COMPARING ATMOSPHERIC CORRECTION METHODS FOR LANDSAT OLI DATA

Esthi Kurnia Dewi, Bambang Trisakti

Abstract

Landsat data used for monitoring activities to land cover because it has spatial resolution and high temporal. To monitor land cover changes in an area, atmospheric correction is needed to be performed in order to obtain data with precise digital value picturing current condition. This study compared atmospheric correction methods namely Quick Atmospheric Correction (QUAC), Dark Object Subtraction (DOS) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH). The correction results then were compared to Surface Reflectance (SR) imagery data obtained from the United States Geological Survey (USGS) satelite. The three atmospheric correction methods were applied to Landsat OLI data path/row126/62 for 3 particular dates. Then, sample on vegetation, soil and bodies of water (waterbody) were retrieved from the image. Atmospheric correction results were visually observed and compared with SR sample on the absolute value, object spectral patterns, as well as location and time consistency. Visual observation indicates that there was a contrast change on images that had been corrected by using FLAASH method compared to SR, which mean that the atmospheric correction method was quite effective. Analysis on the object spectral pattern, soil, vegetation and waterbody of images corrected by using FLAASH method showed that it was not good enough eventhough the reflectant value differed greatly to SR image. This might be caused by certain variables of aerosol and atmospheric models used in Indonesia. QUAC and DOS made more appropriate spectral pattern of vegetation and water body than spectral library. In terms of average value and deviation difference, spectral patterns of soil corrected by using DOS was more compatible than QUAC.

Keywords

Landsat; atmospheric correction; QUAC; FLAASH; DOS; surface reflectance

Full Text:

PDF

References

Caselles V., García MJL, (1989), An alternative simple approach to estimate atmospheric correction in multitemporal studies. International Journal of Remote Sensing 10(6): 1127-1134. doi: http://dx.doi.org/ 10.1080/01431168908903951.

Ceccarelli T., Smiraglia D., Bajocco S., Rinaldo S., Angelis AD, Salvati L., Perini L., (2013), Land cover data from Landsat single-date imagery: an approach integrating pixel-based and object-based classifiers. European Journal of Remote Sensing 46 : 699 – 717.

Chavez PS, (1988), An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote sensing of Environment 24 (3): 459-479. doi: http://dx.doi.org/10.1016/ 0034-4257(88)90019-.

Chavez PS, (1996), Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing 62 (9): 1025-1036. Available at: http://www.unc.edu/courses/2008spring/geog/577/001/www/Chavez96-PERS.pdf/ (lastaccessed: 24/10/2016).

Cui L., Li G., Ren H., He L., Liao H., Ouyang N., Zhang Y., (2014), Assessment of atmospheric correction methods for historical Landsat TM images in the coastal zone: A case study in Jiangsu, China. European Journal of Remote Sensing 47: 701-716. doi: 10.5721/EuJRS20144740

Ginting AY, Latifah S., Rahmawaty, (2012), Analisis Perubahan Tutupan Lahan Kabupaten Karo (Analysis of Karo Regency Land Cover Changes). Peronema Forestry Science Journal 1(1).

Guo Y., Zeng F., (2012), Atmospheric Correction Comparison of Spot-5 Image Based on Model Flaash and Model Quac. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7: 7-11

Lee SB, La HP, Eo YD, Pyeon MW, (2015), Generation of Simulated Image from Atmospheric Corrected Landsat TM Images. Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography 33(1): 1-9.

Moran E., (2002), Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. ACT Publication No 02-06.

Nazeer M., Nichol JE, Yung Y., (2014), Evaluation of atmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International Journal of Remote Sensing 35(16): 6271 – 6291. doi: 10.1080/01431161.2014. 951742.

Nguyen HC, Jung J., Lee J., Choi S., Hong S., Heo J., (2015), Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor. Sensors 15(8): 18865–18886. doi: 10.3390/s150818865.

Rahayu, Candra DS, (2014), Koreksi Radiometrik Citra Landsat-8 Kanal Multispektral Menggunakan Top of Atmosphere (Toa) untuk Mendukung Klasifikasi Penutup Lahan. Seminar Nasional Penginderaan Jauh.

Smith GM, Milton EJ, (1999), The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing 20 (13): 2653-2662,doi: http://dx.doi.org/10.1080/0143 11699211994.

Somdatta C., Chakrabarti S., (2011), Pre­ processing of Hyperspectral Data: A case study of Henry and Lothian Islands in Sunderban Region, West Bengal, India. International Journal of Geomatics And Geosciences 2(2).

Tampubolon T., Jeddah Y., (2015), Aplikasi Pemanfaatan Citra Satelit Landsat untuk Mengidentifikasi Perubahan Lahan Kritis di Kota Medan danSekitarnya. Spektra: Jurnal Fisika dan Aplikasinya 16(2): 15-19.

Trisakti B., Suwarnana N., Cahyono JS, (2014), Pemanfaatan Data Penginderan Jauh untuk Memantau Parameter Status Ekosistem Perairan Danau (Studi Kasus: Danau Rawa Pening). Seminar Nasional Penginderaan Jauh.

Tuni MS, Barus B., Iskandar, (2013), Prediksi Perubahan Tutupan Lahan dan Perencanaan Penggunaan Lahan Pascatambang Nikel di Kabupaten Halmahera Timur. Globe 15(2): 146 – 152.

Vincent RK, (1972), An ERTS multispectral scanner experiment for mapping iron compounds. Proceedings of the Eighth International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, II: 1239-1247. Available at: http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19730001633.pdf/(last accessed: 24/09/2014).

Yulius, Tanto TA, Ramdhan M., Putra A., Salim HL, (2014), Perubahan Tutupan Lahan di Pesisir Bungus Teluk Kabung, Sumatra Barat Tahun 2003-2013 Menggunakan Sistem Informasi Geografis. Jurnal Ilmu dan Teknologi KelautanTropis, 6 (2): 311-318.

Zhang X., Yang H., Shuai T., (2010). Comparision of FLAASH versus Empirical Line Approach for Atmospheric Correction of OMIS-II Imagery. Journal Chinese Academy of Sciences, Beijing.

Zhang Z., He G., Wang X., (2010), A practical DOS model-based atmospheric correction algorithm. International Journal of Remote Sensing 31(11): 2837-2852, doi: http:// dx.doi.org/10.1080/01431160903124682.

Refbacks

  • There are currently no refbacks.