Studi Potensi Daerah Genangan Banjir Pasang (rob) Perairan Meulaboh dengan Sistem Informasi Geografis (SIG) Studi Potensi Daerah Genangan Banjir Pasang (rob) Perairan Meulaboh dengan Sistem Informasi Geografis (SIG) (Kajian Teknis)

Abstrak Kota Meulaboh adalah salah satu daerah di provinsi Nanggroe Aceh Darussalam yang berada di wilayah perairan Lautan Samudra Hindia sebagai sarana Pelabuhan. Pelabuhan Meulaboh sangat berperan penting dalam meningkatkan perekonomian dan memenuhi kebutuhan masyarakat khususnya di Wilayah Kabupaten Aceh Barat. Daerah perairan pantai sangat rentan terhadap banjir pasang (rob) akibat fluktuasi muka air laut dengan kejadian pasang tertinggi (Highest Water Level) yang berpotensi mengakibatkan daerah genangan banjir di sekitar wilayah pemukiman penduduk. Studi penelitian dilakukan untuk menganalisis pemetaan potensi daerah genangan banjir pasang (rob) menggunakan Sistem Informasi Geografis (SIG). Elevasi tinggi pasang surut dianalisis dengan metode Admiralty, penggambaran elevasi kontur permukaan tanah dan banjir pasang (rob) dianalisis berdasarkan data Digital Elevation Model (DEM), dan pemetaan potensi daerah genangan banjir antara elevasi permukaan banjir pasang (rob) dengan peta administratif Kota Meulaboh dilakukan dengan Sistem Informasi Geografis (SIG) yang menggunakan software Arcgis. Hasil penelitian menunjukan elevasi muka air banjir pasang (rob) tertinggi berada di ketinggian 0.78 m di atas permukaan laut rata-rata (MSL) dan mengakibatkan potensi luas daerah genangan banjir mencapai 18.18 Km2. Luas daerah genangan banjir mengakibatkan 11 desa terkena dampak dan mengakibatkan kerugian bagi masyarakat khususnya di wilayah Desa Kampung Pasir, Suak Indrapuri, Suak Raya, dan Suak Nie. Kata-kata Kunci: Meulaboh, pasang surut, admiralty, dan sistem informasi geografis. Abstract Meulaboh City is one of the regions in the province of Nanggroe Aceh Darussalam in the territorial waters Indian Ocean as a port. Meulaboh Port is very important in improving the economy and meeting the needs of the community, especially in the District of West Aceh. Coastal waters are very susceptible to tidal flooding (rob) due to sea level fluctuations with the highest water level which has the potential to cause flooding areas around residential areas. The research study was conducted to analyze the mapping of potential tidal flood areas (rob) using Geographic Information Systems (GIS). Tidal height analysis is carried out using the Admiralty method, depiction of ground surface contour elevation and tidal flooding (rob) is analyzed based on Digital Elevation Model (DEM) data, and mapping of potential inundation areas between tidal flood surface elevations (ROB) with administrative maps of Meulaboh City carried out with a Geographic Information System (GIS) that uses Arcgis software. The results showed that the highest tidal flood water level (rob) was at an altitude of 0.78 m above the mean sea level (MSL) and resulted in the potential area of ​​flood inundation areas reaching 18.18 Km2. The total of ​​flood inundation area affected 11 villages and caused losses to the community, especially in the villages of Kampung Pasir, Suak Indrapuri, Suak Raya, and Suak Nie. Keywords: Meulaboh, tides, admiralty, and geographic information systems.

Penentuan Batas DAS Tojo Berbasis GIS Menggunakan Perangkat HEC HMS 4.4 dan QGIS 3.16

Watershed delineation is the process of determining an area that contributes to the flow of rainfall (precipitation) into runoff to the outlet. The watershed delineation analysis in this study used the HEC-HMS version 4.4 and QGIS 3.16 . HEC HMS version 4.4 has GIS tools that have been directly integrated to facilitate the watershed delineation process. The study area in this research is the Tojo watershed which is located in Central Sulawesi Province. The digital elevation model (DEM) data source used is DEMNAS BIG with a spatial resolution is 8,1 meters. The results of watershed delineation using HEC-HMS 4.4 software are the same as the results of watershed delineation using QGIS 3.16 software. There is a slight difference in the watershed boundary in some parts when zoomed in. The watershed area produced by each GIS Softwares are HEC-HMS 212,583 km2, QGIS 212,5404 km2. The HEC-HMS 4.4 software gives quite good results on the boundary analysis of the Tojo watershed, so the HEC-HMS 4.4 software can also be used in other researches in the field of water resources.

LANDSLIDE MONITORING USING CLOSE RANGE PHOTOGRAMMETRY

This paper presents the preliminary results of a simulation study on the production of low cost Digital Elevation Model (DEM) for a landslide study area in Seri Iskandar, Perak. The important objective of this paper is to present the potentiality of Close Range Photogrammetry (CRP) as a data acquisition tool in producing a Digital Elevation Model (DEM) by using data from surface measurement. This method was applied using stereopair photographs captured data from ground level detection, or known as close range photogrammetry with the use of a digital camera mounted on a tripod as a tool for data collection. Close Range Photogrammetry (CRP) applications is useful for mapping of areas that are difficult and risky to point manpower on terrain that consist of steep and dangerous slopes. Conventional methods require measurement using Electronic Distance Measuring (EDM), but this method is very costly and requires a survey team placed on the land site area. The research data were carried out with two different epoch data. The outcome proves that CRP can produce DEM with less cost compared to other methods.

CARTOGRAPHY OF MOROCCAN ARGAN TREE USING COMBINED OPTICAL AND SAR IMAGERY INTEGRATED WITH DIGITAL ELEVATION MODEL

Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.