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Outputs (49)

Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy (2004)
Journal Article
Bowyer, P., & Danson, F. (2004). Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy. Remote Sensing of Environment, 92(3), 297-308. https://doi.org/10.1016/j.rse.2004.05.020

Wildland fires burn large areas of the earth's land surface annually, causing significant environmental damage and danger to human health. In order to mitigate the effects, and to better manage the incidence of such fires, fire behaviour models are u... Read More about Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy.

Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy (2004)
Journal Article
Bowyer, P., & Danson, F. (2004). Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy. Remote Sensing of Environment, 92(3), 297-308. https://doi.org/10.1016/j.rse.2004.05.020

Wildland fires burn large areas of the earth's land surface annually, causing significant environmental damage and danger to human health. In order to mitigate the effects, and to better manage the incidence of such fires, fire behaviour models are u... Read More about Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy.

Estimating live fuel moisture content from remotely sensed reflectance (2004)
Journal Article
Danson, F., & Bowyer, P. (2004). Estimating live fuel moisture content from remotely sensed reflectance. Remote Sensing of Environment, 92(3), 309-321. https://doi.org/10.1016/j.rse.2004.03.017

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content whic... Read More about Estimating live fuel moisture content from remotely sensed reflectance.

Landscape dynamics and risk modeling of human alveolar echinococcosis (2004)
Journal Article
Danson, F., Craig, P., Man, W., Shi, D., & Giraudoux, P. (2004). Landscape dynamics and risk modeling of human alveolar echinococcosis. Photogrammetric engineering and remote sensing, 70(3), 359-366. https://doi.org/10.1017/S0031182003003639

Human alveolar echinococcosis (AE) is a rare but fatal liver disease caused by a parasitic tapeworm. Between 1994 and 1997 a medical survey in a rural area in central China revealed the highest incidence rate of the disease recorded in the world to d... Read More about Landscape dynamics and risk modeling of human alveolar echinococcosis.

Landscape dynamics and risk modeling of human alveolar echinococcosis (2004)
Journal Article
Danson, F., Craig, P., Man, W., Shi, D., & Giraudoux, P. (2004). Landscape dynamics and risk modeling of human alveolar echinococcosis. Photogrammetric engineering and remote sensing, 70(3), 359-366. https://doi.org/10.1017/S0031182003003639

Human alveolar echinococcosis (AE) is a rare but fatal liver disease caused by a parasitic tapeworm. Between 1994 and 1997 a medical survey in a rural area in central China revealed the highest incidence rate of the disease recorded in the world to d... Read More about Landscape dynamics and risk modeling of human alveolar echinococcosis.

Training a neural network with a canopy reflectance model to estimate crop leaf area index. (2003)
Journal Article
Danson, F., Rowland, C., & Baret, F. (2003). Training a neural network with a canopy reflectance model to estimate crop leaf area index. International Journal of Remote Sensing, 24(23), 4891-4905. https://doi.org/10.1080/0143116031000070319

This paper outlines the strategies available for estimating the biophysical properties of crop canopies from remotely sensed data. Spectral reflectance and biophysical data were obtained over 132 plots of sugarbeet (Beta vulgaris var. saccharifera) a... Read More about Training a neural network with a canopy reflectance model to estimate crop leaf area index..

Training a neural network with a canopy reflectance model to estimate crop leaf area index. (2003)
Journal Article
Danson, F., Rowland, C., & Baret, F. (2003). Training a neural network with a canopy reflectance model to estimate crop leaf area index. International Journal of Remote Sensing, 24(23), 4891-4905. https://doi.org/10.1080/0143116031000070319

This paper outlines the strategies available for estimating the biophysical properties of crop canopies from remotely sensed data. Spectral reflectance and biophysical data were obtained over 132 plots of sugarbeet (Beta vulgaris var. saccharifera) a... Read More about Training a neural network with a canopy reflectance model to estimate crop leaf area index..

Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge (2000)
Journal Article
Lucas, N., Curran, P., Plummer, S., Danson, F., & et, A. (2000). Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge. International Journal of Remote Sensing, 21(4), 619-631. https://doi.org/10.1080/014311600210461

A general forest ecosystem model (FOREST-BGC) driven by remotely sensed and meteorological data was used to estimate stem carbon production (SCP) for a forest in mid-Wales. Key inputs to the model were spatial estimates of leaf area index (LAI) and l... Read More about Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge.

Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge (2000)
Journal Article
Lucas, N., Curran, P., Plummer, S., Danson, F., & et, A. (2000). Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge. International Journal of Remote Sensing, 21(4), 619-631. https://doi.org/10.1080/014311600210461

A general forest ecosystem model (FOREST-BGC) driven by remotely sensed and meteorological data was used to estimate stem carbon production (SCP) for a forest in mid-Wales. Key inputs to the model were spatial estimates of leaf area index (LAI) and l... Read More about Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge.