Fast Landform Position Classification to Improve the Accuracy of Remote Sensing Land Cover Mapping
Volume 7, Issue 1, February 2018, Pages: 23-33
Received: Oct. 11, 2017;
Accepted: Oct. 24, 2017;
Published: Jan. 19, 2018
Views 2105 Downloads 57
Wenjuan Qi, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Science, Beijing, PR China
Xiaomei Yang, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
Zhihua Wang, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Science, Beijing, PR China
Zhi Li, University of Chinese Academy of Science, Beijing, PR China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumchi, PR China
Fengshuo Yang, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China; University of Chinese Academy of Science, Beijing, PR China
Zhiling Zheng, Heilongjiang Institute of Technology, Harbin, PR China
Follow on us
With the increase in the availability of high resolution remote sensing imagery, land cover classification and mapping by high-resolution remote sensing images is becoming an increasingly useful technique for providing a large area of detailed land-cover information. High-resolution images have the characteristics of abundant geometric and detail information, which are beneficial to detailed classification and mapping. However, in such images, similar features may present different land-cover types in various topographic positions, but these differences are hard to recognize in high remote sensing images. When dealing with such problems, ground surveys or rough classifications of elevations are common methods. Ground surveys are time and labor consuming and lack strong real-time capability. A rough classification cannot reflect subtle changes in terrain. In order to make full use of characteristics of high remote sensing images and avoid their insufficient, a topographic position index landform position classification method is utilized in this research. The meaning of using this method is to reduce the amount of misclassification and wrongly mapping land cover types. The Topographic Position Index landform position classification method compares the elevation of each pixel in a digital elevation model to the mean elevation of the neighborhood and defines landform position class of the each pixel. Such landform position classification method allows a variety of nested landforms to be distinguished. This gives a new input for remote sensing land cover classification and mapping. The experimental results in this research proved that a GaoFen-1（GF-1）remote sensing image land cover classification accuracy is significantly improved by using the Topographic Position Index landform position classification method after image segmentation and classification.
High Resolution Remote Sensing Images, Land Cover, Topographic Position Index (TPI), Topographic Position Index Landform Position
To cite this article
Fast Landform Position Classification to Improve the Accuracy of Remote Sensing Land Cover Mapping, Earth Sciences.
Vol. 7, No. 1,
2018, pp. 23-33.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Gong, M. G; Zhang Y.; et al. The Discussion of the Relation between the Scale and the Best Spatial Resolution in Making Cartographies with RS. Science of Surveying and Mapping, 34 (4), 2009, 232-233+60.
Lu, D., and Q. Weng. A Survey of Image Classification Methods and Techniques for Improving Classification Performance. International Journal of Remote Sensing 2007. 28: 823–870. doi:10.1080/01431160600746456.
Li D., Liu D., Zhao J. Study on Land Use Change in Mountainous Area Based on DEM [J]. Journal of Soil and Water Conservation, 2014, 01: 66-70 + 2.
Dhruba Pikha Shrestha, J Alfred Zinck. Land use classification in mountainous areas: integration of image processing, digital elevation data and field knowledge (application to Nepal), International Journal of Applied Earth Observation and Geoinformation, Volume 3, Issue 1, 2001，Pages 78-85, ISSN 0303-2434, http://dx.doi.org/10.1016/S0303-2434(01)85024-8.
Hui Fan Land-cover mapping in the Nujiang Grand Canyon: integrating spectral, textural, and topographic data in a random forest classifier, International Journal of Remote Sensing, 2013, 34:21, 7545-7567, DOI: 10.1080/01431161.2013.820366.
Jenness, J. Topographic Position Index (tpi_jen.avx) Extension for ArcView 3.x. http://www.jennessent.com/arcview/tpi.htm.Accessed 25 June 2008.
Yang Y. Q. Comparison of Remote Sensing Image Classification Method Based on Different Geomorphic Elements [J]; Shanxi Normal University; 2016.
Zhang, X. P.; Jiao F; Li R; et al. The Indices and Method Approach of Land Sustainable Use Evaluation in Land Patch Scale-Take the Zhifang gully in Ansai, Shaanxi province as example. Chinese Journal of Environmental Engineering, 7, 1998, 29-33.
Yang, X. X.; He Z. M.; Yang X. L. Remedying Land Grading Method based on Comparison of the Change of Land Plot and Verification of Land Price. Resour. Sci. 28(5), 2006, 80-85.
Hu, T. G.; Zhu W. Q.; et al. Farmland Parcel Extraction Based on High Resolution Remote Sensing Image. Spectroscopy and Spectral Analysis Spectrosc. Spect. Anal. 29(10), 2009, 2703-2707.
Dizdaroglu D.; Yigitcanlar T. A parcel-scale assessment tool to measure sustainability through urban ecosystem components: The MUSIX model. Ecol. Indic. 41,. 2014 115-130. http://doi.org/10.1016/j.ecolind.2014.01.037.
Weiss A. Topographic position and landforms analysis．ERSI User Conference, San Diego, USA. 2001.
Carrao, H.; Goncalves, P.; Caetano, M.. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens. Environ. 112(3), 2008 986-997. http://doi.org/10.1016/j.rse.2007.07.002.
Nemmour, H., et al. Fuzzy neural network architecture for change detection in remotely sensed imagery. Int. J. of Remote Sens. 27(4), 2006. 705-717. http://dx.doi.org/10.1080/01431160500275648.
Ben-David S. A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering. Mach. Learn. 66(2), 2007. 243-257. doi:10.1007/s10994-006-0587-3.
Detlef D.; Rolf S.; Michael S. A multiscale soil–landform relationship in the glacial‐drift area based on digital terrain analysis and soil attributes. J. Plant Nutr. Soil Sc. 173, 2010, 843-851. doi: 10.1002/jpln.200900094.
Kang X.; Wang Y. W.; et al. Multi-analysis of the comprehensive classification of terrain elements classification method [J]. Geography, 2016, 35 (09): 1637-1646.