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Chunxiang Cao
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China

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Journal article
Published: 29 March 2021 in Journal of Cleaner Production
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Aerosol pollution has become an increasingly serious problem in China. Among the multiple factors causing aerosol pollution, wildfires in China are occurring more frequently and have gradually become one of the most important contributing factors. However, little is known about their potential causality trends or spatial characteristics. In this research, satellite data of fire events and atmospheric aerosol datasets from 2001 to 2016 were applied in the geographical, statistical “Geodetector model” (GDM) to better understand their causal relationship. From long term observation data in China, we found that the increase in wildfires over the study period greatly enhanced their impacts on the aerosol optical depth (AOD) in recent years. The contribution of burning areas to AOD was 18.29% in 2001 and increased to 38.94% in 2016, and the contribution of fire radiative power (FRP) was 18.80% in 2001 and became 36.05% in 2016. In addition, seasonal research suggested that wildfires contributed rapidly to aerosol pollution, usually from April to September. The regional results in China showed that wildfires can be a relatively dominant factor for aerosol pollution in the southern regions, so that more importance should be attached to the complicated pollution conditions. Overall, our findings highlight the causal effects of wildfires on atmospheric aerosol pollution in China. We suggest that the rising contributions of wildfires to AOD in China should be noticed, and attention should be given to adaptions to local conditions regarding wildfires and aerosol pollution management.

ACS Style

Yiyu Chen; Chunxiang Cao; Yunfeng Cao; Barjeece Bashir; Min Xu; Bo Xie; Kaimin Wang. Observed evidence of the growing contributions to aerosol pollution of wildfires with diverse spatiotemporal distinctions in China. Journal of Cleaner Production 2021, 298, 126860 .

AMA Style

Yiyu Chen, Chunxiang Cao, Yunfeng Cao, Barjeece Bashir, Min Xu, Bo Xie, Kaimin Wang. Observed evidence of the growing contributions to aerosol pollution of wildfires with diverse spatiotemporal distinctions in China. Journal of Cleaner Production. 2021; 298 ():126860.

Chicago/Turabian Style

Yiyu Chen; Chunxiang Cao; Yunfeng Cao; Barjeece Bashir; Min Xu; Bo Xie; Kaimin Wang. 2021. "Observed evidence of the growing contributions to aerosol pollution of wildfires with diverse spatiotemporal distinctions in China." Journal of Cleaner Production 298, no. : 126860.

Journal article
Published: 06 November 2020 in Remote Sensing
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Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

ACS Style

Xiaojuan Lin; Min Xu; Chunxiang Cao; Yongfeng Dang; Barjeece Bashir; Bo Xie; Zhibin Huang. Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry. Remote Sensing 2020, 12, 3649 .

AMA Style

Xiaojuan Lin, Min Xu, Chunxiang Cao, Yongfeng Dang, Barjeece Bashir, Bo Xie, Zhibin Huang. Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry. Remote Sensing. 2020; 12 (21):3649.

Chicago/Turabian Style

Xiaojuan Lin; Min Xu; Chunxiang Cao; Yongfeng Dang; Barjeece Bashir; Bo Xie; Zhibin Huang. 2020. "Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry." Remote Sensing 12, no. 21: 3649.

Journal article
Published: 13 August 2020 in Remote Sensing
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Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

ACS Style

Barjeece Bashir; Chunxiang Cao; Shahid Naeem; Mehdi Zamani Joharestani; Xie Bo; Huma Afzal; Kashif Jamal; Faisal Mumtaz. Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors. Remote Sensing 2020, 12, 2612 .

AMA Style

Barjeece Bashir, Chunxiang Cao, Shahid Naeem, Mehdi Zamani Joharestani, Xie Bo, Huma Afzal, Kashif Jamal, Faisal Mumtaz. Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors. Remote Sensing. 2020; 12 (16):2612.

Chicago/Turabian Style

Barjeece Bashir; Chunxiang Cao; Shahid Naeem; Mehdi Zamani Joharestani; Xie Bo; Huma Afzal; Kashif Jamal; Faisal Mumtaz. 2020. "Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors." Remote Sensing 12, no. 16: 2612.

Journal article
Published: 12 February 2020 in Remote Sensing
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Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation.

ACS Style

Min Xu; Chunxiang Cao; Peng Jia. Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data. Remote Sensing 2020, 12, 608 .

AMA Style

Min Xu, Chunxiang Cao, Peng Jia. Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data. Remote Sensing. 2020; 12 (4):608.

Chicago/Turabian Style

Min Xu; Chunxiang Cao; Peng Jia. 2020. "Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data." Remote Sensing 12, no. 4: 608.

Journal article
Published: 22 January 2020 in Remote Sensing
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Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

ACS Style

Bo Xie; Chunxiang Cao; Min Xu; Barjeece Bashir; Ramesh P. Singh; Zhibin Huang; Xiaojuan Lin. Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data. Remote Sensing 2020, 12, 360 .

AMA Style

Bo Xie, Chunxiang Cao, Min Xu, Barjeece Bashir, Ramesh P. Singh, Zhibin Huang, Xiaojuan Lin. Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data. Remote Sensing. 2020; 12 (3):360.

Chicago/Turabian Style

Bo Xie; Chunxiang Cao; Min Xu; Barjeece Bashir; Ramesh P. Singh; Zhibin Huang; Xiaojuan Lin. 2020. "Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data." Remote Sensing 12, no. 3: 360.

Journal article
Published: 02 January 2020 in Remote Sensing
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Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

ACS Style

Zhibin Huang; Chunxiang Cao; Wei Chen; Min Xu; Yongfeng Dang; Ramesh P. Singh; Barjeece Bashir; Bo Xie; Xiaojuan Lin. Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts. Remote Sensing 2020, 12, 156 .

AMA Style

Zhibin Huang, Chunxiang Cao, Wei Chen, Min Xu, Yongfeng Dang, Ramesh P. Singh, Barjeece Bashir, Bo Xie, Xiaojuan Lin. Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts. Remote Sensing. 2020; 12 (1):156.

Chicago/Turabian Style

Zhibin Huang; Chunxiang Cao; Wei Chen; Min Xu; Yongfeng Dang; Ramesh P. Singh; Barjeece Bashir; Bo Xie; Xiaojuan Lin. 2020. "Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts." Remote Sensing 12, no. 1: 156.

Journal article
Published: 02 December 2019 in International Journal of Environmental Research and Public Health
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Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

ACS Style

Bipin Kumar Acharya; Wei Chen; Zengliang Ruan; Gobind Prasad Pant; Yin Yang; Lalan Prasad Shah; Chunxiang Cao; Zhiwei Xu; Meghnath Dhimal; Hualiang Lin. Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models. International Journal of Environmental Research and Public Health 2019, 16, 4845 .

AMA Style

Bipin Kumar Acharya, Wei Chen, Zengliang Ruan, Gobind Prasad Pant, Yin Yang, Lalan Prasad Shah, Chunxiang Cao, Zhiwei Xu, Meghnath Dhimal, Hualiang Lin. Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models. International Journal of Environmental Research and Public Health. 2019; 16 (23):4845.

Chicago/Turabian Style

Bipin Kumar Acharya; Wei Chen; Zengliang Ruan; Gobind Prasad Pant; Yin Yang; Lalan Prasad Shah; Chunxiang Cao; Zhiwei Xu; Meghnath Dhimal; Hualiang Lin. 2019. "Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models." International Journal of Environmental Research and Public Health 16, no. 23: 4845.

Journal article
Published: 04 July 2019 in Atmosphere
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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

ACS Style

Mehdi Zamani Joharestani; Chunxiang Cao; Xiliang Ni; Barjeece Bashir; Somayeh Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere 2019, 10, 373 .

AMA Style

Mehdi Zamani Joharestani, Chunxiang Cao, Xiliang Ni, Barjeece Bashir, Somayeh Talebiesfandarani. PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere. 2019; 10 (7):373.

Chicago/Turabian Style

Mehdi Zamani Joharestani; Chunxiang Cao; Xiliang Ni; Barjeece Bashir; Somayeh Talebiesfandarani. 2019. "PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data." Atmosphere 10, no. 7: 373.

Articles
Published: 10 June 2019 in Geocarto International
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A forest spectral library would be helpful for tree species monitoring and management. To meet the demand of standard spectral information for the subtropical forest, a three-level spectral library including leaf spectra of 67 typical subtropical tree species was built using two spectrometers in this study. Towards the spectra measured by different spectrometers, the spectra consistency was tested using one-way Analysis of Variance (ANOVA) and Jeffries-Matusita distance (J-M distance). To find the optimal band range for subtropical tree species discrimination, the spectral separability of these 67 species were assessed using J-M distance based on original spectra, first order and second order derivative spectra on visible-near infrared band (VNIR, 350-1000 nm), shortwave infrared band (SWIR, 1000-2500 nm) and full band (350-2500 nm). Two kinds of dimension-reduced spectra in these three spectral band range were also analyzed for the tree species separability. It was found that: 1) the spectra measured by the two spectrometers with the same spectral resolution were significantly different, but the derivative spectra were not; 2) the SWIR spectral band was optimal for separating these subtropical tree species; 3) the selection method outperformed transform method for reducing spectra dimension and separating subtropical tree species. This study was expected to provide a reference for spectral library development and subtropical tree species discrimination.

ACS Style

Shanning Bao; Chunxiang Cao; Wei Chen; Tianyu Yang; Chunying Wu. Towards a subtropical forest spectral library: spectra consistency and spectral separability. Geocarto International 2019, 36, 226 -240.

AMA Style

Shanning Bao, Chunxiang Cao, Wei Chen, Tianyu Yang, Chunying Wu. Towards a subtropical forest spectral library: spectra consistency and spectral separability. Geocarto International. 2019; 36 (2):226-240.

Chicago/Turabian Style

Shanning Bao; Chunxiang Cao; Wei Chen; Tianyu Yang; Chunying Wu. 2019. "Towards a subtropical forest spectral library: spectra consistency and spectral separability." Geocarto International 36, no. 2: 226-240.

Journal article
Published: 27 February 2019 in ISPRS International Journal of Geo-Information
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Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.

ACS Style

Mingbo Liu; Chunxiang Cao; Wei Chen; Xuejun Wang. Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data. ISPRS International Journal of Geo-Information 2019, 8, 106 .

AMA Style

Mingbo Liu, Chunxiang Cao, Wei Chen, Xuejun Wang. Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data. ISPRS International Journal of Geo-Information. 2019; 8 (3):106.

Chicago/Turabian Style

Mingbo Liu; Chunxiang Cao; Wei Chen; Xuejun Wang. 2019. "Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data." ISPRS International Journal of Geo-Information 8, no. 3: 106.

Journal article
Published: 21 February 2019 in Science of The Total Environment
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Wetland is one of the three major ecosystems on the earth and has fundamental ecological functions and plays an irreplaceable role in serving biological survival and human development. Considering the characteristics of five types of wetlands, this study constructed a wetland ecological health evaluation indicator system using a wide variety of data from statistical report, field sampling, remote sensing, and questionnaire survey. In this study, we have selected 13 indicators (related to water, soil, biological, landscape and social factors) for ecological health evaluation of 19 wetlands in Beijing-Tianjin-Hebei region of China which have national importance. The detailed analysis shows a comprehensive health index of 5.53. There was significant spatial heterogeneity in the health status of the 19 wetlands in this region. The evaluation results and analysis provides scientific services for developing reasonable and targeted wetland protection and utilization policies of wetlands.

ACS Style

Wei Chen; Chunxiang Cao; Di Liu; Rong Tian; Chunying Wu; Yiqun Wang; Yifan Qian; Guoqiang Ma; Daming Bao. An evaluating system for wetland ecological health: Case study on nineteen major wetlands in Beijing-Tianjin-Hebei region, China. Science of The Total Environment 2019, 666, 1080 -1088.

AMA Style

Wei Chen, Chunxiang Cao, Di Liu, Rong Tian, Chunying Wu, Yiqun Wang, Yifan Qian, Guoqiang Ma, Daming Bao. An evaluating system for wetland ecological health: Case study on nineteen major wetlands in Beijing-Tianjin-Hebei region, China. Science of The Total Environment. 2019; 666 ():1080-1088.

Chicago/Turabian Style

Wei Chen; Chunxiang Cao; Di Liu; Rong Tian; Chunying Wu; Yiqun Wang; Yifan Qian; Guoqiang Ma; Daming Bao. 2019. "An evaluating system for wetland ecological health: Case study on nineteen major wetlands in Beijing-Tianjin-Hebei region, China." Science of The Total Environment 666, no. : 1080-1088.

Journal article
Published: 29 January 2019 in Forests
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Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m.

ACS Style

Mingbo Liu; Chunxiang Cao; Yongfeng Dang; Xiliang Ni. Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data. Forests 2019, 10, 105 .

AMA Style

Mingbo Liu, Chunxiang Cao, Yongfeng Dang, Xiliang Ni. Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data. Forests. 2019; 10 (2):105.

Chicago/Turabian Style

Mingbo Liu; Chunxiang Cao; Yongfeng Dang; Xiliang Ni. 2019. "Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data." Forests 10, no. 2: 105.

Articles
Published: 01 January 2019 in Geomatics, Natural Hazards and Risk
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Dysentery is a major infectious diseases that affects public health and the quality of life in Jiangsu Province. Since the development of the railways, the incidence of dysentery in the general population living near railway lines has increased. This research investigates the relationship between the geographical distribution of the railways and the incidence of dysentery in Jiangsu Province, and suggests a method for predicting dysentery in such areas. We used previously published literature, statistics, and known physiological properties of Shigella to analyse bacterial dysentery data for Jiangsu Province for the period from 2001 to 2012. The results indicate that railways could be indicative of the transmission of dysentery. Analysis of the incidence of dysentery in relation to the distribution of the railways using linear regression found that the incidence of dysentery in Jiangsu Province for 2001–2012 was linearly related to the buffer distance from the particular railway line. Furthermore, both regression parameters were affected by meteorological parameters, and the railway site was linearly related to the incidence of dysentery in cities along the railway.

ACS Style

Tianyu Yang; Chunxiang Cao; Jianhong Guo; Min Xu; Haijing Tian. Analysis of factors evident in the relation between railways and the incidence of dysentery using linear regression. Geomatics, Natural Hazards and Risk 2019, 10, 1459 -1474.

AMA Style

Tianyu Yang, Chunxiang Cao, Jianhong Guo, Min Xu, Haijing Tian. Analysis of factors evident in the relation between railways and the incidence of dysentery using linear regression. Geomatics, Natural Hazards and Risk. 2019; 10 (1):1459-1474.

Chicago/Turabian Style

Tianyu Yang; Chunxiang Cao; Jianhong Guo; Min Xu; Haijing Tian. 2019. "Analysis of factors evident in the relation between railways and the incidence of dysentery using linear regression." Geomatics, Natural Hazards and Risk 10, no. 1: 1459-1474.

Journal article
Published: 01 November 2018 in Sustainability
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Using Landsat remote-sensing data combined with geological information extracted from ALOS and Sentinel-1A radar data, the ecological environment was evaluated in the years 2007, 2008, 2013, and 2017 through gray correlation analysis on the basis of the construction of the pressure-state-response model. The main objective of this research was to assess the ecological environment changes in Wenchuan County before and after the earthquake, and to provide reference for future social development and policy implementation. The grading map of the ecological environment was obtained for every year, and the ecological restoration status of Wenchuan County after the earthquake was evaluated. The results showed that the maximum area cover at a “safe” ecological level was over 46.4% in 2007. After the 2008 earthquake, the proportion of “unsafe” and “very unsafe” ecological levels was 40.0%, especially around the Lancang River and the western mountain area in Wenchuan County. After five years of restoration, ecological conditions were improved, up to 48.0% in the region. The areas at “critically safe” and above recovered to 85.5% in 2017 within nine years after the deadly Wenchuan earthquake of May 12, 2008. In this paper, we discuss the results of detailed analysis of ecological improvements and correlation with the degrees of pressure, state, and response layers of the Pressure-State-Response (PSR) model.

ACS Style

Zhibin Huang; Min Xu; Wei Chen; Xiaojuan Lin; Chunxiang Cao; Ramesh P. Singh. Postseismic Restoration of the Ecological Environment in the Wenchuan Region Using Satellite Data. Sustainability 2018, 10, 3990 .

AMA Style

Zhibin Huang, Min Xu, Wei Chen, Xiaojuan Lin, Chunxiang Cao, Ramesh P. Singh. Postseismic Restoration of the Ecological Environment in the Wenchuan Region Using Satellite Data. Sustainability. 2018; 10 (11):3990.

Chicago/Turabian Style

Zhibin Huang; Min Xu; Wei Chen; Xiaojuan Lin; Chunxiang Cao; Ramesh P. Singh. 2018. "Postseismic Restoration of the Ecological Environment in the Wenchuan Region Using Satellite Data." Sustainability 10, no. 11: 3990.

Journal article
Published: 08 October 2018 in Sustainability
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Due to urban expansion, economic development, and rapid population growth, land use/land cover (LULC) is changing in major cities around the globe. Quantitative analysis of LULC change is important for studying the corresponding impact on the ecosystem service value (ESV) that helps in decision-making and ecosystem conservation. Based on LULC data retrieved from remote-sensing interpretation, we computed the changes of ESV associated with the LULC dynamics using the benefits transfer method and geographic information system (GIS) technologies during the period of 1992–2018 following self-modified coefficients which were corrected by net primary productivity (NPP). This improved approach aimed to establish a regional value coefficients table for facilitating the reliable evaluation of ESV. The main objective of this research was to clarify the trend and spatial patterns of LULC changes and their influence on ecosystem service values and functions. Our results show a continuous reduction in total ESV from United States (US) $1476.25 million in 1992, to US $1410.17, $1335.10, and $1190.56 million in 2001, 2009, and 2018, respectively; such changes are attributed to a notable loss of farmland and forest land from 1992–2018. The elasticity of ESV in response to changes in LULC shows that 1% of land transition may have caused average changes of 0.28%, 0.34%, and 0.50% during the periods of 1992–2001, 2001–2009, and 2009–2018, respectively. This study provides important information useful for land resource management and for developing strategies to address the reduction of ESV.

ACS Style

Xiaojuan Lin; Min Xu; Chunxiang Cao; Ramesh P. Singh; Wei Chen; Hongrun Ju. Land-Use/Land-Cover Changes and Their Influence on the Ecosystem in Chengdu City, China during the Period of 1992–2018. Sustainability 2018, 10, 3580 .

AMA Style

Xiaojuan Lin, Min Xu, Chunxiang Cao, Ramesh P. Singh, Wei Chen, Hongrun Ju. Land-Use/Land-Cover Changes and Their Influence on the Ecosystem in Chengdu City, China during the Period of 1992–2018. Sustainability. 2018; 10 (10):3580.

Chicago/Turabian Style

Xiaojuan Lin; Min Xu; Chunxiang Cao; Ramesh P. Singh; Wei Chen; Hongrun Ju. 2018. "Land-Use/Land-Cover Changes and Their Influence on the Ecosystem in Chengdu City, China during the Period of 1992–2018." Sustainability 10, no. 10: 3580.

Journal article
Published: 01 October 2018 in Global Ecology and Conservation
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Global land degradation and sustainable development has become a serious challenge for the terrestrial ecosystems. Shrub plays a crucial role in global ecosystem protection, ecological reconstruction, which is especially important in arid and semi-arid sandland ecosystem. Shrub above ground biomass (AGB) is a proxy of carbon sequestration capacity. Shrub AGB in Mu Us Sandland was estimated using different methods based on Landsat Thematic Mapper (TM) data, topography data, combined with in situ survey data. Linear regression model, multiple stepwise regression model, machine learning model and geometric optical model were used to estimate shrub biomass in combination with in situ data, respectively and their effects were validated and compared. Results showed that shrub AGB predicted from one multiple stepwise regression model with Ratio Vegetation Index (RVI) and Brightness from K-T transformation as input variables reached highest accuracy. For both high and low shrub coverage regions, shrub AGB distribution maps derived by this multiple stepwise regression model achieved higher precision. All these findings will provide a scientific support for ecological sustainable development in eco-vulnerable ecosystems.

ACS Style

Wei Chen; Jian Zhao; Chunxiang Cao; Haijing Tian. Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology. Global Ecology and Conservation 2018, 16, e00479 .

AMA Style

Wei Chen, Jian Zhao, Chunxiang Cao, Haijing Tian. Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology. Global Ecology and Conservation. 2018; 16 ():e00479.

Chicago/Turabian Style

Wei Chen; Jian Zhao; Chunxiang Cao; Haijing Tian. 2018. "Shrub biomass estimation in semi-arid sandland ecosystem based on remote sensing technology." Global Ecology and Conservation 16, no. : e00479.

Original paper
Published: 04 September 2018 in International Journal of Biometeorology
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Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran’s I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.

ACS Style

Bipin Kumar Acharya; Chunxiang Cao; Tobia Lakes; Wei Chen; Shahid Naeem; Shreejana Pandit. Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model. International Journal of Biometeorology 2018, 62, 1973 -1986.

AMA Style

Bipin Kumar Acharya, Chunxiang Cao, Tobia Lakes, Wei Chen, Shahid Naeem, Shreejana Pandit. Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model. International Journal of Biometeorology. 2018; 62 (11):1973-1986.

Chicago/Turabian Style

Bipin Kumar Acharya; Chunxiang Cao; Tobia Lakes; Wei Chen; Shahid Naeem; Shreejana Pandit. 2018. "Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model." International Journal of Biometeorology 62, no. 11: 1973-1986.

Journal article
Published: 12 July 2018 in ISPRS International Journal of Geo-Information
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Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.

ACS Style

Sornkitja Boonprong; Chunxiang Cao; Wei Chen; Xiliang Ni; Min Xu; Bipin Kumar Acharya. The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS International Journal of Geo-Information 2018, 7, 274 .

AMA Style

Sornkitja Boonprong, Chunxiang Cao, Wei Chen, Xiliang Ni, Min Xu, Bipin Kumar Acharya. The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS International Journal of Geo-Information. 2018; 7 (7):274.

Chicago/Turabian Style

Sornkitja Boonprong; Chunxiang Cao; Wei Chen; Xiliang Ni; Min Xu; Bipin Kumar Acharya. 2018. "The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy." ISPRS International Journal of Geo-Information 7, no. 7: 274.

Journal article
Published: 12 July 2018 in ISPRS International Journal of Geo-Information
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Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

ACS Style

Bipin Kumar Acharya; Chunxiang Cao; Min Xu; Laxman Khanal; Shahid Naeem; Shreejana Pandit. Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal. ISPRS International Journal of Geo-Information 2018, 7, 275 .

AMA Style

Bipin Kumar Acharya, Chunxiang Cao, Min Xu, Laxman Khanal, Shahid Naeem, Shreejana Pandit. Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal. ISPRS International Journal of Geo-Information. 2018; 7 (7):275.

Chicago/Turabian Style

Bipin Kumar Acharya; Chunxiang Cao; Min Xu; Laxman Khanal; Shahid Naeem; Shreejana Pandit. 2018. "Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal." ISPRS International Journal of Geo-Information 7, no. 7: 275.

Letter
Published: 23 May 2018 in Remote Sensing
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Burnt forest recovery is normally monitored with a time-series analysis of satellite data because of its proficiency for large observation areas. Traditional methods, such as linear correlation plotting, have been proven to be effective, as forest recovery naturally increases with time. However, these methods are complicated and time consuming when increasing the number of observed parameters. In this work, we present a random forest variable importance (RF-VIMP) scheme called multilevel RF-VIMP to compare and assess the relationship between 36 spectral indices (parameters) of burnt boreal forest recovery in the Great Xing’an Mountain, China. Six Landsat images were acquired in the same month 0, 1, 4, 14, 16, and 20 years after a fire, and 39,380 fixed-location samples were then extracted to calculate the effectiveness of the 36 parameters. Consequently, the proposed method was applied to find correlations between the forest recovery indices. The experiment showed that the proposed method is suitable for explaining the efficacy of those spectral indices in terms of discrimination and trend analysis, and for showing the satellite data and forest succession dynamics when applied in a time series. The results suggest that the tasseled cap transformation wetness, brightness, and the shortwave infrared bands (both 1 and 2) perform better than other indices for both classification and monitoring.

ACS Style

Sornkitja Boonprong; Chunxiang Cao; Wei Chen; Shanning Bao. Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring—Multilevel RF-VIMP. Remote Sensing 2018, 10, 807 .

AMA Style

Sornkitja Boonprong, Chunxiang Cao, Wei Chen, Shanning Bao. Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring—Multilevel RF-VIMP. Remote Sensing. 2018; 10 (6):807.

Chicago/Turabian Style

Sornkitja Boonprong; Chunxiang Cao; Wei Chen; Shanning Bao. 2018. "Random Forest Variable Importance Spectral Indices Scheme for Burnt Forest Recovery Monitoring—Multilevel RF-VIMP." Remote Sensing 10, no. 6: 807.