Satellite monitoring of spatio-temporal dynamics of China’s coastal zone eco-environments: preliminary analysis on the relationship between the environment, climate change and human behavior

Abstract

In this paper, temporal dynamics of eco-environmental changes in coastal areas of China during 1981–2000 are investigated based on four key surface parameters including normalized difference vegetation index (NDVI), thermal index, moisture index and surface broadband albedo derived from quantitative remote sensing techniques and meteorological data. Firstly, land surface temperature (LST) and land surface broadband albedo are retrieved by the split-window algorithms and high-order polynomial regression method, respectively, using NOAA/AVHRR series images. Then, moisture index and thermal index, indicators of climate and moisture conditions in the study area, are computed from meteorological data and LST using principal component analysis (PCA). Finally, long-term dynamics of these eco-environmental factors and the reasons responsible for these changes are analyzed further. The results show that during the years from 1981 to 2000, the study area experienced a gradual increase in annual NDVI and climate factors and a decrease in surface annual broadband albedo, which indicates that the coastal thermal and moisture conditions and the subsistence conditions of natural vegetation have changed to a considerable extent. According to the results, a warming and wetting tendency over the last two decades is obvious in the China’s coastal zone that are mainly due to land use changes as of growing urbanization, exhaust emissions from industries and transportations and, partly global climate change. Uncontrolled rapid development of the study area may be blamed for these negative changes as a major driving force. The positive feedback mechanisms between albedo, NDVI and climate factors also partly explain these changes. This study suggests that the method integrating biophysical parameters retrieved from remote sensed images and meteorologic data provides a novel and feasible way to monitor large scale eco-environmental changes.