Evaluation of multi-spectral remote sensing data capability in order to identify and differentiate burned pastures during the grazing gradient (Case study: Semi-arid rangelands of CHB province, Iran)

Document Type : Research Paper

Authors

1 Assistant Professor, Research Division of Forests and Rangeland, Khorramabad Agricultural and Natural Resources Research and Education Center, Agricultural Research Education and Extension Organization (AREEO), Khorramabad, Iran

2 Associate Professor, Department of Nature Engineering, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Iran

3 Associate Professor, Research Division of of Forests and Rangeland, Khorramabad Agricultural and Natural Resources Research and Education Center, Agricultural Research Education and Extension Organization (AREEO), Khorramabad, Iran.

Abstract

Background and objectives: The evaluation of disturbances using satellite imagery is a crucial sub-field in natural resource science, serving as a tool for monitoring changes such as fire and livestock grazing in forest and pasture ecosystems. Grazing intensity and fires are significant disturbances in arid and semi-arid rangelands. Remote sensing for burned area mapping has been extensively studied. Spectral indices are widely used to monitor vegetation cover changes, particularly post-fire, and to generate burned area maps. Given the vastness and inaccessibility of Iran's mountain rangelands, multi-spectral remote sensing data was employed to identify burned areas. This research aimed to determine the most effective auxiliary data to enhance classification accuracy for identifying and delineating burned semi-steppe rangelands, and to estimate burned pasture extent for post-fire management using Landsat-8 imagery.
Methodology: Burned rangeland sites were selected based on information from the Chaharmahal and Bakhtiari Province Department of Natural Resources Protection and local expert input. Old fire occurrence and grazing intensity at selected sites were determined. Twenty-seven burned sites, representing old fire occurrences and high to medium grazing intensity, were delineated using GPS polygons. Maximum likelihood classification (MLC) in Idrisi TerrSet software was used to identify burned pasture areas. Auxiliary data, including pan-sharpened raw bands, Tasseled Cap transformation components, digital elevation model (DEM) derivatives, principal component analysis (PCA) components, and the Normalized Burn Ratio-Thermal (NBRT) index, were evaluated to improve classification accuracy. Overall and Kappa accuracies were assessed using error matrices, and Friedman's rank test was used to compare the effectiveness of different data combinations.
Results: The combination of DEM derivatives and the NBRT index significantly improved the classification accuracy of burned areas with varying grazing intensities, achieving an overall accuracy of 66% and a Kappa accuracy of 63%. The NBRT index, based on near-infrared (NIR), short-wave infrared (SWIR1 and SWIR2), and thermal infrared (Thermal1) bands, effectively distinguished burned semi-arid rangelands with different fire ages and grazing intensities. The high sensitivity of these bands to post-fire vegetation changes contributes to the NBRT index's effectiveness.
Conclusion: Multi-spectral remote sensing data, particularly Landsat-8 imagery, combined with auxiliary data layers, effectively identifies and delineates burned semi-steppe pastures. MLC demonstrated capability despite spectral similarities between regenerated vegetation and surrounding areas. The NBRT index's effectiveness stems from the high sensitivity of its constituent bands (NIR, SWIR1, SWIR2, and Thermal1) to post-fire vegetation changes, enabling accurate identification of burned semi-steppe grasslands.
 

Keywords

Main Subjects

References

-Abedini, M., Shishegaran, M. and Ghale, E., 2022. Monitoring and estimating the fire-affected areas of the Zagros mountains using landsat satellite images. Geography and Environmental Planning, 33(4): 49-62 (In Persian).
-Alhaj Khalaf, M., Shataee, S. and Jahdi, R., 2020. Ability and sensitivity study of spectral indices for wildfire severity mapping (Case study: Arabdagh-Golestan reforestations). Forest and Wood Products, 73(1): 97-110 (In Persian).
-Anderson, J.R., 1976. A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.
-Ariza, A., Salas Rey, J. and Merino de Miguel, S., 2019. Comparison of maximum likelihood estimators and regression models for burn severity mapping in Mediterranean forests using Landsat TM and ETM+ data. Revista cartográfica, (98): 145-177.
-Atak, B.K. and Tonyaloğlu, E.E., 2020. Evaluating spectral indices for estimating burned areas in the case of Izmir/Turkey. Eurasian Journal of Forest Science, 8(1): 49-59.
-Baig, M.H.A., Zhang, L., Shuai, T. and Tong, Q., 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5(5): 423-431.
-Barsi, J.A., Alhammoud, B., Czapla-Myers, J., Gascon, F., Haque, M.O., Kaewmanee, M., Leigh, L. and Markham, B.L., 2018. Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites. European Journal of Remote Sensing, 51(1): 822-837.
-Bastarrika, A., Chuvieco, E. and 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.
-Bastin, G., Ludwig, J., Eager, R., Liedloff, A., Andison, R. and Cobiac, M., 2003. Vegetation changes in a semiarid tropical savanna, northern Australia: 1973–2002. The Rangeland Journal, 25(1): 3-19.
-ChaharMahal Bakhtiari Province Management and Planning Organization, 2017. Chaharmahal va Bakhtiyari Province statistics Yearbook, 768 p (In Persian).
-Chen, W., Moriya, K., Sakai, T., Koyama, L. and Cao, C.X., 2016. Mapping a burned forest area from Landsat TM data by multiple methods. Geomatics, Natural Hazards and Risk, 7(1): 384-402.
-Chuvieco, E., Mouillot, F., Van der Werf, G.R., San Miguel, J., Tanase, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I. and Heil, A., 2019. Historical background and current developments for mapping burned area from satellite earth observation. Remote Sensing of Environment, 225: 45-64.
-Demsar, J., 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7: 1-30.
-Fathizad, H., Shamsi, R.F., Mahdavi, A. and Arekhi, S., 2015. Comparison of two classification methods of maximum probability and artificial neural network of fuzzy Artmap to produce rangeland cover maps (Case study: rangeland of Doviraj, Dehloran). Iranian Journal of Range and Desert Research, 22(1): 59-72 (In Persian).
-Filipponi, F., 2018, March. BAIS2: Burned area index for Sentinel-2. In Proceedings, 2(7), p. 364. MDPI.
-Fornacca, D., Ren, G. and Xiao, W., 2018. Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a mountainous region of Northwest Yunnan, China. Remote Sensing, 10(8): 1196.
-Henry, M.C., 2008. Comparison of single-and multi-date Landsat data for mapping wildfire scars in Ocala National Forest, Florida. Photogrammetric Engineering and Remote Sensing, 74(7): 881-891.
-Holden, Z.A., Smith, A.M.S., Morgan, P., Rollins, M.G. and Gessler, P.E., 2005. Evaluation of novel thermally enhanced spectral indices for mapping fire perimeters and comparisons with fire atlas data. International Journal of Remote Sensing, 26(21): 4801-4808.
-Karimi, K., Karamidehkordi, E. and Badsar, M., 2016. The role of rural communities in conservation of rangelands in Mahneshan Township. Rural Development Strategies, 3(1): 1-21 (In Persian).
-Kaufman, Y.J., Wald, A.E., Remer, L.A., Gao, B.C., Li, R.R. and Flynn, L., 1997. The MODIS 2.1-/spl mu/m channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1286-1298.
-Lu, B., He, Y. and Tong, A., 2015. Evaluation of spectral indices for estimating burn severity in semiarid grasslands. International Journal of Wildland Fire, 25(2): 147-157.
-Meddens, A.J., Kolden, C.A. and Lutz, J.A., 2016. Detecting unburned areas within wildfire perimeters using Landsat and ancillary data across the northwestern United States. Remote Sensing of Environment, 186: 275-285.
-Mitri, G.H. and Gitas, I.Z., 2004. A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery. International Journal of Wildland Fire, 13(3): 367-376.
-Mohammadian, A., Asadi Borujeni, E., Ebrahimi, A., Tahmasebi, P. and Naghipour Borj, A.A., 2022. Capability of derived vegetation indices from remotely sensed data for burned area discrimination in semi-steppic rangeland (Case study of CHB province, Iran). Journal of Range and Watershed Managment, 74(4): 837-850 (In Persian).
-Pacheco, A.D.P., da Silva Junior, J.A., Ruiz-Armenteros, A.M., Henriques, R.F.F. and de Oliveira Santos, I., 2023. Analysis of spectral separability for detecting burned areas using landsat-8 OLI/TIRS images under different biomes in Brazil and Portugal. Forests, 14(4): 663.
-Pflugmacher, D., Rabe, A., Peters, M. and Hostert, P., 2019. Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221: 583-595.
-Roy, D.P., 1999. Multi-temporal active-fire based burn scar detection algorithm. International Journal of Remote Sensing, 20(5): 1031-1038.
-Shahriary, E., Palmer, M.W., Tongway, D.J., Azarnivand, H., Jafari, M. and Saravi, M.M., 2012. Plant species composition and soil characteristics around Iranian piospheres. Journal of Arid Environments, 82: 106-114 (In Persian).
-Silva, J.M., Pereira, J.M., Cabral, A.I., Sá, A.C., Vasconcelos, M.J., Mota, B. and Grégoire, J.M., 2003. An estimate of the area burned in southern Africa during the 2000 dry season using SPOT‐VEGETATION satellite data. Journal of Geophysical Research: Atmospheres, 108(D13): 1-34.
-Stroppiana, D., Bordogna, G., Carrara, P., Boschetti, M., Boschetti, L. and Brivio, P.A., 2012. A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm. ISPRS Journal of Photogrammetry and Remote Sensing, 69: 88-102.
-Tarhouni, M., Salem, F.B., Belgacem, A.O. and Neffati, M., 2010. Acceptability of plant species along grazing gradients around watering points in Tunisian arid zone. Flora-Morphology, Distribution, Functional Ecology of Plants, 205(7): 454-461(In Persian).
-Thariqa, P., Sitanggang, I.S. and Syaufina, L., 2016. Comparative analysis of spatial decision tree algorithms for burned area of peatland in Rokan Hilir Riau. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(2): 684-691.
-Trager, M.D., Wilson, G.W. and Hartnett, D.C., 2004. Concurrent effects of fire regime, grazing and bison wallowing on tallgrass prairie vegetation. The American midland naturalist, 152(2): 237-247.
-Tran, B.N., Tanase, M.A., Bennett, L.T. and Aponte, C., 2018. Evaluation of spectral indices for assessing fire severity in Australian temperate forests. Remote Sensing, 10(11): 1680.
-Verlinden, A. and Laamanen, R., 2006. Long term fire scar monitoring with remote sensing in northern Namibia: relations between fire frequency, rainfall, land cover, fire management and trees. Environmental Monitoring and Assessment, 112: 231-253.
-Veraverbeke, S., Harris, S. and Hook, S., 2011. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sensing of Environment, 115(10): 2702-2709.

Files

History

  • Receive Date: 23 February 2024
  • Revise Date: 19 June 2024
  • Accept Date: 23 November 2024

Share

How to cite

Statistics