ANALYSIS ACTIVITY 14C OF CORAL REEF IN KAYANGAN ISLAND

The work is to determine absorption capacity, optimum time analysis, efficiency enumeration (TDCR), specific activityof 14C and coral age. Steps taken are physical and chemical washing, CO2 absorption, and analysis using liquid scintillation counter Hidex 300 SL. Due to washing, the weight loss was 4.74%. Total carbon absorbed was 1,056 grams. CO2 absorption capacity using KOH was 47% while optimum time analysis by LSC was 30 minutes and average efficiency enumeration (TDCR) was 0.6877.  It was concluded that specific activityof 14C was 14.7361 DPM/gC and coral age in Kayangan Island was 310.49 years

Combining environmental DNA and visual surveys can inform conservation planning for coral reefs

Environmental DNA (eDNA) metabarcoding has the potential to revolutionize conservation planning by providing spatially and taxonomically comprehensive data on biodiversity and ecosystem conditions, but its utility to inform the design of protected areas remains untested. Here, we quantify whether and how identifying conservation priority areas within coral reef ecosystems differs when biodiversity information is collected via eDNA analyses or traditional visual census records. We focus on 147 coral reefs in Indonesia’s hyper-diverse Wallacea region and show large discrepancies in the allocation and spatial design of conservation priority areas when coral reef species were surveyed with underwater visual techniques (fishes, corals, and algae) or eDNA metabarcoding (eukaryotes and metazoans). Specifically, incidental protection occurred for 55% of eDNA species when targets were set for species detected by visual surveys and 71% vice versa. This finding is supported by generally low overlap in detection between visual census and eDNA methods at species level, with more overlap at higher taxonomic ranks. Incomplete taxonomic reference databases for the highly diverse Wallacea reefs, and the complementary detection of species by the two methods, underscore the current need to combine different biodiversity data sources to maximize species representation in conservation planning

ANALISIS SPASIAL UNTUK UJI AKURASI DAN PENGEMBANGAN ALGORITMA PEMETAAN OBYEK DASAR PERAIRAN DANGKAL DENGAN MENGGUNAKAN CITRA ALOS AVNIR 2

ABSTRAK TEKNOSAINS 2010Identifikasi obyek dasar permukaan perairan dangkal (ODPD) memerlukan kajian dan pendekatan khusus. Terdapat beberapa metode yang dapat digunakan untuk mengidentifikasi ODPD secara lebih baik dan lebih akurat yakni metode klasifikasi dan metode algoritma. Penelitian ini difokuskan pada metode klasifikasi gambar citra satelit ALOS AVNIR II. Beberapa metode yang diuji adalah Attenuated Lyzenga Method (ALM), metode Re-Class dan Composit Citra yakni Box Classification (parallelepiped) dan metode Minimum Distance yang terbentuk terhadap rata-rata algoritma , serta metode Maximum Likelihood. Uji akurasi dan penentuan model yang terbaik menggunakan Uji Penanda dan Uji Kappa. Hasil penelitian menunjukkan metode Re-class dari ALM, dan composite Citra 312 dengan metode klasifikasi Minimum Distance serta Maximum Likelihood dapat digunakan untuk mengidentifikasi obyek dasar permukaan perairan dangkal. Tes akurasi menunjukkan bahwa Image Composit 312 dengan metode klasifikasi maximum likelihood merupakan model terbaik yang bisa digunakan untuk identifikasi obyek dasar permukaan peraira

THE ACCURACY TEST OF SEVERAL IMAGE'S CLASSIFICATION METHODS USING ALOS AVNIR II IMAGE

The identification of shallow-water surface objects needs special study for its identification processes. There are many methods that could be used for good identification in algorithm and classification model. This research was focused more in the classification method of the satellite images of ALOS AVNIR II. Method used was the Attenuated Lyzenga Method (ALM) with Re-class and image composite with Box Classification (parallelepiped), minimum distance to mean algorithm and maximum likelihood. The accuracy test and the determination of the model were best performed with the sign test and the kappa Test. Results of the previous research showed that Re-class from the ALM, and image composite 312 with classification method of minimum distance and maximum likelihood could be used for identification of the object of shallow-water surface; and the accuracy Test showed that image composite 312 with the classification method of maximum likelihood was the best model to be used in the identification of the shallow-water surface objects