Darvishzadeh, R.

Neinavaz, E.

Skidmore, A.K.

English

The leaf area index (LAI)- as one of the most important vegetation biophysical variables, has been retrieved in vegetation canopies using data from different remote sensing platforms. LAI was recently proposed as a remote sensing-enabled essential biodiversity variable. To our knowledge, however, the retrieval of the LAI using hyperspectral thermal infrared (i.e., TIR 8-14 m) data has been addressed only under controlled laboratory conditions and has not yet been accomplished using thermal infrared hyperspectral data acquired from an airborne platform. Therefore, the primary goal of this study is to determine the accuracy of LAI prediction using thermal infrared hyperspectral data acquired from an airborne platform. The field campaign was conducted in July 2017 in the Bavarian Forest National Park in southeast Germany, and biophysical parameters, including LAI, were measured for 36 plots. Concurrently, thermal hyperspectral data were obtained using the Twin Otter aircraft operated by NERC-ARF (i.e., the U.K. Natural Environment Research Council- Airborne Research Facility) and the AISA Owl sensor. LAI was retrieved using an artificial neural network Levenberg-Marquardt algorithm. The results indicated that thermal infrared hyperspectral data could estimate LAI with relatively high accuracy (R= 0.734, RMSE=0.554). The study showed the significance of using an artificial neural network. It proved the possibility of using hyperspectral thermal infrared data to estimate vegetation biophysical properties at the canopy level and over a large forest area

University of Twente Research Information

PREDICTION OF LEAF AREA INDEX USINGHYPERSPECTRAL THERMAL INFRAREDIMAGERY OVER THE MIXED TEMPERATEFORESTElnaz Neinavaz, Roshanak Darvishzadeh, Andrew K. SkidmoreIntroduction3,0 8,0Visible Near InfraredThermal InfraredMid-wave InfraredWavelength (μm)ReflectanceLong-wave InfraredLeaf area index (LAI) is an extremely important variable of terrestrial ecosystems due to the strongcorrelation with many ecosystem processes such as water balance and evapotranspiration and its directrelation to the plant energy balance and gas exchanges.• Upscaling from leaf to canopy level• Apply as an input in different model and for various applications.3LAI was predicted usingunivariate and multivariateas well as artificial neuralnetwork under laboratory-controlled condition usingTIR hyperspectral data.4Vegetation indices PLSRArtificial neural networkResearch ObjectiveEvaluate the retrieval of LAI applying artificial neuralnetwork using TIR hyperspectral remote sensing data.5Study AreaThe study area is in Bavaria Forest National Park.Elevation in this area ranges from 600 m to 1453 m above sea level. The climate of the region is temperate. Precipitation varies from 1200 to 1800 mm/year (of which 50% is snow)Above 1100 m, at high altitude, there are sub-alpinespruce forests with Norway spruce (Picea abies) andsome Mountain ash (Sorbus aucuparia).On the slopes, between 600 m and 1100 m, mountainmixed forests with Norway spruce, Silver fir (Abies alba),European beech (Fagus sylvatica), and Norway maple(Acer pseudoplatanus) can be found.In wet depressions in the lower valley (i.e. valley bottoms),Spruce forests mixed with Norway spruce, Mountain ash,and Birches (Betula pendula, and Betula pubescens)occur. 6Flight Campaign: European Facility for Airborne Research (EUFAR)Airborne Remote Sensing for Monitoring Essential Biodiversity Variables in Forest Ecosystems (RS4forestEBV)78TIR hyperspectral images of the study area were acquired under clear skies on the 6th of July 2017.The data was collected based on 30 flight lines over the Northern and Southern parts of the BNFP operated by NERC-ARF.Specim's AISA Fenix and AISA Owl were mounted on the aircraft.During the flight a pair of internal blackbodies that were mechanically switchedconsecutively for calibration of the sensor.Specim’s Thermal Sensor Aisa-OWL9Specim's thermal sensor OWL is a fast push-broom hyperspectral system designed to provide high-quality remote sensing data. The AISA Owl covers the contiguous spectral range from 7.6 to 12.3μm in 102 spectral channels. The spatial resolution is approximately 3m based on the flight height of 2083m.Pre-processing10Field Data Collection11The coordinates of 36 plots (30m X30m) were collected.Differential Global PositioningSystem (Leica 1200 ) was used torecord the center location of eachnorth-oriented plot.Leaf area index was measured using LI-COR 2200C Canopy AnalyzerA mosaic of flight lines showing the retrieved land surface temperature from TIR hyperspectral data.A mosaic of all flight lines showing the retrieved emissivity from TIR hyperspectral data.1213Training algorithm:14Levenberg-MarquardtBayesian-RegularizationSelected-conjugate gradientTraining algorithm:Levenberg-Marquardt 15ResultsTraining algorithm:Bayesian-Regularization16Training algorithm:Selected-conjugate gradient17Conclusion18Leaf area index can be retrieved using airborne TIR hyperspectral data with moderate accuracy by means of artificial neural network.The results of the LAI prediction accuracy using airborne TIR hyperspectral data relay on selected training algorithm.Developing TIR based vegetation index Further investigation needs to be done using Physical model.Fusion of TIR hyperspectral and LiDAR dataTHANK YOU!ELNAZ NEINAVAZE.NEINAVAZ@UTWENTE.NLWWW.ITC.NL/WWW.ITC.NL/EO4ALL/

Prediction of leaf area index using hyperspectral thermal infrared imagery over the mixed temperate forest

https://ris.utwente.nl/ws/files/285174317/Elnaz_Neinavaz_et_al._2022_EARSeL.pdf

Prediction of leaf area index using hyperspectral thermal infrared imagery over the mixed temperate forest

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