Forest and wetland are crucial ecosystems to global sustainable development. Both play irreplaceable roles in carbon sequestration and climate change mitigation. In 2015, the 2030 Agenda for Sustainable Development that is comprised of 17 sustainable development goals (SDGs) was endorsed by all UN member states. The SDGs are a blueprint and worldwide demand for a more peaceful, prosperous, and sustainable future that ensures that no one is left behind. While all of the goals are interconnected, Goal 15 (Life on land: protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss) draws our attention to this special issue. In addition, the increasing availability of multisource remote sensing data (e.g., multispectral, hyperspectral, LiDAR, and SAR) with various platforms (e.g., satellite, airborne, unmanned aerial vehicle, and terrestrial) and advanced artificial intelligence (e.g., machine learning, deep learning, and transfer learning) provide unprecedented potential and opportunity to comprehensively understand our forest and wetland ecosystems under climate change. Thus, this special issue will provide a platform for cutting-edge research on the sustainable forest and wetland under climate change using multisource remote sensing data. Well-prepared, unpublished submissions that address one or more of the following topics are solicited (but not be limited to this list):
- Mapping, monitoring, and modeling the dynamics of forest or wetland including deforestation, afforestation, desertification, wetland degradation or restoration, biodiversity loss
- Forest/wetland classification or species-level classification
- Estimation of vegetation structures and biophysical properties of forest or wetlands
- Multiscale estimation and its spatial uncertainty, and change analysis of biomass forest or wetland
- Monitoring and modelling carbon fluxes in forests or wetlands
- Deep learning or innovative artificial intelligence algorithms for forest/wetland monitoring
- The impact of climate change on carbon source and sink distribution of forest and wetland
- Responses of forest and wetland to extreme weather events (e.g., heavy precipitation, drought, sand and dust storms) or disturbances (e.g., wildfire, insects)
