scholarly journals Mapping of forest leaf area index (LAI) using airborne LiDAR data in an urban park, Shinjyuku Gyoen, Tokyo

2008 ◽  
Vol 47 (3) ◽  
pp. 37-47
Author(s):  
Hiroshi P. SATO ◽  
Mamoru KOARAI ◽  
Toshiyuki GODA ◽  
Asako ITO
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Airborne Lidar ◽  
Urban Park ◽  
Lidar Data ◽  
Area Index ◽  
Airborne 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 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

Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests

2015 ◽  
Vol 36 (10) ◽  
pp. 2569-2583 ◽  
Author(s):  
Janne Heiskanen ◽  
Lauri Korhonen ◽  
Jesse Hietanen ◽  
Petri K.E. Pellikka
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Gap ◽  
Airborne Lidar ◽  
Montane Forests ◽  
Area Index ◽  
Tropical Montane Forests ◽  
Gap Fraction
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