scholarly journals Characterizing Leaf Area Index (LAI) and Vertical Foliage Profile (VFP) over the United States

2015 ◽  
Vol 12 (16) ◽  
pp. 13675-13710 ◽  
Author(s):  
H. Tang ◽  
S. Ganguly ◽  
G. Zhang ◽  
M. A. Hofton ◽  
R. F. Nelson ◽  
...  
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Structure ◽  
The United States ◽  
Data Set ◽  
Area Index ◽  
Continental Scale ◽  
Comparison Results ◽  
Waveform Lidar

Abstract. Leaf area index (LAI) and vertical foliage profile (VFP) are among the important canopy structural variables. Recent advances in lidar remote sensing technology have demonstrated the capability of accurately mapping LAI and VFP over large areas. The primary objective of this study was to derive and validate a LAI and VFP product over the contiguous United States using spaceborne waveform lidar data. This product was derived at the footprint level from the Geoscience Laser Altimeter System (GLAS) using a biophysical model. We validated GLAS derived LAI and VFP across major forest biomes using airborne waveform lidar. The comparison results showed that GLAS retrievals of total LAI were generally accurate with little bias (r2 = 0.67, bias = −0.13, RMSE = 0.75). The derivations of GLAS retrievals of VFP within layers was not as accurate overall (r2 = 0.36, bias = −0.04, RMSE = 0.26), and these varied as a function of height, increasing from understory to overstory −0 to 5 m layer: r2 = 0.04, bias = 0.09, RMSE = 0.31; 10 to 15 m layer: r2 = 0.53, bias = −0.08, RMSE = 0.22; and 15 to 20 m layer: r2 = 0.66, bias =−0.05, RMSE = 0.20. Significant relationships were also found between GLAS LAI products and different environmental factors, in particular elevation and annual precipitation. In summary, our results provide a unique insight into vertical canopy structure distribution across North American ecosystems. This data set is a first step towards a baseline of canopy structure needed for evaluating climate and land use induced forest changes at continental scale in the future and should help deepen our understanding of the role of vertical canopy structure on terrestrial ecosystem processes across varying scales.

scholarly journals Characterizing leaf area index (LAI) and vertical foliage profile (VFP) over the United States

2016 ◽  
Vol 13 (1) ◽  
pp. 239-252 ◽  
Author(s):  
H. Tang ◽  
S. Ganguly ◽  
G. Zhang ◽  
M. A. Hofton ◽  
R. F. Nelson ◽  
...  
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Structure ◽  
The United States ◽  
Data Set ◽  
Area Index ◽  
Continental Scale ◽  
Comparison Results ◽  
Waveform Lidar

Abstract. Leaf area index (LAI) and vertical foliage profile (VFP) are among the important canopy structural variables. Recent advances in lidar remote sensing technology have demonstrated the capability of accurately mapping LAI and VFP over large areas. The primary objective of this study was to derive and validate a LAI and VFP product over the contiguous United States (CONUS) using spaceborne waveform lidar data. This product was derived at the footprint level from the Geoscience Laser Altimeter System (GLAS) using a biophysical model. We validated GLAS-derived LAI and VFP across major forest biomes using airborne waveform lidar. The comparison results showed that GLAS retrievals of total LAI were generally accurate with little bias (r2 =  0.67, bias  =  −0.13, RMSE  =  0.75). The derivations of GLAS retrievals of VFP within layers were not as accurate overall (r2 =  0.36, bias  =  −0.04, RMSE  =  0.26), and these varied as a function of height, increasing from understory to overstory – 0 to 5 m layer: r2 =  0.04, bias  =  0.09, RMSE  =  0.31; 10 to 15 m layer: r2 =  0.53, bias  =  −0.08, RMSE  =  0.22; and 15 to 20 m layer: r2 =  0.66, bias  =  −0.05, RMSE  =  0.20. Significant relationships were also found between GLAS LAI products and different environmental factors, in particular elevation and annual precipitation. In summary, our results provide a unique insight into vertical canopy structure distribution across North American ecosystems. This data set is a first step towards a baseline of canopy structure needed for evaluating climate and land use induced forest changes at the continental scale in the future, and should help deepen our understanding of the role of vertical canopy structure in terrestrial ecosystem processes across varying scales.

scholarly journals Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States

2021 ◽  
Vol 175 ◽  
pp. 71-87
Author(s):  
Luke A. Brown ◽  
Richard Fernandes ◽  
Najib Djamai ◽  
Courtney Meier ◽  
Nadine Gobron ◽  
...  
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
The United States ◽  
Area Index ◽  
Level 2 ◽  
Sentinel 2

scholarly journals A leaf area index data set acquired in Sahelian rangelands of Gourma in Mali over the 2005–2017 period

2019 ◽  
Vol 11 (2) ◽  
pp. 675-686
Author(s):  
Eric Mougin ◽  
Mamadou Oumar Diawara ◽  
Nogmana Soumaguel ◽  
Ali Amadou Maïga ◽  
Valérie Demarez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Model Development ◽  
Land Surface Model ◽  
Product Evaluation ◽  
Tree Canopy ◽  
Surface Model ◽  
Data Set ◽  
Area Index

Abstract. The leaf area index of Sahelian rangelands and related variables such as the vegetation cover fraction, the fraction of absorbed photosynthetically active radiation and the clumping index were measured between 2005 and 2017 in the Gourma region of northern Mali. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1 km linear sampling transects for five contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5 km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation. This paper aims to present the field work that was carried out during 13 successive rainy seasons, the measured vegetation variables, and the associated open database. Finally, a few examples of data use are shown. DOI of the referenced data set: https://doi.org/10.17178/AMMA-CATCH.CE.Veg_Gh.

Yield, leaf-area index and photosynthetic rate in some perennial ryegrass (Lolium perenne L.) selections

1972 ◽  
Vol 78 (3) ◽  
pp. 509-511 ◽  
Author(s):  
Ian Rhodes
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lolium Perenne ◽  
Photosynthetic Rate ◽  
Perennial Ryegrass ◽  
Canopy Structure ◽  
Base Population ◽  
Area Index ◽  
Lolium Perenne L ◽  
Unit Leaf

SUMMARYYield, critical LAI and apparent photosynthetic rate per unit leaf area were measured in four families selected from L. perenne S. 321. Differences in yield were attributable to differences in canopy structure producing differing critical LAI. The most productive family, which was 33% more productive than the base population, produced the largest critical LAI but had the lowest photosynthetic rate.

scholarly journals Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1359-1377 ◽  
Author(s):  
Sujay V. Kumar ◽  
David M. Mocko ◽  
Shugong Wang ◽  
Christa D. Peters-Lidard ◽  
Jordan Borak
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Vegetation Changes ◽  
Area Index ◽  
Terrestrial Water

Abstract Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

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