Forest biophysical parameters such as forest structure, stand volume and above-ground biomass (AGB) are critical in monitoring forest health and can also be used to indicate the impacts of climate change under UNFCCC. Terrestrial ecosystems around the world have been severely affected by deforestation, disturbance of vegetation, natural disasters (forest fire, earthquake, landslides, etc.), and socio-economic development activities. Protecting the world’s forests is necessary to preserve these terrestrial ecosystems in their present state. Monitoring of forest cover and measuring forest biophysical parameters on a regular basis will aid in understanding the health of the forest ecosystem and in strategizing conservation measures. Reducing deforestation and forest disturbance will also help address global climate change. Time series analysis of forest biophysical parameters would provide insight into the productivity of forest stands and assessment of anthropological pressure on the forests.
Forest biophysical parameters can be measured through field surveys; however, these are not practical for large areas as they are resource-intensive and time-consuming. Satellite remote sensing is a more effective approach. Specifically, Synthetic Aperture Radar (SAR) data with its all-weather imaging capability and higher penetration through vegetation is ideal for monitoring forest biophysical parameters in all seasons, and hence much attention has been devoted to developing advanced SAR remote sensing techniques in the past couple of decades. It has also been recognized that using multi-baseline, multi-angle, multi-temporal and multi-polarimetric SAR data can provide greater insights into the structural properties of the forest. The techniques of Polarimetric SAR (PolSAR), Polarimetric Interferometry SAR (PolInSAR), and Polarimetric Tomographic SAR (PolTomSAR) are extensively useful to exploit the information contained in multi-polarimetric and multi-baseline/multi-angle SAR acquisitions. These techniques retrieve enhanced information of the forest, including forest biomass, disturbance, phenology and degradation, structures, dielectric constant, canopy- and sub-canopy scattering characteristics. Satellite missions planned specifically for terrestrial ecosystem monitoring would improve our estimation techniques by further reducing the uncertainties, and helping to develop specific models for different global forest ecosystems and degradation/disturbances. Presently space-borne SAR data is being acquired in X-, C- and L- band while the acquisition in S- and P- band space-borne sensors are about to be in service shortly (NISAR for L- & S-band, and BIOMASS for P-band).
This Research Topic welcomes submissions on the following topics:
• SAR remote sensing methods for characterization of forest structure
• SAR remote sensing of slow and abrupt forest change monitoring
• SAR based development of forest degradation characterization techniques
• Time Series SAR data analysis for forest dynamics
• Spatial and temporal distribution of forest parameters
• Advanced PolSAR, PolInSAR and TomoSAR techniques for forests
• Any other related topics
Forest biophysical parameters such as forest structure, stand volume and above-ground biomass (AGB) are critical in monitoring forest health and can also be used to indicate the impacts of climate change under UNFCCC. Terrestrial ecosystems around the world have been severely affected by deforestation, disturbance of vegetation, natural disasters (forest fire, earthquake, landslides, etc.), and socio-economic development activities. Protecting the world’s forests is necessary to preserve these terrestrial ecosystems in their present state. Monitoring of forest cover and measuring forest biophysical parameters on a regular basis will aid in understanding the health of the forest ecosystem and in strategizing conservation measures. Reducing deforestation and forest disturbance will also help address global climate change. Time series analysis of forest biophysical parameters would provide insight into the productivity of forest stands and assessment of anthropological pressure on the forests.
Forest biophysical parameters can be measured through field surveys; however, these are not practical for large areas as they are resource-intensive and time-consuming. Satellite remote sensing is a more effective approach. Specifically, Synthetic Aperture Radar (SAR) data with its all-weather imaging capability and higher penetration through vegetation is ideal for monitoring forest biophysical parameters in all seasons, and hence much attention has been devoted to developing advanced SAR remote sensing techniques in the past couple of decades. It has also been recognized that using multi-baseline, multi-angle, multi-temporal and multi-polarimetric SAR data can provide greater insights into the structural properties of the forest. The techniques of Polarimetric SAR (PolSAR), Polarimetric Interferometry SAR (PolInSAR), and Polarimetric Tomographic SAR (PolTomSAR) are extensively useful to exploit the information contained in multi-polarimetric and multi-baseline/multi-angle SAR acquisitions. These techniques retrieve enhanced information of the forest, including forest biomass, disturbance, phenology and degradation, structures, dielectric constant, canopy- and sub-canopy scattering characteristics. Satellite missions planned specifically for terrestrial ecosystem monitoring would improve our estimation techniques by further reducing the uncertainties, and helping to develop specific models for different global forest ecosystems and degradation/disturbances. Presently space-borne SAR data is being acquired in X-, C- and L- band while the acquisition in S- and P- band space-borne sensors are about to be in service shortly (NISAR for L- & S-band, and BIOMASS for P-band).
This Research Topic welcomes submissions on the following topics:
• SAR remote sensing methods for characterization of forest structure
• SAR remote sensing of slow and abrupt forest change monitoring
• SAR based development of forest degradation characterization techniques
• Time Series SAR data analysis for forest dynamics
• Spatial and temporal distribution of forest parameters
• Advanced PolSAR, PolInSAR and TomoSAR techniques for forests
• Any other related topics
