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Wetlands are one of the most important ecosystems on Earth. There is an urgent need to quantify the biophysical parameters (e.g., plant height, aboveground biomass) and map total remaining areas of wetlands in order to evaluate the ecological status of wetlands. In this study, Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) dual-polarization C-band data acquired in 2005 is tested to investigate radar backscattering mechanisms with the variation of hydrological conditions during the growing cycle of two types of herbaceous wetland species, which colonize lake borders with different elevation in Poyang Lake region, China. Phragmites communis (L.) Trin. is semi-aquatic emergent vegetation with vertical stem and blade-like leaves, and the emergent Carex spp. has rhizome and long leaves. In this study, the potential of ASAR data in HH-, HV-, and VV-polarization in mapping different wetland types is examined, by observing their dynamic variations throughout the whole flooding cycle. The sensitivity of ASAR backscattering coefficients to vegetation parameters of plant height, fresh and dry biomass, and vegetation water content is also analyzed for Phragmites communis (L.) Trin. and Carex spp. The research for Phragmites communis (L.) Trin. shows that HH polarization is more sensitive to plant height and dry biomass than HV polarization. ASAR backscattering coefficients are relatively less sensitive to fresh biomass, especially in HV polarization. However, both are highly dependent on canopy water content. In contrast, the dependence of HH-and HV-backscattering from Carex community on vegetation parameters is poor, and the radar backscattering mechanism is controlled by ground water level.

Abstract:As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM 2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA-GAM) was proposed to estimate PM 2.5 concentrations in this study. The reliability of PCA-GAM for estimating PM 2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA-GAM outperforms traditional LUR modelling with relatively higher adjusted R 2 (0.94) and lower RMSE (4.08 µg/m 3 ). The CV-adjusted R 2 (0.92) is high and close to the model-adjusted R 2 , proving the robustness of the PCA-GAM model. The PCA-GAM model enhances PM 2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA-GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.

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Commission VII, WG VII/6: Remote Sensing Data Fusion KEY WORDS: land cover classification, decision tree, C5.0, MLC ABSTRACT:Traditional land classification techniques for large areas that use LANDSAT TM imagery are typically limited to the fixed spatial resolution of the sensors. For modeling habitat characteristics is often difficult when a study area is large and diverse and complete sampling of environmental variables is unrealistic. We also did some researches on this field, in this paper we firstly introduced the decision tree classification based on C5.0, and then introduced the classification workflow. The study results were compared with the Maximum Likelihood Classification result. Victoria of Australia was as the study area, the LANDSAT ETM+ images were used to classify. Experiments show that the decision tree classification method based on C5.0 is better.