Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems.

Remote sensing can be used to map tillage practices at large spatial and temporal scales. However, detecting such management practices in smallholder systems is challenging given that the size of fields is smaller than historical readily-available satellite imagery. In this study we used newer, higher-resolution satellite data from Sentinel-1, Sentinel-2, and Planet to map tillage practices in the Eastern Indo-Gangetic Plains in India. We specifically tested the classification performance of single sensor and multiple sensor random forest models, and the impact of spatial, temporal, or spectral resolution on classification accuracy. We found that when considering a single sensor, the model that used Planet imagery (3 m) had the highest classification accuracy (86.55%) while the model that used Sentinel-1 data (10 m) had the lowest classification accuracy (62.28%). When considering sensor combinations, the model that used data from all three sensors achieved the highest classification accuracy (87.71%), though this model was not statistically different from the Planet only model when considering 95% confidence intervals from bootstrap analyses. We also found that high levels of accuracy could be achieved by only using imagery from the sowing period. Considering the impact of spatial, temporal, and spectral resolution on classification accuracy, we found that improved spatial resolution from Planet contributed the most to improved classification accuracy. Overall, it is possible to use readily-available, high spatial resolution satellite data to map tillage practices of smallholder farms, even in heterogeneous systems with small field sizes.

Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR.

Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numerous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and temporal determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these landscape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual-sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially variable visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.

Assessment and valuation of recreational ecosystem services of landscapes.

Recreational ecosystem services (RES), understood as the numerous benefits people obtain from landscapes and the natural environment, are a topical area of policy, research and society. This Editorial introduces the current state of RES research, provides an overview of the 21 contributions comprising this Special Issue of Ecosystem Services, and outlines opportunities for further research. This issue's publications employ diverse methods for assessing and valuing RES at different scales in Europe and beyond. The papers present advancements in mapping and valuation, provide evidence for the contributions of biodiversity and landscapes to the generation of RES and human well-being, and shed light on distributional effects across different beneficiaries. Taken together, contributions emphasize that RES may be a prime vehicle for reconnecting people with nature with positive effects on societal well-being. The diversity of approaches currently applied in RES research reflects much creativity and new insights, for example by harnessing georeferenced social media data. Future research should aim towards harmonizing datasets and methods to enhance comparability without compromising the need for context-specific adaptations. Finally, more research is needed on options for integrating RES information in decision making, planning and management in order to enhance actual uptake in public and private decisions.

Continental-scale quantification of landscape values using social media data.

Individuals, communities, and societies ascribe a diverse array of values to landscapes. These values are shaped by the aesthetic, cultural, and recreational benefits and services provided by those landscapes. However, across the globe, processes such as urbanization, agricultural intensification, and abandonment are threatening landscape integrity, altering the personally meaningful connections people have toward specific places. Existing methods used to study landscape values, such as social surveys, are poorly suited to capture dynamic landscape-scale processes across large geographic extents. Social media data, by comparison, can be used to indirectly measure and identify valuable features of landscapes at a regional, continental, and perhaps even worldwide scale. We evaluate the usefulness of different social media platforms-Panoramio, Flickr, and Instagram-and quantify landscape values at a continental scale. We find Panoramio, Flickr, and Instagram data can be used to quantify landscape values, with features of Instagram being especially suitable due to its relatively large population of users and its functional ability of allowing users to attach personally meaningful comments and hashtags to their uploaded images. Although Panoramio, Flickr, and Instagram have different user profiles, our analysis revealed similar patterns of landscape values across Europe across the three platforms. We also found variables describing accessibility, population density, income, mountainous terrain, or proximity to water explained a significant portion of observed variation across data from the different platforms. Social media data can be used to extend our understanding of how and where individuals ascribe value to landscapes across diverse social, political, and ecological boundaries.

Combining exploratory scenarios and participatory backcasting: using an agent-based model in participatory policy design for a multi-functional landscape.

While the merits of local participatory policy design are widely recognised, limited use is made of model-based scenario results to inform such stakeholder involvement. In this paper we present the findings of a study using an agent based model to help stakeholders consider, discuss and incorporate spatial and temporal processes in a backcasting exercise for rural development. The study is carried out in the Dutch region called the Achterhoek. Region-specific scenarios were constructed based on interviews with local experts. The scenarios are simulated in an agent based model incorporating rural residents and farmer characteristics, the environment and different policy interventions for realistic projection of landscape evolution. Results of the model simulations were presented to stakeholders representing different rural sectors at a workshop. The results indicate that illustration of the spatial configuration of landscape changes is appreciated by stakeholders. Testing stakeholders' solutions by way of model simulations revealed that the effectiveness of local interventions is strongly related to exogenous processes such as market competition and endogenous processes like local willingness to engage in multifunctional activities. The integration of multi-agent modelling and participatory backcasting is effective as it offers a possibility to initiate discussion between experts and stakeholders bringing together different expertise.