Spatial Pattern, Dynamic Monitoring And Simulation Of The Vegetation In The Yellow River Delta

The Yellow River Delta is one of the most active regions of land-ocean interaction among the large river deltas in the world. The area of perennial waterlogged wetlands in the Yellow River Delta including rivers, lakes, estuary waters, pits and ponds, reservoirs, channels, salt lakes, shrimp and crab pools, and tidal flats is 964.8 km2, accounts for 63.06% of total area. The Yellow River Delta is regarded as the largest and most well-preserved wetland in Chinese temperate area. The Yellow River Delta also has become an important over-wintering and breeding site for migrating birds in Northeast Asian Inland and Western Pacific Rim. And the delta is a complex depositional environment, with multiscale sedimentological, hydrological and ecological progress and georelational environment variables, which makes the Yellow River Delta to be a typical site for biodiversity conservation, environmental monitoring, multiscale analysis on vegetation-environmental relationships and spatially-explicit simulation using combined RS and GIS methodology.We tend to derive a holistic understanding of vegetation-environment relationships at mesoscales using an integrated remote sensing and GIS method. Canonical correspondence analysis (CCA) was employed to specify the relationships between vegetation pixels that obtained from high-resolution imagine and a group of biophysical, geographical and anthropogenic variables at different aggregation levels in the Yellow River Delta. Our study found that the statistic correlations between vegetation composition and environment variables increased as the grain sizes increased. The first CCA axes were closely related to the environmental variable of SS (soil salinity) and temperature vegetation dryness index (TVDI) at all the five scales, and mainly reflected the gradient of soil moisture-salinity interaction. The changes in other environmental variables that significantly related to the first axes at different scales may associate with the different processes and mechanisms that dominate on soils moisture and salinity. Several environmental variables used to depict anthropogenic activity were closely related to the second CCA axes, which indicated that the human disturbances had imposed obvious impacts on vegetation pattern in the delta. Our study confirmed that the relationships between vegetation and environmental factors at mesoscales in the delta were scale-dependent. The integration of GIS and remote sensing could be a promising method to detect relationships between vegetation and environment at different spatial scales.At larger scale, the elevation gradient and the redistribution of water and soil solutes always interact as a whole to determine regional vegetation pattern, especially in the region with small variation in elevation. The objective of this section is to test if water redistribution affects vegetation pattern at large scales in a coastal ecosystem in the Yellow River Delta, using an integrated remote sensing and GIS method with the Landsat images and a series of topographic variables. Results showed that:(1) NDVI was highly different among four prevailing communities, which was determined by the differences in habitat structure of coastal plant; (2) the spatial lagged model was statistically sound to obtain less significant Moran's I index in model residuals and reasonable and robust ecological conclusions; (3) topographical variables affected the vegetation pattern via the scale-dependent adjustment and handle on soil moisture and salinity. At small scales, topographic factors regulate soil water and salinity through the evaporation from soil surface. At large scales, however, topographic factors redistribute the soil water and salinity through the runoff and groundwater system.A couple of vegetation index and other derived index, coupled by threshold determination methods, were selected to monitor regional environmental changes, with the randomly selected points in 1992,1996,2001 and 2005 as the validation points. The result showed that the combination of two vegetation index (NDVI and OSAVI) and Expectation Maximization (EM) provided considerable performance in environmental monitoring. The result also showed that the adoption of ANN also showed good performance in the study.With the aim to investigate the dynamic changes in land use, the combination of remote sensing and geographic information system were adopted to interpret the land use dynamics of the Yellow River Delta from five Landsat images from 1992 to 2008. As shown by the land use maps, the coastline in Diaokou river mouth was relatively gently, achieving to equilibrium since 2001, while, the coastline near the new river mouth showed rapid expansion. From 1992 to 2008, the land use changes in the delta were rapid and complex, with frequent transition among tide land, shrub and grass land, bare land and water body. Condition of water resources and anthropic activities were the main driving forces for land use changes. The most dynamic area and less dynamic area mainly located in the areas where the anthropic activities were intense. And there were also dynamic and less dynamic areas distributing in the new river mouth.Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower AICc (Akaike's information criterion corrected for small sample size) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes. Our holistic and systemtic research on the spatial pattern, monitoring and dynamic simulation relied on the combination of remote sensing and GIS techniques. The result indicated that the integrated application of remote sensing and GIS not only provided scale-dependent knowledge on multiscale vegetation-environment relationships, but also provide effective ways to monitor, detect and simulate environmental changes and spatiotemporal dynamics at landscape scale. The results also clearly showed the necessity to pay special attention to scale and spatial autocorrelation when using integrated remote sensing and GIS. The analysis that carried at multiple scales would provide more holistic and comprehensive knowledge on the underlying operating processes. The incorporation of spatial autocorrelation into ecological studies would eliminate the disturbance of spatial autocorrelation, and provide more sound and reliable ecological conclusions and more accurate model performance.