Analisis Komparasi Melalui Citra Satelit Sentinel dan Landsat untuk Pemantauan Pelaksanaan Reklamasi Lahan Bekas Tambang Batubara

Tedy Meilyanto (1) , Hery Suhartoyo (2) , Yansen Yansen (3) , Wiryono Wiryono (4) , Agus Susatya (5)
(1) a:1:{s:5:"en_US";s:143:"Program Studi Pengelolaan Sumber Daya Alam Fakultas Pertanian Universitas Bengkulu, Jl. WR. Supratman, Kandang Limun, Bengkulu 38371, Indonesia";} , Indonesia
(2) Jurusan Kehutanan Fakultas Pertanian Universitas Bengkulu, Jalan WR. Supratman, Kandang Limun, Bengkulu 38371, Indonesia , Indonesia
(3) Jurusan Kehutanan Fakultas Pertanian Universitas Bengkulu, Jalan WR. Supratman, Kandang Limun, Bengkulu 38371, Indonesia , Indonesia
(4) Jurusan Kehutanan Fakultas Pertanian Universitas Bengkulu, Jalan WR. Supratman, Kandang Limun, Bengkulu 38371, Indonesia , Indonesia
(5) Jurusan Kehutanan Fakultas Pertanian Universitas Bengkulu, Jalan WR. Supratman, Kandang Limun, Bengkulu 38371, Indonesia , Indonesia

Abstract

Mining activities can cause environmental degradation, especially in the form of changes in land cover and loss of vegetation. Therefore, monitoring the success of reclamation is important to ensure the restoration of post-mining ecosystems. This study aims to (1) monitor the implementation of post-mining land reclamation of PT. Bukit Sunur by analyzing canopy density or vegetation cover through Landsat and Sentinel imagery using the ArcGIS 10.8 platform, and (2) compare the NDVI values from both satellite images in the 2016, 2020, and 2024 periods to see the differences in the level of reclamation success detected by each satellite sensor. This study uses a remote sensing analysis method, where Landsat and Sentinel images are processed to extract NDVI (Normalized Difference Vegetation Index) values to identify changes in vegetation density over time. Vegetation density classification is carried out using the Kappa index and Overall Accuracy (OA) calculations to measure the accuracy of image classification. The results show that the reclamation of post-mining land of PT. Bukit Sunur experienced a significant increase in vegetation density. In 2016, most of the area was still dominated by the Low to Moderate Vegetation category, but in 2020 there was an increase in the area of High Vegetation due to the transition from the Fairly High Vegetation class. Furthermore, in 2024, Very High Vegetation increased drastically from 0 ha to 267 ha, indicating the success of reclamation in improving land cover. Furthermore, a comparison of Landsat and Sentinel imagery shows a similar trend of NDVI changes, although Sentinel provides a higher resolution in detecting detailed vegetation changes. Overall Accuracy (OA) and Kappa Index (KA) values reached 97%-100%, indicating a very high level of classification accuracy. The conclusion of this study is that reclamation monitoring using satellite imagery has proven effective in identifying vegetation changes over time, with Sentinel showing superiority in more detailed resolution co mpared to Landsat. The results of this study can be used as a basis for evaluating mine reclamation programs and planning for post-mining ecosystem sustainability.


Keywords: Landsat, Mining Land Reclamation, NDVI, Sentinel, Vegetation Density

Full text article

Generated from XML file

References

Ahmed, N. (2020). Application of NDVI in Vegetation Monitoring Using GIS and Remote Sensing in Northern Ethiopian Highlands. Abyssinia Journal of Science and Technology, 1(1), 12–17.
Alexiou, S., Deligiannakis, G., Pallikarakis, A., Papanikolaou, I., Psomiadis, E., & Reicherter, K. (2021). Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection. ISPRS International Journal of Geo-Information, 10(6), 367–377. https://doi.org/10.3390/ijgi10060367
Andini, S. W., Prasetyo, Y., & Sukmono, A. (2018). Analisis Sebaran Vegetasi Dengan Citra Satelit Sentinel Menggunakan Metode Ndvi dan Segmentasi. Jurnal Geodesi Undip, 7(1), 14–24. https://doi.org/https://doi.org/10.14710/jgundip.2017.19295
Bostjančić, I., Gulam, V., Frangen, T., & Hećej, N. (2023). Relation between relief and Badland spatial distribution in the Paleogene Pazin Basin, Croatia. Journal of Maps, 19(1), 1–10. https://doi.org/10.1080/17445647.2022.2163196
BPS. (2024). Ekspor Batu Bara Menurut Negara Tujuan Utama, 2012-2023. https://www.bps.go.id/id/statistics-table/1/MTAzNCMx/ekspor-batu-bara-menurut-negara-tujuan-utama-2012-2022.html
Buta, M., Blaga, G., Paulette, L., Păcurar, I., Roșca, S., Borsai, O., Grecu, F., Sînziana, P. E., & Negrușier, C. (2019). Soil Reclamation of Abandoned Mine Lands by Revegetation in Northwestern Part of Transylvania: A 40-Year Retrospective Study. Sustainability, 11(12), 339–345. https://doi.org/10.3390/su11123393
ESDM. (2024). Minerba One Map Indonesia (MODI). Kementerian ESDM.
Hu, J., Ye, B., Bai, Z., & Hui, J. (2022). Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data. Land, 11(3), 325–330. https://doi.org/10.3390/land11030325
Irawan, S., & Malau, A. O. (2016). Analisis Persebaran Mangrove di Pulau Batam Menggunakan Teknologi Penginderaan Jauh. Jurnal Integrasi, 8(2), 80–87.
Neugirg, F., Stark, M., Kaiser, A., Vlacilova, M., Della Seta, M., Vergari, F., Schmidt, J., Becht, M., & Haas, F. (2016). Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal high-resolution terrestrial LiDAR and UAV surveys. Geomorphology, 269(2), 8–22. https://doi.org/10.1016/j.geomorph.2016.06.027
Rafsenja, U., Jaya, L. M. G., Sawaludin, S., & Rahim, S. (2020). Analisis Perbandingan Citra Landsat 8 dan Citra Sentinel 2-A untuk Mengidentifikasi Sebaran Mangrove. Jurnal Geografi Aplikasi Dan Teknologi, 4(1), 1–10. https://doi.org/http://dx.doi.org/10.33772/jagat.v4i1.11901
Ridayat, R., & Suroso, S. (2022). Analisis Kesehatan Mangrove Berbasis Algoritma NDVI Menggunakan Citra Sentinel 2A di Kecamatan Tugu Kota Semarang. Geo-Image, 11(1), 1–10. https://doi.org/https://doi.org/10.15294/geoimage.v11i1.54461
Zhao, J., Li, J., Liu, Q., Wang, H., Chen, C., Xu, B., & Wu, S. (2018). Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize. Remote Sensing, 10(1), 68–75. https://doi.org/10.3390/rs10010068

Authors

Tedy Meilyanto
tedymeilyanto@gmail.com (Primary Contact)
Hery Suhartoyo
Yansen Yansen
Wiryono Wiryono
Agus Susatya
Meilyanto, T., Suhartoyo, H., Yansen, Y., Wiryono, W., & Susatya, A. (2025). Analisis Komparasi Melalui Citra Satelit Sentinel dan Landsat untuk Pemantauan Pelaksanaan Reklamasi Lahan Bekas Tambang Batubara. Naturalis: Jurnal Penelitian Pengelolaan Sumberdaya Alam Dan Lingkungan, 14(02), 136–145. https://doi.org/10.31186/naturalis.14.02.43723

Article Details