Direct and indirect estimates of Leaf Area Index (LAI) for lodgepole and loblolly pine stands

Trees ◽  
1995 ◽  
Vol 9 (3) ◽  
pp. 119-122 ◽  
Author(s):  
David Arthur Sampson ◽  
H. Lee Allen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Loblolly Pine ◽  
Area Index ◽  
Pine Stands

Leaf Area Index (LAI) of Loblolly Pine and Emergent Vegetation Following a Harvest

2011 ◽  
Vol 54 (6) ◽  
pp. 2057-2066 ◽  
Author(s):  
D. A. Sampson ◽  
D. M. Amatya ◽  
C. D. Blanton Lawson ◽  
R. W. Skaggs
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Loblolly Pine ◽  
Emergent Vegetation ◽  
Area Index

Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands

1994 ◽  
Vol 14 (7-8-9) ◽  
pp. 981-995 ◽  
Author(s):  
P. Stenberg ◽  
S. Linder ◽  
H. Smolander ◽  
J. Flower-Ellis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Scots Pine ◽  
Plant Canopy ◽  
Area Index ◽  
Pine Stands ◽  
Scots Pine Stands

Estimating leaf area index at multiple heights within the understorey component of Loblolly pine forests from airborne discrete-return lidar

2015 ◽  
Vol 37 (1) ◽  
pp. 78-99 ◽  
Author(s):  
Matthew J. Sumnall ◽  
Thomas R. Fox ◽  
Randolph H. Wynne ◽  
Christine Blinn ◽  
Valerie A. Thomas
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Loblolly Pine ◽  
Pine Forests ◽  
Area Index

scholarly journals A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1140
Author(s):  
Stephen M. Kinane ◽  
Cristian R. Montes ◽  
Timothy J. Albaugh ◽  
Deepak R. Mishra
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Loblolly Pine ◽  
Vegetation Indices ◽  
Pine Plantations ◽  
Estimation Model ◽  
Moisture Index ◽  
Modeling Framework ◽  
Area Index ◽  
Extensive Evaluation

Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.

scholarly journals Validation of an Integrated Estimation of Loblolly Pine (Pinus taeda L.) Leaf Area Index (LAI) Using Two Indirect Optical Methods in the Southeastern United States

2008 ◽  
Vol 32 (3) ◽  
pp. 101-110 ◽  
Author(s):  
John S. Iiames ◽  
Russell Congalton ◽  
Andrew Pilant ◽  
Timothy Lewis
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Pinus Taeda ◽  
Loblolly Pine ◽  
Southeastern United States ◽  
Area Ratio ◽  
Allometric Equations ◽  
Area Index ◽  
Whole Tree

Abstract Quality assessment of satellite-derived leaf area index (LAI) products requires appropriate ground measurements for validation. Since the National Aeronautics and Space Administration launch of Terra (1999) and Aqua (2001), 1-km, 8-day composited retrievals of LAI have been produced for six biome classes worldwide. The evergreen needle leaf biome has been examined at numerous validation sites, but the dominant commercial species in the southeastern United States, loblolly pine (Pinus taeda), has not been investigated. The objective of this research was to evaluate an in situ optical LAI estimation technique combining measurements from the Tracing Radiation and Architecture of Canopies (TRAC) optical sensor and digital hemispherical photography (DHP) in the southeastern US P.taeda forests. Stand-level LAI estimated from allometric regression equations developed from whole-tree harvest data were compared to TRAC–DHP optical LAI estimates at a study site located in the North Carolina Sandhills Region. Within-shoot clumping, (i.e., the needle-to-shoot area ratio [γE]) was estimated at 1.21 and fell within the range of previously reported values for coniferous species (1.2–2.1). The woody-to-total area ratio (α = 0.31) was within the range of other published results (0.11–0.34). Overall, the indirect optical TRAC–DHP method of determining LAI was similar to LAI estimates that had been derived from allometric equations from whole-tree harvests. The TRAC–DHP yielded a value 0.14 LAI units below that retrieved from stand-level whole-tree harvest allometric equations. DHP alone yielded the best LAI estimate, a 0.04 LAI unit differential compared with the same allometrically derived LAI.

scholarly journals Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

2020 ◽  
Vol 12 (9) ◽  
pp. 1406 ◽  
Author(s):  
Chris W. Cohrs ◽  
Rachel L. Cook ◽  
Josh M. Gray ◽  
Timothy J. Albaugh
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Resolution ◽  
Loblolly Pine ◽  
Southeastern United States ◽  
Model Fit ◽  
Support Vector ◽  
Area Index ◽  
Sentinel 2

Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.

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