A leaf area index, LAI, data set acquired in Sahelian rangelands (Gourma, Mali) over the 2005-2017 period

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.

Download Full-text

Analysis of a multiyear global vegetation leaf area index data set

Journal of Geophysical Research Atmospheres ◽

10.1029/2001jd000975 ◽

2002 ◽

Vol 107 (D22) ◽

Cited By ~ 69

Author(s):

Wolfgang Buermann

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Data Set ◽

Area Index ◽

Index Data ◽

Global Vegetation

Download Full-text

Supplementary material to "A leaf area index, LAI, data set acquired in Sahelian rangelands (Gourma, Mali) over the 2005–2017 period"

10.5194/essd-2018-113-supplement ◽

2018 ◽

Author(s):

Eric Mougin ◽

Mamadou Oumar Diawara ◽

Nogmana Soumaguel ◽

Ali Amadou Maiga ◽

Valerie Demarez ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Data Set ◽

Area Index ◽

Supplementary Material

Download Full-text

Leaf area index estimation of even-aged oak (Quercus petraea) forests using in situ stand dendrometric parameters

10.1101/2021.08.05.454476 ◽

2021 ◽

Author(s):

Briere Maxime ◽

Christophe Francois ◽

Francois Lebourgeois ◽

Ingrid Seynave ◽

Francois Ningre ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Basal Area ◽

Quercus Petraea ◽

Forest Stand ◽

Stand Age ◽

Estimation Errors ◽

Data Set ◽

Area Index ◽

Net Productivity

The leaf area index (LAI) is a key characteristic of forest stand aboveground net productivity (ANP), and many methods have been developed to estimate the LAI. However, every method has flaws, e.g., methods may be destructive, require means or time and/or show intrinsic bias and estimation errors. A relationship using basal area (G) and stand age to estimate LAI was proposed by Sonohat et al. (2004). We used literature data in addition to data form measurements campaign made in the northern half of France to build a data set with large ranges of pedoclimatic conditions, stand age and measured LAI. We validated the Sonohat et al. (2004) relationship and attempted to improve or modify it using other stand/dendrometric characteristics that could be predictors of the LAI. The result is a series of three models using the G, age and/or quadratic mean diameter (Dg), and the models were able to estimate the LAI of an oak only even-aged forest stand with good confidence (root mean square error, RMSE < 0.75) While G is the main predictor here, age and Dg could be used conjointly or exclusively given the available data, with variable precision in the estimations. Although these models could not, by construction, relate to the interannual variability of the LAI, they may provide the theoretical LAI of an untouched forest (no meteorological, biotic or anthropogenic perturbation) in recent years. additionally, the use of this model may be more interesting than an LAI measurement campaign, depending on the means to be invested in such a campaign.

Download Full-text

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

Biogeosciences ◽

10.5194/bg-13-239-2016 ◽

2016 ◽

Vol 13 (1) ◽

pp. 239-252 ◽

Cited By ~ 14

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.

Download Full-text

Influences of Leaf Area Index estimations on water balance modeling in a Mediterranean semi-arid basin

Natural Hazards and Earth System Science ◽

10.5194/nhess-9-979-2009 ◽

2009 ◽

Vol 9 (3) ◽

pp. 979-991 ◽

Cited By ~ 23

Author(s):

V. Gigante ◽

V. Iacobellis ◽

S. Manfreda ◽

P. Milella ◽

I. Portoghese

Keyword(s):

Water Balance ◽

Leaf Area Index ◽

Soil Water ◽

Leaf Area ◽

Data Set ◽

Area Index ◽

Spectral Bands ◽

Basin Scale ◽

Vegetation Heterogeneity ◽

Scale Calibration

Abstract. In the present work, the role played by vegetation parameters, necessary to the hydrological distributed modeling, is investigated focusing on the correct use of remote sensing products for the evaluation of hydrological losses in the soil water balance. The research was carried out over a medium-sized river basin in Southern Italy, where the vegetation status is characterised through a data-set of multi-temporal NDVI images. The model adopted uses one layer of vegetation whose status is defined by the Leaf Area Index (LAI), which is often obtained from NDVI images. The inherent problem is that the vegetation heterogeneity – including soil disturbances – has a large influence on the spectral bands and so the relation between LAI and NDVI is not unambiguous. We present a rationale for the basin scale calibration of a non-linear NDVI-LAI regression, based on the comparison between NDVI values and literature LAI estimations of the vegetation cover in recognized landscape elements of the study catchment. Adopting a process-based model (DREAM) with a distributed parameterisation, the influence of different NDVI-LAI regression models on main features of water balance predictions is investigated. The results show a significant sensitivity of the hydrological losses and soil water regime to the alternative LAI estimations. These crucially affects the model performances especially in low-flows simulation and in the identification of the intermittent regime.

Download Full-text

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

Biogeosciences Discussions ◽

10.5194/bgd-12-13675-2015 ◽

2015 ◽

Vol 12 (16) ◽

pp. 13675-13710 ◽

Cited By ~ 2

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.

Download Full-text

A leaf area index, LAI, data set acquired in Sahelian rangelands (Gourma, Mali) over the 2005–2017 period

10.5194/essd-2018-113 ◽

2018 ◽

Author(s):

Eric Mougin ◽

Mamadou Oumar Diawara ◽

Nogmana Soumaguel ◽

Ali Amadou Maiga ◽

Valerie 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, LAI, of Sahelian rangelands and related variables such as the vegetation cover fraction, fCover, the fraction of absorbed photosynthetically active radiation, fAPAR, and the clumping index, λo, were measured in the Gourma region (Mali) during 13 successive rainy seasons, between 2005 and 2017. 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 5 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.

Download Full-text