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Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+

Overview of attention for article published in Carbon Balance and Management, April 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#37 of 238)
  • High Attention Score compared to outputs of the same age (88th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

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1 blog
policy
2 policy sources
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7 X users
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1 Facebook page

Citations

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194 Dimensions

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mendeley
694 Mendeley
Title
Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+
Published in
Carbon Balance and Management, April 2017
DOI 10.1186/s13021-017-0078-9
Pubmed ID
Authors

Anthea L. Mitchell, Ake Rosenqvist, Brice Mora

Abstract

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 694 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Unknown 693 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 127 18%
Student > Ph. D. Student 95 14%
Researcher 93 13%
Student > Bachelor 55 8%
Student > Doctoral Student 44 6%
Other 88 13%
Unknown 192 28%
Readers by discipline Count As %
Environmental Science 176 25%
Earth and Planetary Sciences 95 14%
Agricultural and Biological Sciences 78 11%
Engineering 45 6%
Computer Science 18 3%
Other 60 9%
Unknown 222 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 October 2022.
All research outputs
#1,729,636
of 23,506,079 outputs
Outputs from Carbon Balance and Management
#37
of 238 outputs
Outputs of similar age
#34,978
of 311,221 outputs
Outputs of similar age from Carbon Balance and Management
#3
of 7 outputs
Altmetric has tracked 23,506,079 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 238 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.4. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,221 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.