دورية أكاديمية
Assessing land use changes' effect on river water quality in the Dez Basin using land change modeler.
العنوان: | Assessing land use changes' effect on river water quality in the Dez Basin using land change modeler. |
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المؤلفون: | Goodarzi MR; Department of Civil Engineering, Yazd University, Yazd, Iran. Goodarzimr@yazd.ac.ir., Niknam ARR; Department of Civil Engineering, Water Resources Management Engineering, Yazd University, Yazd, Iran., Rahmati SH; Department of Environmental Engineering, Science & Research Branch, Islamic Azad University, Tehran, Iran., Attar NF; Department of Statistical Sciences, University of Padova, Via Cesare Battisti, Padua, 35121, Italy. |
المصدر: | Environmental monitoring and assessment [Environ Monit Assess] 2023 May 31; Vol. 195 (6), pp. 774. Date of Electronic Publication: 2023 May 31. |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE |
أسماء مطبوعة: | Publication: 1998- : Dordrecht : Springer Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981- |
مواضيع طبية MeSH: | Water Quality* , Rivers*, Environmental Monitoring/methods ; Urbanization ; Agriculture |
مستخلص: | Changes in land use due to urbanization, industrialization, and agriculture will adversely affect water quality at all scales. This study examined the possible effects of future land use on the water quality of the Dez River located in Iran. The QUAL2Kw dynamic model was used to simulate the water quality of the Dez River. Data and information available in July 2019 and 2013 were used for calibration and validation. According to the comparison of the RMSE, RMSE%, and percent bias error indices for the model during the calibration and validation period, the QUAL2Kw model of Dez River had high accuracy with acceptable values of errors. The land use changes in the Dez river basin were modeled and predicted by the LCM model after simulating water quality. The images from Landsat 8/OLI were used for 2013, 2016, and 2019, respectively. Based on the accurate evaluation of classified images, Kappa coefficients for 2013, 2016, and 2019 were 88.19, 87.46, and 89.91, respectively. Modeling land use and land cover changes was conducted to predict 2030. As a result of the study, agricultural and built-up areas and water bodies will increase in 2030. The possible effects of land use changes in 2030 on river water quality were examined as a final step. Based on the results of the water quality simulation in 2030, biochemical oxygen demand, chemical oxygen demand, and NO (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.) |
References: | Abdovis Sabdovis, S., Sedghi, H., Hassonizadeh, H., & Babazadeh, H. (2020). The quality study and simulation by Qual2k model in Dez River, Iran. Irrigation Sciences and Engineering, 43(3), 71–85. https://doi.org/10.22055/jise.2018.25434.1752. (PMID: 10.22055/jise.2018.25434.1752) Al-sharif, A. A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291–4301. https://doi.org/10.1007/s12517-013-1119-7. (PMID: 10.1007/s12517-013-1119-7) Araya, Y. H., & Cabral, P. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), 1549–1563. https://doi.org/10.3390/rs2061549. (PMID: 10.3390/rs2061549) Ayele, G., Hayicho, H., & Alemu, M. (2019). Land use land cover change detection and deforestation modeling. In Delomena District of Bale Zone, Ethiopia. Journal of Environmental Protection, 10, 532–561. https://doi.org/10.4236/jep.2019.104031. (PMID: 10.4236/jep.2019.104031) Azari, M., Tayyebi, A., Helbich, M., & Reveshty, M. A. (2016). Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: Application to Maragheh, Iran. Giscience & Remote Sensing, 53(2), 183–205. https://doi.org/10.1080/15481603.2015.1137111. (PMID: 10.1080/15481603.2015.1137111) Baker, A. (2003). Land use and water quality. Hydrological Processes, 17(12), 2499–2501. https://doi.org/10.1002/hyp.5140. (PMID: 10.1002/hyp.5140) Bowie, G. L., Laboratory, E. R., & Tech, T. (1985). Rates, constants, and kinetics formulations in surface water quality modeling. Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency. Retrieved March 3, 2021, from https://books.google.com/books?id=RpuGnQEACAAJ. Bu, H., Zhang, Y., Meng, W., & Song, X. (2016). Effects of land-use patterns on in-stream nitrogen in a highly-polluted river basin in Northeast China. Science of the Total Environment, 553, 232–242. https://doi.org/10.1016/j.scitotenv.2016.02.104. (PMID: 10.1016/j.scitotenv.2016.02.104) Buck, O., Niyogi, D. K., & Townsend, C. R. (2004). Scale-dependence of land use effects on water quality of streams in agricultural catchments. Environmental Pollution, 130(2), 287–299. https://doi.org/10.1016/j.envpol.2003.10.018. (PMID: 10.1016/j.envpol.2003.10.018) Camara, M., Jamil, N. R., & Abdullah, A. F. B. (2019). Impact of land uses on water quality in Malaysia: a review. Ecological Processes, 8(1), 10. https://doi.org/10.1186/s13717-019-0164-x. (PMID: 10.1186/s13717-019-0164-x) Chapra, S. C., Pelletier, G. J., & Tao, H. (2008). QUAL2K: A modeling framework for simulating river and stream water quality, Version 2.11. USA: Documentation and User’s Manual. Civil and Environmental Engineering Department, Tufts University, Medford. Charbonneau, P., & Knapp, B. (1995). A user’s guide to PIKAIA 1.0 (No. NCAR/TN-418+IA). University Corporation for Atmospheric Research. https://doi.org/10.5065/D69P2ZKK. Clark Labs. (2015). TerrSet geospatial monitoring and modeling software. In: Clark Labs, Clark University Worcester, MA. Dey, N. N., Al Rakib, A., Kafy, A. A., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh. Environmental Challenges, 4, 100148. https://doi.org/10.1016/j.envc.2021.100148. Eastman, R. (2012). IDRISI for windows: IDRISI Selva Manual. Clark University, New York. Fathizad, H., Rostami, N., & Faramarzi, M. (2015). Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Environmental Monitoring and Assessment, 187(10), 629. https://doi.org/10.1007/s10661-015-4805-y. (PMID: 10.1007/s10661-015-4805-y) Gashaw, T., Bantider, A., & Mahari, A. (2014). Evaluations of land use/land cover changes and land degradation in Dera District, Ethiopia: GIS and remote sensing based analysis. International Journal of Scientific Research in Environmental Sciences, 2, 199–208. https://doi.org/10.12983/ijsres-2014-p0199-0208. (PMID: 10.12983/ijsres-2014-p0199-0208) Gashaw, T., Tulu, T., & Argaw, M. (2017). Erosion risk assessment for prioritization of conservation measures in Geleda watershed, Blue Nile basin, Ethiopia. Environmental Systems Research, 6(1), 1. https://doi.org/10.1186/s40068-016-0078-x. (PMID: 10.1186/s40068-016-0078-x) Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9). https://doi.org/10.1016/j.heliyon.2020.e05092. Girma, R., Fürst, C., & Moges, A. (2022). Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environmental Challenges, 6, 100419. https://doi.org/10.1016/j.envc.2021.100419. Goodarzi, M. R., Niknam, A. R. R., Jamali, V., & Pourghasemi, H. R. (2022). Aquifer vulnerability identification using DRASTIC-LU model modification by fuzzy analytic hierarchy process. Modeling Earth Systems and Environment, 8(4), 5365–5380. https://doi.org/10.1007/s40808-022-01408-4. (PMID: 10.1007/s40808-022-01408-4) Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20), 3761–3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009. (PMID: 10.1016/j.ecolmodel.2011.09.009) Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015. (PMID: 10.1016/j.apgeog.2015.06.015) Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421. https://doi.org/10.3390/su10103421. (PMID: 10.3390/su10103421) Hepinstall, J. A., Alberti, M., & Marzluff, J. M. (2008). Predicting land cover change and avian community responses in rapidly urbanizing environments. Landscape Ecology, 23(10), 1257–1276. https://doi.org/10.1007/s10980-008-9296-6. (PMID: 10.1007/s10980-008-9296-6) Huang, J., Zhan, J., Yan, H., Wu, F., & Deng, X. (2013). Evaluation of the impacts of land use on water quality: A case study in The Chaohu Lake Basin. The Scientific World Journal, 2013, 329187. https://doi.org/10.1155/2013/329187. Kafy, A.-A., Naim, M. N. H., Subramanyam, G., Faisal, A.-A., Ahmed, N. U., Rakib, A. A., Kona, M. A., & Sattar, G. S. (2021). Cellular automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh. Environmental Challenges, 4, 100084. https://doi.org/10.1016/j.envc.2021.100084. Kafy, A. A., Rahman, M. S., Faisal, A.-A., Hasan, M. M., & Islam, M. (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment, 18, 100314. https://doi.org/10.1016/j.rsase.2020.100314. Kalburgi, P., Shareefa, R., & Deshannavar, U. (2015). Development and evaluation of BOD–DO model for River Ghataprabha near Mudhol (India), using QUAL2K. International Journal of Engineering and Manufacturing, 5, 15–25. https://doi.org/10.5815/ijem.2015.01.02. (PMID: 10.5815/ijem.2015.01.02) Kannel, P. R., Lee, S., Kanel, S. R., Lee, Y.-S., & Ahn, K.-H. (2007). Application of QUAL2Kw for water quality modeling and dissolved oxygen control in the river Bagmati. Environmental Monitoring and Assessment, 125(1), 201–217. https://doi.org/10.1007/s10661-006-9255-0. (PMID: 10.1007/s10661-006-9255-0) Khuzestan Water and Power Authority (KWPA). (2000). An assessment of pollutants in Karun River. A report prepared by the Water Quality Assessment section, Khuzestan Water and Power Authority, Khuzestan, Iran. Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia. Sustainability, 13(7), 3740. https://www.mdpi.com/2071-1050/13/7/3740. Li, S., Gu, S., Liu, W., Han, H., & Zhang, Q. (2008). Water quality in relation to land use and land cover in the upper Han River Basin, China. CATENA, 75(2), 216–222. https://doi.org/10.1016/j.catena.2008.06.005. (PMID: 10.1016/j.catena.2008.06.005) Li, Y. L., Liu, K., Li, L., & Xu, Z. X. (2012). Relationship of land use/cover on water quality in the Liao River basin, China. Procedia Environmental Sciences, 13, 1484–1493. https://doi.org/10.1016/j.proenv.2012.01.140. (PMID: 10.1016/j.proenv.2012.01.140) Morales-Barquero, L., Lyons, M. B., Phinn, S. R., & Roelfsema, C. M. (2019). Trends in remote sensing accuracy assessment approaches in the context of natural resources. Remote Sensing, 11, 1–16. (PMID: 10.3390/rs11192305) Mas, J.-F., Kolb, M., Paegelow, M., Camacho Olmedo, M. T., & Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51, 94–111. https://doi.org/10.1016/j.envsoft.2013.09.010. (PMID: 10.1016/j.envsoft.2013.09.010) Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9(4), 249. https://doi.org/10.1007/s12517-015-2138-3. (PMID: 10.1007/s12517-015-2138-3) Mishra, V. N., Rai, P. K., Prasad, R., Punia, M., & Nistor, M.-M. (2018). Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: A comparison of hybrid models. Applied Geomatics, 10(3), 257–276. https://doi.org/10.1007/s12518-018-0223-5. (PMID: 10.1007/s12518-018-0223-5) Mohammady, S., Delavar, M. R., & Pahlavani, P. (2014). Urban growth modeling using an artificial neural network a case study of Sanandaj City, Iran. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W3, 203-208. https://doi.org/10.5194/isprsarchives-XL-2-W3-203-2014. Mosammam, H. M., Nia, J. T., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 103–116. https://doi.org/10.1016/j.ejrs.2016.08.002. (PMID: 10.1016/j.ejrs.2016.08.002) Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153. (PMID: 10.13031/2013.23153) Nagaraju, A., Sunil Kumar, K., & Thejaswi, A. (2014). Assessment of groundwater quality for irrigation: A case study from Bandalamottu lead mining area, Guntur District, Andhra Pradesh, South India. Applied Water Science, 4(4), 385–396. https://doi.org/10.1007/s13201-014-0154-1. (PMID: 10.1007/s13201-014-0154-1) Ngoye, E., & Machiwa, J. F. (2004). The influence of land-use patterns in the Ruvu river watershed on water quality in the river system. Physics and Chemistry of the Earth, 29, 1161–1166. (PMID: 10.1016/j.pce.2004.09.002) Ni, X., Parajuli, P. B., Ouyang, Y., Dash, P., & Siegert, C. (2021). Assessing land use change impact on stream discharge and stream water quality in an agricultural watershed. CATENA, 198, 105055. https://doi.org/10.1016/j.catena.2020.105055. Nikakhtar, M., Rahmati, S. H., & Bavani, A. R. M. (2019). Impact of climate change on the future quality of surface waters: Case study of the Ardak River, northeast of Iran. Journal of Water and Climate Change, 11(3), 685–702. https://doi.org/10.2166/wcc.2019.132. (PMID: 10.2166/wcc.2019.132) Nouri, J., Gharagozlou, A., Arjmandi, R., Faryadi, S., & Adl, M. (2014). Predicting urban land use changes using a CA–Markov model. Arabian Journal for Science and Engineering, 39(7), 5565–5573. https://doi.org/10.1007/s13369-014-1119-2. (PMID: 10.1007/s13369-014-1119-2) Olusola, A., David, O., & Durowoju, O. (2018). Analysis of Organic Matter and Carbonate Mineral Distribution in Shallow Water Surface Sediments, 1, 106–110. Pelletier, G. J., Chapra, S. C., & Tao, H. (2006). QUAL2Kw — A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration. Environmental Modelling & Software, 21(3), 419–425. https://doi.org/10.1016/j.envsoft.2005.07.002. (PMID: 10.1016/j.envsoft.2005.07.002) Rafiee, M., Akhond Ali, A. M., Moazed, H., Lyon, S. W., Jaafarzadeh, N., & Zahraie, B. (2014). A case study of water quality modeling of the Gargar River, Iran. Journal of Hydraulic Structures, 1(2), 10–22. https://doi.org/10.22055/jhs.2014.10533. (PMID: 10.22055/jhs.2014.10533) Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002. (PMID: 10.1016/j.ejrs.2015.02.002) Regasa, M. S., Nones, M., & Adeba, D. (2021). A review on land use and land cover change in Ethiopian basins. Land, 10(6), 585. https://doi.org/10.3390/land10060585. (PMID: 10.3390/land10060585) Rimal, B., Zhang, L., Keshtkar, H., Haack, B. N., Rijal, S., & Zhang, P. (2018). Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov chain. ISPRS International Journal of Geo-Information, 7(4), 154. https://www.mdpi.com/2220-9964/7/4/154. Rodrigues, V., Estrany, J., Ranzini, M., de Cicco, V., Martín-Benito, J. M. T., Hedo, J., & Lucas-Borja, M. E. (2018). Effects of land use and seasonality on stream water quality in a small tropical catchment: The headwater of Córrego Água Limpa, São Paulo (Brazil). Science of the Total Environment, 622–623, 1553–1561. https://doi.org/10.1016/j.scitotenv.2017.10.028. (PMID: 10.1016/j.scitotenv.2017.10.028) Samal, D. R., & Gedam, S. S. (2015). Monitoring land use changes associated with urbanization: An object based image analysis approach. European Journal of Remote Sensing, 48(1), 85–99. https://doi.org/10.5721/EuJRS20154806. (PMID: 10.5721/EuJRS20154806) Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019. (PMID: 10.1016/j.mcm.2010.11.019) Shafizadeh-Moghadam, H., Tayyebi, A., & Helbich, M. (2017). Transition index maps for urban growth simulation: Application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation. Environmental Monitoring and Assessment, 189(6), 300. https://doi.org/10.1007/s10661-017-5986-3. (PMID: 10.1007/s10661-017-5986-3) Sharifi, A. (2018). Estimation of biophysical parameters in wheat crops in Golestan province using ultra-high resolution images. Remote Sensing Letters, 9(6), 559–568. https://doi.org/10.1080/2150704X.2018.1452058. (PMID: 10.1080/2150704X.2018.1452058) Sharifi, A., Amini, J., & Tateishi, R. (2016). Estimation of forest biomass using multivariate relevance vector regression. Photogrammetric Engineering & Remote Sensing, 82(1), 41–49. https://doi.org/10.14358/PERS.83.1.41. (PMID: 10.14358/PERS.83.1.41) Sharifi, A., & Hosseingholizadeh, M. (2019). The effect of rapid population growth on urban expansion and destruction of green space in Tehran from 1972 to 2017. Journal of the Indian Society of Remote Sensing, 47(6), 1063–1071. https://doi.org/10.1007/s12524-019-00966-y. (PMID: 10.1007/s12524-019-00966-y) Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S., & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using Earth observation datasets and geo-information. Environmental Processes, 2(1), 61–78. https://doi.org/10.1007/s40710-015-0062-x. (PMID: 10.1007/s40710-015-0062-x) Subedi, P., Subedi, K., & Thapa, B. (2013). Application of a hybrid cellular automaton ¨C Markov (CA-Markov) model in land-use change prediction: A case study of Saddle Creek Drainage Basin. Florida. Applied Ecology and Environmental Sciences, 1(6), 126–132. http://pubs.sciepub.com/aees/1/6/5. Tahiru, A. A., Doke, D. A., & Baatuuwie, B. N. (2020). Effect of land use and land cover changes on water quality in the Nawuni Catchment of the White Volta Basin, Northern Region, Ghana. Applied Water Science, 10(8), 198. https://doi.org/10.1007/s13201-020-01272-6. (PMID: 10.1007/s13201-020-01272-6) Tayyebi, A., & Pijanowski, B. C. (2014). Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools. International Journal of Applied Earth Observation and Geoinformation, 28, 102–116. https://doi.org/10.1016/j.jag.2013.11.008. (PMID: 10.1016/j.jag.2013.11.008) Tong, S. T. Y., & Chen, W. (2002). Modeling the relationship between land use and surface water quality. Journal of Environmental Management, 66(4), 377–393. https://doi.org/10.1006/jema.2002.0593. (PMID: 10.1006/jema.2002.0593) USEPA. (2003). Human health toxicity values in superfund risk assessments. OSWER Directive, 9285.753. Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37, 360–363. Wang, S. W., Munkhnasan, L., & Lee, W.-K. (2021). Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environmental Challenges, 2, 100017. https://doi.org/10.1016/j.envc.2020.100017. Wang, W., Zhang, C., Allen, J. M., Li, W., Boyer, M. A., Segerson, K., & Silander, J. A. (2016). Analysis and prediction of land use changes related to invasive species and major driving forces in the state of Connecticut. Land, 5(3), 25. https://doi.org/10.3390/land5030025. (PMID: 10.3390/land5030025) Wnęk, A., Kudas, D., & Stych, P. (2021). National level land-use changes in functional urban areas in Poland, Slovakia, and Czechia. Land, 10(1), 39. https://doi.org/10.3390/land10010039. (PMID: 10.3390/land10010039) Yirsaw, E., Wu, W., Shi, X., Temesgen, H., & Bekele, B. (2017). Land use/land cover change modeling and the prediction of subsequent changes in ecosystem service values in a coastal area of China, the Su-Xi-Chang Region. Sustainability, 9(7), 1204. https://doi.org/10.3390/su9071204. (PMID: 10.3390/su9071204) Zadbagher, E., Becek, K., & Berberoglu, S. (2018). Modeling land use/land cover change using remote sensing and geographic information systems: Case study of the Seyhan Basin, Turkey. Environmental Monitoring and Assessment, 190(8), 494. https://doi.org/10.1007/s10661-018-6877-y. (PMID: 10.1007/s10661-018-6877-y) Zhao, J., Lin, L., Yang, K., Liu, Q., & Qian, G. (2015). Influences of land use on water quality in a reticular river network area: A case study in Shanghai, China. Landscape and Urban Planning, 137, 20–29. https://doi.org/10.1016/j.landurbplan.2014.12.010. (PMID: 10.1016/j.landurbplan.2014.12.010) |
فهرسة مساهمة: | Keywords: Dez River; LCM; Land use; Landsat 8; Water quality |
تواريخ الأحداث: | Date Created: 20230531 Date Completed: 20230602 Latest Revision: 20230602 |
رمز التحديث: | 20231215 |
DOI: | 10.1007/s10661-023-11265-y |
PMID: | 37256385 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1573-2959 |
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DOI: | 10.1007/s10661-023-11265-y |