Performa NDVI dan NDMI Landsat 8 sebagai Prediktor Spektral Kehadiran Otus jolandae di Rinjani
Downloads
How to Cite
Pemetaan habitat satwa endemik di kawasan pegunungan tropis Indonesia sering terhambat oleh medan sulit dan biaya survei yang tinggi. Studi ini mengevaluasi performa Normalized Difference Vegetation Index (NDVI) dan Normalized Difference Moisture Index (NDMI) dari citra Landsat 8 OLI/TIRS tunggal resolusi 30 m sebagai prediktor spektral kehadiran Celepuk Rinjani (Otus jolandae) di Resort Timbanuh, Taman Nasional Gunung Rinjani. Citra perekaman 8 Februari 2025 dikoreksi secara atmosferik menggunakan metode Dark Object Subtraction sebelum ekstraksi indeks. Validasi melibatkan 41 titik kehadiran hasil reconnaissance survey malam selama 24 malam dan 27 titik pseudo-absence. Tiga prosedur statistik diaplikasikan: Mann-Whitney U, korelasi Pearson, dan analisis ROC. Nilai NDVI di titik kehadiran (rata-rata 0,420±0,047) berbeda signifikan dari pseudo-absence (0,323±0,059), dengan U=104, p<0,001, dan effect size r=0,683. Pola serupa muncul pada NDMI: kehadiran 0,240±0,024 versus pseudo-absence 0,196±0,037 (U=179, p<0,001, r=0,569). Korelasi Pearson NDVI–NDMI sebesar 0,412 (p<0,001) menunjukkan komplementaritas moderat-positif tanpa redundansi. Analisis ROC menghasilkan AUC NDVI 0,906 (sangat baik) dan AUC NDMI 0,838 (baik). Keduanya menunjukkan performa diskriminasi yang memadai dalam konteks area studi. Hasil ini menunjukkan bahwa NDVI dan NDMI berpotensi digunakan sebagai prediktor spektral awal untuk pemetaan habitat O. jolandae, meskipun validasi multi-musim dan multi-lokasi masih diperlukan untuk memastikan generalisasi threshold.
Performance of Landsat 8-Derived NDVI and NDMI as Spectral Predictors of Otus jolandae Occurrence in Rinjani
Abstract
Mapping the habitat of endemic wildlife in Indonesia's tropical mountain regions is frequently hampered by difficult terrain and high survey costs. This study evaluates the performance of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI) derived from a single Landsat 8 OLI/TIRS image at 30 m resolution as spectral predictors of the presence of the Rinjani Scops-Owl (Otus jolandae) in the Timbanuh Resort, Mount Rinjani National Park. An image acquired on 8 February 2025 was atmospherically corrected using the Dark Object Subtraction method prior to index extraction. Validation drew on 41 presence points recorded during reconnaissance night surveys conducted over 24 nights, and 27 pseudo-absence points. Three statistical procedures were applied: the Mann–Whitney U test, Pearson correlation, and ROC analysis. NDVI values at presence points (mean 0.420 ± 0.047) differed significantly from those at absence points (0.323 ± 0.059), with U = 104, p < 0.001, and effect size r = 0.683. A similar pattern emerged for NDMI: presence 0.240 ± 0.024 versus absence 0.196 ± 0.037 (U = 179, p < 0.001, r = 0.569). The Pearson correlation between NDVI and NDMI was 0.412 (p < 0.001), indicating moderate positive complementarity without redundancy. ROC analysis yielded an AUC of 0.906 for NDVI (excellent) and 0.838 for NDMI (good), both reflecting adequate discriminatory performance within the study area. These results suggest that NDVI and NDMI have potential as preliminary spectral predictors for mapping O. jolandae habitat, although multi-season and multi-site validation remains necessary to confirm the generalizability of the identified thresholds.
Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3(2), 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
Bibby, C. J., Burgess, N. D., Hill, D. A., & Mustoe, S. H. (2000). Bird Census Techniques (2nd ed.). Academic Press.
Chavez, P. S. (1996). Image-based atmospheric corrections – revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1036.
Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE Publications.
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18. https://doi.org/10.1037/a0024338
Gao, B. C. (1996). NDWI — A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
He, K. S., Bradley, B. A., Cord, A. F., Rocchini, D., Tuanmu, M. N., Schmidtlein, S., Turner, W., Wegmann, M., & Pettorelli, N. (2015). Will remote sensing shape the next generation of species distribution models? Remote Sensing in Ecology and Conservation, 1(1), 4–18. https://doi.org/10.1002/rse2.7
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). John Wiley & Sons.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
Jin, S., & Sader, S. A. (2005). Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment, 94(3), 364–372. https://doi.org/10.1016/j.rse.2004.10.012
König, C., & Weick, F. (2008). Owls of the World (2nd ed.). A&C Black Publishers.
Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F., & Hansen, M. C. (2014). Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change, 4(8), 730–735. https://doi.org/10.1038/nclimate2277
Marks, J. S., Cannings, R. J., & Mikkola, H. (1999). Family Strigidae (typical owls). In J. del Hoyo, A. Elliott, & J. Sargatal (Eds.), Handbook of the Birds of the World (Vol. 5, pp. 76–242). Lynx Edicions.
Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2016). Framing the concept of satellite remote sensing essential biodiversity variables: challenges and future directions. Remote Sensing in Ecology and Conservation, 2(3), 122–131. https://doi.org/10.1002/rse2.15
Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839–848. https://doi.org/10.1111/1365-2664.12261
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503–510. https://doi.org/10.1016/j.tree.2005.05.011
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third ERTS-1 Symposium (pp. 309–317). NASA.
Sangster, G., King, B. F., Verbelen, P., & Trainor, C. R. (2013). A new owl species of the genus Otus (Aves: Strigidae) from Lombok, Indonesia. PLOS ONE, 8(2), e53712. https://doi.org/10.1371/journal.pone.0053712
Sodhi, N. S., Koh, L. P., Brook, B. W., & Ng, P. K. L. (2004). Southeast Asian biodiversity: an impending disaster. Trends in Ecology & Evolution, 19(12), 654–660. https://doi.org/10.1016/j.tree.2004.09.006
Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment, 75(2), 230–244. https://doi.org/10.1016/S0034-4257(00)00169-3
Suana, I. W., Ahyadi, H., Hadiprayitno, G., Amin, S., Kalih, L. A. T. T. W. S., & Sudaryanto, F. X. (2020). Lombok scops owl (Otus jolandae) habitat preference in Kerandangan Nature Tourism Park. Biodiversitas, 21(8), 3658–3664. https://doi.org/10.13057/biodiv/d210834
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293. https://doi.org/10.1126/science.3287615
Turner, W., Rondinini, C., Pettorelli, N., Mora, B., Leidner, A. K., Szantoi, Z., Buchanan, G., Dech, S., Dwyer, J., Herold, M., Koh, L. P., Leimgruber, P., Taubenboeck, H., Wegmann, M., Wikelski, M., & Woodcock, C. (2015). Free and open-access satellite data are key to biodiversity conservation. Biological Conservation, 182, 173–176. https://doi.org/10.1016/j.biocon.2014.11.048
USGS. (2019). Landsat 8 (L8) Data Users Handbook (Version 5.0). U.S. Geological Survey.
Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment, 80(3), 385–396. https://doi.org/10.1016/S0034-4257(01)00318-2
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Allen, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., ... Zhu, Z. (2019). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147. https://doi.org/10.1016/j.rse.2019.02.015
Zar, J. H. (2010). Biostatistical Analysis (5th ed.). Pearson Prentice Hall.
Copyright (c) 2026 andrie ridzki prasetyo, Yuda Radian Surya Gana, Muhamad Husni Idris, Niechi Valentino, Sitti Latifah, Sri Wahyuni
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Empiricism Journal agree to the following terms:
- For all articles published in Empiricism Journal, copyright is retained by the authors. Authors give permission to the publisher to announce the work with conditions. When the manuscript is accepted for publication, the authors agrees to implement a non-exclusive transfer of publishing rights to the journals.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
