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Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda

Received: 27 February 2021     Accepted: 11 March 2021     Published: 22 March 2021
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Abstract

The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By means of Remote Sensing and GIS tools, Land use/Land cover (LULC) changes were detected. Possible causes linked to historical changes were highlighted accordingly. Multi-temporal remote sensing images (Landsat imagery) were used to generate land use/land cover (LULC) maps. Two temporal satellite images were collected, preprocessed, and classified through supervised Image classification stages in ENVI 5.1. Corresponding maps were exported by ArcGIS 10.7. Seven important classes including water, bare land, wetlands, agriculture, vegetation, forest, and built-up area were classified and detected for changes using both Image change workflow and Thematic change workflow tools in ENVI 5.1. Among seven classes of land use/land cover (LULC), four experienced gains while built-up area, forest, and bare land witnessed decrease/losses over the last 15 years period (2005-2020). Like Forest diminished from 197.8821 km2 in 2005 to 56.9304 km2 in 2020. Several factors including government policies and regulations, population growth, and economic development can be attributed to these changes. The present work can provide important insights on land use planning and management for the area under consideration and we believe this work to contribute to the literature on the application of ENVI and related remote sensing tools.

Published in American Journal of Remote Sensing (Volume 9, Issue 1)
DOI 10.11648/j.ajrs.20210901.13
Page(s) 23-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Land Use/Land Cover, LULC, ENVI, Rwanda, Change Detection, Supervised Classification

References
[1] Sala O. E et al. (2000). Global biodiversity scenarios for the year 2100. Science, Vol. 287, pp. 1770-1774., DOI: 10.1126/science.287.5459.1770.
[2] Alawamy, J. S., Balasundram, S. K., Hanif, A. H. M., & Sung, C. T. B. (2020). Detecting and analyzing land use and land cover changes in the Region of Al-Jabal Al-Akhdar, Libya using time-series landsat data from 1985 to 2017. Sustainability (Switzerland). https://doi.org/10.3390/su12114490.
[3] 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. https://doi.org/10.1007/s12517-013-1119-7.
[4] Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M., Mengistu, D. T., & Teshale, E. Z. (2018). Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air, Soil and Water Research. https://doi.org/10.1177/1178622117751603.
[5] Sharma, R., Nguyen, T. T., & Grote, U. (2018). Changing consumption patterns-drivers and the environmental impact. In Sustainability (Switzerland). https://doi.org/10.3390/su10114190.
[6] Omar, P. J., Gupta, N., Tripathi, R. P., Shekhar, S., &. S. (2017). A Study of Change in Agricultural and Forest Land in Gwalior City Using Satellite Imagery. SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology. https://doi.org/10.18090/samriddhi.v9i02.10870.
[7] DeFries, R., Hansen, A., Turner, B. L., Reid, R., & Liu, J. (2007). Land use change around protected areas: Management to balance human needs and ecological function. In Ecological Applications. https://doi.org/10.1890/05-1111.
[8] Ganasri B. P and Dwarakish G. S. (2015). Study of Land use/land Cover Dynamics through Classification Algorithms for Harangi Catchment Area, Karnataka State, INDIA. Aquatic Procedia, Vol. 4, pp. 1413–1420, DOI: 10.1016/j.aqpro.2015.02.183.
[9] Kafi K. M., Shafri H. Z. M and Shariff A. B. M. (2014). An analysis of LULC change detection using remotely sensed data: A Case study of Bauchi City. IOP Conference Series: Earth and Environmental Science, Vol. 20, DOI: 10.1088/1755-1315/20/1/012056.
[10] Lillesand T. M., Kiefer R. W and Chipman J. W. (2015). Remote sensing and image interpretation, 7th ed, Wiley, 2015, pp. 1-770.
[11] Lin Y. P., Verburg P. H., Chang C. R., Chen H. Y., and Chen M. H. (2009). Developing and comparing optimal and empirical land-use models for the development of an urbanized watershed forest in Taiwan. Landscape and Urban Planning, Vol. 92, pp. 242-254, DOI: 10.1016/j.landurbplan.2009.05.003.
[12] Su C. et al. (2011). Land use change and anthropogenic driving forces: A case study in Yanhe River Basin. Chinese Geographical Science, Vol. 21, No. 5, pp. 587-599, DOI: 10.1007/s11769-011-0495-8.
[13] Roy D. P., Lewis P. E., and Justice C. O. (2002). Burned area mapping using multi-temporal moderate spatial resolution data-a bi-directional reflectance model-based expectation approach. Remote Sensing of Environment, Vol. 83, pp. 263-286, DOI: 10.1016/S0034-4257(02)00077-9.
[14] Dewan A. M. and Yamaguchi Y. (2009) ‘‘Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization’’, Applied Geography, Vol. 29, pp. 390-401, DOI: 10.1016/j.apgeog.2008.12.005.
[15] Barnsley M. J., Longley P. A., Batty M., and Howes D. (2010). Predicting Temporal Patterns in Urban Development from Remote Imagery: in Remote Sensing and Urban Analysis, 1st ed, CRC Press, 2010, pp. 1-20.
[16] Gilmore M. S. et al. (2008). Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sensing of Environment, Vol. 112, pp. 4048- 4060, DOI: 10.1016/j.rse.2008.05.020.
[17] Duraisamy V., Bendapudi R., and Jadhav A. (2018). Identifying hotspots in land use land cover change and the drivers in a semi-arid region of India. Environ. Monit. Assess., Vol. 190, No. 9, pp. 1-21, DOI: 10.1007/s10661-018-6919-5.
[18] Roy A. and Inamdar A. B. (2019). Multi-temporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy. Heliyon, Vol. 5, No. e01478, DOI: 10.1016/j.heliyon.2019.e01478.
[19] REMA. (2019). Transforming Eastern Province of Rwanda’ s capacity to adapt to climate change through forests and landscape restoration. Rwanda Environment Management Authority, pp. 1-12.
[20] Liping C., Yujun S., and Saeed S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS One, Vol. 13, No. 5, pp. 1-23, DOI: 10.1371/journal.pone.0200493.
[21] Coppin P., Jonckheere I., Nackaerts K., Muys B., and Lambin E. (2004). Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, Vol. 29, No. 5, pp. 1565-1596, DOI: 10.1080/0143116031000101675.
[22] Cooley T. et al. (2002). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its applications and validation. International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1414-1418, DOI: 10.1109/igarss.2002.1026134.
[23] Mathur A. and Foody G. M. (2008). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, Vol. 29, No. 8, pp. 2227-2240, DOI: 10.1080/01431160701395203.
[24] Nambajimana J. D et al. (2020). Land use change impacts on water erosion in Rwanda. Sustainability, Vol. 12, No. 50, pp. 1-23, DOI: 10.3390/SU12010050.
[25] Food and Agriculture Organization of the United Nations (FAO). FAO in Rwanda: Rwanda at a glance. Accessed on 11/02/2021 at http://www.fao.org/rwanda/our-office-in-rwanda/rwanda-at-a-glance/en/.
[26] The World Bank. Population density (people per sq.km of land area)-Rwanda. Accessed on 11/02/2021 at https://data.worldbank.org/indicator/EN.POP.DNST?locations=RW&most_recent_year_desc=false.
[27] Rwanda Ministry of Infrastructure. (2015). National Housing Policy. Accessed on 11/02/2021 at https://www.rha.gov.rw/fileadmin/user_upload/documents/General_documents/Housing_regulations_and_standards/Policies/National_Housing_Policy.pdf.
[28] Tooth S. et al. (2015). 10 reasons why the geomorphology of wetlands is important”, Climate Change Consortium of Wales (C3W), Accessed on 11/02/2021 at http://wetlandsindrylands.net/wp-content/uploads/2015/10/10-Reasons-Geomorphology-of-Wetlands-NEAR-FINAL-FULL-COLOUR.pdf.
Cite This Article
  • APA Style

    Jean Paul Nkundabose, Felix Nshimiyimana, Gratien Twagirayezu, Olivier Irumva. (2021). Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda. American Journal of Remote Sensing, 9(1), 23-32. https://doi.org/10.11648/j.ajrs.20210901.13

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    ACS Style

    Jean Paul Nkundabose; Felix Nshimiyimana; Gratien Twagirayezu; Olivier Irumva. Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda. Am. J. Remote Sens. 2021, 9(1), 23-32. doi: 10.11648/j.ajrs.20210901.13

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    AMA Style

    Jean Paul Nkundabose, Felix Nshimiyimana, Gratien Twagirayezu, Olivier Irumva. Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda. Am J Remote Sens. 2021;9(1):23-32. doi: 10.11648/j.ajrs.20210901.13

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  • @article{10.11648/j.ajrs.20210901.13,
      author = {Jean Paul Nkundabose and Felix Nshimiyimana and Gratien Twagirayezu and Olivier Irumva},
      title = {Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda},
      journal = {American Journal of Remote Sensing},
      volume = {9},
      number = {1},
      pages = {23-32},
      doi = {10.11648/j.ajrs.20210901.13},
      url = {https://doi.org/10.11648/j.ajrs.20210901.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20210901.13},
      abstract = {The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By means of Remote Sensing and GIS tools, Land use/Land cover (LULC) changes were detected. Possible causes linked to historical changes were highlighted accordingly. Multi-temporal remote sensing images (Landsat imagery) were used to generate land use/land cover (LULC) maps. Two temporal satellite images were collected, preprocessed, and classified through supervised Image classification stages in ENVI 5.1. Corresponding maps were exported by ArcGIS 10.7. Seven important classes including water, bare land, wetlands, agriculture, vegetation, forest, and built-up area were classified and detected for changes using both Image change workflow and Thematic change workflow tools in ENVI 5.1. Among seven classes of land use/land cover (LULC), four experienced gains while built-up area, forest, and bare land witnessed decrease/losses over the last 15 years period (2005-2020). Like Forest diminished from 197.8821 km2 in 2005 to 56.9304 km2 in 2020. Several factors including government policies and regulations, population growth, and economic development can be attributed to these changes. The present work can provide important insights on land use planning and management for the area under consideration and we believe this work to contribute to the literature on the application of ENVI and related remote sensing tools.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda
    AU  - Jean Paul Nkundabose
    AU  - Felix Nshimiyimana
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    AB  - The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By means of Remote Sensing and GIS tools, Land use/Land cover (LULC) changes were detected. Possible causes linked to historical changes were highlighted accordingly. Multi-temporal remote sensing images (Landsat imagery) were used to generate land use/land cover (LULC) maps. Two temporal satellite images were collected, preprocessed, and classified through supervised Image classification stages in ENVI 5.1. Corresponding maps were exported by ArcGIS 10.7. Seven important classes including water, bare land, wetlands, agriculture, vegetation, forest, and built-up area were classified and detected for changes using both Image change workflow and Thematic change workflow tools in ENVI 5.1. Among seven classes of land use/land cover (LULC), four experienced gains while built-up area, forest, and bare land witnessed decrease/losses over the last 15 years period (2005-2020). Like Forest diminished from 197.8821 km2 in 2005 to 56.9304 km2 in 2020. Several factors including government policies and regulations, population growth, and economic development can be attributed to these changes. The present work can provide important insights on land use planning and management for the area under consideration and we believe this work to contribute to the literature on the application of ENVI and related remote sensing tools.
    VL  - 9
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Author Information
  • School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, PR China

  • School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, PR China

  • School of Environmental and Municipal Engineering, Lanzhou Jiaotong University, Lanzhou, PR China

  • School of Eco-Environmental Engineering, Guizhou Minzu University, Guiyang, PR China

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