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Integration of remotely sensed spatial and spectral information for change detection using FAHP

Year 2016, Volume: 66 Issue: 2, 524 - 538, 01.07.2016
https://doi.org/10.17099/jffiu.90466

Abstract

Integration of remotely sensed spatial and spectral information for change detection using FAHP

Abstract: Land use change mapping is one of the basic requirements for effective monitoring and management of the environment. Although several change detection approaches have been proposed and used, past experience has shown that each method may have its own advantages. Selecting the best approach is an important challenge. The integration of spectral/spatial criteria seems to lead to better results. In this study, for change detection analysis, we have used the Landsat Thematic Mapper (TM®) imagery of Zanjan city,acquired in 1985 and 2010. To map the changes, the spectral/spatial criteria were integrated with different weights by using the Fuzzy Analytical Hierarchy Process (FAHP). Results indicate that the FAHP methods generally resulted in a higher accuracy than spectral and spatial criteria. The proposed semivariogram features have a better, early and automatic change detection performance. When using spatial information, both the omission and commission errors decreased significantly. In addition, the uncertainty derived from choosing only one separate method decreases when using the FAHP method. This method will likely be widely used for the change detection of two images that have, on the one hand, the same spectral characteristics and, on the other, different texture characteristics.

Keywords: Changes detection, FAHP, spectral/spatial information

Değişim tespiti için FAHP kullanılarak uzaktan algılanan konumsal ve spektral bilginin entegrasyonu

Özet: Arazi kullanım değişim haritalaması, çevrenin etkin izlenmesi ve yönetiminde temel gereksinimlerden biridir. Çeşitli değişim tespit yaklaşımları önerilip kullanılmasına ragmen, eski deneyimler her bir metodun kendine ait bir avantajı olduğunu gösteriyor. En iyi yaklaşımı seçmek önemli ve zorlu bir iştir. Spektral/konumsal kriterlerin entegrasyonu iyi sonuçlar vermektedir. Bu çalışmada, değişim tespiti analizi için, Zanjan şehrinin 1985 ve 2010 yıllarında alınan Landsat Thematic Mapper (TM®) tasviri kullanılmıştır. Değişimleri haritalamak için, spektral/konumsal kriterler Fuzzy Analytical Hierarchy Process (FAHP) kullanılarak farklı ağırlıklarla entegre edilmiştir. Sonuçlar, FAHP metodlarının genellikle spektral/konumsal kriterlere göre daha yüksek doğrulukla sonuçlandığını göstermiştir. Önerilen semivariogram özellikleri daha iyi, erken ve otomatik değişim tespiti performansına sahiptir. Konumsal bilgi kullanılırken hem eksiklik hem de komisyon hataları önemli ölçüde azalmıştır. Ayrıca tek bir ayrı metodun seçilmesinden kaynaklanan belirsizlik FAHP metodu kulanıldığında azalmaktadır. Bu metodun, bir yandan aynı konumsal karakteristiklerine ve diğer yandan farklı doku karakteristiklerine sahip olan iki tasvirin de değişim tespiti için yaygın biçimde kullanılması muhtemel görünmektedir.

Anahtar kelimeler: Değişim tespiti, FAHP , spectral / konumsal bilgi

Received (Geliş): 21.10.2015 - Revised (Düzeltme): 09.11.2015 -  Accepted (Kabul): 30.11.2015

Cite (Atıf) : Eisavi, V., Homayouni, S., Karami, J., 2016. Integration of remotely sensed spatial and spectral information for change detection using FAHP.  Journal of the Faculty of Forestry Istanbul University 66(2): 524-538. DOI: 10.17099/jffiu.90466

References

  • Atkinson, P.M., Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers &Geosciences 26: 361–371.
  • Balaguer, A., Ruizb, L.A., Hermosillab, T., Reciob, J.A., 2010. Definition of a comprehensive set o f texture semivariogram features and their evaluation for object-oriented image classification. Computers &Geosciences 36: 231-240.
  • Berberoglu, S., Curran, P.J., Lloyd, C.D., Atkinson, P.M., 2007. Texture classification of Mediterranean land cover.
  • International Journal of Applied Earth Observation and Geoinformation 9: 322-334.
  • Buckley, J.J., 1985. Fuzzy hierarchical analysis. Fuzzy Sets Systems 17: 233–247.
  • Carleer, A.P., Wolff, E., 2006. Urban land cover multi-level region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing 27: 1035–1051.
  • Carreiras, J.M.B., Pereira, J.M.C., Campagnolo, M.L., Shimabukuro, Y.E., 2006. Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Remote Sensing of Environment 101: 283-298.
  • Conchedda, G., Durieuxb, L., Mayauxa, P., 2008. An object-based method for map-ping and change analysis in mangrove ecosystems. ISPRS J. Photogramm 63: 578–589.
  • Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25: 1565–1596.
  • Curran, P.J., Atkinson, P.M., 1998. Geostatistics and remote sensing. Prog.Phys.Geogr 22: 61-78.
  • Dekker, R.J., 2003. Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands. IEEE Transactions on Geoscience and Remote Sensing 41: 1950−1958.
  • Deng, H., 1999. Multicriteria analysis with fuzzy pairwise comparisons. International Journal of Approximate Reasoning 22: 215–231.
  • Desclee, B., Bogaert, P., Defourny, P., 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment 102: 1-11.
  • Dewan, A.M., Yamaguchi, Y., 2009. Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl. Geogr 29: 390–401.
  • Dobson, J.E., Bright, A.E., 1992. Coast Watch Change Analysis Program (C-CAP) Chesapeake Bay regional project. Global change and education 1: 109-110.
  • Elvidge, C.D., Yuan, D., Weerackoon, R.D., Lunetta, R.S., 1995. An automated scattergram-controlled regression technique for image normalization. Photogramm Eng. Rem. Sens 61: 1255–1260.
  • Fraser, R.H., Abuelgasim, A., Latifovic, R., 2005. A method for detecting large-scale forest covers change using coarse spatial resolution imagery. Remote Sensing of Environment 95: 414–427.
  • Gong, P., Marceau, D.J., Howarth, P.J., 1992. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing of Environment 40: 137–151.
  • Hartter, J., Lucas, C., Gaughan, A.E., Aranda, L.L., 2008. Detecting tropical dry forest succession in a shifting cultivation mosaic of the Yucatan Peninsula. Appl. Geogr 28: 134-149.
  • He, C., Wei, A., Shi, P., Zhangc, Qi., Zhaoa, Y., 2011 Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis. International Journal of Applied Earth Observation and Geoinformation 13: 572–585.
  • Helmy, A.K., El-Taweel, GhS., 2010. Neural Network Change Detection Model for Satellite Images Using Textural and Spectral Characteristics. American J. of Engineering and Applied Sciences 3: 604-610.
  • Jensen, J.R., Toll, D.L., 1982. Detecting residential land-use development at the urban fringe. Photogramm. Eng. Rem. Sens 48: 629–643.
  • Li, X., Yeh, A., 1998. Principal component analysis of stacked multitemporal images for the monitoring of rapid urban expansion in the Pearl River Delta. International Journal of Remote Sensing 19: 1501–1518.
  • Lund, H.G., 1983. now you see it—now you don’t! Paper presented at the Proceedings of the International Conference on Renewable Resource Inventories for Monitoring Changes and Trends Oregon State University ,Corvallis ,OR,USA (Corvallis, OR :Oregon State University).
  • Macleod, R.D., Congalton, R.G., 1998. A quantitative Vomparison of change – detection algorithms for monitoring eel from remotely sensed data. PEGRS 94: 207-216.
  • Milne, A.K., 1988. Change direction analysis using Landsat imagery: a review of methodology. Proceedings of the IGARSS’88 Symposium Edinburgh, Scotland, ESA SP-284 (Noordwijk, Netherlands: ESA), 541-544.
  • Önüt, S., Efendigil, T., Soner Kara, S., 2010. A combined fuzzy MCDM approach for selecting shopping center site: An example from Istanbul, Turkey. Expert Systems with Applications 37: 1973-1980.
  • Pacifici, F., Chini, M., Emery, W.J., 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic. Remote Sensing of Environment 113: 1276–1292.
  • Singh, A., 1986. Change detection in the tropical forest environment of North-eastern India using Landsat. . In Remote Sensing and Tropical Land Management (New York: John Wiley and Sons), 237–254.
  • Singh, A., 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10: 989–1003.
  • Townshend, J.R.G., Justice, C.O., 1995, Spatial variability of images and the monitoring of changes in the normalized difference vegetation index. International Journal of Remote Sensing 16: 2187–2195.
  • Vahidnia, M., Alesheikh, A., Alimohammadi, A., 2009. Hospital site selection using fuzzy AHP and derivatives. Journal of Enviromental Management 90: 3048-3056.
  • Van, Oort, P.A.J., 2007. Interpreting the change detection error matrix. Remote Sensing of Environment 108: 1-8.
  • Yuan, D., Elvidge, C., 1998. NALC land cover change detection pilot study: Washington D.C. area experiments. Remote Sensing of Environment 66: 166–178.
  • Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E., 2005. Land cover Classification and change analysis of the Twin Cities (Minnesota) Metropolitan area by multi-temporal Landsat remote sensing. Remote Sensing of Environment 98: 317-328

Değişim tespiti için FAHP kullanılarak uzaktan algılanan konumsal ve spektral bilginin entegrasyonu

Year 2016, Volume: 66 Issue: 2, 524 - 538, 01.07.2016
https://doi.org/10.17099/jffiu.90466

Abstract

Arazi kullanım değişim haritalaması, çevrenin etkin izlenmesi ve yönetiminde temel gereksinimlerden biridir. Çeşitli değişim tespit yaklaşımları önerilip kullanılmasına ragmen, eski deneyimler her bir metodun kendine ait bir avantajı olduğunu gösteriyor. En iyi yaklaşımı seçmek önemli ve zorlu bir iştir. Spektral/konumsal kriterlerin entegrasyonu iyi sonuçlar vermektedir. Bu çalışmada, değişim tespiti analizi için, Zanjan şehrinin 1985 ve 2010 yıllarında alınan Landsat Thematic Mapper (TM®) tasviri kullanılmıştır. Değişimleri haritalamak için, spektral/konumsal kriterler Fuzzy Analytical Hierarchy Process (FAHP) kullanılarak farklı ağırlıklarla entegre edilmiştir. Sonuçlar, FAHP metodlarının genellikle spektral/konumsal kriterlere göre daha yüksek doğrulukla sonuçlandığını göstermiştir. Önerilen semivariogram özellikleri daha iyi, erken ve otomatik değişim tespiti performansına sahiptir. Konumsal bilgi kullanılırken hem eksiklik hem de komisyon hataları önemli ölçüde azalmıştır. Ayrıca tek bir ayrı metodun seçilmesinden kaynaklanan belirsizlik FAHP metodu kulanıldığında azalmaktadır. Bu metodun, bir yandan aynı konumsal karakteristiklerine ve diğer yandan farklı doku karakteristiklerine sahip olan iki tasvirin de değişim tespiti için yaygın biçimde kullanılması muhtemel görünmektedir.

References

  • Atkinson, P.M., Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers &Geosciences 26: 361–371.
  • Balaguer, A., Ruizb, L.A., Hermosillab, T., Reciob, J.A., 2010. Definition of a comprehensive set o f texture semivariogram features and their evaluation for object-oriented image classification. Computers &Geosciences 36: 231-240.
  • Berberoglu, S., Curran, P.J., Lloyd, C.D., Atkinson, P.M., 2007. Texture classification of Mediterranean land cover.
  • International Journal of Applied Earth Observation and Geoinformation 9: 322-334.
  • Buckley, J.J., 1985. Fuzzy hierarchical analysis. Fuzzy Sets Systems 17: 233–247.
  • Carleer, A.P., Wolff, E., 2006. Urban land cover multi-level region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing 27: 1035–1051.
  • Carreiras, J.M.B., Pereira, J.M.C., Campagnolo, M.L., Shimabukuro, Y.E., 2006. Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Remote Sensing of Environment 101: 283-298.
  • Conchedda, G., Durieuxb, L., Mayauxa, P., 2008. An object-based method for map-ping and change analysis in mangrove ecosystems. ISPRS J. Photogramm 63: 578–589.
  • Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25: 1565–1596.
  • Curran, P.J., Atkinson, P.M., 1998. Geostatistics and remote sensing. Prog.Phys.Geogr 22: 61-78.
  • Dekker, R.J., 2003. Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands. IEEE Transactions on Geoscience and Remote Sensing 41: 1950−1958.
  • Deng, H., 1999. Multicriteria analysis with fuzzy pairwise comparisons. International Journal of Approximate Reasoning 22: 215–231.
  • Desclee, B., Bogaert, P., Defourny, P., 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment 102: 1-11.
  • Dewan, A.M., Yamaguchi, Y., 2009. Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl. Geogr 29: 390–401.
  • Dobson, J.E., Bright, A.E., 1992. Coast Watch Change Analysis Program (C-CAP) Chesapeake Bay regional project. Global change and education 1: 109-110.
  • Elvidge, C.D., Yuan, D., Weerackoon, R.D., Lunetta, R.S., 1995. An automated scattergram-controlled regression technique for image normalization. Photogramm Eng. Rem. Sens 61: 1255–1260.
  • Fraser, R.H., Abuelgasim, A., Latifovic, R., 2005. A method for detecting large-scale forest covers change using coarse spatial resolution imagery. Remote Sensing of Environment 95: 414–427.
  • Gong, P., Marceau, D.J., Howarth, P.J., 1992. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing of Environment 40: 137–151.
  • Hartter, J., Lucas, C., Gaughan, A.E., Aranda, L.L., 2008. Detecting tropical dry forest succession in a shifting cultivation mosaic of the Yucatan Peninsula. Appl. Geogr 28: 134-149.
  • He, C., Wei, A., Shi, P., Zhangc, Qi., Zhaoa, Y., 2011 Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis. International Journal of Applied Earth Observation and Geoinformation 13: 572–585.
  • Helmy, A.K., El-Taweel, GhS., 2010. Neural Network Change Detection Model for Satellite Images Using Textural and Spectral Characteristics. American J. of Engineering and Applied Sciences 3: 604-610.
  • Jensen, J.R., Toll, D.L., 1982. Detecting residential land-use development at the urban fringe. Photogramm. Eng. Rem. Sens 48: 629–643.
  • Li, X., Yeh, A., 1998. Principal component analysis of stacked multitemporal images for the monitoring of rapid urban expansion in the Pearl River Delta. International Journal of Remote Sensing 19: 1501–1518.
  • Lund, H.G., 1983. now you see it—now you don’t! Paper presented at the Proceedings of the International Conference on Renewable Resource Inventories for Monitoring Changes and Trends Oregon State University ,Corvallis ,OR,USA (Corvallis, OR :Oregon State University).
  • Macleod, R.D., Congalton, R.G., 1998. A quantitative Vomparison of change – detection algorithms for monitoring eel from remotely sensed data. PEGRS 94: 207-216.
  • Milne, A.K., 1988. Change direction analysis using Landsat imagery: a review of methodology. Proceedings of the IGARSS’88 Symposium Edinburgh, Scotland, ESA SP-284 (Noordwijk, Netherlands: ESA), 541-544.
  • Önüt, S., Efendigil, T., Soner Kara, S., 2010. A combined fuzzy MCDM approach for selecting shopping center site: An example from Istanbul, Turkey. Expert Systems with Applications 37: 1973-1980.
  • Pacifici, F., Chini, M., Emery, W.J., 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic. Remote Sensing of Environment 113: 1276–1292.
  • Singh, A., 1986. Change detection in the tropical forest environment of North-eastern India using Landsat. . In Remote Sensing and Tropical Land Management (New York: John Wiley and Sons), 237–254.
  • Singh, A., 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 10: 989–1003.
  • Townshend, J.R.G., Justice, C.O., 1995, Spatial variability of images and the monitoring of changes in the normalized difference vegetation index. International Journal of Remote Sensing 16: 2187–2195.
  • Vahidnia, M., Alesheikh, A., Alimohammadi, A., 2009. Hospital site selection using fuzzy AHP and derivatives. Journal of Enviromental Management 90: 3048-3056.
  • Van, Oort, P.A.J., 2007. Interpreting the change detection error matrix. Remote Sensing of Environment 108: 1-8.
  • Yuan, D., Elvidge, C., 1998. NALC land cover change detection pilot study: Washington D.C. area experiments. Remote Sensing of Environment 66: 166–178.
  • Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E., 2005. Land cover Classification and change analysis of the Twin Cities (Minnesota) Metropolitan area by multi-temporal Landsat remote sensing. Remote Sensing of Environment 98: 317-328
There are 35 citations in total.

Details

Primary Language English
Journal Section Research Articles (Araştırma Makalesi)
Authors

Vahid Eisavi

Saeid Homayouni This is me

Jalal Karami This is me

Publication Date July 1, 2016
Published in Issue Year 2016 Volume: 66 Issue: 2

Cite

APA Eisavi, V., Homayouni, S., & Karami, J. (2016). Integration of remotely sensed spatial and spectral information for change detection using FAHP. Journal of the Faculty of Forestry Istanbul University, 66(2), 524-538. https://doi.org/10.17099/jffiu.90466
AMA Eisavi V, Homayouni S, Karami J. Integration of remotely sensed spatial and spectral information for change detection using FAHP. J FAC FOR ISTANBUL U. July 2016;66(2):524-538. doi:10.17099/jffiu.90466
Chicago Eisavi, Vahid, Saeid Homayouni, and Jalal Karami. “Integration of Remotely Sensed Spatial and Spectral Information for Change Detection Using FAHP”. Journal of the Faculty of Forestry Istanbul University 66, no. 2 (July 2016): 524-38. https://doi.org/10.17099/jffiu.90466.
EndNote Eisavi V, Homayouni S, Karami J (July 1, 2016) Integration of remotely sensed spatial and spectral information for change detection using FAHP. Journal of the Faculty of Forestry Istanbul University 66 2 524–538.
IEEE V. Eisavi, S. Homayouni, and J. Karami, “Integration of remotely sensed spatial and spectral information for change detection using FAHP”, J FAC FOR ISTANBUL U, vol. 66, no. 2, pp. 524–538, 2016, doi: 10.17099/jffiu.90466.
ISNAD Eisavi, Vahid et al. “Integration of Remotely Sensed Spatial and Spectral Information for Change Detection Using FAHP”. Journal of the Faculty of Forestry Istanbul University 66/2 (July 2016), 524-538. https://doi.org/10.17099/jffiu.90466.
JAMA Eisavi V, Homayouni S, Karami J. Integration of remotely sensed spatial and spectral information for change detection using FAHP. J FAC FOR ISTANBUL U. 2016;66:524–538.
MLA Eisavi, Vahid et al. “Integration of Remotely Sensed Spatial and Spectral Information for Change Detection Using FAHP”. Journal of the Faculty of Forestry Istanbul University, vol. 66, no. 2, 2016, pp. 524-38, doi:10.17099/jffiu.90466.
Vancouver Eisavi V, Homayouni S, Karami J. Integration of remotely sensed spatial and spectral information for change detection using FAHP. J FAC FOR ISTANBUL U. 2016;66(2):524-38.