Abstract
This study was conducted to predict annual soil loss at the district level in Nigeria for proper conservation measures. The method applied was the revised universal soil loss equation (RUSLE) and all factors used in RUSLE were calculated using Earth Engine’s public data archive. The pattern of soil loss was obtained using the spatial autocorrelation (Morans I) statistic. Ordinary least squares (OLS) linear regression model was used to estimate soil loss in terms of the relationships to its factors R, K, LS, C, and P. The grouping analysis tool was used to group districts based on soil loss. The results indicate that the estimated spatial average soil erosion was 7141 t ha−1 y−1 in Nigeria. The pattern of soil loss at the district level was found highly clustered with a z score of 10.045. The results obtained from linear regression were statistically significant p value (p < 0.01) and adjusted R-Squared (0.87). Twenty-six districts were identified in the very high category of soil loss based on standardized residuals above 1.5. The grouping analysis shows that the districts within groups 2 and 3 are in the outlier positions of soil loss due to the high LS factor. This work highlights valuable information for decision-makers and planners to take suitable land administration measures to minimize the soil loss in the districts of high soil loss. It, therefore, indicates Google Earth Engine is a significant platform to analyze the RUSLE model for evaluating and mapping soil erosion quantitatively and spatially.
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References
Abu Hammad A, Lundekvam H, Børresen T (2004) Adaptation of RUSLE in the eastern part of the Mediterranean region. Environ Manage 34(6):829–841. https://doi.org/10.1007/s00267-003-0296-7
Ajayi J, Ade F, Toyin O, Kirk-Greene (2022) Anthony hamilton millard and udo, reuben kenrick. Nigeria. Encyclopedia Britannica. https://www.britannica.com/place/Nigeria
Almagro A, Thomé TC, Colman CB, Pereira RB, Marcato Junior J, Rodrigues DBB, Oliveira PTS (2019) Improving cover and management factor (C-factor) estimation using remote sensing approaches for tropical regions. Int Soil Water Conserv Res 7(4):325–334. https://doi.org/10.1016/j.iswcr.2019.08.005
Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C et al (2017) An assessment of the global impact of 21st century land use change on soil erosion. Nat Commun 8(1):1–13
Brocca L, Filippucci P, Hahn S, Ciabatta L, Massari C, Camici S, Wagner W (2019) SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations. Earth Syst Sci Data 11(4):1583–1601. https://doi.org/10.5194/essd-11-1583-2019
Chabot D, Stapleton S, Francis CM (2022) Using Web images to train a deep neural network to detect sparsely distributed wildlife in large volumes of remotely sensed imagery: a case study of polar bears on sea ice. Eco Inform 68:101547. https://doi.org/10.1016/J.ECOINF.2021.101547
Chen H, Oguchi T, Wu P (2017) Assessment for soil loss by using a scheme of alterative sub-models based on the RUSLE in a Karst Basin of Southwest China. J Integrat Agric 16(2):377–388. https://doi.org/10.1016/S2095-3119(16)61507-1
de Brogniez D, Ballabio C, Stevens A, Jones RJA, Montanarella L, van Wesemael B (2015) A map of the topsoil organic carbon content of Europe generated by a generalized additive model. Eur J Soil Sci 66(1):121–134
Demirci A, Karaburun A (2012) Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed, northwest Turkey. Environ Earth Sci 66(3):903–913. https://doi.org/10.1007/s12665-011-1300-9
Durigon VL, Carvalho DF, Antunes MAH, Oliveira PTS, Fernandes MM (2014) NDVI time series for monitoring RUSLE cover management factor in a tropical watershed. Int J Remote Sens 35(2):441–453. https://doi.org/10.1080/01431161.2013.871081
Ehui SK, Kang BT, Spencer DSC (1990) Economic analysis of soil erosion effects in alley cropping, no-till and bush fallow systems in South Western Nigeria. Agric Syst 34(4):349–368. https://doi.org/10.1016/0308-521X(90)90013-G
Evans R, Boardman J (2016) The new assessment of soil loss by water erosion in Europe. Panagos P. et al., 2015 Environmental Science & Policy 54, 438–447-A response. Environ Sci Policy 58:11–15. https://doi.org/10.1016/j.envsci.2015.12.013
Fagbohun BJ, Anifowose AYB, Odeyemi C, Aladejana OO, Aladeboyeje AI (2016) GIS-based estimation of soil erosion rates and identification of critical areas in Anambra sub-basin, Nigeria. Model Earth Syst Environ. https://doi.org/10.1007/s40808-016-0218-3
Fiener P, Neuhaus P, Botschek J (2013) Long-term trends in rainfall erosivity-analysis of high-resolution precipitation time series (1937–2007) from Western Germany. Agric Meteorol 171–172:115–123. https://doi.org/10.1016/j.agrformet.2012.11.011
Ganasri BP, Ramesh H (2016) Assessment of soil erosion by RUSLE model using remote sensing and GIS-A case study of Nethravathi Basin. Geosci Front 7(6):953–961
GEEa (2022) Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2018 and IMERG 2014-2018. https://doi.org/10.5281/zenodo.1435912
GEEb (2022) MOD13A2.006 terra vegetation indices 16-Day Global 1km. NASA. https://doi.org/10.5067/MODIS/MOD13A2.006
Ghosal K, Das Bhattacharya S (2020) A review of RUSLE model. J Indian Soc Remote Sens 48(4):689–707. https://doi.org/10.1007/s12524-019-01097-0
Hateffard F, Mohammed S, Alsafadi K, Enaruvbe GO, Heidari A, Abdo HG, Rodrigo-Comino J (2021) CMIP5 climate projections and RUSLE-based soil erosion assessment in the central part of Iran. Sci Rep. https://doi.org/10.1038/S41598-021-86618-Z
He Y, Wang L, Niu Z, Nath B (2022) Vegetation recovery and recent degradation in different karst landforms of southwest China over the past two decades using GEE satellite archives. Eco Inform 68:101555. https://doi.org/10.1016/J.ECOINF.2022.101555
Hengl T, Wheeler I (2018) Soil organic carbon content in x 5 g/kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (Version v02). Zenodo. https://doi.org/10.5281/zenodo.1475457
James DE, Sherman PB (2013) Dryland management: economic case studies. Dryland Manag Econ Case Stud. https://doi.org/10.4324/9781315066325
Kinnell PIA (2014) Applying the RUSLE and the USLE-M on hillslopes where runoff production during an erosion event is spatially variable. J Hydrol 519(PD), 3328–3337. https://doi.org/10.1016/j.jhydrol.2014.10.016
Kouli M, Soupios P, Vallianatos F (2009) Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete Greece. Environ Geol 57(3):483–497. https://doi.org/10.1007/s00254-008-1318-9
Lal R (1976) Soil erosion on alfisols in western Nigeria III. effects of rainfall characteristics. Geoderma 16(5):389–401. https://doi.org/10.1016/0016-7061(76)90003-3
Lal R (1981) Soil erosion problems on alfisols in Western Nigeria, VI. Effects of erosion on experimental plots. Geoderma 25(3–4): 215–230. https://doi.org/10.1016/0016-7061(81)90037-9
Lal R (1985) Soil erosion and sediment transport research in tropical Africa. Hydrol Sci J 30(2):239–256. https://doi.org/10.1080/02626668509490987
Lehner B, Verdin K, Jarvis A (2008) New global hydrography derived from spaceborne elevation data. Eos Trans AGU 89(10):93–94
Liu BY, Nearing MA, Risse LM (1994) Slope gradient effects on soil loss for steep slopes. Trans ASAE 37(6):1835–1840
Liu K, Tang G, Jiang L, Zhu AX, Yang J, Song X (2015) Regional-scale calculation of the LS factor using parallel processing. Comput Geosci 78:110–122. https://doi.org/10.1016/j.cageo.2015.02.001
Lu D, Li G, Valladares GS, Batistella M (2004) Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land Degrad Dev 15(5):499–512. https://doi.org/10.1002/ldr.634
McCool DK, Foster GR, Mutchler CK, Meyer LD (1989) Revised slope length factor for the Universal Soil Loss Equation. Trans ASAE 32(5):1571–1576
Montanarella L, Badraoui M, Chude V, Costa I, Baptista, saurinda D. S., Mamo, T., Yemefack, M., Aulang, M. S., Yagi, K., Hong, S. Y., Vijarnsorn, P., Zhang, G. L., Arrouays, D., Black, H., Krasilnikov, P., Sobocá, J., Alegre, J., Henriquez, C. R., Mendonça-Santos, M. de L., … McKenzie, N. (2015). Status of the World’s Soil Resources. In Intergovernmental Technical Panel on Soils. http://www.fao.org/3/a-i5199e.pdf
Moore ID, Wilson JP (1992) Length-slope factors for the Revised Universal Soil Loss Equation: Simplified method of estimation. J Soil Water Conserv 47(5):423–428
Okoye CU (1998) Comparative analysis of factors in the adoption of traditional and recommended soil erosion control practices in N. Soil Tillage Res 45(3–4):251–263. https://doi.org/10.1016/S0933-3630(96)00137-7
Panagos P, Meusburger K, Ballabio C, Borrelli P, Alewell C (2014) Soil erodibility in Europe: a high-resolution dataset based on LUCAS. Sci Total Environ 479–480(1):189–200. https://doi.org/10.1016/j.scitotenv.2014.02.010
Parsons AJ (2019) How reliable are our methods for estimating soil erosion by water? Sci Total Environ 676:215–221. https://doi.org/10.1016/j.scitotenv.2019.04.307
Phinzi K, Ngetar NS (2019) The assessment of water-borne erosion at catchment level using GIS-based RUSLE and remote sensing: a review. In: International Soil and Water Conservation Research (Vol. 7, Issue 1, pp. 27–46). International Research and Training Center on Erosion and Sedimentation and China Water and Power Press. https://doi.org/10.1016/j.iswcr.2018.12.002
Prasannakumar V, Vijith H, Abinod S, Geetha N (2012) Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geosci Front 3(2):209–215. https://doi.org/10.1016/j.gsf.2011.11.003
Sileshi M, Kadigi R, Mutabazi K, Sieber S (2019) Determinants for adoption of physical soil and water conservation measures by smallholder farmers in Ethiopia. Int Soil Water Conserv Res 7(4):354–361. https://doi.org/10.1016/j.iswcr.2019.08.002
Tamene L, Le QB (2015) Estimating soil erosion in sub-Saharan Africa based on landscape similarity mapping and using the revised universal soil loss equation (RUSLE). Nutr Cycl Agroecosyst 102: 17–31. https://doi.org/10.1007/s10705-015-9674-9
Tanyaş H, Kolat Ç, Süzen ML (2015) A new approach to estimate cover-management factor of RUSLE and validation of RUSLE model in the watershed of Kartalkaya Dam. J Hydrol 528:584–598. https://doi.org/10.1016/j.jhydrol.2015.06.048
Tian P, Zhu Z, Yue Q, He Y, Zhang Z, Hao F, Guo W, Chen L, Liu M (2021) Soil erosion assessment by RUSLE with improved P factor and its validation: case study on mountainous and hilly areas of Hubei Province, China. Int Soil Water Conserv Res 9(3):433–444. https://doi.org/10.1016/j.iswcr.2021.04.007
Van Remortel RD, Hamilton ME, Hickey RJ (2001) Estimating the LS factor for RUSLE through iterative slope length processing of digital elevation data within arclnfo grid. Cartography 30(1):27–35. https://doi.org/10.1080/00690805.2001.9714133
Vatandaşlar C, Yavuz M (2017) Modeling cover management factor of RUSLE using very high-resolution satellite imagery in a semiarid watershed. Environ Earth Sci. https://doi.org/10.1007/s12665-017-6388-0
Wischmeier WH, Smith DD (1965) Predicting rainfall-erosion losses from cropland east of the Rocky Mountains: Guide for selection of practices for soil and water conservation (No. 282). Agricultural Research Service, US Department of Agriculture
Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration.
Wuepper D, Borrelli P, Finger R (2020) Countries and the global rate of soil erosion. Nat Sustain 3(1):51–55. https://doi.org/10.1038/s41893-019-0438-4
Wu TY, Yeh KT, Hsu HC, Yang CK, Tsai MJ, Kuo YF (2022) Identifying Fagaceae and Lauraceae species using leaf images and convolutional neural networks. Eco Inform 68:101513. https://doi.org/10.1016/J.ECOINF.2021.101513
Xu L, Xu X, Meng X (2013) Risk assessment of soil erosion in different rainfall scenarios by RUSLE model coupled with Information Diffusion Model: a case study of Bohai Rim, China. CATENA 100:74–82. https://doi.org/10.1016/j.catena.2012.08.012
Yue T, Xie Y, Yin S, Yu B, Miao C, Wang W (2020) Effect of time resolution of rainfall measurements on the erosivity factor in the USLE in China. Int Soil Water Conserv Res 8(4):373–382. https://doi.org/10.1016/j.iswcr.2020.06.001
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Islam, Z. Soil loss assessment by RUSLE in the cloud-based platform (GEE) in Nigeria. Model. Earth Syst. Environ. 8, 4579–4591 (2022). https://doi.org/10.1007/s40808-022-01467-7
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DOI: https://doi.org/10.1007/s40808-022-01467-7