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This working group intends to provide a platform for the community to exchange the latest findings (e.g., new approaches to multi-source remote sensing, that include biophysical, social and economic aspects land use and management, better interpretation of RS products for land use and land management, “knowledge gaps” or “land use and management data policy gaps”) in the land use/cover mapping field in the context of remotely sensed big data. On the one hand, the land use data requirement from LSS communities can be shared with RS scientists; on the other hand, the LSS scientists can be informed about the most recent progresses in land use mapping in this platform. With ongoing development in remote sensing technology as well as the improvement of mapping algorithms, a set of national and global scale land cover/use products with higher spatial and temporal resolutions have been developed and validated to address this gap between LSS and RS. We welcome any working group members whose work is related to the application of remote sensing within land system sciences, including multi-sensor, multi-resolution, multi-temporal remote sensing analysis, and specific case studies in different regions of the world.
The main goal of this working group is to support land system science by using new advances in the new remote sensing era and to close the gap between LSS and RS. The specific objectives of this Working Group include:
As a key component of global change, land cover and land use maps have been increasingly important for improved understanding of global environmental change and feedbacks between social and environmental systems. Land cover/use data are fundamental for understanding land system sciences (LSS); however, existing land cover/use products cannot fully support the LSS studies given the missing more specific land use and management information, for example, current efforts focus on biophysical remote sensing that misses key social and economic information that is needed for understanding LSS. With the increasing availability of open access high spatial, temporal and/or spectral remote sensing (RS) data, we are entering an unprecedented era of big data in remote sensing, that opens opportunities and challenges. A multitude of new algorithms and approaches using the power of the time series data analysis has improved the existing efforts from mapping land cover to land use and land management. More specific land use information, such as crop types (e.g., rice paddies, soybean), forest types (e.g., rubber plantation, oil palm), and land management (e.g., irrigation, cropping intensity, rangeland management) have become accessible in the new remote sensing era. This progress provides new and improved knowledge to support current GLP themes and global efforts to implement the 2030 agenda for Sustainable Development and associated goals (SDGs).
GLP Methods: Remote Sensing
The NASA Applied Remote Sensing Training Program (ARSET) is offering a free online training series to introduce participants to the use of satellite data for conservation and biodiversity applications. The series will highlight specific projects that have successfully used satellite data. Examples include monitoring chimpanzee habitat loss, decreasing whale mortality, detecting penguins, monitoring wildfires, and biodiversity observation networks.