关键词:
international rapeseed trade;winter rape;spatio-temporal patterns;MYR;time-series MODIS data
摘要:
Rapeseed is an important oil product in China. China's current soybean trade issues with major soybean producing countries have caused a large decline in soybean import since 2017. This may bring the increasing needs of rapeseed import, which would have an impact on domestic production. However, our knowledge on the effects of international rapeseed trade on domestic production remains unknown. It is thus important to understand the pattern of rapeseed in China under this scenario, as it may provide necessary information for all relevant stakeholders. With this goal, this study aims to investigate the spatial and temporal patterns of winter rape in China's major winter rape production region, the Middle Reaches of the Yangtze River Valley (MYR), during 2003-2015 using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data. A decision tree according to the difference in enhanced vegetation index (EVI) profiles of land-cover types was built to extract winter rape. The results show that there is an essential decrease in both the number and density of winter rape patches under the opening global rapeseed market. There are significant hotspots of winter rape gain and loss, within which the loss dominated the trend. The significant cost advantage of rapeseed in the international market may largely reduce the domestic cultivation in China through telecoupling effects. Understanding the spatio-temporal dynamics of winter rape on the MYR has significant economic and policy implications and can provide great supports for the agricultural production, policy-making, and oil products trade in the international market.
摘要:
Global food demand will increase over the next few decades, and sustainable agricultural intensification on current cropland may be a preferred option to meet this demand. Mapping cropping intensity with remote sensing data is of great importance for agricultural production, food security, and agricultural sustainability in the context of global climate change. However, there are some challenges in large-scale cropping intensity mapping. First, existing indicators are too coarse, and fine indicators for measuring cropping intensity are lacking. Second, the regional, intra-class variations detected in time-series remote sensing data across vast areas represent environment-related clusters for each cropping intensity level. However, few existing studies have taken into account the intra-class variations caused by varied crop patterns, crop phenology, and geographical differentiation. In this research, we first presented a new definition, a normalized cropping intensity index (CII), to quantify cropping intensity precisely. We then proposed a Bayesian network model fusing prior knowledge (BNPK) to address the issue of intra-class variations when mapping CII over large areas. This method can fuse regional differentiation factors as prior knowledge into the model to reduce the uncertainty. Experiments on five sample areas covering the main grain-producing areas of mainland China proved the effectiveness of the model. Our research proposes the framework of obtain a CII map with both a finer spatial resolution and a fine temporal resolution at a national scale.
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
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.
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.