Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu, Indonesia
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Bibliographic record
Abstract
The study aims to assess the rate of erosion that occurred in Manjunto Watershed and financial loss using Geographic Information System and Remote Sensing. Model used to determine the erosion is E30 models. The basis for the development of this model is to integrate with the slope of the slope between NDVI. The value of NDVI obtained from satellite imagery. Slope factor obtained through the DEM processing. To determine the amount of economic losses caused by erosion used the shadow prices. The amount of nutrients lost converted to fertilizer price. The results showed that the eroded catchment area has increased significantly. The rate of average annual erosion in the watershed Manjunto in 2000 amounted to 3 Mg ha-1 yr-1. The average erosion rate in the watershed Manjunto annual increase to 27 Mg ha-1 yr-1 in the year 2009. Economic losses due to erosion in 2009 was Rp200,000,- for one hectare. Total losses due to erosion for the total watershed area is Rp15,918,213,133, -. The main factor causing the high rate of erosion is high rainfall, slope and how to grow crops that do not pay attention to the rules of conservation.Keywords: Soil erosion, digital elevation model, GIS, remote sensing, valuation erosion[How to Cite: Gunawan G, D Sutjiningsih, H Soeryantono and S Widjanarko. 2013.Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu-Indonesia. J Trop Soils 18 (2): 141-148. Doi: 10.5400/jts.2013.18.2.141][Permalink/DOI: www.dx.doi.org/10.5400/jts.2013.18.2.141]REFERENCESAksoy E, G Ozsoy and MS Dirim. 2009. Soil mapping approach in GIS using Landsat satellite imagery and DEM data. Afr J Agric Res 4: 1295-1302.Ananda J and G Herath. 2003. Soil erosion in developing countries: a socio-economic appraisal. J Environ Manage 68: 343-353.Ananda J, G Herath and A Chisholm. 2001. Determination of yield and Erosion Damage Functions Using Subjectivly Elicited Data: application to Smallholder Tea in Sri Lanka. 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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it