Does the Data Resolution/Origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

Abstract

ABSTRACT:Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study

“…The point based accuracy assessment results illustrated that the supervised image classifiers evaluated in this study generally produced better user and overall accuracies than the unsupervised classifiers for mapping H. pomanensis. The poor performance of the unsupervised image classifiers could be attributed to the low spectral resolution (approximately 100nm wide bands) of the utilized UAV imagery [53]. The evaluated unsupervised image classifiers depend only on the spectral resolution of the imagery because they make use of a linear comparison to assign a pixel/segment to a class according to a similarity measure that only takes into account a spectral mean or a median vector of the pixel/segment without taking into consideration textural and spatial information [41].…”

“…This is explained by the generally low user and producer accuracies for the K-mediuns, Euclidian length and Isoseg classifiers. On the other hand, the supervised classifiers make use of probability models to assign pixels/segments to a class and that is why they outperformed their unsupervised counterparts for classifying low spectral resolution UAV imagery [53,54]. In addition to the probabilistic models, supervised image classifiers make use of training data-sets to guide the classifier using not only single pixels/segments but a sample group of pixels/segments to train the classifier through machine learning [50].…”

“…Supervised or unsupervised (pixel, object based and hybrid) classification approaches are tested for mapping IAPs [22]. In particular, iterative semiautomated object based classification approaches are tested for mapping IAPs such as Heracleum mantegazzianum (giant hogweed) from high spatial resolution UAV-derived data [24]. For very high resolution imagery, the object-based image classification techniques have demonstrated improved performances over the pixel based approach [25].…”

Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring

“…Moreover, extremely high detail of UAV data generates problems with precise location of validation data due to the limits of available GNSS instruments (especially in forested or hilly environments). Still, despite these limits, the UAV technology holds great potential for invasive species assessment and monitoring (Calviño-Cancela et al, 2014; Michez et al, 2016; Müllerová et al, 2016, 2017; Table 1). …”

Open-Source Processing and Analysis of Aerial Imagery Acquired with a Low-Cost Unmanned Aerial System to Support Invasive Plant Management

“…Unmanned Aerial Systems (UAS), popularly known as drones, introduce a new remote sensing technique that may become an applicable and affordable alternative to conventional approaches, as they reduce costs and increase the spatial resolution of aerial images (Wan et al, 2014;Dvořák et al, 2015;Chabot et al, 2016;Hill et al, 2016;Müllerová et al, 2016Müllerová et al, , 2017. The technical development, component miniaturization, and increased sales in recent years resulted in the rapid growth of UAS as an environmental remote-sensing platform.…”