Forecasting tree mortality using change metrics derived from MODIS satellite data

Abstract

Insect-induced tree mortality can cause substantial timber and carbon losses in many regions of the world. There is a critical need to forecast tree mortality to guide forest management decisions. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery provides inexpensive and frequent coverage over large areas, facilitating forest health monitoring. This study examined time series of MODIS satellite images to forecast tree mortality for a Pinus radiata plantation in southern New South Wales, Australia. Dead tree density derived from ADS40 aerial imagery was used to evaluate the performance of change metrics derived from time series of MODIS-based vegetation indices. Continuous subset selection by LASSO regression and model assessment using a variant of the bootstrap were used to select the best performing change metrics out of a large amount of predictor variables to account for over-fitting. The results suggest that 250 m 16-daily MODIS images are effective for forecasting tree mortality. Seasonal change metrics derived from the Normalized Difference Vegetation Index (NDVI) outperformed the Enhanced Vegetation Index (EVI) and the Normalized Difference Infrared Index (NDII). Temporal analysis illustrated that optimal forecasting power was obtained using change metrics based on three years of satellite data for this population. The forecast could be used to optimise the scheduling of detailed forest health surveys and silvicultural operations which currently are planned based on stratified, annual assessments. This coarse-scale, spatio-temporal analysis represents a potentially cost-effective early warning approach to forecasting tree mortality in pine plantations by identifying compartments that require more detailed investigation.