An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang, Hongliang; Baret, Frédéric; Plummer, S.; Schaepman‐Strub, Gabriela

doi:10.1029/2018rg000608

Cited by 518 publications

(346 citation statements)

References 698 publications

(843 reference statements)

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“…Hemiview calculates an effective plant area index (PAI) that has to be corrected by clumping factors. However, some studies have considered that in forests, effective PAI is almost identical to true LAI, as the underestimation of LAI due to clumping effects could be compensated by the overestimation due to woody structures [39]. In the rest of the paper, effective PAI estimated through Hemiview is therefore considered equal to true LAI and simply referred to as LAI.…”

Section: Leaf Area Indexmentioning

confidence: 99%

Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

et al. 2019

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Leaf pigment contents, such as chlorophylls a and b content (C a b ) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants’ and forests’ health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI < 1 m 2 /m 2 ), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C a b and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C a b , and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m 2 /m 2 , R 2 = 0.81); the band ratio ρ 0.750 μ m ρ 0.550 μ m retrieved C a b more accurately than other chlorophylls indices (RMSE = 5.21 μ g/cm 2 , R 2 = 0.73); RMSE over the 0.5–0.55 μ m interval showed encouraging results for Car estimations.

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Section: Leaf Area Indexmentioning

confidence: 99%

Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

et al. 2019

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“…Winter v d in boreal regions with coniferous forests is dominated by uptake to cuticles (Figures a, c, e, and g). While LAI from the satellite‐based climatology used in GEOS‐Chem suggests nearzero LAI in boreal forests during winter and thus an overestimate of LAI there in AM3DD (Figure S1), satellite‐based estimates of LAI over boreal regions are particularly uncertain due to snow contamination and low solar zenith angle (Fang et al, , ).…”

Section: Impact Of Dynamic Ozone Dry Deposition On Present‐day Surfacmentioning

confidence: 99%

Influence of Dynamic Ozone Dry Deposition on Ozone Pollution

et al. 2020

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Identifying the contributions of chemistry and transport to observed ozone pollution using regional-to-global models relies on accurate representation of ozone dry deposition. We use a recently developed configuration of the NOAA GFDL chemistry-climate model -in which the atmosphere and land are coupled through dry deposition-to investigate the influence of ozone dry deposition on ozone pollution over northern midlatitudes. In our model, deposition pathways are tied to dynamic terrestrial processes, such as photosynthesis and water cycling through the canopy and soil. Small increases in winter deposition due to more process-based representation of snow and deposition to surfaces reduce hemispheric-scale ozone throughout the lower troposphere by 5-12 ppb, improving agreement with observations relative to a simulation with the standard configuration for ozone dry deposition. Declining snow cover by the end of the 21st-century tempers the previously identified influence of rising methane on winter ozone. Dynamic dry deposition changes summer surface ozone by −4 to +7 ppb. While previous studies emphasize the importance of uptake by plant stomata, new diagnostic tracking of depositional pathways reveals a widespread impact of nonstomatal deposition on ozone pollution. Daily variability in both stomatal and nonstomatal deposition contribute to daily variability in ozone pollution. Twenty-first century changes in summer deposition result from a balance among changes in individual pathways, reflecting differing responses to both high carbon dioxide (through plant physiology versus biomass accumulation) and water availability. Our findings highlight a need for constraints on the processes driving ozone dry deposition to test representation in regional-to-global models.

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“…The indirect methods can be divided into two different categories: allometric equations and optical methods. The former estimates LAI based on empirical regression with other forest variables, such as diameter at breast height (DBH) while the latter uses a logarithmic relationship based on gap fraction measurements or canopy transmittance [17]. Optical methods (e.g., Canopy Light Analyzer LAI-2000/2200/2200C or hemispherical cameras) have become the most commonly implemented in field measurements and a wide range of reviews have covered the devices available for LAI field measurements (e.g., [11,16]).…”

Section: Introductionmentioning

confidence: 99%

Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

Prada¹,

Cabo

Hernández-Clemente

et al. 2020

Remote Sensing

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Forest management treatments often translate into changes in forest structure. Understanding and assessing how forests react to these changes is key for forest managers to develop and follow sustainable practices. A strategy to remotely monitor the development of the canopy after thinning using satellite imagery time-series data is presented. The aim was to identify optimal remote sensing Vegetation Indices (VIs) to use as time-sensitive indicators of the early response of vegetation after the thinning of sweet chestnut (Castanea Sativa Mill.) coppice. For this, the changes produced at the canopy level by different thinning treatments and their evolution over time (2014–2019) were extracted from VI values corresponding to two trials involving 33 circular plots (r = 10 m). Plots were subjected to one of the following forest management treatments: Control with no intervention (2800–3300 stems ha−1), Treatment 1, one thinning leaving a living stock density of 900–600 stems ha−1 and Treatment 2, a more intensive thinning, leaving 400 stems ha−1. Time series data from Landsat-8 and Sentinel-2 were collected to calculate values for different VIs. Canopy development was computed by comparing the area under curves (AUCs) of different VI time-series annually throughout the study period. Soil-Line VIs were compared to the Normalized Vegetation Index (NDVI) revealing that the Second Modified Chlorophyll Absorption Ratio Index (MCARI2) more clearly demonstrated canopy evolution tendencies over time than the NDVI. MCARI2 data from both L8 and S2 reflected how the influence of treatment on the canopy cover decreases over the years, providing significant differences in the thinning year and the year after. Metrics derived from the MCARI2 time-series also demonstrated the capacity of the canopy to recovery to pretreatment coverage levels. The AUC method generates a specific V-shaped time-signature, the vertex of which coincides with the thinning event and, as such, provides forest managers with another tool to assist decision making in the development of sustainable forest management strategies.

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An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Cited by 518 publications

References 698 publications

Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

Monitoring LAI, Chlorophylls, and Carotenoids Content of a Woodland Savanna Using Hyperspectral Imagery and 3D Radiative Transfer Modeling

Influence of Dynamic Ozone Dry Deposition on Ozone Pollution

Assessing Canopy Responses to Thinnings for Sweet Chestnut Coppice with Time-Series Vegetation Indices Derived from Landsat-8 and Sentinel-2 Imagery

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