Search

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios. [ABSTRACT FROM AUTHOR]

Using toponym data, population data, and night-time light data, we visualized the development index of the Yi, Wa, Zhuang, Naxi, Hani, and Dai ethnic groups on ArcGIS as well as the distribution of 25 ethnic minorities in the study area. First, we extracted the toponym data of 25 ethnic minorities in the study area, combined with night-time light data and the population proportion data of each ethnic group, then we obtained the development index of each ethnic group in the study area. We compared the development indexes of the Yi, Wa, Zhuang, Naxi, Hani, and Dai ethnic groups with higher development indexes. The results show that the Yi nationality's development index was the highest, reaching 28.86 (with two decimal places), and the Dai nationality's development index was the lowest (15.22). The areas with the highest minority development index were concentrated in the core area of the minority development, and the size varied with the minority's distance. According to the distribution of ethnic minorities, we found that the Yi ethnic group was distributed in almost the entire study area, while other ethnic minorities had obvious geographical distribution characteristics, and there were multiple ethnic minorities living together. This research is of great significance to the cultural protection of ethnic minorities, the development of ethnic minorities, and the remote sensing mapping of lights at night. [ABSTRACT FROM AUTHOR]