A Study On The Eco-environment Evolution Of Yangtze River Delta Region Based On The Retrieval & Reconstruction Of MODIS Time Series Datasets

Long term (over 10 a) time series data from satellite-based sensors promise to improve remote sensing data observation capabilities and increase our understanding of land use/land cover changes and eco-environment evolutions and tendencies. MODIS(Moderate-resolution Imaging spectro radiometer) provides an opportunity to construct some new analysis models based on time series with contemporaneous measurements of the same spatial and temporal scales. In this thesis, the MODIS remote sensing imagery was used to study the eco-environment evolution and tendency of the Yangtze River delta area from 2000 to 2010.1) The technology, methodology and process which used to retrieve and reconstruct the time series data of eco-environment based on MODIS were proposed.2) The spatial and temporal processes, conditions and dynamic characteristics of the eco-environment evolutions of the Yangtze River delta area were analyzed based on remote sensing time series datasets.3) On the basis above, the time series was used to analyze the eco-environment evolution trends of Yangtze River delta area.4) Using DI and SVI model, a study on interannual disturbance and seasonal variation mutations of the eco-environment was performed on 2010 based on the long time series datasets.The MODIS data preprocessing, retrieval and reconstruction of the time series datasets are the basis of this thesis. The major works are as follows:(1) According to the characteristics, contents and practical scope of MODIS data, three data products were chose in the research, including MOD13Q1, MOD11A2 and MOD09A1. (2) Because the spatial and temporal resolutions, projection and coordinate are different among MOD13Q1, MOD09A2 and MOD09A1, all the data products are required to simulated to the unified spatial resolution, temporal resolution and projection. The "maximum value" and "minimum vaule" synthetic methods were used to generate 8-day LST and 16-day MOD09A1 products respectively, which had greatly improved data quality. (3) The BISE model was improved and combined with the Savizky-Golay filter for reconstruction of time series, the reconstruction quality had improved. (4) A new classification method named SAM-MDM was introduced with the advantages of both SAM (Spectral Angle Mapper) and MDM (Minimum Distance Method), and performed on the EVI classification, the Kappa coefficient and overall accuracy of 2000's LULC classification were 0.72506 and 84.09% respectively, while in 2005, they were 0.71265 and 83.70%.Many indices, such as the fraction of vegetation cover (FVC), land surface temperature (LST), surface water capacity index (SWCI), land use and land cover (LULC) and so on were derived from MODIS data, they were ecological elements used to analyze the eco-environment evolution of the Yangtze River Delta.1) Studies showed that it is feasible to apply EVI instead of NDVI to produce FVC, the characteristics of temporal-spatial distributions and changes of plants were studied using the FVC retrieved from EVI.2) the temporal-spatial change progress of LST during 2000 to 2010 was analyzed. The temporal-spatial eco-environment heat distribution and its dynamic change characteristics were studied from the contrast among different areas and different years.3) The temporal and spatial distribution of LULC and its dynamic changes were analyzed using dynamic degree model of LULC. It indicated that these transfer patterns of LULC work as wave-like evolutions. The author also built the transition probability matrix for predicting the trend of LUCC with Markov process.4) The temporal and spatial characteristics of the evolution of SWCI was explained by analyzing the temporal and spatial distribution, results revealed the wave-like features of inter-annual alternation and spatial round action for eco-environment evolution.5) the spatial and temporal characteristics of the subinterval eco-environments and its elements were released by analyzing SWCI, such as the differences between Northern China and Southern China for vegetation coverage, land, and surface water, the difference between hilly areas and plain areas, the difference between urban and rural.In the study of eco-environment elements'evolution and their relationships, the Correlation Analysis, Multivariate Regression Analysis, Transition Probability Matrix and Time Series Analysis were used.1) Three models—the eco-environment elements correlation, multiple regression analysis model, time series self-correlation model—were built, using the 11a time-series data to analyze the extent of temporal and spatial variation of the eco-environmental evolutional elements.2) The correlation between LST, SWCI, EVI and ground observed temperature and precipitation was calculated, the results showed a high relevance.3) The result indicated that the Yangtze River-the Dabie Mountain line (1200mm precipitation line) is the dividing line of Northern China and Southern China to study the distribution and evolution of various eco-environment elements.4) The result also showed that the core area of the Yangtze River was affected evidently by human activity and Shanghai was the most affected area. From 2000 to 2010, the forest and grassland reduced significantly, the low-coverage land increased evidently, the cultivated land increased little, and water-body changed slightly, those tendencies had a positive correlation with the human activity. It could figure out that the farmland, grassland and woodland had larger transition probability.5) Using disturbance index (DI), an analysis on spatial distribution of eco-environment disturbance was performed on 2010, and had tried to build the seasonal variation index (SVI) to analyze the spatial distribution of the seansonal mutation of eco-environment in 2010, the both result validations with ground-based observations, the current literature and research reports showed it got a certain effect.The study on the eco-environment evolutions and its trends such as disturbance beased on remote sensing time series datasets is a very comprehensive and complex issue, this thesis got certain results, but not complete success. The further research issues are as follows. First, to improve the eco-environmental factor models to reveal the evolving relationship better; Second, to perform the quantitative analysis on the states of eco-environmental factors; Third, the time series trend forecasting models need further improvement; Fourth, the study on disturbance models needs to improve for detection and assessment precision of eco-environment evolution.