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This paper analyzes the English teaching model’s structure, function, and characteristics and constructs the basic structure of the classroom innovation teaching model. Then, the English teaching model is constructed, and the English teaching evaluation index system is established based on the algorithms of structural equation model, multigroup structural equation and multigroup parameter estimation. Finally, the quantitative analysis of index weights and satisfaction was conducted with the example of college A. The results showed that the weights of the primary indicators were 0.2873, 0.1294, 0.2546, and 0.3287 for personalized needs, perceived quality, perceived value, and satisfaction, respectively, which indicated that the multigroup structural model could help universities to realize the innovation of English teaching mode and to adopt diversified teaching methods to meet student’s diverse needs in the teaching process. Diversified the needs of students and improved their satisfaction.

The main problems of forest parameter extraction and forest stand volume estimation using unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology are the lack of precision in individual tree segmentation and the inability to directly obtain the diameter at breast height (DBH) parameter. To address such limitations, the study proposed an improved individual tree segmentation method combined with a DBH prediction model to obtain the tree height (H) and DBH for calculating the volume of trees, thus realizing the accurate estimation of forest stand volume from individual tree segmentation aspect. The method involves the following key steps: (1) The local maximum method with variable window combined with the Gaussian mixture model were used to detect the treetop position using the canopy height model for removing pits. (2) The measured tree DBH and H parameters of the sample trees were used to construct an optimal DBH-H prediction model. (3) The duality standing tree volume model was used to calculate the forest stand volume at the individual tree scale. The results showed that: (1) Individual tree segmentation based on the improved Gaussian mixture model with optimal accuracy, detection rate r, accuracy rate p, and composite score F were 89.10%, 95.21%, and 0.921, respectively. The coefficient of determination R2 of the accuracy of the extracted tree height parameter was 0.88, and the root mean square error RMSE was 0.84 m. (2) The Weibull model had the optimal model fit for DBH-H with predicted DBH parameter accuracy, the R2 and RMSE were 0.84 and 2.28 cm, respectively. (3) Using the correctly detected trees from the individual tree segmentation results combined with the duality standing tree volume model estimated the forest stand volume with an accuracy AE of 90.86%. In conclusion, using UAV-LiDAR technology, based on the individual tree segmentation method and the DBH-H model, it is possible to realize the estimation of forest stand volume at the individual tree scale, which helps to improve the estimation accuracy. [ABSTRACT FROM AUTHOR]

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands. [ABSTRACT FROM AUTHOR]