scholarly journals Analysis of temporal variability of MODIS leaf area index (LAI) product over temperate forest in Korea

2005 ◽  
Author(s):  
Sun-Hwa Kim ◽  
Ji-Hoon Park ◽  
Choong-Sik Woo ◽  
Kyu-Sung Lee
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Temporal Variability ◽  
Temperate Forest ◽  
Area Index

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

scholarly journals Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil Using the Sentine-2

2019 ◽  
Vol 11 (15) ◽  
pp. 187
Author(s):  
Carolina Jaramillo-Giraldo ◽  
Williams Pinto Marques Ferreira ◽  
Humberto Paiva Fonseca ◽  
Marcelo de Freitas Ribeiro ◽  
Laís Maria Rodrigues Silva ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Temporal Variability ◽  
Crop Coefficient ◽  
Area Index ◽  
Directional Information ◽  
Production Area ◽  
Spatio Temporal ◽  
The Relationship ◽  
Sentinel 2

Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.

scholarly journals A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3036
Author(s):  
Jinling Song ◽  
Xiao Zhu ◽  
Jianbo Qi ◽  
Yong Pang ◽  
Lei Yang ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Temperate Forest ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Area Index ◽  
Waveform Data ◽  
Cloud Data ◽  
Full Waveform

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.

Sign in / Sign up

Export Citation Format

Share Document