Back

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

Maxime, B.; Francois, C.; Lebourgeois, F.; Seynave, I.; Ningre, F.; Vincent, G.; Korboulewsky, N.; Perot, T.; Dufrene, E.; Perret, S.

2021-08-06 ecology
10.1101/2021.08.05.454476 bioRxiv
Show abstract

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.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Forest Ecology and Management
25 papers in training set
Top 0.1%
42.4%
2
Peer Community Journal
254 papers in training set
Top 0.2%
9.0%
50% of probability mass above
3
Scientific Reports
3102 papers in training set
Top 15%
6.7%
4
Methods in Ecology and Evolution
160 papers in training set
Top 0.7%
4.6%
5
PLOS ONE
4510 papers in training set
Top 36%
3.9%
6
Ecological Indicators
20 papers in training set
Top 0.1%
2.5%
7
Science of The Total Environment
179 papers in training set
Top 3%
2.2%
8
Journal of Environmental Management
11 papers in training set
Top 0.4%
1.8%
9
Frontiers in Plant Science
240 papers in training set
Top 4%
1.6%
10
Basic and Applied Ecology
11 papers in training set
Top 0.1%
1.6%
11
PeerJ
261 papers in training set
Top 9%
1.4%
12
Remote Sensing in Ecology and Conservation
10 papers in training set
Top 0.2%
1.0%
13
Plant Methods
39 papers in training set
Top 0.6%
1.0%
14
Ecological Modelling
24 papers in training set
Top 0.5%
0.8%
15
Global Ecology and Conservation
25 papers in training set
Top 1.0%
0.8%
16
Plants
39 papers in training set
Top 2%
0.8%
17
Biotropica
15 papers in training set
Top 0.4%
0.8%
18
Agriculture, Ecosystems & Environment
15 papers in training set
Top 0.3%
0.8%
19
Agronomy
18 papers in training set
Top 0.8%
0.8%
20
Journal of Clinical Medicine
91 papers in training set
Top 7%
0.7%
21
Global Ecology and Biogeography
41 papers in training set
Top 0.7%
0.7%
22
Ecological Informatics
29 papers in training set
Top 0.8%
0.7%
23
BMC Genomics
328 papers in training set
Top 6%
0.7%
24
Tree Physiology
21 papers in training set
Top 0.2%
0.5%
25
Royal Society Open Science
193 papers in training set
Top 6%
0.5%
26
Hydrobiologia
11 papers in training set
Top 0.4%
0.5%