GeoSpatial Tools and Analysis David Currie

Transcription

1 GeoSpatial Tools and Analysis David Currie GeoSpatial Tools and Analysis Data for Good Sept 23, 2014 Focus on open source tools Bias towards remote sensing topics Some examples from completed projects David Currie, P.Eng Open Source Geo Tools A good place to start: GUI tools for GIS and Image Analysis QGIS, OpenJUMP, Grass Command line utilities GDAL/OGR GEOS Proj4 Ossim Databases and Web Services PostGIS/PostgreSQL Mapserver, OpenLayers/GeoMoose, Leaflet 1

2 Django/GeoDjango Scripting tools R Python Quantum GIS (QGIS) Useful for Raster and Vector Exploration Basic mapping capabilities Scriptable using Python Grass Very capable raster and vector processing Good performance Steep learning curve GDAL/OGR Raster and Vector format translations Datum and projection transformations many useful utilities, eg gdalwarp 2

3 GEOS topology library computes useful things like INTERSECTION and CONVEXHULL included in PostGIS Web Services Mapserver custom map generation on demand 3

4 can also function as a tileserver GeoDjango Python based CMS with PostGIS in the background Client-side tools (javascript) Openlayers/GeoMoose Leaflet PostGIS Spatial engine running on PostgreSQL handles vector and raster data provides Proj4 and GEOS functionality for projections and topology library("rpostgresql") v<-dbsendquery(con,"select gid, length, width from feature_polygon where featuretype_id=22") exfeat<-fetch(v,n=-1) hist(exfeat$length,main="length of Extreme Features",xlab="Length (m)",breaks="fd",col="lightblue") Length of Extreme Features Frequency Length (m) 4

5 Scripting: R R-project free and open source powerful statistical capabilities package system to link external capabilities, including GDAL, GEOS, GRASS, Raster, Spatial, SQL... - RStudio IDE 5

6 Scripting: Python free and open source lightweight, useful for web servers lower level programming interface than R, can give higher performance 6

7 useful GIS libraries available: fiona, shapely, pyproj, Rtree Open Geo Data Worldwide Data - OpenStreetMap - Natural Earth - NASA nasa.gov/ - USGS Canada Specific - GeoBase - GeoGratis - Altalis - Calgary Open Data 7

8 8

9 Project Examples: Web Data Capture The problem: capture dimensions of thousands of sea ice features from hundreds of images in order to quantify the variation in size by season and location. The deliverable: Probability of exceedence statistics for each zone and season 9

10 Exceedence Probability Area 2 Iceberg Diameter (m) Note Log Scale n:6470 m:0 Web Data Capture Tools PostGIS/PostgreSQL Python with GDAL for data loading Grass for image enhancement Mapserver with GeoMoose for web based digitizing Django for project administration and reporting R and QGIS for generation of results 10

11 Web Data Capture 11

12 12

13 Web Data Capture 13

14 14

15 Web Data Capture 15

16 Web Data Capture Results: - Selected 408 images from a set of 2073 (Landsat and Aster) - 45,578 icebergs detected and measured - 3,292 other significant features detected - Multiple dates between allowed movement to be detected and mapped 16

17 Project Examples: Raster Data Mining 17

18 The problem: Arctic polynyas are ephemeral features that are critical to wildlife and human survival. Where and when they occur is difficult to map due to access and weather conditions. The deliverable: Spatio-temporal analysis of open water occurrence for period July June 2013 Tools: - R with spatial and raster packages 18

19 Raster Data Mining Starting from MODIS sea surface temperature grids. - 1 km resolution - Two grids produced per day 19

20 Raster Data Mining Day and Night grids give up to 6 observations of each square kilometer Cloud and lack of sunlight affect available data 20

21 21

22 Raster Data Mining Over 110K images collected, registered, classified, and stacked 22

23 23

24 Raster Data Mining Although daily data is available, the coverage was not continuous Daily data was stacked and filtered to generate a weekly time series A set of open water composite maps were generated for each week of the 11 year span 24

25 25

26 Raster Data Mining Landsat imagery was used to tune the analysis and quality control the result. 26

27 Raster Data Mining Summary statistics from the resulting grids were extracted and analyzed Open water fraction gives a normalized comparison 27

28 28

29 Raster Data Mining Polynyas are most easily recognizable in the early Spring Comparing maps from specific week between years gives a good indication of variation 29

30 30

31 31

32 Raster Data Mining Specific locations were of interest, including the Last Ice Area 32

33 33

34 34

35 Thanks! 35