Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water and energy exchange between canopy and atmosphere, and is widely applied in the process models to simulate production and hydrological cycle in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have not been fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (i.e. Pinus massoniana – Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber – Cyclobalanopsis glauca evergreen broadleaved forests) during period from April, 2014 to January, 2015. Spatial heterogeneity of LAI and its controlling factors were analysed by using geostatistics method the generalised additive models (GAMs), respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for three forests measured in January and for the L. glaber – C. glauca forest in April, July and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stand basal area, crown coverage, crown width, proportion of deciduous species on basal area basis and forest types affected the spatial variations in LAI values in January, while species richness, crown coverage, stem number and forest types affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

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Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

Biogeosciences ◽

10.5194/bg-13-3819-2016 ◽

2016 ◽

Vol 13 (12) ◽

pp. 3819-3831 ◽

Cited By ~ 13

Author(s):

Wenjuan Zhu ◽

Wenhua Xiang ◽

Qiong Pan ◽

Yelin Zeng ◽

Shuai Ouyang ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Seasonal Variations ◽

Basal Area ◽

Floristic Composition ◽

Controlling Factors ◽

Stem Number ◽

Deciduous Species ◽

Area Index ◽

Subtropical Forests ◽

Area Basis

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water, and energy exchange between canopy and atmosphere and is widely applied in process models that simulate production and hydrological cycles in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have yet to be fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (Pinus massoniana–Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber–Cyclobalanopsis glauca evergreen broadleaved forests) from April 2014 to January 2015. Spatial heterogeneity of LAI and its controlling factors were analysed using geostatistical methods and the generalised additive models (GAMs) respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for the three forests measured in January and for the L. glaber–C. glauca forest in April, July, and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stem number, crown coverage, proportion of evergreen conifer species on basal area basis, proportion of deciduous species on basal area basis, and forest types affected the spatial variations in LAI values in January, while stem number and proportion of deciduous species on basal area basis affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity, and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

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Supplementary material to "Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters"

10.5194/bg-2016-5-supplement ◽

2016 ◽

Author(s):

Wenjuan Zhu ◽

Wenhua Xiang ◽

Qiong Pan ◽

Yelin Zeng ◽

Shuai Ouyang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Seasonal Variations ◽

Floristic Composition ◽

Secondary Forests ◽

Area Index ◽

Supplementary Material

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Improving accuracy of optical methods in estimating leaf area index through empirical regression models in multiple forest types

Trees ◽

10.1007/s00468-016-1437-y ◽

2016 ◽

Vol 30 (6) ◽

pp. 2101-2115 ◽

Cited By ~ 1

Author(s):

Zhili Liu ◽

Guangze Jin

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Regression Models ◽

Optical Methods ◽

Forest Types ◽

Area Index ◽

Improving Accuracy

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Algorithm for assessing forest stand productivity index using leaf area index

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i3.pp1311-1319 ◽

2019 ◽

Vol 16 (3) ◽

pp. 1311

Author(s):

Faid Abdul Manan ◽

Muhammad Buce Saleh ◽

I Nengah Surati Jaya ◽

Uus Saepul Mukarom

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Basal Area ◽

Forest Stand ◽

Productivity Index ◽

Estimation Model ◽

Area Index ◽

Study Objective ◽

Stand Productivity ◽

Variable Combination

This paper describes a development of an algorithm for assessing stand productivity by considering the stand variables. Forest stand productivity is one of the crucial information that required to establish the business plan for unit management at the beginning of forest planning activity. The main study objective is to find out the most significant and accurate variable combination to be used for assessing the forest stand productivity, as well as to develop productivity estimation model based on leaf area index. The study found the best stand variable combination in assessing stand productivity were density of poles (X2), volume of commercial tree having diameter at breast height (dbh) 20-40 cm (X16), basal area of commercial tree of dbh >40 cm (X20) with Kappa Accuracy of 90.56% for classifying into 5 stand productivity classes. It was recognized that the examined algorithm provides excellent accuracy of 100% when the stand productivity was classified into only 3 classes. The best model for assessing the stand productivity index with leaf area index is y = 0.6214x - 0.9928 with R2= 0.71, where y is productivity index and x is leaf area index.

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Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data

Remote Sensing of Environment ◽

10.1016/j.rse.2006.07.013 ◽

2006 ◽

Vol 105 (4) ◽

pp. 286-298 ◽

Cited By ~ 105

Author(s):

S. Garrigues ◽

D. Allard ◽

F. Baret ◽

M. Weiss

Keyword(s):

Remote Sensing ◽

Spatial Heterogeneity ◽

Leaf Area Index ◽

Leaf Area ◽

Spatial Resolution ◽

Remote Sensing Data ◽

Linear Estimation ◽

Area Index ◽

Sensing Data ◽

Non Linear

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Dynamics of leaf area index and canopy openness of three forest types in a warm temperate zone

Frontiers of Forestry in China ◽

10.1007/s11461-008-0062-3 ◽

2008 ◽

Vol 3 (4) ◽

pp. 416-421 ◽

Cited By ~ 3

Author(s):

Weiguo Sang ◽

Sha Chen ◽

Guangqi Li

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Temperate Zone ◽

Canopy Openness ◽

Forest Types ◽

Area Index ◽

Warm Temperate Zone ◽

Warm Temperate

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Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations

Remote Sensing ◽

10.3390/rs12111843 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1843 ◽

Cited By ~ 1

Author(s):

Andrew Revill ◽

Anna Florence ◽

Alasdair MacArthur ◽

Stephen Hoad ◽

Robert Rees ◽

...

Keyword(s):

Spatial Heterogeneity ◽

Leaf Area Index ◽

Leaf Area ◽

Growth Stages ◽

Two Stage ◽

Area Index ◽

Field Surveys ◽

Red Edge ◽

Calibration Approach ◽

Sentinel 2

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.

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A PROPOSED METHODOLOGY FOR THE CORRECTION OF THE LEAF AREA INDEX MEASURED WITH A CEPTOMETER FOR PINUS AND EUCALYPTUS FORESTS

Revista Árvore ◽

10.1590/0100-67622016000500008 ◽

2016 ◽

Vol 40 (5) ◽

pp. 845-854 ◽

Cited By ~ 2

Author(s):

Domingos Mendes Lopes ◽

Nigel Walford ◽

Helder Viana ◽

Carlos Roberto Sette Junior

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Correction Factor ◽

Basal Area ◽

Allometric Equations ◽

Nutrient Cycle ◽

Area Index ◽

Physiological Processes ◽

The Difference ◽

Non Destructive

ABSTRACT Leaf area index (LAI) is an important parameter controlling many biological and physiological processes associated with vegetation on the Earth's surface, such as photosynthesis, respiration, transpiration, carbon and nutrient cycle and rainfall interception. LAI can be measured indirectly by sunfleck ceptometers in an easy and non-destructive way but this practical methodology tends to underestimated when measured by these instruments. Trying to correct this underestimation, some previous studies heave proposed the multiplication of the observed LAI value by a constant correction factor. The assumption of this work is LAI obtained from the allometric equations are not so problematic and can be used as a reference LAI to develop a new methodology to correct the ceptometer one. This new methodology indicates that the bias (the difference between the ceptometer and the reference LAI) is estimated as a function of the basal area per unit ground area and that bias is summed to the measured value. This study has proved that while the measured Pinus LAI needs a correction, there is no need for that correction for the Eucalyptus LAI. However, even for this last specie the proposed methodology gives closer estimations to the real LAI values.

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