Estimating canopy density parameters time-series for winter wheat using uas mounted lidar

Drone; Gap fraction; Green area index; Leaf area index; Light detection and ranging; Multispectral; Plant area index; Unmanned aircraft system; Winter wheat; Budget control; Crops; Forestry; Optical sensors; Parameter estimation; Reflection; Remote sensing; Unmanned aerial vehicles (UAV); Above ground biomass; Airborne lidar systems; Chlorophyll contents; Destructive measurements; Ground measurements; Spectral reflectances; Optical radar

Abstract :

[en] Monitoring of canopy density with related metrics such as leaf area index (LAI) makes a significant contribution to understanding and predicting processes in the soil–plant–atmosphere system and to indicating crop health and potential yield for farm management. Remote sensing methods using optical sensors that rely on spectral reflectance to calculate LAI have become more mainstream due to easy entry and availability. Methods with vegetation indices (VI) based on mul-tispectral reflectance data essentially measure the green area index (GAI) or response to chlorophyll content of the canopy surface and not the entire aboveground biomass that may be present from non-green elements that are key to fully assessing the carbon budget. Methods with light detection and ranging (LiDAR) have started to emerge using gap fraction (GF) to estimate the plant area index (PAI) based on canopy density. These LiDAR methods have the main advantage of being sensitive to both green and non-green plant elements. They have primarily been applied to forest cover with manned airborne LiDAR systems (ALS) and have yet to be used extensively with crops such as winter wheat using LiDAR on unmanned aircraft systems (UAS). This study contributes to a better understanding of the potential of LiDAR as a tool to estimate canopy structure in precision farming. The LiDAR method proved to have a high to moderate correlation in spatial variation to the multi-spectral method. The LiDAR-derived PAI values closely resemble the SunScan Ceptometer GAI ground measurements taken early in the growing season before major stages of senescence. Later in the growing season, when the canopy density was at its highest, a possible overestimation may have occurred. This was most likely due to the chosen flight parameters not providing the best depictions of canopy density with consideration of the LiDAR’s perspective, as the ground-based destructive measurements provided lower values of PAI. Additionally, a distinction between total LiDAR-derived PAI, multispectral-derived GAI, and brown area index (BAI) is made to show how the active and passive optical sensor methods used in this study can complement each other throughout the growing season. © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

Disciplines :

Earth sciences & physical geography
Environmental sciences & ecology

Author, co-author :

Bates, Jordan ; Université de Liège - ULiège > Département de géographie > Earth Observation and Ecosystem Modelling (EOSystM Lab)

Montzka, Carsten; Institute of Bio-and Geosciences Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52428, Germany

Schmidt, Marius; Institute of Bio-and Geosciences Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52428, Germany

Jonard, François ; Université de Liège - ULiège > Département de géographie > Systèmes d'information géographiques

Language :

English

Title :

Estimating canopy density parameters time-series for winter wheat using uas mounted lidar

Publication date :

2021

Journal title :

Remote Sensing

eISSN :

2072-4292

Publisher :

MDPI AG

Volume :

Issue :

Pages :

1-23

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 21 September 2021

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