Remote Sensing of Coniferous Forest Leaf Area

Running, Steven W.; Peterson, D. L.; Spanner, Michael; Teuber, Kurt B.

doi:10.2307/1938532

Cited by 143 publications

(68 citation statements)

References 13 publications

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“…Despite the logistical problems involved, several groups have managed to produce predictive relationships (e.g., Jensen and Hodgson 1985, Badhwar et al 1986a, 1986b, Running et al 1986, Danson 1987, Herwitz et al 1990, Spanner et al 1990). For example, Peterson et al (1987) reported a correlation coefficient between LAI and NIR/R of 0.95 for forests in the Pacific Northwest.…”

Section: Remote Sensing Of Laimentioning

confidence: 99%

Dynamics of Canopy Structure and Light Interception in Pinus Elliottii Stands, North Florida

Gholz¹,

Vogel²,

Cropper³

et al. 1991

Ecological Monographs

207

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This is the published version of the paper.This version of the publication may differ from the final published version. Abstract. In order to develop a model of the carbon cycle for mature slash pine (Pinus elliottii) stands in north Florida, we studied seasonal variation in leaf area index (LAI, allsided), aboveground biomass increment and litterfall, and light penetration through the forest canopy, over a 3-yr period. The primary approach to establishing monthly LAI included annual destructive analyses and monthly measurements of needle fall and elongation. Imagery from the Landsat Thematic Mapper (TM) and patterns of light penetration were also used in attempts to derive less arduous estimates; the TM imagery was most promising. Permanent repository linkLAIs ranged from 3.0 to 6.5 on control plots over the 3 yr, with repeated fertilization increasing maximum LAI by >40%. Seasonal variation was high (40%), as was variation from year to year. An average of 3 1% of the incident photosynthetically active radiation (PAR) penetrated the canopies annually, ranging from 18 to 42% seasonally. Seasonal light penetration could not be described using a simple application of the Beer-Lambert law, perhaps due to the highly aggregated nature of the canopies. Models incorporating more information on canopy structure are necessary to predict light penetration through slash pine stands accurately.A model of needle litterfall was derived that could account for much of the seasonal and annual variation using stand basal area and climate conditions from the spring of the previous year; this model may be useful for developing climate-driven predictions of LAI. Efficiencies of use of incoming and intercepted PAR were low compared to other forest types. Low light interception and high nutrient-use efficiencies (demonstrated in earlier studies) are important adaptive characteristics of slash pine stands to these relatively warm and nutrient-poor sites.

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Section: Remote Sensing Of Laimentioning

confidence: 99%

Dynamics of Canopy Structure and Light Interception in Pinus Elliottii Stands, North Florida

Gholz¹,

Vogel²,

Cropper³

et al. 1991

Ecological Monographs

207

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“…A selection of literature results is presented in Table 1. Most of these studies were carried out in western coniferous forests with large LAI gradients (Running et al, 1986;Spanner et al, 1990b;White et al, 1997). In coniferous forest plantations, the ranges in LAI are usually lower and relationships between LAI and vegetation indices (VI) may be disturbed as other biophysical stand characteristics (such as stem density, canopy closure, tree height, etc.)…”

Section: Introductionmentioning

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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“…(or similar measure of foliar mass) with an accuracy adequate for deriving a predictive relationship (Curran and Williamson, 1985). Despite the logistical problems involved, several groups have managed to produce such relationships (e.g., Jensen and Hodgson, 1985;Running et al, 1986;Badhwar et al, 1986aBadhwar et al, , 1986bDanson, 1987;Spanner et al, 1990;Running et al, 1989;Sader et al, 1989;Herwitz et al, in press;Spanner et al, in review). For instance, Peterson et al (1987) reported a correlation coefficient between LA!…”

Section: Introductionmentioning

confidence: 99%

Seasonal LAI in slash pine estimated with landsat TM

Curran

Dungan

Gholz

1992

Remote Sensing of Environment

142

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SUMMARYThe leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LA! seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values, derived using Landsat Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on three dates.Linear relationships existed between NDVI and LAI with R 2 values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March 1989. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass.Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6% of the mean LAI. This demonstrates the potential use of Landsat TM data for studying seasonal dynamics in forest canopies.

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Remote Sensing of Coniferous Forest Leaf Area

Cited by 143 publications

References 13 publications

Dynamics of Canopy Structure and Light Interception in Pinus Elliottii Stands, North Florida

Dynamics of Canopy Structure and Light Interception in Pinus Elliottii Stands, North Florida

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

Seasonal LAI in slash pine estimated with landsat TM

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