Leaf litterfall, leaf area index, and radiation transmittance in cypress wetlands and slash pine plantations in north-central Florida

1996 ◽  
Vol 4 (4) ◽  
pp. 257-271 ◽  
Author(s):  
S. Liu ◽  
H. Riekerk ◽  
H. L. Gholz
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Slash Pine ◽  
Pine Plantations ◽  
Area Index ◽  
North Central ◽  
Central Florida ◽  
Leaf Litterfall

Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper

1997 ◽  
Vol 61 (2) ◽  
pp. 229-245 ◽  
Author(s):  
Karin S. Fassnacht ◽  
Stith T. Gower ◽  
Mark D. MacKenzie ◽  
Erik V. Nordheim ◽  
Thomas M. Lillesand
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Thematic Mapper ◽  
Landsat Thematic Mapper ◽  
Area Index ◽  
North Central

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 Leaf Area of Overstory and Understory in Pine Plantations in the Flatwoods

2010 ◽  
Vol 34 (4) ◽  
pp. 154-160 ◽  
Author(s):  
Alicia Peduzzi ◽  
H. Lee Allen ◽  
Randolph H. Wynne
Keyword(s):  
Leaf Area ◽  
Loblolly Pine ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Slash Pine ◽  
Pine Plantations ◽  
Site Factors ◽  
Biophysical Parameters ◽  
Area Index

Abstract Leaf area index (LAI) was measured in summer and winter for the overstory and understory in 7- and 10-year-old loblolly and slash pine plantations on poorly drained, somewhat poorly drained, and moderately well-drained soils. LAI and vegetation indices (simple ratio [SR], normalized difference vegetation index [NDVI], vegetation index, and enhanced vegetation index) were also calculated using Landsat imagery. LAI values observed for the overstory were low in most of the plots (around 2 m2 m−2 in slash pine and around 3 m2 m−2 in loblolly pine), whereas the understory LAI was very high (around 2 m2 m−2), which can be attributed to the lack of canopy closure observed in all plots. No significant differences were found in the understory LAI values across soil drainage classes. Total LAI (overstory LAI plus understory LAI) values were weakly correlated with the vegetation indices. The LAI values estimated using Landsat data were typically half of the values estimated on the ground. Significant correlations were observed between the vegetation indices (SR and NDVI) and stand and site factors, suggesting that the satellite-derived indices were more related to the stand biophysical parameters than to in situ LAI estimates.

scholarly journals Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data

2012 ◽  
Vol 270 ◽  
pp. 54-65 ◽  
Author(s):  
Alicia Peduzzi ◽  
Randolph H. Wynne ◽  
Thomas R. Fox ◽  
Ross F. Nelson ◽  
Valerie A. Thomas
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanner ◽  
Pine Plantations ◽  
Scanner Data ◽  
Area Index ◽  
Airborne Laser Scanner ◽  
Airborne Laser ◽  
Intensively Managed

scholarly journals Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 222 ◽  
Author(s):  
Christine Blinn ◽  
Matthew House ◽  
Randolph Wynne ◽  
Valerie Thomas ◽  
Thomas Fox ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Loblolly Pine ◽  
Empirical Model ◽  
Vegetation Indices ◽  
Pine Plantations ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Area Index ◽  
The Impact

Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region.

scholarly journals Observation-based implementation of ecophysiological processes for a rubber plant functional type in the community land model (CLM4.5-rubber_v1)

2018 ◽  
Author(s):  
Ashehad A. Ali ◽  
Yuanchao Fan ◽  
Marife D. Corre ◽  
Martyna M. Kotowska ◽  
Evelyn Hassler ◽  
...  
Keyword(s):  
Land Use ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Net Primary Productivity ◽  
Measured Data ◽  
Biogeochemical Processes ◽  
Water Fluxes ◽  
Area Index ◽  
Community Land Model ◽  
Leaf Litterfall

Abstract. Land-use change has a strong impact on carbon, energy and water fluxes and its effect is particularly pronounced in tropical regions. Uncertainties exist in the prediction of future land-use change impacts on these fluxes by land surface models due to scarcity of suitable measured data for parametrization and poor representation of key biogeochemical processes associated with tropical vegetation types. Rubber plantations (Havea brasilliensis) are a crucial land-use type across tropical landscapes that has greatly expanded in recent decades. Here, we first synthesize the relevant data for describing the biogeochemical processes of rubber from our past measurement campaigns in Jambi province, Indonesia. We then use these data-sets to develop a rubber plant functional type (PFT) for the Community Land Model (CLM4.5). Field measured data from small-holder plantations on leaf litterfall, soil respiration, latex harvest, leaf area index, transpiration, net primary productivity, and above-ground and fine root biomass were used to develop and calibrate a new PFT-based model (CLM4.5-rubber). CLM-rubber predictions adequately captured the annual net primary productivity and above-ground biomass as well as the seasonal dynamics of leaf litterfall, soil respiration, soil moisture and leaf area index. All of the predicted water fluxes of CLM-rubber were very similar to a site-specific calibrated soil water model. Including temporal variations in leaf life span enabled CLM-rubber to better capture the seasonality of leaf litterfall. Increased sensitivity of stomata to soil water stress and the enhancement of growth and maintenance respiration of fine roots in response to soil nutrient limitation enabled CLM-rubber to capture the magnitude of transpiration and leaf area index. Since CLM-rubber predicted reasonably well the carbon and water use, we think that the current model can be used for larger-scale simulations within Jambi province because more than 99 % of the rubber plantations are smallholder owned in Jambi province and have low soil fertility.

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