Datum
2021-07-13Autor
Wengert, MatthiasPiepho, Hans-PeterAstor, ThomasGraß, RüdigerWijesingha, JayanWachendorf, MichaelSchlagwort
630 Landwirtschaft, Veterinärmedizin PräzisionslandwirtschaftAgroforstwirtschaftFlugkörperGersteErnteMetadata
Zur Langanzeige
Aufsatz
Assessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensing
Zusammenfassung
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.
Zitierform
In: Remote sensing Volume 13 / Issue 14 (2021-07-13) eissn:2072-4292Förderhinweis
Gefördert durch den Publikationsfonds der Universität KasselZitieren
@article{doi:10.17170/kobra-202107234399,
author={Wengert, Matthias and Piepho, Hans-Peter and Astor, Thomas and Graß, Rüdiger and Wijesingha, Jayan and Wachendorf, Michael},
title={Assessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensing},
journal={Remote sensing},
year={2021}
}
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2021-07-23T09:07:59Z 2021-07-23T09:07:59Z 2021-07-13 doi:10.17170/kobra-202107234399 http://hdl.handle.net/123456789/13033 Gefördert durch den Publikationsfonds der Universität Kassel eng Namensnennung 4.0 International http://creativecommons.org/licenses/by/4.0/ UAV agroforestry multispectral barley alley cropping predictive modellingeng SFM 630 Assessing spatial variability of barley whole crop biomass yield and leaf area index in silvoarable agroforestry systems using UAV-borne remote sensing Aufsatz 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. open access Wengert, Matthias Piepho, Hans-Peter Astor, Thomas Graß, Rüdiger Wijesingha, Jayan Wachendorf, Michael doi:10.3390/rs13142751 Präzisionslandwirtschaft Agroforstwirtschaft Flugkörper Gerste Ernte publishedVersion eissn:2072-4292 Issue 14 Remote sensing Volume 13 false 2751
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