Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

Franklin, Steven E.; Lavigne, M. B.; Deuling, M. J.; Wulder, M.; Hunt, E. Raymond

doi:10.1080/014311697216973

Cited by 69 publications

(35 citation statements)

References 13 publications

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“…This index is correlated to LAI (Baret & Guyot, 1991;Franklin et al, 1997;Peterson et al, 1987;Tucker, 1979). This index also differentiates between green vegetation and soil background, and is also useful for landcover classification.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Yılmaz

Hunt

Goins

et al. 2008

Remote Sensing of Environment

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Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R 2 of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the nonfoliar components of vegetation. Published by Elsevier Inc.

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Section: Vegetation Indicesmentioning

confidence: 99%

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Yılmaz

Hunt

Goins

et al. 2008

Remote Sensing of Environment

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“…To improve LAI mapping, it has been suggested to derive land cover specific VI-LAI relationships using land cover maps (Chen et al, 2002;Cohen et al, 2003;Franklin et al, 1997). This statistical stratification of sampled stands is usually based on the species type.…”

Section: Study Site and Field Measurementsmentioning

confidence: 99%

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Schlerf¹,

Atzberger²,

Hill³

2005

Remote Sensing of Environment

278

171

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This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables (n=40). Ratio-based and soil-linerelated broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R 2 =0.67, RMSE=0.69 m 2 m À2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2 =0.77, RMSE=0.54 m 2 m À2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) (R 2 =0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved. D

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“…However, once full cover is reached, the NDVI changes little, even as the LAI continues to increase (Carlson and Ripley 1997). Normally, the NDVI saturates when the LAI exceeds 4 (Franklin et al 1997), although the LAI can vary from 5 to 8 (tropical forest), 3 to 7 (temperate forest), and 1 to 8 (boreal forest).…”

Section: Discussionmentioning

confidence: 96%

Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images

Idris

Kuraji

Suzuki

2005

Journal of Forest Research

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Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

Cited by 69 publications

References 13 publications

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images

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