Spatial modelling of flood-prone areas will provide maximum results if it is supported by the accuracy of the data acquired, mainly related to elevation data or the area’s topography. Spatial modelling generated from accurate topographic data can estimate the river’s carrying capacity. This study built a spatial model using data from aerial, terrestrial, and hydrographic surveys. Aerial surveys were conducted using UAV corrected by terrestrial surveys, GCP, and ICP. Testing the accuracy of the spatial model is carried out by comparing the results of current field velocity with the results of 2D Hec-Ras numerical simulations using a variation of the manning coefficient. The combination of aerial, terrestrial, and hydrographic surveys produces a cross-sectional spatial model of the river, which is used in calculating the river’s carrying capacity. The river’s capacity is calculated using a 2D numerical simulation method using Hec-Ras software and verified by a mathematical approach based on the flood hydrograph curve. The results showed that the horizontal accuracy of the GCP was 2.8 cm and the vertical accuracy was 6.5 cm. The results of testing the vertical elevation accuracy of aerial photographs on terrestrial topographic data measured in the field (ICP) have a Mean Absolute Percentage Error (MAPE) value of 5.81%. According to the spatial model, the manning roughness value is 0.06-0.09. The river’s capacity based on numerical simulations is 1.700.766 m3, and the results of the verification using a mathematical approach are 1.683.433 m3 with a difference of 1.02%.
Indeks Kekritisan Lingkungan (ECI) didefinisikan sebagai kondisi kritis lingkungan akibat peningkatan suhu permukaan tanah (LST) dan berkurangnya indeks kerapatan vegetasi (NDVI). Secara sederhana dapat dijelaskan bahwa ECI berbanding lurus dengan peningkatan suhu dan berbanding terbalik dengan tutupan vegetasi. Penelitian ini bertujuan untuk mengkaji ECI di Kota Makassar dengan memanfaatkan citra satelit landsat 8 OLI/TIRS perekaman tahun 2013-2018. Metode penelitian untuk analisa ECI selain menggunakan algoritma LST dan NDVI juga dilakukan dengan menggunakan persamaan deduktif modifikasi dengan penambahan algoritma indeks kawasan terbangun (NDBI) dan indeks kebasahan (NDWI) untuk meningkatkan akurasi klsifikasi ECI di wilayah Kota Makassar.Hasil penelitian menunjukkan bahwa terjadi tren peningkatan LST dan NDBI serta penurunan NDVI di wilayah Kota Makassar tahun 2013 – 2018. . Algoritma indeks kebasahan NDWI juga dapat digunakan sebagai tambahan dalam formula deduktif ECI dan mampu meningkatkan akurasi klasifikasi dengan mengeliminasi tubuh air sebagai kategori lingkungan kritis. Hasil klasifikasi indeks kekritisan lingkungan ECI menunjukkan bahwa 46,69% wilayah Kota Makassar termasuk dalam kategori tidak kritis, 51,55% masuk kategori kritis dan 1,76% termasuk kategori sangat kritis. Wilayah dengan kategori sangat kritis tersebar di Jalan Tamalate, wilayah Jalan Maccini Raya, Jalan Teuku Umar 11, Jalan Tinumbu Jalan Abdul Rahman Hakim, Jalan Manuruki, dan sekitar jalan Tol Reformasi.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.