Lodging stress seriously affects the yield, quality, and mechanical harvesting of maize, and is a major natural disaster causing maize yield reduction. The aim of this study was to obtain light detection and ranging (LiDAR) data of lodged maize using an unmanned aerial vehicle (UAV) equipped with a RIEGL VUX-1UAV sensor to analyze changes in the vertical structure of maize plants with different degrees of lodging, and thus to use plant height to quantitatively study maize lodging. Based on the UAV-LiDAR data, the height of the maize canopy was retrieved using a canopy height model to determine the height of the lodged maize canopy at different times. The profiles were analyzed to assess changes in maize plant height with different degrees of lodging. The differences in plant height growth of maize with different degrees of lodging were evaluated to determine the plant height recovery ability of maize with different degrees of lodging. Furthermore, the correlation between plant heights measured on the ground and LiDAR-estimated plant heights was used to verify the accuracy of plant height estimation. The results show that UAV-LiDAR data can be used to achieve maize canopy height estimation, with plant height estimation accuracy parameters of R2 = 0.964, RMSE = 0.127, and nRMSE = 7.449%. Thus, it can reflect changes of plant height of lodging maize and the recovery ability of plant height of different lodging types. Plant height can be used to quantitatively evaluate the lodging degree of maize. Studies have shown that the use of UAV-LiDAR data can effectively estimate plant heights and confirm the feasibility of LiDAR data in crop lodging monitoring.

The negative impact of rapid urbanization in developing countries has led to a deterioration of urban and regional air quality. Much attention has been given to the impact of fine particulate pollution on urban public health. However, very little attention has been given to its impact on the regional ecosystem such as the agricultural ecosystem. Thus, we evaluate the direct impact of air pollution on the reduction of wheat photosynthesis by fine particulate matter (PM) pollution in the world's most heavily polluted area, the North China Plain, using remote sensing observations and ground measurements. We found the following to be true: (1) Heavy PM pollution could significantly reduce wheat photosynthesis and cause an expositional relationship between the PM concentration and wheat photosynthesis (R = 0.9824, P < 0.05); (2) Heavy PM pollution makes up 2% for the reduction in wheat photosynthesis at all wheat-plant farmlands in the North China Plain, approximately covering an area of 354,400 km; (3) Increasing heavy PM pollution significantly reduced wheat photosynthesis by 87% in wheat-planted farmland during 1999-2011. We hope the results presented here could draw attention to the effect of PM pollution on the agricultural ecosystem and encourage further studies to evaluate the feedback of atmospheric pollution on the agricultural ecosystem using remote sensing. Abbreviation: Northern China Plain (NCP); normalized difference vegetation index (NDVI); The Moderate Resolution Imaging Spectroradiometer (MODIS); fine particulate matter (PM).

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

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