A volumetric approach to population estimation using lidar remote sensing

Photogrammetric Engineering and Remote Sensing

Volumn 77, Issue 11, 2011, Pages 1145-1156

  Lu, Zhenyu ^a   Im, Jungho ^a   Quackenbush, Lindi ^a  

^a 402 Baker Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; FORESTRY; HOUSING; LINEAR REGRESSION; OPTICAL RADAR; POPULATION STATISTICS; REGRESSION ANALYSIS; REMOTE SENSING; SIGNAL DETECTION; SURVEYS; TREES (MATHEMATICS);

HOUSING CHARACTERISTIC; LIDAR REMOTE SENSING; MULTIPLE LINEAR REGRESSIONS; POPULATION ESTIMATIONS; REGRESSION TREE MODELS; RESIDENTIAL BUILDING; SIMPLE LINEAR REGRESSION; VOLUME-BASED MODELS;

BUILDINGS;

ALGORITHM; CALIBRATION; CENSUS; DATA SET; DETECTION METHOD; HETEROGENEITY; HOUSING CONDITIONS; LIDAR; NUMERICAL MODEL; PIXEL; POPULATION ESTIMATION; REGRESSION ANALYSIS; REMOTE SENSING; RESIDENTIAL LOCATION; URBAN AREA;

COLORADO; DENVER; UNITED STATES;

EID: 80755176683     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.77.11.1145     Document Type: Article

References (26)

1. Gong, B., J. Im, and G. Mountrakis, 2011. An artificial immune network approach to multi-sensor land use/land cover classification, Remote Sensing of Environment, 115: 600-614.
2. Harvey, J.T., 2002. Estimating census district populations from satellite imagery: Some approaches and limitations, International Journal of Remote Sensing, 23:2071-2095.
3. Huang, C., and J.R.G. Townshend, 2003. A stepwise regression tree for nonlinear approximation: Applications to estimating subpixel land cover, International Journal of Remote Sensing, 24:75-90.
4. Im, J., J.R. Jensen, M. Coleman, and E. Nelson, 2009. Hyperspectral remote sensing analysis of short rotation woody crops grown with controlled nutrient and irrigation treatments, Geocarto International, 24(4):293-312.
5. Im, J., Z. Lu, J. Rhee, and L.J. Quackenbush, 2011. Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data, Remote Sensing of Environment, doi:10.1016/j.rse.2011.06.024.
6. Jensen, J.R., and D.J. Cowen, 1999. Remote sensing of urban/suburban infrastructure and socio-economic attributes, Photogrammetric Engineering & Remote Sensing, 65(6):611-622.
7. Jensen, R.R., J.P. Hardin, and W.M. Jackson, 2010. Spectral modeling of population density: A study of Utah's Wasatch Front, Photogrammetric Engineering & Remote Sensing, 76(7):797-806.
8. Ke, Y., L.J. Quackenbush, and J. Im, 2010. Synergistic use of QuickBird multispectral imagery and LIDAR data for objectbased forest species classification, Remote Sensing of Environment, 114(6):1141-1154.
9. Kocev, D., S. Dzeroski, M.D. White, G.R. Newell, and P. Griffioen, 2009. Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition, Ecological Modelling, 220:1159-1168.
10. Langford, M., 2006. Obtaining population estimates in non-census reporting zones: An evaluation of the 3-class dasymetric method, Computers, Environment and Urban Systems, 30:161-180.
11. Lawrence, R.L. and A. Wright, 2001. Rule-based classification systems using classification and regression tree (CART) analysis, Photogrammetric Engineering & Remote Sensing, 67(11):1137-1142.
12. Li, G., and Q.H. Weng, 2005. Using Landsat ETM + imagery to measure population density in Indianapolis, Indiana, USA, Photogrammetric Engineering & Remote Sensing, 71(9):947-958.
13. Li, G., and Q.H. Weng, 2010. Fine-scale population estimation: how Landsat ETM_ imagery can improve population distribution mapping?, Canadian Journal of Remote Sensing, 36:155-165.
14. Liu, X.H., P.C. Kyriakidis, and M.F. Goodchild, 2008. Populationdensity estimation using regression and area-to-point residual kriging, International Journal of Geographical Information Science, 22:431-447.
15. Lo, C.P., 1995, Automated population and dwelling unit estimation from high resolution satellite images: A GIS approach, International Journal of Remote Sensing, 16:17-34.
16. Lo, C.P., 2008. Population estimation using geographically weighted regression, GIScience and Remote Sensing, 45:131-148.
17. Lu, D.S., Q.H. Weng, and G.Y. Li, 2006. Residential population estimation using a remote sensing derived impervious surface approach, International Journal of Remote Sensing, 27:3553-3570.
18. Lu, Z., J. Im, L.J. Quackenbush, and K. Halligen, 2010. Population estimation based on multi-sensor data fusion, International Journal of Remote Sensing, 31:5587-5604.
19. Meng, X., L. Wang, and N. Currit, 2009a. Morphology-based building detection from airborne lidar data, Photogrammetric Engineering & Remote Sensing, 75(4):437-442.
20. Meng, X., L. Wang, L.C. Jose, and C. Nate, 2009b. A multi-directional ground filtering algorithm for airborne lidar, ISPRS Journal of Photogrammetry and Remote Sensing, 64:117-124.
21. Tullis, J.A., and J.R. Jensen, 2003. Expert system house detection in high spatial resolution imagery using size, shape, and context, Geocarto International, 18(1):5-15.
22. Wu, C., and A.T. Murray, 2005. A cokriging method for estimating population density in urban areas, Computers, Environment and Urban Systems, 29:558-579.
23. Wu, C., and A.T. Murray, 2007. Population estimation using Landsat Enhanced Thematic Mapper imagery, Geographical Analysis, 39:26-43.
24. Wu, S.S., L. Wang, and X. Qui, 2008. Incorporating GIS building data and census housing statistics for sub-block population estimation, The Professional Geographer, 60:121-135.
25. Xu, M., P. Watanachaturaporn, P.K. Varshney, and M.K. Arora, 2005. Decision tree regression for soft classification of remote sensing data, Remote Sensing of Environment, 97:322-336.
26. Yuan, Y., R.M. Smith, and W.F. Limp, 1997. Remodeling census population with spatial information from Landsat TM imagery, Computer, Environment and Urban Systems, 21:245-258.