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001-es BibID:	BIBFORM087952
035-os BibID:	(Scopus)85092450960 (WoS)000565086800013
Első szerző:	Phinzi, Kwanele
Cím:	Comparison of Rusle and Supervised Classification Algorithms for Identifying Erosion-Prone Areas in a Mountainous Rural Landscape / Kwanele Phinzi, Njoya Silas Ngetar, Osadolor Ebhuoma, Szilárd Szabó
Dátum:	2020
Megjegyzések:	The identification of erosion prone areas with reasonably high accuracy is a prerequisite for formulating relevant soil conservation measures especially in rural areas where there is much reliance on subsistence agriculture. The aim of this paper was to compare and exploit the complementary advantage of fusing three independent methods including the Revised Universal Soil Loss Equation (RUSLE) and two supervised image classification algorithms: Random Forest (RF) and Maximum Likelihood (ML). All analyses were conducted using a GIS proprietary software, ArcGIS. The results indicated that RF was the best with the highest overall accuracy (OA), producer's accuracy (PA), and user's accuracy (UA) of 87%, 78%, and 95%, respectively. RUSLE poorly performed relative to other methods, scoring the lowest PA (34%) and OA (66%), but slightly outperformed ML in terms of UA. From the user's perspective, the performance of individual methods was satisfactory with each method achieving an UA of greater than 90% although ML and RUSLE were not satisfactory from the producer's perspective, recording respective PAs of 56% and 34%. When the results from individual methods were fused, the accuracy increased above 90% across all accuracy indices, which is far above the 85% acceptable level for planning and management purposes.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk RUSLE supervised classification random forest maximum likelihood soil erosion
Megjelenés:	Carpathian Journal of Earth and Environmental Science. - 15 : 2 (2020), p. 405-413. -
További szerzők:	Ngetar Njoya Silas Ebhuoma Osadolor Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:	TKP - Thematic Excellence Programme of the Ministry for Innovation and Technology in Hungary (ED 18-1-2019-0028) Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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