Research Article
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Year 2024, Volume: 11 Issue: 3, 30 - 48, 28.09.2024
https://doi.org/10.30897/ijegeo.1516280

Abstract

References

  • Atalay, I., Efe, R., Öztürk, M. (2014). Ecology and Classification of Forests in Turkey. Procedia - Social and Behavioral Sciences, 120, 788-805. doi.org/10.1016/ J.Sbspro.2014.02.163
  • Baillarin, S. J., Meygret, A., Dechoz, C., Petrucci, B., Lacherade, S., Tremas, T., Isola, C., Martimort, P., Spoto, F. (2012). Sentinel-2 Level 1 Products and Image Processing Performances. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B1, 197–202. doi.org/10.5194/Isprsarchives-XXXIX-B1-197-2012
  • Bastarrika, A., Chuvieco, E., Martín, M. P. (2011). Mapping Burned Areas from Landsat TM/ETM+ Data with a Two-Phase Algorithm: Balancing Omission and Commission Errors. Remote Sensing of Environment, 115(4), 1003-1012, doi.org/10.1016/J.Rse. 2010.12.005
  • Carper, W. J., Lillesand, T. M., Kiefer, R. W. (1990). The Use of Intensity-Hue-Saturation Transformations for Merging Spot Panchromatic and Multispectral Image Data. Photogrammetric Engineering Remote Sensing, 56(4), 459-467.
  • Choi, M. (2006). A New Intensity-Hue-Saturation Fusion Approach to Image Fusion with a Tradeoff Parameter. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 672–1682.
  • Chu, T., Guo, X. (2013). Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review. In Remote Sensing 6(1), 470-520. doi.org.10.3390/Rs6010470
  • Chuvieco, E., Martín, M. P., Palacios, A. (2002). Assessment of Different Spectral Indices in the Red-Near-Infrared Spectral Domain For Burned Land Discrimination. International Journal of Remote Sensing, 23(23), 5103-5110.
  • Chuvieco, E., Mouillot, F., Van Der Werf, G. R., San Miguel, J., Tanasse, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., Giglio, L. (2019). Historical Background and Current Developments for Mapping Burned Area from Satellite Earth Observation. Remote Sensing of Environment, 225, 45-64.
  • Cihlar, J., Xiao, Q., Chen, J., Beaubien, J., Fung, K., Latifovic, R. (1998). Classification by Progressive Generalization: A New Automated Methodology for Remote Sensing Multichannel Data. International Journal of Remote Sensing, 19(14), 2685-2704. doi.org.10.1080/014311698214451
  • Congalton, R. G. (2015). Remote Sensing and Image Interpretation. 7th Edition. Photogrammetric Engineering Remote Sensing, 81(8). doi.org/ 10.14358/Pers.81.8.615
  • Congalton, R. G., Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition. In Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition.
  • Delegido, J., Verrelst, J., Alonso, L., Moreno, J. (2011). Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green Lai and Chlorophyll Content. Sensors, 11(7), 89-108.
  • Díaz-Delgado, R., Lloret, F., Pons, X., Terradas, J. (2002). Satellite Evidence of Decreasing Resilience in Mediterranean Plant Communities after Recurrent Wildfires. Ecology, 83(8):2293-2303
  • Fernández-García, V., Marcos, E., Huerta, S., Calvo, L. (2021). Soil-Vegetation Relationships in Mediterranean Forests after Fire. Forest Ecosystems, 8(2), 18.
  • Foody, G. (2010). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. The Photogrammetric Record, 25(130). 9780429143977
  • Foody, G. M. (2002). Status of Land Cover Classification Accuracy Assessment. In Remote Sensing of Environment 80(1), 185-201. doi.org/10.1016/S0034-4257 (01)00295-4
  • General Directorate of Forestry. (2020). 2020’s Statistics in Forestry. Republic of Turkey Ministry of Agriculture and Forestry.
  • General Directorate of Forestry. (2022). 2022’s Statistics in Forestry.
  • Gigović, L., Pourghasemi, H. R., Drobnjak, S., Bai, S. (2019). Testing a New Ensemble Model Based on Svm and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park. Forests, 10(5), 408.
  • Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, 24, 417-441.
  • Huang, D., Jiang, F., Li, K., Tong, G., Zhou, G. (2022). Scaled PCA: A New Approach to Dimension Reduction. Management Science, 68(3). doi.org/10.1287/ Mnsc.2021.4020
  • Kavgacı, A., Başararan, E.A. (2023). Orman Yangınları
  • Key, C. H., Benson, N. C. (2006). Landscape Assessment (La) Sampling and Analysis Methods. In Usda Forest Service - General Technical Report Rmrs-Gtr (Issues 164 Rmrs-Gtr).
  • Khorrami, B., Gunduz, O., Patel, N., Ghouzlane, S., Najar, M. (2019). Land Surface Temperature Anomalies In Response To Changes In Forest Cover. International Journal of Engineering and Geosciences, 4(3). doi.org/10.26833/Ijeg.549944
  • Lanorte, A., Manzi, T., Nolè, G., Lasaponara, R. (2015). On The Use of the Principal Component Analysis (PCA) For Evaluating Vegetation Anomalies from Landsat-TM NDVI Temporal Series in The Basilicata Region (Italy). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9158. doi.org.10.1007/978-3-319-21410-8_16
  • Lasaponara, R. (2006). On The Use of Principal Component Analysis (PCA) For Evaluating Interannual Vegetation Anomalies from Spot/Vegetation NDVI Temporal Series. Ecological Modelling, 194(4). doi.org.10.1016/J.Ecolmodel. 2005.10.035
  • Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., Benson, N. C. (2006). Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. In International Journal of Wildland Fire (Vol. 15, Issue 3). doi.org.10.1071/Wf05097
  • Lentile, L. B., Holden, Z. A., Smith, A. M., Falkowski, M. J., Hudak, A. T., Morgan, P., Others, Benson, N. C. (2006). Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. International Journal of Wildland Fire, 15(3), 319–345.
  • Leung, Y., Liu, J., Zhang, J. (2014). An Improved Adaptive Intensity-Hue-Saturation Method for the Fusion Of Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 11(5). Doi.org.10.1109/Lgrs.2013.2284282
  • Lillesand, T. M., Kiefer, R. W. (1994). Remote Sensing and Image Interpretation. 3rd Edition. Remote Sensing and Image Interpretation. 3rd Edition.
  • Liu, S., Zheng, Y., Dalponte, M., Tong, X. (2020). A Novel Fire Index-Based Burned Area Change Detection Approach Using Landsat-8 OLI Data. European Journal of Remote Sensing, 53(1), 104-112. doi.org. 10.1080/22797254.2020.1738900
  • Lu, S. L., Zou, L. J., Shen, X. H., Wu, W. Y., Zhang, W. (2011). Multi-Spectral Remote Sensing Image Enhancement Method Based On PCA and IHS Transformations. Journal of Zhejiang University: Science A, 12(6), 453-460, doi.org.10.1631/ Jzus.A1000282
  • Lutes, D. C., Keane, R. E., Caratti, J. F., Key, C. H., Benson, N. C., Gang, L. J. (2006). Firemon: Fire Effects Monitoring and Inventory System. USA Forest Service, Rocky Mountain Research Station, General Technical Report.
  • Mallinis, G., Koutsias, N. (2012). Comparing Ten Classification Methods for Burned Area Mapping in a Mediterranean Environment Using Landsat TM Satellite Data. International Journal of Remote Sensing, 33(14), 4408-4433 doi.org.10.1080/ 01431161.2011.648284
  • Mandanici, E., Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sensing 8(12), 1014, 1-9.
  • Petropoulos, G. P., Kontoes, C., Keramitsoglou, I. (2011). Burnt Area Delineation from A Uni-Temporal Perspective Based On Landsat TM Imagery Classification Using Support Vector Machines. International Journal of Applied Earth Observation and Geoinformation, 13(1), 70-80.
  • Richards, J. A. (2013). Remote Sensing Digital Image Analysis: An Introduction. In Remote Sensing Digital Image Analysis: An Introduction (Vol. 9783642300622). doi.org.10.1007/978-3-642-30062-2
  • Richards, J. A., Jia, X. (2006). Remote Sensing Digital Image Analysis: An Introduction. In Remote Sensing Digital Image Analysis: An Introduction. doi.org.10.1007/3-540-29711-1
  • Röder, A., Hill, J., Duguy, B., Alloza, J. A., Vallejo, R. (2008). Using Long Time Series of Landsat Data to Monitor Fire Events and Post-Fire Dynamics and Identify Driving Factors. A Case Study in the Ayora Region (Eastern Spain). Remote Sensing of Environment, 112(1), 259-273. doi.org.10.1016/J.Rse. 2007.05.001
  • Roteta, E., Bastarrika, A., Padilla, M., Storm, T., Chuvieco, E. (2019). Development of a Sentinel-2 Burned Area Algorithm: Generation of a Small Fire Database For Sub-Saharan Africa. Remote Sensing of Environment, 222, 1-17. doi.org.10.1016/J.Rse.2018.12.011
  • Sabuncu, A., Özener, H. (2019). Uzaktan Algılama Teknikleri ile Yanmış Alanların Tespiti: İzmir Seferihisar Orman Yangını Örneği. Doğal Afetler Ve Çevre Dergisi, 5(2), 317-326. doi.org/ 10.21324/dacd.511688
  • San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., De Rigo, D., Ferrari, D., Maianti, P., Artes Vivancos, T., Costa, H., Lana, F. (2020). Advance Effis Report On Forest Fires in Europe, Middle East and North Africa 2019. In Joint Research Center EC (Issue March).
  • Sentinel-2 Mission Overview. (2015). ESA. www.esa.int/
  • Singh, A., Harrison, A. (1985). Standardized Principal Components. International Journal of Remote Sensing, 6(6), 883-896. doi.org.10.1080/01431168508948511
  • Sunar Erbek, F., Özkan, C., Taberner, M. (2004). Comparison of Maximum Likelihood Classification Method with Supervised Artificial Neural Network Algorithms for Land Use Activities. International Journal of Remote Sensing, 25(9), 1733-1748. doi.org.10.1080/0143116031000150077
  • Tucker, C. J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8(2), 127-150. doi.org.10.1016/0034-4257 (79) 90013-0
  • Tuia, D., Volpi, M., Copa, L., Kanevski, M., Muñoz-Marí, J. (2011). A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification. IEEE Journal On Selected Topics in Signal Processing, 5(3), 606-617. doi.org.10.1109/Jstsp.2011.2139193
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W. W., Goossens, R. (2011). Evaluation of Pre/Post-Fire Differenced Spectral Indices for Assessing Burn Severity in A Mediterranean Environment with Landsat Thematic Mapper. International Journal of Remote Sensing, 32(12), 3521-3537. doi.org.10.1080/ 01431161003752430
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W., Goossens, R. (2010). Assessing Burn Severity Using Satellite Time Series. Wit Transactions on Ecology and The Environment, 137. doi.org.10.2495/Fiva100101
  • Walsh, S. J., Cooper, J. W., Von Essen, I. E., Gallager, K. R. (1990). Image Enhancement of Landsat Thematic Mapper Data and GIS Data Integration for Evaluation of Resource Characteristics. Photogrammetric Engineering Remote Sensing. 56(8), 162-175
  • Wintz, P. A. (1973). Information Ex. traction, Snr Improvement, And Data Compression in Multispectral Imagery. IEEE Transactions On Communications, 21(10), 1121-1131. doi.org.10.1109/Tcom. 1973.1091550

Enhancing Burned Area Mapping Accuracy: Integrating Multi-temporal PCA with NDVI Analysis

Year 2024, Volume: 11 Issue: 3, 30 - 48, 28.09.2024
https://doi.org/10.30897/ijegeo.1516280

Abstract

Forested lands in the west coast of Turkey, with their similarity to Mediterranean forests, are often found to be highly susceptible to wildfires, necessitating the development of a forest management program to refine and quantify forest fires and their impacts on the environment. In light of this fact, a multi-temporal approach combining Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) analysis derived from Sentinel-2 imagery is suggested in the current study. Through PCA of carefully selected bands of Sentinel-2, both recent and historic fire impacts are attempted to be captured. It was found that the first two principal components (PC1 and PC2) predominantly describe landscape characteristics, while the third and fourth components (PC3 and PC4) have high abilities in detecting burn scars. It is worth noting that an increase in the ability to detect burn scars was observed with the inclusion of NDVI and its difference in time (dNDVI) within the PCA process. A high effectiveness level in distinguishing burnt areas from unburnt landscapes was presented by the multi-temporal PCA approach, particularly with dNDVI integration. PC2 and PC3, especially with dNDVI integration, are found to be strong indicative factors of burnt areas. In the classification result, accuracies of different years of fire events differed, and a high accuracy of 98.76% was found in the last fire event year of 2019. However, slight underestimation and overestimation were also observed in older fire scars. Mean accuracy, on average, for the PCA-dNDVI method was found to be higher than that of the MLC method. Furthermore, significant vegetation losses by fire, particularly by the 2019 fire incident, were realized through NDVI assessment. Although it worked well in recent fire scars, overestimating the extent in the case of burned areas from previous years was observed. The potential of multi-temporal PCA integration with NDVI for analysis in mapping burned areas at different scales in fire-prone ecosystems in western Turkey is underlined by the results of this work. Much more successful forest management and assessment strategies after fires have occurred in these ecosystems are helped to be created by this approach. Moreover, the approach is suggested to be one of the strong tools for monitoring fire induced damages across many time scales toward better understanding and management of long-term impacts caused by forest fires in the region.

References

  • Atalay, I., Efe, R., Öztürk, M. (2014). Ecology and Classification of Forests in Turkey. Procedia - Social and Behavioral Sciences, 120, 788-805. doi.org/10.1016/ J.Sbspro.2014.02.163
  • Baillarin, S. J., Meygret, A., Dechoz, C., Petrucci, B., Lacherade, S., Tremas, T., Isola, C., Martimort, P., Spoto, F. (2012). Sentinel-2 Level 1 Products and Image Processing Performances. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B1, 197–202. doi.org/10.5194/Isprsarchives-XXXIX-B1-197-2012
  • Bastarrika, A., Chuvieco, E., Martín, M. P. (2011). Mapping Burned Areas from Landsat TM/ETM+ Data with a Two-Phase Algorithm: Balancing Omission and Commission Errors. Remote Sensing of Environment, 115(4), 1003-1012, doi.org/10.1016/J.Rse. 2010.12.005
  • Carper, W. J., Lillesand, T. M., Kiefer, R. W. (1990). The Use of Intensity-Hue-Saturation Transformations for Merging Spot Panchromatic and Multispectral Image Data. Photogrammetric Engineering Remote Sensing, 56(4), 459-467.
  • Choi, M. (2006). A New Intensity-Hue-Saturation Fusion Approach to Image Fusion with a Tradeoff Parameter. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 672–1682.
  • Chu, T., Guo, X. (2013). Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review. In Remote Sensing 6(1), 470-520. doi.org.10.3390/Rs6010470
  • Chuvieco, E., Martín, M. P., Palacios, A. (2002). Assessment of Different Spectral Indices in the Red-Near-Infrared Spectral Domain For Burned Land Discrimination. International Journal of Remote Sensing, 23(23), 5103-5110.
  • Chuvieco, E., Mouillot, F., Van Der Werf, G. R., San Miguel, J., Tanasse, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., Giglio, L. (2019). Historical Background and Current Developments for Mapping Burned Area from Satellite Earth Observation. Remote Sensing of Environment, 225, 45-64.
  • Cihlar, J., Xiao, Q., Chen, J., Beaubien, J., Fung, K., Latifovic, R. (1998). Classification by Progressive Generalization: A New Automated Methodology for Remote Sensing Multichannel Data. International Journal of Remote Sensing, 19(14), 2685-2704. doi.org.10.1080/014311698214451
  • Congalton, R. G. (2015). Remote Sensing and Image Interpretation. 7th Edition. Photogrammetric Engineering Remote Sensing, 81(8). doi.org/ 10.14358/Pers.81.8.615
  • Congalton, R. G., Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition. In Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition.
  • Delegido, J., Verrelst, J., Alonso, L., Moreno, J. (2011). Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green Lai and Chlorophyll Content. Sensors, 11(7), 89-108.
  • Díaz-Delgado, R., Lloret, F., Pons, X., Terradas, J. (2002). Satellite Evidence of Decreasing Resilience in Mediterranean Plant Communities after Recurrent Wildfires. Ecology, 83(8):2293-2303
  • Fernández-García, V., Marcos, E., Huerta, S., Calvo, L. (2021). Soil-Vegetation Relationships in Mediterranean Forests after Fire. Forest Ecosystems, 8(2), 18.
  • Foody, G. (2010). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. The Photogrammetric Record, 25(130). 9780429143977
  • Foody, G. M. (2002). Status of Land Cover Classification Accuracy Assessment. In Remote Sensing of Environment 80(1), 185-201. doi.org/10.1016/S0034-4257 (01)00295-4
  • General Directorate of Forestry. (2020). 2020’s Statistics in Forestry. Republic of Turkey Ministry of Agriculture and Forestry.
  • General Directorate of Forestry. (2022). 2022’s Statistics in Forestry.
  • Gigović, L., Pourghasemi, H. R., Drobnjak, S., Bai, S. (2019). Testing a New Ensemble Model Based on Svm and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park. Forests, 10(5), 408.
  • Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, 24, 417-441.
  • Huang, D., Jiang, F., Li, K., Tong, G., Zhou, G. (2022). Scaled PCA: A New Approach to Dimension Reduction. Management Science, 68(3). doi.org/10.1287/ Mnsc.2021.4020
  • Kavgacı, A., Başararan, E.A. (2023). Orman Yangınları
  • Key, C. H., Benson, N. C. (2006). Landscape Assessment (La) Sampling and Analysis Methods. In Usda Forest Service - General Technical Report Rmrs-Gtr (Issues 164 Rmrs-Gtr).
  • Khorrami, B., Gunduz, O., Patel, N., Ghouzlane, S., Najar, M. (2019). Land Surface Temperature Anomalies In Response To Changes In Forest Cover. International Journal of Engineering and Geosciences, 4(3). doi.org/10.26833/Ijeg.549944
  • Lanorte, A., Manzi, T., Nolè, G., Lasaponara, R. (2015). On The Use of the Principal Component Analysis (PCA) For Evaluating Vegetation Anomalies from Landsat-TM NDVI Temporal Series in The Basilicata Region (Italy). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9158. doi.org.10.1007/978-3-319-21410-8_16
  • Lasaponara, R. (2006). On The Use of Principal Component Analysis (PCA) For Evaluating Interannual Vegetation Anomalies from Spot/Vegetation NDVI Temporal Series. Ecological Modelling, 194(4). doi.org.10.1016/J.Ecolmodel. 2005.10.035
  • Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., Benson, N. C. (2006). Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. In International Journal of Wildland Fire (Vol. 15, Issue 3). doi.org.10.1071/Wf05097
  • Lentile, L. B., Holden, Z. A., Smith, A. M., Falkowski, M. J., Hudak, A. T., Morgan, P., Others, Benson, N. C. (2006). Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. International Journal of Wildland Fire, 15(3), 319–345.
  • Leung, Y., Liu, J., Zhang, J. (2014). An Improved Adaptive Intensity-Hue-Saturation Method for the Fusion Of Remote Sensing Images. IEEE Geoscience and Remote Sensing Letters, 11(5). Doi.org.10.1109/Lgrs.2013.2284282
  • Lillesand, T. M., Kiefer, R. W. (1994). Remote Sensing and Image Interpretation. 3rd Edition. Remote Sensing and Image Interpretation. 3rd Edition.
  • Liu, S., Zheng, Y., Dalponte, M., Tong, X. (2020). A Novel Fire Index-Based Burned Area Change Detection Approach Using Landsat-8 OLI Data. European Journal of Remote Sensing, 53(1), 104-112. doi.org. 10.1080/22797254.2020.1738900
  • Lu, S. L., Zou, L. J., Shen, X. H., Wu, W. Y., Zhang, W. (2011). Multi-Spectral Remote Sensing Image Enhancement Method Based On PCA and IHS Transformations. Journal of Zhejiang University: Science A, 12(6), 453-460, doi.org.10.1631/ Jzus.A1000282
  • Lutes, D. C., Keane, R. E., Caratti, J. F., Key, C. H., Benson, N. C., Gang, L. J. (2006). Firemon: Fire Effects Monitoring and Inventory System. USA Forest Service, Rocky Mountain Research Station, General Technical Report.
  • Mallinis, G., Koutsias, N. (2012). Comparing Ten Classification Methods for Burned Area Mapping in a Mediterranean Environment Using Landsat TM Satellite Data. International Journal of Remote Sensing, 33(14), 4408-4433 doi.org.10.1080/ 01431161.2011.648284
  • Mandanici, E., Bitelli, G. (2016). Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sensing 8(12), 1014, 1-9.
  • Petropoulos, G. P., Kontoes, C., Keramitsoglou, I. (2011). Burnt Area Delineation from A Uni-Temporal Perspective Based On Landsat TM Imagery Classification Using Support Vector Machines. International Journal of Applied Earth Observation and Geoinformation, 13(1), 70-80.
  • Richards, J. A. (2013). Remote Sensing Digital Image Analysis: An Introduction. In Remote Sensing Digital Image Analysis: An Introduction (Vol. 9783642300622). doi.org.10.1007/978-3-642-30062-2
  • Richards, J. A., Jia, X. (2006). Remote Sensing Digital Image Analysis: An Introduction. In Remote Sensing Digital Image Analysis: An Introduction. doi.org.10.1007/3-540-29711-1
  • Röder, A., Hill, J., Duguy, B., Alloza, J. A., Vallejo, R. (2008). Using Long Time Series of Landsat Data to Monitor Fire Events and Post-Fire Dynamics and Identify Driving Factors. A Case Study in the Ayora Region (Eastern Spain). Remote Sensing of Environment, 112(1), 259-273. doi.org.10.1016/J.Rse. 2007.05.001
  • Roteta, E., Bastarrika, A., Padilla, M., Storm, T., Chuvieco, E. (2019). Development of a Sentinel-2 Burned Area Algorithm: Generation of a Small Fire Database For Sub-Saharan Africa. Remote Sensing of Environment, 222, 1-17. doi.org.10.1016/J.Rse.2018.12.011
  • Sabuncu, A., Özener, H. (2019). Uzaktan Algılama Teknikleri ile Yanmış Alanların Tespiti: İzmir Seferihisar Orman Yangını Örneği. Doğal Afetler Ve Çevre Dergisi, 5(2), 317-326. doi.org/ 10.21324/dacd.511688
  • San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., De Rigo, D., Ferrari, D., Maianti, P., Artes Vivancos, T., Costa, H., Lana, F. (2020). Advance Effis Report On Forest Fires in Europe, Middle East and North Africa 2019. In Joint Research Center EC (Issue March).
  • Sentinel-2 Mission Overview. (2015). ESA. www.esa.int/
  • Singh, A., Harrison, A. (1985). Standardized Principal Components. International Journal of Remote Sensing, 6(6), 883-896. doi.org.10.1080/01431168508948511
  • Sunar Erbek, F., Özkan, C., Taberner, M. (2004). Comparison of Maximum Likelihood Classification Method with Supervised Artificial Neural Network Algorithms for Land Use Activities. International Journal of Remote Sensing, 25(9), 1733-1748. doi.org.10.1080/0143116031000150077
  • Tucker, C. J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8(2), 127-150. doi.org.10.1016/0034-4257 (79) 90013-0
  • Tuia, D., Volpi, M., Copa, L., Kanevski, M., Muñoz-Marí, J. (2011). A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification. IEEE Journal On Selected Topics in Signal Processing, 5(3), 606-617. doi.org.10.1109/Jstsp.2011.2139193
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W. W., Goossens, R. (2011). Evaluation of Pre/Post-Fire Differenced Spectral Indices for Assessing Burn Severity in A Mediterranean Environment with Landsat Thematic Mapper. International Journal of Remote Sensing, 32(12), 3521-3537. doi.org.10.1080/ 01431161003752430
  • Veraverbeke, S., Lhermitte, S., Verstraeten, W., Goossens, R. (2010). Assessing Burn Severity Using Satellite Time Series. Wit Transactions on Ecology and The Environment, 137. doi.org.10.2495/Fiva100101
  • Walsh, S. J., Cooper, J. W., Von Essen, I. E., Gallager, K. R. (1990). Image Enhancement of Landsat Thematic Mapper Data and GIS Data Integration for Evaluation of Resource Characteristics. Photogrammetric Engineering Remote Sensing. 56(8), 162-175
  • Wintz, P. A. (1973). Information Ex. traction, Snr Improvement, And Data Compression in Multispectral Imagery. IEEE Transactions On Communications, 21(10), 1121-1131. doi.org.10.1109/Tcom. 1973.1091550
There are 51 citations in total.

Details

Primary Language English
Subjects Geomatic Engineering (Other)
Journal Section Research Articles
Authors

Souad Ghouzlane 0000-0001-5781-5874

Okan Fıstıkoğlu 0000-0002-9483-1563

Early Pub Date September 3, 2024
Publication Date September 28, 2024
Submission Date July 15, 2024
Acceptance Date September 3, 2024
Published in Issue Year 2024 Volume: 11 Issue: 3

Cite

APA Ghouzlane, S., & Fıstıkoğlu, O. (2024). Enhancing Burned Area Mapping Accuracy: Integrating Multi-temporal PCA with NDVI Analysis. International Journal of Environment and Geoinformatics, 11(3), 30-48. https://doi.org/10.30897/ijegeo.1516280