Exploring Spectral Index Band and Vegetation Indices for Estimating Vegetation Area

https://doi.org/10.22146/ijg.38981

Iswari Nur Hidayati(1*), R. Suharyadi(2), Projo Danoedoro(3)

(1) Faculty of Geography Universitas Gadjah Mada, Yogyakarta
(2) Faculty of Geography, Universitas Gadjah Mada
(3) Faculty of Geography, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not fully representative for urban vegetation studies. Meanwhile, the spectral range of high-resolution imagery is usually limited to visible wavelengths for the image transformation. Worldview-2 imagery provides a new breakthrough with a high spatial resolution and supports various spectral resolutions. This study aims to explore the spectral value of the Worldview-2 image index for estimation of vegetation density. Normalized indices were made for 56 band combinations and Otsu thresholding was implemented for the threshold selection to separate vegetation and non-vegetation areas. This thresholding was done by minimizing classes’ variances between two groups of pixels which are distinguished by system or classification. The image binarization process was performed to differentiate between vegetation and non-vegetation. For the accuracy testing, a total of 250 samples was produced by a stratified random sampling method. Our results show that the combination of indices from red channel, red-edge, NIR-1, and NIR-2 provides the best accuracy for semantic accuracy. Vegetation area extracted from the index was then compared with the results of the visual analysis. Although the index results in area difference of 2.32 m2 compared to visual analysis, the combination of NIR-2 and red bands can give an accuracy of 96.29 %.

Keywords


vegetation density, spectral index, normalized index

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References

Caroline, A. H., & Hidayati, I. N. (2016). Pemanfaatan Citra Quickbird dan SIG untuk Pemetaan Tingkat Kenyamanan Permukiman di Kecamatan Semarang Barat dan Kecamatan Semarang Utara. Majalah Geografi Indonesia, 30(1), 1–8.


Chengyang, W., Shixiong, H., Wenjing, S., & Wei, C. (2012). Fractal dimension of coal particles and their CH4 adsorption. International Journal of Mining Science and Technology, 22(6), 855–858. https://doi.org/10.1016/j.ijmst.2012.11.003


Congalton, R. G. (2010). Remote Sensing: An Overview. GIScience & Remote Sensing, 47(4), 443–459. https://doi.org/10.2747/1548-1603.47.4.443


De Benedetto, D., Castrignanò, A., Rinaldi, M., Ruggieri, S., Santoro, F., Figorito, B., … Tamborrino, R. (2013). An approach for delineating homogeneous zones by using multi-sensor data. Geoderma, 199, 117–127. https://doi.org/10.1016/j.geoderma.2012.08.028


Digital Globe. (2010). Radiometric Use of WorldView-2 Imagery Technical Note 1 WorldView-2 Instrument Description, (November), 1–17.


Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272. https://doi.org/10.1016/j.rse.2011.11.020


Gago, E. J., Roldan, J., Pacheco-Torres, R., & Ordóñez, J. (2013). The city and urban heat islands: A review of strategies to mitigate adverse effects. Renewable and Sustainable Energy Reviews, 25, 749–758. https://doi.org/10.1016/j.rser.2013.05.057


Gitelson, A. A., Peng, Y., Masek, J. G., Rundquist, D. C., Verma, S., Suyker, A., … Meyers, T. (2012). Remote estimation of crop gross primary production with Landsat data. Remote Sensing of Environment, 121, 404–414. https://doi.org/10.1016/j.rse.2012.02.017


Hidayati, I. N. (2013). Ekstraksi Data Indeks Vegetasi untuk Evaluasi Ruang Terbuka Hijau berdasarkan Citra ALOS di Kecamatan Ngaglik Kabupaten Sleman Yogyakarta. Agroteknologi, 3(2), 27–34.


Hidayati, I. N., Suharyadi, R., & Danoedoro, P. (2018). Kombinasi Indeks Citra untuk Analisis Lahan Terbangun dan Vegetasi Perkotaan. Majalah Geografi Indonesia, 32(1), 24–32.


Kabisch, N. (2015). Ecosystem service implementation and governance challenges in urban green space planning-The case of Berlin, Germany. Land Use Policy, 42, 557–567. https://doi.org/10.1016/j.landusepol.2014.09.005


Kaspersen, P. S., Fensholt, R., & Drews, M. (2015). Using Landsat vegetation indices to estimate impervious surface fractions for European cities. Remote Sensing, 7(6), 8224–8249. https://doi.org/10.3390/rs70608224


Kimm, H., & Ryu, Y. (2015). Seasonal variations in photosynthetic parameters and leaf area index in an urban park. Urban Forestry and Urban Greening, 14(4), 1059–1067. https://doi.org/10.1016/j.ufug.2015.10.003


Kumar, A., Pandey, A. C., & Jeyaseelan, A. T. (2012). Built-up and vegetation extraction and density mapping using WorldView-II. Geocarto International, 27(7), 557–568. https://doi.org/10.1080/10106049.2012.657695


Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sensing Letters, 1(4), 187–194. https://doi.org/10.1080/01431161003743173


Liu, T., & Yang, X. (2013). Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis. Remote Sensing of Environment, 133, 251–264. https://doi.org/10.1016/j.rse.2013.02.020


Lizarazo, I. (2014). Accuracy assessment of object-based image classification: another STEP. International Journal of Remote Sensing, 35(16), 6135–6156. https://doi.org/10.1080/01431161.2014.943328


Nenzén, H. K., & Araújo, M. B. (2011). Choice of threshold alters projections of species range shifts under climate change. Ecological Modelling, 222(18), 3346–3354. https://doi.org/10.1016/j.ecolmodel.2011.07.011


Prabhakara, K., Dean Hively, W., & McCarty, G. W. (2015). Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International Journal of Applied Earth Observation and Geoinformation, 39, 88–102. https://doi.org/10.1016/j.jag.2015.03.002


Priebe, C. E., Naiman, D. Q., & Cope, L. M. (2001). Importance sampling for spatial scan analysis: Computing scan statistic p-values for marked point processes. Computational Statistics and Data Analysis, 35(4), 475–485. https://doi.org/10.1016/S0167-9473(00)00017-7


Rougier, S., Puissant, A., Stumpf, A., & Lachiche, N. (2016). Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images. International Journal of Applied Earth Observation and Geoinformation, 51, 60–73. https://doi.org/10.1016/j.jag.2016.04.005


Roy, P. S., Behera, M. D., Murthy, M. S. R., Roy, A., Singh, S., Kushwaha, S. P. S., … Ramachandran, R. M. (2015). New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39, 142–159. https://doi.org/10.1016/j.jag.2015.03.003

Salsbury, T. I., & Alcala, C. F. (2017). A method for setpoint alarming using a normalized index. Control Engineering Practice, 60(May 2016), 1–6. https://doi.org/10.1016/j.conengprac.2016.12.002


Schelin, L., & Sjöstedt-De Luna, S. (2014). Spatial prediction in the presence of left-censoring. Computational Statistics and Data Analysis, 74, 125–141. https://doi.org/10.1016/j.csda.2014.01.004


Stefanov, W. L., & Netzband, M. (2005). Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center. Remote Sensing of Environment, 99(1–2), 31–43. https://doi.org/10.1016/j.rse.2005.04.024


Stehman, S. V., Hansen, M. C., Broich, M., & Potapov, P. V. (2011). Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery. Remote Sensing of Environment, 115(2), 650–658. https://doi.org/10.1016/j.rse.2010.10.009


Sun, G., Chen, X., Jia, X., Yao, Y., & Wang, Z. (2016). Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(5), 2081–2092. https://doi.org/10.1109/JSTARS.2015.2478914


Valentinitsch, A., Patsch, J. M., Deutschmann, J., Schueller-Weidekamm, C., Resch, H., Kainberger, F., & Langs, G. (2012). Automated threshold-independent cortex segmentation by 3D-texture analysis of HR-pQCT scans. Bone, 51(3), 480–487. https://doi.org/10.1016/j.bone.2012.06.005

Weng, Q., & Fu, P. (2014). Modeling annual parameters of clear-sky land surface temperature variations and evaluating the impact of cloud cover using time series of Landsat TIR data. Remote Sensing of Environment, 140, 267–278. https://doi.org/10.1016/j.rse.2013.09.002

Zhang, Y., Gao, J., Liu, L., Wang, Z., Ding, M., & Yang, X. (2013). NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas. Global and Planetary Change, 108, 139–148. https://doi.org/10.1016/j.gloplacha.2013.06.012

Zhao, B., Duan, A., Ata-Ul-Karim, S. T., Liu, Z., Chen, Z., Gong, Z., … Ning, D. (2018). Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. European Journal of Agronomy, 93(December 2017), 113–125. https://doi.org/10.1016/j.eja.2017.12.006




DOI: https://doi.org/10.22146/ijg.38981

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