Grassland mowing event detection from multi-sensor time series to evaluate agri-environmental measures – a model comparison exercise
Grassland areas are an essential part of the agricultural landscapes across Europe and provide key ecological functions and services in a multifunctional agriculture. The conservation and sustainable use of grassland areas directly contributes to policies, strategies and measures at European and national level that aim at a transition towards more sustainable agriculture within the scope of the European Green Deal.
Grassland areas are managed intensively or extensively for the provision of food, fodder or raw biomass for energy production. Due to their high relevance for the preservation of biological diversity, they are subject to agricultural policy measures as well as nature protection. Besides overall conversion embargos, those measures are also targeted towards a more extensive use of grassland, which is known to have positive impact on biodiversity. On the other hand, the management intensity in grasslands can also be an indicator for the evaluation of climate protection measures (e.g., in peatlands). For meadows, the management intensity can be described by proxies such as the mowing frequency where a higher number of cuts indicates higher intensities. Besides mowing frequency, the date of mowing (e.g., date of first cut) is also a relevant parameter for the evaluation of agri-environmental measures under the Common Agricultural Policy (CAP), e.g., for fallow land.
However, as information on grassland management is usually not reported there is a lack of knowledge on the spatial distribution of grassland use intensity. Lately, it has been shown that the availability of dense time series of remote sensing data enables to fill this gap. Various approaches have been published that make use of radar data, optical data or a combination of data from both domains. Two recently published studies of the authors underlined the overall potential of dense time series from combined Sentinel- and Landsat-data (Lobert et al. 2021; Schwieder, et al. 2021). In another recent publication, de Vroey et al. (2021) evaluated the rule-based approach of the Sen4Cap processor for the detection of mowing events for Belgium, which was developed in the Sentinels for Common Agricultural Policy framework, and makes use of Sentinel-1 and Sentinel-2 time series. All studies highlight the potential as well as limitations of the proposed data and methods for selected regions and provide accuracy measures for the evaluation of their results. However, the accuracies among the different studies are hardly comparable due to different settings and measures of the validation framework.
Against this background we conducted a model comparison study to predict mowing date and mowing frequency in grassland and fallow land from dense time series of Sentinel-data and third-party missions. We here present the concept and result of the comparison study where we estimated mowing events and dates for the federal state of Brandenburg using i) a convolutional neural network approach with time series of Sentinel-1, Sentinel-2 and Landsat 8 data (Lobert et al. 2021), ii) a rule-based algorithm using time series of Sentinel-2 and Landsat 8 data (Schwieder et al. 2021), iii) a rule-based algorithm based on Sentinel-1, 2 and Landsat 8 data (Schwieder et al. 2019) and iv) the Sen4Cap processor (de Vroey et al. 2021). The validation concept builds on parcel boundaries as an input, for which we used the Land Parcel Identification System (LPIS), which enabled to differentiate fallow land, temporally used and permanent grassland. However, as these data are not always widely available and do usually not allow to differentiate management variations within grassland parcels (e.g., partly mown and/or pasture use), we additionally derived parcel boundaries for Brandenburg based on a processor for the fully automatic parcel delineation from S1/S2 data as proposed by Tetteh et al. 2021, and compared the output from both data sets, including an independent validation from field management information and PlanetScope data.
We will present the results of the comparison exercise as well as a feasibility use case, in which we use the estimated grassland management information for the assessment of measures in nature protection (e.g., FFH areas) and agricultural policy (e.g., ecoschemes for the next CAP period). The output of this feasibility study will be summarized as an outlook for the future application of Sentinel-based indicators of grassland management within the monitoring and evaluation framework of the CAP.
De Vroey, M.D., Radoux, J., Zavagli, M., Vendictis, L.D., Heymans, D., Bontemps, S., & Defourny, P. (2021). Performance Assessment of the Sen4CAP Mowing Detection Algorithm on a Large Reference Data Set of Managed Grasslands. In, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 743-746).
Lobert, F., Holtgrave, A.-K., Schwieder, M., Pause, M., Vogt, J., Gocht, A., & Erasmi, S. (2021). Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series. Remote Sensing of Environment, 267, 112751.
Schwieder, M., Frantz, D., Loibl, D., Griffiths, P., Pfoch, K., Lilienthal, H., Hostert, P. (2019). Grassland Use Intensity Metrics from Sentinel-1 and Sentinel-2 Data. In, ESA Living Planet Symposium Milan, Italy.
Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2021). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 112795. https://doi.org/10.1016/J.RSE.2021.112795
Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719.