Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats

Daniele Ventura; Francesca Napoleone; Silvia Cannucci; Samuel Alleaume; Emiliana Valentini; Edoardo Casoli; Sabina Burrascano

doi:10.1016/j.jenvman.2022.115723

Integrating low-altitude drone based-imagery and OBIA for mapping and manage semi natural grassland habitats

J Environ Manage. 2022 Nov 1:321:115723. doi: 10.1016/j.jenvman.2022.115723. Epub 2022 Aug 19.

Authors

Daniele Ventura¹, Francesca Napoleone², Silvia Cannucci³, Samuel Alleaume⁴, Emiliana Valentini⁵, Edoardo Casoli¹, Sabina Burrascano¹

Affiliations

¹ Department of Environmental Biology, Sapienza University of Rome, P.le Aldo Moro 5, 00185, Rome, Italy.
² Department of Environmental Biology, Sapienza University of Rome, P.le Aldo Moro 5, 00185, Rome, Italy. Electronic address: francesca.napoleone@uniroma1.it.
³ Department of Life Sciences, University of Siena, Via P.A. Mattioli 4, 53100, Siena, Italy.
⁴ UMR TETIS, Univ. Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France.
⁵ Institute of Polar Sciences of the Italian National Research Council (ISP - CNR), Rome, Italy.

PMID: 35994965
DOI: 10.1016/j.jenvman.2022.115723

Abstract

Monitoring semi-natural grasslands is difficult and costly because they occur in highly dynamic and extremely complex habitat mosaics. We combined the use of a low-cost unmanned aerial vehicle (UAV) and Structure from Motion (SfM) photogrammetry to acquire high spatial resolution (∼1.5 cm pixel) RGB imagery. After image classification through Object-Based Image Analysis (OBIA), we accurately were able to distinguish three semi-natural grassland types, one of which is a habitat of conservation concern. The use of orthomosaics, digital elevation models (DEMs), and canopy height models (CHMs) yielded excellent overall classification accuracies (>89%) assessed through both remotely validated and ground-truthed points. We identified two layers of woody vegetation with a user's (UA) and producer's (PA) accuracies >73% and three grassland types: closed grassland (UA = 94%; PA = 97%), open grassland habitat (UA = 97%; PA = 93%) and open grasslands with soil erosion (UA = 96%; PA = 98%). The grassland types differed substantially in the cover of vegetation, rocks, stones, and bare soil measured in the field, as well as in the number and relative cover of the habitat diagnostic species. The proposed methodology is highly promising for mapping and monitoring semi-natural grassland of conservation concern in support of tailored management actions.

Keywords: DSM; Habitat directive; OBIA; Point clouds; Semi-natural grasslands; SfM photogrammetry.

MeSH terms

Altitude
Conservation of Natural Resources* / methods
Ecosystem
Grassland*
Unmanned Aerial Devices