The Development of a Geographic Information System(GIS) Database for Jiuzhaigou National Nature Reserve and Its Application

The objective of this paper is to develop a GIS(Geographic Information System) database for Jiuzhaigou National Nature Reserve(Jiuzhaigou,hereafter) in China and demonstrate its application as a research tool.A cost-effective procedure was developed to compile a variety of geographical and biological data of the study area in terms of popular GIS format such as shape files.These files were further calibrated and validated using field surveys data.The developed GIS database was used to quantify the distributions of the wildlife(amphibians,mammals,and birds) using the distances of the wildlife to the centerline of the bus-tour routes.The Pearson correlation coefficient was used to quantify the correlation in space between pairs of different wildlife using the number of habitats for given space contexts.An ArcObject-based macro was developed to perform the analysis.The results showed the majority of the habitats of wildlife are located in the proximity of the tour-bus routes with an average distance ranging from 564 to 894 m depending on types of wildlife.This indicates a possibility of the disturbance to the wildlife by human activities.The correlation coefficient of the wildlife ranged from 0.36 to 0.64 depending on pairs of wildlife,indicating some correlations in space.However,due to the limited sample size,the statistical significances need to be further investigated.This paper has successfully demonstrated the use of the GIS-based database as a research tool for environmental study.

Current and near-term advances in Earth observation for ecological applications

There is an unprecedented array of new satellite technologies with capabilities for advancing our understanding of ecological processes and the changing composition of the Earth’s biosphere at scales from local plots to the whole planet.We identified 48 instruments and 13 platforms with multiple instruments that are of broad interest to the environmental sciences that either collected data in the 2000s,were recently launched,or are planned for launch in this decade.We have restricted our review to instruments that primarily observe terrestrial landscapes or coastal margins and are available under free and open data policies.We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum.The suite of instruments we describe measure land surface characteristics,including land cover,but provide a more detailed monitoring of ecosystems,plant communities,and even some species then possible from historic sensors.The newer instruments have potential to greatly improve our understanding of ecosystem functional relationships among plant traits like leaf mass area(LMA),total nitrogen content,and leaf area index(LAI).They provide new information on physiological processes related to photosynthesis,transpiration and respiration,and stress detection,including capabilities to measure key plant and soil biophysical properties.These include canopy and soil temperature and emissivity,chlorophyll fluorescence,and biogeochemical contents like photosynthetic pigments(e.g.,chlorophylls,carotenoids,and phycobiliproteins from cyanobacteria),water,cellulose,lignin,and nitrogen in foliar proteins.These data will enable us to quantify and characterize various soil properties such as iron content,several types of soil clays,organic matter,and other components.Most of these satellites are in low Earth orbit(LEO),but we include a few in geostationary orbit(GEO)because of their potential to measure plant physiological traits over diurnal periods,improving estimates of water and carbon budgets.We also include a few spaceborne active LiDAR and radar imagers designed for quantifying surface topography,changes in surface structure,and 3-dimensional canopy properties such as height,area,vertical profiles,and gap structure.We provide a description of each instrument and tables to summarize their characteristics.Lastly,we suggest instrument synergies that are likely to yield improved results when data are combined.

Classification and detection of dominant factors in geospatial patterns of traditional settlements in China

The geospatial distribution pattern in traditional Chinese settlements(TCSs)reflects the traditional harmony between humans and nature,which has been learned over centuries.However,TCSs have experienced serious disturbances by urbanization and migration.It is crucial to explore the local wisdom of geospatial patterns and dominant factors for TCSs at the national scale in China.This study sought to determine the geospatial wisdom of traditional settlements to enrich our future settlement development with the aim of establishing Chinese settlement values for modern living.Herein,a dataset of 4000 TCSs were analyzed and clustered for environmental factors that affect their geospatial patterns by machine learning algorithms.We concluded that(1)five geospatial patterns of TCSs were clustered on a national scale,and the threshold of environmental factors of TCS groups was detected.(2)Environmental conditions and settlement concepts interacted and determined the similarities and differences among TCS groups.(3)The key boundary for TCSs and the dominant factors for each zone were determined,and topographical conditions and hydrologic resources played significant roles in all five TCS zones.This study provides a better understanding of the adaptability of the environment in relation to the TCSs and aids in planning TCS conservation and rural revitalization.