On paleodrainage evolution in mid-late Epipleistocene based on radar remote sensing in northeastern Ejin Banner,Inner Mongolia

Radar remote sensing can acquire information of sub-surface covered by sand in arid area, detect surface roughness and vegetation coronet's layer and linear feature such as linear structure and channel sensitively. With sediment facies analysis, this paper studies the features of environmental evolution in mid-late Epipleistocene (60 ka BP-20 ka BP) in northeastern Ejin Banner. The conclusions are listed as follows: (1) The evolution of the three lakes, i.e. Gaxunnur, Sugunur and Tian'e lakes, are dominated by faults and regional climate. (2) By analyzing sedimentary section of old Juyanze Lake, the three lakes used to be a large outflow lake before 50 ka BP in northeastern Ejin Banner, and at 50 ka BP, temperature declined rapidly in northwestern China. The event caused the lake's shrinkage. (3) By fault activity uplift in the northern part of old Juyan Lake and depression in the southern part, the lake's water followed from north to south at around 35 ka BP, old Juyanze fluvial fan was formed. At the same time, Juyan Lake separated from Sugunur Lake and Wentugunr old channel was abandoned. (4) In recent 2000 years, Ruoshui River is a wandering river, sometimes it flows into Juyan Lake and sometimes Sugunur and Gaxunnur lakes. Due to human activities and over exploitation, the oasis ecosystem is rapidly degenerated in 15 years (1986-2000).

A new approach to dual-band polarimetric radar remote sensing image classification

It is very important to efficiently represent the target scattering characteristics in applications of polarimetric radar remote sensing. Three probability mass functions are introduced in this paper for target representation： using similarity parameters to describe target average scattering mechanism, using the eigenvalues of a target coherency matrix to describe target scattering randomness, and using radar received power to describe target scattering intensity. The concept of cross-entropy is employed to measure the difference between two scatterers based on the probability mass functions. Three parts of difference between scatterers are measured separately as the difference of average scattering mechanism, the difference of scattering randomness and the difference of scattering intensity, so that the usage of polarimetric data can be highly efficient and flexible. The supervised/unsupervised image classification schemes and their simplified versions are established based on the minimum cross-entropy principle. They are demonstrated to have better classification performance than the maximum likelihood classifier based on the Wishart distribution assumption, both in supervised and in unsupervised classification.

Inventory of mangrove wetlands in the Pearl River Estuary of China using remote sensing

Mangrove forests have important ecological functions in protecting the environment. However, the mangrove wetlands have been largely lost because of intensified human activities in the study area. Remote sensing can be conveniently used for the inventory of mangrove forests because field investigation is very difficult. In this study, a knowledge-based system is developed to retrieve spatio-temporal dynamics of mangrove wetlands using multi-temporal remote sensing data. Radar remote sensing data are also used to provide complementary information for the quantitative analysis of mangrove wetlands. Radar remote sensing is able to penetrate mangrove forests and obtain the trunk information about mangrove structures. The integration of radar remote sensing with optical remote sensing can significantly improve the accuracies of classifying mangrove wetland types and estimating wetland biomass.

The INSAR technique:its principle and applications to mapping the deformation field of earthquakes

The development, state of art and prospects of application of the radar remote sensing technique are presented. The principle of the INSAR (Interferometric Synthetic Aperture Radar) technique is expounded in more details. Some applications of this technique in measuring seismic dislocations are given. Finally, it is pointed out that INSAR has a non replaceable application potential in observing ground surface vertical deformations; it would provide an entirely new means and method for monitoring the dynamic field of earthquakes and give an extremely great impetus to the future earthquake prediction work.

First order sea clutter cross section for bistatic shipborne HFSWR

This paper studies the development on the first order sea clutter cross section for bistatic high frequency surface wave radar (HFSWR). Based on the received first order electric field expression, a closed-form of cross sections is derived to account for the case of receiving antenna array being mounted on the shipborne platform. The uniform linear motion and sway motion components are assumed to be responsible for the observed differences in comparison with the bistatic fixed antenna case. Correspondingly, simulations are conducted to study the sea clutter spectral characteristics for these two cases versus different system parameters and sea state conditions. It is shown numerically that the forward motion component will spread the Bragg lines severely and the influence triggered by the sway motion can be explained as the Bessel function modulation of the ordinary sea clutter spectra. The obtained results have important implications in the application of shipborne HFSWR technology to ocean remote sensing and target detection.

Flood susceptibility assessment using artificial neural networks in Indonesia

Flood incidents can massively damage and disrupt a city economic or governing core.However,flood risk can be mitigated through event planning and city-wide preparation to reduce damage.For,governments,firms,and civilians to make such preparations,flood susceptibility predictions are required.To predict flood susceptibility nine environmental related factors have been identified.They are elevation,slope,curvature,topographical wetness index(TWI),Euclidean distance from a river,land-cover,stream power index(SPI),soil type and precipitation.This work will use these environmental related factors alongside Sentinel-1 satellite imagery in a model intercomparison study to back-predict flood susceptibility in Jakarta for the January 2020 historic flood event across 260 key locations.For each location,this study uses current environmental conditions to predict flood status in the following month.Considering the imbalance between instances of flooded and non-flooded conditions,the Synthetic Minority Oversampling Technique(SMOTE)has been implemented to balance both classes in the training set.This work compares predictions from artificial neural networks(ANN),k-Nearest Neighbors algorithms(k-NN)and Support Vector Machines(SVM)against a random baseline.The effects of the SMOTE are also assessed by training each model on balanced and imbalanced datasets.The ANN is found to be superior to the other machine learning models.