Examining the link between vegetation leaf area and land-atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data

Abstract. Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the large-scale link between vegetation and surface fluxes by combining MODIS leaf area index with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation between leaf area index and water and energy fluxes depends on vegetation and aridity. In water-limited conditions, the link between vegetation and water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no vegetation control on water and energy fluxes. In contrast to water and energy fluxes, we found a strong correlation between leaf area index and gross primary productivity that was independent of vegetation type and aridity index. This study provides insight in the large-scale link between vegetation and surface fluxes. The study indicates that for modelling or extrapolating large-scale surface fluxes, LAI can be useful in savanna and grassland, but only of limited use in deciduous broadleaf forest and evergreen needleleaf forest.

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Examining the link between vegetation leaf area and land–atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data

Biogeosciences ◽

10.5194/bg-17-4443-2020 ◽

2020 ◽

Vol 17 (17) ◽

pp. 4443-4457

Author(s):

Anne J. Hoek van Dijke ◽

Kaniska Mallick ◽

Martin Schlerf ◽

Miriam Machwitz ◽

Martin Herold ◽

...

Keyword(s):

Leaf Area ◽

Primary Productivity ◽

Vegetation Type ◽

Carbon Fluxes ◽

Gross Primary Productivity ◽

Surface Fluxes ◽

Energy Fluxes ◽

Area Index ◽

Vegetation Control ◽

Yearly Average

Abstract. Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the link between vegetation and surface fluxes by combining the yearly average MODIS leaf area index (LAI) with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation of the LAI with water and energy fluxes depends on the vegetation type and aridity. Under water-limited conditions, the link between the LAI and the water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no link between the LAI and water and energy fluxes. In contrast to water and energy fluxes, we found a strong spatial correlation between the LAI and gross primary productivity that was independent of vegetation type and aridity. This study provides insight into the link between vegetation and surface fluxes. It indicates that for modelling or extrapolating surface fluxes, the LAI can be useful in savanna and grassland, but it is only of limited use in deciduous broadleaf forest and evergreen needleleaf forest to model variability in water and energy fluxes.

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Faculty Opinions recommendation of First direct landscape-scale measurement of tropical rain forest Leaf Area Index, a key driver of global primary productivity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1098484.617254 ◽

2009 ◽

Author(s):

Peter Reich

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Primary Productivity ◽

Rain Forest ◽

Tropical Rain Forest ◽

Landscape Scale ◽

Area Index ◽

Key Driver

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Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

Remote Sensing ◽

10.3390/rs13081427 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1427

Author(s):

Kasturi Devi Kanniah ◽

Chuen Siang Kang ◽

Sahadev Sharma ◽

A. Aldrie Amir

Keyword(s):

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Primary Productivity ◽

Peninsular Malaysia ◽

Area Index ◽

Mangrove Area ◽

The Mean ◽

Shape Complexity ◽

The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

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Leaf area index and net primary productivity along subtropical to alpine gradients in the Tibetan Plateau

Global Ecology and Biogeography ◽

10.1111/j.1466-822x.2004.00094.x ◽

2004 ◽

Vol 13 (4) ◽

pp. 345-358 ◽

Cited By ~ 87

Author(s):

Tianxiang Luo ◽

Yude Pan ◽

Hua Ouyang ◽

Peili Shi ◽

Ji Luo ◽

...

Keyword(s):

Tibetan Plateau ◽

Leaf Area Index ◽

Leaf Area ◽

Primary Productivity ◽

Net Primary Productivity ◽

The Tibetan Plateau ◽

Area Index

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Faculty Opinions recommendation of First direct landscape-scale measurement of tropical rain forest Leaf Area Index, a key driver of global primary productivity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1098484.554574 ◽

2007 ◽

Author(s):

Christian Körner

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Primary Productivity ◽

Rain Forest ◽

Tropical Rain Forest ◽

Landscape Scale ◽

Area Index ◽

Key Driver

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Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography

Annals of Botany ◽

10.1093/aob/mcaa097 ◽

2020 ◽

Vol 126 (4) ◽

pp. 765-773 ◽

Cited By ~ 1

Author(s):

Yingpu Che ◽

Qing Wang ◽

Ziwen Xie ◽

Long Zhou ◽

Shuangwei Li ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Plant Height ◽

Large Scale ◽

High Efficiency ◽

Point Clouds ◽

Phenotypic Traits ◽

Area Index ◽

Area Density ◽

Leaf Area Density

Abstract Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R2 = 0.67), compared with that of nadir photography (slope = 0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7–3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.

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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

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0237.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1359-1377 ◽

Cited By ~ 14

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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Plant- and Soil-Parameter-Caused Uncertainty of Predicted Surface Fluxes

Monthly Weather Review ◽

10.1175/mwr3046.1 ◽

2005 ◽

Vol 133 (12) ◽

pp. 3498-3516 ◽

Cited By ~ 20

Author(s):

Nicole Mölders

Keyword(s):

Heat Flux ◽

Leaf Area Index ◽

Leaf Area ◽

Stomatal Resistance ◽

Surface Fluxes ◽

Critical Parameters ◽

Net Radiation ◽

Area Index ◽

Vegetation Fraction ◽

Ground Heat Flux

Abstract Simulated surface fluxes depend on one or more empirical plant or soil parameters that have a standard deviation (std dev). Thus, simulated fluxes will have a stochastic error (or std dev) resulting from the parameters’ std dev. Gaussian error propagation (GEP) principles are used to calculate the std dev for fluxes predicted by the hydro–thermodynamic soil–vegetation scheme to identify prediction limitations due to stochastic errors, parameterization weaknesses, and critical parameters, and to prioritize which parameters to measure with higher accuracy. Relative errors of net radiation, sensible, latent, and ground heat flux, on average, are 7%, 10%, 6%, and 26%, respectively. The analysis identified the parameterization of thermal conductivity as the dominant influence on the std dev of ground heat flux. For net radiation, critical parameters are vegetation fraction and ground emissivity; for sensible and latent heat fluxes, vegetation fraction. Minimum stomatal resistance and leaf area index dominate the std dev of stomatal resistance for most vegetation and soil types. The empirical parameters with the highest relative error are not necessarily the greatest contributors to the std dev of the predicted flux. Based on the analysis high priority should be given to measurements of vegetation fraction, ground emissivity, minimum stomatal resistance, leaf area index in general, and the permanent wilting point and field capacity for clay and clay loam. Moreover, further specification of clay-type soils and tundra-type vegetation may improve the accuracy of the lower boundary condition in Arctic numerical weather prediction. Since GEP showed itself able to identify critical parameters and (parts of) parameterizations, GEP analysis could form a basis for parameterization intercomparisons and for parameter determination aimed at improving models.

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