Forest ecosystem simulation modelling: the role of remote sensing

Lucas, Neil; Curran, Paul J.

doi:10.1191/030913399668122549

Cited by 9 publications

(10 citation statements)

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“…The number of bands included into the model was controlled by the p-values for entry Table 1 Specification of the five absorption features used in the regression analysis. Causal absorption bands adapted from Curran (1989), Fourty et al (1996) and Lucas and Curran (1999 and removal. The RMSEcv value above the break in slope was used to define the maximum number of bands.…”

Section: Regression Modelsmentioning

confidence: 99%

“…To interpret the relation of the selected wavebands to foliar constituents a list of absorption bands related to leaf chlorophyll and leaf nitrogen was compiled from Curran (1989), Fourty et al (1996) and Lucas and Curran (1999), namely: 640 and 660 nm (chlorophyll), 1200 nm (water), 1690, 1730, 2130, 2180, 2240, 2300, and 2350 nm (protein) ( Table 1). Following Curran et al (2001) a selected waveband was considered as directly related when it occurred within 30 nm around the central absorption band of the chemical of interest.…”

Section: Interpretation Of Selected Wavebandsmentioning

confidence: 99%

See 1 more Smart Citation

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

Schlerf¹,

Atzberger²,

Hill

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

135

107

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Section: Regression Modelsmentioning

confidence: 99%

Section: Interpretation Of Selected Wavebandsmentioning

confidence: 99%

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

Schlerf¹,

Atzberger²,

Hill

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

135

107

View full text Add to dashboard Cite

“…Whilst they may vary in their conceptual approach and scale of operation, all are simplified versions of reality and are typically aspatial (Lucas and Curran, 1999). The aspatial nature of many process models has made it difficult to scale up biological and physiological data or process to understand the functioning of ecosystems and their link to atmospheric transport processes.…”

Section: Ecosystem Modelling and Remote Sensingmentioning

confidence: 99%

“…Several hundred models of ecosystem productivity and function exist with varying levels of organization, scope, temporal resolution (seconds-years) and spatial explicitness (leaves -global), e.g., Register of Ecological Models (Schwalm and Ek, 2001). During this time a number of reviews have assessed the role and capability of models for scaling biological data at various temporal (hours-decades) and spatial (leaf-ecosystem) resolutions (e.g., Sklar and Costanza, 1990;Baldocchi, 1993;Bazzaz, 1993;Jarvis and Dewar, 1993;Bonan, 1995;Leuning et al, 1995;Landsberg and Gower, 1997a;Lucas and Curran, 1999;Running et al, 1999;Valentini et al, 1999). Additionally, reviews of individual ecosystem models and comparisons between several models for a number of representative ecosystems have also been conducted and these papers are outlined within Appendix 1.…”

Section: Introductionmentioning

confidence: 99%

Ecosystem process models at multiple scales for mapping tropical forest productivity

Nightingale

Phinn

Held

2004

Progress in Physical Geography: Earth and Environment

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Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf -tree, plot -stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.

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“…It is essential to understand how image data relate to forest stand characteristics (Lucas and Curran 1999;Visser and Stampfer 2003). Empirical models including correlation and regression are important tools for relating field-measured stand parameters to remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of forest parameters based on TM imagery and statistical analysis

Cai

Zhao

2007

J. of For. Res.

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One of the primary forestry research interests lies in estimating forest stand parameters by applying empirical or semi-empirical model to establish the relationship between the forest stand parameters and remote sensing data. Using remote sensing image and the inventory data from 2 compartments in northeast Florida, U.S.A., this paper explored the correlation between forest stand parameters and Landsat TM spectral digital number (DN) value. Results showed that less than 50% of the total variance could be explained by linear regression models with only either a single band or such vegetation indices as vegetation index (VI) or normalized difference vegetation index (NDVI) as predicators. In consequence, multi-linear regression models which synthesized more predicators were introduced to estimate forest parameters. Regression results were tested in terms of the other group of data, and verification showed a better capability of explaining over 75% variance except for forest density. The weakness and further improvement of prediction models were also discussed in the article. This paper is expected to provide a better understanding of the relationship between TM spectral and forest characteristics

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Forest ecosystem simulation modelling: the role of remote sensing

Cited by 9 publications

References 0 publications

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

Ecosystem process models at multiple scales for mapping tropical forest productivity

Estimation of forest parameters based on TM imagery and statistical analysis

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