Systematic analysis of the LUT-based inversion of PROSAIL using full range hyperspectral data for the retrieval of leaf area index in view of the future EnMAP mission

2015 ◽  
Author(s):  
M. Locherer ◽  
T. Hank ◽  
M. Danner ◽  
W. Mauser
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Full Range ◽  
Hyperspectral Data ◽  
Systematic Analysis ◽  
Area Index ◽  
The Future

scholarly journals Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

2021 ◽  
Vol 95 ◽  
pp. 102242 ◽  
Author(s):  
Rui Xie ◽  
Roshanak Darvishzadeh ◽  
Andrew K. Skidmore ◽  
Marco Heurich ◽  
Stefanie Holzwarth ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gaussian Processes ◽  
Temperate Forest ◽  
Hyperspectral Data ◽  
Area Index

Rice Leaf Area Index (LAI) estimates from hyperspectral data

2003 ◽  
Author(s):  
Xiuzhen Wang ◽  
Jingfeng Huang ◽  
Yunmei Li ◽  
Renchao Wang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Data ◽  
Area Index ◽  
Rice Leaf

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.

scholarly journals LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 99-105
Author(s):  
Elnaz Neinavaz ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Thomas A. Groen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Narrow Band ◽  
Hyperspectral Data ◽  
Partial Least Square ◽  
Least Square ◽  
Growth Potential ◽  
Partial Least Square Regression ◽  
Area Index ◽  
Thermal Data

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R<sup>2</sup> < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.

scholarly journals Leaf Area Index Estimation Model for UAV Image Hyperspectral Data Based on Wavelength Variable Selection and Machine Learning Methods

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Tao Cheng ◽  
Wei Guo ◽  
Xin Xu ◽  
Xinming Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Data ◽  
Learning Methods ◽  
Successive Projections Algorithm ◽  
Area Index ◽  
Machine Learning Methods ◽  
Successive Projections

Abstract Background In order to accurately estimate leaf area index (LAI) of winter wheat by using unmanned aerial vehicle (UAV) hyperspectral imagery.Methods The UAV hyperspectral imaging data, alternating slice-wise diagonalization (ASD) spectral data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments.The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models.Results Our results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information.The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model.Conclusions The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. Our results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.

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