Читать книгу Climate Impacts on Sustainable Natural Resource Management - Группа авторов - Страница 39

Remote sensing coupled with artificial intelligence (AI) provides essential technical supports to natural resource monitoring using various applications, including target detection, quantitative extraction of information, change detection and analysis, as well as multi‐source remote sensing information processing (SuperMap 2019).

Land use control is the primary means of developing and protecting land space. It protects the land by ensuring that all‐natural resources are utilized strictly according to the established plan. On the basis of target detection tools and technologies, the targets and scenes of prime interest can be precisely detected in the raster image and their size‐position can be instantly confirmed to identify different natural resources accurately. By recognizing the location of natural resources and their mutual relationships, the technology can aid the land space utilization control along with geological disaster prevention, ecological restoration and provide valuable information to law enforcement inspectors. AI image segmentation and classification technology can be used for quick high‐precision image classification. The quantitative indicators and boundaries of different natural resources can be automatically acquired, thereby assisting in the monitoring and evaluating of these natural resources. The supervision of natural resources and protection of farmland require automatic comparison, self‐inspection, self‐reporting, and regular verification of large land‐use areas. Earlier, this supervision was mainly dependent on the analysis of remote sensing images conducted visually by monitoring experts. With the integration of AI technology, changes in different natural resource categories can be quickly detected for a particular area and time.

Figure 2.6 Satellite and LiDAR data fusion for natural resource management.

The technology involving data processing and its optimization from different remote sensing sources can enrich the overall data quality. This technology can further improve the overall strength of the natural resources management process and may include:

1 LiDAR 3D point cloud processing techniques based on AI can enhance the monitoring data like buildings and terrains and improve natural resource management accuracy.

2 Optimization of remote sensing image quality using AI can improve the accuracy of image interpretation. Super‐resolution reconstruction and de‐clouding techniques can enhance the image quality and add more value to its use.

3 Hyperspectral image processing using AI can highlight various natural resources' subtle features and improve the natural resource classification results.