Conference Agenda

Regional application, topographic influence, and mixed pixel decomposition have become the three major scientific problems in the retrieval of forest parameters by multi-source remote sensing data. This project has proposed some methods for the prediction of the mountain forest height, the canopy closure, and the effective leaf area index. Then, the forest above ground biomass model was constructed based on vegetation indices, topographic indices and these structure parameters with physical significance.

In the aspect of three-dimensional parameter inversion of forest based on SAR, the quantitative processing methods of satellite-airborne PolSAR and InSAR data is innovated, which reduce the influence of terrain and improve the estimation accuracy of forest structure parameters. Moreover, a feature selection method for PolSAR based on genetic algorithm is proposed, which reduces the impact of feature redundancy on the accuracy of PolSAR classification and quantitative estimation. In addition, the adaptive TomoSAR spectrum analysis method is innovated, which effectively improve the quality of TomoSAR profile imaging. And a series of PolSAR/PolInSAR calibration and application research are carried out, the development of the PolSARpro software module and the translation of PolSAR classics are completed.

Biodiversity underpins the health of ecosystems and the services they provide to society. With the help of forest vertical structure parameters derived from LiDAR data, the SVR model for species diversity estimation show better result than using GF-2 multi-spectral or hyperspectral data only. The merging derived waveform parameters from synthesize waveform and the variation of spectral indices and texture that derived from GF-2 imagery showed a satisfied result for four different species diversity estimation. Synergizing passive and active remote sensing offers tremendous potential for forest species diversity estimation at regional scale.

In Dragon 4 project 32396, it involves the use of geomatic methods on remotely sensed data from both ESA and Chinese side and other geospatial databases for explored the land degradation surveillance of drylands in china. This aim frames the following concrete objectives:1)To develop methods of vegetation & soil bio-physical variables retrieval based on satellite data in drylands, at local and regional scale;2)To enhance, benchmark and validate two novel approaches, i.e. Rain Use Efficiency (2dRUE) method and Climate response in Net Primary Productivity (CRNPP)method to land degradation surveillance by remote sensing 3)To use the said approaches to map land degradation in a study area defined by the Potential Extent of Desertification in China (PEDC). After four years joint-research, following result have been achieved. 1) It is found that incorporating the red-edge bands of Sentinel-2 in LSMM could improve the accuracy of Non-Photosynthetic Vegetation Fraction estimation, utiliz-ing the VV/VH bands of Sentinel-1 was helpful for distinguishing shrub and grassland coverage and combing time series of photosynthetic and non-photosynthetic vegetation could effectively estimate the SOM in topsoil of desertified land.(2) Both two methods of land degradation could reflect the spatial distribution, driving force and the rate of degradation / restoration in the PEDC well.