Detecting Forest Disturbance in Northeast China from GLASS LAI Time Series Data Using a Dynamic Model

Jian Wang,Hongmin Zhou,Zhiqiang Xiao,Jindi Wang

doi:10.3390/rs9121293

Abstract

Large-scale forest disturbance often leads to changes in forest cover and structure, which imposes a great uncertainty in the estimation of the forest carbon cycle and biomass and affects other applications. In northeastern China, the Daxinganling region has abundant forest resources, where the forest coverage is about 30%. The Global LAnd Surface Satellite (GLASS) leaf area index (LAI) time series data provide important information to monitor the possible change of forests. In this study, we developed a new method to detect forest disturbances using GLASS LAI data over the Daxinganling region of Northeast China. As a dynamic model, the season-trend model has a higher sensitivity toward a seasonal change in LAI. Based on the accumulation of multi-year GLASS LAI products from 1997 to 2002, the dynamic model of LAI time series for each pixel is established first. The time-stepping modeling (TSM) process was designed by using the season-trend method, and sequential tests for detecting disturbances from a time series of pixels. Significant changes in the model parameters were captured as disturbance signals. Then, the near-infrared and shortwave-infrared bands of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance are used as auxiliary information to distinguish the types of forest disturbances. Here, the algorithm led to the detection of two different types of disturbances: fire and other (e.g., insect, drought, deforestation). In this study, we took the forest region as the study area, used the 8-day composite GLASS LAI data at 1000-m spatial resolution to identify each pixel as a fire disturbance, other disturbance, or non-disturbance. Validation was performed using reference burned area data derived from Landsat 30 m imagery. Results were also compared with the MCD64 product. The validation results were based on confusion matrices showing the overall accuracy (OA) exceeded 92% for our method and the MCD64 product. Statistical tests identified that TSM’s product accuracy is higher than that of MCD64. This study demonstrated that the TSM algorithm using a season-trend model provides a simple and automated approach to identify and map forest disturbance.

Highlights

Forest disturbances are discrete events that cause tree mortality and destruction of plant biomass
Current remote sensing approaches in monitoring forest disturbance detection are mainly based on vegetation indices (VI) and other vegetation parameters [19,20,21,22,23,24], such as the normalized difference vegetation index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) [11], to determine the disturbance by analyzing the changes in a long time series
The leaf area index (LAI) in 2003 compared to the previous two years had a large degree of reduction; at the same time, the value of normalized burn ratio (NBR) in 2003 was generally at a low level compared with that of the previous two years

Summary

Introduction

Forest disturbances are discrete events that cause tree mortality and destruction of plant biomass. An effective method to detect the temporal and spatial distribution of forest disturbance in a large area is to use remote sensing time series data. Current remote sensing approaches in monitoring forest disturbance detection are mainly based on vegetation indices (VI) and other vegetation parameters [19,20,21,22,23,24], such as the normalized difference vegetation index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) [11], to determine the disturbance by analyzing the changes in a long time series. HANDS is designed to produce annual maps of burned forests by combining the active fire detection product with NDVI differencing, a common change detection technique. The MGDI algorithm was designed to contrast annual changes in vegetation density and land surface temperature (LST) following disturbance by enhancing the signal to effectively detect the location and intensity of disturbances

Methods

Results

Conclusion