Analisis Perubahan Kerapatan Ekosistem Mangrove Menggunakan Algoritma Indeks Vegetasi NDVI dan SAVI Citra Satelit Multitemporal (Studi Kasus: Pesisir Utara Surabaya)

Arindi, Yurike Nisa (2018) Analisis Perubahan Kerapatan Ekosistem Mangrove Menggunakan Algoritma Indeks Vegetasi NDVI dan SAVI Citra Satelit Multitemporal (Studi Kasus: Pesisir Utara Surabaya). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Adanya dinamika lingkungan di wilayah Pesisir Utara Surabaya menyebabkan adanya perubahan di sekitar wilayah pesisir. Salah satu ekosistem yang dapat terganggu akibat adanya perubahan tersebut adalah ekosistem mangrove. Adanya perubahan lingkungan di wilayah pesisir dapat menyebabkan perubahan kerapatan mangrove. Mangrove memiliki banyak manfaat bagi kelangsungan hidup makhluk hidup. Ekosistem mangrove merupakan salah satu obyek yang dapat diidentifikasi menggunakan teknologi penginderaan jauh yakni memanfaatkan citra satelit. Data citra satelit yang digunakan dalam penelitian ini adalah data citra Landsat 8 multitemporal tahun 2015, 2016, 2017 dan 2018. Sedangkan metode algoritma indeks vegetasi yang digunakan adalah NDVI dan SAVI untuk mengetahui tingkat kerapatan mangrove. Hasil klasifikasi indeks vegetasi tersebut, selanjutnya akan diuji akurasi menggunakan data kerapatan tajuk pada 34 titik sampel di lapangan. Uji klasifikasi algoritma NDVI menghasilkan nilai Overall Accuracy sebesar 94,12%. Sedangkan Overall Accuracy yang dihasilkan SAVI sebesar 88,24%. Sehingga hasil kedua klasifikasi dapat digunakan untuk proses selanjutnya. Berdasarkan hasil pengolahan indeks vegetasi, persebaran serta luas hasil klasifikasi tiap transformasi indeks vegetasi menunjukkan hasil yang bervariasi. Luas ekosistem mangrove selama tahun 2015-2018 terus mengalami penurunan. Luas mangrove berdasarkan NDVI tahun 2015-2018 sebesar sebesar 184,77, 184,59, 184,14 dan 183,60 hektar dan SAVI sebesar 184,41, 184,14, 183,51 dan 182,97 hektar. Total perubahan luas mangrove selama tahun 2015-2018 berdasarkan algoritma NDVI sebesar 1,17 hektar dan SAVI sebesar 1,44 hektar. Selain itu, hasil penelitian menunjukkan bahwa korelasi (R) antara NDVI dengan kerapatan jenis menghasilkan nilai korelasi yang lebih baik yakni 0,80 dibandingkan SAVI yang berkorelasi sebesar 0,78. Selain itu, hasil perhitungan korelasi indeks vegetasi dengan sampel parameter air di lapangan menunjukkan bahwa sampel salinitas memiliki korelasi (R) sangat baik terhadap nilai NDVI sebesar 0,90 (stasiun Tambak Wedi) dan terhadap SAVI sebesar 0,97 (stasiun Tambak Wedi). Sedangkan untuk korelasi antara NDVI degan pH berkorelasi (R) negatif yakni sebesar -0,48 (stasiun Tambak Wedi) dan korelasi dengan SAVI sebesar -0,47 (stasiun Romokalisari). Sehingga indeks vegetasi NDVI merupakan salah satu indeks vegetasi yang memiliki hubungan paling besar dengan sampel parameter lapangan. ============= The existence of environmental dynamics in the North Coast of Surabaya caused the changes in coastal areas. One of the ecosystems that can be disrupted by the changes was mangrove ecosystem. The existence of environmental changes in coastal areas can cause the changes in mangrove density. Mangrove have many benefits for the survival of living things. The mangrove ecosystem is one of the objects that can be identified using remote sensing technology which used satelitte imagery. Sattelite imagery data was used in this research are multitemporal of Landsat 8 imagery during 2015, 2016, 2017 and 2018. While the vegetation index algorithm was used are NDVI and SAVI. Vegetation index aims to determine of mangrove density, especially in the North Coast of Surabaya. The results of the vegetation index classification, will be tested by using canopy density data at 34 sample points in the field. NDVI algorithm classification test resulted in overall accuracy value was 94.12%. While the overall accuracy produced of SAVI was 88.24%. So the results of both classifications can be used for the next process. Based on the results of the vegetation index processing, the spread and the result of the classification of each vegetation transformation index showed varying results. It was caused by the differences vegetation index that was used. The area of mangrove ecosystems during 2015-2018 was decrease continously. The mangrove area based on NDVI year 2015-2018 are 184.77, 184.59, 184.14 and 183.60 hectares and SAVI at 184.41, 184.14, 183.51 and 182.97 hectares. The total change of mangrove area during 2015-2018 based on NDVI algorithm of 1.17 hectares and SAVI of 1.44 hectares. In addition, the results showed that the correlation (R) between NDVI with the density of the desnsity types had better correlation value that was 0.80 compared to SAVI which correlates 0.78. In addition, the result of correlation calculation of vegetation index with water parameter samples in the field showed that salinity samples have a very good correlation (R) to NDVI value of 0.90 (Tambak Wedi station) and SAVI of 0.97 (Tambak Wedi station). As for the correlation between NDVI degan pH correlated (R) negative of -0.48 (Tambak Wedi station) and SAVI of -0.47 (Romokalisari station). So the vegetation index NDVI is one of the vegetation index that has the greatest relationship with the sample of field parameters.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Kerapatan, Mangrove, NDVI, SAVI, Density, penginderaan jauh, citra satelit, remote sensing technology, satelitte imagery
Subjects: G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing
Q Science > QH Biology > QH75 Nature conservation. Landscape protection. Biodiversity conservation. Endangered species and ecosystems (General). Habitat conservation. Ecosystem management. Conservation biology
Divisions: Faculty of Civil, Environmental, and Geo Engineering > Geomatics Engineering > (S1) Undergraduate Theses
Depositing User: Yurike Nisa Arindi
Date Deposited: 30 Aug 2018 05:16
Last Modified: 30 Aug 2018 05:16
URI: http://repository.its.ac.id/id/eprint/52457

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