Measuring How Much Wood is in the World’s Forests: Why Statistics Matter

Shaun Quegan ()
Additional contact information
Shaun Quegan: University of Sheffield, National Centre for Earth Observation

A chapter in UK Success Stories in Industrial Mathematics, 2016, pp 19-26 from Springer

Abstract: Abstract In March 2013 the European Space Agency (ESA) selected, against very strong competition, the BIOMASS mission as its 7th Earth Explorer, for launch in 2020. This is the first space mission using P-band (wavelength 70 cm) radar, which gives the capability to provide global maps of forest biomass and height every 6 months during the 5-year operational lifetime, at 200 m resolution and with an accuracy of 20 % for biomass and 20–30 % for height. Crucial for selection was demonstration that the accuracy and resolution requirements could be met in the presence of “speckle”, an intrinsic statistical variability permeating radar measurements. In original radar images, speckle obeys an exponential distribution, with equal mean and standard deviation and a long positive tail, making the images seem very noisy. Speckle can be reduced by averaging pixel values; this preserves the mean and reduces the variance, but also degrades the resolution. The basic resolution of BIOMASS is 50 m, so at most 16 pixels can be averaged (i.e. a $$4\times 4$$ 4 × 4 block) before the resolution exceeds 200 m; this is insufficient to give 20 % accuracy in biomass. This impasse can be circumvented by linearly combining multi-temporal polarized images to yield unbiased output images with minimum variance. Solving this optimization problem shows that combining data from just three times allows the resolution and accuracy requirements to be met. This finding underpins ESA’s decision to select BIOMASS at a cost of € 470M, with € 280M going to industry, and with far-reaching impacts for science, policy, the environment and society.

Keywords: Radio Wave; European Space Agency; Forest Biomass; Radar Satellite; Positive Tail (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-25454-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9783319254548

DOI: 10.1007/978-3-319-25454-8_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().