2 research outputs found

    Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

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    The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy

    Field Spectroscopy in the VNIR-SWIR region to discriminate between Mediterranean native plants and exotic-invasive shrubs based on leaf tannin content

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    The invasive shrub, Acacia longifolia, native to southeastern Australia, has a negative impact on vegetation and ecosystem functioning in Portuguese dune ecosystems. In order to spectrally discriminate A. longifolia from other non-native and native species, we developed a classification model based on leaf reflectance spectra (350–2500 nm) and condensed leaf tannin content. High variation of leaf tannin content is common for Mediterranean shrub and tree species, in particular between N-fixing and non-N-fixing species, as well as within the genus, Acacia. However, variation in leaf tannin content has not been studied in coastal dune ecosystems in southwest Portugal. We hypothesized that condensed tannin concentration varies significantly across species, further allowing for distinguishing invasive, nitrogen-fixing A. longifolia from other vegetation based on leaf spectral reflectance data. Spectral field measurements were carried out using an ASD FieldSpec FR spectroradiometer attached to an ASD leaf clip in order to collect 750 in situ leaf reflectance spectra of seven frequent plant species at three study sites in southwest Portugal. We applied partial least squares (PLS) regression to predict the obtained leaf reflectance spectra of A. longifolia individuals to their corresponding tannin concentration. A. longifolia had the lowest tannin concentration of all investigated species. Four wavelength regions (675–710 nm, 1060–1170 nm, 1360–1450 nm and 1630–1740 nm) were identified as being highly correlated with tannin concentration. A spectra-based classification model of the different plant species was calculated using a principal component analysis-linear discriminant analysis (PCA-LDA). The best prediction of A. longifolia was achieved by using wavelength regions between 1360–1450 nm and 1630–1740 nm, resulting in a user’s accuracy of 98.9%. In comparison, selecting the entire wavelength range, the best user accuracy only reached 86.5% for A. longifolia individuals
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