Based on a field survey at the beginning of 2019, it shows that the growth and development of land use in the South Lampung Regency area leads to uncontrolled conditions, causing disruption of land function both in the area itself and the area below. This condition can be overcome by making efforts to determine land conservation areas. One of the study documents to determine conservation areas in an area is the distribution of degraded land, so a study of degraded land is absolutely necessary. GIS technology can be used to answer the challenge of determining critical land through the superimpose method using several map layers with weighting techniques. The superimpose study requires data on thematic maps and the distribution of existing land cover. Remote sensing technology is utilized to produce existing land cover maps through classification and image interpretation techniques. Thematic map data supporting other analyzes utilize spatial data from the RTRW of the research area. The largest distribution of degraded land is in Merbau Mataram and Katibung districts which require immediate action to be implemented by the Conservation program. Conservation areas that have been defined in RTRW must be maintained, it is necessary to establish additional protected areas on the Sutet border area. As a disaster mitigation effort, all disaster areas need to be designated as conservation areas.
The eruption of the Anak Krakatoa volcano (GAK) in December 2018 caused part of the body of GAK to collapse into the sea and caused a tsunami. This avalanche also caused changes in the topography of GAK. If there is a repeat of the disaster with the current GAK topography, it will certainly cause changes in tsunami wave height at the shoreline which will affect changes in the tsunami inundation area. Because the location of the Lampung Bay coastal area which is quite close to GAK makes the Lampung Bay coastal area vulnerable to the tsunami disaster. So, it is necessary to study the tsunami inundation area due to changes in the current GAK topography in the coastal area of Lampung Bay. This study was conducted using non-numerical methods to obtain wave heights at the shoreline and the Berryman methods to obtain tsunami inundation areas in the coastal areas of Lampung bay by making three scenarios. Based on the results of the study, it is known that the height of tsunami waves, which are 13 meters, 26 meters, and 39 meters with an average time of arrival of tsunami waves on the shoreline is 57 minutes. Where there are seven sub-districts submerged by the tsunami with a distance of about 160 meters to 1.6 kilometers.
Abtrak -Kerusakan terumbu karang di Pulau Tegal berdampak terhadap berkurangnya habitat terumbu karang, sehingga perlu dilakukan monitoring. Monitoring dilakukan dengan analisis luasan dan perubahannya dengan memanfaatkan teknologi penginderaan jauh untuk pemetaan kondisi eksistingnya. Data yang digunakan yaitu citra landsat pada tahun 1998, 2008, 2015 dan 2018. Pengolahan citra digital dilakukan mulai dari koreksi citra, perhitungan algoritma lyzenga, interpretasi citra dan validasi lapangan, serta dilakukan uji akurasi habitat terumbu karang menggunakan matriks konfusi. Hasil penelitian menunjukan bahwa terjadi perubahan luasan terumbu karang dari tahun 1998 -2018. Kelas terumbu karang mengalami pengurangan seluas 11,22 ha. Kelas terumbu karang yang berubah menjadi kelas pasir seluas 9,13 ha (29,49%) dan lamun seluas 4,38 ha (14,15%). Kelas pasir yang berubah menjadi terumbu karang seluas 2,08 ha (13,52%) dan kelas lamun yang berubah menjadi terumbu karang seluas 0,21 ha (0,25%). Perubahan yang paling besar yaitu perubahan terumbu karang menjadi pasir seluas 9,13 ha (29,49%), sedangkan perubahan paling kecil yaitu perubahan lamun menjadi terumbu karang seluas 0,21 ha (0,25%). Pada kelas lainnya perubahan luasan paling besar yaitu perubahan lamun menjadi pasir seluas 5,76 ha (6,96%), sedangkan perubahan paling kecil yaitu perubahan pasir menjadi lamun seluas 2,67 ha (17,35%).Abstract -Damage to coral reefs on Tegal Island has an impact on reducing coral reef habitats, so monitoring needs to be done. Monitoring is done by analyzing the extent and changes by utilizing remote sensing technology to map the existing conditions. The data used are Landsat imagery in 1998, 2008, 2015 and 2018. Digital image processing is done starting from image correction, lyzenga algorithm calculation, image interpretation and field validation, and accuracy testing of coral reef habitats using a confusion matrix. The results showed that there was a change in the area of coral reefs from 1998 to 2018. The coral reef class experienced a reduction of 11.22 ha. Coral classes that changed into sand classes were 9.13 ha (29.49%) and seagrasses were 4.38 ha (14.15%). The class of sand that turned into coral reefs was 2.08 ha (13.52%) and seagrass classes that turned into coral reefs were 0.21 ha (0.25%). The biggest change is the change in the coral reef to sand covering an area of 9.13 ha (29.49%), while the smallest change is the change in seagrass into a coral reef covering an area of 0.21 ha (0.25%). In the other classes, the biggest change in area was seagrass change into sand covering an area of 5.76 ha (6.96%), while the smallest change was the change in the sand to seagrass covering an area of 2.67 ha (17.35%).
Abstract. Oil palm plantations are the largest oil-producing plant and are included in the multipurpose plant category as a contributing economy and reducing the poverty. Oil palm age is one of the factors that support the growth and production process of oil palm. We used Landsat 8 Operational Land Imager (OLI) to estimate the oil palm age. The aims in this study are to analyse the relationship between NDVI (Normalized Difference Vegetation Index) and oil palm age in variation of age by using logarithmic regression and to estimate the distribution of oil palm age. Methodology in this study are collecting of Landsat 8, preprocessing, making of Regions of Interest for each of oil palm age, making of relationship analysis between NDVI and oil palm age, and estimating the oil palm age. The study shows that NDVI have positive correlation which R 2 = 0,66 and equation y = 0.0425 ln(x) + 0.723 means the higher NDVI value the higher the age. The result of this study show that the majority of age in the study area is 0-5 year.
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