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.

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.

scholarly journals Metsa lehepinnaindeksi kaardistamine lennukilidari, satelliidipiltide ja maapealsete mõõtmiste abil Järvselja VALERI testalal / Leaf area index mapping with airborne lidar, satellite images and ground measurements in Järvselja VALERI test site

2011 ◽  
Vol 55 (1) ◽  
pp. 11-32 ◽  
Author(s):  
Ave Kodar ◽  
Mait Lang ◽  
Tauri Arumäe ◽  
Alo Eenmäe ◽  
Jan Pisek ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Random Noise ◽  
Field Measurements ◽  
Test Site ◽  
Tree Canopy ◽  
Airborne Lidar ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Area Index

Abstract The aim of this study was to compile the leaf area index (LAI) map of a 3×3 km VALERI test site in Järvselja, Estonia. Canopy transmittance measurements of LAI by LAI-2000 Plant Canopy Analyzers and digital cameras supplied with fisheye converters were carried out on 42 elementary sampling units at ground level and at breast height level. The vegetation LAI was estimated as a sum of the tree canopy true green LAI obtained from the inversion of canopy gap fraction data and of the ground vegetation effective LAI. Red channel from SPOT-4 HRV-IR image and airborne lidar data based canopy transmittance were used for up-scaling. The LAI map was compared with standard LAI products from Terra MODIS and ENVISAT MERIS. We found that lidar data based LAI estimate on up-scaled map saturated at high values (LAI > 4.5) compared to the LAI estimates based on SPOT-4 HRV-IR red channel. Validation of MODIS LAI product revealed substantial underestimates of LAI compared to the up-scaled field measurements and rather large random noise. ENVISAT MERIS LAI product was more similar to up-scaled field measurements; however, rather large unexpected random variations exist in its time series.

scholarly journals Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations

2017 ◽  
Vol 9 (2) ◽  
pp. 163 ◽  
Author(s):  
Haotian You ◽  
Tiejun Wang ◽  
Andrew Skidmore ◽  
Yanqiu Xing
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Coniferous Forest ◽  
Airborne Lidar ◽  
Area Index

Optimising prediction of forest leaf area index from discrete airborne lidar

2017 ◽  
Vol 200 ◽  
pp. 220-239 ◽  
Author(s):  
Grant D. Pearse ◽  
Justin Morgenroth ◽  
Michael S. Watt ◽  
Jonathan P. Dash
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Airborne Lidar ◽  
Area Index

scholarly journals An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

2008 ◽  
Vol 35 (10) ◽  
pp. 1070 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Anthony R. Palmer ◽  
Daniel Taylor ◽  
Melanie Zeppel ◽  
Rhys Whitley ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gap Analysis ◽  
Digital Camera ◽  
Field Measurements ◽  
Accurate Estimation ◽  
Image Capture ◽  
Area Index ◽  
Modis Lai ◽  
Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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