Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm

Abstract

Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island.

Monitoring forest gain and loss based on LandTrendr algorithm and Landsat images in KTH Pati social forestry area, Indonesia

“…Sentinel-2 is the alternative offering higher spatial resolution (10 m for visible and near-infrared bands) with dense temporal resolution (up to 5 days). However, as the first observation of Sentinel-2 started in 2015, it limits the observation period to less than 10 years (Yin et al, 2022). Future studies can consider the employment of higher spatial resolution either derived from the pan-sharpening method or the use of Sentinel-2 images in LandTrendr algorithm.…”

“…The dynamic change of vegetation can be monitored by observing the spectral trajectory of an individual pixel over time (Yang et al, 2018). Taking advantage of prolonged Landsat data acquisition, Kennedy et (Shen et al, 2022;Yin et al, 2022). This study aimed to identify the trends of forest change using the LandTrendr approach, taking a case study in a social forestry area in Pati, Indonesia.…”

ResNet152-U-Net: Transfer Learning for the Semantic Segmentation of Forest Restoration Site Using Aerial Images

“…Traditional methods for forest monitoring, such as ground surveys using the Global Navigation Satellite System (GNSS) and remote sensing techniques, have been widely used. Remote sensing, in particular, enables the detection of various land disturbances over time [5][6][7][8][9][10][11][12]. However, remote sensing methods may be limited in early-stage detection when ground access is restricted [13].…”