The University of Southampton
University of Southampton Institutional Repository

On the measurability of change in Amazon vegetation from MODIS

On the measurability of change in Amazon vegetation from MODIS
On the measurability of change in Amazon vegetation from MODIS
The Amazon rainforest is a critical hotspot for bio-diversity, and plays an essential role in global carbon, water and energy fluxes and the earth's climate. Our ability to project the role of vegetation carbon feedbacks on future climate critically depends upon our understanding of this tropical ecosystem, its tolerance to climate extremes and tipping points of ecosystem collapse. Satellite remote sensing is the only practical approach to obtain observational evidence of trends and changes across large regions of the Amazon forest; however, inferring these trends in the presence of high cloud cover fraction and aerosol concentrations has led to widely varying conclusions. Our study provides a simple and direct statistical analysis of a measurable change in daily and composite surface reflectance obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) based on the noise level of data and the number of available observations. Depending on time frame and data product chosen for analysis, changes in leaf area need to exceed up to 2 units leaf area per unit ground area (expressed as m2 m? 2) across much of the basin before these changes can be detected at a 95% confidence level with conventional approaches, roughly corresponding to a change in NDVI and EVI of about 25%. A potential way forward may be provided by advanced multi-angular techniques, such as the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), which allowed detection of changes of about 0.6–0.8 units in leaf area (2–6% change in NDVI) at the same confidence level. In our analysis, the use of the Enhanced Vegetation Index (EVI) did not improve accuracy of detectable change in leaf area but added a complicating sensitivity to the bi-directional reflectance, or view geometry effects.
amazon, MAIAC, MOD09, MODIS, sensitivity analysis, vegetation change
0034-4257
233-242
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40
Lyapustin, Alexei I.
ee8fd005-4cb8-491c-a7c5-38d57a562608
Hall, Forrest G.
19da6ee8-b54b-4eee-b5b6-e8e3a92f6bcf
Myneni, Ranga
11d5d989-a245-4725-b4de-a9063fa9fcb1
Knyazikhin, Yuri
84e2bfd7-e1bd-4f11-8167-9cabe2c060e3
Wang, Yujie
6915380d-4c23-4fef-a172-6880ddeff699
Tucker, Compton J.
3aaff73d-aa1f-49c0-9d16-7099c218b274
Sellers, Piers J.
c9d7b8a6-3ed9-4e9f-9318-cc287e746315
Hilker, Thomas
c7fb75b8-320d-49df-84ba-96c9ee523d40
Lyapustin, Alexei I.
ee8fd005-4cb8-491c-a7c5-38d57a562608
Hall, Forrest G.
19da6ee8-b54b-4eee-b5b6-e8e3a92f6bcf
Myneni, Ranga
11d5d989-a245-4725-b4de-a9063fa9fcb1
Knyazikhin, Yuri
84e2bfd7-e1bd-4f11-8167-9cabe2c060e3
Wang, Yujie
6915380d-4c23-4fef-a172-6880ddeff699
Tucker, Compton J.
3aaff73d-aa1f-49c0-9d16-7099c218b274
Sellers, Piers J.
c9d7b8a6-3ed9-4e9f-9318-cc287e746315

Hilker, Thomas, Lyapustin, Alexei I., Hall, Forrest G., Myneni, Ranga, Knyazikhin, Yuri, Wang, Yujie, Tucker, Compton J. and Sellers, Piers J. (2015) On the measurability of change in Amazon vegetation from MODIS. Remote Sensing of Environment, 166, 233-242. (doi:10.1016/j.rse.2015.05.020).

Record type: Article

Abstract

The Amazon rainforest is a critical hotspot for bio-diversity, and plays an essential role in global carbon, water and energy fluxes and the earth's climate. Our ability to project the role of vegetation carbon feedbacks on future climate critically depends upon our understanding of this tropical ecosystem, its tolerance to climate extremes and tipping points of ecosystem collapse. Satellite remote sensing is the only practical approach to obtain observational evidence of trends and changes across large regions of the Amazon forest; however, inferring these trends in the presence of high cloud cover fraction and aerosol concentrations has led to widely varying conclusions. Our study provides a simple and direct statistical analysis of a measurable change in daily and composite surface reflectance obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) based on the noise level of data and the number of available observations. Depending on time frame and data product chosen for analysis, changes in leaf area need to exceed up to 2 units leaf area per unit ground area (expressed as m2 m? 2) across much of the basin before these changes can be detected at a 95% confidence level with conventional approaches, roughly corresponding to a change in NDVI and EVI of about 25%. A potential way forward may be provided by advanced multi-angular techniques, such as the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), which allowed detection of changes of about 0.6–0.8 units in leaf area (2–6% change in NDVI) at the same confidence level. In our analysis, the use of the Enhanced Vegetation Index (EVI) did not improve accuracy of detectable change in leaf area but added a complicating sensitivity to the bi-directional reflectance, or view geometry effects.

Full text not available from this repository.

More information

Accepted/In Press date: 23 May 2015
e-pub ahead of print date: 8 June 2015
Published date: 1 September 2015
Keywords: amazon, MAIAC, MOD09, MODIS, sensitivity analysis, vegetation change
Organisations: Geography & Environment

Identifiers

Local EPrints ID: 384673
URI: https://eprints.soton.ac.uk/id/eprint/384673
ISSN: 0034-4257
PURE UUID: b11033e6-9733-4d5b-9fde-8f67c2b42b05

Catalogue record

Date deposited: 11 Jan 2016 16:31
Last modified: 17 Jul 2017 20:03

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×