Electronic Theses and Dissertation

Penelitian ini dilatarbelakangi oleh terjadinya bencana hidrometeorologi yang memengaruhi kondisi vegetasi dan tutupan lahan di daerah aliran sungai (das) krueng meureudu. perubahan tutupan vegetasi dapat menurunkan kemampuan das dalam mengatur tata air sehingga meningkatkan risiko banjir, erosi, dan degradasi lingkungan. pemanfaatan teknologi penginderaan jauh melalui citra satelit landsat 8 dan metode normalized difference vegetation index (ndvi) menjadi salah satu pendekatan yang efektif untuk memantau perubahan kondisi vegetasi secara spasial dan temporal. penelitian ini bertujuan untuk menganalisis kondisi indeks vegetasi sebelum dan sesudah bencana hidrometeorologi serta mengetahui besarnya kehilangan vegetasi yang terjadi di das krueng meureudu. penelitian dilaksanakan pada april–juni 2026 di das krueng meureudu yang meliputi wilayah kabupaten pidie dan kabupaten pidie jaya. data yang digunakan berupa citra satelit landsat 8 tahun 2025 (sebelum banjir) dan tahun 2026 (sesudah banjir), data batas das, serta data hasil ground check sebanyak 100 titik sampel. analisis dilakukan menggunakan metode ndvi untuk memperoleh kelas kerapatan vegetasi, sedangkan klasifikasi tutupan lahan dilakukan dengan metode supervised classification. validasi hasil klasifikasi dilakukan menggunakan confusion matrix melalui perhitungan kappa accuracy. hasil penelitian menunjukkan bahwa kondisi vegetasi das krueng meureudu mengalami perubahan setelah terjadinya bencana hidrometeorologi. pada tahun 2025, kelas kehijauan tinggi mendominasi dengan luas 28.492,08 ha (71,12%), sedangkan pada tahun 2026 menurun menjadi 19.252,08 ha (48,05%). sebaliknya, kelas kehijauan sedang meningkat dari 9.428,97 ha (23,53%) menjadi 17.367,10 ha (43,35%), disertai peningkatan kelas kehijauan rendah, kehijauan sangat rendah, dan tanpa vegetasi. hasil uji akurasi menunjukkan overall accuracy sebesar 95% dan kappa accuracy sebesar 90,96%, sehingga klasifikasi yang dihasilkan memiliki tingkat ketelitian yang sangat baik. berdasarkan hasil penelitian dapat disimpulkan bahwa telah terjadi penurunan kerapatan vegetasi di das krueng meureudu pada periode 2025–2026. luas kelas kehijauan tinggi mengalami penurunan sebesar 9.240,00 ha, yang menunjukkan adanya kehilangan vegetasi selama periode pengamatan. perubahan tersebut tidak hanya berkaitan dengan dampak bencana hidrometeorologi, tetapi juga dipengaruhi oleh kondisi alami vegetasi, aktivitas manusia, serta faktor lingkungan lainnya. hasil penelitian ini diharapkan dapat menjadi informasi dasar dalam pengelolaan das, rehabilitasi hutan, serta penyusunan strategi mitigasi bencana berbasis data penginderaan jauh.

This study was motivated by the occurrence of a hydrometeorological disaster that affected vegetation conditions and land cover in the Krueng Meureudu Watershed. Changes in vegetation cover can reduce the watershed's capacity to regulate water flow, thereby increasing the risk of flooding, erosion, and environmental degradation. Utilizing remote sensing technology—specifically Landsat 8 satellite imagery and the Normalized Difference Vegetation Index (NDVI) method—offers an effective approach for monitoring changes in vegetation conditions across spatial and temporal dimensions. This study aims to analyze vegetation index conditions before and after the hydrometeorological disaster and to quantify the extent of vegetation loss within the Krueng Meureudu Watershed. The research was conducted from April to June 2026 in the Krueng Meureudu Watershed, covering areas within Pidie Regency and Pidie Jaya Regency. The data utilized included Landsat 8 satellite imagery from 2025 (pre-flood) and 2026 (post-flood), watershed boundary data, and ground-truth data from 100 sample points. Analysis was performed using the NDVI method to determine vegetation density classes, while land cover classification was carried out using the Supervised Classification method. Classification results were validated using a confusion matrix and the calculation of Kappa Accuracy. The research results indicate that vegetation conditions in the Krueng Meureudu Watershed underwent changes following the hydrometeorological disaster. In 2025, the high-greenness class was dominant, covering an area of ​​28,492.08 hectares (71.12%); however, by 2026, this area had decreased to 19,252.08 hectares (48.05%). Conversely, the greenness class increased from 9,428.97 ha (23.53%) to 17,367.10 ha (43.35%), accompanied by increases in the low greenness, very low greenness, and non-vegetated classes. Accuracy assessment results showed an Overall Accuracy of 95% and a Kappa Accuracy of 90.96%, indicating a high level of precision in the resulting classification. Based on the findings, it can be concluded that a decline in vegetation density occurred in the Krueng Meureudu watershed during the 2025–2026 period. The area of ​​the high greenness class decreased by 9,240.00 ha, indicating a loss of vegetation during the observation period. These changes are not only linked to the impacts of hydrometeorological disasters but are also influenced by natural vegetation conditions, human activities, and other environmental factors. These research findings are expected to serve as baseline information for watershed management, forest rehabilitation, and the formulation of disaster mitigation strategies based on remote sensing data.