Urban Remote Sensing. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ S., Masetti, M., Nghiem, S.V., and Sorichetta, A. (2014). Use of scatterometer data in groundwater vulnerability assessment. Rendiconti Online: Della Societa Geologica Italiana 30: 45–50. doi:10.3301/ROL.2014.10.

      73 Stevenazzi, S., Masetti, M., Nghiem, S.V., and Sorichetta, A. (2015). Groundwater vulnerability maps derived from time dependent method using satellite scatterometer data. Hydrogeology Journal 23(4): 631–647. doi:10.1007/s10040‐015‐1236‐3.

      74 Stevenazzi, S., Bonfanti, M., Masetti, M., Nghiem, S.V., and Sorichetta, A. (2017). A versatile method for groundwater vulnerability projections in future scenarios. Journal of Environmental Management 187: 365–374. doi:10.1016/j.jenvman.2016.10.057.

      75 Strozzi, T., and Wegmuller, U. (1998). Delimitation of urban areas with SAR interferometry. 1998 IEEE International Geoscience and Remote Sensing. Symposium (IGARSS) Proceedings 3: 1632–1634. doi: 10.1109/IGARSS.1998.691660.

      76 Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., and Dech, S. (2012). Monitoring urbanization in mega cities from space. Remote Sensing of Environment 117: 162–176. doi:10.1016/j.rse.2011.09.015.

      77 Taubenböck, H., Weigand, M., Esch, T., Staab, J., Wurm, M., Mast, J., and Dech, S. (2019) A new ranking of the world's largest cities—do administrative units obscure morphological realities? Remote Sensing of Environment 232: 111353. doi:10.1016/j.rse.2019.111353.

      78 Teo, T.A., and Shih, T.Y. (2013). Lidar‐based change detection and change‐type analysis determination in urban areas. International Journal of Remote Sensing, 34(3): 968–981. doi:10.1080/01431161.2012.714504.

      79 Thiele, A., Cadario, E., Schulz, K., Thonnessen, U., and Soergel, U. (2007). Building recognition from multi‐aspect high‐resolution InSAR data in urban areas. IEEE Transactions on Geoscience and Remote Sensing 45(11): 3583–3593. doi:10.1109/TGRS.2007.898440.

      80 Tsang, L., Kong, J.A., and Shin, R.T. (1985). Theory of Microwave Remote Sensing. Hoboken, NJ: Wiley‐Interscience.

      81 Ulaby, F.T., Moore, R.K., and Fung, A.K. (1986). Microwave Remote Sensing Active and Passive, Vol. I‐III. Norwood, MA: Artech House.

      82 United Nations (2014). World Urbanization Prospects: The 2014 Revision ‐ Highlights. New York: United Nations.

      83 US Census (2019). United States Census Bureau – American FactFinder. https://factfinder.census.gov (accessed 17 December 2019).

      84 Vu, T.T., Matsuoka, M., and Yamazaki, F. (2004). LIDAR‐based change detection of buildings in dense urban areas. IEEE International Geoscience and Remote Sensing Symposium 3413–3416. doi:10.1109/IGARSS.2004.1370438.

      85 Wentz, E.A., York, A.M., Alberti, M., Conrow, L., Fischer, H., Inostroza, L., Jantz, C., Pickett, S.T.A., Seto, K.C., and Taubenbock, H. (2018). Six fundamental aspects for conceptualizing multidimensional urban form: a spatial mapping perspective. Landscape and Urban Planning 179: 55–62. doi:10.1016/j.landurbplan.2018.07.007.

      86 Wu, Q., Chen, R., Sun, H., and Cao, Y. (2011). Urban building density detection using high resolution SAR imagery. 2011 Joint Urban Remote Sensing Event (Munich), 45–48. doi:10.1109/JURSE.2011.5764715.

      87 Yu, B., Liu, H., Wu, J., and Lin, W.M. (2009). Investigating impacts of urban morphology on spatio‐temporal variations of solar radiation with airborne LIDAR data and a solar flux model: a case study of downtown Houston. International Journal of Remote Sensing 30(17): 4359–4385. doi:10.1080/01431160802555846.

      88 Zha, Y., Gao, J., and Ni, S. (2003). Use of normalized difference built‐up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 24(3): 583–594. doi:10.1080/01431160304987.

      89 Zhang, Q., and Seto, K.C. (2013). Can night‐time light data identify typologies of urbanization? A global assessment of successes and failures. Remote Sensing 5(7): 3476–3494. doi:10.3390/rs5073476.

      90 Zhao, Y., Ovando‐Montejo, G.A, Frazier, A.E., Mathews, A.J., Flynn, K.C., and Ellis, E.A. (2017). Estimating home and work population using lidar‐derived building volumes. International Journal of Remote Sensing 38(4): 1180–1196. doi:10.1080/01431161.2017.1280634.

CHAPTER 3 Opportunities and Challenges of Unmanned Aircraft Systems for Urban Applications

       Feilin Lai1, Austin Bush2, Xiaojun Yang1, and David Merrick2

       1 Department of Geography, College of Social Sciences and Public Policy, Florida State University, Tallahassee, FL, USA

       2 Emergency Management & Homeland Security Program, Florida State University, Tallahassee, FL, USA

      Abstract

       Unmanned aircraft systems (UAS) are an emerging technology with extensive applications in various domains and industries. This technology lends itself greatly to the need in generating high‐quality geospatial data with a high temporal resolution, while also maintaining a high degree of safety. With flexible operations, low implementation costs, and high‐quality data outputs, UAS are ideal tools for urban applications that require rapid mapping, assessment, or management. However, increasingly these applications are facing regulatory challenges from local, state, and national governing bodies. This chapter discusses the opportunities and challenges of UAS for urban remote sensing research. It begins with an introduction to the concept of UAS and some common types of UAS models and cameras onboard, followed by a discussion of a typical UAS data collection procedure including mission planning, flight operations, and data processing. Several urban applications using UAS are discussed, including disaster relief efforts, building inspection, disorder detection, and smart cities construction, along with a case study to demonstrate how UAS can be used for 3D mapping of urban structures. Finally, the major challenges of using UAS for urban studies are discussed, which are related to regulations, operations, and data processing. Recognizing a new frontier of remote sensing applications emerging with UAS platforms, this chapter helps better understand the potential of UAS in urban applications and points out potential future research directions.

3.1 INTRODUCTION

Cities make up 55% of the population in this world, and this number keeps increasing with the continued global trend of urbanization (UN DESA, 2018). Drastic urban growth can be accompanied by various issues such as air pollution, СКАЧАТЬ