Fine scale plant community assessment in coastal meadows using UAV based multispectral data

Abstract

Highlights:-Plant communities in coastal wetlands are at risk due to the impacts of global change-Knowing the distribution of plant communities is essential for nature conservation-Communities distribution maps were produced using a UAV-based multispectral sensor-The Random Forest classifier yielded the highest classification accuracy-Species diversity and aboveground biomass affect the classification performance ABSTRACT Coastal meadows worldwide are subjected to habitat degradation due to abandonment, intensification and the impacts of global change. In order to protect and restore these habitats and ensure the supply of valuable ecosystem services, it is necessary to know the extent and location of plant communities in coastal meadows. In this study, five plant communities were mapped at very high resolution in three different study sites in West Estonia. A fixed wing UAV was used to obtain multispectral images and derive a set of vegetation indices. Two different image classification techniques were used to cluster the vegetation indices maps and produce plant community distribution maps. The highest classification accuracy was obtained using a Random Forest classifier and 13 vegetation indices. Additionally, the spectral characteristics of the training samples were correlated with aboveground biomass and species diversity. Both biomass and species diversity were positively correlated with the spectral diversity of training samples and are thus likely to have an effect on the classification accuracy. The results of this study highlight the need to utilize a wide array of vegetation indices and assess the spectral characteristics of training samples in order to obtain high classification accuracies and understand the nature of misclassification errors. The resulting maps provide a solid foundation for global change impact assessment and habitat management and restoration in coastal meadows.

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

“…The UAS data was post-processed in SenseFly eMotion 3 [40] using receiver independent exchange (RINEX) format data provided by the GNSS CORS (Continuously Operating Reference Station) of Estonia [41] for post-processing kinematics (PPK) corrections. This post-process provided an increase in the geotagging accuracy [42] of the UAS images from 5 m error to under 0.06 m, where the method and accuracy obtained is similar to [43]; and thus less than the one-pixel size in our study. Pix4D v.4.3.31 ® (Pix4D SA, 1015 Lausanne, Switzerland) software was utilized to process and radiometrically correct (default in Pix4D) the imagery and generate the multispectral orthomosaics.…”

Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types

“…Spectral vegetation indices have been used by many researchers for the clustering and classification of vegetation types. For example, Villoslada et al [ 58 ] highlighted the need to utilize a wide array of vegetation indices for the improved classification of vegetation types in coastal wetlands. Similarly, Kobayashi et al [ 59 ] utilized spectral indices calculated from a Sentinel-2 multispectral instrument for crop classification.…”

Comparison and assessment of different object-based classifications using machine learning algorithms and UAVs multispectral imagery: a case study in a citrus orchard and an onion crop

“…Several studies proved that the vegetation indices (VIs) enhanced spectral differences between vegetation/non-vegetation objects in UAV images (Gašparović et al, 2020;López-Granados et al, 2016;Solano et al, 2019;Torres-Sánchez et al, 2014;Villoslada et al, 2020). Even in our previous work (De Luca et al, 2019), the use of a VI, such as the normalized difference vegetation index (NDVI) (Eq.…”

“…In these cases, as explained in Pande-Chhetri et al (2017), the training phase's spectral features could not be enough to differentiate these classes. However, according to several scholars (Gašparović et al, 2020;López-Granados et al, 2016;Solano et al, 2019;Torres-Sánchez et al, 2014;Villoslada et al, 2020), the use of GNDVI enhanced spectral differences between vegetation and novegetation classes in VHR images, and it allowed to obtain good results of accuracy, despite the same spectral response of the plant coverings. This research was implemented in two heterogeneous study sites in terms of field structure and plant phenological activity.…”