In the current study, remotely sensed sea surface ocean temperature (SST) and sea surface chlorophyll (SSC), an indicator of tuna abundance, were used to determine the optimal feeding habitat zone of the southern Indian Ocean (SIO) albacore using a habitat suitability model applied to the 2000–2016 Taiwanese longline fishery data. The analysis showed a stronger correlation between the 2-month lag SSC and standardized catch per unit effort (CPUE) than 0-, 1-, 3-, and 4-month lag SSC. SST also exhibited a stronger correlation with standardized CPUE. Therefore, SST and SSC_2 were selected as final variables for model construction. An arithmetic mean model with SST and SSC_2 was deemed suitable to predict the albacore feeding habitat zone in the SIO. The preferred ranges of SSC_2 and SST for the feeding habitat of immature albacore were 0.07–0.09 mg m⁻³ and 16.5–18.5 °C, respectively, and mainly centralized at 17.5 °C SST and 0.08 mg m⁻³ SSC_2. The selected habitat suitability index model displayed a high correlation (R² = 0.8276) with standardized CPUE. Overall, temperature and ocean chlorophyll were found to be essential for albacore habitat formation in the SIO, consistent with previous studies. The results of this study can contribute to ecosystem-based fisheries management in the SIO by providing insights into the habitat preference of immature albacore tuna in the SIO.

Skipjack tuna (Katsuwonus pelamis) is the most dominating tuna species caught in the Indian Ocean. However, tuna fishermen in the Indian Ocean still face difficulties determining potential skipjack tuna fishing grounds due to limited geographic information. An attempt to improve information regarding potential skipjack tuna fishing grounds is through modeling the SST, SSC, SSH, and SSS oceanographic parameters with skipjack tuna distribution using GIS. The characteristics of the skipjack tuna habitat can be found through the generalized additive model (GAM) statistical analysis using data of skipjack tuna catch and oceanographic parameters from satellite imagery. The integrated GIS and GAM methods can improve the study of the habitat of skipjack tuna species. Therefore, this research is aimed at analyzing the condition and SST, SSC, SSH, and SSS variations in 2016-2021 in the South Java Sea; at predicting the correlation of SST, SSC, SSH, and SSS towards skipjack tuna catch in 2016-2021 in the South Java Sea using GAM; and at creating a map of skipjack tuna seasonal migration zones. The data included skipjack tuna catches, number of trips, and skipjack tuna fishing coordinates in 2016-2021 from PPS Cilacap and PPP Pondokdadap. The oceanographic parameter data in 2016-2021 was derived from Aqua MODIS level 3 (SST and SSC) and CMEMS (SSH and SSS) satellite imagery. The results showed that the average values of oceanographic parameters at skipjack tuna fishing ground coordinates in the South Java Sea (2016-2021) were SST (26.050-30.816C), SSC (0.094-0.564mg/m³), SSH (0.268-0.639m), and SSS (33.075-34.514psu). The best GAM modeling was the combination of four oceanographic parameter variables with an AIC value of 45357.92. Skipjack tuna in the South Java Sea migrates southeast during the west monsoon season to the first transitional season and tends to migrate northwest during the east monsoon season to the second transitional season.

The southern waters of Java are suitable to be the largest supplier of Yellowfin tuna exports in Indonesia, but have not efficiently produced the expected yield. This research minimizes these constraints by modeling the yellowfin tuna fishing grounds in the southern waters of Java based on oceanographic factors such as Sea Surface Temperature (SST), chlorophyll-a (CHL_A), Sea Surface Salinity (SSS), Sea Surface Height (SSH) using an integration between remote sensing, Geographic Information Systems (GIS), and the Generalized Additive Model (GAM) statistical method. This study used oceanographic factor data from Aqua MODIS Level-3 and Copernicus, while yellowfin tuna fishery production was obtained from Palabuhanratu Nusantara Fishing Port (NFP), Cilacap Ocean Fishing Port (OFP), and Pondokdadap Coastal Fishing Port (CFP). The modeling process used 80% of the data, while the remaining 20% was used to validate the model results. The order of influence of oceanographic parameters from largest to smallest is SST > SSS > SSH > CHL-A. The best model from the GAM analysis showed that the combination of four oceanographic parameters had the greatest influence on yellowfin tuna CPUE. The catch per unit effort (CPUE) of yellowfin tuna was predicted to be high in May-October and low in November-April. The prediction model had high accuracy because most of the fishing activity was in the HSI 0.4-0.5 range and the RMSEP value was 0.63. Yellowfin tuna were suitable in habitats distributed from inshore to offshore in June and July, but less suitable in December.

Mangrove merupakan tumbuhan intertidal yang memilki peran ekologi, fisik, dan sosial ekonomi untuk daerah pesisir. Mangrove di Indonesia menunjukkan penurunan kondisi seiring berjalannya waktu, termasuk di wilayah Probolinggo, Jawa Timur. Faktor utama yang memicu berkurangnya mangrove yaitu aktivitas manusia yang mengubah area mangrove menjadi tambak, permukiman, dan sebagainya. Oleh karena itu, penelitian ini bertujuan untuk memantau tutupan mangrove di Probolinggo melalui integrasi penginderaan jauh dan sistem informasi geografis sebagai bentuk kontribusi dalam kegiatan pemantauan yang diharapkan dapat menjadi acuan dalam penentuan arah pengelolaan mangrove. Luas mangrove dianalisis dari citra Sentinel 2A menerapkan indeks vegetasi Normalized Difference Vegetation Index (NDVI) yang diolah menggunakan Google Earth Engine (GEE). Hasil penelitian menunjukkan terjadi perubahan luas mangrove di Probolinggo setiap tahunnya. Total luas mangrove tertinggi terjadi pada tahun 2020 bernilai 687,15 Ha dan nilai luas mangrove terendah terjadi pada tahun 2022 sebesar 241,13 Ha. Nilai NDVI tertinggi selama tahun 2019-2023 memiliki nilai paling besar 0,87 (rapat) pada tahun 2020-2021 dan nilai paling kecil 0,55 (jarang) pada tahun 2022. Hasil uji akurasi memiliki nilai Overall Accuracy sebesar 86% sehingga hasil klasifikasi sebaran mangrove dari citra Sentinel 2A menggunakan indeks vegetasi NDVI di Probolinggo dapat diterima. Akan tetapi, penambahan sampel uji akurasi masih diperlukan supaya dapat menghasilkan tingkat akurasi yang lebih baik. Mangroves are intertidal plants that have ecological, physical, and socioeconomic roles for coastal areas. Mangroves in Indonesia show a decline in condition over time, including in the Probolinggo region, East Java. The main factor that triggers mangrove decline is human activities that convert mangrove areas into ponds, settlements, etc. Therefore, this study aimed to monitor mangrove cover in Probolinggo through the integration of remote sensing and geographic information systems as a way of monitoring activities that are expected to be a reference in determining the direction of mangrove management. The mangrove area was analyzed from Sentinel 2A imagery applying the Normalized Difference Vegetation Index (NDVI) processed using Google Earth Engine (GEE). The results showed that there were changes in mangrove area in Probolinggo every year. The highest total mangrove area occurred in 2020 amounting to 687.15 Ha and the lowest total mangrove area occurred in 2022 amounting to 241.13 Ha. The highest NDVI value during 2019-2023 had the greatest value of 0.87 (dense) in 2020-2021 and the smallest value of 0.55 (sparse) in 2022. The accuracy test results had an Overall Accuracy value of 86% so that the classification results of mangrove distribution from Sentinel 2A images using the NDVI vegetation index in Probolinggo were acceptable. However, additional accuracy test samples are still needed to produce a better accuracy level.

This study presented a non-destructive method for estimating mangrove biomass and carbon stock by integrating species-specific allometric equations with hemispherical canopy photography. Field surveys were conducted across 15 plots at two coastal sites in East Java - Banyuurip Mangrove Center (BMC) and Lewean Mangrove Park (LMP). Measurements of Circumference at Breast Height (CBH), tree height, and canopy images were collected, covering twelve identified species, with Avicennia marina, Rhizophora stylosa, and R. mucronata being dominant. Canopy cover was extracted from hemispherical images using ImageJ, an open-source image analysis software. Aboveground biomass ranged from 34.04 to 154.99 Mg/ha and carbon stock from 24.64 to 69.89 Mg C/ha, with canopy cover consistently above 81%. This integrated, low-cost, and scalable approach offered a replicable solution for carbon assessment in data-limited mangrove regions.

The increasing rate of population growth and urbanization will lead to an increase in escalating greenhouse gas (GHG) emissions from the transportation sector, which brings various negative impacts of climate change to people's lives. In this study, an inventory and projection of GHG emissions in the transportation sector in Malang City for 10 years were carried out using the Intergovernmental Panel on Climate Change (IPCC) tier 1 method. The potential of carbon sequestration from trees along urban roads or in green corridors was measured as a mitigation measure to reduce GHG emissions in the transportation sector. It was estimated with i-Tree canopy, along with determining the baseline GHG emission target to be reduced. The results showed that GHG emissions in the transportation sector in Malang City amounted to 511,011.24 tons CO2-eq and are projected to reach 6.043.940 tons CO2-eq in 2034. The highest potential is observed in the green corridor in Klojen District, totaling 12,816 trees and dominated by Swietenia mahagoni. Street trees in Malang City’s green corridors in the 2024 - 2034 interval have the potential to reduce GHG emissions by 10.195,90 tons CO2-eq in Malang City. The green corridor is projected to contribute 1,69 % to Malang's regional target in GHG mitigation.