Forest ecosystem simulation modelling: the role of remote sensing

Lucas, Neil; Curran, Paul J.

doi:10.1177/030913339902300304

Cited by 21 publications

(7 citation statements)

References 122 publications

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“…Models can also be used to interpret remote sensing data [Plummer, 2000], although this is not addressed here. For more general reviews of remote sensing and model synthesis, see Turner et al [2004], Nightingale et al [2004], Plummer [2000], and Lucas and Curran [1999].…”

Section: Recent Studies Linking Models and Remote-sensing Data On Vegmentioning

confidence: 99%

Linking models and data on vegetation structure

et al. 2010

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[1] For more than a century, scientists have recognized the importance of vegetation structure in understanding forest dynamics. Now future satellite missions such as Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynI) hold the potential to provide unprecedented global data on vegetation structure needed to reduce uncertainties in terrestrial carbon dynamics. Here, we briefly review the uses of data on vegetation structure in ecosystem models, develop and analyze theoretical models to quantify model-data requirements, and describe recent progress using a mechanistic modeling approach utilizing a formal scaling method and data on vegetation structure to improve model predictions. Generally, both limited sampling and coarse resolution averaging lead to model initialization error, which in turn is propagated in subsequent model prediction uncertainty and error. In cases with representative sampling, sufficient resolution, and linear dynamics, errors in initialization tend to compensate at larger spatial scales. However, with inadequate sampling, overly coarse resolution data or models, and nonlinear dynamics, errors in initialization lead to prediction error. A robust model-data framework will require both models and data on vegetation structure sufficient to resolve important environmental gradients and tree-level heterogeneity in forest structure globally.

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Section: Recent Studies Linking Models and Remote-sensing Data On Vegmentioning

confidence: 99%

Linking models and data on vegetation structure

et al. 2010

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“…Remotely sensed vegetation properties are essential for ecological modelling of carbon and nutrient cycles, and estimating vegetation production on regional and global scales (Asner, 1998;Lucas and Curran, 1999;Lucas et al, 2000). Several photosynthetic (Xiao et al, 2005), biogeochemical (Ruimy et al, 1996), and production (Potter et al, 1993;Running et al, 2004) models of vegetation have been parameterized by remote-sensing products; however, significant discrepancies were found between model predictions and ground-based measurements (Running et al, 1999;Drolet et al, 2005;Martel et al, 2005;Turner et al, 2006;Friend et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence

Malenovský

Mishra

Zemek³

et al. 2009

Journal of Experimental Botany

138

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State-of-the-art optical remote sensing of vegetation canopies is reviewed here to stimulate support from laboratory and field plant research. This overview of recent satellite spectral sensors and the methods used to retrieve remotely quantitative biophysical and biochemical characteristics of vegetation canopies shows that there have been substantial advances in optical remote sensing over the past few decades. Nevertheless, adaptation and transfer of currently available fluorometric methods aboard air- and space-borne platforms can help to eliminate errors and uncertainties in recent remote sensing data interpretation. With this perspective, red and blue-green fluorescence emission as measured in the laboratory and field is reviewed. Remotely sensed plant fluorescence signals have the potential to facilitate a better understanding of vegetation photosynthetic dynamics and primary production on a large scale. The review summarizes several scientific challenges that still need to be resolved to achieve operational fluorescence based remote sensing approaches.

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“…To correctly model biophysical aspects of global change, it is critical to begin with an accurate baseline estimate of the vegetation distribution of a region. To this end, land cover classification maps based on satellite imagery are commonly used in terrestrial ecosystem models to predict the potential impacts of climate change on land‐atmosphere exchanges of energy, water, carbon and greenhouse gases [e.g., Nemani and Running , 1996; Lucas and Curran , 1999; Markon and Peterson , 2002; Cox et al , 2004; Krinner et al , 2005]. Furthermore, an accurate assessment of the extent of wetlands and water bodies is critical in studies of hydrology, water resources, ecology, land‐atmosphere interactions and trace gas emissions [e.g., Kling et al , 1991; Vörösmarty et al , 1997; Mitsch and Gosselink , 2000; Malcom et al , 2002].…”

Section: Introductionmentioning

confidence: 99%

How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground‐truth database for West Siberia

Frey

Smith

2007

Global Biogeochemical Cycles

126

120

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An unprecedented collection of 2161 geolocated, irregularly spaced field observations of land cover spanning ∼106 km2 throughout West Siberia suggests that currently available land cover classification products are remarkably poor indicators of vegetation type and water body extent in this northern wetland environment. The ground‐truth data are compared with (1) the Global Land Cover Characteristics database derived from Advanced Very High Resolution Radiometer data (GLCC.AVHRR), (2) the Global Land Cover Classification derived from Moderate Resolution Imaging Spectroradiometer data (GLCC.MODIS), (3) the Global Lakes and Wetlands Database (GLWD), and (4) the West Siberian Lowland Peatland Database (WSLPD) using: (1) all land cover categories and (2) permanent wetland and water body categories only. Overall agreement with ground observations of land cover is only 21% for the GLCC.AVHRR database and 11% for the GLCC.MODIS database. However, at a much broader scale (one degree of latitude) there is improved qualitative agreement between vegetation classes, with some notable exceptions: (1) at low latitudes (∼54°N–58°N), both the GLCC.AVHRR and GLCC.MODIS databases underestimate woody savannas in favor of croplands; (2) at midlatitudes (∼58°N–64°N), the GLCC.AVHRR database underestimates evergreen needleleaf forest in favor of mixed forest; and (3) at high latitudes (∼64°N–68°N), both the GLCC.AVHRR and GLCC.MODIS databases underestimate deciduous needleleaf forest in favor of woody savannas or open shrublands, respectively. It is clear that all four data databases significantly underestimate permanent wetlands and water bodies, although those specifically developed for this purpose (GLWD and WSPLD) perform better than the more widely used, multiclass land cover products. For permanent wetlands, agreement with our ground data is only 2% for GLCC.MODIS, 23% for GLCC.AVHRR, 45% for GLWD and 56% for WSLPD. Agreement with open water bodies is even poorer (0–5%). These results raise into question the efficacy of incorporating currently available land cover products into terrestrial ecosystem models in northern wetland environments.

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Forest ecosystem simulation modelling: the role of remote sensing

Cited by 21 publications

References 122 publications

Linking models and data on vegetation structure

Linking models and data on vegetation structure

Scientific and technical challenges in remote sensing of plant canopy reflectance and fluorescence

How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground‐truth database for West Siberia

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