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All Journal The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Ratih Dewanti Dimyati
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Earth & Planetary Sciences

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STUDY OF SHORT MACKEREL CATH, SEA SURFACE TEMPERATURE, AND CHLOROPHYLL -A IN THE MAKASSAR STRAIT Bambang Semedi; Ratih Dewanti Dimyati
International Journal of Remote Sensing and Earth Sciences Vol. 6 (2009)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2009.v6.a1241

Abstract

The Makassar Strait is the major fishing ground for Short Mackerel (Rastrelliger spp) fisheries in South Sulawesi, Indonesia using both commercial fishing vessels and boats with traditional fishing gear. Though Short Mackerel is one of dominant commercial food fishes in South Sulawesi, the annual Cath per Unit Effort (CPUE) has been decreasing from year to year. In 2000, the total of annual CPUE was 22,117 tons and in 2007, it was 17,596 tons. The purpose of this research was to forecast the fishing ground of Short Mackerel employing Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images in Makassar Strait territory with the study interest of 3 S and to 5 S and 118 E to 120 E. This research was conductade from September 15 to October 20, 2007. Fishing data were collected from the fishermen including fishing locations, catch, sea surface temperature, and chlorophyll concentrations. To determine the relationship between cacth and oceanographic parameters, linear regression was employed. We also examined sea surface temperature (SST) and Chlorophyll-a concentration field data vs. MODIS satellite data. The result showed that SST andChlorophyll distributions have close relationship with the distribution of fishing location of Short Mackerel. The fishing location tends to spread on the waters with the SST ranged from 26 degree of celcius to 29 degree of celcius and Chlorophyll concentration from 1.19 mg per m to 1.25 mg per m.
RANDOM FOREST CLASSIFICATION FOR MANGROVE CANOPY COVER SPATIAL ANALYSIS IN BENOA BAY – BALI, INDONESIA Nanin; Noverita Dian Takarina; Ratih Dewanti Dimyati; Dwi Nowo Martono; Evi Frimawaty; Rahmadi; A. A. Md. Ananda Putra Suardana
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.13466

Abstract

Mangroves play a crucial role in maintaining the stability of coastal ecosystems by providing habitats for diverse species, protecting shorelines from erosion, and acting as a carbon sink. The importance of conserving and developing mangrove areas can be effectively monitored using remote sensing data and classification methods, such as Random Forest (RF), ensuring an accurate assessment and management of these vital ecosystems. This research aims to develop and evaluate an RF classification model to produce accurate spatial information on mangrove canopy cover. The research area, Benoa Bay in Bali, Indonesia, is known for its dynamic and ecologically complex mangrove habitats. The inputs for RF classification are bands on Sentinel-2A satellite imagery, Mangrove Vegetation Index (MVI), Normalized Difference Vegetation Index (NDVI), Enhanced Mangrove Index (EMI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and the Normalized Difference Salinity Index (NDSalI), along with topographic variables such as elevation and slope. Model validation was conducted using high-resolution imagery from Google Earth Pro and cross-referenced with the 2024 National Mangrove Map. The classification of coastal land cover is divided into water bodies, mangroves, open land, built-up land, and non-mangrove vegetation, with an overall accuracy of 0.98 and a kappa statistic of 0.98. In contrast, the accuracy of the classification of mangrove canopy cover concerning the national mangrove map produces an overall accuracy of 0.97 and a kappa value of 0.86. These findings demonstrate the robustness of the RF model and its potential for supporting data-driven coastal management practices.
Co-Authors A. A. Md. Ananda Putra Suardana Bambang Semedi Evi Frimawaty Martono, Dwi Nowo Nanin Noverita Dian Takarina Dian Takarina Rahmadi