Retrieval of the Forest Leaf Area Index Based on the Laser Penetration Ratio from the GLAS Waveform Lidar Data

2019 ◽  
Author(s):  
Lei Cui ◽  
Jing Guo ◽  
Ziti Jiao ◽  
Mei Sun ◽  
Yadong Dong ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lidar Data ◽  
Area Index ◽  
Penetration Ratio ◽  
Waveform Lidar

Estimating leaf area index of maize using airborne full-waveform lidar data

2015 ◽  
Vol 7 (2) ◽  
pp. 111-120 ◽  
Author(s):  
Sheng Nie ◽  
Cheng Wang ◽  
Pinliang Dong ◽  
Xiaohuan Xi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lidar Data ◽  
Area Index ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar

scholarly journals Direct Estimation of Forest Leaf Area Index based on Spectrally Corrected Airborne LiDAR Pulse Penetration Ratio

2020 ◽  
Vol 12 (2) ◽  
pp. 217 ◽  
Author(s):  
Yonghua Qu ◽  
Ahmed Shaker ◽  
Lauri Korhonen ◽  
Carlos Alberto Silva ◽  
Kun Jia ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Extinction Coefficient ◽  
Remotely Sensed ◽  
Lidar Data ◽  
Area Index ◽  
Physically Based Model ◽  
Gap Fraction ◽  
Penetration Ratio ◽  
Physically Based

The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, and such methods can only be applied when the field-based LAI data are available. Compared with an empirical model, a physically-based model—e.g., the Beer–Lambert law based light extinction model—is more attractive due to its independent dataset with training. However, two challenges are encountered when applying the physically-based model to estimate LAI from discrete LiDAR data: i.e., deriving the gap fraction and the extinction coefficient from the LiDAR data. We solved the first problem by integrating LiDAR and hyperspectral data to transfer the LiDAR penetration ratio to the forest gap fraction. For the second problem, the extinction coefficient was estimated from tiled (1 km × 1 km) LiDAR data by nonlinearly optimizing the cost function of the angular LiDAR gap fraction and simulated gap fraction from the Beer–Lambert law model. A validation against LAI-2000 measurements showed that the estimates were significantly correlated to the reference LAI with an R2 of 0.66, a root mean square error (RMSE) of 0.60 and a relative RMSE of 0.15. We conclude that forest LAI can be directly estimated by the nonlinear optimization method utilizing the Beer–Lambert model and a spectrally corrected LiDAR penetration ratio. The significance of the proposed method is that it can produce reliable remotely sensed forest LAI from discrete LiDAR and spectral data when field-measured LAI are unavailable.

scholarly journals Estimation of Biomass and Leaf Area Index in the Western Ghats Forest Ecosystem by the Integrated Analysis of Hyperspectral Data and Space Borne LiDAR Data

2019 ◽  
pp. 1-12
Author(s):  
Indu Indirabai ◽  
M. V. Harindranathan Nair ◽  
Jaishanker R. Nair ◽  
Rama Rao Nidamanuri
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Western Ghats ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Lidar Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
The Western Ghats

The Western Ghats regions of India are characterised by highly complex and biodiverse forest ecosystem with heterogeneous tree species. The integration of LiDAR data with multispectral remote sensing has limitations in the case of spectral information abundance. The objective of this study was to undertake biophysical characterisation in the Western Ghats regions of India by the integration of GLAS ICESat data and AVIRIS-NG hyperspectral data. The methodology of the study includes pre-processing of the hyperspectral and ICESat GLAS data followed by the integration of the two data sets based on pixel based fusion strategy in order to estimate the biophysical parameters of forests. Biomass was estimated by Support Vector Regression method. The structural characteristics extracted from the LiDAR data are integrated with spectral characteristics from the AVIRIS NG imagery based on the pixel level so that biophysical characteristics including canopy height, biomass, Leaf Area Index are estimated. The integrated product on further analysis revealed the applicability of this approach to extract more spectral information and forest parameters. The key findings of the study include biophysical parameters both structural as well as abundant spectral information can be retrieved successfully by the methodology used which have strong correlation with the in situ measurements. The study concluded that biophysical parameters including Leaf Area Index, biomass and canopy height can be effectively estimated by the integration of AVIRIS-NG imagery and GLAS data, which cannot be possible when used independently. It is recommended to have continuous retrieval of LiDAR foot prints instead of discrete, to make modelling of the biophysical parameters a little more effective.

Forest Leaf Area Index (LAI) Estimation Using Airborne Discrete-Return Lidar Data

2013 ◽  
Vol 56 (3) ◽  
pp. 233-242 ◽  
Author(s):  
LUO She-Zhou ◽  
WANG Cheng ◽  
ZHANG Gui-Bin ◽  
XI Xiao-Huan ◽  
LI Gui-Cai
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lidar Data ◽  
Area Index

scholarly journals Remote Sensing of Leaf Area Index from LiDAR Height Percentile Metrics and Comparison with MODIS Product in a Selectively Logged Tropical Forest Area in Eastern Amazonia

2021 ◽  
Author(s):  
Yonghua Qu ◽  
Ahmed Shaker ◽  
Carlos Alberto Silva ◽  
Carine Klauberg ◽  
Ekena Rangel Pinagé
Keyword(s):  
Tropical Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Tropical Forests ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Area Index ◽  
Dense Forest ◽  
Modis Lai ◽  
Airborne Lidar Data

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data.

Clumping Effects in Leaf Area Index Retrieval From Large-Footprint Full-Waveform LiDAR

2021 ◽  
pp. 1-20
Author(s):  
Hailan Jiang ◽  
Shiyu Cheng ◽  
Guangjian Yan ◽  
Andres Kuusk ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar
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