412 Shojanoori and Shafri: Review on the Use of Remote Sensing for Urban Forest Monitoring Qu, W., S. Zhao, and Y. Sun. 2014. Spatiotemporal patterns of ur- banization over the past three decades: A comparison between two large cities in Southwest China. Urban Ecosystems pp. 1–17. Rapinal, S., B. Clement, S. Magnanon, V. Sellin, and L. Hubert-Moy. 2014. Identification and mapping of natural vegetation on a coastal site using a WorldView-2 satellite image. Journal of En- vironmental Management 144:236–246. Reitberger, J., P. Krzystek, and U. Stilla. 2008. Analysis of full wave- form LiDAR data for the classification of deciduous and conifer- ous trees. International Journal of Remote Sensing 29:1407–1431. Rouse, J.W., R.H. Haas, J.A. Schell, D.W. Deering, and J.C. Harlan. 1974. Monitoring the vernal advancement of retro gradation of natural vegetation. Greenbelt, MD, NASA/GSFC, type III, final report 371. Shafri, H.Z.M., E. Taherzadeh, S. Mansor, and R. Ashurov. 2012. Hyperspectral remote sensing of urban areas: an overview of techniques and applications. Research Journal of Applied Sci- ences, Engineering, and Technology 4:1557–1565. Shafri, H.Z.M., N. Hamdan, and M.I. Saripan. 2011. Semi-automat- ic detection and counting of oil palm trees from high spatial resolution airborne imagery. International Journal of Remote Sensing 32:2095–2115. Shahidan, M.F., M.K.M. Shariff, P. Jones, E. Salleh, and A.M. Abdul- lah. 2010. A comparison of Mesua ferrea L. and Hura crepitans L. for shade creation and radiation modification in improving thermal comfort. Landscape and Urban Planning 97:168–181. Shen, C., M. Li, F. Li, J. Chen, and Y. Lu. 2010. Study on Urban Green Space Extraction from Quickbird Imagery Based on Decision Tree. 18th IEEE International Conference on Geoin- formatics, Beijing, China. pp. 1–4. Shouse, M., L. Liang, and S. Fei. 2013. Identification of understory invasive exotic plants with remote sensing in urban forests. International Journal of Applied Earth Observation and Geoin- formation 21:525–534. Sun, C.Y., H.T. Lin, and W.S. Ou. 2007. The relationship between urban greening and thermal environment. Urban Remote Sens- ing Joint Event, Paris, France. pp. 1–6. Sung, C. 2012. Evaluating the efficacy of a local tree protection policy using LiDAR remote sensing data. Landscape and Urban Planning 104:19–25. Tigges, J., T. Lakes, and P. Hostert. 2013. Urban vegetation clas- sification: Benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment 136:66–75. Tooke, T.R., Coops, N.C., Voogt, J.A. 2009. Assessment of Urban Tree Shade Using Fused LIDAR and High Spatial Resolution Imagery. Urban Remote Sensing Joint Event, Shanghai, China. pp. 1–6. Tooke, T.R., N.C. Coops, N.R. Goodwin, and J.A. Voogt. 2009. Extracting urban vegetation characteristics using spectral mix- ture analysis and decision tree classifications. Remote Sensing of Environment 113:398–407. Tso, B., and P.M. Mather. 2001. Classification Methods for Remotely Sensed Data. Taylor and Francis, New York, New York, U.S. Van Der Linden, S., A. Janz, B. Waske, M. Eiden, and P. Hostert. 2007. Classifying segmented hyperspectral data from a hetero- geneous urban environment using support vector machines. Journal of Applied Remote Sensing 1(1). Vapnik, V. 1996. The Nature of Statistical Learning Theory, first edi- tion. Springer, New York, New York, U.S. Voss, M., and R. Sugumaran. 2008. Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors 8:3020–3036. Walton, J., D. Nowak, and E. Greenfield. 2008. Assessing urban for- est canopy cover using airborne or satellite imagery. Arboricul- ture & Urban Forestry 34:334–340. Wania, A., and C. Weber. 2007. Hyperspectral imagery and urban green observation. Urban Remote Sensing Joint Event, Paris, France. pp. 1–8. Wolf, B.M., and C. Heipke. 2007. Automatic extraction and delinea- tion of single trees from remote-sensing data. Machine Vision and Applications 18:317–330. Wu, B., B. Yu, W. Yue, J. Wu, and Y. Huang. 2012. Voxel-based Marked Neighborhood Searching Method for Identifying Street Trees Using Vehicle-borne Laser Scanning Data. Second Inter- national Workshop on Earth Observation and Remote Sensing Application. pp. 327–331. Wulder, M.A., W.A. Kurz, and M. Gillis. 2004. National level for- est monitoring and modeling in Canada. Progress in Planning 61:365–381. Xiao, Q., and E. McPherson. 2005. Tree health mapping with mul- tispectral remote-sensing data at UC Davis, California. Urban Ecosystems 8:349–361. Xie, Y., Z. Sha, and M. Yu. 2008. Remote sensing imagery in vegeta- tion mapping: A review. Journal of Plant Ecology 1:9–23. Yao, W., and Y. Wei. 2013. Detection of 3-D individual trees in urban areas by combining airborne LiDAR data and imagery. IEEE Geoscience and Remote Sensing Letters 10:1355–1359. Yu, Q., P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer. 2006. Object-based detailed vegetation classification with air- borne high spatial resolution remote-sensing imagery. Photo- grammetric Engineering and Remote Sensing 72:799–811. Zhang, C., and F. Qiu. 2012. Mapping individual tree species in an urban forest using airborne lidar data and hyperspectral imag- ery. Photogrammetric Engineering & Remote Sensing 78:1079– 1087. Zhang, J., P. Li, A. Mazher, and J. Liu. 2012. Impervious surface extraction with very high resolution imagery in urban areas: Reducing tree obscuring effect. International Conference on Computer Vision in Remote Sensing (CVRS), Xiamen, China. pp. 94–99. Zhang, W., X. Zhang, L. Li, and Z. Zhang. 2007. Urban forest in Jinan City: Distribution, classification, and ecological signifi- cance. Catena 69:44–50. Zheng, Y., and H.L. Qui. 2012. Mapping Urban Landuse Types in Los Angeles Using Multi-date Moderate-Resolution Imaging Spectroradiometer Vegetation Image Products. Second Inter- national Workshop on Earth Observation and Remote Sensing Applications, Shanghai, China. pp. 282–286. Zhou, W. 2013. An object-based approach for urban land cover classification: Integrating LiDAR height and intensity data. IEEE Geoscience and Remote Sensing Letters 10:928–931. ©2016 International Society of Arboriculture
November 2016
| Title Name |
Pages |
Delete |
Url |
| Empty |
Search Text Block
Page #page_num
#doc_title
Hi $receivername|$receiveremail,
$sendername|$senderemail wrote these comments for you:
$message
$sendername|$senderemail would like for you to view the following digital edition.
Please click on the page below to be directed to the digital edition:
$thumbnail$pagenum
$link$pagenum
Your form submission was a success. You will be contacted by Washington Gas with follow-up information regarding your request.
This process might take longer please wait
