Eurasian Journal of Soil Science

Volume 13, Issue 2, Mar 2024, Pages 101-110
DOI: 10.18393/ejss.1402168
Stable URL: http://ejss.fess.org/10.18393/ejss.1402168
Copyright © 2024 The authors and Federation of Eurasian Soil Science Societies



Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques

X

Article first published online: 08 Dec 2023 | How to cite | Additional Information (Show All)

Author information | Publication information | Export Citiation (Plain Text | BibTeX | EndNote | RefMan)

CLASSICAL | APA | MLA | TURABIAN | IEEE | ISO 690

Abstract | References | Article (XML) | Article (HTML) | PDF | 61 | 235

Demirağ Turan,İ., Dengiz,O., Pacci,S., Agbor,D., 2024. Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques. Eurasian J Soil Sci 13(2):101-110. DOI : 10.18393/ejss.1402168
Demirağ Turan,İ.,Dengiz,O.Pacci,S.,& Agbor,D. Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques Eurasian Journal of Soil Science, 13(2):101-110. DOI : 10.18393/ejss.1402168
Demirağ Turan,İ.,Dengiz,O.Pacci,S., and ,Agbor,D."Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques" Eurasian Journal of Soil Science, 13.2 (2024):101-110. DOI : 10.18393/ejss.1402168
Demirağ Turan,İ.,Dengiz,O.Pacci,S., and ,Agbor,D. "Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques" Eurasian Journal of Soil Science,13(Mar 2024):101-110 DOI : 10.18393/ejss.1402168
İ,Demirağ Turan.O,Dengiz.S,Pacci.D,Agbor "Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques" Eurasian J. Soil Sci, vol.13, no.2, pp.101-110 (Mar 2024), DOI : 10.18393/ejss.1402168
Demirağ Turan,İnci ;Dengiz,Orhan ;Pacci,Sena ;Agbor,David Tavi Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques. Eurasian Journal of Soil Science, (2024),13.2:101-110. DOI : 10.18393/ejss.1402168

How to cite

Demirağ Turan, İ., Dengiz, O., Pacci, S., Agbor, D., 2024. Determination of change in the land use and land cover of the Samsun Bafra Delta Plain from 1990 to 2020 using GIS and Remote Sensing Techniques. Eurasian J. Soil Sci. 13(2): 101-110. DOI : 10.18393/ejss.1402168

Author information

İnci Demirağ Turan , Samsun University, Faculty of Human and Social Sciences, Department of Geography, Samsun, Türkiye
Orhan Dengiz , Ondokuz Mayıs University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Samsun, Türkiye
Sena Pacci , Ondokuz Mayıs University, Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Samsun, Türkiye
David Tavi Agbor , Department of Agronomic and Applied Molecular Sciences, Faculty of Agriculture and Veterinary Medicine, University of Buea, Cameroon

Publication information

Article first published online : 08 Dec 2023
Manuscript Accepted : 03 Dec 2023
Manuscript Received: 09 Jul 2023
DOI: 10.18393/ejss.1402168
Stable URL: http://ejss.fesss.org/10.18393/ejss.1402168

Abstract

Land use and land cover changes can have detrimental effects on the ecology, if they are not properly aligned with the characteristics of the land. This study aims to evaluate the temporal changes in land use and land cover of Bafra Delta plain, situated in the east of Samsun province. The region is one of the most significant plains within the Black Sea area. Remote sensing technique was utilized in this research which made use of Landsat images from 1990, 2000, 2010, and 2020. Supervised classification was applied in ENVI 5.3v software to perform calculations, resulting in six main classes. Field work was applied to classify the unclassified classes. The resulting six land use-land cover classes were agriculture lands, forest, dune, marshy, water surface, and artificial areas. To determine land use efficiency, analogue data was digitised and transferred to a GIS database. The agricultural areas occupy the largest portion of the plain, followed by hazelnut and artificial areas. The changes over the last decade, notably the growth of artificial areas and water surfaces, and the reduction of arable lands, highlight significant variations in size across the areas. Furthermore, the study indicated that remote sensing and geographic information system techniques play a crucial role in identifying and monitoring land cover and land use trends on a large-scale to produce accurate and timely data. Poorly adapted land use changes can cause major ecological damage. The aim of this study is to identify the changes over time in land use and land cover of Bafra Delta plain, located to the east of Samsun city and one of the most significant plains in the Black Sea region, using remote sensing techniques. To this end, Landsat images from 1990, 2000, 2010 and 2020 are utilized. To perform the calculations, ENVI 5.3v software was employed, applying a supervised classification technique that resulted in forming six main classes. Fieldwork was conducted to classify the unclassified classes. The resulting land-use and land-cover classes were agricultural land, forest, dunes, marshland, water surface, and artificial areas. To evaluate land-use efficiency, analogue data were digitalised and imported into a GIS database. The plain's most extensive land-use areas consist of agricultural lands, followed by hazelnut and artificial areas. In the last decade, the rise in artificial and water surfaces and the decline in agricultural areas highlights significant changes in the region's size. This study also emphasises the crucial role of remote sensing and geographic information system techniques in generating fast and consistent data for monitoring large-scale land cover and land use trends.

Keywords

Land use-land cover, change analysis, Bafra Plain

Corresponding author

References

Abdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M.A.A., Hassan, Q.K., Dewan, A., 2019. Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing 11(7): 790.

Aboelnour, M., Engel, B.A., 2018. Application of remote sensing techniques and geographic information systems to analyze land surface temperature in response to land use/land cover change in Greater Cairo Region, Egypt. Journal of Geographic Information System 10(1): 57-88.

Arora, G., Wolter, P.T., 2018. Tracking land cover change along the western edge of the US Corn Belt from 1984 through 2016 using satellite sensor data: Observed trends and contributing factors. Journal of Land Use Science 13(1-2): 59-80.

Bağcı, H.R., Bahadır, M., 2019. Land use and temporal change in Kızılırmak Delta (Samsun) (1987-2019). The Journal of Academic Social Science Studies 78: 295-312. [in Turkish]

Bansod, R.D., Dandekar, U.M., 2018. Evaluation of Morna river catchment with RS and GIS techniques. Journal of Pharmacognosy and Phytochemistry 7(1): 1945-1948.

Chen, J., Theller, L., Gitau, M.W., Engel, B.A., Harbor, J.M., 2017. Urbanization impacts on surface runoff of the contiguous United States. Journal of Environmental Management 187: 470-481.

Cui, J., Zhu, M., Liang, Y., Qin, G., Li, J., Liu, Y., 2022. Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on google earth Engine from 2000 to 2020. ISPRS International Journal of Geo-Information 11(3): 163.

Devkota, P., Dhakal, S., Shrestha, S., Shrestha, U.B., 2023. Land use land cover changes in the major cities of Nepal from 1990 to 2020. Environmental and Sustainability Indicators 13: 100227.

Govender, T., Dube, T., Shoko, C., 2022. Remote sensing of land use-land cover change and climate variability on hydrological processes in Sub-Saharan Africa: key scientific strides and challenges. Geocarto International 37(25): 10925-10949.

Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H.M., Ali, M., Masood, N., Amin, A., Farid, H.U., Sultana, R.R., Fahad, S., Wang, D., Nasim, W.,2020. Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan. Environmental Science and Pollution Research 27(32): 39676-39692.

Ibharim, N.A., Mustapha, M. A., Lihan, T., Mazlan, A.G., 2015. Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries. Ocean & Coastal Management 114: 64-76.

Iqbal, M.F., Khan, I.A., 2014. Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. The Egyptian Journal of Remote Sensing and Space Science 17(2): 209-229.

Koç, A., Yener, H., 2001. Uzaktan algılama verileriyle İstanbul çevresi ormanlarının alansal ve yapısal değişikliklerinin saptanması. İstanbul Üniversitesi, Orman Fakültesi Dergisi 51(2): 17-36. [in Turkish]

Lin, C., Wu, C.C., Tsogt, K., Ouyang, Y.C., Chang, C.I., 2015. Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery. Information Processing in Agriculture 2(1): 25–36.

Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., Chi, W., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences 24(2): 195-210.

Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L.W., Merchant, J.W., 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing 21(6-7): 1303-1330.

Lu, D., Li, G., Moran, E., Hetrick, S., 2013. Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon. International Journal of Remote Sensing 34(16): 5953–5978.

MohanRajan, S.N., Loganathan, A., Manoharan, P., 2020. Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges. Environmental Science and Pollution Research 27(24): 29900-29926.

Nguyen, L.H., Joshi, D.R., Clay, D.E., Henebry, G.M., 2020. Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier. Remote Sensing of Environment 238: 111017.

Orimoloye, I.R., Mazinyo, S.P., Nel, W., Kalumba, A.M., 2018. Spatiotemporal monitoring of land surface temperature and estimated radiation using remote sensing: human health implications for East London, South Africa. Environmental Earth Sciences 77: 77.

Pushpanjali, Osman, M.D., Srinivas, R.K., Kumar, P.P., Josily, S., Karthikeyan, K., Sammi, R.K., 2022. Land-use change mapping and analysis using remote sensing and GIS for watershed evaluation-A case study. Journal of Soil and Water Conservation 21(1): 1-6.

Özdemir, İ., Özkan, Y.U., 2003. Monitoring the changes of forest areas using landsat satellite images in Armutlu forest district. Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi 4(1): 55-66. [in Turkish]

Pflugmacher, D., Rabe, A., Peters, M., Hostert, P., 2019. Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment 221: 583-595.

Roy, P.S., Ramachandran, R.M., Paul, O., Thakur, P.K., Ravan, S., Behera, M.D., Sarangi, C., Kanawade, V.P., 2022. Anthropogenic land use and land cover changes—A Review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing 50: 1615–1640.

Samie, A., Deng, X., Jia, S., Chen, D., 2017. Scenario-based simulation on dynamics of land-use-land-cover change in Punjab Province, Pakistan. Sustainability 9(8): 1285.

Soil Survey Staff, 1999. Soil taxonomy: A basic of soil classification for making and ınterpreting soil survey. USDA Natural Resources Conservation Service, Agriculture Handbook No: 436. Washington DC, USA. Available at [Access date: 09.07.2023]: https://www.nrcs.usda.gov/sites/default/files/2022-06/Soil%20Taxonomy.pdf

Stefanski, J., Chaskovskyy, O., Waske, B., 2014. Mapping and monitoring of land use changes in post-Soviet western Ukraine using remote sensing data. Applied Geography 55: 155-164.

Song, W., Song, W., Gu, H., Li, F., 2020. Progress in the remote sensing monitoring of the ecological environment in mining areas. International Journal of Environmental Research and Public Health 17(6): 1846.

Souza, C.M., Jr., Z. Shimbo, J., Rosa, M.R., Parente, L.L., A. Alencar, A., Rudorff, B.F.T., Hasenack, H., Matsumoto, M.G., Ferreira, L., Souza-Filho, P.W.M., de Oliveira, S.W., Rocha, W.F., Fonseca, A.W., Marques, C.B., Diniz, C.G., Costa, D., Monteiro, D., Rosa, E.R., Vélez-Martin, E., Weber, E.J., Lenti, F.E.B., Paternost, F.F., Pareyn, F.G.C., Siqueira, J.A., Viera, J.L., Neto, L.C.F., Saraiva, M.M., Sales, M.H., Salgado, M.P.G., Vasconcelos, R., Galano, S., Mesquita, V.V., Azevedo, T., 2020. Reconstructing three decades of land use and land cover changes in brazilian biomes with landsat archive and earth engine. Remote Sensing 12(17): 2735.

USGS, 2020. USGS EarthExplorer. Available at [Access date: 15.05.2020]: https://earthexplorer.usgs.gov/  

Usman, M., Liedl, R., Shahid, M. A., Abbas, A., 2015. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. Journal of Geographical Sciences 25(12): 1479–1506.

Wu, J., 2019. Linking landscape, land system and design approaches to achieve sustainability. Journal of Land Use Science 14(2): 173-189.

Xu, J., Xiao, P., 2022. A bibliometric analysis on the effects of land use change on ecosystem services: Current status, progress, and future directions. Sustainability 14(5): 3079.

Yasir, M., Hui, S., Binghu, H., Rahman, S. U., 2020. Coastline extraction and land use change analysis using remote sensing (RS) and geographic information system (GIS) technology–A review of the literature. Reviews on Environmental Health 35(4): 453-460.

Zhang, Z., Liu, S., Wei, J., Xu, J., Guo, W., Bao, W., Jiang, Z., 2016. Mass change of glaciers in Muztag Ata–Kongur Tagh, Eastern Pamir, China from 1971/76 to 2013/14 as derived from remote sensing data. PLoS One 11(1): e0147327.

Abstract

Land use and land cover changes can have detrimental effects on the ecology, if they are not properly aligned with the characteristics of the land. This study aims to evaluate the temporal changes in land use and land cover of Bafra Delta plain, situated in the east of Samsun province. The region is one of the most significant plains within the Black Sea area. Remote sensing technique was utilized in this research which made use of Landsat images from 1990, 2000, 2010, and 2020. Supervised classification was applied in ENVI 5.3v software to perform calculations, resulting in six main classes. Field work was applied to classify the unclassified classes. The resulting six land use-land cover classes were agriculture lands, forest, dune, marshy, water surface, and artificial areas. To determine land use efficiency, analogue data was digitised and transferred to a GIS database. The agricultural areas occupy the largest portion of the plain, followed by hazelnut and artificial areas. The changes over the last decade, notably the growth of artificial areas and water surfaces, and the reduction of arable lands, highlight significant variations in size across the areas. Furthermore, the study indicated that remote sensing and geographic information system techniques play a crucial role in identifying and monitoring land cover and land use trends on a large-scale to produce accurate and timely data. Poorly adapted land use changes can cause major ecological damage. The aim of this study is to identify the changes over time in land use and land cover of Bafra Delta plain, located to the east of Samsun city and one of the most significant plains in the Black Sea region, using remote sensing techniques. To this end, Landsat images from 1990, 2000, 2010 and 2020 are utilized. To perform the calculations, ENVI 5.3v software was employed, applying a supervised classification technique that resulted in forming six main classes. Fieldwork was conducted to classify the unclassified classes. The resulting land-use and land-cover classes were agricultural land, forest, dunes, marshland, water surface, and artificial areas. To evaluate land-use efficiency, analogue data were digitalised and imported into a GIS database. The plain's most extensive land-use areas consist of agricultural lands, followed by hazelnut and artificial areas. In the last decade, the rise in artificial and water surfaces and the decline in agricultural areas highlights significant changes in the region's size. This study also emphasises the crucial role of remote sensing and geographic information system techniques in generating fast and consistent data for monitoring large-scale land cover and land use trends.

Keywords: Land use-land cover, change analysis, Bafra Plain

References

Abdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M.A.A., Hassan, Q.K., Dewan, A., 2019. Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing 11(7): 790.

Aboelnour, M., Engel, B.A., 2018. Application of remote sensing techniques and geographic information systems to analyze land surface temperature in response to land use/land cover change in Greater Cairo Region, Egypt. Journal of Geographic Information System 10(1): 57-88.

Arora, G., Wolter, P.T., 2018. Tracking land cover change along the western edge of the US Corn Belt from 1984 through 2016 using satellite sensor data: Observed trends and contributing factors. Journal of Land Use Science 13(1-2): 59-80.

Bağcı, H.R., Bahadır, M., 2019. Land use and temporal change in Kızılırmak Delta (Samsun) (1987-2019). The Journal of Academic Social Science Studies 78: 295-312. [in Turkish]

Bansod, R.D., Dandekar, U.M., 2018. Evaluation of Morna river catchment with RS and GIS techniques. Journal of Pharmacognosy and Phytochemistry 7(1): 1945-1948.

Chen, J., Theller, L., Gitau, M.W., Engel, B.A., Harbor, J.M., 2017. Urbanization impacts on surface runoff of the contiguous United States. Journal of Environmental Management 187: 470-481.

Cui, J., Zhu, M., Liang, Y., Qin, G., Li, J., Liu, Y., 2022. Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on google earth Engine from 2000 to 2020. ISPRS International Journal of Geo-Information 11(3): 163.

Devkota, P., Dhakal, S., Shrestha, S., Shrestha, U.B., 2023. Land use land cover changes in the major cities of Nepal from 1990 to 2020. Environmental and Sustainability Indicators 13: 100227.

Govender, T., Dube, T., Shoko, C., 2022. Remote sensing of land use-land cover change and climate variability on hydrological processes in Sub-Saharan Africa: key scientific strides and challenges. Geocarto International 37(25): 10925-10949.

Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H.M., Ali, M., Masood, N., Amin, A., Farid, H.U., Sultana, R.R., Fahad, S., Wang, D., Nasim, W.,2020. Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan. Environmental Science and Pollution Research 27(32): 39676-39692.

Ibharim, N.A., Mustapha, M. A., Lihan, T., Mazlan, A.G., 2015. Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries. Ocean & Coastal Management 114: 64-76.

Iqbal, M.F., Khan, I.A., 2014. Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan. The Egyptian Journal of Remote Sensing and Space Science 17(2): 209-229.

Koç, A., Yener, H., 2001. Uzaktan algılama verileriyle İstanbul çevresi ormanlarının alansal ve yapısal değişikliklerinin saptanması. İstanbul Üniversitesi, Orman Fakültesi Dergisi 51(2): 17-36. [in Turkish]

Lin, C., Wu, C.C., Tsogt, K., Ouyang, Y.C., Chang, C.I., 2015. Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery. Information Processing in Agriculture 2(1): 25–36.

Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., Chi, W., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences 24(2): 195-210.

Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, Z., Yang, L.W., Merchant, J.W., 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing 21(6-7): 1303-1330.

Lu, D., Li, G., Moran, E., Hetrick, S., 2013. Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon. International Journal of Remote Sensing 34(16): 5953–5978.

MohanRajan, S.N., Loganathan, A., Manoharan, P., 2020. Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges. Environmental Science and Pollution Research 27(24): 29900-29926.

Nguyen, L.H., Joshi, D.R., Clay, D.E., Henebry, G.M., 2020. Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier. Remote Sensing of Environment 238: 111017.

Orimoloye, I.R., Mazinyo, S.P., Nel, W., Kalumba, A.M., 2018. Spatiotemporal monitoring of land surface temperature and estimated radiation using remote sensing: human health implications for East London, South Africa. Environmental Earth Sciences 77: 77.

Pushpanjali, Osman, M.D., Srinivas, R.K., Kumar, P.P., Josily, S., Karthikeyan, K., Sammi, R.K., 2022. Land-use change mapping and analysis using remote sensing and GIS for watershed evaluation-A case study. Journal of Soil and Water Conservation 21(1): 1-6.

Özdemir, İ., Özkan, Y.U., 2003. Monitoring the changes of forest areas using landsat satellite images in Armutlu forest district. Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi 4(1): 55-66. [in Turkish]

Pflugmacher, D., Rabe, A., Peters, M., Hostert, P., 2019. Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment 221: 583-595.

Roy, P.S., Ramachandran, R.M., Paul, O., Thakur, P.K., Ravan, S., Behera, M.D., Sarangi, C., Kanawade, V.P., 2022. Anthropogenic land use and land cover changes—A Review on its environmental consequences and climate change. Journal of the Indian Society of Remote Sensing 50: 1615–1640.

Samie, A., Deng, X., Jia, S., Chen, D., 2017. Scenario-based simulation on dynamics of land-use-land-cover change in Punjab Province, Pakistan. Sustainability 9(8): 1285.

Soil Survey Staff, 1999. Soil taxonomy: A basic of soil classification for making and ınterpreting soil survey. USDA Natural Resources Conservation Service, Agriculture Handbook No: 436. Washington DC, USA. Available at [Access date: 09.07.2023]: https://www.nrcs.usda.gov/sites/default/files/2022-06/Soil%20Taxonomy.pdf

Stefanski, J., Chaskovskyy, O., Waske, B., 2014. Mapping and monitoring of land use changes in post-Soviet western Ukraine using remote sensing data. Applied Geography 55: 155-164.

Song, W., Song, W., Gu, H., Li, F., 2020. Progress in the remote sensing monitoring of the ecological environment in mining areas. International Journal of Environmental Research and Public Health 17(6): 1846.

Souza, C.M., Jr., Z. Shimbo, J., Rosa, M.R., Parente, L.L., A. Alencar, A., Rudorff, B.F.T., Hasenack, H., Matsumoto, M.G., Ferreira, L., Souza-Filho, P.W.M., de Oliveira, S.W., Rocha, W.F., Fonseca, A.W., Marques, C.B., Diniz, C.G., Costa, D., Monteiro, D., Rosa, E.R., Vélez-Martin, E., Weber, E.J., Lenti, F.E.B., Paternost, F.F., Pareyn, F.G.C., Siqueira, J.A., Viera, J.L., Neto, L.C.F., Saraiva, M.M., Sales, M.H., Salgado, M.P.G., Vasconcelos, R., Galano, S., Mesquita, V.V., Azevedo, T., 2020. Reconstructing three decades of land use and land cover changes in brazilian biomes with landsat archive and earth engine. Remote Sensing 12(17): 2735.

USGS, 2020. USGS EarthExplorer. Available at [Access date: 15.05.2020]: https://earthexplorer.usgs.gov/  

Usman, M., Liedl, R., Shahid, M. A., Abbas, A., 2015. Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. Journal of Geographical Sciences 25(12): 1479–1506.

Wu, J., 2019. Linking landscape, land system and design approaches to achieve sustainability. Journal of Land Use Science 14(2): 173-189.

Xu, J., Xiao, P., 2022. A bibliometric analysis on the effects of land use change on ecosystem services: Current status, progress, and future directions. Sustainability 14(5): 3079.

Yasir, M., Hui, S., Binghu, H., Rahman, S. U., 2020. Coastline extraction and land use change analysis using remote sensing (RS) and geographic information system (GIS) technology–A review of the literature. Reviews on Environmental Health 35(4): 453-460.

Zhang, Z., Liu, S., Wei, J., Xu, J., Guo, W., Bao, W., Jiang, Z., 2016. Mass change of glaciers in Muztag Ata–Kongur Tagh, Eastern Pamir, China from 1971/76 to 2013/14 as derived from remote sensing data. PLoS One 11(1): e0147327.

 

 

 



Eurasian Journal of Soil Science