Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

Abstract

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

“…In particular, the existence of several vegetation indices in GEE allows conducting vegetation studies in efficient and quick manners. GEE has been widely used for vegetation mapping [57], [58], vegetation dynamics monitoring [59], [60], deforestation [61], [62], vegetation and forest expansion [63], [64], forest health monitoring [65], [66], forest mapping [67], [68], pasture monitoring [49], [69], and rangeland assessment [70], [71]. For instance, the full archive of the Landsat imagery was processed within GEE to map the vegetation dynamics from 1988 to 2017 in Queensland, Australia [59].…”

Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

“…There are a number of recent studies that argue in support of a combined use of SAR and optical data for tropical forest monitoring [3,[50][51][52][53]. Recent results indicate that a combined use can improve tropical forest monitoring for burnt area detection [14], for forest/non-forest mapping [52,54,55], for biomass assessment [56][57][58], and for deforestation and degradation monitoring [45,51,59]. A combination of ALOS PALSAR data and Landsat data was also used to enhance the discrimination of mature forest, secondary forest, and non-forest areas [60].…”

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

“…Most approaches of recent TSA studies that use LANDSAT or Sentinel-2 data, apply harmonic regressions (ordinary least square models) on the generated time series to characterise seasonality of vegetation canopy (Zhu and Woodcock 2014;Jönsson et al 2018;Shimizu et al 2019;Deijns et al 2020).…”