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This study focuses on the urban landscape design of city A, and uses information fusion technology to evaluate. Through the analysis of the measured data by the entropy weighting method and the ensemble optimal weighting method, and compared with the established index system, the results reveal the weights of different landscape elements. Regarding ecological landscape, the CR value was 0.003, in which landscape diversity and green space rate were the leading indicators, and the weights were 0.2976. The weights of landscape dominance and uniformity were relatively medium, both of which were 0.1579. In the evaluation of aesthetic landscape, the CR value was 0.0309, and the highway alignment and urban landscape indicators in the urban landscape were the most important, with a weight of 0.2044, while the weight of the indication information was the lowest, only 0.0266. The CR value of the cultural landscape was 0.0518, of which the weight of scenic connectivity was the highest (0.5559), followed by the number and scale of scenic spots (0.3537), and the weight of historical and cultural elements was the lowest (0.0904). In the past few years, landscape design has accounted for the most significant proportion of landscape construction investment, especially reaching a peak of about 57,000 yuan in 2022. From 2018 to 2022, the cumulative investment in urban landscape design is about 246664 yuan.

This paper determines the influencing factors of architectural acoustic environment comfort assessment from the perspective of green building acoustic environment comfort, and constructs a two-by-two comparison judgment matrix for each questionnaire survey result. Through the consistency test of the judgment matrix, the weights of the factors influencing the comfort of the building sound environment are obtained, and the construction of the assessment model of the comfort of the building sound environment is completed. Based on the architectural acoustic environment comfort assessment model, optimization variables are selected and multi-objective optimization is used to determine the objective function and constraints of architectural acoustic environment comfort. The comfort of the acoustic environment of the campus building is evaluated and analyzed through simulation analysis. The results show that the two measurement points, point 8 and point 15, located on the north side of the library and information building and the center courtyard, respectively, have relatively low continuous sound pressure levels Leq of 51.7 dB and 44.4 dB, respectively, which are relatively favorable for the creation of a comfortable acoustic environment. Improving the building arrangement through the multi-objective optimization method under the acoustic environment assessment model can provide good environmental protection for people’s daily lives and work.

Monitoring winter wheat Soil–Plant Analysis Development (SPAD) values using Unmanned Aerial Vehicles (UAVs) is an effective and non-destructive method. However, predicting SPAD values during the booting stage is less accurate than other growth stages. Existing research on UAV-based SPAD value prediction has mainly focused on low-altitude flights of 10–30 m, neglecting the potential benefits of higher-altitude flights. The study evaluates predictions of winter wheat SPAD values during the booting stage using Vegetation Indices (VIs) from UAV images at five different altitudes (i.e., 20, 40, 60, 80, 100, and 120 m, respectively, using a DJI P4-Multispectral UAV as an example, with a resolution from 1.06 to 6.35 cm/pixel). Additionally, we compare the predictive performance using various predictor variables (VIs, Texture Indices (TIs), Discrete Wavelet Transform (DWT)) individually and in combination. Four machine learning algorithms (Ridge, Random Forest, Support Vector Regression, and Back Propagation Neural Network) are employed. The results demonstrate a comparable prediction performance between using UAV images at 120 m (with a resolution of 6.35 cm/pixel) and using the images at 20 m (with a resolution of 1.06 cm/pixel). This finding significantly improves the efficiency of UAV monitoring since flying UAVs at higher altitudes results in greater coverage, thus reducing the time needed for scouting when using the same heading overlap and side overlap rates. The overall trend in prediction accuracy is as follows: VIs + TIs + DWT > VIs + TIs > VIs + DWT > TIs + DWT > TIs > VIs > DWT. The VIs + TIs + DWT set obtains frequency information (DWT), compensating for the limitations of the VIs + TIs set. This study enhances the effectiveness of using UAVs in agricultural research and practices. [ABSTRACT FROM AUTHOR]