Conference Agenda

Overview and details of the sessions and sub-session of this conference. Please select a date or session to show only sub-sessions at that day or location. Please select a single sub-session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in CEST. The current conference time is: 13th Dec 2021, 09:45:03am CET

 
 
Session Overview
Session
Dr4 S.1.4: ECOSYSTEMS, FOREST & GRASSLANDS
Time:
Wednesday, 21/July/2021:
8:30am - 9:30am

Session Chair: Prof. Laurent Ferro Famil
Session Chair: Prof. Erxue Chen
Workshop: Dragon 4

ID. 31470 FOREST DRAGON 
ID. 32396 Drylands in China

Session finishes at 09:10 CEST, 15:10 CST


Show help for 'Increase or decrease the abstract text size'
Presentations
8:30am - 8:50am
Accepted
ID: 314 / Dr4 S.1.4: 1
Oral Presentation for Dragon 4
Land & Environment: 31470 - Forest biophysical retrievals and land cover dynamics using multi-temporal, multi-sensor (SAR-optical-LiDAR) and multi-resolution EO sensors for China and selected Asian regions (FOREST Dragon 4)

Forest Dragon 4 Final Results

Zengyuan Li1, Christiana Schmullius2, Erxue Chen1, Laurent Ferro-Famil3, Yong Pang1, Xin Tian1

1Chinese Academy of Forestry, P.R.China; 2University Jena, Germany; 3IETR/University of Rennes 1, France

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.

Li-Forest Dragon 4 Final Results-314Oral4.pdf


8:50am - 9:10am
Accepted
ID: 261 / Dr4 S.1.4: 2
Oral Presentation for Dragon 4
Land & Environment: 32396 - Land Degradation Surveillance of Drylands in China

Final Report Of Dragon 4 Project (ID:32396) :Land Degradation Surveillance Of Ddrylands In China

Zhihai Gao1,2, Gabriel del Barrio3, Li Xiaosong4, Jaime Martinez-Valderrama5, Bin Sun1,2, Maria Sanjuan3, Yan Ziyu1,2, Alberto Ruiz3

1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; 2Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100094, China; 3Estacion Experimental de Zonas Aridas, Consejo Superior de Investigaciones Científicas, La Cañada de San Urbano, Almeria 04120, Spain.; 4Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; 5Instituto para la Investigación del Madio Ramón Margalef, San Vicente del Raspeig 03690, Alicante, Spain

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.

Gao-Final Report Of Dragon 4 Project-261Oral4.pdf


 
Contact and Legal Notice · Contact Address:
Privacy Statement · Conference: 2021 Dragon Symposium
Conference Software - ConfTool Pro 2.6.142+TC
© 2001 - 2021 by Dr. H. Weinreich, Hamburg, Germany