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Enregistrement W4241303754 · doi:10.5400/jts.2013.v18i2.141-148

Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu, Indonesia

2013· article· en· W4241303754 sur OpenAlex

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Notice bibliographique

RevueJournal of Tropical Soils · 2013
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueSoil and Land Suitability Analysis
Établissements canadiensnon disponible
Organismes subventionnairesFakultas Teknik Universitas IndonesiaUniversitas Indonesia
Mots-clésWatershedErosionEnvironmental scienceHydrology (agriculture)Digital elevation modelSoil conservationSurface runoffNormalized Difference Vegetation IndexRemote sensingGeologyGeographyClimate changeAgriculture

Résumé

récupéré en direct d'OpenAlex

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. Aust J Agric Resour Ec 45: 275-289.Ande OT, Y Alaga and GA Oluwatosin. 2009. Soil erosion prediction using MMF model on highly dissected hilly terrain of Ekiti environs in southwestern Nigeria. Int J Phys Sci 4: 053-057.Arnold JG, BA Engel and R Srinivasan. 1998. A continuous time grid cell watershed model. Proc. of application of Advanced Technology for management of Natural Resources.Arsyad S. 2010. Konservasi Tanah dan Air. IPB Press. Bogor-Indonesia (in Indonesian).Asdak C.1995. Hydrology and Watershed Management. Gadjah Mada University Press, Yogyakarta.Barlin RD and ID Moore. 1994. Role of buffer strips in management of waterway pollution: a review. Environ Manage 18: 543-58.Brough PA.1986. Principle of Geographical Information Systems For Land Resources Assessment. Oxford University Press, 194p.Clark B and J Wallace. 2003. Global connections: Canadian and world issues. Toronto, Canada: Pearson Education Canada, Inc.Cochrane T A and DC Flanagan. 1999. Assessing water erosion in small watershed using WEPP with GIS and digital elevation models. J Soil Water Conserv 54: 678 685.Dames TWg. 1955. The Soils of East Central Java; with a Soil Map 1:250,000. Balai Besar Penjelidikan Pertanian, Bogor, Indonesia.Dixon JA, LF Scura, RA Carpenter and PB Sherman. 2004. Economic Analysis of Environmental Impacts 2nd ed. Eartscans Publication Ltd., London.Fistikoglu O and NB Harmancioglu. 2002. Integration of GIS with USLE in Assessment of Soil Erosion. Water Resour Manage 16: 447-467.Green K. 1992. Spatial imagery and GIS: integrated data for natural resource management. J Forest 90: 32-36.Hazarika MK and H Honda. 2001. Estimation of Soil Erosion Using Remote Sensing and GIS, Its Valuation & Economic Implications on Agricultural Productions. The 10th International Soil Conservation Organization Meeting at Purdue University and the USDA-ARS Soil Erosion Research Laboratory.Hazarika S, R Parkinson, R Bol, L Dixon, P Russell, S Donovan and D Allen. 2009. Effect of tillage system and straw management on organic matter dynamics. Agron Sustain Develop 29: 525-533. doi: 10.1051/agro/2009024. Honda KL, A Samarakoon, Y Ishibashi, Mabuchi and S Miyajima.1996. Remote Sensing and GIS technologies for denudation estimation in Siwalik watershed of Nepal,p. B21-B26. Proc. 17th Asian Conference on Remote Sensing, Colombo, Sri lanka.Kefi M and K Yoshino. 2010. Evaluation of The Economic Effects of Soil Erosion Risk on Agricultural Productivity Using Remote Sensing: Case of Watershed in Tunisia. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto Japan.Kefi M, K Yoshino, K Zayani and H Isoda. 2009. Estimation of soil loss by using combination of Erosion Model and GIS: case of study watersheds in Tunisia. J Arid Land Stud 19: 287-290.Lal R. 1998. Soil erosion impact on agronomic productivity and environment quality: Critical Review. Plant Sci 17: 319-464.Lal. 2001. Soil Degradation by Erosion. Land Degrad Develop12: 519-539.Lanya I. 1996. Evaluasi Kualitas lahan dan Produktivitas Lahan Kering Terdegradasi di Daerah Transmigrasi WPP VII Rengat Kabupaten Indragiri Hulu, Riau. [Disertasi Doktor]. Program Pasca Sarjana IPB, Bogor (in Indonesian).Mermut AR and H Eswaran. 2001. Some major developments in soil science since the mid 1960s. Geoderma 100: 403-426.Mongkolsawat C, P Thurangoon and Sriwongsa.1994. Soil erosion mapping with USLE and GIS. Proc. Asian Conf. Rem. Sens., C-1-1 to C-1-6.Morgan RPC, Morgan DDV and Finney HJ. 1984. A predictive model for the assessment of erosion risk. J Agric Eng Res 30: 245-253.Morgan RPC. 2005. Soil Erosion and Conservation. 3rd ed. Malden, MA: Blackwell Publishing Co.Panuju DR, F Heidina, BH Trisasongko, B Tjahjono, A Kasno, AHA Syafril. 2009. Variasi nilai indeks vegetasi MODIS pada siklus pertumbuhan padi. J.Ilmiah Geomat. 15, 9-16 (in Indonesian).Pimentel D, C Harvey, P Resosudarmo, K. Sinclair, D Kurz, M Mc Nair, S Christ, L Shpritz, L Fitton, R Saffouri and R Balir. 1995. Environmental and Economic Costs of Soil Erosion and Conservation Benefits. Science 267: 1117-1123.Saha SK and LM Pande. 1993. Integrated approach towards soil erosion inventory for environmental conservation using satellite and agrometeorological data. Asia Pac Rem Sens J 5: 21-28.Saha SK, Kudrat M and Bhan SK.1991. Erosional soil loss prediction using digital satellitee data and USLE. In: S Murai (ed). Applications of Remote Sensing in Asia and Oceania – Environmental Change Monitoring. Asian Association of Remote Sensing, pp. 369-372.Salehi MH, Eghbal MK and Khademi H. 2003. Comparison of soil variability in a detailed and a reconnaissance soil map in central Iran. Geoderma 111: 45-56.Soil Survey Staff. 1998. Keys to Soil Taxonomy. Eighth Edition. United States Department of Agriculture Natural Resources Conservation Service. Washington, D.C.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,213
Score d'incertitude au seuil0,318

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,218
Écart entre enseignants0,205 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle