Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia
<p>Location map of the study area: (<b>a</b>) River basins of Ethiopia, Upper Blue Nile Basin, and Lake Tana subbasin; (<b>b</b>) Lake Tana subbasin, Lake Tana, and Gumara watershed; and (<b>c</b>) Gumara watershed boundary, location of towns, road networks, river networks, and elevation map of the Gumara watershed.</p> "> Figure 2
<p>False color composites (NIR, red and green bands) of Landsat-5/TM (<b>a</b>–<b>c</b>) and Landsat-8/OLI (<b>d</b>) images used for LULC classification for the years (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. The deep red areas represent areas covered with scattered plants; the darker red areas represent densely vegetated areas.</p> "> Figure 3
<p>Methodological framework of LULC classification and change detection.</p> "> Figure 4
<p>Methodological framework for future LULC prediction.</p> "> Figure 5
<p>Computed NDVI images of the Gumara watershed for the years (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. In the figures, dark greens (maximum NDVI values example, NDVI = 0.4) represent vegetated areas, while dark reds (minimum NDVI values) represent bare soils or agricultural lands.</p> "> Figure 6
<p>Computed SAVI images of the Gumara watershed for the years (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. In the figures, dark greens (maximum SAVI values, for example, SAVI ≥ 0.6) represent highly vegetated areas, while dark reds (minimum SAVIvalues) represent bare soils or agricultural lands.</p> "> Figure 7
<p>Map of driver variables: (<b>a</b>) elevation, (<b>b</b>) slope, (<b>c</b>) distance from streams, (<b>d</b>) distance from roads, (<b>e</b>) distance from towns, and (<b>f</b>) evidence likelihood.</p> "> Figure 8
<p>LULC maps of the Gumara watershed for (<b>a</b>) 1985, (<b>b</b>) 2000, (<b>c</b>) 2010, and (<b>d</b>) 2019. The values in the legend indicate the percentage of each LULC class.</p> "> Figure 9
<p>Area of each LULC class in the Gumara watershed for the four historical years (1985, 2000, 2010, and 2019).</p> "> Figure 10
<p>(<b>a</b>) UA and (<b>b</b>) PA assessment results for each class for the LULC maps for the years 1985, 2000, 2010, and 2019.</p> "> Figure 11
<p>Relative variable importance (%) for the four datasets used for mapping LULC in the Gumara watershed: (<b>a</b>) Landsat-5/TM (1985), (<b>b</b>) Landsat-5/TM (2000), (<b>c</b>) Landsat 5/TM (2010), and (<b>d</b>) Landsat-8/OLI (2019).</p> "> Figure 12
<p>Net change (gain-loss) in each LULC class for the four study periods (1985–2000, 2000–2010, 2010–2019, and 1985–2019).</p> "> Figure 13
<p>Contribution of each LULC class to the net change in cultivated land: (<b>a</b>) 1985–2000, (<b>b</b>) 2000–2010, (<b>c</b>) 2010–2019, and (<b>d</b>) 1985–2019.</p> "> Figure 14
<p>Potential for transition: (<b>a</b>) shrubland to cultivated land and (<b>b</b>) cultivated land to settlement. TP is the transition potential. The greater the TP is, the greater the possibility of a transition from one class to another. The gray shaded regions show the orientation gradients of the transition potential, wherein the maximum transitions are oriented along the northeastern part of the watershed for both transitions. The areas bordered by circles indicate the maximum values of transition suitability. The triangle symbol in both of the figures indicates the location of the town Debre Tabor.</p> "> Figure 15
<p>LULC maps (2019): (<b>a</b>) reference LULC map and (<b>b</b>) CA–Markov model-predicted LULC map under the BAU scenario.</p> "> Figure 16
<p>Comparison of the reference (baseline) and predicted areas of the LULC classes in the Gumara watershed in 2019.</p> "> Figure 17
<p>Predicted LULC maps of the Gumara watershed: (<b>a</b>) for 2035 and (<b>b</b>) for 2065 under the BAU scenario; (<b>c</b>) for 2035 and (<b>d</b>) for 2065 under the GOV scenario.</p> "> Figure 18
<p>Net changes (gain-losses): (<b>a</b>) net change (2019–2065) under the BAU scenario and (<b>b</b>) net change (2019–2065) under the GOV scenario.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Used
2.3. Overview of the Methodology
2.4. LULC Classification and Change Detection
2.4.1. LULC Classification
2.4.2. Input Variables
2.4.3. Variable Importance
2.4.4. Accuracy Assessment
2.4.5. LULC Change Detection
2.5. LULC Prediction
2.5.1. LULC Change Driver Variables
2.5.2. Transition Probability Matrix (TPM)
2.5.3. Transition Suitability Map (TSM)
2.5.4. CA–Markov Model
2.5.5. Validation of the CA–Markov Model
2.5.6. Scenario-Based LULC Prediction
3. Results
3.1. LULC Classification
3.2. Accuracy Assessment
3.3. Variable Importance
3.4. Change Detection (1985–2019)
3.4.1. Percentage Change and Annual Rate of Change
3.4.2. Gain, Loss, and Net Change
3.4.3. Contribution to the Net Change in Cultivated Land
3.5. LULC Change Driver Variables
3.6. Transition Probability Matrix (TPM)
3.7. Transition Suitability Maps (TSMs)
3.8. Validation of the CA–Markov Model
3.9. LULC Prediction
3.10. Change Detection (2019–2065)
4. Discussion
4.1. LULC Classification and Change Detection
4.2. Impacts of LULC Change on Socioeconomic and Environmental Conditions
4.3. Relevance of Scenario-Based LULC Change Detection and Prediction to Policy and Practice
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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---|---|---|---|---|
Landsat-5/TM | 1 January–30 April 1985 | 169/052 | 6 | 30 |
Landsat-5/TM | 1 January–30 March 2000 | 169/052 | 6 | 30 |
Landsat-5/TM | 1 January–30 March 2010 | 170/052 | 3 | 30 |
Landsat-8/OLI | 1 January–30 March 2019 | 169/052 | 6 | 30 |
LULC Class | Description |
---|---|
Forest | Areas covered with open forest, dense forest, and woodland. This class mainly includes Eucalyptus tree and other woody plantations of the watershed. |
Shrubland | Area of land covered with open and closed bushes and shrubs mainly found along the banks of rivers and streams. |
Grassland | Areas covered with grasslands mainly used for grazing. |
Cultivated land | Areas of agricultural land mainly used for crop cultivation. It also includes rice cultivation which concentrates at wetland part of the watershed. |
Settlement | Areas of urban and rural settlements and other developments like roads. |
LULC Class | Area (1985) | Area (2000) | Area (2010) | Area (2019) | ||||
---|---|---|---|---|---|---|---|---|
(km2) | (%) | (km2) | (%) | (km2) | (%) | (km2) | (%) | |
Forest | 74.60 | 5.22 | 27.62 | 1.93 | 40.77 | 2.85 | 37.01 | 2.59 |
Shrubland | 449.49 | 31.72 | 280.00 | 19.58 | 216.00 | 15.11 | 182.81 | 12.79 |
Grassland | 62.90 | 4.40 | 39.96 | 2.79 | 30.64 | 2.14 | 19.47 | 1.36 |
Cultivated land | 837.79 | 58.60 | 1077.14 | 75.62 | 1136.11 | 79.76 | 1184.48 | 83.08 |
Settlement | 0.81 | 0.06 | 0.99 | 0.07 | 1.88 | 0.13 | 2.63 | 0.18 |
LULC Map | Overall Accuracy | Kappa Coefficient | Status of Agreement |
---|---|---|---|
1985 | 94.39 | 0.92 | Perfect agreement |
2000 | 94.84 | 0.94 | Perfect agreement |
2010 | 93.13 | 0.90 | Perfect agreement |
2019 | 91.13 | 0.88 | Perfect agreement |
LULC Class | 1985–2000 | 2000–2010 | 2010–2019 | 1985–2019 | ||||
---|---|---|---|---|---|---|---|---|
PΔ | RΔ | PΔ | RΔ | PΔ | RΔ | PΔ | RΔ | |
Forest | −62.98 | −3.13 | 37.64 | 1.32 | −9.24 | −0.42 | −50.40 | −1.11 |
Shrubland | −38.26 | −11.57 | −22.86 | −6.40 | −15.36 | −3.69 | −59.69 | −7.96 |
Grassland | −36.48 | −1.53 | −23.33 | −0.93 | −36.46 | −1.24 | −69.05 | −1.28 |
Cultivated land | 29.05 | 16.22 | 5.45 | 5.90 | 4.15 | 5.26 | 41.74 | 10.28 |
Settlement | 22.31 | 0.01 | 39.32 | 0.09 | 40.29 | 0.08 | 34.86 | 0.05 |
LULC Class | 1985–2000 | 2000–2010 | 2010–2019 | 1985–2019 | ||||
---|---|---|---|---|---|---|---|---|
Gain (km2) | Loss (km2) | Gain (km2) | Loss (km2) | Gain (km2) | Loss (km2) | Gain (km2) | Loss (km2) | |
Forest | 4.00 | 52.45 | 14.58 | 1.57 | 8.43 | 12.01 | 11.15 | 50.16 |
Shrubland | 91.32 | 262.25 | 64.17 | 125.20 | 56.74 | 91.61 | 51.74 | 318.56 |
Grassland | 23.87 | 46.45 | 21.29 | 31.16 | 9.86 | 21.72 | 9.15 | 53.45 |
Cultivated land | 322.28 | 80.18 | 139.77 | 83.46 | 113.48 | 63.88 | 399.17 | 51.16 |
Settlement | 0.71 | 0.57 | 2.00 | 0.57 | 2.29 | 1.72 | 2.86 | 0.71 |
Driver Variables | Cramer’s V | p Value |
---|---|---|
Elevation | 0.2105 | 0.0000 |
Slope | 0.0437 | 0.0000 |
Distance from streams | 0.0868 | 0.0000 |
Distance from roads | 0.1004 | 0.0000 |
Distance from towns | 0.0521 | 0.0000 |
Evidence likelihood | 0.4885 | 0.0000 |
(a) | |||||
---|---|---|---|---|---|
1985 | 2000 | ||||
Forest | Shrubland | Grassland | Cultivated Land | Settlement | |
Forest | 0.1848 1 | 0.4651 | 0.0043 | 0.3447 | 0.0010 |
Shrubland | 0.0053 | 0.2888 | 0.0087 | 0.6970 | 0.0002 |
Grassland | 0.0013 | 0.0643 | 0.2597 | 0.6747 | 0.0000 |
Cultivated land | 0.0020 | 0.0614 | 0.0218 | 0.9141 | 0.0007 |
Settlement | 0.0000 | 0.0048 | 0.0000 | 0.1208 | 0.8744 |
(b) | |||||
2000 | 2010 | ||||
Forest | Shrubland | Grassland | Cultivated Land | Settlement | |
Forest | 0.8073 | 0.0872 | 0.0017 | 0.0526 | 0.0012 |
Shrubland | 0.0710 | 0.2508 | 0.0092 | 0.6678 | 0.0012 |
Grassland | 0.0072 | 0.0997 | 0.1275 | 0.7650 | 0.0006 |
Cultivated land | 0.0045 | 0.0598 | 0.0191 | 0.9148 | 0.0018 |
Settlement | 0.0018 | 0.0248 | 0.0071 | 0.1546 | 0.8117 |
(c) | |||||
2010 | 2019 | ||||
Forest | Shrubland | Grassland | Cultivated Land | Settlement | |
Forest | 0.4571 | 0.2724 | 0.0030 | 0.2653 | 0.0022 |
Shrubland | 0.0298 | 0.2198 | 0.0059 | 0.7432 | 0.0013 |
Grassland | 0.0050 | 0.0305 | 0.1657 | 0.7978 | 0.0010 |
Cultivated land | 0.0051 | 0.0461 | 0.0087 | 0.9381 | 0.0020 |
Settlement | 0.0036 | 0.0246 | 0.0034 | 0.1434 | 0.8249 |
(d) | |||||
1985 | 2019 | ||||
Forest | Shrubland | Forest | Cultivated Land | Forest | |
Forest | 0.5800 | 0.1526 | 0.0020 | 0.2594 | 0.0059 |
Shrubland | 0.0082 | 0.2225 | 0.0000 | 0.7691 | 0.0002 |
Grassland | 0.0006 | 0.0126 | 0.2335 | 0.7533 | 0.0000 |
Cultivated land | 0.0034 | 0.0204 | 0.0051 | 0.9699 | 0.0012 |
Settlement | 0.0016 | 0.0020 | 0.0000 | 0.4784 | 0.8180 |
LULC Class | Reference (2019) | BAU (2035) | BAU (2065) | GOV (2035) | GOV (2065) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | |
Forest | 37.01 | 2.59 | 20.53 | 1.44 | 15.50 | 1.08 | 52.05 | 3.64 | 67.30 | 4.71 |
Shrubland | 182.81 | 12.79 | 149.50 | 10.46 | 119.70 | 8.37 | 81.74 | 5.72 | 73.28 | 5.13 |
Grassland | 19.47 | 1.36 | 11.12 | 0.78 | 10.10 | 0.71 | 29.65 | 2.07 | 31.44 | 2.20 |
Cultivated land | 1184.48 | 83.08 | 1236.61 | 86.77 | 1268.62 | 89.01 | 1259.44 | 88.36 | 1250.86 | 87.76 |
Settlement | 2.63 | 0.18 | 8.06 | 0.56 | 11.90 | 0.83 | 2.93 | 0.20 | 2.96 | 0.21 |
(a) | ||||||
---|---|---|---|---|---|---|
LULC | 2019–2035 | 2035–2065 | 2019–2065 | |||
Gain | Loss | Gain | Loss | Gain | Loss | |
Forest | 0.00 | 6.72 | 0.00 | 3.14 | 0.00 | 9.86 |
Shrubland | 3.43 | 37.87 | 2.57 | 10.29 | 2.57 | 44.73 |
Grassland | 0.00 | 7.29 | 0.00 | 1.00 | 0.00 | 8.29 |
Cultivated land | 48.59 | 5.15 | 11.86 | 3.86 | 60.45 | 9.00 |
Settlement | 5.15 | 0.02 | 3.86 | 0.05 | 9.00 | 0.02 |
(b) | ||||||
LULC | 2019–2035 | 2035–2065 | 2019–2065 | |||
Gain | Loss | Gain | Loss | Gain | Loss | |
Forest | 26.73 | 0.00 | 15.29 | 0.00 | 42.02 | 0.00 |
Shrubland | 0.01 | 20.15 | 0.05 | 8.43 | 0.09 | 28.58 |
Grassland | 11.15 | 0.02 | 1.86 | 0.03 | 13.01 | 0.00 |
Cultivated land | 0.00 | 17.72 | 0.00 | 8.58 | 0.00 | 26.30 |
Settlement | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Belay, H.; Melesse, A.M.; Tegegne, G. Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia. Land 2024, 13, 396. https://doi.org/10.3390/land13030396
Belay H, Melesse AM, Tegegne G. Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia. Land. 2024; 13(3):396. https://doi.org/10.3390/land13030396
Chicago/Turabian StyleBelay, Haile, Assefa M. Melesse, and Getachew Tegegne. 2024. "Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia" Land 13, no. 3: 396. https://doi.org/10.3390/land13030396