Jinxi Yao

The Xianning-Jiujiang expressway (Xian-Jiu expressway) is a key project for improving the highway network of Hubei and Jiangxi Provinces. The significant environmental characteristics of the region along the expressway make it crucial to study the ecological quality changes within roadside. Utilizing a remote sensing cloud computing platform and coordinating Landsat series remote sensing data, high-quality images were synthesized using a dynamic threshold method. Annual data for greenness, wetness, heat, and dryness were collected between 2019 and 2023. The Remote Sensing Ecological Index (RSEI) was constructed using principal component analysis to evaluate ecological environmental quality. The results revealed the following: (1) The image quality can be effectively improved through multi-source data coordination and automatic annual image synthesis. (2) Spatially, the overall ecological environmental quality within the 5 km roadside of the Xian-Jiu expressway is relatively high. However, the ecological quality is poorer in areas with dense urban distribution and within the primary and secondary buffer zones along the expressway. (3) Temporally, the overall ecological environmental quality has not changed significantly, showing a trend where the rate of ecological decline in the early stage is lower than that in the later stage. Due to the influence of segmented engineering construction, the ecological quality changes within the roadside exhibit a segmented pattern, with significant ecological changes observed along the expressway and at both endpoints. (4) With the continued advancement of construction, the ecological environmental quality within the primary and secondary roadside of the Xian-Jiu expressway has significantly decreased. The research results can provide a scientific basis and reference for the construction and subsequent maintenance of the Xian-Jiu expressway.

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning.