Literature DB >> 33501565

New tools for old problems - comparing drone- and field-based assessments of a problematic plant species.

Jens Oldeland1,2, Rasmus Revermann3, Jona Luther-Mosebach3, Tillmann Buttschardt4, Jan R K Lehmann4.   

Abstract

Plant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

Entities:  

Keywords:  Elymus athericus; Hallig; Nature conservation; Nordstrandischmoor; OBIA; Salt marsh; UAV; Vegetation mapping

Mesh:

Year:  2021        PMID: 33501565      PMCID: PMC7838141          DOI: 10.1007/s10661-021-08852-2

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  4 in total

1.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

2.  Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery.

Authors:  Teja Kattenborn; Jana Eichel; Fabian Ewald Fassnacht
Journal:  Sci Rep       Date:  2019-11-27       Impact factor: 4.379

3.  Geographic Object-Based Analysis of Airborne Multispectral Images for Health Assessment of Capsicum annuum L. Crops.

Authors:  Jesús A Sosa-Herrera; Moisés R Vallejo-Pérez; Nohemí Álvarez-Jarquín; Néstor M Cid-García; Daniela J López-Araujo
Journal:  Sensors (Basel)       Date:  2019-11-05       Impact factor: 3.576

4.  Monitoring the invasion of Spartina alterniflora using very high resolution unmanned aerial vehicle imagery in Beihai, Guangxi (China).

Authors:  Huawei Wan; Qiao Wang; Dong Jiang; Jingying Fu; Yipeng Yang; Xiaoman Liu
Journal:  ScientificWorldJournal       Date:  2014-05-04
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.