Wijayanti, Regita Faridatunisa (2021) REMOTE SENSING-BASED DROUGHT ANALYSIS USING STANDARD AND COMPOSITE PRECIPITATION INDICES. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Java Island is the center of Indonesia's economic, government and tourism activities. Drought is a disaster that causes severe consequences without water supplies. Drought monitoring can help us determine drought conditions and prevent significant losses and severe impacts of the catastrophe. This study used precipitation data in 2015-2019 from Tropical Rainfall Measuring Mission (TRMM) and considered the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Sentinel-3 Level-2 in 2018 - 2019. Standardized Drought Analysis is conducted to establish the Standardized Precipitation Index (SPI) for different timescales (SPI 3, SPI 6, and SPI 9). Furthermore, composite drought analysis was used to determine the correlation between NDVI, LST, and SPI. Moreover, the estimation model is derived by Multivariate Linear Regression (MLR) and Geographically Weighted Regression (GWR). This study found that the GWR generates a better model than MLR because the GWR has a higher coefficient of determination for every month of each time scale. Besides, the standard deviation in GWR generates the same pattern as the observation model. The fine-resolution SPI (1 km) was composed of Sentinel-3 SLSTR NDVI and LST and matched the original SPI trend with a 27.75 km resolution. Furthermore, the most drought season happened in 2019, especially in November 2019, and the most drought location occurred in Central Java.
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Pulau Jawa merupakan pusat kegiatan ekonomi, pemerintahan dan
pariwisata di Indonesia. Kekeringan merupakan bencana yang menimbulkan
dampak yang parah karena kurangnya pasokan air. Pemantauan kekeringan dapat
membantu kita menentukan kondisi kekeringan dan mencegah kerugian yang
signifikan dan dampak parah dari bencana tersebut. Penelitian ini menggunakan
data curah hujan tahun 2015-2019 dari Tropical Rainfall Measuring Mission
(TRMM) dan mempertimbangkan Land Surface Temperature (LST) dan
Normalized Difference Vegetation Index (NDVI) dari Sentinel-3 Level-2 tahun
2018 - 2019. Standardized Drought Analysis dilakukan untuk menetapkan
Standardized Precipitation Index (SPI) untuk rentang waktu yang berbeda (SPI 3,
SPI 6, dan SPI 9). Selanjutnya, analisis komposit kekeringan digunakan untuk
mengetahui hubungan antara NDVI, LST, dan SPI. Selain itu, model estimasi
diturunkan dengan Multivariate Linear Regression (MLR) dan Geographically
Weighted Regression (GWR). Studi ini menemukan bahwa GWR menghasilkan
model yang lebih baik daripada MLR karena GWR memiliki koefisien determinasi
yang lebih tinggi untuk setiap bulan dari setiap skala waktu. Selain itu, standar
deviasi pada GWR menghasilkan pola yang sama dengan model pengamatan. SPI
resolusi tinggi (1 km) terdiri dari Sentinel-3 SLSTR NDVI dan LST dan cocok
dengan tren SPI asli dengan resolusi 27,75 km. Selanjutnya, musim kemarau
terbanyak terjadi pada tahun 2019, khususnya pada bulan November 2019, dan
lokasi kekeringan terbanyak terjadi di Jawa Tengah.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Drought analysis, Remote sensing, SPI, NDVI, LST |
| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) > G70.217 Geospatial data G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing G Geography. Anthropology. Recreation > GE Environmental Sciences H Social Sciences > HA Statistics > HA30.3 Time-series analysis H Social Sciences > HA Statistics > HA31.38 Data envelopment analysis. Q Science > QC Physics > QC871 Meteorology--Observations. T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29101-(S2) Master Thesis |
| Depositing User: | Regita Faridatunisa Wijayanti |
| Date Deposited: | 19 Sep 2021 01:40 |
| Last Modified: | 19 Sep 2021 01:40 |
| URI: | http://repository.its.ac.id/id/eprint/91985 |
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