Ecological Remote Sensing

Background and Aim

Remote sensing or Earth observation offers a unique set of measurement, mapping, monitoring, and modelling tools for use in:

(1) Fundamental ecological studies, examining structures, processes and relationships between living organisms and their physical environment; and

(2) A wide range of government, community. and industry contexts, including, but not limited to, conservation biology, resource management, agriculture/grazing/horticulture/aquaculture, and forestry, in terrestrial–aquatic–atmospheric and marine ecosystems.

Remote sensing data sets and analysis techniques provide scale-specific approaches, in spatial and temporal contexts, for measurement and monitoring ecosystems at the individual, population, community, ecosystem, and biosphere levels.

Ecological remote sensing needs to be supported by robust work linking field and process-based measurements, to satellite, airborne, and drone image data sets, to develop and validate algorithms and applications for use across academic, government, community, and industry sectors. These applications are inherently multi-disciplinary and require effective collaborations.

This section on Ecological Remote Sensing provides a fast and robust reviewing process on new ideas involving the use of remote sensing for ecological studies. Papers in this section build the knowledge, applications, and capacity base for advancing our global ecological remote sensing capabilities in a robust, diverse, and equitable manner by encouraging and supporting works that explicitly link field and remote sensing data sets and expertise across the range of disciplines that contribute to ecology.

Scope

Examples of the primary ecological remote sensing challenges this section will address include (i) measuring and monitoring ecological structures and processes from plant to global scales, (ii) monitoring the impacts of environmental management practices on ecological structures and processes, (iii) separating anthropogenic impacts from natural environment variability, (iv) assessing error and communication effectively for ecological remote sensing application, and (v) linking indigenous ecological understanding with Earth observations.