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Polygon-based aggregation of remotely sensed data for regional ecological analyses

Polygon-based aggregation of remotely sensed data for regional ecological analyses
Polygon-based aggregation of remotely sensed data for regional ecological analyses
Given current concerns about global climate change, there is an urgent need to quantify and monitor accurately the magnitude of present day terrestrial carbon sinks. This may be achieved by driving ecosystem simulation models (ESMs) spatially with remotely sensed estimates of ecological variables, such as leaf area index (LAI). Conventional procedures for analysing digital remotely sensed images rely upon pixel-based methods, using spectral information from each pixel to allocate it to a land cover type or estimate a surface property (e.g. LAI). Groups of pixels, within areas assumed to be ‘thematically homogeneous’, will not necessarily provide the same allocation or estimation due to data noise, atmospheric effects and natural variation of the surface. Pixels on the boundary between areas are an additional problem as their spectral information derives from more than one surface type. If contextual information on the spatial pattern and structure of the landscape could be included in the analysis (e.g. forest inventory polygons, agricultural land parcels), then the accuracy of the allocations or estimations (e.g. of LAI) could be increased. Polygon-based approaches, where all pixels within a defined area are presumed similar and so can be combined prior to analysis, offer a solution. These approaches are implemented most efficiently within an integrated GIS where raster and ancillary data can be analysed with reference to vector land polygons. A procedure using remotely sensed data in a polygon format to produce accurate spatial estimates of LAI (on which to drive an ESM) is described. In relation to a pixel-based procedure, the polygon-based procedure provided: (1) increased accuracy, (2) more appropriate and realistic representations of the environment and (3) a powerful and flexible framework for further data analysis.
Ecosystem simulation, LAI estimation
0303-2434
161-173
Wicks, T.E.
a43a6197-48b3-42aa-b168-20a1584ade9b
Smith, G.M.
13bdc231-4f94-424f-b80f-ceffef7d55a2
Curran, P.J.
3f5c1422-c154-4533-9c84-f2afb77df2de
Wicks, T.E.
a43a6197-48b3-42aa-b168-20a1584ade9b
Smith, G.M.
13bdc231-4f94-424f-b80f-ceffef7d55a2
Curran, P.J.
3f5c1422-c154-4533-9c84-f2afb77df2de

Wicks, T.E., Smith, G.M. and Curran, P.J. (2002) Polygon-based aggregation of remotely sensed data for regional ecological analyses. International Journal of Applied Earth Observation and Geoinformation, 4 (2), 161-173. (doi:10.1016/S0303-2434(02)00025-9).

Record type: Article

Abstract

Given current concerns about global climate change, there is an urgent need to quantify and monitor accurately the magnitude of present day terrestrial carbon sinks. This may be achieved by driving ecosystem simulation models (ESMs) spatially with remotely sensed estimates of ecological variables, such as leaf area index (LAI). Conventional procedures for analysing digital remotely sensed images rely upon pixel-based methods, using spectral information from each pixel to allocate it to a land cover type or estimate a surface property (e.g. LAI). Groups of pixels, within areas assumed to be ‘thematically homogeneous’, will not necessarily provide the same allocation or estimation due to data noise, atmospheric effects and natural variation of the surface. Pixels on the boundary between areas are an additional problem as their spectral information derives from more than one surface type. If contextual information on the spatial pattern and structure of the landscape could be included in the analysis (e.g. forest inventory polygons, agricultural land parcels), then the accuracy of the allocations or estimations (e.g. of LAI) could be increased. Polygon-based approaches, where all pixels within a defined area are presumed similar and so can be combined prior to analysis, offer a solution. These approaches are implemented most efficiently within an integrated GIS where raster and ancillary data can be analysed with reference to vector land polygons. A procedure using remotely sensed data in a polygon format to produce accurate spatial estimates of LAI (on which to drive an ESM) is described. In relation to a pixel-based procedure, the polygon-based procedure provided: (1) increased accuracy, (2) more appropriate and realistic representations of the environment and (3) a powerful and flexible framework for further data analysis.

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More information

Published date: 2002
Keywords: Ecosystem simulation, LAI estimation

Identifiers

Local EPrints ID: 14908
URI: http://eprints.soton.ac.uk/id/eprint/14908
ISSN: 0303-2434
PURE UUID: 1870b545-215a-46c4-a8ae-348ada01e165

Catalogue record

Date deposited: 09 Mar 2005
Last modified: 15 Mar 2024 05:32

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Contributors

Author: T.E. Wicks
Author: G.M. Smith
Author: P.J. Curran

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