Pemetaan Ekosistem Mangrove Menggunakan Citra Multisumber (Sentinel-2A dan Sentinel-1) Berbasis Cloud Computing di Teluk Balikpapan
Bibliographic record
Abstract
Not separated from the role and function as a balancer of coastal ecosystems, the existence of mangroves is very important so it requires preservation and prevention of damage such as degradation and deforestation. The sustainability of mangrove ecosystems in Balikpapan Bay needs to be considered, this is due to the existence of human activities that have the potential to adversely affect mangroves, such as nickel mining, industrial development, shipping lanes, crude oil spills in 2018, and IKN infrastructure development whose development locations entered the mangrove ecosystem. The objectives of this study are, (1) to map the distribution of mangrove ecosystem land changes in mangrove areas in Balikpapan Bay using multisource images in 2019 and 2022, (2) to analyze the mangrove density level in 2019 and 2022. This research was conducted land cover classification process with Random Forest algorithm model of mangrove ecosystem using Sentinel-2A image, Sentinel-1 SAR image, and SRTM DEM on Google Earth Engine (GEE) platform. The use of multisource images aims to obtain a more precise and accurate area. Land cover objects are classified into 5 classes, mangrove vegetation, non-mangrove vegetation, built-up land, open land, and water bodies. The results showed that the dominance of land cover area in 2019 and 2022 was in the non-mangrove vegetation class of 102,014.078 Ha and 108,166.27 Ha. The results of mangrove vegetation land cover class are obtained for processing vegetation density level using NDVI index with sparse, moderate, and dense density. It is identified that the mangrove ecosystem in Balikpapan Bay is dominated by dense vegetation density with an area in 2019 of 14.985,81 Ha and in 2022 of 10.661,27 Ha. The result of accuracy test conducted in 2019 amounted to 88.89% and in 2022 amounted to 91.67%. There are changes in mangrove land within the 3-year period, both adding and subtracting land. This is due to restoration activities with reforestation which resulted in an increase in mangrove land area and varied vegetation density, while the reduction of land is due to the transfer of land into non-mangrove land for various reasons. Evaluation and handling of various possibilities that have the potential for damage to the mangrove ecosystem is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".