Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra

https://doi.org/10.22146/ijg.35046

Ratih Dewanti Dimyati(1*), Projo Danoedoro(2), Hartono Hartono(3), Kustiyo Kustiyo(4), Muhammad Dimyati(5)

(1) Gadjah Mada University; and LAPAN
(2) Gadjah Mada University
(3) Gadjah Mada University
(4) LAPAN
(5) Directorate General of Strengthening for Research and Development, Ministry of Research, Technology and Higher Education; and University of Indonesia
(*) Corresponding Author

Abstract


This paper presents an interoperability of annual tile-based mosaic (MTB) images, as well as a verification of the validity of the model for the time series land cover analysis purposes. The primary data used are MTB image of Landsat-8 of the central part of Sumatra, acquired from January 2015 to June 2017. The method used for the interoperability validation is the digital analysis of three-years time series land cover. The classification was performed with four band spectral groups. Training samples are taken from the image of 2016. The results are then reclassified to improve the overall accuracy score based on Jefferies Matusita (JM) distance. The interoperability can be measured by the average of overall accuracy (AOA) score, namely Good (scores > 80%), Fair (70.0% -79.9%), and Bad (< 70%). The results show that the use of the groups Bands 6-5-4-3-2 performs the consistent accuracy level of Good with an AOA score of 86% for six classes object. Whereas the use of the groups Bands 6-5-4-3-2, Bands 6-5-4, and Bands 6-5 shows the consistent accuracy level of Good up to four classes object with an AOA score of 89%, 82%, and 81%, respectively. It means that the annual mosaic image of MTB model is accepted for the image interoperability with an AOA score of > 80% for six and four classes object. Thus the most efficient for interoperability is the use of Bands 6-5 to analyze four class object of land cover. 

Keywords


Interoperability; Mosaic Tile Based model; annual mosaic image; time series land cover analysis; the spectral consistency.

Full Text:

PDF


References

Andie Setiyoko, Riyan Mahendra Saputra, Abdul Asyiri, Gusti Dharma, & Yudha. (2016). Analisis Kesesuaian Pelayanan Data Penginderaan Jauh Terhadap Kebutuhan Pengguna (in Bahasa). In Seminar Nasional Penginderaan Jauh 2016 (pp. 424–527). Retrieved from www.lapan.go.id


Badan Standardisasi Nasional (BSN). SNI 7645:2010: Klasifikasi penutup lahan (in Bahasa), Pub. L. No. SNI 7645: 2010, SNI 7645:2 28 (2010). Jakarta, Indonesia.

Badan Standardisasi Nasional (BSN). SNI 8033:2014: Metode penghitungan perubahan tutupan hutan berdasarkan hasil penafsiran citra penginderaan jauh optik secara visual (in Bahasa), Pub. L. No. SNI 8033:2014, 9 (2014). Jakarta, Indonesia.

Badan Standarisasi Nasional (BSN). RSNI-1 (2015): Kelas Penutupan Lahan dalam Penafsiran Citra Optis Resolusi Sedang (in Bahasa), Pub. L. No. RSNI-1 ICS, 17. Indonesia.

Belinda Arunarwati Margono; Peter V. Potapov; Svetlana Turubanova; Fred Stolle; Matthew C. Hansen (2014). Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change, 4(August), 730–735. https://doi.org/10.1038/NCLIMATE2277

Bodart, C., Eva, H., Beuchle, R., Raši, R., Simonetti, D., Stibig, H. J., Achard, F. (2011). Pre-processing of a sample of multi-scene and multi-date Landsat imagery used to monitor forest cover changes over the tropics. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 555–563. https://doi.org/10.1016/j.isprsjprs.2011.03.003

Broich, M., Hansen, M. C., Potapov, P., Adusei, B., Lindquist, E., & Stehman, S. V. (2011). Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia. International Journal of Applied Earth Observation and Geoinformation, 13 (2), 277–291. https://doi.org/10.1016/j.jag. 2010.11.004

Centre for Remote Imaging, Sensing and Processing (CRISP). (2001). Cloud-Free Mosaics. Retrieved March 9, 2017, from https://crisp.nus.edu.sg/~research/cloudfree_mosaic/ cloudfree_mosaic.htm

Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205 (December 2017), 338–351. https://doi.org/10.1016/j.rse.2017.11.024

Costachioiu, T., Lazarescu, V., & Datcu, M. (2011). Classification of scene evolution patterns from satellite Image Time Series based on spectro-temporal signatures. ISSCS 2011 - International Symposium on Signals, Circuits and Systems, Proceedings, 305–308. https://doi.org/10.1109/ISSCS.2011.5978721

Danoedoro, P. (2012). Pengantar Penginderaan Jauh Digital (in Bahasa) (First). Yogyakarta: Andi Offset.

De Vries, C., Danaher, T., Denham, R., Scarth, P., & Phinn, S. (2007). An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites. Remote Sensing of Environment, 107(3), 414–429. https://doi.org/10.1016/ j.rse.2006.09.019

Dimyati, R. D., Danoedoro, P., Hartono, Kustiyo (2018). A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based. International Journal of Electrical and Computer Engineering, 8 (1). https://doi.org/10.11591/ijece.v8i1.pp360-371

Dimyati M., Dimyati RD, Kustiyo, Danoedoro P., Hartono. (2018). Interpretability Evaluation of Annual Multitemporal Tile Based Mosaic of Landsat-8 Operational Land Imager for Land Cover Changes Analysis in the Central Part of Sumatra. Telkomnika, Vol. 16/03 (June 2018), 1–14. https://doi.org/http://dx.doi.org/10.12928/ telkomnika.v16i3.9331

Furby, S. (2002). National carbon accounting system Land Cover Change Specification for Remote Sensing Analysis, Technical Report No. 9. Canberra.

Furby, S. L., Caccetta, P. A., Wu, X., A., & Chia, J. (2006). Continental Scale Land Cover Change Monitoring in Australia using Landsat Imagery. CSIRO Mathematical and Information Sciences.

Gastellu-Etchegorry, J. P. (1988). Monthly Probabilities For Acquiring Remote Sensed Data of Indonesia with Cloud Cover Less than 10, 20 and 30 Percent. The Indonesian Journal of Geography, 18 (55), 11–28.

Ghosh, D., & Kaabouch, N. (2016). A survey on image mosaicing techniques. Journal of Visual Communication and Image Representation, 34, 1–11. https://doi.org/10.1016/j.jvcir.2015.10.014

Gómez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55–72. https://doi.org/10.1016/j.isprsjprs.2016.03.008

Gu, J., Chen, J., Zhou, Q. M., Zhang, H. W., & Ma, L. (2008). Quantitative Textural Parameter Selection for Residential Extraction from High-Resolution Remotely Sensed Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 ⎧, Vol. XXXVI (2008), 1371–1376.

Guo, Y., Li, F., Caccetta, P., And, D. D., & Berman, M. (2016). Cloud Filtering for Landsat TM Satellite Images Using Multiple Temporal Mosaicing. IEEE IGARSS 2016, 7240–7243.

Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66–74. https://doi.org/10.1016/j.rse.2011.08.024

Hansen, M. C., Roy, D. P., Lindquist, E., Adusei, B., Justice, C. O., & Altstatt, A. (2008). A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, 112 (5), 2495–2513. https://doi.org/10.1016/j.rse.2007.11.012

Islam, K., Jashimuddin, M., Nath, B., & Nath, T. K. (2016). Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. Egyptian Journal of Remote Sensing and Space Science, 21 (1), 37–47. https://doi.org/10.1016/j.ejrs.2016.12.005

Kevin Butler. (2018). Band Combinations for Landsat 8. Retrieved from https://blogs.esri.com/esri/arcgis/2013/07/24/band-combinations-for-landsat-8/

Kushardono, D., & Dewanti, R. (2016). Pemetaan Kebutuhan Sensor Optik Satelit Penginderaan Jauh di Indonesia (The Mapping of Remote Sensing Satellite Optical Sensor Needs in Indonesia) (in Bahasa). Majalah Inderaja, VII(9 Edisi November 2016), 20–27.

Kustiyo. (2016). Development of Annual Landsat 8 Composite Over Central Kalimantan, Indonesia Using Automatic Algorithm to Minimize Cloud. International Journal of Remote Sensing and Earth Sciences, 13 (1), 51–58.

Kustiyo, Dewanti, R., & Lolitasari, I. (2014). Pengembangan Metode Koreksi Radiometrik Citra SPOT 4 Multi-Spektral dan Multi-Temporal untuk Mosaik Citra. In Seminar Nasional Penginderaan Jauh 2014 (pp. 79–87).

Kustiyo, Roswintiarti, O., Tjahjaningsih, A., Dewanti, R., Furby, S., & Wallace, J. (2015). Annual forest monitoring as part of the Indonesia’s National Carbon Accounting System. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40 (7W3), 441–448. https://doi.org/10.5194/ isprsarchives-XL-7-W3-441-2015

M. Dabboor; S. Howell; M. Shokr; J. Yackel. (2014). The Jeffries – Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data. International Journal of Remote Sensing, 35(19), 6859–6873. https://doi.org/10.1080/01431161.2014.960614

Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y. (2017). A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 277–293. https://doi.org/10.1016/j.isprsjprs.2017.06.001

Margono, B. A., Usman, A. B., Budiharto, & Sugardiman, R. A. (2016). Indonesia ’ s Forest Resource Monitoring. Indonesian Journal of Geography, 48(1), 7–20.

Mausel, P. W., Kramber, W. J., & Lee, J. K. (1990). Optimum Band Selection for Supervised Classification of Multispectral Data, 56(1), 55–60.

Mitchell, A. L., Milne, A., Tapley, I., Lowell, K., Caccetta, P., Lehmann, E., Held, A. (2011). Interoperability of radar and optical data for forest information assessment. International Geoscience and Remote Sensing Symposium (IGARSS), 1397–1400. https://doi.org/10.1109/IGARSS.2011.6049327

Mitchell A.L, Williams M, Tapley I, Milne A.K. (2012). Interoperability of Multi-Frequency SAR Data for Forest Information Extraction in Support of National MRV Systems. IEEE IGARSS 2012, 978-1–4673, 3166–3169.

Mouginis-mark, P. J., Owensby, P., Chellis, C., & Lo, J. (2001). Cloud-Free Satellite Mosaics for Disaster Management, 00(C), 822–823.

Roswintiarti Orbita, Ratih Dewanti, Furby Suzanne, Wallace Jeremy (2015). The Remote Sensing Monitoring Program of Indonesia’s National Carbon Accounting System: Methodology and Products. Jakarta.

Peacock, R. (2014). Land Cover Classification, Accuracy Assessment of Supervised and Unsupervised Classification Using Landsat Imagery of Little Rock, Arkansas. Northwest Missouri State University Master Thesis.

Presiden Republik Indonesia. Undang-undang Republik Indonesia Nomor 4 Tahun 2011 tentang Informasi Geospasial (in Bahasa), No. NOMOR 4 TAHUN 2011 (2011).

Presiden Republik Indonesia. Peraturan Pemerintah Republik Indonesia Nomor 8 Tahun 2013 tentang Ketelitian Peta Rencana Tata Ruang (in Bahasa), PP No 8 Tahun 2013 (2013).

Presiden Republik Indonesia. Undang-undang Republik Indonesia Nomor 21 Tahun 2013 tentang Keantariksaan (in Bahasa), NOMOR 21 TAHUN 2013 (2013). Indonesia. Retrieved from www.lapan.go.id

Presiden Republik Indonesia. Undang-undang Republik Indonesia Nomor 6 tahun 2014 tentang Desa (in Bahasa), Presiden Republik Indonesia (2014). https://doi.org/10.1017/ CBO9781107415324.004

Presiden Republik Indonesia. Undang Undang Republik Indonesia Nomor 20 Tahun 2015 tentang Standardisasi dan Penilaian Kesesuaian (in Bahasa) (2014). Indonesia.

Presiden Republik Indonesia. Peraturan Presiden Republik Indonesia Nomor 9 Tahun 2016 tentang Percepatan Pelaksanaan Kebijakan Satu Peta pada Tingkat Ketelitian Peta Skala 1: 50.000 (in Bahasa), Pub. L. No. No 9 Year 2016 (2016).

Presiden Republik Indonesia. Peraturan Pemerintah Nomor 11 Tahun 2018 tentang Tata Cara Penyelenggaraan Kegiatan Penginderaan Jauh, No. PP No 11 Tahun 2018, 45 (in Bahasa) (2018). Indonesia.

Queensland Department of Science, Information Technology (2014). Land cover change in Queensland 2010–11: a Statewide Landcover and Trees Study (SLATS) report. Brisbane.

Richards, J. A., & Jia, X. (2006). Remote Sensing Digital Image Analysis, An Introduction 4th (4th Editio). Canberra: Springer.

Setiawan, Y., Lubis, M. I., Yusuf, S. M., & Prasetyo, L. B. (2015). Identifying Change Trajectory over the Sumatra’s Forestlands Using Moderate Image Resolution Imagery. Procedia Environmental

Sciences, 24, 189–198. https://doi.org/10.1016/j.proenv. 2015.03.025

Sonobe, R., Tani, H., & Wang, X. (2017). An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data. Geocarto International, 32 (2), 128–138. https://doi.org/10.1080/10106049.2015.1130085

Sutanto (2013). Metode Penelitian Penginderaan Jauh (in Bahasa). Badan Penerbit Fakultas Geografi Universitas Gadjah Mada, Penerbit Ombak Yogyakarta.

USGS. (2018). Landsat Missions How do Landsat 8 band combinations differ from Landsat 7 or Landsat 5 satellite data? USGS. Retrieved from https://landsat.usgs.gov/how-do-landsat-8-band-combinations-differ-landsat-7- or-landsat-5-satellite-data

USGS. (2015). Landsat Mission, What are the best spectral bands to use for my study?

Wijaya, A., Sugardiman, R. A., Budiharto, B., Tosiani, A., Murdiyarso, D., & Verchot, L. V. (2015). Assessment of large scale land cover change classifications and drivers of deforestation in Indonesia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40 (7W3), 557–562. https://doi.org/10.5194/isprsarchives-XL-7-W3-557-2015

Wulansari, H. (2017). Uji Akurasi Klasifikasi Penggunaan Lahan dengan Menggunakan Metode Defuzzifikasi Maximum Likelihood Berbasis Citra ALOS AVNIR-2 (in Bahasa). Bhumi, 3–No 1 (Mei), 98–110.

Zhongyang, L., Zixuan, D., Huailiang, C., & Chunhui, Z. (2011). Study on the Land Use and Cover Classification of Zhengzhou Based on Decision Tree.




DOI: https://doi.org/10.22146/ijg.35046

Article Metrics

Abstract views : 3351 | views : 1923

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2018 Indonesian Journal of Geography

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

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC