Can We Monitor Seedling Stands Using Landsat Time Series?

Abstract

Monitoring the status of seedling stands is crucial for determining whether seedlings have achieved the required density and height and assessing the need for weeding. We studied the potential of Landsat Time Series (LTS) data for monitoring young seedling stands in Liperi, eastern Finland. We assessed the ability of Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI) to estimate stand-level attributes like stem density, height, and identify the need for weeding of both broadleaved and coniferous trees. According to the results, the variation within a stand is typically very high, and thus it is difficult to give a single prediction for an entire stand. In our study, the indices could not capture the structural variation in seedling stands, although some trends could be found between NDVI and the number of coniferous trees (R2 = 0.22), height of the deciduous trees (R2 = 0.26) and height difference between coniferous and deciduous trees (R2 = 0.25). Our study also shows that the prediction of the need for weeding or tending using a binary decision-making process achieved an accuracy of 81% and a Cohen's kappa value of 0.55. Our study demonstrates that the LTS data can be used with a reasonable accuracy to monitor seedling stands characteristics.