Jianrong Fan

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture (Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle (UAV), and a digital elevation model (DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include QuickBird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events. Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Characteristics of rainfall erosivity based on tropical rainfall measuring mission data in Tibet, China

Rainfall erosivity in Tibet from 2000 to 2010 was estimated based on simplified erosion prediction model using daily rainfall data derived from the Tropical Rainfall Measurement Misssion (TRMM) 3B42 rainfall measurement algorithm. Semimonthly erosive rainfall and rainfall erosivity were validated using weather station data. The spatial distribution of annual rainfall erosivity as well as its seasonal and annual variation in Tibet was also examined. Results showed that TRMM 3B42 data could serve as an alternative data source to estimate rainfall erosivity in the area where only data from sparsely distributed weather stations are available. The spatial distribution of rainfall erosivity in Tibet generally resembles the distribution of multi-year average of annual rainfall. Annual rainfall erosivity in Tibet decreased from the southeast to the northwest. The concentration degree of rainfall erosivity shows an increasing trend from the southeast to the northwest. High rainfall erosivity accompanies low rainfall erosivity concentration degree and vice versa. Rainfall erosivity increased in the middle and western Tibet and decreased in the southeastern Tibet during the 11 years of this study.

Rapid Assessment of Secondary Disasters Induced by the Wenchuan Earthquake

The Wenchuan earthquake of 12 May 2008 produced numerous secondary mountain disasters, including the formation of dangerous barrier lakes resulting from blocked river channels. By exploiting real-time, multiplatform remote sensing imagery data, researchers located these barrier lakes, acquired their distribution information, and assessed the threats to inform mitigation efforts and help prevent potentially disastrous consequences.

Cause analysis of gully erosion in Yuanmou basin of Jinshajiang valley

Some factors (i.e. lithology, topography, climate, the change of population as well as land use during the past 50 years) that could have great influence on the development of gully in the arid-hot basin of Jinshajiang valley were investigated. The results show that the factors leading to the strong gully erosion in this area include: the widely distributed Yuanmou group stratum, which promotes the development of gully erosion; the unique geomorphologic configuration that is prone to rock fall and gully erosion; the strong and time-concentrated rainfall; the arid-humid alternate climate characteristics that prepares the ground for the development of fissures in soils; the arid-hot climate that goes against the growth and recovery of vegetation; and the unreasonable and abusive human activities.

Analysis and assessment of earthquake-induced secondary mountain disaster chains based on multi-platform remote sensing

The Wenchuan earthquake was a destructive earthquake that measured at 7.9 Mw occurred at 14∶28∶01.42 CST on May 12, 2008 in Sichuan province of China, which induced large quantities of secondary mountain disasters such as collapse, landslides, debris flows and barrier lakes. These disasters occurred and more seriously formed disaster chains causing great destruction, and costing many lives after both the earthquake and thousands of aftershocks. To gather a proper understanding of disaster and formed chains, it is necessary to consider disaster chains for the dilapidated areas. In this paper, we focused on dynamically monitoring and analyzing the disaster chains patterns, spatial distribution and trying to choose the best time to cut the chains for preventing the prospective disasters. The multi-sensor remote sensing imageries before and after earthquakes were used to discover the disasters induced by earthquake and aftershocks, then to acquire the distributions of these disasters and rapidly analysis and assess the disaster chains to prevent possible disaster consequences.

Research on Fusion Approach for Hyperspectral Image and Multispectral Image of HJ-1A

The vacancy of hyperspectral image (HSI) in China is made up by HJ-1A satellite, which makes more study and application possible. But comparing with other HSI, low spatial resolution turns into a big limiting obstacle for application. In order to improve the HSI quality and make full use of the existing RS data, this paper proposed a fusion approach basing on 3D wavelet transform (3D WT) to fusing HJ-1A HSI and Multispectral image (MSI) using their 3D structure. Contrasting with the principal component transform (PCA) and Gram-Schmidt fusion approach, which are mature at present, 3D WT fusion approach use all bands of MSI to its advantage and the fusion result perform better in both spatial and spectral quality.

Evaluating the ecological suitability for Olive tree in Sichuan using GIS and comprehensive fuzzy method: Methodological development and application

We have used the Geographic Information System’s (GIS) spatial analysis functions and comprehensive fuzzy sets to evaluate and analyze the ecological suitability of the olive trees in Sichuan, China. The over-years experience of olive planting and amelioration data has also been used as an aid to this analysis taking consideration of climate, soil, and topography which are closely related to the olive growing habit. The results are representing the actual distribution of olive while providing alternative adaptive regions for olive growing, thus we are able to offer guidance for the olive cultivation in the region.

The application of high spatial resolution remote sensing image for vegetation type recognition in Dagou Valley

This paper present a detail processing procedure about SPOT5 image applied for vegetation type recognition, and determines the capacity of high spatial resolution satellite image data to discriminate vegetation type in a complex ecosystem. A high spatial resolution SPOT5 image, captured in April 2005, and coincident field data covering the Dagou valley, was used in this analysis. Image geometric rectification and image fusion are then introduced to take prepare for classification. Subsequently, a maximum likelihood classification algorithm was applied to the SPOT5 image data to map the vegetation classes. Field validation and accuracy assessment are crucial to ensure the reliability of classification results. The strategy of field work and the resulting accuracy evaluations were presented, and yielded the high classification accuracy (overall accuracy=83.86%, Kappa=80.23%). The result showed that the information on vegetation types can be mapped effectively from high spatial resolution satellite image data.