Detection of Vegetation Changes in Agricultural Lands of Sistan Plain, using Remote Sensing Technique

Document Type : Research Paper

Authors

1 Soil and Water Research Department, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization(AREEO), Shahrekord, Iran.

2 Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

Abstract

Information about the state of vegetation is very important for environmental planning, land preparation and achieving sustainable development. In this study normalized differential vegetation index (NDVI) values were calculated based on Landsat 8 satellite images in order to show temporal and spatial changes in the vegetation cover of agricultural lands in Sistan plain over ten years (2011 to 2020) using the Google Earth Engine platform. Additionally, the NDVI index were classified using decision tree algorithm in order to analyze vegetation changes using thematic change workflow method. By comparing classified images with reference samples which collected from ground sampling, validation was carried out. Then, in order to assess accuracy of vegetation maps, the error matrix was prepared, the overall accuracy and kappa indices were determined. The values of overall accuracy and kappa indices indicated optimal accuracy and it can be stated that there is moderate agreement between ground samples and the classified images (i.e., kappa index is 0.48 to 0.7). The central areas of Sistan plain have a decline in vegetation, whereas areas in northern and eastern have an increase. The cover vegetation on lands of Sistan plain decreased in 19260.4 ha while increased over 25633.2 ha throughout ten years. Examination of NDVI index shows instability of production in this area due to aforementioned factors.

Keywords

References

References
Alavipanah,, S.K., 2003. Application of remote sensing in earth science. 5 th ed., University of Tehran Press, Tehran.

Alavipanah,, S.K., Ehsani, E., Metinfar, H., Rafii Imam, A., Amiri R., 2006. Comparison of information content of TM and ETM+ sensor bands in desert and urban environments of Iran. Journal of Geographical Studies. 47; 56-64.

Alqurashi, A.F., Kumar, L., 2013. Investigating the use of remote sensing and GIS tech- niques to detect land use and land cover change: a review. Advances in Remote Sensing. 2; 193–204.

Braun, A., Fakhri, F., Hochschild, V., 2019. Refugee camp monitoring and environmental change assessment of Kutupalong, Bangladesh, based on radar imagery of Sentinel-1 and ALOS-2. Remote Sensing.11; 1–34.

Congalton R. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing Environmental 37: 35–46.   

Congalton, R.G., and Green, K., 1998. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton.

Fatemi S.B., Rezaie Y. 2012. Basics of remote sensing. Azadeh. Iran.

 Firouzi, F., Tavossi, T., Mahmoudi, P. 2019. Investigating the sensitivity of two vegetation indices, NDVI and EVI, to droughts and droughts in arid and semi-arid regions; Case study: Sistan plain of Iran. Scientific - Research Quarterly of Geographical Data (SEPEHR). 28, 163- 179.

Gandhi. M., Parthiban, S., Thummalu, N., Christy, A., 2015. Ndvi: Vegetation change detection using remote sensing and GIS – A case study of Vellore District. Procedia Computer Science 57, 1199 – 1210.

Gondwe, J.F., Lin, S., Munthali, R.M., 2021. Analysis of Land Use and Land Cover Changes in Urban Areas Using Remote Sensing: Case of Blantyre City. Discrete Dynamics in Nature and Society. 2021, 1-17.

Kotaridis, I., Lazaridou, M., 2018. Environmental change detection study in the wider area of lignite mines. Civil Engineering and Architecture. 6; 108–114.

Lu, D., Mausel, P., Brondízio, E., Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing. 25; 2365-2407.

Mohammadyari, F., Mirsanjari, M.M., Zarandian, A., 2019. Monitoring of vegetation changes in Karaj watershed using NDVI index and gradient analysis. Journal of RS and GIS for Natural Resource. 4; 55-72.

Mansourmoghaddam, M., Ghafarian  Ghafarian Malamiri, Arabi Aliabad, F., Fallah Tafti, M., Haghani, M., H.R., Shojaei, S. 2022. The Separation of the Unpaved Roads and Prioritization of Paving These Roads Using UAV Images. Air, Soil and Water Research. 15.  

 Mansourmoghaddam, M., Naghipur, N., Rousta, I., Ghaffarian, H.R. 2022. Temporal and Spatial Monitoring and Forecasting of Suspended Dust Using Google Earth Engine and Remote Sensing Data (Case Study: Qazvin Province). Desert Management. 10; 77-98.

Mousavi, M.N.,  Sarli, R., Khodadad, M. 2017. Revealing changes in land use and vegetation in Poldakhter city using Landsat satellite images. Environmental studies of Haft Hesar. 26; 103-115.

 Nateghi, S.,  Nohegar, A.,  Ehsani, A.H.,  Bazrafshan, O., 2018. Evaluating the vegetation changes upon vegetation index by using remote sensing. Iranian Journal of Rangeland and Desert Research. 4; 778-790.

Pettorelli, N.,Vik, O., Mysterud. A., Gaillard, J. M,. Tucker, C. J. and Stenseth.N. C., 2005. Using the satellite–derived NDVI to assess ecological responses to environmental change. Journal Trends in Ecology and Evolution. 9; 200-216.

Rahman, M., Islam, M., Chowdhury, T., 2019. Change of vegetation cover at Rohingya refugee occupied areas in Cox’s Bazar District of Bangladesh: evidence from remotely sensed data. Journal of Environmental Science and Natural Resources. 11; 9–16.

Reynolds, J. F.; D.M. Smith, E.F. Lambin, B.L. Turner, M. Mortimore, S.P. Batterbury, T. E.Downing, H. Dowlatabadi, R.J. Fernandez, J.E. Herrick, E. Huber-Sannwald, H. Jiang, R. Leemans, T. Lynam, F.T. Maestre, M. Ayarza, and B. Walker. 2007. Global desertification: building a science for dryland development. Science. 316; 847-851.

Rwanga, S.S., Ndambuki, J.M. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences. 8, 1-12.

Shafei, H., Hosseini, S. M., 2012. A study of vegetation in Sistan region through satellite data. Journal of Plant Ecophysiology. 9; 91-105.

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing. 164; 152–170.

Veron, S.R., Paruelo, J.M., Oesterheld, M., 2006. Assessing desertification. Journal of Arid Environments. 66; 753-763.

Xie, Z., Phinn, S.R., Game, E.T., Pannell, D.J., Hobbs, R.J., Briggs, P.R., McDonald-Madden, E. 2019. Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands - A first step towards identifying degraded lands for conservation. Remote Sensing of Environment. 232; 111317.

Desert
Volume 28, Issue 1
June 2023
Pages 49-63

Files

Share

How to cite

Statistics