scholarly journals Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring

2018 ◽  
Vol 10 (2) ◽  
pp. 179 ◽  
Author(s):  
Irina Melnikova ◽  
Yoshio Awaya ◽  
Taku Saitoh ◽  
Hiroyuki Muraoka ◽  
Takahiro Sasai
Keyword(s):  
Simple Model ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Resolution ◽  
Mountain Forest ◽  
Central Japan ◽  
Area Index ◽  
Seasonal Monitoring

Testing high spatial resolution WorldView-2 imagery for retrieving the leaf area index

2015 ◽  
Author(s):  
Eufemia Tarantino ◽  
Antonio Novelli ◽  
Maurizio Laterza ◽  
Andrea Gioia
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Area Index

scholarly journals Assessing Spatial Variability of Barley Whole Crop Biomass Yield and Leaf Area Index in Silvoarable Agroforestry Systems Using UAV-Borne Remote Sensing

2021 ◽  
Vol 13 (14) ◽  
pp. 2751
Author(s):  
Matthias Wengert ◽  
Hans-Peter Piepho ◽  
Thomas Astor ◽  
Rüdiger Graß ◽  
Jayan Wijesingha ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spatial Resolution ◽  
Biomass Yield ◽  
Alley Cropping ◽  
High Spatial Resolution ◽  
Plant Traits ◽  
Agroforestry Systems ◽  
Area Index

Agroforestry systems (AFS) can provide positive ecosystem services while at the same time stabilizing yields under increasingly common drought conditions. The effect of distance to trees in alley cropping AFS on yield-related crop parameters has predominantly been studied using point data from transects. Unmanned aerial vehicles (UAVs) offer a novel possibility to map plant traits with high spatial resolution and coverage. In the present study, UAV-borne red, green, blue (RGB) and multispectral imagery was utilized for the prediction of whole crop dry biomass yield (DM) and leaf area index (LAI) of barley at three different conventionally managed silvoarable alley cropping agroforestry sites located in Germany. DM and LAI were modelled using random forest regression models with good accuracies (DM: R² 0.62, nRMSEp 14.9%, LAI: R² 0.92, nRMSEp 7.1%). Important variables for prediction included normalized reflectance, vegetation indices, texture and plant height. Maps were produced from model predictions for spatial analysis, showing significant effects of distance to trees on DM and LAI. Spatial patterns differed greatly between the sampled sites and suggested management and soil effects overriding tree effects across large portions of 96 m wide crop alleys, thus questioning alleged impacts of AFS tree rows on yield distribution in intensively managed barley populations. Models based on UAV-borne imagery proved to be a valuable novel tool for prediction of DM and LAI at high accuracies, revealing spatial variability in AFS with high spatial resolution and coverage.

scholarly journals Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

2015 ◽  
Vol 7 (5) ◽  
pp. 5329-5346 ◽  
Author(s):  
Shinya Tanaka ◽  
Kensuke Kawamura ◽  
Masayasu Maki ◽  
Yasunori Muramoto ◽  
Kazuaki Yoshida ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Spectral Index ◽  
Central Japan ◽  
Area Index ◽  
Gifu Prefecture
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