Delin Xu

Remote sensing of aquatic vegetation distribution in Taihu Lake using an improved classification tree with modified thresholds.

Classification trees (CT) have been used successfully in the past to classify aquatic vegetation from spectral indices (SI) obtained from remotely-sensed images. However, applying CT models developed for certain image dates to other time periods within the same year or among different years can reduce the classification accuracy. In this study, we developed CT models with modified thresholds using extreme SI values (CT(m)) to improve the stability of the models when applying them to different time periods. A total of 903 ground-truth samples were obtained in September of 2009 and 2010 and classified as emergent, floating-leaf, or submerged vegetation or other cover types. Classification trees were developed for 2009 (Model-09) and 2010 (Model-10) using field samples and a combination of two images from winter and summer. Overall accuracies of these models were 92.8% and 94.9%, respectively, which confirmed the ability of CT analysis to map aquatic vegetation in Taihu Lake. However, Model-10 had only 58.9-71.6% classification accuracy and 31.1-58.3% agreement (i.e., pixels classified the same in the two maps) for aquatic vegetation when it was applied to image pairs from both a different time period in 2010 and a similar time period in 2009. We developed a method to estimate the effects of extrinsic (EF) and intrinsic (IF) factors on model uncertainty using Modis images. Results indicated that 71.1% of the instability in classification between time periods was due to EF, which might include changes in atmospheric conditions, sun-view angle and water quality. The remainder was due to IF, such as phenological and growth status differences between time periods. The modified version of Model-10 (i.e. CT(m)) performed better than traditional CT with different image dates. When applied to 2009 images, the CT(m) version of Model-10 had very similar thresholds and performance as Model-09, with overall accuracies of 92.8% and 90.5% for Model-09 and the CT(m) version of Model-10, respectively. CT(m) decreased the variability related to EF and IF and thereby improved the applicability of the models to different time periods. In both practice and theory, our results suggested that CT(m) was more stable than traditional CT models and could be used to map aquatic vegetation in time periods other than the one for which the model was developed.

Spatio-Temporal Variability of Aquatic Vegetation in Taihu Lake over the Past 30 Years

It is often difficult to track the spatio-temporal variability of vegetation distribution in lakes because of the technological limitations associated with mapping using traditional field surveys as well as the lack of a unified field survey protocol. Using a series of Landsat remote sensing images (i.e. MSS, TM and ETM+), we mapped the composition and distribution area of emergent, floating-leaf and submerged macrophytes in Taihu Lake, China, at approximate five-year intervals over the past 30 years in order to quantify the spatio-temporal dynamics of the aquatic vegetation. Our results indicated that the total area of aquatic vegetation increased from 187.5 km2 in 1981 to 485.0 km2 in 2005 and then suddenly decreased to 341.3 km2 in 2010. Similarly, submerged vegetation increased from 127.0 km2 in 1981 to 366.5 km2 in 2005, and then decreased to 163.3 km2. Floating-leaf vegetation increased continuously through the study period in both area occupied (12.9 km2 in 1981 to 146.2 km2 in 2010) and percentage of the total vegetation (6.88% in 1981 to 42.8% in 2010). In terms of spatial distribution, the aquatic vegetation in Taihu Lake has spread gradually from the East Bay to the surrounding areas. The proportion of vegetation in the East Bay relative to that in the entire lake has decreased continuously from 62.3% in 1981, to 31.1% in 2005 and then to 21.8% in 2010. Our findings have suggested that drastic changes have taken place over the past 30 years in the spatial pattern of aquatic vegetation as well as both its relative composition and the amount of area it occupies.

Wetlands of Northeast Asia and High Asia: an overview

This review reports background information on wetlands in the Northeast Asia and High Asia areas, including wetland coverage and type, significance for local populations, and threats to their vitality and protection, with particular focus on the relationship of how global change influenced wetlands. Natural wetlands in these areas have been greatly depleted and degraded, largely due to global climate change, drainage and conversion to agriculture and silviculture, hydrologic alterations, exotics invasions, and misguided management policies. Global warming has caused wetland and ice-sheet loss in High Asia and permafrost thawing in tundra wetlands in Northeast Asia, and hence induced enormous reductions in water-storage sources in High Asia and carbon loss in Northeast Asia. This, in the long term, will exacerbate chronic water shortage and positively feed back global warming. Recently, better understanding of the vital role of healthy wetland ecosystems to Asia’s sustainable economic development has led to major efforts in wetland conservation and restoration. Nonetheless, collaborative efforts to restore and protect the wetlands must involve not only the countries of Northeast and High Asia but also international agencies. Research has been productive but the results should be more effectively integrated with policy-making and wetland restoration practices under future climatic scenarios.