elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data - a comparison of sensors, algorithms, and predictor sets

Schucknecht, Anne and Seo, Bumsuk and Krämer, Alexander and Asam, Sarah and Atzberger, Clement and Kiese, Ralf (2022) Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data - a comparison of sensors, algorithms, and predictor sets. Biogeosciences Discussions. Copernicus Publications. doi: 10.5194/bg-2021-250. ISSN 1810-6277. (Submitted)

Full text not available from this repository.

Official URL: https://bg.copernicus.org/preprints/bg-2021-250/

Abstract

Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UAS) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (Linear Model; Random Forests, RF; Gradient Boosting Machines, GBM) and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors, but was not available in our study. Therefore, we tested the added value of this structural information with in-situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in Southern Germany to obtain in-situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized and all model set-ups were run with a six-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor-predictor set combinations with average (avg) R2cv of 0.48, RMSEcv, avg of 53.0 g m2 and rRMSEcv, avg of 15.9 % for DM, and with R2cv, avg of 0.40, RMSEcv, avg of 0.48 wt.% and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R2cv = 0.67, RMSEcv = 41.9 g m2, rRMSEcv = 12.6 %) was achieved with a RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of a RF model with all predictors and SEQ sensor data (R2cv = 0.47, RMSEcv = 0.45 wt.%, rRMSEcv = 14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating ML algorithm improved the model performance substantially, which shows the importance of this step.

Item URL in elib:https://elib.dlr.de/147826/
Document Type:Article
Title:Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data - a comparison of sensors, algorithms, and predictor sets
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Schucknecht, Anneanne.schucknecht (at) kit.eduUNSPECIFIED
Seo, BumsukKIT Institute for Meteorology and Climate Research, Atmospheric Environmental ResearchUNSPECIFIED
Krämer, Alexanderalexander.kraemer (at) wwl-web.deUNSPECIFIED
Asam, Sarahsarah.asam (at) dlr.dehttps://orcid.org/0000-0002-7302-6813
Atzberger, ClementBoKu WienUNSPECIFIED
Kiese, RalfKIT Institute for Meteorology and Climate Research, Atmospheric Environmental ResearchUNSPECIFIED
Date:2022
Journal or Publication Title:Biogeosciences Discussions
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.5194/bg-2021-250
Publisher:Copernicus Publications
ISSN:1810-6277
Status:Submitted
Keywords:grassland, biomass, nitrogen, UAV, alpine,multispectral, machine learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Asam, Dr. Sarah
Deposited On:10 Jan 2022 10:44
Last Modified:10 Jan 2022 10:44

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.