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All Journal Buletin PSP Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Harpodon Borneo Panrita Abdi - Jurnal Pengabdian pada Masyarakat Jurnal Penelitian Sains EnviroScienteae Tropical Wetland Journal Jurnal Biogenerasi Bulletin of Computer Science Research Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Fish Scientiae : Jurnal Ilmu-Ilmu Perikanan dan Kelautan JRIIN :Jurnal Riset Informatika dan Inovasi Tashdiq: Jurnal Kajian Agama dan Dakwah MCSIJ (Marine, Coastal and Small Islands Journal) : Jurnal Tugas Akhir Mahasiswa Ilmu Kelautan
Muhammad Syahdan
Universitas Lambung Mangkurat
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Journal : EnviroScienteae

 1 
PENENTUAN TINGKAT RESIKO KEBAKARAN HUTAN DAN LAHAN MENGGUNAKAN METODE INDEKS CUACA KEBAKARAN (FIRE WEATHER INDEX) DAN JUMLAH TITIK PANAS (HOTSPOT) DI KABUPATEN BANJAR PROVINSI KALIMANTAN SELATAN Rizqi Nur Fitriani; Bambang Joko Priatmadi; Akhmad Rizalli Saidy; Muhammad Syahdan
EnviroScienteae Vol 19, No 2 (2023): ENVIROSCIENTEAE VOLUME 19 NOMOR 2, MEI 2023
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v19i2.16202

Abstract

Various indicators of hotspot occurrence as a cause of forest and land fires (karhutla) in Banjar District are still difficult to determine due to limited information. The analysis of FFMC (Fine Fuel Moisture Code) and DC (Drought Code) as well as the monitoring of the number of hotspots aims to determine the level of risk of forest and land fire hazards and can be an early picture of future forest and land fire disasters in the Banjar District of South Kalimantan Province. The data processing process to obtain the FFMC and DC values and their relationship with the number of hotspots is to calculate the FFMC and DC values of station observations and model observations through the Microsoft Excel Add-In (FWI Add-in) program. The two models will then be verified with a scatter plot and through the Pearson correlation test the relationship between the FFMC and DC of the ERA5 model and the number of hotspots can be found. As a result, the FFMC and DC (station observation and model) showed extreme risk levels for the 2014, 2015 and 2018 forest and land fires. Both models show a positive and linear relationship on the scatter plot. And in the Pearson correlation test, both variables between the FFMC and DC of the ERA5 model and the number of hotspots are moderately to strongly correlated. This condition indicates that an increase in the risk level of forest and land fires will be followed by a significant increase in the incidence of forest and land fires in the Banjar Regency area of South Kalimantan Province.
ANALYSIS OF SEAWEED CULTIVATION DEVELOPMENT STRATEGIES IN MUARA PAGATAN VILLAGE SUB-DISTRICT KUSAN HILIR DISTRICT TANAH BUMBU Al Mughni, Muhammad Johari; Rifa’i, Muhammad Ahsin; Tony, Frans; Syahdan, Muhammad
EnviroScienteae Vol 20, No 3 (2024): ENVIROSCIENTEAE VOLUME 20 NOMOR 3, AGUSTUS 2024
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v20i3.20016

Abstract

Seaweed is a food commodity with benefits in the food industry, pharmaceuticals and others. Muara Pagatan Village is located in Kusan Hilir District, Tanah Bumbu Regency. According to Mughni (2021), Muara Pagatan Village has 12 households with seaweed cultivation. Based on that, it is necessary to study the development strategy of seaweed cultivation in Muara Pagatan Village. The approach used in the acquisition of seaweed aquaculture development strategies is the SWOT approach (strengths, weaknesses, opportunities and threats) and a participatory approach involving stakeholders, government and the community of Muara Pagatan Village, this research uses questionnaires distributed to representatives of RTP Muara Pagatan Village. The results of this study include 4 SWOT parameters which include, strengths in the form of land feasibility/potential, growth rate, accessibility and quality of seeds, for weaknesses in the form of limited capital, less innovative and not optimal production results. Meanwhile, opportunities include high demand, cooperation with other parties, government assistance, lack of competitors and availability of labor, for threat parameters include industrial activities, pests and diseases, weather, abrasion and prices. After that, SWOT analysis was carried out in obtaining seaweed cultivation development strategies, there were 9 priority strategies obtained based on SWOT parameters.
ANALISIS BANJIR SUNGAI MENGGUNAKAN MODEL HEC-RAS DI WILAYAH DAS TABANIO KABUPATEN TANAH LAUT PROVINSI KALIMANTAN SELATAN Prayogo, Shonu Dwi; Syahdan, Muhammad; Ridwan, Ichsan; Rifa’i, Muhammad Ahsin
EnviroScienteae Vol 19, No 4 (2023): ENVIROSCIENTEAE VOLUME 19 NOMOR 4, NOVEMBER 2023
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v19i4.17817

Abstract

This study aims to determine the amount of water runoff and water depth in the Tabanio Watershed, Tanah Laut Regency. The study was conducted using the HEC-RAS software to generate a flood simulation model. Data analysis included Digital Elevation Model for National Scale (DEMNAS) data, flood discharge data, rainfall data, river bathymetry, and tidal data. DEMNAS data was obtained from the website of the National Geospatial Information Agency (BIG), rainfall data was obtained from the Center for Hydrometeorology and Remote Sensing (CHRS) website. Rainfall data analysis involves selecting the maximum daily rainfall value to determine the flood discharge. The hourly flood discharge is calculated using the Synthetic Unit Hydrograph method with the Nakayasu approach. River bathymetry data is processed as additional data for DEM, with interval recording done every 0.2 seconds. Tide data was obtained through prediction based on the location constant closest to the research area and analyzed using the Pasut.exe software for a period of 6 days. Based on the results of flood analysis research using the HEC-RAS model in the Tabanio Watershed, Tanah Laut Regency, South Kalimantan Province, it can be concluded that the model results show the water depth and area of water runoff at maximum discharge. as follows: less than 0.5 meters with a very low category covering an area of 4,168.86 hectares, 0.51 to 1.5 meters with a low category covering an area of 6,417.79 hectares, 1.51 to 2.5 meters with a medium category covering an area of 1,987, 25 hectares, 2.51 to 3.5 meters with a high category of 741.47 hectares, and above 3.51 meters with a very high category of 113.69 hectares. So that the total water runoff reaches 13,429.05 hectares.
PEMETAAN KERAPATAN DAN PENERAPAN METODE DIFERENSIASI OBIA UNTUK DIFERENSIASI JENIS MANGROVE DI KAWASAN TANJUNG PEMANCINGAN, KOTABARU Melkyanus, Melkyanus; Syahdan, Muhammad; Asyari, Mufidah; Sofarini, Dini
EnviroScienteae Vol 20, No 4 (2024): ENVIROSCIENTEAE VOLUME 20 NOMOR 4, NOVEMBER 2024
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/es.v20i4.20470

Abstract

This study aims to identify mangrove species and map the density of mangrove vegetation in the Tanjung Pemancingan area, Kotabaru, using an object-based classification method (OBIA) applied to Sentinel-2 imagery and Unmanned Aerial Vehicle (UAV) data. Mangroves play a crucial role in protecting coastlines from erosion and serving as habitats for various species, making an in-depth understanding of mangrove distribution and types essential for coastal conservation and environmental management. The OBIA method allows for more accurate mapping by considering texture, shape, and more complex spatial patterns compared to traditional pixel-based methods. This study employs the Support Vector Machine (SVM) algorithm in the classification process to enhance the accuracy of mangrove species identification. The analysis utilizes Sentinel-2 satellite imagery with a spatial resolution of 10x10 meters and UAV data for higher resolution. The results show that the NDVI values for mangroves in the study area range from -0.30 to 0.686, which were classified into three canopy density classes: sparse (-0.30 to 0.026), moderate (0.027 to 0.356), and dense (0.357 to 0.686). The OBIA method combined with the SVM algorithm successfully discriminated between seven mangrove species with an overall accuracy (OA) of 72.46%. The identified mangrove species include Avicennia alba, Avicennia marina, Avicennia officinalis, Avicennia rumphiana, Bruguiera gymnorhiza, Rhizophora apiculata, and Sonneratia alba, with Avicennia rumphiana being the most dominant species, covering an area of 13.87 hectares. The mangrove vegetation density was successfully mapped, providing valuable information that can be used in conservation planning, coastal resource management, and ecotourism development in the area. Furthermore, these results have significant implications for further research in mangrove ecosystem monitoring and the application of remote sensing technology in environmental management.
Co-Authors Abdul Hakim, Ilham Abdul Rozaq, Muhammad Adirawan, Muhammad Aditya Rahman Agus Atmadipoera Agus Syahrani Aida Sukma Hati Akbar, Hassanal Akbar, Zainuddin Akhmad Rizalli Saidy Al Mughni, Muhammad Johari Algui Sumas Ponaru Algui Sumas Ponaru Asyari, Mufidah Attijani, Fatur Rahmat Badawi, Achmad Bagus Setiawan Baharuddin Baharuddin Baharuddin Baharuddin Bambang Joko Priatmadi Dafiuddin Salim, Dafiuddin Dini Sofarini Domu Simbolon Elman Sudri, Abdurrobi Fatur Rahmat Attijani Hadiratul Kudsiah Hadiratul Kudsiah Hadiratul Kudsiah, Hadiratul Hamdani Hamdani Hamdani Hamdani Heidiani Ikasari, Ines Ichsan Ridwan Ira Puspita Dewi, Ira Puspita Iskandar Yusuf Jauhari, Isynu Lutfhi Khadafi, Faraluna Putri Kurniawan, Euis Sri Wahyuni Lestarina, Putri Mudhlika M Sauqi Mubarok M. Ahsin Rifa’i M. Fedi A. Sondita Melkyanus Melkyanus Melkyanus, Melkyanus Mirna Sari Mubarok, M Sauqi Muh. Afdal Muhammad Faisal Amin Muhammad Yusuf Dibisono Muzdalifah Muzdalifah nanang nanang Nasution, Ali Napiah Nirwan Nirwan Nur Salam Nurliana NURSALAM Nursalam . Nursalam Nursalam Oktoviandi Oktoviandi Oktoviandi, Oktoviandi Parinduri, Sulthon Ponaru, Algui Sumas Ponaru, Algui Sumas Prayogo, Shonu Dwi Raafi, Muhammad Rempil, Nicolas Teah Batara Rifa'i, Muhammad Ahsin Rifa’i, Muhammad Ahsin Risman Risman Rizhansyah, Rifqi Rizqi Nur Fitriani Rohim, Gusti Akhmad Santoso Santoso Sari Ginting, Makharani Setiawan, Muhammad Ade Noor Setiyadi, Yusnar Silaban, Christianly Yery Suryaningrat, Suryaningrat Syaeful Machfud Syahril, Akhmad Syamsul Arifin Tengku Zia Ulqodry Tony, Frans Wantoro Wantoro, Wantoro Yulisman Yulisman Yuliyanto Yuliyanto Yunida, Rania