Abstract
Remote sensing by Unmanned Aerial Systems (UAS) is a dynamic evolving technology. UAS are particularly useful in environmental monitoring and management because they have the capability to provide data at high temporal and spatial resolutions. Moreover, data acquisition costs are lower than those of conventional methods such as extensive ground sampling, manned airplanes, or satellites. Small fixed-wing UAS in particular offer further potential benefits as they extend the operational coverage of the area under study at lower operator risks and accelerate data deployment times. Taking these aspects into account, UAS might be an effective tool to support management of invasive plant based on early detection and regular monitoring. A straightforward UAS approach to map invasive plant species is presented in this study with the intention of providing ready-to-use field maps essential for action-oriented management. Our UAS utilizes low-cost sensors, free-of-charge software for mission planning and an affordable, commercial aerial platform to reduce operational costs, reducing expenses with personnel while increasing overall efficiency. We illustrate our approach using a real example of invasion by Acacia mangium in a Brazilian Savanna ecosystem. A. mangium was correctly identified with an overall accuracy of 82.7% from the analysis of imagery. This approach provides land management authorities and practitioners with new prospects for environmental restoration in areas where invasive plant species are present.
Highlights
Biological invasions by plant species increased rapidly since the past century, with major impacts on nature, landscapes, and human health, which carries high economic and ecological costs throughout the globe (Mack et al, 2000; Vilà et al, 2011)
VIS-near infrared (NIR)-Based Vegetation Indices To test the capabilities of VIS-NIR-based vegetation indices (VI), we focused on the Green Normalized Difference Vegetation Index (GNDVI) and the Green Difference Vegetation Index (GDVI)—both are popular and frequently used chlorophyll indices—using the Raster Calculator in Quantum GIS (QGIS)
To illustrate image processing results and methods of analysis in more detail we selected an one-hectare subscene intensely invaded by A. mangium
Summary
Biological invasions by plant species increased rapidly since the past century, with major impacts on nature, landscapes, and human health, which carries high economic and ecological costs throughout the globe (Mack et al, 2000; Vilà et al, 2011). Invasive trees and shrubs have been recognized to be among the most problematic invaders (Richardson and Rejmánek, 2011; Richardson et al, 2014). They can alter ecosystem structure and functioning, and intensively compete with native species for space, resources, and light. Successful control or eradication depend on the availability of precise distribution data (e.g., field maps) of invasive plants in management areas. Fine-scale field maps requiring regular updates generate high personnel, data, and software costs that often hinder invasive species management efforts (Spring et al, 2017)
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