PENGGUNAAN ALGORITMA SURFACE ENERGY BALANCE SYSTEM (SEBS) PADA CITRA LANDSAT 8 UNTUK ESTIMASI EVAPOTRANSPIRASI AKTUAL

Evapotranspirasi aktual (ETa) yang merupakan salah satu proses yang terjadi di siklus hidrologi dapat diektrasksi dari citra penginderaan jauh menggunakan algoritma Surface Energy Balance System (SEBS). Tujuan penelitian ini adalah untuk mengetahui kemampuan Landsat 8 menurunkan parameter estimasi ETa, mengetahui akurasi ETa berdasarkan data stasiun meteorologi dan klimatologi, serta mengetahui distribusi spasial ETa berdasarkan penutup lahan. Parameter yang dibutukan untuk SEBS adalah: albedo, emisivitas, suhu permukaan, NDVI, fraksi vegetasi, LAI, kekasaran permukaan transfer momentum (Z0m), tinggi kanopi, dan DEM. Hasilnya menunjukan bahwa semua parameter memiliki akurasi yang baik berdasarkan data referensi. Akurasi ETa adalah 0.99, 2.18, dan 2.66 mm/hari pada 3 lokasi stasiun yang berbeda. Nilai ETa tertinggi dan terendah berada di objek tubuh air dengan 9.6 mm/hari dan atap seng dengan 5.6 mm/hari

HOTSPOT VALIDATION OF THE HIMAWARI-8 SATELLITE BASED ON MULTISOURCE DATA FOR CENTRAL KALIMANTAN

The Advanced Himawari Imager (AHI) is the sensor aboard the remote-sensing satellite Himawari-8 which records the Earth’s weather and land conditions every 10 minutes from a geostationary orbit. The imagery produced known as Himawari-8 has 16 bands which cover visible, near infrared, middle infrared and thermal infrared wavelength potentials to monitor forestry phenomena. One of these is forest/land fires, which frequently occur in Indonesia in the dry season. Himawari-8 can detect hotspots in thermal bands 5 and band 7 using absolute fire pixel (AFP) and possible fire pixel (PFP) algorithms. However, validation has not yet been conducted to assess the accuracy of this information. This study aims to validate hotspots identified from Himawari images based on information from Landsat 8 images, field surveys and burnout data. The methodology used to validate hotspots comprises AFP and PFP extraction, determining firespots from Landsat 8, buffering at 2 km from firespots, field surveys, burnout data, and calculation of accuracy. AFP and PFP hotspot validation of firespots from Landsat-8 is found to have higher accuracy than the other options. In using Himawari-8 hotspots to detect land/forest fires in Central Kalimantan, the AFP algorithm with 2km radius has accuracy of 51.33% while the PFP algorithm has accuracy of 27.62%

ENVIRONMENT QUALITY IDENTIFICATION USING LANDSAT-8 IN THE PERIOD OF COVID-19 LOCKDOWN IN JAKARTA

The quality of the urban environment during the Covid-19 lockdown became a concern because it was reported that it had improved but the spatial studies were still limited. Spatial information at regional scale can be extracted from Landsat-8 imagery. This study aims to spatially and temporally analyze environmental quality variables from Landsat-8 Imagery and compare environmental quality indices before, during and after the Covid-19 lockdown in Jakarta. Environmental quality variables extracted from Landsat-8 imagery are PM10, LST, NDVI, NDWI, NDMI. Radiometric correction and masking were applied to obtain Landsat-8 reflectance and radian values. PM10 concentrations were estimated using linear regression between station data and visible-near infrared (VNIR) reflectance band values. The variable land surface temperature (LST) is obtained from the brightness temperature band 10 extraction. NDVI, NDWI, and NDMI are extracted from the transformation of the reflectance band index. The environmental quality index is extracted from a weighted linear combination method where each variable has a weighted value of 50% PM10, 31% LST, 11% NDVI, 5% NDWI, and 3% NDMI. The results of the distribution of the environmental quality index before, during and after the Covid-19 lockdown show changes. Before the lockdown, some areas in Jakarta had a poor environmental quality index, while during the lockdown, only a few areas were still of poor quality, including the reclamation island and the Cilincing industrial area, North Jakarta. After the lockdown, the environmental quality index decreased again i.e. good, medium and bad categories but the distribution was not as wide as before the lockdown

DETECTING DEFORMATION DUE TO THE 2018 MERAPI VOLCANO ERUPTION USING INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) FROM SENTINEL-1 TOPS

This paper describes the application of Sentinel-1 TOPS (Terrain Observation with Progressive Scans), the latest generation of SAR satellite imagery, to detect displacement of the Merapi volcano due to the May–June 2018 eruption. Deformation was detected by measuring the vertical displacement of the surface topography around the eruption centre. The Interferometric Synthetic Aperture Radar (InSAR) technique was used to measure the vertical displacement. Furthermore, several Landsat-8 Thermal Infra Red Sensor (TIRS) imageries were used to confirm that the displacement was generated by the volcanic eruption. The increasing temperature of the crater was the main parameter derived using the Landsat-8 TIRS, in order to determine the increase in volcanic activity. To understand this phenomenon, we used Landsat-8 TIRS acquisition dates before, during and after the eruption. The results show that the eruption in the May–June 2018 period led to a small negative vertical displacement. This vertical displacement occurred in the peak of volcano range from -0.260 to -0.063 m. The crater, centre of eruption and upper slope of the volcano experienced negative vertical displacement. The results of the analysis from Landsat-8 TIRS in the form of an increase in temperature during the 2018 eruption confirmed that the displacement detected by Sentinel-1 TOPS SAR was due to the impact of volcanic activity. Based on the results of this analysis, it can be seen that the integration of SAR and thermal optical data can be very useful in understanding whether deformation is certain to have been caused by volcanic activity

Pengaruh Metode Koreksi Radiometrik Citra Alos Avnir-2 Terhadap Akurasi Hasil Estimasi Karbon Vegetasi Tegakan Di Wilayah Kota Semarang BagianTimur

Vegetasi dikenal sebagai media yang berperan aktif dalam pengendalianpencemaran udara di wilayah kota, sehubungan dengan kemampuannya dalam menyerapkarbon dioksidan dan memproduksi oksigen. Terkait dengan peran itu, penginderaan jauhtelah banyak digunakan untuk mengestimasi stok karbon vegetasi, baik di daratan maupundi pesisir. Dalam berbagai penelitian tersebut, seringkali data satelit multispektral diprosesdengan koreksi radiometrik terlebih dahulu untuk menghilangkan pengaruh gangguanatmosfer. Meskipun demikian, kajian penginderaan jauh tentang pengaruh koreksiradiometrik terhadap akurasi model estimasi yang dihasilkan masih jarang dilakukan.Penelitian ini mengkaji pengaruh jenis dan tingkat koreksi radiometrik citra multispektralALOS AVNIR-2 terhadap akurasi model estimasi stok karbon vegetasi tegakan diSemarang bagian timur. Dalam penelitian ini, digunakan empat jenis koreksi radiometrikmeliputi (a) penyesuaian histogram atas nilai piksel asli, (b) kalibrasi bayangan, (c)koreksi at-sensor reflectance, dan (d) koreksi at-surface reflectance. Pada tiap jeniskoreksi, citra diproses dengan enam indeks vegetasi, yaitu (a) EVI2, (b) NDVI, (c) TVI, (d)ARVI, (e) SAVI, dan (f) MSARVI. Masing-masing jenis koreksi dan transformasi indeksvegetasi kemudian dikorelasikan dengan stok karbon vegetasi tegakan di atas permukaanhasil pengumpulan data biomassa di lapangan. Hasil penelitian menunjukkan bahwa (1)koreksi at-sensor dan at-surface reflectance merupakan metode koreksi yang paling efektifdan sekaligus stabil untuk dijadikan basis bagi estimasi stok karbon, karena secara statistiklayak dilanjutkan dalam pemodelan estimasi dengan model regresi untuk semuatransformasi indeks vegetasi; (2) MSARVI dengan model regresi eksponensial berbasiskoreksi at-sensor reflectance dan at-surface reflectance merupakan jenis transformasi yangpaling akurat untuk estimasi stok karbon vegetasi tegakan di daerah penelitian.Hal.673-68