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Satellite-derived aerosol optical depth (AOD) is an important parameter for studies related to atmospheric environment, climate change, and biogeochemical cycle. Unfortunately, the relatively high data missing ratio of satellite-derived AOD limits the atmosphere-related research and applications to a certain extent. Accordingly, numerous AOD fusion algorithms have been proposed in recent years. However, most of these algorithms focused on merging AOD products from multiple passive sensors, which cannot complementarily recover the AOD missing values due to cloud obscuration and the misidentification between optically thin cloud and aerosols. In order to address these issues, a spatiotemporal AOD fusion framework combining active and passive remote sensing based on Bayesian maximum entropy methodology (AP-BME) is developed to provide satellite-derived AOD data sets with high spatial coverage and good accuracy in large scale. The results demonstrate that AP-BME fusion significantly improves the spatial coverage of AOD, from an averaged spatial completeness of 27.9%-92.8% in the study areas, in which the spatial coverage improves from 91.1% to 92.8% when introducing Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) AOD data sets into the fusion process. Meanwhile, the accuracy of recovered AOD nearly maintains that of the original satellite AOD products, based on evaluation against ground-based Aerosol Robotic Network (AERONET) AOD. Moreover, the efficacy of the active sensor in AOD fusion is discussed through overall accuracy comparison and two case analyses, which shows that the provision of key aerosol information by the active sensor on haze condition or under thin cloud is important for not only restoring the real haze situations but also avoiding AOD overestimation caused by cloud optical depth (COD) contamination in AOD fusion results.
Xinghui Xia; Bin Zhao; Tianhao Zhang; Luyao Wang; Yu Gu; Kuo-Nan Liou; Feiyue Mao; Boming Liu; Yanchen Bo; Yusi Huang; Jiadan Dong; Wei Gong; Zhongmin Zhu. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -13.
AMA StyleXinghui Xia, Bin Zhao, Tianhao Zhang, Luyao Wang, Yu Gu, Kuo-Nan Liou, Feiyue Mao, Boming Liu, Yanchen Bo, Yusi Huang, Jiadan Dong, Wei Gong, Zhongmin Zhu. Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-13.
Chicago/Turabian StyleXinghui Xia; Bin Zhao; Tianhao Zhang; Luyao Wang; Yu Gu; Kuo-Nan Liou; Feiyue Mao; Boming Liu; Yanchen Bo; Yusi Huang; Jiadan Dong; Wei Gong; Zhongmin Zhu. 2021. "Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-13.
High-level satellite remote sensing products of Earth surface play an irreplaceable role in global climate change, hydrological cycle modeling and water resources management, environment monitoring and assessment. Earth surface high-level remote sensing products released by NASA, ESA and other agencies are routinely derived from any single remote sensor. Due to the cloud contamination and limitations of retrieval algorithms, the remote sensing products derived from single remote senor are suspected to the incompleteness, low accuracy and less consistency in space and time. Some land surface remote sensing products, such as soil moisture products derived from passive microwave remote sensing data have too coarse spatial resolution to be applied at local scale. Fusion and downscaling is an effective way of improving the quality of satellite remote sensing products.
We developed a Bayesian spatio-temporal geostatistics-based framework for multiple remote sensing products fusion and downscaling. Compared to the existing methods, the presented method has 2 major advantages. The first is that the method was developed in the Bayesian paradigm, so the uncertainties of the multiple remote sensing products being fused or downscaled could be quantified and explicitly expressed in the fusion and downscaling algorithms. The second advantage is that the spatio-temporal autocorrelation is exploited in the fusion approach so that more complete products could be produced by geostatistical estimation.
This method has been applied to the fusion of multiple satellite AOD products, multiple satellite SST products, multiple satellite LST products and downscaling of 25 km spatial resolution soil moisture products. The results were evaluated in both spatio-temporal completeness and accuracy.
Yanchen Bo. Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling. 2020, 1 .
AMA StyleYanchen Bo. Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling. . 2020; ():1.
Chicago/Turabian StyleYanchen Bo. 2020. "Bayesian Spatio-temporal Geostatistics-based Method for Multiple Satellite Products Fusion and Downscaling." , no. : 1.
Scientific research of land surface dynamics in heterogeneous landscapes often require remote sensing data with high resolutions in both space and time. However, single sensor could not provide such data at both high resolutions. In addition, because of cloud pollution, images are often incomplete. Spatiotemporal data fusion methods is a feasible solution for the aforementioned data problem. However, for existing data fusion methods, it is difficult to address the problem constructed regular and cloud-free dense time-series images with high spatial resolution. To address these limitations of current spatiotemporal data fusion methods, in this paper, we presented a novel data fusion method for fusing multi-source satellite data to generate s a high-resolution, regular and cloud-free time series of satellite images.
We incorporates geostatistical theory into the fusion method, and takes the pixel value as a random variable which is composed of trend and a zero-mean second-order stationary residual. To fuse satellite images, we use the coarse-resolution image with high frequency observation to capture the trend in time, and use Kriging interpolation to obtain the residual in fine-resolution scale to provide the informative spatial information. In this paper, in order to avoid the smoothing effect caused by spatial interpolation, Kriging interpolation is performed only in time dimension. For certain region, the temporal correlation between pixels is fixed after the data reach stationary. So for getting the weight in temporal Kriging interpolation, we can use the residuals obtained from coarse-resolution images to construct the temporal covariance model. The predicted fine-resolution image can be obtained by returning the trend value of pixel to their own residual until the each pixel value was obtained. The advantage of the algorithm is to accurately predict fine-resolution images in heterogeneous areas by integrating all available information in the time-series images with fine spatial resolution.
We tested our method to fuse NDVI of MODIS and Landsat at Bahia State where has heterogeneous landscape, and generated 8-day time series of NDVI for the whole year of 2016 at 30m resolution. By cross-validation, the average R2 and RMSE between NDVI from fused images and from observed images can reach 95% and 0.0411, respectively. In addition, experiments demonstrated that our method also can capture correct texture patterns. These promising results demonstrated this novel method can provide effective means to construct regular and cloud-free time series with high spatiotemporal resolution. Theoretically, the method can predict the fine-resolution data required on any given day. Such a capability is helpful for monitoring near-real-time land surface and ecological dynamics at the high-resolution scales most relevant to human activities.
Aojie Shen; Yanchen Bo; Duoduo Hu. A Spatiotemporal data Fusion method for generating a high-resolution, regular and cloud-free time series of satellite images. 2020, 1 .
AMA StyleAojie Shen, Yanchen Bo, Duoduo Hu. A Spatiotemporal data Fusion method for generating a high-resolution, regular and cloud-free time series of satellite images. . 2020; ():1.
Chicago/Turabian StyleAojie Shen; Yanchen Bo; Duoduo Hu. 2020. "A Spatiotemporal data Fusion method for generating a high-resolution, regular and cloud-free time series of satellite images." , no. : 1.
Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.
Xiaolong Liu; Zhengtao Shi; Guangcai Huang; Yanchen Bo; Guangjie Chen. Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events? Remote Sensing 2020, 12, 612 .
AMA StyleXiaolong Liu, Zhengtao Shi, Guangcai Huang, Yanchen Bo, Guangjie Chen. Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events? Remote Sensing. 2020; 12 (4):612.
Chicago/Turabian StyleXiaolong Liu; Zhengtao Shi; Guangcai Huang; Yanchen Bo; Guangjie Chen. 2020. "Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?" Remote Sensing 12, no. 4: 612.
Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.
Xiaolong Liu; Zhengtao Shi; Guangcai Huang; Yanchen Bo; Guangjie Chen. Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events? 2019, 1 .
AMA StyleXiaolong Liu, Zhengtao Shi, Guangcai Huang, Yanchen Bo, Guangjie Chen. Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events? . 2019; ():1.
Chicago/Turabian StyleXiaolong Liu; Zhengtao Shi; Guangcai Huang; Yanchen Bo; Guangjie Chen. 2019. "Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?" , no. : 1.
Wenzhi Zhao; Lichao Mou; Jiage Chen; Yanchen Bo; William J. Emery. Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 2720 -2731.
AMA StyleWenzhi Zhao, Lichao Mou, Jiage Chen, Yanchen Bo, William J. Emery. Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (4):2720-2731.
Chicago/Turabian StyleWenzhi Zhao; Lichao Mou; Jiage Chen; Yanchen Bo; William J. Emery. 2019. "Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4: 2720-2731.
Given the limitations of current approaches for disease relative risk mapping, it is necessary to develop a comprehensive mapping method not only to simultaneously downscale various epidemiologic indicators, but also to be suitable for different disease outcomes. We proposed a three-step progressive statistical method, named disease relative risk downscaling (DRRD) model, to localize different spatial epidemiologic relative risk indicators for disease mapping, and applied it to the real world hand, foot, and mouth disease (HFMD) occurrence data over Mainland China. First, to generate a spatially complete crude risk map for disease binary variable, we employed ordinary and spatial logistic regression models under Bayesian hierarchical modeling framework to estimate county-level HFMD occurrence probabilities. Cross-validation showed that spatial logistic regression (average prediction accuracy: 80.68%) outperformed ordinary logistic regression (69.75%), indicating the effectiveness of incorporating spatial autocorrelation effect in modeling. Second, for the sake of designing a suitable spatial case–control study, we took spatial stratified heterogeneity impact expressed as Chinese seven geographical divisions into consideration. Third, for generating different types of disease relative risk maps, we proposed local-scale formulas for calculating three spatial epidemiologic indicators, i.e., spatial odds ratio, spatial risk ratio, and spatial attributable risk. The immediate achievement of this study is constructing a series of national disease relative risk maps for China’s county-level HFMD interventions. The new DRRD model provides a more convenient and easily extended way for assessing local-scale relative risks in spatial and environmental epidemiology, as well as broader risk assessment sciences.
Chao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Jiangang Guo; Huibin Yang. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment 2019, 33, 1815 -1833.
AMA StyleChao Song, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, Jiangang Guo, Huibin Yang. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment. 2019; 33 (10):1815-1833.
Chicago/Turabian StyleChao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Jiangang Guo; Huibin Yang. 2019. "Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China." Stochastic Environmental Research and Risk Assessment 33, no. 10: 1815-1833.
Hyperspectral image classification is a challenging task when a limited number of training samples are available. It is also known that the classification performance highly depends on the quality of the labeled samples. In this work, a cluster-based conditional generative adversarial net (CCGAN) is proposed as an effective solution to increase the size and quality of the training data set. The proposed method is able to automatically select the most representative initial samples with a subtractive clustering-based strategy, which keeps the diversity for sample generation. Moreover, compared to the traditional semisupervised classification frameworks, the CCGAN is able to generate realistic spectral profiles by considering the class-specific labels. Experiments on well-known Pavia University data set demonstrate that the proposed CCGAN can significantly boost the classification accuracy, even using a small number of initial labeled samples.
Wenzhi Zhao; Xuehong Chen; Yanchen Bo; Jiage Chen. Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net. IEEE Geoscience and Remote Sensing Letters 2019, 17, 539 -543.
AMA StyleWenzhi Zhao, Xuehong Chen, Yanchen Bo, Jiage Chen. Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net. IEEE Geoscience and Remote Sensing Letters. 2019; 17 (3):539-543.
Chicago/Turabian StyleWenzhi Zhao; Xuehong Chen; Yanchen Bo; Jiage Chen. 2019. "Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net." IEEE Geoscience and Remote Sensing Letters 17, no. 3: 539-543.
Urban scenes refer to city blocks which are basic units of megacities, they play an important role in citizens’ welfare and city management. Remote sensing imagery with largescale coverage and accurate target descriptions, has been regarded as an ideal solution for monitoring the urban environment. However, due to the heterogeneity of remote sensing images, it is difficult to access their geographical content at the object level, let alone understanding urban scenes at the block level. Recently, deep learning-based strategies have been applied to interpret urban scenes with remarkable accuracies. However, the deep neural networks require a substantial number of training samples which are hard to satisfy, especially for high-resolution images. Meanwhile, the crowed-sourced Open Street Map (OSM) data provides rich annotation information about the urban targets but may encounter the problem of insufficient sampling (limited by the places where people can go). As a result, the combination of OSM and remote sensing images for efficient urban scene recognition is urgently needed. In this paper, we present a novel strategy to transfer existing OSM data to high-resolution images for semantic element determination and urban scene understanding. To be specific, the object-based convolutional neural network (OCNN) can be utilized for geographical object detection by feeding it rich semantic elements derived from OSM data. Then, geographical objects are further delineated into their functional labels by integrating points of interest (POIs), which contain rich semantic terms, such as commercial or educational labels. Lastly, the categories of urban scenes are easily acquired from the semantic objects inside. Experimental results indicate that the proposed method has an ability to classify complex urban scenes. The classification accuracies of the Beijing dataset are as high as 91% at the object-level and 88% at the scene level. Additionally, we are probably the first to investigate the object level semantic mapping by incorporating high-resolution images and OSM data of urban areas. Consequently, the method presented is effective in delineating urban scenes that could further boost urban environment monitoring and planning with high-resolution images.
Wenzhi Zhao; Yanchen Bo; Jiage Chen; Dirk Tiede; Thomas Blaschke; William J. Emery. Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM). ISPRS Journal of Photogrammetry and Remote Sensing 2019, 151, 237 -250.
AMA StyleWenzhi Zhao, Yanchen Bo, Jiage Chen, Dirk Tiede, Thomas Blaschke, William J. Emery. Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM). ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 151 ():237-250.
Chicago/Turabian StyleWenzhi Zhao; Yanchen Bo; Jiage Chen; Dirk Tiede; Thomas Blaschke; William J. Emery. 2019. "Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM)." ISPRS Journal of Photogrammetry and Remote Sensing 151, no. : 237-250.
Land surface albedo is a key parameter in regulating surface radiation budgets. The gridded remote sensing albedo product often represents information concerning an area larger than the nominal spatial resolution because of the large viewing angles of the observations. It is essential to quantify the spatial representativeness of remote sensing products to better guide the sampling strategy in field experiments and match products from different sources. This study quantifies the spatial representativeness of the MODerate Resolution Image Spectroradiometer (MODIS) (collection V006) 500 m daily albedo product (MCD43A3) using the high-resolution product as intermediate data for different land cover types. A total of 1820 paired high-resolution Landsat Thematic Mapper (TM) and coarse-resolution (MODIS) albedo data from five land cover types were used. The TM albedo data was used as the spatial-complete high resolution data to evaluate the spatial representativeness of the MODIS albedo product. Semivarioagrams were estimated from 30 m Landsat data at different spatial scales. Surface heterogeneity was evaluated with sill value and relative coefficient of variation. The 30 m Landsat albedo data was aggregated to 450 m–1800 m using two different methods and compared with MODIS albedo product. The spatial representativeness of MODIS albedo product was determined according to the surface heterogeneity and the consistency of MODIS data and the aggregated TM value. Results indicated that for evergreen broadleaf forests, deciduous broadleaf forests, open shrub lands, woody savannas and grasslands, the MODIS 500 m daily albedo product represents a spatial scale of approximately 630 m. For mixed forests and croplands, the representative spatial scale was approximately 690 m. The difference obtained was primarily because of the complexity of the landscape structure. For mixed forests and croplands, the structure of the landscape was relatively complex due to the presence of different forest and plant types in the pixel area, whereas the other landscape structures were considerably simpler.
Hongmin Zhou; Shunlin Liang; Tao He; Jindi Wang; Yanchen Bo; Dongdong Wang. Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites. Remote Sensing 2019, 11, 547 .
AMA StyleHongmin Zhou, Shunlin Liang, Tao He, Jindi Wang, Yanchen Bo, Dongdong Wang. Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites. Remote Sensing. 2019; 11 (5):547.
Chicago/Turabian StyleHongmin Zhou; Shunlin Liang; Tao He; Jindi Wang; Yanchen Bo; Dongdong Wang. 2019. "Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites." Remote Sensing 11, no. 5: 547.
As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R2 (R2 greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.
Shupeng Gao; Xiaolong Liu; Yanchen Bo; Zhengtao Shi; Hongmin Zhou. Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna. Remote Sensing 2019, 11, 496 .
AMA StyleShupeng Gao, Xiaolong Liu, Yanchen Bo, Zhengtao Shi, Hongmin Zhou. Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna. Remote Sensing. 2019; 11 (5):496.
Chicago/Turabian StyleShupeng Gao; Xiaolong Liu; Yanchen Bo; Zhengtao Shi; Hongmin Zhou. 2019. "Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna." Remote Sensing 11, no. 5: 496.
Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate effective feature representations, in order to recognize objects with complex image patterns. However, the rich spatial information still remains unexploited, since most of the deep learning algorithms only focus on small image patches that overlook the contextual information at larger scales. To utilize these contextual information and improve the classification performance for high-resolution imagery, we propose a graph-based model in order to capture the contextual information over semantic segments of the image. First, we explore semantic segments which build on the top of deep features and obtain the initial classification result. Then, we further improve the initial classification results with a higher-order co-occurrence model by extending the existing conditional random field (HCO-CRF) algorithm. Compared to the pixel- and object-based CNN methods, the proposed model achieved better performance in terms of classification accuracy.
Wenzhi Zhao; William J. Emery; Yanchen Bo; Jiage Chen. Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model. Remote Sensing 2018, 10, 1713 .
AMA StyleWenzhi Zhao, William J. Emery, Yanchen Bo, Jiage Chen. Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model. Remote Sensing. 2018; 10 (11):1713.
Chicago/Turabian StyleWenzhi Zhao; William J. Emery; Yanchen Bo; Jiage Chen. 2018. "Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model." Remote Sensing 10, no. 11: 1713.
Pediatric hand, foot, and mouth disease (HFMD) has generally been found to be associated with climate. However, knowledge about how this association varies spatiotemporally is very limited, especially when considering the influence of local socioeconomic conditions. This study aims to identify multi-sourced HFMD environmental factors and further quantify the spatiotemporal nonstationary effects of various climate factors on HFMD occurrence. We propose an innovative method, named spatiotemporally varying coefficients (STVC) model, under the Bayesian hierarchical modeling framework, for exploring both spatial and temporal nonstationary effects in climate covariates, after controlling for socioeconomic effects. We use data of monthly county-level HFMD occurrence and data of related climate and socioeconomic variables in Sichuan, China from 2009 to 2011 for our experiments. Cross-validation experiments showed that the STVC model achieved the best average prediction accuracy (81.98%), compared with ordinary (68.27%), temporal (72.34%), spatial (75.99%) and spatiotemporal (77.60%) ecological models. The STVC model also outperformed these models in the Bayesian model evaluation. In this study, the STVC model was able to spatialize the risk indicator odds ratio (OR) into local ORs to represent spatial and temporal varying disease-climate relationships. We detected local temporal nonlinear seasonal trends and spatial hot spots for both disease occurrence and disease-climate associations over 36 months in Sichuan, China. Among the six representative climate variables, temperature (OR = 2.59), relative humidity (OR = 1.35), and wind speed (OR = 0.65) were not only overall related to the increase of HFMD occurrence, but also demonstrated spatiotemporal variations in their local associations with HFMD. Our findings show that county-level HFMD interventions may need to consider varying local-scale spatial and temporal disease-climate relationships. Our proposed Bayesian STVC model can capture spatiotemporal nonstationary exposure-response relationships for detailed exposure assessments and advanced risk mapping, and offers new insights to broader environmental science and spatial statistics.
Chao Song; Xun Shi; Yanchen Bo; Jinfeng Wang; Yong Wang; Dacang Huang. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China. Science of The Total Environment 2018, 648, 550 -560.
AMA StyleChao Song, Xun Shi, Yanchen Bo, Jinfeng Wang, Yong Wang, Dacang Huang. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China. Science of The Total Environment. 2018; 648 ():550-560.
Chicago/Turabian StyleChao Song; Xun Shi; Yanchen Bo; Jinfeng Wang; Yong Wang; Dacang Huang. 2018. "Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China." Science of The Total Environment 648, no. : 550-560.
Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.
Chao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Huibin Yang. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. International Journal of Environmental Research and Public Health 2018, 15, 1476 .
AMA StyleChao Song, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, Huibin Yang. Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models. International Journal of Environmental Research and Public Health. 2018; 15 (7):1476.
Chicago/Turabian StyleChao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Huibin Yang. 2018. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models." International Journal of Environmental Research and Public Health 15, no. 7: 1476.
Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.
Xiaokang Kou; Lingmei Jiang; Yanchen Bo; Shuang Yan; Linna Chai. Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sensing 2016, 8, 105 .
AMA StyleXiaokang Kou, Lingmei Jiang, Yanchen Bo, Shuang Yan, Linna Chai. Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sensing. 2016; 8 (2):105.
Chicago/Turabian StyleXiaokang Kou; Lingmei Jiang; Yanchen Bo; Shuang Yan; Linna Chai. 2016. "Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method." Remote Sensing 8, no. 2: 105.
The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area.
Xiaolong Liu; Yanchen Bo; Jian Zhang; Yaqian He. Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data. Remote Sensing 2015, 7, 15244 -15268.
AMA StyleXiaolong Liu, Yanchen Bo, Jian Zhang, Yaqian He. Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data. Remote Sensing. 2015; 7 (11):15244-15268.
Chicago/Turabian StyleXiaolong Liu; Yanchen Bo; Jian Zhang; Yaqian He. 2015. "Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data." Remote Sensing 7, no. 11: 15244-15268.
The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.
Xin Tian; Zengyuan Li; Erxue Chen; Qinhuo Liu; Guangjian Yan; Jindi Wang; Zheng Niu; Shaojie Zhao; Xin Li; Yong Pang; Zhongbo Su; Christiaan van der Tol; Qingwang Liu; Chaoyang Wu; Qing Xiao; Le Yang; Xihan Mu; Yanchen Bo; Yonghua Qu; Hongmin Zhou; Shuai Gao; Linna Chai; Huaguo Huang; Wenjie Fan; Shihua Li; Junhua Bai; Lingmei Jiang; Ji Zhou. The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE). PLoS ONE 2015, 10, e0137545 .
AMA StyleXin Tian, Zengyuan Li, Erxue Chen, Qinhuo Liu, Guangjian Yan, Jindi Wang, Zheng Niu, Shaojie Zhao, Xin Li, Yong Pang, Zhongbo Su, Christiaan van der Tol, Qingwang Liu, Chaoyang Wu, Qing Xiao, Le Yang, Xihan Mu, Yanchen Bo, Yonghua Qu, Hongmin Zhou, Shuai Gao, Linna Chai, Huaguo Huang, Wenjie Fan, Shihua Li, Junhua Bai, Lingmei Jiang, Ji Zhou. The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE). PLoS ONE. 2015; 10 (9):e0137545.
Chicago/Turabian StyleXin Tian; Zengyuan Li; Erxue Chen; Qinhuo Liu; Guangjian Yan; Jindi Wang; Zheng Niu; Shaojie Zhao; Xin Li; Yong Pang; Zhongbo Su; Christiaan van der Tol; Qingwang Liu; Chaoyang Wu; Qing Xiao; Le Yang; Xihan Mu; Yanchen Bo; Yonghua Qu; Hongmin Zhou; Shuai Gao; Linna Chai; Huaguo Huang; Wenjie Fan; Shihua Li; Junhua Bai; Lingmei Jiang; Ji Zhou. 2015. "The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)." PLoS ONE 10, no. 9: e0137545.
Sea surface temperature (SST) plays a vital role in the Earth's atmosphere and climate systems. Complete and accurate SST observations are in great demand for forecasting tropical cyclones and projecting climate change. Satellite remote sensing has been used to retrieve SST globally, but missing values and biased observations impose difficulties on practical applications of these satellite-derived SST data. Conventional spatial statistics methods such as kriging have been widely used to fill the gaps. However, when such conventional methods are used to analyze a massive satellite data set of size n, the inversion of the n × n covariance matrix may require O(n 3 ) computations, which make the computation very intensive or even infeasible. The fixed rank kriging (FRK) performs dimension reduction through multiresolution wavelet analysis so that it can dramatically reduce the computation cost of various kriging methods. However, the FRK cannot directly be used for incomplete data over spatially irregular regions such as SSTs, and the potential bias in the satellite data is not addressed. In this paper, we construct a data-driven bias-correction model for the correction of the bias in satellite SSTs and develop a robust FRK (R-FRK) method so that the dimension reduction can be used to the satellite data in irregular regions with missing data. We implement the bias-correction model and the R-FRK to the level-3 mapped night Moderate Resolution Imaging Spectroradiometer SSTs. The accuracy of the resulting predictions is assessed using the colocated drifting buoy SST observations, in terms of mean bias (bias), root-mean-squared error, and R squared (R 2 ). The spatial completeness is assessed by the availability of ocean pixels. The assessment results show that the spatially.
Yuxin Zhu; Emily Kang; Yanchen Bo; Qingxin Tang; Jiehai Cheng; Yaqian He. A Robust Fixed Rank Kriging Method for Improving the Spatial Completeness and Accuracy of Satellite SST Products. IEEE Transactions on Geoscience and Remote Sensing 2015, 53, 5021 -5035.
AMA StyleYuxin Zhu, Emily Kang, Yanchen Bo, Qingxin Tang, Jiehai Cheng, Yaqian He. A Robust Fixed Rank Kriging Method for Improving the Spatial Completeness and Accuracy of Satellite SST Products. IEEE Transactions on Geoscience and Remote Sensing. 2015; 53 (9):5021-5035.
Chicago/Turabian StyleYuxin Zhu; Emily Kang; Yanchen Bo; Qingxin Tang; Jiehai Cheng; Yaqian He. 2015. "A Robust Fixed Rank Kriging Method for Improving the Spatial Completeness and Accuracy of Satellite SST Products." IEEE Transactions on Geoscience and Remote Sensing 53, no. 9: 5021-5035.
Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area.
Xiaolong Liu; Yanchen Bo. Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data. Remote Sensing 2015, 7, 922 -950.
AMA StyleXiaolong Liu, Yanchen Bo. Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data. Remote Sensing. 2015; 7 (1):922-950.
Chicago/Turabian StyleXiaolong Liu; Yanchen Bo. 2015. "Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data." Remote Sensing 7, no. 1: 922-950.
Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.
Yaqian He; Yanchen Bo; Rogier De Jong; Aihua Li; Yuxin Zhu; Jiehai Cheng. Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China. International Journal of Remote Sensing 2015, 36, 300 -317.
AMA StyleYaqian He, Yanchen Bo, Rogier De Jong, Aihua Li, Yuxin Zhu, Jiehai Cheng. Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China. International Journal of Remote Sensing. 2015; 36 (1):300-317.
Chicago/Turabian StyleYaqian He; Yanchen Bo; Rogier De Jong; Aihua Li; Yuxin Zhu; Jiehai Cheng. 2015. "Comparison of vegetation phenological metrics extracted from GIMMS NDVIg and MERIS MTCI data sets over China." International Journal of Remote Sensing 36, no. 1: 300-317.