Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset

Abstract

While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods.