Remotely sensed birch forest resilience against climate change in the northern China forest-steppe ecotone

Abstract

Assessments of forest resilience to climate change are becoming increasingly urgent with more frequent drought events. In this study, we examined warming-induced variations in canopy greenness and water content in developing a robust approach to monitor forest resilience with the dense time series of the Landsat normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII). Remote sensing observations and field data from five sites in the northern China forest-steppe ecotone were selected for assessment. The results reveal that the NDVI and NDII were negatively correlated with the investigated mortality ratios (with R2 values of 0.45 and 0.27, respectively), corresponding to the canopy greenness reduction and water loss, respectively, in the context of forest mortality. We further observed four patterns of birch forest resilience based on the NDVI-NDII coordinated variation trends identified from the Mann-Kendall (MK) test. Accordingly, arid timberline forests were found to exhibit greater resilience with more significant recovery of greenness and water content even after a canopy decline period of more than 10 years. Larger standard deviation (SD) values of the NDVI residual time series (all above 0.07) and a longer time lag of the NDII variations relative to those of the NDVI were observed, indicating that the greenness changes dominated the canopy dynamics observed in birch forest. Compared to traditional field surveys, remote sensing techniques focus on the continuous and quasi-synchronous monitoring of canopy dynamics, contributing to a more accurate detection and prediction of semiarid forest resilience on large scales.

Keywords

• Resilience
• Remote sensing
• Spectral indices
• Time series
• Semiarid forests
• Mortality