Special Issue on Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems

Dear Colleagues,

Forests are one of the major ecosystems of the earth, supporting a wide diversity of plants and animals and a significant number of services to humankind. At the same time, trees outside forests (TOF) including rural forestry, agroforestry and urban trees also contribute to the global carbon cycle and climate regulation. They also represent a non-negligible component of the tree biomass in the national forest inventories, in addition to their important ecological and cultural values. Thus, monitoring current state of forest ecosystems and TOF with their changes is of prime importance for public policy and land management.

Hyperspectral remote sensing and imaging spectroscopy offers a unique way to characterize forests and TOF as the fine spectral detail allows to characterize the canopy chemistry and structure. Current airborne imagery and future hyperspectral missions such as EnMAP (Environmental Mapping and Analysis Program), PRISMA (Precursore IperSpettrale della Missione Applicativa), HYPXIM (HYPerspectral X Imagery) or HyspIRI (Hyperspectral Infrared Imager) open new opportunities for the analysis of these ecosystems at broad scale. Enhanced ability of hyperspectral data for the analysis of complex systems comes along with increased difficulties for data processing due to the very high dimensionality and size, requiring specific developments in order to fully exploit these images. Characterizing TOF in terms of structure and composition is also challenging because of their high diversity and heterogeneity, combined with limited spatial extent.

This special issue on “Hyperspectral Remote Sensing of Forest and Trees outside Forests Ecosystems” aims to publish original research works based on hyperspectral data to improve forest and TOF ecosystems monitoring. It includes (but is not limited to): taxonomic and functional biodiversity assessment, biochemical parameter estimation, mapping of ecosystem services, and multi-source data fusion (hyperspectral with LiDAR, time series)…

Dr. Mathieu Fauvel Dr. David Sheeren Dr. Jean-Baptiste Feret Dr. Ben Somers Guest Editors