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Article

An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones

1
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA
2
Green Drone AZ, the Grady Lab, Center for Adaptable Western Landscapes, Northern Arizona University, Flagstaff, AZ 86001, USA
3
Spatial Analysis Research Center (SPARC), School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA
*
Author to whom correspondence should be addressed.
Drones 2021, 5(1), 19; https://doi.org/10.3390/drones5010019
Received: 28 December 2020 / Revised: 10 February 2021 / Accepted: 22 February 2021 / Published: 8 March 2021
(This article belongs to the Special Issue Drones in Geography)
Mapping invasive vegetation species in arid regions is a critical task for managing water resources and understanding threats to ecosystem services. Traditional remote sensing platforms, such as Landsat and MODIS, are ill-suited for distinguishing native and non-native vegetation species in arid regions due to their large pixels compared to plant sizes. Unmanned aircraft systems, or UAS, offer the potential to capture the high spatial resolution imagery needed to differentiate species. However, in order to extract the most benefits from these platforms, there is a need to develop more efficient and effective workflows. This paper presents an integrated spectral–structural workflow for classifying invasive vegetation species in the Lower Salt River region of Arizona, which has been the site of fires and flooding, leading to a proliferation of invasive vegetation species. Visible (RGB) and multispectral images were captured and processed following a typical structure from motion workflow, and the derived datasets were used as inputs in two machine learning classifications—one incorporating only spectral information and one utilizing both spectral data and structural layers (e.g., digital terrain model (DTM) and canopy height model (CHM)). Results show that including structural layers in the classification improved overall accuracy from 80% to 93% compared to the spectral-only model. The most important features for classification were the CHM and DTM, with the blue band and two spectral indices (normalized difference water index (NDWI) and normalized difference salinity index (NDSI)) contributing important spectral information to both models. View Full-Text
Keywords: UAV; vegetation mapping; machine learning; random forest; species classification; non-native species; flooding; hydrology UAV; vegetation mapping; machine learning; random forest; species classification; non-native species; flooding; hydrology
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MDPI and ACS Style

Kedia, A.C.; Kapos, B.; Liao, S.; Draper, J.; Eddinger, J.; Updike, C.; Frazier, A.E. An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones. Drones 2021, 5, 19. https://doi.org/10.3390/drones5010019

AMA Style

Kedia AC, Kapos B, Liao S, Draper J, Eddinger J, Updike C, Frazier AE. An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones. Drones. 2021; 5(1):19. https://doi.org/10.3390/drones5010019

Chicago/Turabian Style

Kedia, Arnold C., Brandi Kapos, Songmei Liao, Jacob Draper, Justin Eddinger, Christopher Updike, and Amy E. Frazier. 2021. "An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones" Drones 5, no. 1: 19. https://doi.org/10.3390/drones5010019

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