Skip to main content

Advertisement

SpringerLink
Log in

Diachronic Study of Land Cover of the Medjerda Watershed and Estimation of RUSLE-C Factor Using NDVI-Based Equation, Remote Sensing, and GIS

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Remote sensing and geographic information systems are very effective when studying land cover evolution and detecting changes. This research has been carried out in the Medjerda watershed. It is aimed to study the dynamism of land cover from multiple satellite images from 1994 to 2017 as well as the evolution of the C factor and its relationship to water erosion. The classification has been supervised by the use of the maximum likelihood method, the multi-date comparison along with masks technique. Results have shown that the classification accuracy varies from 76 to 91% and the Medjerda landscape largely contains soils without vegetation. During the period of the study, both vegetation classes and bare soil go through considerable changes following an undulating tendency. During the period from 2004 to 2010, an annual vegetation regression rate of around 55% was observed. However, an increase of 66% was observed during the period 2010–2017. The estimation of the factor C has shown that the use of the exponential regression model is effective and the vegetation in the study area is insufficient to protect the basin against possible erosion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Abbreviations

CE:

The coefficient of efficiency

ETM:

The enhanced thematic mapper

NDVI:

Normalized difference vegetation index

OA:

The overall accuracy

OLI:

The operational land imager

RMSE:

Root-mean-square error

RUSLE:

Revised universal soil loss equation

TIRS:

Thermal infrared sensor

TM:

Thematic mapper

VHSR:

Very high spatial resolution

WSC:

Water and soil conservation

References

  • Alam, A., Bhat, M. S., & Maheen, M. (2020). Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal, 85, 1529–1543. https://doi.org/10.1007/s10708-019-10037-x

    Article  Google Scholar 

  • Almagro, A., Thais, C. T., Carina, B. C., Rodrigo, B. P., José, M. J., Dulce, B. B. R., & Paulo, T. S. O. (2019). Improving cover and management factor (C-factor) estimation using remote sensing approaches for tropical regions. International Soil and Water Conservation Research, 7(4), 325–334. https://doi.org/10.1016/j.iswcr.2019.08.005 ISSN 2095-6339.

    Article  Google Scholar 

  • Amani, M., Kakooei, M., Moghimi, A., Ghorbanian, A., Ranjgar, B., Mahdavi, S., & Mohammadzadeh, A. (2020). Application of Google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. RemoteSensing, 12(21), 3561. https://doi.org/10.3390/rs12213561

    Article  Google Scholar 

  • Amri, R. (2013). Estimation régionale de l'évapotranspiration sur la plaine de Kairouan (Tunisie) à partir de données satellites multi-capteurs. Thèse de doctorat de l’Université de Toulouse, 176 p.

  • Ayalew, D. A., Deumlich, D., Šarapatka, B., & Doktor, D. (2020). Quantifying the sensitivity of NDVI-based C factor estimation and potential soil erosion prediction using spaceborne earth observation data. RemoteSensing, 12(7), 1136. https://doi.org/10.3390/rs12071136

    Article  Google Scholar 

  • Benhadj, I. (2008). Observation spatiale de l’irrigation d’agrosystèmes semi-arides et Gestion durable de la ressource en eau en plaine de Marrakech. Thèse de l’Université de Toulouse, 296 p.

  • Benkadja, R., Boussag, F., & Benkadja, A. (2015). Identification et évaluation du risque d’érosion sur le bassin versant du K’sob (Est Algérien). Bulletin of Engineering Geology and the Environment, 74, 91–102. https://doi.org/10.1007/s10064-014-0611-y

    Article  Google Scholar 

  • Boulaassal, H., Anaki, S., Yazidi, O. A., Maatouk, M., & Wahbi, M. (2020). Cartographie des changements de l’occupation du sol entre 2002 et 2016 à partir des images Landsat. Cas de la région Tanger Tetouan Al-Hoceima (Maroc). African Journal on Land Policy and Geospatial Sciences, 3(2), 14–31.

    Google Scholar 

  • Brahim, B., Meshram, S. G., Abdallah, D., Larbi, B., Drisss, S., Khalid, M., & Khedher, K. M. (2020). Mapping of soil sensitivity to water erosion by RUSLE model: Case of the Inaouene watershed (Northeast Morocco). Arabian Journal of Geosciences, 13(21), 1–15.

    Article  Google Scholar 

  • Cabrera-Bosquet, L., Molero, G., Stellacci, A. M., et al. (2015). NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications, 39, 147–159. https://doi.org/10.1556/CRC.39.2011.1.15

    Article  Google Scholar 

  • Chafai, A., Brahim, N., & Shimi, N. S. (2020). Mapping of water erosion by GIS/RUSLE approach: Watershed Ayda. Arabian Journal of Geosciences, 13(810), 1–14. https://doi.org/10.1007/s12517-020-05774-0

    Article  Google Scholar 

  • Charlotte, P. (2017). Cartographie de l’occupation des sols à partir de séries temporelles d’images satellitaires à hautes résolutions Identification et traitement des données mal étiquetées. Thèse de l’Université de Toulouse, 4 p

  • Chowdhury, M., Hasan, M. E., & Abdullah-Al-Mamun, M. M. (2020). Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 63–75. https://doi.org/10.1016/j.ejrs.2018.11.003

    Article  Google Scholar 

  • Chuenchum, P., Xu, M., & Tang, W. (2020). Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang–Mekong River by using the modified RUSLE model. International Soil and Water Conservation Research, 8(3), 213–227. https://doi.org/10.1016/j.iswcr.2020.06.006

    Article  Google Scholar 

  • Claverie, M., Demarez, B., Duchemin, O., Hagolle, D., Ducrot, C., Marais Sicre, J. F., & Dejoux, M. (2012). Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. RemoteSensing of Environment, 124, 844–857.

    Google Scholar 

  • Cormary, Y., & Masson, J. (1964). Etude de conservation des eaux et du sol au Centre de Recherches du Génie Rural de Tunisie: Application à un projet-type de la formule de perte de sols de Wischmeier. Cahiers ORSTOM. Série Pédologie, 2(3), 3–26.

    Google Scholar 

  • Coyen, & Billier. (2011). Etude d’avant-projet détaille du barrage de OuldjetMellegue, wilaya de tebessa. MISSION 11, Rapport 11378-RP-1101.

  • De Jong, S. M., Paracchini, M. L., & Bertolo, F. (1999). Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. CATENA, 37(3–4), 291–308. https://doi.org/10.1016/S0341-8162(99)00038-7

    Article  Google Scholar 

  • De Jong, S. M., & Riezebos, H. T. (1997). SEMMED: A distributed approach to soil erosion modelling. Remote Sensing, 96, 199–204.

    Google Scholar 

  • Dicara, B., & Tech, G. A. B. (2020). Suivi par télédétection de la saisonnalité de la végétation et du climat dans la zone de transition forêt-savane en République Centrafricaine. http://hdl.handle.net/2268.2/8725.

  • Efthimiou, N., Psomiadis, E., & Panagos, P. (2020). Fire severity and soil erosion susceptibility mapping using multi-temporal Earth Observation data: The case of Mati fatal wildfire in Eastern Attica. Greece. Catena, 187, 104320.

    Article  Google Scholar 

  • Feddema, J. J., Oleson, K. W., Bonan, G. B., Mearns, L. O., Buja, L. E., Meehl, G. A., & Washington, W. M. (2005). The importance of land-cover change in simulating future climates. Science, 310(5754), 1674–1678.

    Article  Google Scholar 

  • Ferreira, V., Panagopoulos, T., Cakula, A., Andrade, R., & Arvela, A. (2015). Predicting soil erosion after land use changes for irrigating agriculture in a large reservoir of southern Portugal. Agriculture and Agricultural Science Procedia, 4, 40–49.

    Article  Google Scholar 

  • GCOS. (2016). The global observing system for climate: Implementation needs. Global Climate Observing System Implementation Plan 2016.

  • Houghton, R. A., House, J. I., Pongratz, J., Van der Werf, G. R., DeFries, R. S., Hansen, M. C., et al. (2012). Carbon emissions from land use and landcover change. Biogeosciences, 9(12), 5125–5142.

    Article  Google Scholar 

  • Inglada, J. A., Vincent, M., Arias, B., Tardy, D., & Rodes, I. (2017). Operational high resolution land cover map production at the country scale using satellite image time series. Remote Sensing, 9(1), 95.

    Article  Google Scholar 

  • IPCC. (2014). Intergovernmental panel on climate change. Climate Change, 2014.

  • Knijff, J. M., Jones, R. J. A., & Montanarella, L. (2000). Soil erosion risk assessment in Europe. Joint Research Centre, European Commission, EUR 19044 EN (2000).

  • 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. Environmental Geology, 57(3), 483–497. https://doi.org/10.1007/s00254-008-1318-9

    Article  Google Scholar 

  • Li, D., Tian, P., Luo, H., Hu, T., Dong, B., Cui, Y., & Luo, Y. (2020). Impacts of land use and land cover changes on regional climate in the Lhasa River basin, Tibetan Plateau. Science of the Total Environment, 742, 140570. https://doi.org/10.1016/j.scitotenv.2020.140570

    Article  Google Scholar 

  • Lin, C. Y., Lin, W. T., & Chou, W. C. (2002). Soil erosion prediction and sediment yield estimation: The Taiwan experience. Soil and Tillage Research, 68(2), 143–152. https://doi.org/10.1016/S01671987(02)001149

    Article  Google Scholar 

  • Liu, J., Gao, G., Wang, S., et al. (2018). The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 28, 59–78. https://doi.org/10.1007/s11442-018-1459-z

    Article  Google Scholar 

  • Lunetta, R. S., Knight, J. F., Ediriwickrema, J., Lyon, J. G., & Worthy, L. D. (2006). Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment, 105, 142–154. https://doi.org/10.1016/J.RSE.2006.06.018

    Article  Google Scholar 

  • Markose, V. J., & Jayappa, K. S. (2016). Soil loss estimation and prioritization of sub-watersheds of Kali River basin, Karnataka, India, using RUSLE and GIS. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-016-5218-2

    Article  Google Scholar 

  • Meddi, M., Toumi, S., & Assani, A. (2016). Spatial and temporal variability of the rainfall erosivity factor in Northern Algeria. Arabian Journal of Geosciences, 9(4), 282. https://doi.org/10.1007/s12517-015-2303-8

    Article  Google Scholar 

  • Meusburger, K., Bänninger, D., & Alewell, C. (2010). Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery. International Journal of Applied Earth Observation and Geoinformation, 12, 201–207. https://doi.org/10.1016/j.jag.2010.02.009

    Article  Google Scholar 

  • Mjejra, M. (2015). Étude de l’évapotranspiration dans le bassin versant de Mejerda (en Tunisie): Apport de la télédétection satellitaire et des Systèmes d’Information Géographique. thèse de doctorat, université de Medjaz el bab. Tunisie. Pp. 47–55.

  • Monroe, A. P., Aldridge, C. L., O’Donnell, M. S., Manier, D. J., Homer, C. G., & Anderson, P. J. (2020). Using remote sensing products to predict recovery of vegetation across space and time following energy development. Ecological Indicators, 110, 105872. https://doi.org/10.1016/j.ecolind.2019.105872

    Article  Google Scholar 

  • Niu, X., Tang, J., Wang, S., & Fu, C. (2019). Impact of future land use and land cover change on temperature projections over East Asia. ClimDyn, 52, 6475–6490. https://doi.org/10.1007/s00382-018-4525-4

    Article  Google Scholar 

  • Pham, T. G., Degener, J., & Kappas, M. (2018). Integrated universal soil loss equation (USLE) and geographical information system (GIS) for soil erosion estimation in A Sap basin: Central Vietnam. International Soil and Water Conservation Research., 6(2), 99–110. https://doi.org/10.1016/j.iswcr.2018.01.001

    Article  Google Scholar 

  • Pielke, R. A. (2005). Land use and climate change. Science, 310(5754), 1625–1626.

    Article  Google Scholar 

  • Pijl, A., Quarella, E., Vogel, T. A., D’Agostino, V., & Tarolli, P. (2020). Remote sensing vs. field-based monitoring of agricultural terrace degradation. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2020.09.001

    Article  Google Scholar 

  • Prabhakara, K., Dean Hively, W., & McCarty, G. W. (2015). Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International Journal of Applied Earth Observation and Geoinformation., 39, 88–102. https://doi.org/10.1016/j.jag.2015.03.002

    Article  Google Scholar 

  • Provencher, L., & Dubois, J. M. M. (2007). Précis de télédétection-Volume 4 (Vol. 4). Puq.

  • Quinton, J. N., Edwards, G. M., & Morgan, R. P. C. (1997). The influence of vegetation species and plant properties on runoff and soil erosion, results from a rainfall simulation study in south east Spain. Soil Use and Management, 13(3), 143–148.

    Article  Google Scholar 

  • Rodier, J., Colombani, J., Claude, J., &Kallel, R. (1981). Le bassin de la mdjerdah. Monographie hydrologique N6 (pp. 4–66). Paris: OROSTOM.

  • Samaali, H. (2011). Etude de l’évolution de l’occupation et de l’utilisation du sol dans le delta de Mejerda par télédétection et SIG. domain_other. Faculté des Sciences Humaines et Sociales de Tunis. Français. tel-00612952v1

  • Seo, B., Lee, J., Lee, K. D., Hong, S., & Kang, S. (2019). Improving remotely-sensed crop monitoring by NDVI-based crop phenology estimators for corn and soybeans in Iowa and Illinois, USA. Field Crops Research, 238, 113–128. https://doi.org/10.1016/j.fcr.2019.03.015

    Article  Google Scholar 

  • Stoian, A., Poulain, V., Inglada, J., Poughon, V., & Derksen, D. (2019). Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems. Remote Sensing, 11(17), 1986. https://doi.org/10.3390/rs11171986

    Article  Google Scholar 

  • Touaibia, B., & Achite, M. (2003). Contribution à la cartographie de l’érosion spécifique du bassin versant de l’Oued Mina en zone semi-aride de l’Algérie septentrionale. Hydrological Sciences Journal, 48(2), 235–242. https://doi.org/10.1623/hysj.48.2.235.44691

    Article  Google Scholar 

  • Toubal, A. K., Achite, M., Ouillon, S., & Dehni, A. (2018). Soil erodibility mapping using the RUSLE model to prioritize erosion control in the WadiSahouat basin, North-West of Algeria. Environmental Monitoring and Assessment, 190, 210. https://doi.org/10.1007/s10661-018-6580-z

    Article  Google Scholar 

  • Toumi, S., Meddi, M., Mahé, G., & Brou, Y. T. (2013). Cartographie de l’érosion dans le bassin versant de l’Oued Mina en Algérie par télédétection et SIG. Hydrological Sciences Journal, 58(7), 1542–1558. https://doi.org/10.1080/02626667.2013.824088 In French.

    Article  Google Scholar 

  • Tricart, J. (1963). Oscillations et modifications de caractère de la zone aride en Afrique et en Amérique latine lors des périodes glaciaires des hautes latitudes. In Changes of climates (pp. 415–419).

  • Tsewoue, M. R., Tchamba, M., Avana, M. L., & Tanougong, A. D. (2020). Dynamique spatio-temporelle de l’occupation du sol dans le Moungo, Région du Littoral, Cameroun: Influence sur l’expansion des systèmes agroforestiers à base de bananiers. International Journal of Biological and Chemical Sciences, 14(2), 486–500. https://doi.org/10.4314/ijbcs.v14i2.15

    Article  Google Scholar 

  • Uddin, K., Murthy, M. S. R., Wahid, S. M., & Matin, M. A. (2016). Estimation of soil erosion dynamics in the Koshi basin using GIS and remote sensing to assess priority areas for conservation. PLoS ONE, 11(3), e0150494. https://doi.org/10.1371/journal.pone.0150494

    Article  Google Scholar 

  • Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., et al. (2016). Production of a dynamic cropland mask by processing remote sensing image series at high temporal and spatial resolutions. Remote Sensing, 8(1), 55.

    Article  Google Scholar 

  • Van der Knijff, J. M. F., Jones, R. J. A., & Montanarella, L. (2000). Soil erosion risk assessment in Europe. European Soil Bureau Research Report EUR 19044 ENp, p. 34.

  • Vatandaşlar, C., & Yavuz, M. (2017). Modeling cover management factor of RUSLE using very high-resolution satellite imagery in a semiarid watershed. Environmental Earth Sciences, 76(2), 65. https://doi.org/10.1007/s12665-017-6388-0

    Article  Google Scholar 

  • Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. Department of Agriculture, Science and Education Administration.

  • Zhang, M., Yuan, X., & Otkin, J. A. (2020). Remote sensing of the impact of flash drought events on terrestrial carbon dynamics over China. Carbon Balance and Management, 15, 20. https://doi.org/10.1186/s13021-020-00156-1

    Article  Google Scholar 

  • Zhong, Z., Han, X., Xu, Y., Zhang, W., Fu, S., Liu, W., et al. (2019). Effects of land use change on organic carbon dynamics associated with soil aggregate fractions on the Loess Plateau, China. Land Degradation and Development, 30(9), 1070–1082. https://doi.org/10.1002/ldr.3294

    Article  Google Scholar 

  • Zhou, P., Luukkanen, O., Tokola, T., & Nieminen, J. (2008). Effect of vegetation cover on soil erosion in a mountainous watershed. CATENA, 75(3), 319–325. https://doi.org/10.1016/j.catena.2008.07.010

    Article  Google Scholar 

Download references

Acknowledgements

This work was carried out within the framework of the Young Teams ENSH- IRD Blida (Young Teams IRD-ENSH Blida).

Funding

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Author information

Authors and Affiliations

  1. National Agronomic Institute, University of Carthage, Tunis, Tunisia

    Amine Menasria & Hamadi Habaieb

  2. Higher National School for Hydraulics, LGEE, Blida, Algeria

    Amine Menasria & Mohamed Meddi

Authors
  1. Amine Menasria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Meddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamadi Habaieb
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Meddi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Menasria, A., Meddi, M. & Habaieb, H. Diachronic Study of Land Cover of the Medjerda Watershed and Estimation of RUSLE-C Factor Using NDVI-Based Equation, Remote Sensing, and GIS. J Indian Soc Remote Sens 50, 451–468 (2022). https://doi.org/10.1007/s12524-021-01472-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-021-01472-w

Keywords

Search

Navigation