Quantifying risk dynamics on Rawa Pening floodplain using optical images gathered by satellite and unmanned aerial vehicle
Bibliographic record
Abstract
The floodplain of lake Rawa Pening, experience spatio-temporal dynamics due to regime shifts of wet and dry season as well as a more persisten land use changes in the upland area. The high yield of rice agriculture in the floodplain has also been bothered by additional entity rooted on the socio-economic value of the plain. Our research focused on floodplain in the vicinity of the Torong River, Banyubiru District that recently incurred river normalization project. Compare to the rest eight catchments delivering effluents into the lake, we assume normalization will change sediment budget, in way the dynamics can be captured well by detailing imagery acquired from Unmanned Aerial Vehicle (UAV) photography.Land use change is observed using high temporal resolution of optical satellite imagery and the verification using UAV images. Sentinel-2 optical imagery is used for the macrozonation. Because of the high temporal resolution, we eliminate images with cloud interference exceeding the specified threshold while assuring data continuity. At time when Sentinel-2 is planned to pass over, we also acquire UAV photos of different heights aimed to detail reality mapping of the area. To get land productivity, we use statistical information and semi-structured interviews of randomly selected samples for each land use class.Our initial results using longer period Google Earth images showed both extreme and gradual changes of land use, partly due to irregular temporal captures. Sentinel-2 is available in shorter historical period providing denser images every 5 days. At the same capture time, UAV capture images to opens potentials for further color manipulations matching the productivity. For the moment, our investigation on land productivity still relied on manual delineation of straight skeleton visible in both approaches. High productivity of ricefield in the floodplain area also still relied on semi-structured interviews and statistical reports by village adminstrations. With the constraints, risk of land use change observed using current satellite images and UAV accords on the manual delineation process. As a result, we found Sentinel-2 images is sufficient to predict risk changes particularly for fish culture and tourism, while spatial ricefield productivity using satellite and UAV images still require complex experimentation on color spectrum and operational acquisition height of the UAV.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".