Have a personal or library account? Click to login
Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali Cover

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

Open Access
|Jan 2024

Figures & Tables

Figure 1.

Topographic map of Mali. Mapping software: Generic Mapping Tools (GMT) scripting toolset. The area of the Inner Niger Delta is indicated by green rotated square. Data source: GEBCO/SRTM. Cartography source: authors.
Topographic map of Mali. Mapping software: Generic Mapping Tools (GMT) scripting toolset. The area of the Inner Niger Delta is indicated by green rotated square. Data source: GEBCO/SRTM. Cartography source: authors.

Figure 2.

Flowchart summarising general steps of data processing. Diagram source: authors (R library ‘DiagrammeR’).
Flowchart summarising general steps of data processing. Diagram source: authors (R library ‘DiagrammeR’).

Figure 3.

The location of the Landsat 8–9 satellite image in Mali, Mopti region of the Inner Niger Delta. The images were downloaded from the EarthExplorer repository, USGS. Background image: ESRI World imagery.
The location of the Landsat 8–9 satellite image in Mali, Mopti region of the Inner Niger Delta. The images were downloaded from the EarthExplorer repository, USGS. Background image: ESRI World imagery.

Figure 4.

Landsat 8–9 images of Inner Niger Delta in natural colour RGB values showing floodplain for six years (always November): (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022.
Landsat 8–9 images of Inner Niger Delta in natural colour RGB values showing floodplain for six years (always November): (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022.

Figure 5.

Concept flowchart of the remote sensing data processing and analysis. Flowchart is prepared using R library ‘DiagrammeR’. Source: authors.
Concept flowchart of the remote sensing data processing and analysis. Flowchart is prepared using R library ‘DiagrammeR’. Source: authors.

Figure 6.

Processing Landsat satellite image in RStudio for extracting NDVI.
Processing Landsat satellite image in RStudio for extracting NDVI.

Figure 7.

NDVI based on Landsat 8–9 images of Inner Niger Delta: (a) 2013, (b) 2015,(c) 2018, (d) 2020, (e) 2021, (f) 2022. Mapping: RStudio. Source: authors.
NDVI based on Landsat 8–9 images of Inner Niger Delta: (a) 2013, (b) 2015,(c) 2018, (d) 2020, (e) 2021, (f) 2022. Mapping: RStudio. Source: authors.

Figure 8.

Histograms of the NDVI of the Landsat 8–9 images on Inner Niger Delta: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Plots: RStudio. Source: authors.
Histograms of the NDVI of the Landsat 8–9 images on Inner Niger Delta: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Plots: RStudio. Source: authors.

Figure 9.

Soil Adjusted Vegetation Index (SAVI) based on the Landsat 8–9 images of Inner Niger Delta for November: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Mapping visualisation: RStudio. Source: authors.
Soil Adjusted Vegetation Index (SAVI) based on the Landsat 8–9 images of Inner Niger Delta for November: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Mapping visualisation: RStudio. Source: authors.

Figure 10.

Histograms of SAVI based on the Landsat 8–9 images of Inner Niger Delta: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Plots: RStudio. Source: authors.
Histograms of SAVI based on the Landsat 8–9 images of Inner Niger Delta: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Plots: RStudio. Source: authors.

Figure 11.

Enhanced Vegetation Index (EVI) based on the Landsat 8–9 images of Inner Niger Delta for November: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Mapping visualisation: RStudio. Source: authors.
Enhanced Vegetation Index (EVI) based on the Landsat 8–9 images of Inner Niger Delta for November: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Mapping visualisation: RStudio. Source: authors.

Figure 12.

Histograms of EVI based on the Landsat 8–9 images of Inner Niger Delta for November: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Plots: RStudio. Source: authors.
Histograms of EVI based on the Landsat 8–9 images of Inner Niger Delta for November: (a) 2013, (b) 2015, (c) 2018, (d) 2020, (e) 2021, (f) 2022. Plots: RStudio. Source: authors.

Figure 13.

Clustering based on the Landsat 8–9 images (bands 4-3-2): (a) 2013 RGB in true colour composites (TCC), (b) 2013 clusters, (c) 2015 RGB in TCC, (d) 2015 clusters, (e) 2018 RGB in TCC, (f) 2018 clusters. Mapping: RStudio. Source: authors.
Clustering based on the Landsat 8–9 images (bands 4-3-2): (a) 2013 RGB in true colour composites (TCC), (b) 2013 clusters, (c) 2015 RGB in TCC, (d) 2015 clusters, (e) 2018 RGB in TCC, (f) 2018 clusters. Mapping: RStudio. Source: authors.

Figure 14.

Clustering based on the Landsat 8–9 images of Inner Niger Delta: (a) 2020 RGB in TCC (4-3-2), (b) 2020 classification, (c) 2021 RGB in TCC, (d) 2021 classification, (e) 2022 RGB in TCC, (f) 2022 classification. Mapping: RStudio. Source: authors.
Clustering based on the Landsat 8–9 images of Inner Niger Delta: (a) 2020 RGB in TCC (4-3-2), (b) 2020 classification, (c) 2021 RGB in TCC, (d) 2021 classification, (e) 2022 RGB in TCC, (f) 2022 classification. Mapping: RStudio. Source: authors.

Figure 15.

Correlation plot showing probability cases for land cover classes in the Inner Niger Delta between 2013 and 2022 based on the results of the clustering of the Landsat 8–9 images using Kendall correlation method. Mapping: Python. Source: authors.
Correlation plot showing probability cases for land cover classes in the Inner Niger Delta between 2013 and 2022 based on the results of the clustering of the Landsat 8–9 images using Kendall correlation method. Mapping: Python. Source: authors.

Metadata of the satellite images used in this study: Landsat 8–9 USGS1_

DateSpacecraft / IDPath/RowEntity Product IDScene IDCloud/Coverage
2013/11/10Landsat 8197/50LC08_L2SP_197050_20131110_20200912_02_T1LC81970502013314LGN010.12
2015/11/16Landsat 8197/50LC08_L2SP_197050_20151116_20200908_02_T1LC81970502015320LGN011.12
2018/11/24Landsat 8197/50LC08_L2SP_197050_20181124_20200830_02_T1LC81970502018328LGN000.00
2020/11/29Landsat 8197/50LC08_L2SP_197050_20201129_20210316_02_T1LC81970502020334LGN000.00
2021/11/16Landsat 8197/50LC08_L2SP_197050_20211116_20211125_02_T1LC81970502021320LGN000.00
2022/11/11Landsat 9197/50LC09_L2SP_197050_20221111_20221113_02_T1LC91970502022315LGN000.00

Results of the NDVI, SAVI and EVI computations of the Landsat 8–9 images_

TimeNDVI Extreme ValuesSAVI Extreme ValuesEVI Extreme Values
minimalmaximalminimalmaximalminimalmaximal
10 November 2013−0.23113770.5879165−0.22351050.4281315−0.35760840.6849960
16 November 2015−0.26192930.5245128−0.16812810.2896889−0.26899850.4634939
24 November 2018−0.26039420.5258653−0.18305710.2938004−0.29288490.4700728
29 November 2020−0.24966930.4980780−0.13840920.2833834−0.22144910.4534074
16 November 2021−0.25224580.5417392−0.15644690.2790245−0.25030890.4464335
11 November 2022−0.30728120.5237642−0.18105410.2929085−0.28967990.4686475
DOI: https://doi.org/10.2478/arsa-2023-0011 | Journal eISSN: 2083-6104 | Journal ISSN: 1509-3859
Language: English
Page range: 278 - 313
Submitted on: Feb 24, 2023
Accepted on: Nov 29, 2023
Published on: Jan 19, 2024
Published by: Polish Academy of Sciences, Space Research Centre
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Polina Lemenkova, Olivier Debeir, published by Polish Academy of Sciences, Space Research Centre
This work is licensed under the Creative Commons Attribution 4.0 License.