David, Ruusa and Rosser, Nick J. and Donoghue, Daniel N.M. (2022) 'Remote sensing for monitoring tropical dryland forests: A review of current research, knowledge gaps and future directions for Southern Africa.', Environmental Research Communications, 4 (4). 042001.
Climate change, manifest via rising temperatures, extreme drought, and associated anthropogenic activities, has a negative impact on the health and development of tropical dryland forests. Southern Africa encompasses significant areas of dryland forests that are important to local communities but are facing rapid deforestation and are highly vulnerable to biome degradation from land uses and extreme climate events. Appropriate integration of remote sensing technologies helps to assess and monitor forest ecosystems and provide spatially explicit, operational, and long-term data to assist the sustainable use of tropical environment landscapes. The period from 2010 onwards has seen the rapid development of remote sensing research on tropical forests, which has led to a significant increase in the number of scientific publications. This review aims to analyse and synthesise the evidence published in peer review studies with a focus on optical and radar remote sensing of dryland forests in Southern Africa from 1997-2020. For this study, 137 citation indexed research publications have been analysed with respect to publication timing, study location, spatial and temporal scale of applied remote sensing data, satellite sensors or platforms employed, research topics considered, and overall outcomes of the studies. This enabled us to provide a comprehensive overview of past achievements, current efforts, major research topics studies, EO product gaps/challenges, and to propose ways in which challenges may be overcome. It is hoped that this review will motivate discussion and encourage uptake of new remote sensing tools (e.g., Google Earth Engine (GEE)), data (e.g., the Sentinel satellites), improved vegetation parameters (e.g., red-edge related indices, vegetation optical depth (VOD)) and methodologies (e.g., data fusion or deep learning, etc.), where these have potential applications in monitoring dryland forests.
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|Publisher Web site:||https://doi.org/10.1088/2515-7620/ac5b84|
|Publisher statement:||Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.|
|Date accepted:||08 March 2022|
|Date deposited:||09 March 2022|
|Date of first online publication:||20 April 2022|
|Date first made open access:||09 March 2022|
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