Leaf Area Index (LAI) Change Detection Analysis on Loblolly Pine (Pinus taeda) Following Complete Understory Removal

2008 ◽  
Vol 74 (11) ◽  
pp. 1389-1400 ◽  
Author(s):  
J.S. Iiames ◽  
R.G. Congalton ◽  
A.N. Pilant ◽  
T.E. Lewis
Keyword(s):  
Change Detection ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Pinus Taeda ◽  
Loblolly Pine ◽  
Area Index ◽  
Change Detection Analysis ◽  
Detection Analysis

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.

Monthly leaf area index estimates from point-in-time measurements and needle phenology for Pinus taeda

2003 ◽  
Vol 33 (12) ◽  
pp. 2477-2490 ◽  
Author(s):  
D A Sampson ◽  
T J Albaugh ◽  
K H Johnsen ◽  
H L Allen ◽  
S J Zarnoch
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Pinus Taeda ◽  
Loblolly Pine ◽  
Significant Treatment ◽  
Area Index ◽  
Annual Cohort ◽  
Harvest Data ◽  
Pinus Taeda L ◽  
Bud Initiation

Leaf area index (LAI) of loblolly pine (Pinus taeda L.) trees of the southern United States varies almost twofold interannually; loblolly pine, essentially, carries two foliage cohorts at peak LAI (September) and one at minimum (March–April). Herein, we present an approach that may be site invariant to estimate monthly LAI for loblolly pine using point-in-time measurements from a LI-COR LAI-2000 plant canopy analyzer (PCA). Our analyses used needle accretion and abscission data from monthly needle counts and destructive harvest data from a replicated 2 × 2 factorial experiment of water and nutrition amendments. No significant treatment effects on relative needle accretion or abscission were observed. Cohort (interannual) differences in needle accretion were found but appeared trivial. Cohort year had variable effects on needle abscission. Abscission of current-year foliage began in July and continued through November of the third year; however, only 7%–9% remained 23 months following bud initiation. A treatment-invariable regression of PCA measurements on cohort foliage biomass (r2 [Formula: see text] 0.98) was used to estimate annual cohort LAI. We derived monthly estimates of LAI from cohort accretion and abscission and cohort LAI. Monthly estimates of LAI for loblolly pine, using point-in-time measurements from the PCA, appear possible, although further testing is required.

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

Influence of Herbaceous Interference on Growth and Biomass Partitioning in Planted Loblolly Pine (Pinus taeda)

Weed Science ◽  
1990 ◽  
Vol 38 (6) ◽  
pp. 497-503 ◽  
Author(s):  
John R. Britt ◽  
Bruce R. Zutter ◽  
Robert J. Mitchell ◽  
Dean H. Gjerstad ◽  
John F. Dickson
Keyword(s):  
Leaf Area ◽  
Pinus Taeda ◽  
Loblolly Pine ◽  
Growth Efficiency ◽  
Biomass Partitioning ◽  
Lower Percentage ◽  
Total Biomass ◽  
Growth Responses ◽  
Area Index ◽  
Weed Interference

Three herbaceous regimes were established, using herbicides, to examine the effects of interference on growth and biomass partitioning in loblolly pine (Pinus taedaL.). Trees were sampled near Auburn and Tallassee, AL. Trees at the Auburn site grown with low weed interference (LWI) had 4, 10, 10, 8, and 4 times greater total aboveground biomass than did trees with high weed interference (HWI) for ages one through five, respectively. Medium weed interference (MWI, Auburn site only) resulted in three times greater biomass the first 4 yr and two times greater total biomass by the fifth year compared to trees grown with HWI. Trees growing with LWI were 5, 8, 10, and 6 times larger than those with HWI for ages one through four, respectively, at the Tallassee site. At all levels of interference, the percentage of total biomass in foliage decreased, and stem and branch components increased, with increasing tree size at both sites. Trees growing with HWI had a lower percentage of total biomass in foliage and a greater percentage of total biomass in stem than those growing with LWI when compared over a common size. Growth efficiency per tree, expressed as annual increase in stem biomass per unit leaf area (g m−2), was slightly greater for trees growing with LWI compared to HWI when leaf area index (LAI3, total surface) was less than 0.2. For LAI values greater than 0.2 the relationship was reversed. The latter contradicts the idea that growth efficiency can be used as a measure of vigor for young loblolly pine. Changes in carbon partitioning to the development of leaf area are suggested to be driving the accelerated growth responses associated with a reduction of weed interference.

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

Erratum: Monthly leaf area index estimates from point-in-time measurements and needle phenology for Pinus taeda

2004 ◽  
Vol 34 (3) ◽  
pp. 762
Author(s):  
D A Sampson ◽  
T J Albaugh ◽  
K H Johnsen ◽  
H L Allen ◽  
S J Zarnoch
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Pinus Taeda ◽  
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 A Trend Analysis of Leaf Area Index and Land Surface Temperature and Their Relationship from Global to Local Scale

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 388
Author(s):  
Azad Rasul ◽  
Sa’ad Ibrahim ◽  
Ajoke R. Onojeghuo ◽  
Heiko Balzter
Keyword(s):  
Surface Temperature ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regional Scale ◽  
Climate Mitigation ◽  
Area Index ◽  
Detection Analysis ◽  
Moderate Resolution Imaging Spectroradiometer

Although the way in which vegetation phenology mediates the feedback of vegetation to climate systems is now well understood, the magnitude of these changes is still unknown. A thorough understanding of how the recent shift in phenology may impact on, for example, land surface temperature (LST) is important. To address this knowledge gap, it is important to quantify these impacts and identify patterns from the global to the regional scale. This study examines the trend and linear regression modeling of the leaf area index (LAI) and LST derived from the moderate resolution imaging spectroradiometer (MODIS) data, specifically to assess their spatial distribution and changing trends at the continental and regional scales. The change detection analysis of interannual variability in the global LAI and LST between two periods (2003–2010 and 2011–2018) demonstrates more positive LAI trends than negative, while for LST most changes were not significant. The relationships between LAI and LST were assessed across the continents to ascertain the response of vegetation to changes in LST. The regression between LAI and LST was negative in Australia (R2 = 0.487 ***), positive but minimal in Africa (R2 = 0.001), positive in North America (R2 = 0.641 ***), negative in Central America (R2 = 0.119), positive in South America (R2 = 0.253 *) and positive in Europe (R2 = 0.740 ***). Medium temperatures enhance photosynthesis and lengthen the growing season in Europe. We also found a significant greening trend in China (trendp = 0.16 ***) and India (trendp = 0.13 ***). The relationships between LAI and LST in these most prominent greening countries of the world are R2 = 0.06 and R2 = 0.25 for China and India, respectively. Our deductions here are twofold—(1) In China, an insignificant association appeared between greening trend and temperature. (2) In India, the significant greening trend may be a factor in lowering temperatures. Therefore, temperature may stabilize if the greening trend continues. We attribute the trends in both countries to the different land use management and climate mitigation policies adopted by these countries.

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