Akurasi Luas Areal Kebakaran Dari Data Landsat-8 Oli Di Wilayah Kalimantan

Zubaidah, Any; Sulma, Sayidah; Suwarsono, Suwarsono; Vetrita, Yeni; Priyatna, Muhammad; Ayu, Kusumaning

doi:10.24895/mig.2017.19-1.442

Cited by 6 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is an omission error if the reference data is an oil object but OSI reports no oil was found. In addition, below is how the accuracy is computed [17,18] :…”

Section: Accuracy Testmentioning

confidence: 99%

Adaptive threshold from the Sentinel-2 oil spill index for oil spill detection

Sulma,

Prayogo,

Hartuti

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

View full text Add to dashboard Cite

One method for detecting oil spills from optical satellite data is using Oil Spill Index (OSI). In several studies, this index was used to visualize oil spill distribution. In order to estimate the coverage area of oil spill that occurs, image segmentation needs to be carried out to separate object of oil spill from non-oil spills. Therefore, this study used comparison of several OSI index applied to Sentinel-2 images and segmentation of oil and non-oil objects from OSI images using index threshold values. The data used consist of Sentinel-2 data date 12 August 2021 in Karawang waters, and 5 October 2019 and 2 May 2021 in Bintan waters. Karawang water image indicated oil spill from oil well platform leakage and Bintan waters image showed oil spills from ship sewage and other unknown sources. Two OSI algorithms were used, that is OSI1 that had been developed using Sentinel-2 image, and OSI2 was developed using MODIS data. OSI image threshold was then used to separate oil and non-oil objects. Other band combinations thresholds were also used to obtain the better results. Based on the result of threshold on OSI1 and OSI2 images, the study found that oil object can be separated better in OSI2 image, however in the two images there were still a lot of non-oil subject mixed with oil class especially in OSI1. Based on analysis of spectral pattern, object separation in OSI1 image need to be carried out further using band 2 and band 9 threshold, while for OSI2 needs to be separated further using band 3 threshold. Based on the comparison of the 2 thresholds, using combination of OSI2 and band 3 gave better result. Accuracy analysis of OSI2 threshold also showed the better result with overall accuracy of 86%.

show abstract

“…It is an omission error if the reference data is an oil object but OSI reports no oil was found. In addition, below is how the accuracy is computed [17,18] :…”

Section: Accuracy Testmentioning

confidence: 99%

Adaptive threshold from the Sentinel-2 oil spill index for oil spill detection

Sulma,

Prayogo,

Hartuti

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

View full text Add to dashboard Cite

show abstract

“…Berdasarkan penelitian lainnyatingkat akurasi areal terbakar yang dilakukan di Pulau Kalimantan dengan menggunakan data Landsat 8 OLI mencapai 96% (Zubaidah et al, 2017). Hal tersebut menunjukkan peluang pengembangan teknologi semi otomatis agar dapat meningkatkan akurasi yang lebih tinggi.…”

Section: Analisis Areal Bekas Kebakaran Hutan Dan Lahanunclassified

Identifikasi Areal Bekas Kebakaran Hutan Dan Lahan Menggunakan Analisis Semi Otomatis Citra Satelit Landsat

Endrawati¹,

Purwanto²,

Nugroho³

et al. 2018

SNG

View full text Add to dashboard Cite

ABSTRAKKebakaran hutan dan lahan yang terjadi pada tahun 2015 merupakan salah satu kebakaran terbesar di Indonesia pasca kebakaran pada tahun 1997/1998. Selama kejadian puncak pada tahun 2015 telah mengakibatkan kerugian finansial hingga Rp 221 triliun atau setara 16,1 miliar dolar AS (The World Bank, 2016). Dampak kebakaran telah menimbulkan potensi kerugian yang tidak bisa dihitung secara finansial berupa hilangnya keanekaragaman hayati, rusaknya habitat hidup satwa liar, dan perubahan ekosistem dan lingkungan. Salah satu metode Identifikasi Areal Bekas Kebakaran Hutan dan Lahan dengan teknologi penginderaan jauh adalah analisis semi otomatis. Data titik panas Citra Modis/Terra Aqua (hotspot) harian digunakan sebagai bahan awal menentukan luas areal terbakar dengan metode otomatis analisis kerapatan titik panas (point density analysis). Data hasil analisis ini selanjutnya ditumpang-susunkan dengan citra Landsat untuk proses delineasi visual areal bekas kebakaran. Hasil deliniasi areal kebakaran dijadikan dasar pengecekan lapangan untuk melakukan verifikasi terhadap hasil deliniasi areal terbakar dengan kondisi sebenarnya di lapangan. Hasil kajian menunjukkan total luas areal yang terbakar tahun 2015 adalah 2,6 juta hektar, dimana seluas 0,4 juta hektar berada pada areal berhutan. Pada tahun 2016, hasil analisis sebaran areal kebakaran hutan dan lahan menunjukkan penurunan sebesar 83%, yaitu menjadi 438.363 hektar. Penghitungan akurasi metode ini melalui cek lapangan menghasilkan nilai akurasi sebesar 85%. Identifikasi Areal Bekas Kebakaran Hutan dan Lahan dengan teknologi penginderaan jauh dapat dilakukan dengan cepat, dan akurasi yang tinggi.Kata kunci: Bekas kebakaran hutan dan lahan, hotspot, Landsat, point density analysis, visual delineasi ABSTRACTThe forest and land fires that occurred in 2015 were the largest fire event in Indonesia since the 1997/1998 fires. During the peak period 2015 the financial losses are estimated up to 221 trillion Rupiah or approximately 16,1 billion USD (The World Bank, 2016). The fires caused potential losses that cannot be measured financially such as biodiversity loss, destruction of wildlife habitats, and impacts on environmental and ecosystems. The Remote sensing technology method to Identification of Forest and Land Burnt Scar Area is semi-automatic analysis. Hotspot data daily of Modis/Terra Aqua sattelite is used as the raw data to identify of burnt area by automatic method of point density analysis. The results of this analysis are overlay with Landsat image to the visual delineation process of the burnt area. The result of the delineation of the burnt area was used as the ground check database to verify the delineation of the burnt area with the actual condition in the field. The study results show the total area of burnt area of 2015 is 2.6 million hectares, of which 0.4 million hectares are in forested areas. In 2016, the analysis of the distribution of forest and land burnt area showed a decline of 83%, to 438,363 hectares. Calculating the accuracy of this method...

show abstract