Detecting harvest events and estimating biomass production on alpine grasslands - A multisensor approach

Kurzfassung

Alpine meadows and pastures fulfil numerous key environmental processes such as carbon and water storage, soil stability or flora and fauna habitat. Recently, alpine grasslands are exposed to changes in climatic conditions such as later frost events in spring or drought periods, higher temperatures and less precipitation in summer. At the same time grassland ecosystems are exposed to diverse land use and management processes such as intensification, leading to multiple and earlier harvesting processes. These processes often occur within a short time span and - especially in the alpine environment - differ locally due to various topographic, meteorological and geographic factors. It becomes necessary to implement innovative and stable approaches for continuously monitoring changes within alpine grassland communities and to overcome sensor-specific drawbacks such as temporal, spectral or spatial resolutions. The technologies and subsequently the available sensors for monitoring alpine grassland communities have increased over the past decades. The enhanced internet connectivity and the development of low-cost sensors have led to the installation of numerous sensor networks. At the same time, the Copernicus initiative comprising the operationalization of the Sentinel satellites has increased the availability of imagery for monitoring grassland. However, their potential of synergetic use for deriving spectral or biophysical information across scales is by far not yet fully exploited. This study is focussing on a synergetic use of four different sensors detecting on separate scales ranging from ground measurement to remote sensing imagery for assessing the Phytomass development and harvesting periods by combining a spectral index (NDVI) and a modelled biophysical variable (LAI). We used a ground spectrometer, station-based spectral reflectance sensor (SRS) measurements together with fixed installed digital imagery (Phenocam) and Sentinel-2 MSI acquisitions to derive NDVI observations on different scales. Researching on alpine meadows we laid the main focus on understanding possible drawbacks resulting from topographic and geographic properties including five different grassland sites spread across South Tyrol and split into meadows and pastures. Additionally, we modelled LAI maps based on Sentinel-2 imagery using the Radiative Transfer modelling of PROSAIL as implemented in the ARTMO Toolbox. The resulting information are statistically confronted to in-situ measurements of both Phytomass and LAI taken in 2017 (n=126) and 2018 (n=38) using Random Forest Regression. By combining spectral NDVI and modelled LAI variables we aim at evaluating and further redefine the derived LAI maps and to extensively study the detectability of LAI and Phytomass for alpine grasslands. By comparing the sensor specific NDVI measurements we noticed that the spectral signatures among sensors behave similar but have bigger differences when it comes to offsetting and saturation of sensors as well as the detectability of short-term events such as snow or harvesting periods. This is mostly due to the spectral properties of the sensors as well as the viewing angles. The NDVI signal reacts differently with the clearest change of signal in SRS and Sentinel-2 signal, depending on the number and frequency of acquisitions. First modelling of the LAI indicates that the over- and underestimation of the LAI represents a big challenge, especially when deriving biomass uptake and the exact occurrence of harvest dates. Also, steepness and exposure of the grassland generate uncertainties in the LAI maps and has to be specifically addressed and parametrized. Even though the NDVI tends to over and underestimate the actual measured LAI and phytomass, first Random Forest statistics reveal a good estimation of LAI values on alpine meadows (R2 ~0.7). The Sentinel-2 MSI sensor and the SRS tend to have the highest impact on the models whereas the Phenocam tend to have the least significance for the Random Forest model. This study shows that the combinability of spectral information in a unified framework with diverse sensors on diverse spatial scales relies heavily on their spectral, geometric and temporal resolution but also on their viewing geometry. Especially when analysing short term changing events these factors have to be closely taken into account. Nevertheless, first results indicate that the combined use led to a good predictability of both LAI and Phytomass for alpine grassland sites.