The Status and Development Trends of Land Remote Sensing

Remote Sensing & GIS for Environmental Monitoring & Exploration


First and Last Name Academic degree E-mail Affiliation
Lei Ren No ds2017qm [at] Kyiv National University of Construction and Architecture
Kyiv, Ukraine
Nadiia Lazorenko-Hevel Ph.D. nadiialg [at] Kyiv National University of Construction and Architecture
Kyiv, Ukraine

I and my co-authors (if any) authorize the use of the Paper in accordance with the Creative Commons CC BY license

First published on this website: 03.10.2020 - 17:14

The land resource is a very important natural resource and the most basic resource necessary for human production activities and social development. The research of changes in land resources is the great significance to global climate change and sustainable development. At the same time, land resources vary greatly in different periods and different regions. This means that it is difficult to monitor land resources with a single method such as aerial photography, using UAVs, laser scanning or some other method of remote sensing, but in combination it is getting easier. Since the 1970s, remote sensing technology has gradually become an important means of monitoring land resources due to its advantages such as all-weather, wide-coverage, short repeated coverage cycles, and strong information acquisition. The article researches the development and applications of remote sensing technology in land resource monitoring. Also the article analyzes future development trends of land remote sensing technology that will be large-scale data fusion, cloud computing and deep learning.

  1. Foley  J.A , DeFries R , Asner G , Barford C , Bonan G , Carpenter SR , Chapin FS , Coe MT , Daily GC , Gibbs HK , et al. (2005) .Global Consequences of Land Use.Science 309:570–573.
  2. Yue Chang, Kang Hou, Xuxiang Li, Yunwei Zhang and Pei Chen. (2011).A study on land use/cover change the need for a new integrated approach (J).In Geographical Research(6), pp.645-652.
  3. Lambin EF, Baulies X, Bockstael N, Fischer G, Krug T, Leemans R, Moran EF, Rindfuss RR, et al. (1999). IGBP-IHDP/LUCC Focus 3: Regional and Global Models. In: IGBP Report 48/IHDP Report 10: Land-Use and Land-Cover Change - Implementation Strategy. pp. 73-88 Stockholm, Sweden: IGBP.
  4. B. L. Turner, Eric F. Lambin, Anette Reenberg. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences Dec 2007, 104 (52) 20666-20671; DOI: 10.1073/pnas.0704119104.
  5. Karpinsky Iu., Lazorenko-Hevel N. (2018). The methods of geospatial data collection for topographic mapping THE OFFICIAL CHRONICLE, EDUCATION, SCIENTIFIC, INDUSTRIAL AND PUBLIC  LIFE Issue I (35), 2018. p.204-211.
  6. IGBP and Earth observation: a co-evolution. . Accessed on 12.09.2020.
  7. The IGBP-DIS global 1km land cover data set, DISCover: First results. .  Accessed on 12.09.2020.
  8. Hansen M., R DeFries, J.R.G. Townshend, and R Sohlberg (1998), UMD Global Land Cover Classification, 1 Kilometer, 1.0, Department of Geography, University of Maryland, College Park, Maryland, 1981-1994.
  9. GlobCover. . Accessed on 12.09.2020.
  10. GlobCover land Cover Maps (GlobCover). . Accessed on 12.09.2020.
  11. J. Chen, J. Chen, A.P. Liao, X. Cao, L.J. Chen, X.H. Chen, C.Y. He, G. Han, S. Peng, M. Lu, W.W. Zhang, X.H. Tong, J. Mills. (2015). Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J. Photogramm. Remote Sens., 103 (2015), pp. 7-27.
  12. Product introduction. . Accessed on 12.09.2020.
  13. Tracking Change Across Time and Space with LCMAP. . Accessed on 12.09.2020.
  14. The National Wetlands Inventory. . Accessed on 12.09.2020.
  15. NWI Program Overview. . Accessed on 12.09.2020.
  16. LCMAP Offers Insight on Dynamic Wetlands. . Accessed on 12.09.2020.
  17. Munthali M.G, Mustak S., Adeola A., Botai J., Singh S.K., Davis N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Rem.Sens. Appl.: Soc. Environ. 17, 100276.
  18. Nanki Sidhu, Edzer Pebesma & Gilberto Câmara. (2018). Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 51:1, 486-500, DOI: 10.1080/22797254.2018.1451782.
  19. Kresse W. (2008). Standardization in photogrammetry and remote sensing. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Proceedings of the XXI ISPRS Congress, Commission IV, Beijing, China, July 3-11, 2008. 37(B4).
  20. L Di. (2003). The Development of Remote-Sensing Related Standards at FGDC, OGC, and ISO TC 211. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003), July 21-25, Toulouse, France. 4p.
  21. H Tamiminia, B Salehi, M Mahdianpari, L Quackenbush, S Adeli, B Brisco. (2020). Google earth engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens., 164 (2020), pp. 152-170.
  22. Goldblatt R., You W., Hanson G., Khandelwal A.K. (2016). Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine. Remote Sens. 2016, 8, 634.
  23. Xie Z., Phinn S.R., Game E.T., Pannell D.J., Hobbs R.J., Briggs P.R., McDonaldMadden E. (2019). Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands-a first step towards identifying degraded lands for conservation. Remote Sens. Environ. 232, 111317.
  24. Ge Y., Hu S., Ren Z., Jia Y., Wang J., Liu M., Zhang D., Zhao W., Luo Y., Fu Y. (2019). Mapping annual land use changes in China’s poverty-stricken areas from 2013 to 2018. Remote Sens. Environ. 232, 111285.
  25. Wang L., Diao C., Xian G., Yin D., Lu Y., Zou S., Erickson T. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sens. Environ. 248,2020.
  26. WHAT IS CREODIAS?. . Accessed on 12.09.2020.
  27. Land-cover maps of Europe from the Cloud. . Accessed on 12.09.2020.
  28. Map of Europe. .  Accessed on 12.09.2020.
  29. Deep Learning. . Accessed on 12.09.2020.
  30. L Khelifi & M Mignotte. (2020). Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis. IEEE Access, vol. 8, pp. 126385-126400, 2020. Doi: 10.1109/ACCESS.2020.3008036.
  31. N Kussul, M Lavreniuk, S Skakun and A Shelestov. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett., vol. 14, no. 5, pp. 778-782, May 2017. DOI: 10.1109/LGRS.2017.2681128.
  32. D Zhang, J Han, G Cheng, Z Liu, S Bu and L Guo. (2015). Weakly Supervised Learning for Target Detection in Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 4, pp. 701-705, April 2015. Doi: 10.1109/LGRS.2014.2358994.


Secretary GeoTerrace
researcher, secretary

Dear authors,
Thank you for your paper submission!

Please add and format the references in your paper according to the conference requirements (APA style). Proper references formatting is essential for conference papers inclusion in the bibliographic databases.

We are looking forward to reviewing your updated paper!
GeoTerrace Secretary

Tue, 11/03/2020 - 19:49

Hello, all references have been revised in accordance with the seventh edition of the APA.

references-apa-72020.pdf142.96 KB
Tue, 11/10/2020 - 17:56

Update, the bug of URL character conversion caused by system encoding.

references-apa-72020-12.pdf142.69 KB
Tue, 11/10/2020 - 18:38

Hello, we have made a major revision to the article.

Sat, 11/14/2020 - 10:05