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Globally, wetlands have experienced significant declines in area and condition. Reedbeds are a key attribute of many wetlands and are typically composed of Phragmites australis (common reed), a globally distributed emergent aquatic perennial grass. Environmental water is increasingly used to support functioning river and floodplain ecosystems, including reedbeds, where maintaining wetland vegetation condition is a common objective. Drone-based remote sensing allows for the consistent collection of high-quality data in locations such as wetlands where access is limited. We used unoccupied aerial vehicles (UAVs) and convolutional neural networks (CNNs) to estimate the cover of Phragmites australis and examine the role of reedbed condition and prior environmental watering in the response of reedbeds to flooding. Data were collected from a large inland reedbed in semi-arid western New South Wales, Australia between October 2019 and March 2021 using UAVs and processed using CNNs. Prior to the flood event, sites that had received environmental water had a significantly greater cover of Phragmites australis. The sites that were not managed with environmental water had very low cover (<1%) of reeds prior to the flood event and transitioned from a Critical condition to a Poor or Medium condition following flooding. Using UAVs and CNNs we demonstrated the role environmental water plays in filling the gaps between large flood events and maintaining the condition and resilience of reedbeds. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) provide high‐spatial‐resolution imagery and allow the collection of data in locations or periods of time where field‐based data collection is challenging or impossible, such as in wetlands and floodplains. Computational deep learning techniques are transforming the way in which remotely sensed imagery and data can be used and are having an increasing role in remote sensing. Here, we describe a method using UAV and machine learning technique convolutional neural networks (CNNs) to estimate the cover of wetland features Phragmites australis reeds, leaf litter, water, bareground, and other vegetation in a large inland floodplain wetland in Western New South Wales (NSW), Australia. We firstly describe the process we took to train, validate, and test the model. We describe the model's performance by calculating a range of performance indicators and provide density maps and results from individual sites. The model had an overall accuracy of 0.947 and recognized and estimated Phragmites australis reeds to a very high accuracy (>98%). Here, we show an effective, accurate, and reproducible way to estimate the cover of Phragmites australis reeds and other wetland features using UAV and CNNs in a semi‐arid wetland. [ABSTRACT FROM AUTHOR]