Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data

Abstract

Abstract:A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs) and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer's accuracy of 96.3% for the classification Random Forest (CRF) and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF). The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought.

Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data

“…have not yet been documented beyond short-term (<5 years) associations during severe drought (Byer & Jin, 2017;Potter, 2016).…”

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

“…Detection of dead trees may be important for forest practitioner especially with respect to bark beetle attacks, when abandoned trees (dead-dry trees) may indicate close presence of the beetle near to these trees. Dead trees detection through satellite imagery was described for example described in [51] or more recently in [52]. Hart and Veblen [51] utilized a National Agriculture Imagery Program (NAIP) for training the Landsat Climate Data Records (LDCR) dataset and reported that on tree level the red-green index was higher for grey attacked trees and on stand level index the decrease (lower values) of NDVI could be noticed.…”

“…Overall accuracy reported was over 88%. Byer and Jin [52] also deployed Random Forest algorithm for classification and regression producing accuracy of 96.3% for the classification Random Forest and a RMSE of 7.19 dead trees per acre for the regression Random Forest. Relatively innovative approach was presented by Baker et al [53], where the authors mapped dead trees attacked by pine beetle by assessing MODIS time series with continuous snow cover, when gaps were used to detect continuous ground snow coverage and a snow-free overstory, and so to detect pixels with crown mortality.…”

Unsupervised Clustering of Forest Response to Drought Stress in Zululand Region, South Africa

“…Secondly, its consequences can be experienced at relatively broad geographic scales and simultaneously across landscapes [26]. Thirdly, drought stimulates other forest disturbances such as fire [27] and insect outbreaks [28], which further impair tree productivity, sometimes leading to tree mortality [29,30]. Lastly, it is challenging to determine the onset and cessation of drought episodes since vegetation responds in different ways [31], making it difficult to evaluate drought using costly and spatially restricted field-based measurements [32].…”