Dear Colleagues,

Land surface changes are mainly reflected in the changes of land use and land cover. Land use/cover changes (LUCC) not only represent the spatial and temporal dynamics of any land surface landscape, but also objectively record the process of transformation by human activities, serving as an agent to reveal the interactions between human activities and the natural environment. Based on multi-source remote sensing data, the spatial and temporal dynamics of land surface change have made an innovative leap from qualitative to quantitative descriptions of global change, environmental health and sustainable development. With the advancement of remote sensing technology, the ability to observe the earth via spatial and temporal scales, as well as total number of electromagnetic wave resources being utilized, has been continuously enhanced, especially with the rapid development of high-spatial/temporal-resolution Earth Observation technology that can clearly identify the traces of human activities. This has enabled LUCC to achieve high-resolution image benchmarks with global coverage and continuous updating at the basic data level. With the improvement of classification and mapping technology, LUCC products at different spatial and temporal scales have been widely used, offering a variety of data support for science, education and industry, and also providing data for global, national, regional and local scales, as well as macroscopic to microscopic scales. However, due to the high complexity of surface changes, the traditional method of relying on human–computer interaction for interpretation brings high human and time costs and slow update cycles, and as a result various automatic classification methods with clear mathematical meaning have been a hot spot for academic research. With the deepening of big data research, massive storage and upgrading of supercomputing technology, the realization of high-precision intelligent extraction of LUCC information driven by deep learning based on the concept of artificial intelligence will continue to be an emerging research hotspot in this field.

In the future, faced with the challenges of the United Nations 2030 Sustainable Development Goals, achieving carbon peaking by 2030 and carbon neutrality by 2060, monitoring, assessment and early warning of land surface environmental health changes are highly valuable information in this situation. Relying on emerging technologies, such as big data, cloud computing and artificial intelligence, the research focuses on the relationship between ecosystem services, human livelihoods and disaster risks under different land use patterns, and combines remote sensing data at higher spatial and temporal scales to integrate the optimal management of spatial patterns of land use with territorial spatial planning, and coordinate a harmonious relationship between health, environment and development. In this way, we can scientifically guide and achieve the goal of sustainable development by using remote sensing technology to diagnose environmental health.

Dr. Chunxiang Cao
Dr. Hao Zhang
Dr. Mehdi Khaki
Guest Editors

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• land use/cover change
• diagnosis of environmental health by remote sensing
• artificial intelligence technology
• territorial spatial optimization
• sustainable development