Vision transformers (ViT) have the characteristics of massive training data and complex model, which cannot be directly applied to polarimetric synthetic aperture radar (PolSAR) image classification tasks. Therefore, a mixed convolutional parallel transformer (MCPT) model based on ViT is proposed for fast PolSAR image classification. First of all, a mixed depthwise convolution tokenization is introduced. It replaces the learnable linear projection in the original ViT to obtain patch embeddings. The process of tokenization can reduce computational and parameter complexity and extract features of different receptive fields as input to the encoder. Furthermore, combining the idea of shallow networks with lower latency and easier optimization, a parallel encoder is implemented by pairing the same modules and recombining to form parallel blocks, which can decrease the network depth and computing power requirement. In addition, the original class embedding and position embedding are removed during tokenization, and a global average pooling layer is added after the encoder for category feature extraction. Finally, the experimental results on AIRSAR Flevoland and RADARSAT-2 San Francisco datasets show that the proposed method achieves a significant improvement in training and prediction speed. Meanwhile, the overall accuracy achieved was 97.9% and 96.77%, respectively.

The transformation of resource-exhausted urban land is an urgent problem for sustainable urban development in the world today. Obtaining the urban land use type and analyzing the changes in their land use can lead to better management of the relationship between economic development and resource utilization. In this paper, a residual-intelligent module network was proposed to solve the problems of low classification accuracy and missing objects edge information in traditional computer classification methods. The classification of four Landsat-TM/OLI images from 1993–2020 for Jiaozuo city (the first batch of resource-exhausted cities in China) was realized by this method. The results (overall accuracy was 98.61%, in 2020 images) were better than the comparison models (support vector machine, 2D-convolutional neural network, hybrid convolution networks; overall accuracy was 87.12%, 96.16%, 98.46%, respectively) and effectively reduced the loss of information on the edge of the ground objects. On this basis, six main land use types were constructed by combining field surveys and other methods. The characteristics and driving forces of spatial-temporal change in land use were explored from the aspect of social, economic and policy factors. The results showed that from 1993 to 2020 the cultivated land, forest land, water body and other land types in the study area decreased by 690.97 km2, 57.54 km2, 47.04 km2 and 59.43 km2, respectively. The construction land and bare land increased by 839.38 km2 and 15.57 km2, respectively. The transfer of land use types was mainly from cultivated land to construction land, with a cumulative conversion of 920.95 km2 within 27 years. The driving forces of land use in the study area were analyzed by principal component analysis (PCA) and regression analysis. The spatial-temporal evolution of land use types was affected by policy changes, the level of social development and the adjustment in the economy, industry and agriculture structure. The investment in fixed assets and per capita net income in rural areas were the top two influencing factors and their cumulative contribution rate was 94.62%. The findings of this study can provide scientific reference and theoretical support for land use planning, land reclamation in mining areas, ecological protection and sustainable development in Jiaozuo and other resource-exhausted cities in the world.

Accurate and rapid access to crop distribution information is a significant requirement for the development of modern agriculture. Improving the efficiency of remote sensing monitoring of winter wheat planting area information, a new method of automatically updating training samples (AUTS), is proposed herein. Firstly, based on the Google Earth Engine (GEE) platform, a Sentinel-2 image with a spatial resolution of 10 m was selected to extract the distribution map of winter wheat in the city of Shijiazhuang in 2017. Secondly, combined with the NDVI time series, the weighted correlation coefficients from 2017, 2018, and 2019 were calculated. Then, the 2017 winter wheat distribution map and its most significant relevant areas were used to extract sample points from 2018 and 2019 automatically. Finally, the distribution map of winter wheat in Shijiazhuang in 2018 and 2019 was generated. In addition, to test the applicability of the automatically updating training sample at different scales and regions, the proposed method was applied to Landsat 8 image data with a spatial resolution of 30 m, as well as to Handan and Baoding. The results showed that the calculated winter wheat planting area is comparable with the officially published statistics, based on Sentinel-2, extracting three years of winter wheat, the R2 values for all three years were above 0.95. The R2 values for 2018 and 2019, based on Landsat 8 extractions, were 0.95 and 0.90, respectively. The R2 values extracted from Handan and Baoding in 2018 were 0.94 and 0.86, respectively. These results indicate that the proposed method has high accuracy and can provide technical support and reference for winter wheat area monitoring and yield estimation.

Regional land-use change is the leading cause of ecosystem carbon stock change; it is essential to investigate the response of LUCC to carbon stock to achieve the strategic goal of “double carbon” in a region. This paper proposes a residual network algorithm, the Residual Multi-module Fusion Network (R-MFNet), to address the problems of blurred feature boundary information, low classification accuracy, and high noise, which are often encountered in traditional classification methods. The network algorithm uses an R-ASPP module to expand the receptive field of the feature map to extract sufficient and multi-scale target features; it uses the attention mechanism to assign weights to the multi-scale information of each channel and space. It can fully preserve the remote sensing image features extracted by the convolutional layer through the residual connection. Using this classification network method, the classification of three Landsat-TM/OLI images of Zhengzhou City (the capital of Henan Province) from 2001 to 2020 was realized (the years that the three images were taken are 2001, 2009, and 2020). Compared with SVM, 2D-CNN, and deep residual networks (ResNet), the overall accuracy of the test dataset is increased by 10.07%, 3.96%, and 1.33%, respectively. The classification achieved using this method is closer to the real land surface, and its accuracy is higher than that of the finished product data obtained using the traditional classification method, providing high-precision land-use classification data for the subsequent carbon storage estimation research. Based on the land-use classification data and the carbon density data corrected by meteorological data (temperature and precipitation data), the InVEST model is used to analyze the land-use change and its impact on carbon storage in the region. The results showed that, from 2001 to 2020, the carbon stock in the study area showed a downward trend, with a total decrease of 1.48 × 107 t. Over the course of this 19-year period, the farmland area in Zhengzhou decreased by 1101.72 km2, and the built land area increased sharply by 936.16 km2. The area of land transfer accounted for 29.26% of the total area of Zhengzhou City from 2001 to 2009, and 31.20% from 2009 to 2020. The conversion of farmland to built land is the primary type of land transfer and the most important reason for decreasing carbon stock. The research results can provide support, in the form of scientific data, for land-use management decisions and carbon storage function protections in Zhengzhou and other cities around the world undergoing rapid urbanization.