Abstract
A number of methods are available for topographic correction of remote sensing data, and various approaches have been proposed for the evaluation of topographic correction effects. However, few studies have evaluated the topographic correction effects for images obtained under all possible sun elevation conditions. This study employed three correction methods, namely, Statistical-Empirical (SE), C-correction (C), and the Sun-Canopy-Sensor + C-correction (SCS + C) methods. These were used for topographic correction of 11 images obtained by the Landsat-5 Thematic Mapper sensor from June to December. An evaluation of the effectiveness of these three methods was carried out based on five evaluation indices, with the goal of investigating the variation in the topographic correction effect with changes in lighting conditions and the adaptability of topographic correction methods to those conditions. The results showed that the SE method effectively reduced the dependence of surface reflectance on the terrain when the solar elevation was greater than 46°, yielding a very stable surface reflectance and a low proportion of outliers. At sun elevation below 46°, the SCS + C method has good correction effect. After topographic correction using these three methods, the correction effect was compromised as the lighting conditions weakened. Within areas where the topographic and geographical conditions are similar to that of this study, the correction effect can be divided into three levels based on the solar elevation while obtaining the images, i.e., > 60°, 50–60° and < 50°. Caution should be taken when conducting quantitative research on images obtained when the solar elevation is < 50°.
Similar content being viewed by others
References
Beckschäfer P (2017) Obtaining rubber plantation age information from very dense Landsat TM and ETM + time series data and pixel-based image compositing. Remote Sens Environ 196:89–100. https://doi.org/10.1016/j.rse.2017.04.003
Blesius L, Weirich F (2005) The use of the Minnaert correction for land-cover classification in mountainous terrain. Int J Remote Sens 26(17):3831–3851. https://doi.org/10.1080/01431160500104194
Cheng Q, Shen H, Zhang L, Yuan Q, Zeng C (2014) Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model. ISPRS J Photogramm Remote Sens 92:54–68. https://doi.org/10.1016/j.isprsjprs.2014.02.015
Civco DL (1989) Topographic normalization of Landsat thematic mapper digital imagery. Photogrammetric Engineering Remote Sensing 55:1303–1309
Defries RS, Townshend JRG (1994) NDVI-derived land cover classifications at a global scale. Int J Remote Sens 15(17):3567–3586. https://doi.org/10.1080/01431169408954345
Dong J, Kaufmann RK, Myneni RB, Tucker CJ, Kauppi PE, Liski J, Hughes MK (2003) Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sens Environ 84(3):393–410. https://doi.org/10.1016/s0034-4257(02)00130-x
Ekstrand S (1996) Landsat TM-based forest damage assessment: Correction for topographic effects. Photogramm Eng Remote Sens 62:151–161
Gao B (1996) NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266. https://doi.org/10.1016/s0034-4257(96)00067-3
Gao Y, Zhang W (2009) A simple empirical topographic correction method for ETM + imagery. Int J Remote Sens 30(9):2259–2275. https://doi.org/10.1080/01431160802549336
Ghasemi N, Mohammadzadeh A, Sahebi MR (2013) Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area. Int J Digit Earth 6(5):504–520. https://doi.org/10.1080/17538947.2011.625049
Gu D, Gillespie A (1998) Topographic normalization of landsat tm images of forest based on subpixel sun–canopy–sensor geometry. Remote Sens Environ 64(2):166–175. https://doi.org/10.1016/s0034-4257(97)00177-6
Hansen MC, Loveland TR (2012) A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ 122:66–74. https://doi.org/10.1016/j.rse.2011.08.024
Hantson S, Chuvieco E (2011) Evaluation of different topographic correction methods for Landsat imagery. Int J Appl Earth Obs Geoinf 13(5):691–700. https://doi.org/10.1016/j.jag.2011.05.001
Jiang H, He GJ, Huang HM, Cao XJ, Wang XQ, Zhang ZM (2017) Construction and validation of combination model of topography-adjusted vegetation index based on band-ratio model. Trans Chin Soc Agric Eng (Trans CSAE) 33(5):156–161 (in Chinese). https://doi.org/10.11975/j.issn.1002-6819.2017.05.023
Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-Temporal segmentation algorithms. Remote Sens Environ 114(12):2897–2910. https://doi.org/10.1016/j.rse.2010.07.008
Kobayashi S, Sanga-Ngoie K (2008) The integrated radiometric correction of optical remote sensing imageries. Int J Remote Sens 29(20):5957–5985. https://doi.org/10.1080/01431160701881889
Minnaert M (1941) The reciprocity principle in lunar photometry. Astrophys J 93:403–410. https://doi.org/10.1086/144279
Myneni RB, Keeling CD, Tucker CJ, Asara G, Nemani RR (1997) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:698–702. https://doi.org/10.1038/386698a0
Ouyang Z, Zheng H, Xiao Y, Polasky S, Liu J, Xu W, Daily GC (2016) Improvements in ecosystem services from investments in natural capital. Science 352(6292):1455–1459. https://doi.org/10.1126/science.aaf2295
Pan N, Feng X, Fu B, Wang S, Ji F, Pan S (2018) Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sens Environ 214:59–72. https://doi.org/10.1016/j.rse.2018.05.018
Riano D, Chuvieco E, Salas J, Aguado I (2003) Assessment of different topographic corrections in landsat-TM data for mapping vegetation types (2003). IEEE Trans Geosci Remote Sens 41(5):1056–1061. https://doi.org/10.1109/tgrs.2003.811693
Richards JA (2013) Remote sensing digital image analysis: An introduction. Springer-Verlag, Berlin Heidelberg, pp 1–26
Richter R, Kellenberger T, Kaufmann H (2009) Comparison of topographic correction methods. Remote Sens 1(3):184–196. https://doi.org/10.3390/rs1030184
Soenen SA, Peddle DR, Coburn CA (2005) SCS + C: a modified Sun-canopy-sensor topographic correction in forested terrain. IEEE Trans Geosci Remote Sens 43(9):2148–2159. https://doi.org/10.1109/tgrs.2005.852480
Sola I, González-Audícana M, Álvarez-Mozos J (2016) Multi-criteria evaluation of topographic correction methods. Remote Sens Environ 184:247–262. https://doi.org/10.1016/j.rse.2016.07.002
Tang DM, Fan H, Zhang Y (2017) Review on Landsat time series change detection methods. J Geo-Inf Sci 19(8):1069–1079. https://doi.org/10.3724/SP.J.1047.2017.01069
Teillet PM, Guindon B, Goodenough DG (1982) On the slope-aspect correction of multispectral scanner data. Can J Remote Sens 8(2):84–106. https://doi.org/10.1080/07038992.1982.10855028
Van Westen CJ (2013) 3.10 Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on Geomorphology, pp 259–298. https://doi.org/10.1016/b978-0-12-374739-6.00051-8
Vanonckelen S, Lhermitte S, Balthazar V, Van Rompaey A (2014) Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas. Int J Remote Sens 35(13):4952–4972. https://doi.org/10.1080/01431161.2014.933280
Wu J, Bauer ME, Wang D, Manson SM (2008) A comparison of illumination geometry-based methods for topographic correction of QuickBird images of an undulant area. ISPRS J Photogramm Remote Sens 63(2):223–236. https://doi.org/10.1016/j.isprsjprs.2007.08.004
Wu Q, Jin Y, Fan H (2016) Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data. Int J Remote Sens 37(19):4712–4730. https://doi.org/10.1080/01431161.2016.1222101
Xue X, Liu H, Mu X, Liu J (2014) Trajectory-based detection of urban expansion using Landsat time series. Int J Remote Sens 35(4):1450–1465. https://doi.org/10.1080/01431161.2013.878058
Zeng C, Shen H, Zhang L (2013) Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method. Remote Sens Environ 131:182–194. https://doi.org/10.1016/j.rse.2012.12.012
Zhang YJ, Fan CK, Huang K, Liu YJ, Zu JX, Zhu JT (2017) Opportunities and challenges in remote sensing applications to ecosystem ecology. Chin J Ecol 36(3):809-823 (in chinese). https://doi.org/10.13292/j.1000-4890.201703.015
Acknowledgements
This research was supported by Project supported by the Joint Fund of the National Natural Science Foundation of China and the Karst Science Research Center of Guizhou province (Grant No. U1812401), the National Natural Science Foundation of China (Grant No.41661088 and No. 41361091), the Research Foundation for Advanced Talents of Liupanshui Normal University (Grant No. LPSSYKYJJ201905). The experiments involved in this article comply with the current laws of China in which they were performed.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
No potential conflicts of interest were reported by the authors.
Rights and permissions
About this article
Cite this article
Ma, S., Zhou, Z., Zhang, Y. et al. Evaluation of Topographic Correction Effects for Landsat-5 Thematic Mapper Images with Complex Lighting Conditions. Earth Sci Inform 14, 1–16 (2021). https://doi.org/10.1007/s12145-020-00512-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-020-00512-2