Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. Thirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites.

Human reactions to conservation interventions can trigger unintended feedbacks resulting in poor conservation outcomes. Understanding unintended feedbacks is a necessary first step toward the diagnosis and solution of environmental problems, but existing anecdotal evidence cannot support decision-making. Using conservation examples, we present a conceptual framework and typology of unintended feedbacks based on a social-ecological systems (SES) approach. Three types of causal mechanisms for unintended feedbacks are distinguished: (1) flow unintended feedbacks when pre-existing feedbacks are enhanced or dampened; (2) deletion unintended feedbacks; and (3) addition unintended feedbacks when interventions, respectively, remove or add actors or links to the SES structure. Application of this typology can improve conservation outcomes by enabling the inclusion of complex relationships into planning and evaluation. We show how widely used tools for conservation planning could produce misleading recommendations, and discuss future work to mitigate the effect of unintended feedbacks in conservation practice. There is an urgent need to collect evidence in a structured way in order to understand the mechanisms by which human decision-making feeds through to conservation outcomes at different scales, thereby minimizing negative unintended feedbacks. The framework presented in this article can support the development of this evidence-base. Copyrigh

2017) Considering cost alongside the effectiveness of management in evidence-based conservation: A systematic reporting protocol,