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.

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.

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.

South Patagonian peat bogs are little studied sources of methane (CH₄). Since CH₄ fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH₄ fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH₄ fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH₄ fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH₄ closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH₄ fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m⁻²·day⁻¹ to 26.8 mg·m⁻²·day⁻¹. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH₄ fluxes when plot-scale fluxes are up-scaled.

Context: The sandy‐savanna ecosystem “Mussununga”, a natural ecosystem that occurs as patches throughout the Atlantic Forest domain, is threatened by anthropogenic factors and biological invasions of Australian Acacia species. Habitat degradation in the Atlantic Forest domain and extensive road networks could facilitate Acacia invasion into Mussununga.Objectives: We investigated whether: (a) landscape permeability (measured by effective conductance) facilitates Acacia invasion; (b) forest fragments are barriers, and roads and highways are corridors for invasive spread of Acacia; and (c) size and shape of Mussununga patches play a role in biological invasion.Methods: Acacia invasion was investigated in 32 Mussununga sites within the Atlantic Forest domain. We tested the effect of a set of landscape permeability scenarios based on circuit analysis and nine other metrics of landscape structure on Acacia occurrence using three buffer‐zone sizes (0.5, 1, and 2 km).Results: The likelihood of Acacia invasion significantly increased with landscape permeability. The best‐fitting landscape permeability scenario designated road networks as corridors, intact forests and water surfaces as barriers, and degraded habitats as non‐barriers. We also found that Mussununga areas within a 0.5 km buffer negatively affected the biological invasion by Acacia.Conclusions: Extensive habitat degradation by deforestation and dense road networks facilitate Acacia invasion into sandy‐savanna Mussununga ecosystems. Landscape permeability may be used as a risk‐assessment tool for biological invasion by Acacia species. Mussununga patches can be protected from Acacia invasion through preserving or restoring forest belts around them and disconnecting them from the road network.

Plant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.