1. A Fully Automated Approach to Classifying Urban Land Use and Cover from LiDAR, Multi-spectral Imagery, and Ancillary Data Jason Parent Qian Lei University of Connecticut

2. Land cover and land use  Land cover: the physical material on the earth’s surface (e.g. water, grass, asphalt, etc.)  Land use: the use of the land by humans (e.g. reservoir, agriculture, parking lot, etc.)  Fundamental to landscape analyses and urban planning. 2

3. Opportunities and challenges for high resolution data  Increasing availability of airborne light detection and ranging (LiDAR) and aerial imagery offers opportunities to study landscapes in great detail.  Technically challenging to process…  require lots of hard drive space.  datasets must be divided into small subsets for processing.  conventional algorithms not well suited to processing large numbers of subsets

4. Study objectives and justification  Develop fully automated algorithm to classify high resolution (1-meter) land cover / land use which is applicable over large areas.  no previously presented algorithm has been feasible to apply over large areas.  Specifically, we developed python scripts with ArcGIS to…  classify 1-meter land cover from LiDAR and multispectral data.  infer land use from object geometry and spatial context of land cover features. 4

5. Study area  Located in eastern Connecticut in the northeastern U.S.  Semi-random stratified sample of 30 1x1 km tiles.  Stratified by % impervious cover (according to Connecticut’s Changing Landscape land cover data). 5 0 - 33 33 - 66 66 – 100 % impervious 4800 km 2

6. Data LiDAR  2010 leaf-off fall acquisition  Small footprint (44 cm)  Near-infrared (1064 nm)  > 1.5 pts/m 2 6 Aerial orthophotos  2012 leaf-off spring acquisition  Blue, green, red, and NIR  0.3 meter resolution

7. Land cover classification rules 7 Land coverPrimary characteristics BuildingHeight > 2.5m; no ground returns Low impervious cover (low IC) Low NDVI; no returns 2 to 4.5 meters above ground Deciduous forestHeight > 3m; high NDVI Coniferous forestHeight > 3m; very high NDVI Medium vegetationHeight 0.5 to 3m; high NDVI WaterNo returns Riparian wetlandsLow reflectance in all bands; adjacent to water Low vegetationHigh return intensity Pixel- and object-based rules using structural and spectral properties

8. Land cover classification example 8 deciduous coniferous med. veg. low veg. water wetland building low IC

9. Land cover class accuracies ClassUser acc. (%)Prod. acc. (%) Water 9685 Building 9997 Low vegetation 9194 Wetland 2635 Low impervious 9391 Med. vegetation 6160 Coniferous trees 9076 Deciduous trees 9596 93% overall Kappa = 0.90 n = 3200  User accuracy: probability that a cell label is correct.  Producer accuracy: probability that a cell is correctly labelled.

10. Land use classification rules 10 Building usePrimary characteristic Non-ResidentialLarge parking area; flat roof; large building size Multi-family residentialLarge parking area; narrow building width; similar building shapes Single family residentialSmall parking area; peak roof; small building size  Parcel cadastral information not used because of limited availability. Object- and parcel- based rules using object shape/size and parcel land cover composition

11. Land use preliminary results deciduous coniferous med. veg. low veg. water wetland building low IC multi-family non-resid. single-family

12. Land use classification assessment 12  small commercial buildings misclassified as single family due to similar structural characteristics  problems caused by mismatch between land cover and parcel data  Qualitative assessment notes… 12

13. Conclusions and future work  Land cover classification:  Use of airborne LiDAR and multi-spectral data proved highly effective in classification of high resolution land cover.  Developed fully automated algorithm that performs well over large area. 13  Land use classification:  Use of building shape and context is promising  Future work will develop rules for classification of…  roads vs. parking lots  urban vs. non-urban forest  agriculture vs. turf

14. A Fully Automated Approach to Classifying Urban Land Use and Cover from LiDAR, Multi-spectral Imagery, and Ancillary Data Jason Parent Qian Lei University of Connecticut Questions?