This Special Issue invites papers demonstrating an innovative application of remote sensing in mapping and monitoring species diversity using a suite of remote sensing techniques, including optical and active domain applied in various vegetation types and biomes.

Submission deadline is 30 September 2021.

Globally, biodiversity at a species level is facing an unprecedented loss. According to a recent Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) assessment report, the loss is attributed to the unregulated land cover and land-use change, pollution, habitat changes, including invasive species, ever-increasing human population, and climate change. There is a need for continuous assessments and monitoring of biodiversity to inform decision-making processes about biodiversity management at a local, national, and international level. Species diversity is cited as one of the essential biodiversity variables (EBVs), which is an indicator of ecosystem health. Conventional biodiversity monitoring activities are often field-based, costly, and time-consuming. Remote sensing provides an alternative technique for assessing and monitoring species diversity, especially that of vegetation at multiple scales (i.e., looking at alpha, beta, and gamma diversity).

There are statistical and mathematical techniques, such as empirical (parametric and non-parametric), physical-based models, and hybrid models, combined with remote sensing used to estimate biophysical (biomass, leaf area index, etc) and biochemical (nitrogen, phosphorus) properties. In addition, there has been some progress in the estimation of species diversity using remote-sensing-derived variables through univariates, multivariates, artificial neural networks, generalized additive models, and recently machine learning techniques. Most of the successes have been evident through the use of commercial remote sensing satellites, which are relatively expensive. Lately, freely available remote sensing data at moderate to high resolution have become available, such as Sentinel 1 and 2, and Landsat 8 has provided an opportunity to map grass and tree species diversity. Landsat imagery, for example, has been used to map species diversity in various vegetation types.

This Special Issue intends to benefit from the emergence of in situ, environmental, and remotely sensed big data and machine learning analytics in detecting and monitoring species diversity. Potential research papers should also address some of the key issues when focusing on remote sensing of species diversity (Rocchini et al. 2015), i) additions to known sensors that are relevant to achieve this, 2) issues of scale-matching remote sensing and in situ derived data, 3) spectral heterogeneity measurement techniques, and 4) types of species taxonomic diversity measures.

Guest Editors

Dr. Abel Ramoelo, Scientific Services, Conservation Services Division, South African National Parks (SANParks), Pretoria, South Africa
Prof. Dr. Samuel Adelabu, Department of Geography, University of the Free State, Bloemfontein, South Africa

For details on how to submit your manuscript, click here

View the official call and submit your manuscript.


 Photo by Pixabay 

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