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All Journal Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Jurnal MEKANIKAL E-Journal Home Economic and Tourism Jurnal Ilmu Lingkungan E-SOSPOL Jurnal Perencanaan Wilayah JITRO (Jurnal Ilmiah dan Teknologi Peternakan Tropis) Ecogreen Buletin Penelitian Sosek BioWallacea Journal of Biological Research Jurnal Sosio Agribisnis JIPHO ( Jurnal Ilmiah Peternakan Halu Oleo) Media Konstruksi : Jurnal Ilmiah Teknik Sipil Jurnal Agribest JURNAL PengaMAS Jurnal Peternakan (Jurnal of Animal Science) Jurnal Sains Sosio Humaniora Jambura Journal of Animal Science Journal of Tropical Animal Science and Technology Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Sibermas (Sinergi Pemberdayaan Masyarakat) International Journal Of Science, Technology & Management (IJSTM) Indonesian Journal of Animal Agricultural Science (IJAAS) Journal Basic Science and Technology Jurnal Peternakan Lokal Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT) Jurnal Pengabdian Masyarakat Bestari (JPMB) JURNAL HUTAN TROPIS Asian Journal of Community Services (AJCS) Chalaza Journal of Animal Husbandry Journal of Livestock and Animal Health (JLAH) Amal Ilmiah: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Multidisciplinary Scientific Studies Joong-Ki Indonesian Journal of Community Services Jurnal Pengabdian Masyarakat AGROLAND: The Agricultural Sciences Journal Animal Production GABBAH : Jurnal Pertanian Dan Perternakan Jakadimas Joong-Ki JIPHO (Jurnal Ilmiah Peternakan Halu Oleo) Jurnal Pengabdian Kepada Masyarakat (JAPIMAS)
Deki Zulkarnain
Fakultas Peternakan Universitas Halu Oleo
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Journal : BioWallacea Journal of Biological Research

 1 
Analisis Sebaran Jasa Ekosistem Penyediaan Pangan dan Air di Daerah Karst (Studi Kasus Kabupaten Buton Tengah) La Baco S; Kahirun Kahirun; Zulkarnain Zulkarnain; Albasri Albasri
BioWallacea : Jurnal Penelitian Biologi (Journal of Biological Research) Vol 7, No 1 (2020): BioWallacea and Sains
Publisher : University of Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (542.131 KB) | DOI: 10.33772/biowallacea.v7i1.11041

Abstract

The karst area is the dominant ecosystem in the area of Central Buton District where around 80 percent is a stretch of karst. This objectives of the study was to analyze the ability of ecosystems to provide food and water and its distribution spatially in Central Buton District. This research was conducted through a study of ecoregion characteristics, including data on landscape, natural vegetation types and land cover. In addition, population data, water availability, food availability and administrative data in Central Buton District were collected. Data analysis includes the calculation of ecosystem service indexes, spatial distribution of ecosystem service indices and indicative status of the carrying capacity of food and water. The average index of environmental services for food supply in the Karst area of Central Buton District was 2.52 with a low category, while the index of environmental services for water supply was lower at 1.96 with also a low category. Food service ecosystem index with a low category dominates Central Buton District with an area of 50,286.63 hectares (60.08%) and a medium category of 26,695.97 hectares (31.89%). Water supply ecosystem services index is very low to low with an area of 54,849.99 hectares (65.53%) and 24,551.24 hectares (29.33%). The overall carrying capacity of food and water in Central Buton District is still a surplus. The area of food surplus in the region reached 66,977.53 hectares (80.02%) and the total food surplus was 30,453,510,374 kcal. The total area of water surplus in Central Buton District is 81,291 hectares (97.12%) with a total surplus of 367,826,651 m3/year.Keywords: carrying capacity, ecoregion, ecosystem service index, karst area. AbstrakKawasan karst merupakan ekosistem dominan di wilayah Kabupaten Buton Tengah dimana sekitar 80 persen merupakan hamparan karst.  Penelitian ini bertujuan untuk menganalisis kemampuan ekosistem menyediakan pangan dan air serta distribusinya secara spasial di Kabupaten Buton Tengah. Penelitian ini dilakukan melalui kajian karakteristik ekoregion, mencakup data bentang lahan, tipe vegetasi alami dan tutupan lahan.  Selain itu dilakukan pengumpulan data kependudukan, ketersediaan air, ketersediaan pangan dan data administrasi Kabupaten Buton Tengah.  Analisis data meliputi perhitungan indeks jasa ekosistem, sebaran spasial indeks jasa ekosistem dan status indikatif daya dukung pangan dan air.  Rerata indeks jasa lingkungan penyediaan pangan Kawasan Karst Kabupaten Buton Tengah adalah 2,52 dengan kategori rendah, sementara itu indeks jasa lingkungan penyediaan air lebih rendah yakni 1,96 dengan kategori juga rendah.  Indeks jasa ekosistem penyediaan pangan dengan kategori rendah mendominasi Kabupaten Buton Tengah dengan luas 50.286,63 hektar (60.08 %) dan kategori sedang seluas 26.695,97 hektar (31,89 %). Indeks jasa ekosistem penyediaan air kategori sangat rendah sampai rendah dengan luas masing-masing 54.849,99 hektar (65,53 %) dan 24.551,24 hektar (29,33 %).  Status daya dukung pangan dan air Kabupaten Buton Tengah secara keseluruhan masih surplus.  Luas daerah surplus pangan di wilayah tersebut mencapai 66.977,53 hektar (80,02 %) dan total surplus pangan sebanyak 30.453.510.374 kkal. Luas daerah surplus air Kabupaten Buton Tengah adalah 81.291 hektar (97,12 %) dengan total surplus sebanyak 367.826.651 m3/tahun..Kata Kunci: daya dukung, ekoregion, indeks jasa ekosistem, kawasan karst, sebaran spasial.
Tinjauan Pemanfaatan Citra Sentinel dan Machine Learning Dalam Pendugaan Volume Tegakan Hutan Zulkarnain Zulkarnain; Muhammad Buce Saleh
BioWallacea : Jurnal Penelitian Biologi (Journal of Biological Research) Vol 9, No 2 (2022): Biodiversitas Wallacea
Publisher : University of Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1043.874 KB) | DOI: 10.33772/biowallacea.v9i2.26227

Abstract

This literature review is carried out systematically and aims to identify and analyze research trends, data collections, methods, and frameworks used in estimating forest stand volume (VTH) research with sentinel and (machine learning) ML from 2014 to 2021 so that it can provide answers. on the research questions of this study. The results of the analysis of 24 selected articles based on inclusion-exclusion criteria showed that VTH studies with sentinel and ML generally combine sentinel with other images, such as ALOS-2 L Band, Landsat, ALOS DSM and DEM data. S2-spectral band, S2-vegetation index, slope, S1-backscatter, elevation, are the variables most widely used as predictors. RF (random forest regression), SVR (support vector regression), MLR (multi linear regression) and kNN (k-nearest neighbor) algorithms are the most widely used algorithms. The potential for significantly increasing the accuracy of VTH estimation results can be done by adding environmental factors as a predictor variable.The study of Mauya et al, Reis et al and Chen et al which combines optical, SAR and topographic data, reported a significant increase in the accuracy of VTH estimation.Furthermore Mauya et al suggested that the weighted average approach of pixels in extracting variables from image based on the position of the field plot produces a better estimation model than using the centroid approach Optimizing the use of red edge bands in VTH estimation conducted by Jiang et al, Ahmadi et al and Hu et al, shows a more significant correlation between red edge bands and VTH than other S2-derived features s proves that the modification of the vegetation index formula by replacing the band (NIR) with a red edge band is proven to significantly increase its correlation with stand volume and performs very well on the RF, SVM and MLR algorithms in estimating VTH. The kriging geostatistical method by Chen et al and Bolat concluded that SVR-kriging and Regression-kriging each outperformed SVR and had better accuracy in predicting VTH. AbstrakTinjauan literatur ini dilakukan secara sistematis dan bertujuan untuk mengidentifikasi serta menganalisis tren penelitian, kumpulan data, metode, dan kerangka kerja yang digunakan dalam penelitian pendugaan volume tegakan hutan (VTH) dengan sentinel dan machine learning (ML) tahun 2014 sampai 2021 sehingga dapat memberikan jawaban atas pertanyaan penelitian kajian ini. Hasil analisis terhadap 24 artikel terpilih berdasarkan kriteria inklusi-eksklusi menunjukan bahwa kajian VTH dengan sentinel dan ML, umumnya mengkombinasikan sentinel dengan citra lain, seperti ALOS-2 L Band, Landsat, ALOS DSM dan data DEM.  Band S2-spektral, S2-indeks vegetasi, slope, S1-backscatter, elevation, adalah variable variable yang paling banyak digunakan sebagai prediktor. Algoritma RF (random forest regression), SVR (support vector regression), MLR (multi linear regression) dan kNN (k-nearest neighbor merupakan algotirma yang ditemukan paling banyak digunakan. Potensi peningkatan akurasi hasil pendugaan VTH secara signifikan dapat dilakukan dengan menambahkan faktor lingkungan sebagai variabel prediktor. Studi Mauya et al, Reis et al dan Chen et al yang mengkombinasikan data optis, SAR dan topografi, melaporkan adanya peningkatan akurasi pendugaan VTH yang signifikan. Selanjutnya Mauya et al mengemukakan bahwa pendekatan rata rata tertimbang dari piksel dalam mengekstraksi variabel dari citra berdasarkan posisi plot lapangan menghasilkan model pendugaan yang lebih baik dari pada menggunakan pendekatan centroid. Optimalisasi pemanfaatan band red edge dalam pendugaan VTH yang dilakukan oleh Jiang et al, Ahmadi et al dan Hu et al, menunjukan koleasi yang lebih signifikan antara band red edge dengan VTH daripada fitur lain yang diturunkan dari S2. Chyrysafis membuktikan bahwa modifikasi rumus indeks vegetasi dengan mengganti band (NIR) dengan band red edge terbukti meningkatkan korelasinya dengan volume tegakan secara signifikan dan berkinerja sangat baik pada algoritma RF, SVM maupun MLR dalam menduga VTH. Metode geostatistik kriging yang dilakukan Chen et al dan Bolat, menyimpulkan bahwa SVR-kriging dan Regresi-kriging masing-masing mengungguli kinerja SVR dan memiliki akurasi yang lebih baik dalam memprediksi VTH. Katakunci: Citra Sentinel, Machine Learning, Tegakan Hutan
Co-Authors A Indi AA Sudharmawan, AA Aang Baitul Mizan Abdi Abdi Abdi Abdi Abdul Rizal Abdul Salam Abdur Razak Achmad Selamet Aku Adrian Tawai Albar, Fahry Albasri Albasri Albasri Albasri Amiluddin Indi Amiluddin Indi Amiluddin Indi, Amiluddin Andarias Julias Wijaya Andi Murlina Tasse Andiwea Andiwea Arwan Arwan Asep Indra Asma Bio Kimestri Asma Bio Kimestri Asyraful Rijal Audza, Fuji Astuti Bana, Sahindomi Beni Surya Purba D P Suryaningsi Damhuri Damhuri Daud, Denvy Meidian Dewi, Fitria Djoko Poernomo Elma Susanti Erwin Yulianto Fadli Ma’mun Pancar Fahmi, Katren Tika Fahria Nadiryati Sadimantara Firman Nasiu Fitria Dewi Fitria Dewi Fitriah Masud Fitrianingsih Fitrianingsih Fitrianingsih Fitrianingsih Fuji Astuty Auza Furqan B, Muhammad Thoksyn Gamar Al Haddar Gerhana Gerhana H Hafid Hairil Adzulyatno Hadini Hamdan Has Harapin Hafid H. Hardiana, Hardiana Hasbullah Syaf Hasmiarni Aris Helpi Pebriani Heriyawan Heriyawan Ida Usman Ilma Sarimustaqima Rianse Iwan Munika Sales Jabuddin, La Ode Jabudin, La Ode Jon Affi, Jon Jusdin Jusdin Kahirun, Kahirun Kasmin, Muh Obi Kasmin, Muh. Obi Kemistri, Asma Bio La Baco S La Malesi La Ode Abdul Malik Fajar La Ode Alimuddin La Ode Arsad Sani La Ode Jabuddin La Ode Muhamad Munadi La Ode Nafiu La Ode Sahaba Laode Diara LaOde Sumarlin Muis Lia Sartika Lies Indriyani Lili Darlian, Lili Liswarti Yusuf Mar wati Marnitasari, Marnitasari Maulana Erwin Syahputra Milasari Milasari Muh. Rusdin Muhammad Amrullah Pagala Muhammad Buce Saleh Muhammad Rajab Muhammad Ulum Al-din Muhammad Yusuf, Fathullah Munadi, Abu Qasim, Muhammad Yusuf, Mukhtar Abu mukhtar mukhtar Mulyadi Nursi Munadi Munadi Munadi, La Ode Muh. Munawarman, Munawarman Musram Abadi Musram Abadi Nasriati Nasriati Natsir Sandiah Nita Nita Nonny Basalama Nur Santy Asminaya Nurhayu Purwnti, Bea Putu Nara Kusuma Prasanjaya R Libriani Rahmatullah Rahmatullah Razid Razid Restu Libriani Ridwan Syah Alimin Rina Astarika, Rina Risawa1, Risma Wilasakti Rusdin, Muh. Rusli Badaruddin Rusli Badarudin Salwati Salwati Sariati Sariati SATRIYAS ILYAS Sitti Marni Ruhana Sitti Marwah Sitti Rahma Ratu Pujian Solihin Solihin Sufardiman Sufardiman Surahmanto Surahmanto Sutiana Sutiana Syamsuddin Syamsuddin Syamsuddin Syamsuddin Syamsuddin Syamsuddin TAKDIR SAILI Umran Sarita Viktor Amrivo Vina Eka Prasetia Anisa Wayan Swastika Yasa Widhi Kurniawan Yamin Yaddi Yuliana Yuliana Yuni Lestari