scholarly journals High NDVI and Potential Canopy Photosynthesis of South American Subtropical Forests despite Seasonal Changes in Leaf Area Index and Air Temperature

Forests ◽  
2014 ◽  
Vol 5 (2) ◽  
pp. 287-308 ◽  
Author(s):  
Piedad Cristiano ◽  
Nora Madanes ◽  
Paula Campanello ◽  
Débora di Francescantonio ◽  
Sabrina Rodríguez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Air Temperature ◽  
Seasonal Changes ◽  
South American ◽  
Canopy Photosynthesis ◽  
Area Index ◽  
Subtropical Forests

Seasonal changes of leaf area index (LAI) in a tropical deciduous forest in west Mexico

1995 ◽  
Vol 74 (1-3) ◽  
pp. 171-180 ◽  
Author(s):  
JoséManuel Maass ◽  
James M. Vose ◽  
Wayne T. Swank ◽  
Angelina Martínez-Yrízar
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Changes ◽  
Deciduous Forest ◽  
Tropical Deciduous Forest ◽  
Area Index ◽  
West Mexico

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

2016 ◽  
Author(s):  
Wenjuan Zhu ◽  
Wenhua Xiang ◽  
Qiong Pan ◽  
Yelin Zeng ◽  
Shuai Ouyang ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Variations ◽  
Basal Area ◽  
Floristic Composition ◽  
Controlling Factors ◽  
Forest Types ◽  
Area Index ◽  
Subtropical Forests

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.

scholarly journals Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

2021 ◽  
Vol 14 (1) ◽  
pp. 148
Author(s):  
Yang Chen ◽  
Lixia Ma ◽  
Dongsheng Yu ◽  
Kaiyue Feng ◽  
Xin Wang ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Complex Terrain ◽  
Absolute Error ◽  
Sensor Data ◽  
Area Index ◽  
Subtropical Forests ◽  
Optical Images ◽  
The Status ◽  
Elevation Model

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

On improving the accuracy of digital hemispherical photography measurements of seasonal leaf area index variation in deciduous broadleaf forests

2015 ◽  
Vol 45 (6) ◽  
pp. 721-731 ◽  
Author(s):  
Zhili Liu ◽  
Xingchang Wang ◽  
Jing M. Chen ◽  
Chuankuan Wang ◽  
Guangze Jin
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Changes ◽  
Optical Methods ◽  
Hemispherical Photography ◽  
Area Index ◽  
Clumping Index ◽  
Area Variation ◽  
Major Factors

Optical methods have been widely used to estimate seasonal changes of the leaf area index (LAI) in forest stands because they are convenient and effective; however, their accuracy in deciduous broadleaf forests has rarely been evaluated. We estimate the seasonal changes in the LAI by combining periodic observations of leaf area variation with litter collection (LAIdir) in deciduous broadleaf forests and use these estimates to evaluate the accuracy of optical LAI measurements made using digital hemispherical photography (DHP). We also propose a method to correct DHP-derived LAI (LAIDHP) values for seasonal changes in major factors that influence the determination of LAI, including the woody to total area ratio (α), the element clumping index (ΩE, using three different methods), and the photographic exposure setting (E). Before these corrections were made, LAIDHP underestimates LAIdir by 14%–55% from 21 May to 1 October but overestimates it by 78% on 12 May and by 226% on 11 October. Although pronounced differences are observed between LAIdir and LAIDHP, they are significantly correlated (R2 = 0.85, RMSE = 0.32, P < 0.001). After considering seasonal changes in α, ΩE, and E, the accuracy of LAIDHP improves markedly, with a mean difference between the corrected LAIDHP and LAIdir of <17% in all periods. The results suggest that the proposed scheme for correcting LAIDHP is useful and effective for estimating seasonal LAI variation in deciduous broadleaf forests.

Seasonal Changes in Leaf Area Index and Chlorophyll in an Indian Grassland

1981 ◽  
Vol 69 (3) ◽  
pp. 797 ◽  
Author(s):  
M. K. Misra ◽  
B. N. Misra
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Changes ◽  
Area Index
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