. Environment and Disasters Monitoring Microsatellite Constellation put forward by China has two satellites of HJ-1A and HJ-1B in orbit. Each satellite has two CCD cameras with four bands to meet the need of mapping burned area. In order to evaluate the capability for mapping the burned area using HJ satellite’s CCD data, a forest fire occurring in Yuxi, Yunnan Province of Southwest China, was selected to analyze the spectral characteristic in the range of visible and near infrared in this paper. The research of mapping burned area was carried out based on the HJ satellites using three spectral indices (NDVI, GEMI and BAI). The color composite images including NIR band could reflect the spectral change in post-fire vegetation with a higher repetition cycle (2 days, or 1 day in some region) and higher spatial resolution (30 m). Through the comparison with the discrimination index M and extraction accuracy, the BAI has higher discrimination capability than NDVI and GEMI, and the highest M value is 2.1943. The extraction of burned area based on BAI showed higher accuracy, and the highest kappa value is 0.8957. Using HJ satellites, the map of burned area with higher temporal–spatial resolution and higher accuracy could provide the potential for dynamic monitoring and analyzing fire behavior.
Wenliang Liu; Litao Wang; Yi Zhou; Shixin Wang; Jinfeng Zhu; Futao Wang. A comparison of forest fire burned area indices based on HJ satellite data. Natural Hazards 2015, 81, 971 -980.
AMA StyleWenliang Liu, Litao Wang, Yi Zhou, Shixin Wang, Jinfeng Zhu, Futao Wang. A comparison of forest fire burned area indices based on HJ satellite data. Natural Hazards. 2015; 81 (2):971-980.
Chicago/Turabian StyleWenliang Liu; Litao Wang; Yi Zhou; Shixin Wang; Jinfeng Zhu; Futao Wang. 2015. "A comparison of forest fire burned area indices based on HJ satellite data." Natural Hazards 81, no. 2: 971-980.
Lightning-caused forest fires can cause serious damage to the social economy and public property and even threaten human life. Therefore, lightning-caused forest fire risk rating assessment is very important for forest management agency, because the risk rating assessment results could provide important information to prevent fires and allocate extinguishing resources. The existing forest fire risk rating assessment methods are more difficult for the area with sparse meteorological stations, imperfect lightning monitoring systems and complex terrain conditions. Based on remote sensing data and case-based reasoning principle, this paper proposed a method to overcome the limitations of existing forest fire risk rating assessment methods. The proposed method uses three dynamic and two static indexes to characterize the potential fire environment. The dynamic indexes are temperature condition index, vegetation condition index and water condition index. The static indexes are terrain fluctuation and LIS/OTD lightning density. In DaXingAn Mountains of China, the fire risk rating spatial distribution maps with 8-day cycle before the occurrences of historical lightning-caused fires were produced by using the lightning-caused forest fire risk rating assessment method during 2000–2006 in this paper. The results showed that most of the historical lightning-caused fires occurred in the region with high fire risk rating, and the spatial–temporal distribution changes of the lightning-caused fire risk rating followed the same trend as the changes in the number of lightning-caused fires. Therefore, the lightning-caused forest fire risk rating assessment method proposed in this paper could assess the fire risk rating effectively, and this method could also provide a reference for other countries and regions with sparse meteorological stations and imperfect lightning monitoring systems.
Wenliang Liu; Shixin Wang; Yi Zhou; Litao Wang; Jinfeng Zhu; Futao Wang. Lightning-caused forest fire risk rating assessment based on case-based reasoning: a case study in DaXingAn Mountains of China. Natural Hazards 2015, 81, 347 -363.
AMA StyleWenliang Liu, Shixin Wang, Yi Zhou, Litao Wang, Jinfeng Zhu, Futao Wang. Lightning-caused forest fire risk rating assessment based on case-based reasoning: a case study in DaXingAn Mountains of China. Natural Hazards. 2015; 81 (1):347-363.
Chicago/Turabian StyleWenliang Liu; Shixin Wang; Yi Zhou; Litao Wang; Jinfeng Zhu; Futao Wang. 2015. "Lightning-caused forest fire risk rating assessment based on case-based reasoning: a case study in DaXingAn Mountains of China." Natural Hazards 81, no. 1: 347-363.
China is considered to be one of the most vulnerable drought-prone countries in the world, and it has recently suffered many severe droughts with large economic and societal losses. Drought events in China have been extracted using run theory based on the Standardized Precipitation Evapotranspiration Index, which covers the period 1961–2013 across 810 stations. The drought events are characterized by three variables: duration, severity and peak. Exponential, Weibull and Pareto functions are then selected to describe the marginal distribution of duration, severity and peak, respectively. The Gumbel–Hougaard Copula was used to construct the joint distribution of Duration–Severity and Duration–Peak, while the Clayton Copula and the Gaussian Copula are used to construct the joint distribution of Severity–Peak and Duration–Severity–Peak, respectively. The results indicate that the return period is dependent on spatial location, variable type and the combination of variables. For extreme droughts, trivariate ‘and’ return periods are longer, with an average of 42.1 years. The short return period is mainly distributed in southern China, especially on the border between Sichuan and Yunnan, the coastal regions of Guangdong, western Hunan and northern Jiangxi. Studies on the identification of spatial distributions of drought return periods across China have therefore been undertaken for drought mitigation and strategy planning.
Xiong-Fei Liu; Shi-Xin Wang; Yi Zhou; Fu-Tao Wang; Guang Yang; Wen-Liang Liu. Spatial analysis of meteorological drought return periods in China using Copulas. Natural Hazards 2015, 80, 367 -388.
AMA StyleXiong-Fei Liu, Shi-Xin Wang, Yi Zhou, Fu-Tao Wang, Guang Yang, Wen-Liang Liu. Spatial analysis of meteorological drought return periods in China using Copulas. Natural Hazards. 2015; 80 (1):367-388.
Chicago/Turabian StyleXiong-Fei Liu; Shi-Xin Wang; Yi Zhou; Fu-Tao Wang; Guang Yang; Wen-Liang Liu. 2015. "Spatial analysis of meteorological drought return periods in China using Copulas." Natural Hazards 80, no. 1: 367-388.
The three-river source region (TRSR, including Yangtze, Yellow and Lancang rivers), located in the Qinghai-Tibetan Plateau, China, is a typical alpine zone with apparent ecosystem vulnerability and sensitivity. In this paper, we introduced many interdisciplinary factors, such as landscape pattern indices (Shannon diversity index and Shannon evenness index) and extreme climate factors (number of extreme high temperature days, number of extreme low temperature days, and number of extreme precipitation days), to establish a new model for evaluating the spatial patterns of ecosystem vulnerability changes in the TRSR. The change intensity (CI) of ecosystem vulnerability was also analyzed. The results showed that the established evaluation model was effective and the ecosystem vulnerability in the whole study area was intensive. During the study period of 2001–2011, there was a slight degradation in the eco-environmental quality. The Yellow River source region had the best eco-environmental quality, while the Yangtze River source region had the worst one. In addition, the zones dominated by deserts were the most severely deteriorated areas and the eco-environmental quality of the zones occupied by evergreen coniferous forests showed a better change. Furthermore, the larger the change rates of the climate factors (accumulative temperature of ≥10°C and annual average precipitation) are, the more intensive the CI of ecosystem vulnerability is. This study would provide a scientific basis for the eco-environmental protection and restoration in the TRSR.
Bing Guo; Yi Zhou; Jinfeng Zhu; Wenliang Liu; Futao Wang; Litao Wang; Fuli Yan; Feng Wang; Guang Yang; Wei Luo; Lin Jiang. Spatial patterns of ecosystem vulnerability changes during 2001–2011 in the three-river source region of the Qinghai-Tibetan Plateau, China. Journal of Arid Land 2015, 8, 23 -35.
AMA StyleBing Guo, Yi Zhou, Jinfeng Zhu, Wenliang Liu, Futao Wang, Litao Wang, Fuli Yan, Feng Wang, Guang Yang, Wei Luo, Lin Jiang. Spatial patterns of ecosystem vulnerability changes during 2001–2011 in the three-river source region of the Qinghai-Tibetan Plateau, China. Journal of Arid Land. 2015; 8 (1):23-35.
Chicago/Turabian StyleBing Guo; Yi Zhou; Jinfeng Zhu; Wenliang Liu; Futao Wang; Litao Wang; Fuli Yan; Feng Wang; Guang Yang; Wei Luo; Lin Jiang. 2015. "Spatial patterns of ecosystem vulnerability changes during 2001–2011 in the three-river source region of the Qinghai-Tibetan Plateau, China." Journal of Arid Land 8, no. 1: 23-35.