CO2 FLUX IN INDONESIAN WATER DETERMINED BY SATELLITE DATA

The oceans was considered to be a major sink for CO2. The improving of quantitative and qualitative description about the ability of sea in uptaking or emitting CO2 is a great scientific concern in meteorological and climatological science. Measurement of the ability of sea in uptake or emitting CO2 could determined by measuring the CO2 exchange coefficient on sea interface and the measuring the different partial pressure of CO2 between the air and sea. In this study, CO2 flux distribution of Indonesian waters in 2007 to 2009 was computed using monthly CO2 exchange and the different partial pressure of CO2 estimated from wind speed, salinity, SST, and sea characteristic, which were obtained from satellite data. The carbon dioxide flux thus was estimated and discussed by two different designs of transfer velocity (k), of Wanninkhof (1992), kW92 relationship and by Nightingale et al. (2000), kN, relationship. The result indicated that generally, Indonesian water was emitting the CO2 to the air. Average CO2 emitting from sea to the air for recent year in 2007 to 2009 are 3.80 (mol m-2year-1) and 2.85 (mol m-2year-1) with kW92 relationship and kN relationship calculation, respectively. The total average CO2 emission from sea to the air in 2007 to 2009 for the Indonesian waters areas are 0.15 (PgC year-1) and 0.12 (PgC year-1) based on kW92 relationship and kN relationship calculations, respectively. Keywords: CO2 flux, salinity, SST, sink and sources of CO2

APLIKASI SISTEM INFORMASI GEOGRAFI (SIG) BERBASIS DATA RASTER UNTUK PENGKELASAN KEMAMPUAN LAHAN DI PROVINSI BALI DENGAN METODE NILAI PIKSEL PEMBEDA = (Application of Geographic Information System (GIS) based raster data

Penggunaan teknologi seperti SIG sangat baik untuk mengelompokkan data keruangan lahan berdasarkan faktor potensi dan penghambat penggunaannya. Dengan mengimprovisasi metode tumpang susun diharapkan mampu mempercepat proses studi tentang pengkelasan kemampuan lahan. Tujuan penelitian ini adalah pengaplikasian SIG berbasis data raster untuk memetakan kelas kemampuan lahan di Provinsi Bali dengan menggunakan metode "nilai piksel pembeda". Hasil penelitian menunjukkan bahwa penggunaan SIG dapat memperlihatkan sebaran kelas kemampuan lahan yang heterogen dan kompleks sehingga mcmperjelas informasi lahan pada satuan unit lahan yang sempit. Selain itu penggunaan metode ini juga membantu mempercepat proses tumpang susun\u27 dan query data. Kelas kemampuan lahan di Provinsi Bali dapat dikelompokkan menjadi 8 kelas, dari kelas I sampai kelas VIII. Sebaran kelas kemampuan lahannya didominasi oleh lahan dengan kelas VI, VII dan VIII yaitu seluas 50,7% dari luas Provinsi Bali. Kabupatcn Buleleng, Jembrana, dan Karangasem berturut-turut merupakan daerah-daerah tcrluas- yang mcmiliki kemampuan lahan kelas VIII. Daerah-daerah tersebut harus lebih instensif dalam menjaga lahan-lahan berkelas VIII agar tidak beralih fungsi dari lahan hutan menjadi lahan non hutan

Pemetaan kondisi hutan mangrove di kawasan pesisir Selat Madura dengan pendekatan Mangrove Health Index memanfaatkan citra satelit Sentinel-2

Abstrak. Pemetaan dan pemantauan kondisi hutan mangrove diperlukan untuk rehabilitasi dan konservasi lingkungan. Mangrove Health Index (MHI) menggunakan analisis citra satelit merupakan pendekatan baru yang bisa digunakan untuk mengetahui kualitas lingkungan ekosistem hutan mangrove. Penelitian ini bertujuan untuk untuk mengetahui struktur komunitas hutan mangrove dan melakukan analisis spasial-temporal MHI di kawasan pesisir Surabaya dan Sidoarjo menggunakan citra satelit. Data yang digunakan untuk analisis struktur komunitas mangrove pada penelitian ini adalah hasil pengamatan lapang di 10 transek. Untuk analisis MHI menggunakan citra Sentinel 2 perekaman tahun 2015, 2018, 2021. Hasil analisis menunjukkan bahwa spesies mangrove yang paling dominan di lokasi penelitian adalah Avicennia marina. Analisis citra satelit mendeteksi pertambahan luas mangrove yang signifikan dari tahun 2015 hingga 2021 yaitu lebih dari 500 Ha. Berdasarkan analisis MHI, terjadi perubahan positif dari kondisi hutan mangrove dominansi buruk (MHI 66,68%). Pertambahan luas hutan mangrove diiringi dengan perbaikan kondisi ekosistem dengan indikator meningkatnya MHI.Abstract. Mapping and monitoring the condition of mangrove forests is needed for environmental rehabilitation and conservation. Mangrove Health Index (MHI) using satellite image analysis is a new approach that can be used to determine the environmental quality of mangrove forest ecosystems. This study aims to determine the structure of the mangrove forest community and conduct a spatial and temporal MHI analysis in the coastal areas of Surabaya and Sidoarjo. The data used in this study were the results of field observations on 10 transects. MHI analysis using Sentinel 2 imagery recorded in 2015, 2018, 2021. The results of the analysis show that the most dominant mangrove species in the research location is Avicennia marina. Analysis of satellite imagery detects a significant increase in mangrove area from 2015 to 2021, which is more than 500 Ha. Based on the MHI analysis, there was a positive change from poor dominant mangrove forest conditions (MHI 66.68%). The increase in the area of mangrove forests is accompanied by improvements in ecosystem conditions with indicators of increasing MHI.

Spatio-Temporal Annual Changes of Mangrove Vegetation Coverages in Porong Estuary Based on Sentinel-2 Imagery

Mangrove is a typical coastal ecosystem with high productivity and has a number of ecosystem services. However this ecosystem is vulnerable particularly in urban areas due to land use change and illegal logging. Sidoarjo is one of the most developed urban area in the East Java Province, with mangrove ecpsystem scattered along the Porong estuary. This estuary is also the location of mudflow from the famous Lapindo mud disaster since 2006. This study aims to analyze the changes of mangrove coverages around the Porong estuary using satellite imagery data. Fractional Vegetation Coverage (Fv) was used to quantify the changes of mangrove vegetation coverage of Mangrove Forest from 2015 – 2021. The results show that there is a change in mangrove coverages in the area of study. The high change from lo vegetation coverage (LVC) to Full Vegetation Covarege (FCV) as found in the mouth of Porong River. This condition maybe caused by sedimentation process due to mudflow from volcanic disasters

APLIKASI SISTEM INFORMASI GEOGRAFI BERBASIS DATA RASTER UNTUK PENGKELASAN KEMAMPUAN LAHAN DI PROVINSI BALI DENGAN METODE NILAI PIKSEL PEMBEDA (Application of Geographic Information System (GIS) Based Raster Data to Classify Land Capability in Bali)

ASBTRAKPenggunaan teknologi seperti SIG sangat baik untuk mengelompokkan data keruangan lahan berdasarkan faktor potensi dan penghambat penggunaannya. Dengan mengimprovisasi metode tumpang susun diharapkan mampu mempercepat proses studi tentang pengkelasan kemampuan lahan. Tujuan penelitian ini adalah pengaplikasian SIG berbasis data raster untuk memetakan kelas kemampuan lahan di Provinsi Bali dengan menggunakan metode ”nilai piksel pembeda”. Hasil penelitian menunjukkan bahwa penggunaan SIG dapat memperlihatkan sebaran kelas kemampuan lahan yang heterogen dan kompleks sehingga memperjelas informasi lahan pada satuan unit lahan yang sempit. Selain itu penggunaan metode ini juga membantu mempercepat proses tumpang susun dan query data. Kelas kemampuan lahan di Provinsi Bali dapat dikelompokkan menjadi 8 kelas, dari kelas I sampai kelas VIII. Sebaran kelas kemampuan lahannya didominasi oleh lahan dengan kelas VI, VII dan VIII yaitu seluas 50,7% dari luas Provinsi Bali. Kabupaten Buleleng, Jembrana, dan Karangasem berturut-turut merupakan daerah-daerah terluas yang memiliki kemampuan lahan kelas VIII. Daerah-daerah tersebut harus lebih instensif dalam menjaga lahan-lahan berkelas VIII agar tidak beralih fungsi dari lahan hutan menjadi lahan non hutan.ABSTRACTThe use of technologies such as GIS  are very good for spatial data classifying based on potential and inhibiting use factors. With improvise an overlay method expected to accelerate study process about land capability classifying. The purpose of this research is the application of GIS based raster data to mapping land capability class in Bali Province by using "differentiator pixel value". The results showed that the use of GIS can show the heterogeneous and complex distribution of land capability classes and can clarify the land information on a narrow land unit. Furthermore, the uses of this method also help to accelerate the overlay and query data process. The distribution of land capability class is dominated by land with class VI, VII and VIII, which is covering 50.7% of the Bali Province. Districts that have a biggest land capability class VII is Buleleng, Jembrana, and Karangasem, respectively. Therefore, these districts should be more intensive to keeping the lands class VIII for not switching function from forest into non-forest land

REGENERASI ALAMI SEMAIAN MANGROVE DI KAWASAN TELUK BENOA, BALI

Mangrove ecosystems have an important role in coastal areas either directly or indirectly. The preservation of the mangrove ecosystem can be described from the seedlings' abundance. Mangrove natural regeneration status was carried out in the Benoa Bay, Bali. The study was aimed to analyze the current natural mangrove regeneration based on the seedling abundance and its correlation with ecological characters. The study area was divided into three zones consist of 30 sampling quadratic plots in total. Seedling and mature stands community structure and environmental parameters data were collected from each plot. Based on the result, the mangrove regeneration state was categorized as fairly good condition. It was implied by seedling abundance compared with tree and sapling density. The highest seedling density was found in zone 2 which was dominated by Rhizophora mucronata with an average of 4800 ± 5610 stands/ha. It was significantly different from the other two zones. Variations of the community structure in the three zones had no significant influence on seedlings distribution. Only two environmental factors i.e. pH and redox potential, had a positive correlation and significant correlation with the abundance of mangrove seedlings. The result indicated that the mangrove regeneration state in this area was maintained even though it had faced variable threats.Ekosistem mangrove memiliki peran penting dalam kawasan pesisir baik secara langsung maupun tidak langsung. Kelestarian ekosistem mangrove dapat digambarkan dari kelimpahan semaian. Penelitian tentang status regenerasi alami mangrove telah dilakukan di kawasan Teluk Benoa, Bali. Tujuan penelitian ini adalah untuk menganalisis tingkat regenerasi mangrove berdasarkan kelimpahan semai, serta hubungannya dengan karakter ekologi mangrove dalam kawasan. Area penelitian dibagi menjadi tiga zona dengan total 30 titik pengambilan sampel dengan distribusi yang proporsional. Pada setiap titik dilakukan pengambilan data struktur komunitas semai, tegakan dewasa (pohon, pancang) dan parameter lingkungan. Hasil penelitian menunjukkan status regenerasi mangrove di kawasan ini termasuk dalam kategori cukup baik, berdasarkan perbandingan dari kelimpahan semaian dengan tegakan kategori pancang dan pohon. Kerapatan semai tertinggi ditemukan pada zona 2 yang didominasi oleh Rhizophora mucronata dengan rata-rata sebesar 4800 ± 5610 tegakan/ha yang berbeda signifikan dengan dua zona lainnya. Variasi kondisi struktur komunitas mangrove pada tiga zona tidak memberikan pengaruh yang signifikan terhadap kelimpahan semai. Sementara itu, dua faktor lingkungan yaitu pH dan potensial redoks memiliki korelasi yang positif dan signifikan memengaruhi jumlah sebaran semai mangrove di dalam kawasan. Hasil penelitian mengindikasikan bahwa tingkat regenerasi mangrove masih mampu bertahan dalam tekanan habitat yang cukup tinggi

BIOMASS ESTIMATION MODEL AND CARBON DIOXIDE SEQUESTRATION FOR MANGROVE FOREST USING SENTINEL-2 IN BENOA BAY, BALI

Remote sensing technology can be used to find out the potential of mangrove forests information. One of the potentials is to be able to absorb three times more CO2 than other forests. CO2 absorbed during the photosynthesis process, produces organic compounds that are stored in the mangrove forest biomass. Utilization of remote sensing technology is able to detect mangrove forest biomass using the density level of the vegetation index. This study focuses on determining the best AGB model based on the vegetation index and the ability of mangrove forests to absorb CO2. This research was conducted in Benoa Bay, Bali Province, Indonesia. The satellite image used is Sentinel-2. Classification of mangroves and non-mangroves using a multivariate random forest algorithm. Furthermore, the mangrove forest biomass model using a semi-empirical approach, while the estimation of CO2 sequestration using allometric equations. Mean Absolute Error (MAE) is used to evaluate the validation of the model results. The classification results showed that the detected area of Benoa Bay mangrove forest reached 1134 ha (OA: 0.98, kappa: 0.95). The best AGB estimation result is the DVI-based AGB model (MAE: 23,525) with a value range of 0 to 468.38 Mg/ha. DVI-based AGB derivatives are BGB with a value range of 0 to 79.425 Mg/ha, TAB with a value range of 0 to 547.8 Mg/ha, TCS with a value range of 0 to 257.47 Mg/ha, and ACS with a value range of 0 to 944.912 Mg/ha