Spatial-temporal responses of Louisiana forests to climate change and hurricane disturbance

This dissertation research focused on three questions: (1) what is the current carbon stock in Louisiana’s forest ecosystems? (2) how will the biomass carbon stock respond to future climate change? and (3) how vulnerable are the coastal forest resources to natural disturbances, such as hurricanes? The research utilized a geographic information system, remote sensing techniques, ecosystem modeling, and statistical approaches with existing data and in-situ measurements. Future climate changes were adapted from predictions by the Community Climate System Model on the basis of low (B1), moderate (A1B), and high (A2) greenhouse gas emission scenarios. The study on forest carbon assessment found that Louisiana’s forests currently store 219.2 Tg of biomass carbon, 90% of which is stored in wetland and evergreen forests. Spatial variation of the carbon storage was mainly affected by forest biomass distribution. No correlation was identified between carbon storage in watersheds with the average watershed slope and drainage density. The modeling study on growth response to future climate found that forest net primary productivity (NPP) would decline from 2000 to 2050 under scenario B1, but may increase under scenarios A1B and A2 due primarily to minimum temperature and precipitation changes. Uncertainties of the NPP prediction were apparent, owing to spatial resolution of the climate variables. The remote sensing study on hurricane disturbance to coastal forests found that increases in the intensity of severe weather in the future would likely increase the turn-over rate of coastal forest carbon stock. Forest attributes and site conditions had a variety of effects on the vulnerability of forests to hurricane disturbance and thereby, spatial patterns of disturbed landscape. Soil groups and stand factors, including forest types, forest coverage, and stand density contributed to 85% of accuracy in the modeling probability of Hurricane Katrina disturbance to forests. In conclusion, this research demonstrated that quantification of forest biomass carbon, using geo-referenced datasets and GIS techniques, provides a credible approach to increase accuracy and constrain the uncertainty of large-scale carbon assessment. A combination of ecosystem modeling and GIS/Remote Sensing techniques can provide insight into future climate change effects on forest carbon change at the landscape scale

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAMComment: 7 pages, accepted at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLA

Genetic Engineering for Breeding for Drought Resistance and Salt Tolerance in \u3ci\u3eAgropyron\u3c/i\u3e Spp. (Wheatgrass)

Genetic engineering for breeding for drought resistance and salt tolerance in wheatgrass, lucerne and tall fescue is one of the main projects in major national programs of 10th five-year national plan: Research of gene transfer in plants and its industrialisation. The project is a large one that has the financial support for forage crops in China and many research institutes and universities take part in it. The Inner Mongolia Agricultural University is in charge of the project on wheatgrass. The research was started in Nov. 2002. The general situation and the primary results are introduced and summarised in this paper

Genetic Engineering for Breeding for Drought Resistance and Salt Tolerance in Agropyron Spp. (Wheatgrass)

Genetic engineering for breeding for drought resistance and salt tolerance in wheatgrass, lucerne and tall fescue is one of the main projects in a major national programs as part of the10th’five-year national plan: “Research of gene transfer in plants and its industrialisation”. It is a large project that has financial support for work on forage crops in China and many research institutes and universities take part in it. The Inner Mongolia Agricultural University is in charge of the project on wheatgrass. The research was started in Nov. 2002. The general situation and the primary results are introduced and summarised in this paper