The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring.

Studies have shown that Sentinel-2 images have advantages over Landsat images in impervious surface area (ISA) extraction. The performance of index-based methods can be affected by different binary methods and subject to seasonal variation. This study marks the first attempt to assess the performance of different spectral indices for ISA extraction using multiseasonal Sentinel-2 images. Specifically, five indices (i.e., the biophysical composition index calculated using the Gram-Schmidt orthogonalization method, biophysical composition index calculated using a principal component-based Procrustes analysis, Normalized Built-up Area Index (NBAI), combinational build-up index, and perpendicular impervious surface index (PISI)) and three impervious surface binary methods (i.e., Otsu's method, manual method, and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) classification method) were tested on multi-seasonal Sentinel-2 images in the main urban area of Wuhan, China. Results showed that PISI combined with the ISODATA classification method achieved the highest accuracy with 92.64% OA, and 0.8410 Kappa coefficient, and NBAI combined with Otsu's method achieved the lowest accuracy with 35.37% OA, and 0.013 Kappa coefficient. Regarding the seasonal sensitivity, PISI is relatively more stable than the other methods. The superior performance of PISI is largely due to its capability in separating ISA from soil and vegetation. In addition, summer is the best season to map ISA from Sentinel-2 images when the impervious surface is generally less confused with bare soil. This study serves as a reference for the selection of spectral indices for ISA extraction from Sentinel-2 images in relation to binary methods and seasonal effects.

Impervious surface mapping incorporating high-resolution remote sensing imagery has continued to attract increasing interest, as it can provide detailed information about urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping yields better performance than the use of high-resolution imagery alone. However, due to LiDAR data’s high cost of acquisition, it is difficult to obtain LiDAR data that was acquired at the same time as the high-resolution imagery in order to conduct impervious surface mapping by multi-sensor remote sensing data. Consequently, the occurrence of real landscape changes between multi-sensor remote sensing data sets with different acquisition times results in misclassification errors in impervious surface mapping. This issue has generally been neglected in previous works. Furthermore, observation differences that were generated from multi-sensor data—including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images—also present obstacles to achieving the final mapping result in the fusion of LiDAR data and high-resolution images. In order to resolve these issues, we propose an improved impervious surface-mapping method incorporating both LiDAR data and high-resolution imagery with different acquisition times that consider real landscape changes and observation differences. In the proposed method, multi-sensor change detection by supervised multivariate alteration detection (MAD) is employed to identify the changed areas and mis-registered areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution image are extracted via independent classification based on the corresponding single-sensor data. Finally, an object-based post-classification fusion is proposed that takes advantage of both independent classification results while using single-sensor data and the joint classification result using stacked multi-sensor data. The impervious surface map is subsequently obtained by combining the landscape classes in the accurate classification map. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and unambiguously improve the performance of impervious surface mapping.

Hyperspectral remote sensing technology has been successfully applied to geological fields. Drill-core hyperspectral imagery has the characteristics of segmented processing and large data volume. Due to simple principle and high accuracy, the Spectral Angle Mapping (SAM) has become the most commonly used method for mineral mapping using drill-core hyperspectral images. However, SAM analyzes the entire spectral form of minerals, and is not sensitive enough to small differences in drill-core mineral spectra. Compared with traditional machine learning methods, deep learning has more powerful feature learning and feature expression capabilities. In order to improve the mineral mapping accuracy, this paper proposes a new approach called Graph Convolutional Neural Networks-Spectral Angle Mapping (GCNNSAM), which integrates the advantages of deep learning and spectral matching to extract mineral information from drill-core hyperspectral images. Taking the drill-core hyperspectral data near the depth of 240m as an example, this paper compares the performances of SAM, GCNN and GCNNSAM mapping methods. The results show that the overall accuracy of the GCNNSAM mapping is 89.23%, and the overall accuracies of SAM and GCNN mapping methods are 80.25%, 83.58%, respectively. Comparing the mineral mapping statistical results of GCNNSAM with the measured geological statistical results, the maximum statistical error of mineral relative content is 1.4%, and the errors are all less than 2%, which verifies the reliability of the proposed method in this study. The method provides a new idea for mineral information acquisition in geological research.

Although many studies have shown that the expansion of impervious surfaces such as artificial buildings and roads is accompanied by a decrease in urban vegetation, recent studies have also pointed out that urban greening measures can promote vegetation growth. The above studies mostly focus on administrative regions such as a certain city, urban agglomeration, river basin, country, etc., ignoring the differences between suburbs and core urban areas with concentrated populations. This paper proposes fractional impervious surface index (FISI) to estimate impervious surfaces and combines it with population information to define core urban area boundaries of megacities. Then, Landsat data are used to analyze dynamics of impervious surfaces and vegetation in core urban areas of 12 megacities around the world from 2013 to 2022. The results indicate a gradual increase in impervious surfaces within the core urban areas of 12 megacities. Most megacities have shown a significant decline in growth rate of impervious surfaces since 2017, and growth rates of all megacities have tended to stabilize after 2019. Across all megacities, lower fractional vegetation cover (FVC) levels account for a higher proportion of area, with a gradual decline toward higher FVC levels. Most megacities have different FVC trends in core urban areas and entire city. The proportions of very high FVC areas in core urban areas of 12 megacities have not changed much, with fluctuations basically not exceeding 5%. This study helps to understand the spatiotemporal dynamic changes of vegetation in core urban areas and provide reference data for sustainable urban development.