Logo 2016 Dragon 3 Final Results Symposium
 
 
Session Overview
Session
F2: ID.10367 Desertification
Time:
Tuesday, 05/Jul/2016:
2:00pm - 3:00pm

Session Chair: Stefano Pignatti
Workshop: Land & Environment
Location: Sun Moon Room -2, 5.5 Floor, Junyi Dynasty Hotel

Presentations
Oral presentation

Report of Project ID 10367: Desertification Monitoring and Assessment in China Based on Remote Sensing

Gabriel del Barrio1, Zhihai Gao2, Maria E. Sanjuán1, Xiaosong Li3, Juan Puigdefabregas1, Bengyu Wang2, Alberto Ruiz1, Bin Sun2

1Arid Zone Research Station, Spanish Council for Scientific Research, Almeria, Spain; 2Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China.; 3Institute of Remote Sensing & Digital Earth, Chinese Academy of Sciences, Beijing, China

The objective of Dragon 3 Project 10367 is to study the techniques for extracting desertification information and develop a quantitative and operational technique system for desertification assessment in China using ESA, Chinese and other relevant EO data in combination with climate-related and environmental data.

The main achievements acquired during the 4 years could be summarized as follows:

(1) Sandy lands identification and classification: Based on object-oriented combining with the classification method of improved support vector machine (SVM), the Otindag Sandy Land were classified using 2 scenes of GF-1 data acquired from 2013 and the optimal segmentation scale of each category was determined by Jefries-Matusita(J-M) value and the classification accuracy. The total accuracy for classification reached 85.61% and the Kappa coefficient was 0.8295.

(2) Improved algorithm for sparse vegetation parameters inversion: Based on the LAI inversion model –a correction Bidirectional Reflectance Distribution Function (BRDF) model and LAI inversion method of LUT (look-up table), the LAI parameter were estimated in the overlap areas of BJ-1, HJ and TM data. The LAI was verified with ground data, in which the accuracy reached 67.6%. Meanwhile, LAI’s estimating ability of the three sensors was evaluated. Based on the improved Carnegie-Ames-Stanford Approach model (CASA), a long-term series of monthly NPP data(2002.4-2012.3) deriving from MERIS FPAR data was calculated. Combined with the same period precipitation data, a long-term series of monthly RUE data set was built. In this project, Zhenglan Qi, located in hinterland of the Otindag Sandy Land,Inner Mongolia, was selected as the study area. By using the medium and high resolution GF-1 images both methods using fixed endmembers and variable endmembers respectively were applied to estimate the fractional cover of photosynthetic vegetation(fPV)and the fractional cover of non-photosynthetic vegetation(fNPV). Results show that the estimation accuracy is higher for both estimations of fPV and fNPV when the different endmember combination methods were used. Also, the GF-1 data displayed the good capability for monitoring sparse vegetation in desertification area.

(3) Assessment and monitoring of land degradation in dry lands of China: This is the frame that has guided the Earth Observation activities of Objectives 3 to 6 of the Dragon 3 project 10367.The method applied (2dRUE) consists of two complementary procedures to estimate both the ecosystem maturity (assessment) and its trends over time (monitoring). This is necessary because interpretations based only on trends can be misleading. Assessment of ecosystem maturity is based on the implementation of Rain Use Efficiency (RUE, i.e. the ratio of Net Primary Productivity to Precipitation) at two temporal scales to account respectively for ecosystem long- and short-term responses. The paradigm is that RUE conveys land maturity states because it is proportional to the soil function of providing vegetation with moisture and nutrients in dry periods between rainfall events. Monitoring of ecosystem trends is based on the paradigm that land trends are associated with changes of biomass over time after discarding the effects of inter-annual rainfall variations. It is implemented through a stepwise regression of biomass against time and aridity. The procedure has the following advantages over existing ones: it lets time and climate to compete for the major driving effects instead assuming that climate is always the dominant driver, it makes statistical proof of the detected effects, and it reports separately climate and time effects. The analysis period for this application spanned 10 hydrological years from April 2002 through March 2012. Input data were a monthly time-series of NPP derived from MERIS fAPAR, and corresponding climate fields of precipitation, mean maximum and mean minimum temperatures from the China Meteorological Forcing Dataset (Institute of Tibetan Plateau Research). Spatial and temporal resolutions were of 4 km and 1 month respectively. Focus was placed on dry lands as defined in the Potential Extent of Desertification in China.

(4) Training of young scientists: During the 4 years, more than eight young scientists have attended the research in the field of sandy lands identification and classification, improved algorithm for sparse vegetation parameters inversion and assessment and monitoring of land degradation in dry lands. Four MS students have attended the advanced training course in land remote sensing. Two Ph.D students and one MS student have graduated under the research of the project 10367.

del Barrio-Report of Project ID 10367_Cn_version.pdf

Poster

Extraction of Elm sparse woods from GF-2 Image based on Object-oriented classification in Zhenglan Banner , Inner Mongolia

Chuanping Xue

Chinese Academy of Forestry, China, People's Republic of

Elm is an important rare plant resources, but also plays an important role in the landscape construction, windbreak and sand fixation and vegetation restoration in Otindag Sandy Land. It is widely distributed in central and eastern Otindag Sandy Land, most of which being Zhenglan Banner. Demostic study of elm sparse woods began in the sixties and seventies of the 20th century. Study time is short, and the research content is limited to the ecological aspects of the community structure, species composition and so on. Based on remote sensing technology to investigate the spatial distribution pattern of elm woodlands is necessary and of great significance. Along with the development of the remote sensing technology, the spatial resolution and temporal resolution of remote sensing images are getting higher and higher. Especially in recent years, with GF series satellite launch, we can obtain high resolution satellite imagery more conveniently. The data used in this paper is GF-2, and its high spatial resolution makes it possible to extract the information of the sparse woods. This article is based on the object-oriented classification method, using ecognition developer 9.01 software by multi-scale segmentation and fuzzy membership function classification method for extraction of elm sparse woods. Classification results are tested and evaluated by field data. Results show that the methods of extracting sand elm based on object-oriented is reliable.

Xue-Extraction of Elm sparse woods from GF-2 Image based_Cn_version.pdf