1. LAND USE/LAND COVER CHANGE IN BEXAR COUNTY, TEXAS 2001-2014 Maryia Bakhtsiyarava FNRM 5262

2. Planet Earth is the place where ALL human and environmental processes occur None of those processes takes place without leaving a trace; land is a limited resource Therefore, it is crucial for a country to know what is happening to its land Land cover/land use pattern is the best source for such knowledge

3. Urban areas – high concentrations of people and increased pressure on natural landscapes San Antonio, occupying roughly 40% of the county, has featured many times in various lists of the fastest-growing cities in the nation. Population of the country increased by 30% from 2001 in 2014 (US Census Bureau).

5. Use multitemporal satellite imagery and image interpretation techniques to assess land cover/land use change (LCLUC) in Bexar County from 2001 to 2014 in view of rapid urbanization in that area

6. Satellite imagery acquired by Landsat 7 and Landsat 8 in July of 2001 and 2014 Object Based Image Classification using eCognition 9 Thematic change analysis in ERDAS IMAGINE 2014 Accuracy Assessment using NAIP imagery

7. Developed Highly Developed Vegetation Cropland Woody Wetland Roads Water Open Space

8. Multiresolution segmentation Parameters: – Scale – Shape – Compactness – Layer weights Spectral difference segmentation

10. Land/Water Mask (the ratio of Short-wave Infrared to Green ) NDVI Brightness Length/Width Rectangular Fit

13. 2001 2014

14. 22% of vegetation changed to developed 4% of vegetation to highly developed 11% of water changed to highly developed 61% of woody wetland changed to vegetation 64% of cropland changed to vegetation 38 % of open space changed to developed

15. Stratified random NAIP imagery from 2004 and 2011 ≈ 30 points per class 2014 – 71% overall accuracy 2001 – 62% overall accuracy

16. Use high resolution imagery to produce more accurate results Use LiDAR data (no free LiDAR in Texas) Use ancillary data (roads, etc) Use more sophisticated rulesets incorporating texture, spatial context or decision tree algorithms A different pair of eyes for accuracy assessment

17. Landsat 7 and Landsat 8 have different band lengths – that could have affected classification and change analysis results Had to use different parameters of brightness and NDVI for the two scenes in eCognition – sensor differences, atmospheric conditions