The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

Deha Agus Umarhadi (1), Projo Danoedoro (2)
(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
Fulltext View | Download
How to cite (IJASEIT) :
Umarhadi, Deha Agus, and Projo Danoedoro. “The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 3, June 2020, pp. 1317-25, doi:10.18517/ijaseit.10.3.7739.
One of the main factors contributing to radiometric distortion on remote sensing data is topographic effect, but it can be reduced by applying topographic correction. This study identifies the effect of topographic correction on canopy density mapping in Menoreh Mountains, Indonesia. Topographic correction methods examined in this research are C-Correction, Minnaert, and Sun-Canopy-Sensor+C (SCS+C). Canopy density estimation was approached using vegetation indices, i.e. Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Aerosol Free Vegetation Index (AFRI) 1.6, and AFRI 2.1 derived from Landsat-8 OLI imagery. We evaluated the performance of topographic correction by visual and statistical analysis prior to comparing the accuracy of canopy density estimation of different vegetation indices and correction methods. The results showed that topographic normalization is able to increase the accuracy of canopy density mapping. The most significant improvement is the model using MSAVI which increases 1.207% using Minnaert method to reach 86.692% accuracy. Meanwhile, NDVI, AFRI 1.6, and AFRI 2.1 have less significant improvement with the maximum increase of 0.241%, 0.057%, and 0.032% using SCS+C method, reaching the accuracy of 88.980%, 83.303%, and 82.308%, respectively. The accuracies were slightly improved since the algorithms have already reduced the effect of topography which are categorized as ratio vegetation indices. SCS+C is the best topographic correction method, because of not only the appropriate assumption of canopy normalization but also its consistency and better accuracy in canopy density estimation among other methods.

J. R. Jensen, Introductory Digital Image Processing - A Remote Sensing Perspective, 3rd ed., Englewood Cliff, N.J.: Pearson Prentice Hall, 2005.

W. Yanzhen, W. Zoucheng, Y. Fupin, and Luoxiaobo, “Research of Improved Minnaert Topographic Correction Model and Application,” Applied Mechanics and Materials Vols. 543-547, 2014.

R. Richter, T. Kellenbeger, and H. Kaufmann, “Comparison of Topographic Correction Methods,” Remote Sens. I, 184-196; doi:10.3390/rs1030184. 2009.

S. Hantson and E. Chuvieco, “Evaluation of Different Topographic Correction Methods for Landsat Imagery,” International Journal of Applied Earth Oservation and Geoinformation 13 (2011) 691-700, 2011.

I. Sola, M. Gonzí¡lez-Audí­cana, and J. ílvarez-Mozos, “Multi-criteria evaluation of topographic correction methods,” Remote Sensing of Environment 184 247-262, 2016.

Q. Wu, Y. Jin, and H. Fan, “Evaluating and comparing performances of topographic correction methods based on multi-source DEMs and Landsat-8 OLI data,” International Journal of Remote Sensing, 37:19, 4712-4730, DOI:10.1080/01431161.2016.1222101, 2016.

Y. Zhou, H. Jiang, Z. Wang, X. Yang, and E. Geng, “Assessment of Four Typical Topographic Corrections In Landsat TM Data For Snow Cover Areas,” in XXIII ISPRS Congress, 12-19 July 2016, Prague, Czech Republic.

Z. Zhang, G. He, X. Zhang, T. Long, G. Wang, and M. Wang, “A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain,” GIScience & Remote Sensing, DOI:10.1080/15481603.2017.1382066, 2017.

S. Vanonckelen, S. Lhermitte, and A. V. Rompaey, “The Effect of Atmospheric and Topographic Correction Methods on Land Cover Classification Accuracy,” International Journal of Applied Earth Observation and Geoinformation 24, 9-21. 2013.

C. Wei, T. Qingjiu, and W. Liming, “A Model of Topographic Correction and Reflectance Retrieval for Optical Satellite Data in Forested Areas,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B6b, Beijing 2008.

H. Adhikari, J. Heiskanen, E. E. Maeda, and P. K. E. Pellikka, “The effect of topographic normalization on fractional tree cover mapping in tropical mountains: An assessment based on seasonal Landsat time series,” International Journal of Applied Earth Observation and Geoinformation, 52, 20-31. 2016.

J. R. Jensen, Remote Sensing of The Environment: An Earth Resource Perspective, 2nd ed., Englewood Cliff, N.J.: Pearson Prentice Hall, 2007.

I. Melnikova, Y. Awaya, T. M. Saitoh, H. Muraoka, and T. Sasai, “Estimation of Leaf Area Index in a Mountain Forest of Central Japan with a 30-m Spatial Resolution Based on Landsat Operational Land Imager Imagery: An Application of a Simple Model for Seasonal Monitoring,” Remote Sens. 10, 179; doi:10.3390/rs10020179. 2018.

D. D. Gupita and S. H. M. B. Santosa, “Soil erosion and its correlation with vegetation cover: An assesment using multispectral imagery and pixel-based geographic information system in Gesing Sub-Watershed, Central Java, Indonesia,” IOP Conf. Series: Earth and Environmental Science, 54, 012047. 2017.

D. A. Umarhadi, P. Danoedoro, P. Wicaksono, P. Widayani, W. Nurbandi, and A. Juniansah, “The Comparison of Canopy Density Measurement Using UAV and Hemispherical Photography for Remote Sensing Based Mapping,” in Int. Conf. on Science and Technology (ICST), Yogyakarta, Indonesia, 7-8 Aug. 2018, DOI: 10.1109/ICSTC.2018.8528670.

D. Riano, E. Chuvieco, J. Salas, and I. Aguado, “Assessment of Different Topographic Corrections in Landsat-TM Data for Mapping Vegetation Types,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 5, May 2003.

P. M. Teillet, B. Guindon, and D. G. Goodenough, “On the Slope-Aspect Correction of Multispectral Scanner Data,” Canadian Journal of Remote Sensing: Vol.8 No.2, 84-106, DOI: 10.1080/07038992.1982.10855028. 1982.

D. Gu and A. Gillespie, “Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry,” Remote Sens. Environ., vol. 64, pp. 166-175,.1998.

S. A. Soenen, D. R. Peddle, and C. A. Coburn, “SCS+C: A Modified Sun-Canopy-Sensor Topographic Correction in Forested Terrain,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 9, September 2005.

J. W. Rouse, R. H. Hass, J. A. Schell, and D. W. Deering, “Monitoring Vegetation Systems in the Great Plains with ERTS,” in Proc. Third Earth Resources Technology Satellite-1 Symposium SP-351, 3010-3017. 1974.

J. Qi, A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian, “A Modified Soil Adjusted Vegetation Index,” Remote Sens. Environ. 48: 119-126. 1994.

A. Karnieli, Y. J. Kaufman, L. Remer, and A. Wald, “AFRI - Aerosol Free Vegetation Index,” Remote Sensing of Environment, 77, 10-21. 2001.

P. Meyer, K. I. Itten, T. Kellenberger, S. Sandmeier, and R. Sandmeier, “Radiometric Corrections of Topographically Induced Effects on Landsat TM Data in an Alpine Environment,” ISPRS Journal of Photogrammetry and Remote Sensing, 48(4): 17-28, 1993.

M. L. Gao, W. J. Zhao, Z. N. Gong, H. L. Gong, Z. Chen, and X. M. Tang, “Topographic Correction of ZY-3 Satellite Images and Its Effects on Estimation of Shrub Leaf Biomass in Mountainous Areas,” Remote Sens. 6, 2745-2764; doi:10.3390/rs6042745. 2014.

M. Vicini and E. Frazzi, “Multitemporal evaluation of topographic normalization methods on deciduous forest TM data,” IEEE Trans. Geosci, Remote Sens, 41[11]: 2586-2590. 2003.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).