Flood is one of the disasters that often hit various regions in Indonesia, specifically in West Kalimantan. The floods in Nanga Pinoh District, Melawi Regency, submerged 18 villages and thousands of houses. Therefore, this study aimed to map flood risk areas in Nanga Pinoh and their environmental impact. Secondary data on the slope, total rainfall, flow density, soil type, and land cover analyzed with the multi-criteria GIS analysis were used. The results showed that the location had low, medium, and high risks. It was found that areas with high, prone, medium, and low risk class are 1,515.95 ha, 30,194.92 ha, 21,953.80 ha, and 3.14 ha, respectively. These findings implied that the GIS approach and multi-criteria analysis are effective tools for flood risk maps and helpful in anticipating greater losses and mitigating the disasters.

saat ini adalah bahan ajar yang relatif kurang praktis dan kurang operasional. Penelitian ini bertujuan untuk: 1) menganalisis kebutuhan modul pembelajaran SIG; 2) mengembangkan pembelajaran SIG berbasis Higher Order Thinking Skill (HOTS); dan 3) untuk mengetahui kelayakan pengembangan modul pembelajaran SIG. Rancangan penelitian yang digunakan adalah penelitian pengembangan. Teknik penelitian menggunakan observasi langsung saat perkuliahan dan wawancara tidak langsung. Alat yang digunakan dalam penelitian ini adalah panduan observasi dan panduan wawancara. Teknik analisis data yang digunakan adalah skoring dan prosentase. Hasil penelitian menunjukkan mahasiswa membutuhkan bahan ajar yang lebih praktis, efektif dan operasional sehinga lebih mudah dipahami. Pengembangan bahan ajar berdasarkan hasil analisa angket adalah berupa modul, karena lebih mudah memfasilitasi mahasiswa dalam belajar dan berfikir lebih kritis dalam menyelesaikan tugas-tugas yang sifatnya adalah proyek. Berdasarkan hasil analisis kelayakan diketahui bahwa kelayakan isi dengan nilai kelayakan sebesar 92,9%, kelayakan penyajian modul dengan nilai kelayakan sebesar 92,5% serta kelayakan kebahasaan memperoleh nilai 93,3%. Dengan demikian, maka modul pembelajaran SIG dalam penelitian ini layak digunakan.

No abstract

In Indonesia, especially in regions where natural conditions and human activity coexist, flood disasters are a strong possibility. Flooding regularly has an impact on Sengah Temila, which is a component j/ of Indonesia's West Kalimantan Province. The issue in Sengah Temila is that there is little knowledge of the distribution of flood susceptibility in this region. The GIS-based flood susceptibility model has been widely used in Indonesia, but research dedicated to validating the model is limited. SAR-based analysis has been used for flood mapping in Indonesia, but its use for validating flood models has been limited.  The objective of this study is to identify the optimal weighting scenario for a GIS-based multi-criteria analysis flood model for use in the Sengah Temila Watershed. The GIS-based model is created by merging spatial parameters, including slope, elevation, flow accumulation, drainage density, land use and land cover (LULC), soil type, normalized difference vegetation index (NDVI), curvature, rainfall, distance to river, and topographic wetness index (TWI) with weighted multi-criteria analysis. In addition, Sentinel-1 GRD images from before and after the floods have been retrieved from Google Earth Engine using past floods of the watershed. In order to create a SAR-based flood model, the researchers then integrated and categorized the results. Eleven weighting scenarios were used to create eleven GIS-based flood models. To calculate the degree of spatial similarity, all of these models were contrasted with the SAR-based model using the Fuzzy Kappa approach. We found that in order to achieve ideal weighting, slope, topographic wetness index (TWI), rainfall, and flow accumulation should each be given a larger value.

<p><em>This study aims to determine 1) the mangrove vegetation density index, 2) the health of mangrove plants in Sungai Batang Village to Kuala Secapah. The data used in this study is the image of Sentinel-2A, dated June 8, 2020. The data taken are vegetation density (NDVI) and mangrove health. The method in this study uses the vegetation index transformation (NDVI). Data analysis used the supervised classification method and the vegetation density index (NDVI). The results showed that the NDVI value of -1 – 0.32 indicates a sparse vegetation density, a value of 0.33 – 0.42 indicates a medium density and 0.43 – 1 indicates a dense density. From this NDVI index value, it can be used as a basis for classifying the health of mangrove vegetation. The health of mangrove vegetation based on the vegetation index value of 0.43 – 1 (meet) indicates that the health of the mangrove vegetation is very good. Vegetation value 0.33 – 0.42 (moderate) indicates good health of mangrove vegetation and vegetation index value -1 – 0.32 (rare) indicates poor vegetation health. Mangrove health level is very good with an area of 3.0314 km², healthy has an area of 0.204806 km² and poor health has an area of 0.625875 km².</em></p>