作者机构：
[O'Connell, Jessica; Tao, Jianbin; Cotten, David L.; Mishra, Deepak R.] Univ Georgia, Dept Geog, Athens, GA 30609 USA.;[Tao, Jianbin] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Hubei, Peoples R China.;[Pahari, Roshani; Leclerc, Monique; Zhang, Gengsheng; Nahrawi, Hafsah Binti] Univ Georgia, Atmospher Biogeosci Grp, Griffin, GA 30223 USA.;[Nahrawi, Hafsah Binti] Univ Malaysia Sarawak, Fac Resource Sci & Technol, Kota Samarahan 94300, Malaysia.
通讯机构：
[Mishra, Deepak R.] U;Univ Georgia, Dept Geog, Athens, GA 30609 USA.
关键词：
MODIS GPP Calibration;MOD17A2;Normalized Distribution Moisture Index;Tide Adjusted Wetland Index;flux GPP;salt marsh;tidal wetlands
摘要：
Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite imagery which can be affected by periodic tidal flooding. Carbon dioxide eddy covariance (EC) towers are installed in only a few wetlands worldwide, and the longest eddy-covariance record from Georgia (GA) wetlands contains only two continuous years of observations. The goals of the present study were to evaluate the performance of existing MODIS Gross Primary Production (GPP) products (MOD17A2) against EC derived GPP and develop a tide-robust Normalized Difference Moisture Index (NDMI) based model to predict GPP within a Spartina alterniflora salt marsh on Sapelo Island, GA. These EC tower-based observations represent a basis to associate CO2 fluxes with canopy reflectance and thus provide the means to use satellite-based reflectance data for broader scale investigations. We demonstrate that Light Use Efficiency (LUE)-based MOD17A2 does not accurately reflect tidal wetland GPP compared to a simple empirical vegetation index-based model where tidal influence was accounted for. The NDMI-based GPP model was capable of predicting changes in wetland CO2 fluxes and explained 46% of the variation in flux-estimated GPP within the training data, and a root mean square error of 6.96 g C m−2 in the validation data. Our investigation is the first to create a MODIS-based wetland GPP estimation procedure that demonstrates the importance of filtering tidal observations from satellite surface reflectance data.
关键词：
time-series MODIS data;phenological feature;peak before wintering;winter wheat mapping
摘要：
By employing the unique phenological feature of winter wheat extracted from peak before winter (PBW) and the advantages of moderate resolution imaging spectroradiometer (MODIS) data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak (MP). The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
期刊：
International Journal of Remote Sensing,2016年37(1):1–13 ISSN：0143-1161
作者机构：
[Jianbin Tao; Yu Wang; Yanbing Zhang] School of Urban and Environmental Sciences, Central China Normal University, Wuhan, PR China;[Ning Shu; Qingwu Hu] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, PR China
摘要：
This article proposes a Gaussian-mixture-model GMM-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution VHR. GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines SVMs, characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity. This article proposes a Gaussian-mixture-model GMM-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution VHR. GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines SVMs, characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity.
期刊：
International Journal of Remote Sensing,2016年37(1):1-13 ISSN：0143-1161
通讯作者：
Tao, Jianbin
作者机构：
[Tao, Jianbin] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.;[Tao, Jianbin; Wang, Yu; Zhang, Yanbing] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan, Peoples R China.;[Shu, Ning; Hu, Qingwu] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430072, Peoples R China.
通讯机构：
[Tao, Jianbin] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.
摘要：
This article proposes a Gaussian-mixture-model (GMM)-based method with optimal Gaussian components to address the high intra-class spectral variability in urban land-cover mapping using remote sensing images with very high resolution (VHR). GMMs can simulate and approximate any data distribution provided the optimal Gaussian components can be found. Through improving the model parameters in view of the characteristic of VHR remote sensing images, the parameter space of GMM is optimized significantly, and the model can find the optimal Gaussian components that are suitable for remote sensing images with different resolutions. Experimental results of Wuhan urban area using two images with different resolutions have demonstrated the efficiency and effectiveness of the model. The optimized GMM-based method performs at least comparably or superior to the state-of-the-art classifiers such as support vector machines (SVMs), characterizes man-made land-cover types better than conventional methods, fuses spectral and textural features of VHR image properly, and meanwhile has lower computational complexity.
期刊：
2014 The Third International Conference on Agro-Geoinformatics,2014年:1-7
作者机构：
[Jianbin Tao; Wenbin Wu; Lei Yu] School of Urban and Environmental Sciences, Central China Normal University, Wuhan, P. R., China;[Yong Zhou] Ministry of Agriculture, Key Laboratory of Agricultural Information Technology, Beijing, P. R., China
摘要：
Carbon dioxide (CO 2 ) is the most important anthropogenic greenhouse gas causing global warming. An increasing number of studies have focused on urban areas recently because cities are major anthropogenic sources of CO 2 and also the main habitats of most human beings. However, the complicated nature of urban landscapes and the inhomogeneous distributions of CO 2 sources and sinks lead to methodological difficulties in CO 2 observation. This paper introduces a new approach to estimate CO 2 concentration from satellite imagery using a Bayesian network. An estimation model based on Bayesian network was built to characterize the quantitative relationships between remote-sensing data and CO 2 concentrations. Comparative analysis of the proposed model and multiple regression models was then carried out. The feasibility of estimating carbon dioxide concentrations in urban areas from satellite imagery was analyzed, and the advantages of modeling land-surface parameters using the Bayesian network were addressed.
摘要：
Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas causing global warming. An increasing number of studies have focused on urban areas recently because cities are major anthropogenic sources of CO2 and also the main habitats of most human beings. However, the complicated nature of urban landscapes and the inhomogeneous distributions of CO2 sources and sinks lead to methodological difficulties in CO2 observation. This paper introduces a new approach to estimate CO2 concentration from satellite imagery using a Bayesian network. An estimation model based on Bayesian network was built to characterize the quantitative relationships between remote-sensing data and CO2 concentrations. Comparative analysis of the proposed model and multiple regression models was then carried out. The feasibility of estimating carbon dioxide concentrations in urban areas from satellite imagery was analyzed, and the advantages of modeling land-surface parameters using the Bayesian network were addressed.
