Landslide Investigation with Remote Sensing and Sensor Network: From Susceptibility Mapping and Scaled-down Simulation towards in situ Sensor Network Design

This paper presents an integrated approach to landslide research based on remote sensing and sensor networks. This approach is composed of three important parts: (i) landslide susceptibility mapping using remote-sensing techniques for susceptible determination of landslide spots; (ii) scaled-down landslide simulation experiments for validation of sensor network for landslide monitoring, and (iii) in situ sensor network deployment for intensified landslide monitoring. The study site is the Taziping landslide located in Hongkou Town (Sichuan, China). The landslide features generated by landslides triggered by the 2008 Wenchuan Earthquake were first extracted by means of object-oriented methods from the remote-sensing images before and after the landslides events. On the basis of correlations derived between spatial distribution of landslides and control factors, the landslide susceptibility mapping was carried out using the Artificial Neural Network (ANN) technique. Then the Taziping landslide, located in the above mentioned study area, was taken as an example to design and implement a scaled-down landslide simulation platform in Tongji University (Shanghai, China). The landslide monitoring sensors were carefully investigated and deployed for rainfall induced landslide simulation experiments. Finally, outcomes from the simulation experiments were adopted and employed to design the future in situ sensor network in Taziping landslide site where the sensor deployment is being implemented

Seawater carbonate chemistry and chlorophyll a, primary productioin and algal community composition

A mesocosm experiment was conducted in Wuyuan Bay (Xiamen), China, to investigate the effects of elevated pCO2 on bloom formation by phytoplankton species previously studied in laboratory-based ocean acidification experiments, to determine if the indoor-grown species performed similarly in mesocosms under more realistic environmental conditions. We measured biomass, primary productivity and particulate organic carbon (POC) as well as particulate organic nitrogen (PON). Phaeodactylum tricornutum outcompeted Thalassiosira weissflogii and Emiliania huxleyi, comprising more than 99% of the final biomass. Mainly through a capacity to tolerate nutrient-limited situations, P. tricornutum showed a powerful sustained presence during the plateau phase of growth. Significant differences between high and low CO2 treatments were found in cell concentration, cumulative primary productivity and POC in the plateau phase but not during the exponential phase of growth. Compared to the low pCO2 (LC) treatment, POC increased by 45.8–101.9% in the high pCO2 (HC) treated cells during the bloom period. Furthermore, respiratory carbon losses of gross primary productivity were found to comprise 39–64% for the LC and 31–41% for the HC mesocosms (daytime C fixation) in phase II. Our results suggest that the duration and characteristics of a diatom bloom can be affected by elevated pCO2. Effects of elevated pCO2 observed in the laboratory cannot be reliably extrapolated to large scale mesocosms with multiple influencing factors, especially during intense algal blooms