This book examines the rural transition and transportation in China. It covers demographic change, settlement transformation, and infrastructure development in rural areas, as well as changes in transportation expenditures, travel behavior, and accessibility of basic public services among rural Chinese residents. The book contributes to the new body of knowledge from an international perspective in three ways. First, it explores the changes in travel behavior of rural residents and related factors. Second, it provides new evidence on the relationship between rural social transformation and transportation infrastructure development. Third, it discusses the impact of transportation investments on rural poverty reduction. The book enhances our understanding of the changes in travel behavior of rural residents. In particular, the results of the multi-source big data analysis will provide new evidence on the link between social transformation and changes in travel behavior in rural China. It also helps planners and politicians to develop policies aimed at improving quality of life, rural poverty, and the efficiency of transportation investments in rural areas. The book is of great interest to scholars interested in rural transformation and development, sustainable transport, regional planning, and environmental policy and is relevant to China and other developing countries.
This book examines the rural transition and transportation in China. It covers demographic change, settlement transformation, and infrastructure development in rural areas, as well as changes in transportation expenditures, travel behavior, and accessibility of basic public services among rural Chinese residents. The book contributes to the new body of knowledge from an international perspective in three ways. First, it explores the changes in travel behavior of rural residents and related factors. Second, it provides new evidence on the relationship between rural social transformation and transportation infrastructure development. Third, it discusses the impact of transportation investments on rural poverty reduction. The book enhances our understanding of the changes in travel behavior of rural residents. In particular, the results of the multi-source big data analysis will provide new evidence on the link between social transformation and changes in travel behavior in rural China. It also helps planners and politicians to develop policies aimed at improving quality of life, rural poverty, and the efficiency of transportation investments in rural areas. The book is of great interest to scholars interested in rural transformation and development, sustainable transport, regional planning, and environmental policy and is relevant to China and other developing countries.
Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. On the other hand, while remote sensing is able to provide spatially distributed measurements, the spatial resolution of multispectral satellite images is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. This book examines the different UAV-based approaches of ET estimation. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are discussed. It also covers the challenges and opportunities for UAVs in ET estimation, with the final chapters devoted to new ET estimation methods and their potential applications for future research.
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