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.

How forests change transit times of transpiration, evaporation and streamflow – a modeling approach

2021 ◽  
Author(s):  
Ingo Heidbüchel ◽  
Jie Yang ◽  
Jan H. Fleckenstein
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Cycle ◽  
Rooting Depth ◽  
Water Fluxes ◽  
Area Index ◽  
Transit Times ◽  
Physically Based ◽  
Forested Areas ◽  
The Individual

<p>In a recent paper we investigated how different catchment and climate properties influence transit time distributions. This was done by employing a physically-based spatially explicit 3D model in a virtual catchment running many different scenarios with different combinations of catchment and climate properties. We found that the velocity distribution of water fluxes through a catchment is more sensitive to certain properties while other factors appear less relevant. Now we expanded the approach by adding vegetation to the model and thus introducing new hydrologic processes (transpiration and evaporation) to the simulated water cycle. On the one hand we wanted to know how these new processes would influence transit times of the water fluxes to the stream, on the other hand we were interested in how exactly differences in the vegetation itself (e.g. rooting depth and leaf area index) would alter the various flux velocities (including transit times of transpiration and evaporation). It was very interesting to observe that streamflow in forested areas appeared to become older on average. We also found that transpiration was generally younger if the vegetation had shallower roots and/or a larger leaf area index. The biggest difference in the age of evaporation was detected for different amounts of subsequent precipitation (evaporation was generally younger in a wetter climate). In conclusion, we found that forests influence the age of the different water fluxes within a catchment. According to our results the overall hydrologic cycle is decelerated when adding vegetation to a model that otherwise only simulates evaporation.</p><p>Still, in order to make meaningful predictions on the age of hydrologic fluxes, it is not constructive to single out specific catchment and climate properties. The multitude of influences from different parameters makes it very challenging to find rules and underlying principles in the integrated catchment response. Therefore it is necessary to look at the individual parameters and their potential interactions and interdependencies in a bottom-up approach.</p>

scholarly journals Extracting Leaf Area Index Using Viewing Geometry Effects - A New Perspective on High-Resolution Unmanned Aerial System Photography.

2020 ◽  
Author(s):  
Lukas Roth ◽  
Helge Aasen ◽  
Achim Walter ◽  
Frank Liebisch
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Ecology ◽  
Growth And Yield ◽  
Unmanned Aerial System ◽  
Area Index ◽  
Handheld Device ◽  
Gap Fraction ◽  
Rgb Images ◽  
New Perspective

Abstract Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI 14 prediction accuracy was comparable with the one of a gap fraction-based handheld device (R2 of 0.92, RMSE of 0.42 m2 m2) and correlated well with destructive LAI measurements (R2 of 0.89, RMSE of 0.41 m2 m2). These results indicate that, if respecting the range (LAI ≤3) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science.

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

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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