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.

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.

Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software, but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology; easier reuse and adaption of workflows; better transferability between operating systems; complete description of the software environment; and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption.

Timely and detailed information on the situation in conflict areas is essential to monitor the impact of conflicts on civilians and to document human rights issues, such as large scale displacement. Remote sensing provides valuable means for conflict monitoring, especially in areas where the ground-based documentation of violence is hampered, e.g. by the limited access to or persistent insecurity of conflict zones. The manual analysis of remote-sensing data is time consuming and labour intensive, but automatic methods can increase the efficiency of corresponding workflows, if the required user interference is minimized. In this study, the use of object-based change analysis for the automatic and selective detection of destructed dwellings in bi-temporal images is explored in test areas in Darfur. The presented approach automatically determines areas of interest (settlements), and detects changes in those areas by analysis of two change features (change of edge intensity and spectral change). It applies automatically defined local reference values and thresholds of these change features to reduce the required user interference. In addition, the extended feature space in the object-based approach (including, e.g. shape, size, and relational features in addition to spectral properties) is used to distinguish destructed dwellings from other, similarly changed objects. The developed method was applied to two study areas using images from three different sensors (GeoEye-1, WorldView-2, and QuickBird) without adaptation of the thresholds or rule sets. This resulted in a producer’s accuracy of 75.4% in the first and 81.2% in the second study area. The achieved user’s accuracy was 73.3% in study area 1 and 77.2% in study area 2. The evaluation of the results shows that the automatic calculation of local reference values and thresholds for the change detection can increase robustness when the proposed method is applied on study areas with different image properties. It also demonstrates the advantages as well as the specific constraints of using object- and context-specific features in this use case for the extraction of a certain structure type on a high level of detail.