Sub-annual tropical forest disturbance monitoring using harmonized Landsat and Sentinel-2 data

Abstract

Accurate sub-annual detection of forest disturbance provides timely baseline information for understanding forest change and dynamics to support the development of sustainable forest management strategies. Traditionally, Landsat imagery was widely used to monitor forest disturbance, but the low temporal density of Landsat observations limits the timely detection of forest disturbance. Recently a new harmonized dataset of Landsat and Sentinel-2 imagery (HLS) has been created to increase the density of observations and provide more frequent images, but the added-value of this dataset for sub-annual tropical forest disturbance monitoring has not been investigated yet. Here, we used all available HLS images acquired from 2016 to 2019 to detect forest disturbance at two tropical forest sites in Tanzania and Brazil. Based on HLS data, forest disturbance was detected by combining normalized difference moisture index (NDMI) and normalized difference vegetation index (NDVI) time series using BFAST monitor and random forest algorithms. To assess the added-value of the HLS time series, we also detected forest disturbance from (i) Landsat-8/OLI time series only and (ii) Sentinel-2 time series only data. The spatial accuracy assessment of forest disturbance detection at the Tanzania site shows that the combined Landsat-8/OLI and Sentinel-2 data achieved the highest overall accuracy (84.5%), more than 3.5% higher than the accuracy of using only Landsat-8/OLI or Sentinel-2. Similarly, for the Brazil site, the overall accuracy of using the combined Landsat-8/OLI and Sentinel-2 data was 95.5%, at least 2% higher than others. In terms of temporal accuracy, the mean time lag of 2.0 months, was achieved from the combined data and Sentinel-2 only at the Tanzania site. This mean time lag is at least one month shorter than that of using Landsat-8/OLI only (3.3 months). At the Brazil site, the mean time lag of forest disturbance detection based on the combined data was 0.22 months, shorter by 0.50 and 0.15 months when compared to using Landsat-8/OLI only (0.72 months) or Sentinel-2 only (0.37 months), respectively. Our results indicate that HLS data is promising for accurate and timely forest disturbance detection particularly in the moist forest where cloud cover is often high.