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

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.

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Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

10.21203/rs.3.rs-1169494/v1 ◽

2021 ◽

Author(s):

Shuang Wu ◽

Lei Deng ◽

Lijie Guo ◽

Yanjie Wu

Keyword(s):

High Resolution ◽

Data Fusion ◽

Leaf Area Index ◽

Leaf Area ◽

Unmanned Aerial Vehicle ◽

Prediction Accuracy ◽

Sensor Data ◽

Support Vector ◽

Area Index ◽

Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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

Forests ◽

10.3390/f5020287 ◽

2014 ◽

Vol 5 (2) ◽

pp. 287-308 ◽

Cited By ~ 23

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

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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 ◽

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.

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Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2006.876028 ◽

2006 ◽

Vol 44 (7) ◽

pp. 1858-1865 ◽

Cited By ~ 17

Author(s):

M.R. Pandya ◽

R.P. Singh ◽

K.N. Chaudhari ◽

G.D. Bairagi ◽

R. Sharma ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Central India ◽

Sensor Data ◽

Area Index ◽

Liss Iii ◽

Modis Lai

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MULTI-SENSOR APPROACH TO LEAF AREA INDEX ESTIMATION USING STATISTICAL MACHINE LEARNING MODELS: A CASE ON MANGROVE FORESTS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-109-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 109-115

Author(s):

K. P. Martinez ◽

D. F. M. Burgos ◽

A. C. Blanco ◽

S. G. Salmo III

Keyword(s):

Machine Learning ◽

Leaf Area Index ◽

Leaf Area ◽

Machine Learning Algorithms ◽

Optical Parameters ◽

Mangrove Forests ◽

Sar Images ◽

Area Index ◽

Optical Images ◽

Sentinel 2

Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.

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