A Comparison of the Signal from Diverse Optical Sensors for Monitoring Alpine Grassland Dynamics

Abstract

Grasslands cover up to 40% of the mountain areas globally and 23% of the European Alps and affect numerous key ecological processes. An increasing number of optical sensors offer a great opportunity to monitor and address dynamic changes in the growth and status of grassland vegetation due to climatic and anthropogenic influences. Vegetation indices (VI) calculated from optical sensor data are a powerful tool in analyzing vegetation dynamics. However, different sensors have their own characteristics, advantages, and challenges in monitoring vegetation over space and time that require special attention when compared to or combined with each other. We used the Normalized Difference Vegetation Index (NDVI) derived from handheld spectrometers, station-based Spectral Reflectance Sensors (SRS), and Phenocams as well as the spaceborne Sentinel-2 Multispectral Instrument (MSI) for assessing growth and dynamic changes in four alpine meadows. We analyzed the similarity of the NDVI on diverse spatial scales and to what extent grassland dynamics of alpine meadows can be detected. We found that NDVI across all sensors traces the growing phases of the vegetation although we experienced a notable variability in NDVI signals among sensors and differences among the sites and plots. We noticed differences in signal saturation, sensor specific offsets, and in the detectability of short-term events. These NDVI inconsistencies depended on sensor-specific spatial and spectral resolutions and acquisition geometries, as well as on grassland management activities and vegetation growth during the year. We demonstrated that the combination of multiple-sensors enhanced the possibility for detecting short-term dynamic changes throughout the year for each of the stations. The presented findings are relevant for building and evaluating a combined sensor approach for consistent vegetation monitoring.

Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs)

“…In this paper, we show that the derived NDVI values from UAVs and PlanetScope CubeSat imagery across treatments are largely consistent with important differences, particularly in their sensitivity to subtle changes to water content (control and water). Studies have shown that vegetation indices, such as NDVI, can be significantly affected by differences in spectral bandwidth [61,69]. The spectral range of the red band has been found to be of importance to NDVI.…”

Radiometric Calibration of ‘Commercial off the Shelf’ Cameras for UAV-Based High-Resolution Temporal Crop Phenotyping of Reflectance and NDVI

“…Some variability occurred between time points in all bands and NDVI; likely a result of the UAV and Tec5 spectrometer obtaining measurements at different spatial resolutions [38] and datasets not being collected on the same date. Rossi et al [44] demonstrated the impact of non-concurrent data collection when comparing different reflectance from different sensors, with a single day lag negatively impacting on correlation results. Variability in accuracy also occurred between bands, particularly for visible versus NIR, which was observed consistently over time.…”

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

“…Considering the aforementioned RTM limitations and the fact that the new satellite missions are providing several bands across the RE and the NIR shoulder regions, further research is expected on the ability of SVIs based on such regions to retrieve LAI and on the spectral response mechanisms at the canopy level in different vegetation types [29], which are not yet fully explored [30,31]. RE SVIs (which make use of the RE spectral region) include the RE-based normalized difference vegetation index (RENDVI [32]) and the chlorophyll index (CIre [33]).…”