Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987–2011)

Abstract

Listen

Translate article

Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker (VCT) model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks (LTSS). The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM+ scenes with different projections due to distinct data sources (Beijing remote sensing ground station and the USGS EROS Center). The validation results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 (38.3 %) and 1992 (56.2 %) in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis-registration errors of the image pairs. Major forest disturbance types existing in our study area were identified as agricultural expansion (39.8 %), urbanization (24.9 %), forest management practice (19.3 %), and mining (12.8 %). In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China’s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.