Using unmanned aerial vehicles for vegetation mapping and identification of botanical species in wetlands

Abstract

High-resolution aerial photographs have important applications in vegetation mapping, especially in environments, such as wetlands, which are not easily accessible by ground operators. Unmanned aerial vehicles (UAVs), equipped with cameras capable of taking photographs of < 1 cm pixel resolution, are promising not only for the vegetation mapping but also for the identification of plant species. This paper illustrated the results of three different flight heights (5 m = 3.5126 mm/pixel; 10 m = 7.0252 mm/pixel; 25 m = 17.5630 mm/pixel), using 12MP images and their magnification, on the identification of vegetation and botanical species in a rewetted peatland populated mainly by Phragmites australis and Myriophyllum aquaticum within the Massaciuccoli Lake basin (Northern Tuscany, Italy). Among the obtained images, we selected the best flight height for the vegetation mapping and the botanical identification of the plant species using both visual and automated image analyses. Images taken from flights at 25 m of height proved to be useful for a sufficiently detailed mapping, while those from 10 m of height were more suitable for the detection of plant microcommunities. However, the most accurate identification of the species (at the taxonomic level of genus/species) was possible only with the images taken from 5 m of height.

Isolation of Four Microalgal Strains From the Lake Massaciuccoli: Screening of Common Pollutants Tolerance Pattern and Perspectives for Their Use in Biotechnological Applications

“…With a surface area of 6.9 km 2 , it represents one of the largest swamps that in ancient times entirely covered the Versilia coastal plain. From an environmental point of view, the Lake Massaciuccoli has been largely studied in the past decades for its eutrophication problems (i.e., Ciurli et al, 2009 ), for mapping the biodiversity of plant species ( Lastrucci et al, 2017 ; Bertacchi et al, 2019 ), to study fish parasites ( Macchioni et al, 2015 ) and also to investigate on the ciliate (protists) community harboring the lake ( Mori et al, 1998 ). Despite its peculiar features, especially the eutrophic characteristics, the microalgal consortia of the Lake Massaciuccoli have never been explored up to now.…”

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

“…Image classification techniques often involved using Esri or AgiSoft software, with occasional classifications made in Python [56], Google Earth Engine [36]), eCognition [10,14,24,27,29,42,63,[69][70][71][72], and ENVI [13,14,73] software. Vegetation classification often used Random Forest algorithms [23,27,55,69,[74][75][76], and there was a significant cluster of studies using object-based image analysis (OBIA) [23,24,29,69,70,77,78]. A recent study used deep learning algorithms such as convolutional neural network architectures to classify coastal wetland land cover [79].…”

“…Four papers embraced an OBIA workflow to map wetland features. For example, the complex vegetation mosaic found in peatlands and heathlands were mapped using OBIA techniques applied to RGB orthomosaics by Bertacchi et al [69] and Díaz-Varella et al [70], respectively. Shang et al [77] used an OBIA method to classify coastal wetland community types in China.…”

“…This may decrease the ability to successfully distinguish landscape classes [42,139,141], especially from spectrally limited RGB imagery [75,76,104]. Although multiple techniques to navigate these challenges have been proposed, such as image texture measures summarizing local heterogeneity at the pixel or local moving window scales and OBIA tools [29,55,69,76,78,104], their use among wetland UAV studies remains somewhat limited and calls for more guiding recommendations. Image texture metrics were used with mixed success in the reviewed studies and showed strong sensitivity to spectral artifacts caused by topographic breaks, orthomosaicking distortions, and similar factors [27,55,68,140,143].…”

“…On the field operations side, a critical precursor of such efficiency lies in navigating the tradeoff among spatial coverage and timing of operations and informative content of the data. For a given camera field of view, reducing flight altitude can increase the flight time of a study site, but also spatial resolution [34,65,67,69,115,133]. Similarly, expanding the overlap among images at a given altitude may improve the quality of photogrammetric products such as DSMs [57,95], but escalate both the time of data collection and postprocessing computational workload [13,65].…”