Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia

Gatot Nugroho; Galdita Aruba Chulafak; Fajar Yulianto

doi:10.15243/jdmlm.2021.083.2731

Rapid detection of land cover change in tropical savanna environment using conditional change vector analysis on remote sensing data in Moyo watershed, Sumbawa Regency, West Nusa Tenggara Province, Indonesia

Gatot Nugroho, Galdita Aruba Chulafak, Fajar Yulianto

J. Degrade. Min. Land Manage. , pp. 2731-2741

Viewed : 347 times

DOI https://doi.org/10.15243/jdmlm.2021.083.2731

Abstract

In environmental management, land cover change is a crucial aspect. The area of tropical savanna environments is vulnerable to land degradation. This study aimed to rapidly detect land cover changes in a tropical savanna environment based on remote sensing data. Conditional change detection was performed using the Change Vector Analysis (CVA) with input parameters such as the Enhanced Vegetation Index (EVI) and Normalized Difference Soil Index (NDSI). The results showed that during the period 2015 to 2019, changes were detected in the Moyo watershed every year. From 2015 to 2016, the Moyo River Basin was dominated by changes with a change magnitude of less than 0.088, which was 63% of the Moyo River Basin area. From 2016 to 2017, the changes were dominated by the change magnitude value of 0.063, which was 58.6% of the Moyo River Basin area. From 2017 to 2018, changes were dominated by the change magnitude value of 0.084 of 55.26% of the Moyo watershed area. From 2018 to 2019, the change was dominated by the change magnitude value of 0.057, which was 47.57% of the Moyo watershed area. The direction of land cover change was dominated by Q2 in 2016, Q4 in 2017 and 2018, and Q2 and Q4 in 2019. These changes generally occurred in the Moyo watershed middle and downstream parts, which are grasslands. The use of the Conditional Change Vector Analysis (CCVA) approach in a tropical savanna environment can detect changes and the direction of change with an accuracy of about 70%.

Keywords

conditional change; vector analysis; Indonesia; Moyo watershed; remote sensing

Full Text:

PDF

References

Abdullah, A.Y.M., Masrur, A., Gani Adnan, M.S., Al Baky, M.A., Hassan, Q.K. and Dewan, A. 2019. Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing 11(7): 1–28, doi: 10.3390/rs11070790.

Afify, H.A. 2011. Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area. Alexandria Engineering Journal 50(2): 187–195, doi: 10.1016/j.aej.2011.06.001.

Aravind, S. and Sivakumar, R. 2016. Agricultural landuse change detection using change vector analysis in lower Velar Watershed. International Journal of Engineering Research & Technology 4(20): 1–6.

Avdan, Z.Y., Kaplan, G., Goncu, S. and Avdan, U. 2019. Monitoring the water quality of small water bodies using high-resolution remote sensing data. ISPRS International Journal of Geo-Information 8(12): 553, doi: 10.3390/ijgi8120553.

Bala, R., Prasad, R., Yadav, V.P. and Sharma, J. 2018. A comparative study of land surface temperature with different indices on heterogeneous land cover using Landsat 8 data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42(5): 389–394, doi: 10.5194/isprs-archives-XLII-5-389-2018.

Beck, H.E., Zimmermann, N.E., McVicar, T.R., Vergopolan, N., Berg, A. and Wood, E.F. 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5: 1–12, doi: 10.1038/sdata.2018.214.

Cheng, L., Zhang, Y. and Sun, H. 2020. Vegetation cover change and relative contributions of associated driving factors in the ecological conservation and development zone of Beijing, China. Polish Journal of Environmental Studies 29(1): 53–65, doi: 10.15244/pjoes/102368.

Chowdhury, M., Hasan, M.E. and Abdullah-Al-Mamun, M.M. 2020. Land use/land cover change assessment of Halda watershed using remote sensing and GIS. Egyptian Journal of Remote Sensing and Space Science 23(1): 63–75, doi: 10.1016/j.ejrs.2018.11.003.

Deng, Y., Wu, C., Li, M. and Chen, R. 2015. RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation 39: 40–48, doi: 10.1016/j.jag.2015.02.010.

Dube, T., Pandit, S., Shoko, C., Ramoelo, A., Mazvimavi, D. and Dalu, T. 2019. Numerical assessments of leaf area index in tropical savanna rangelands, South Africa using Landsat 8 OLI derived metrics and in-situ measurements. Remote Sensing 11(7): 829, doi: 10.3390/rs11070829.

Gómez, D. and Montero, J. 2011. Determining the accuracy in image supervised classification problems. Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2011 and French Days on Fuzzy Logic and Applications, LFA 2011, 1(1): 342–349, doi: 10.2991/eusflat.2011.103.

Ji, X. and Niu, X. 2014. The attribute accuracy assessment of land cover data in the national geographic conditions survey. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II–4(May): 35–40, doi: 10.5194/isprsannals-ii-4-35-2014.

Jiang, Z., Huete, A.R., Didan, K. and Miura, T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112(10): 3833–3845, doi: 10.1016/j.rse.2008.06.006.

Measho, S., Chen, B., Trisurat, Y., Pellikka, P., Guo, L., Arunyawat, S., Tuankrua, V., Ogbazghi, W. and Yemane, T. 2019. Spatio-temporal analysis of vegetation dynamics as a response to climate variability and drought patterns in the Semiarid Region, Eritrea. Remote Sensing 11(6): 724, doi: 10.3390/RS11060724.

Munoth, P. and Goyal, R. 2019. Impacts of land use land cover change on runoff and sediment yield of Upper Tapi River Sub-Basin, India. International Journal of River Basin Management 18(2): 177-189, doi: 10.1080/15715124.2019.1613413.

Polykretis, C., Grillakis, M.G. and Alexakis, D.D. 2020. Exploring the impact of various spectral indices on land cover change detection using change vector analysis: A case study of Crete Island, Greece. Remote Sensing 12(2): 319, doi: 10.3390/rs12020319.

Puletti, N. and Bascietto, M. 2019. Towards a tool for early detection and estimation of forest cuttings by remotely sensed data. Land 8(4): 58, doi: 10.3390/land8040058.

Rahman, S. and Mesev, V. 2019. Change vector analysis, tasselled cap, and NDVI-NDMI for measuring land use/cover changes caused by a sudden short-term severe drought: 2011 Texas event. Remote Sensing 11(19): 2217, doi: 10.3390/rs1192217.

Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M. and Rigol-Sanchez, J.P. 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67(1): 93–104, doi: 10.1016/j.isprsjprs.2011.11.002.

Sankaran, M. and Ratnam, J. 2013. African and Asian Savannas. In: Encyclopedia of Biodiversity: Second Edition (Issue May 2014). Elsevier Ltd., doi: 10.1016/B978-0-12-384719-5.00355-5.

Singh, S. and Talwar, R. 2014. A comparative study on change vector analysis. Sadhana 39(December): 1311–1331.

Yulianto, F., Suwarsono, Sulma, S. 2018. Improving the accuracy and reliability of land use/land cover simulation by the integration of Markov cellular automata and landform-based models - a case study in the upstream Citarum watershed, West Java. Journal of Degraded and Mining Lands Management 6(2): 1675–1696, doi: 10.15243/jdmlm.2019.062.1675.