From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa

Bergamo, Thaisa; Lima, Raul Sampaio de; Kull, Tiiu; Ward, Raymond D.; Sepp, Kalev; Villoslada, Miguel

doi:10.1016/j.jenvman.2023.117693

Cited by 8 publications

(6 citation statements)

References 88 publications

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“…A second challenge is the lack of studies attempting to scale up from small, plot‐level studies to larger landscape‐level or regional extents (Bergamo et al., 2023) or to replicate methods across species or geographies. Of the studies we reviewed, 49% involved a single data collection event at a location that could be imaged in a single flight.…”

Section: Discussionmentioning

confidence: 99%

Systematic review and best practices for drone remote sensing of invasive plants

Singh,

Surasinghe,

Frazier

2024

Methods Ecol Evol

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Drones have emerged as a cost‐effective solution to detect and map plant invasions, offering researchers and land managers flexibility in flight design, sensors and data collection schedules. A systematic review of trends in drone‐based image collection, data processing and analytical approaches is needed to advance the science of invasive species monitoring and management and improve scalability and replicability. We systematically reviewed studies using drones for plant invasion research to identify knowledge gaps, best practices and a path toward advancing the science of invasive plant monitoring and management. We devised a database of 33 standardized reporting parameters, coded each study to those parameters, calculated descriptive statistics and synthesized how these technologies are being implemented and used. Trends show a general increase in studies since 2009 with a bias toward temperate regions in North America and Europe. Most studies have focused on testing the validity of a machine learning or deep learning image classification technique with fewer studies focused on monitoring or modelling spread. Very few studies used drones for assessing ecosystem dynamics and impacts such as determining environmental drivers or tracking re‐emergence after disturbance. Overall, we noted a lack of standardized reporting on field survey design, flight design, drone systems, image processing and analyses, which hinders replicability and scalability of approaches. Based on these findings, we develop a standard framework for drone applications in invasive species monitoring to foster cross‐study comparability and reproducibility. We suggest several areas for advancing the use of drones in invasive plant studies including (1) utilizing standardized reporting frameworks to facilitate scientific research practices, (2) integrating drone data with satellite imagery to scale up relationships over larger areas, (3) using drones as an alternative to in‐person ground surveys and (4) leveraging drones to assess community trait shifts tied to plant fitness and reproduction.

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Section: Discussionmentioning

confidence: 99%

Systematic review and best practices for drone remote sensing of invasive plants

Singh,

Surasinghe,

Frazier

2024

Methods Ecol Evol

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“…A key process required to perform upscaling of remote sensing images is the aggregation of pixel values from a highresolution image to the geographically coincident pixels of a coarser-resolution image. Several studies have performed the aggregation process to a common geographical data frame in the form of a quasi-continuous grid, where all the spectral data are stored (Padró et al, 2018;Riihimäki et al, 2019;Mao et al, 2022;Bergamo et al, 2023). In the present study, the grid was constrained to the limits of each study area, avoiding those overlapping with the edges, excluding transitional areas that do not correspond to the extent of plant communities of interest and submerged areas.…”

Section: Image Processing and Upscalingmentioning

confidence: 99%

Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models

Martínez Prentice,

Villoslada,

Ward

et al. 2024

Biogeosciences

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Abstract. Coastal wetlands provide a range of ecosystem services, yet they are currently under threat from global change impacts. Thus, their monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small-scale studies using drones or national-/regional-/global-scale studies using satellite-derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board unoccupied aerial vehicles (UAVs) to calculate the plant fractional cover (PFC) in Sentinel-2 MultiSpectral Instrument (MSI) sensor grids. A random forest (RF) algorithm was trained and tested with vegetation indices (VIs) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a digital elevation model (DEM). After comparing the models, results show that using DEM with VIs can increase the prediction accuracy of PFC up to 2 times (R2 58 %–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.

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“…However, the robustness of the method ensures its replicability across various latitudes. Moreover, it is feasible to extend its applicability from a local geographic scope to wider areas thought upscaling techniques [17,22]. The present method uses data from field measurements, digital photogrammetry, multispectral analysis, and Machine Learning techniques, mostly through open source software; the method is also straightforward to implement.…”

Section: Spatial Distribution Of Cflsmentioning

confidence: 99%

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

Chávez-Durán,

García,

Olvera-Vargas

et al. 2024

Forests

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Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R2 = 0.75, RMSE = 1.78 Mg, and an average Biasrel = 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management.

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From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa

Cited by 8 publications

References 88 publications

Systematic review and best practices for drone remote sensing of invasive plants

Systematic review and best practices for drone remote sensing of invasive plants

Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

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