Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia
<p>Location map of the Upper Omo–Gibe River basin.</p> "> Figure 2
<p>General flowchart of LULC change detection and prediction using the CA–ANN transition probability model (adopted from [<a href="#B47-remotesensing-15-01148" class="html-bibr">47</a>]).</p> "> Figure 3
<p>Land use/land cover (1991–2022): (<b>a</b>) LULC of 1991, (<b>b</b>) LULC of 1997, (<b>c</b>) LULC of 2004, (<b>d</b>) LULC of 2010, (<b>e</b>) LULC of 2016, and (<b>f</b>) LULC of 2022.</p> "> Figure 4
<p>Change map from (1991–2022), (<b>a</b>) LULC changes (1991–1997), (<b>b</b>) LULC changes (1997–2004), (<b>c</b>) LULC changes (2004–2010), (<b>d</b>) 2010–2016, and (<b>e</b>) LULC changes (2016–2022).</p> "> Figure 4 Cont.
<p>Change map from (1991–2022), (<b>a</b>) LULC changes (1991–1997), (<b>b</b>) LULC changes (1997–2004), (<b>c</b>) LULC changes (2004–2010), (<b>d</b>) 2010–2016, and (<b>e</b>) LULC changes (2016–2022).</p> "> Figure 5
<p>Spatial variables (explanatory variables): (<b>a</b>) elevation (<b>b</b>) slope gradient (<b>c</b>) distance from the main road, (<b>d</b>) distance from the streams, (<b>e</b>) population density, and (<b>f</b>) distance from the urban center.</p> "> Figure 6
<p>Change map (20102016).</p> "> Figure 7
<p>Gain and loss of LULC (1991–2022).</p> "> Figure 8
<p>Neural network learning curve.</p> "> Figure 9
<p>Actual and simulated LULC of 2022.</p> "> Figure 10
<p>Prediction of LULC (2037–2067).</p> "> Figure 11
<p>Change maps (2022–2067).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.2.1. Data Acquisition and Preprocessing
2.2.2. Spatial Variable Identification and Preparation
2.3. Methods
2.3.1. Land Use/Land Cover Classification
2.3.2. Change Evaluation and Modeling Transition Potential
3. Results
3.1. LULC Classification
3.2. Classification Accuracy Assessment
3.3. LULC Change Analysis and Evaluation
3.4. Spatial Variables
3.5. LULC Change Simulation and Prediction Using the CA-ANN Model
3.6. Artificial Neural Network (ANN)-Based LULC Change Transition Potential Modeling
3.7. LULC Prediction
3.8. Change Prediction
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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S.N | Satellite | Sensor | Path | Row | Ground Resolution | Bands | Date of Acquisition |
---|---|---|---|---|---|---|---|
01 | Landsat-5 | TM * | 169 | 054/055 | 30 m | 7 | 10 January 1991 |
170 | 054/055 | 30 m | 7 | 18 February1991 | |||
02 | Landsat-5 | TM | 169 | 054/055 | 30 m | 7 | 14 January 2004 |
170 | 054/055 | 30 m | 7 | 15 February 2004 | |||
03 | Landsat-7 | ETM+ ** | 169 | 054/055 | 30 m | 7 | 7 February 2010 |
170 | 054/055 | 30 m | 7 | 15 January 2010 | |||
04 | Landsat-8 | OLI *** | 169 | 054/055 | 30 m | 7 | 7 February 2016 |
170 | 054/055 | 30 m | 7 | 12 January 2016 | |||
04 | Landsat-8 | OLI | 169 | 054/055 | 30 m | 7 | 31 January 2022 |
170 | 054/055 | 30 m | 7 | 22 January 2022 |
Land Use/Land Cover Type | Description |
---|---|
Forest | Evergreen broad-leafed and evergreen needle-leafed forest, deciduous, woodland, open forest, dense forest, and afro-alpine. |
Agricultural land | Lands occupied by crops, farmland, plantation, and fallow land. |
Waterbody | Rivers, ponds, swamps, and reservoirs. |
Built-up area | Infrastructures include houses, asphalt roads, buildings, and urban areas. |
Shrubland | Include open and closed shrublands. |
Grassland | Open grass lands, vegetation less than 2 m height, and range lands. |
Bare land | Lands without vegetation, crops or grasses, and barren soils. |
(i) | Reference Data | ||||||||||
LULC Cat. | Forest | AgrL | WB | BuA | ShL | GrL | BrnL | RTotal | UA | ||
LULC 1991 | Classified Data | Forest | 69 | 0 | 0 | 0 | 2 | 1 | 0 | 72 | 0.97 |
AgrL | 5 | 64 | 0 | 0 | 1 | 1 | 0 | 71 | 0.90 | ||
WB | 29 | 8 | 34 | 0 | 1 | 0 | 0 | 72 | 0.47 | ||
BuA | 5 | 0 | 0 | 65 | 0 | 1 | 1 | 72 | 0.92 | ||
ShL | 7 | 1 | 2 | 0 | 63 | 0 | 1 | 74 | 0.85 | ||
GrL | 1 | 2 | 0 | 1 | 0 | 68 | 0 | 72 | 0.94 | ||
BrnL | 4 | 4 | 0 | 0 | 4 | 2 | 57 | 71 | 0.80 | ||
CTotal | 120 | 79 | 36 | 66 | 71 | 73 | 59 | 504 | |||
PA | 0.58 | 0.82 | 0.94 | 0.98 | 0.9 | 0.93 | 0.97 | ||||
(ii) | Reference Data | ||||||||||
LULC Cat. | Forest | AgrL | WB | BuA | ShL | GrL | BrnL | Total | UA | ||
LULC of 1997 | Classified Data | Forest | 57 | 14 | 0 | 0 | 2 | 0 | 0 | 73 | 0.79 |
AgrL | 0 | 69 | 1 | 0 | 1 | 1 | 0 | 72 | 0.96 | ||
WB | 22 | 3 | 46 | 0 | 0 | 0 | 0 | 71 | 0.65 | ||
BuA | 0 | 2 | 1 | 69 | 0 | 0 | 0 | 72 | 0.96 | ||
ShL | 0 | 6 | 0 | 0 | 66 | 0 | 0 | 72 | 0.92 | ||
GrassL | 0 | 7 | 0 | 0 | 0 | 65 | 0 | 72 | 0.90 | ||
BarrenL | 0 | 8 | 0 | 0 | 0 | 0 | 64 | 72 | 0.89 | ||
Total | 79 | 109 | 48 | 69 | 69 | 66 | 64 | 504 | |||
PA | 0.72 | 0.63 | 0.96 | 1 | 0.97 | 0.98 | 1 | 0 | 0.87 | ||
(iii) | Reference Data | ||||||||||
LULC Cat. | Forest | AgrL | WB | BuA | ShL | GrL | BrnL | Total | UA | ||
LULC of 2004 | Classified Data | Forest | 72 | 0 | 0 | 0 | 0 | 0 | 0 | 72 | 1 |
AgrL | 3 | 67 | 0 | 0 | 1 | 0 | 0 | 71 | 0.94 | ||
WB | 6 | 13 | 53 | 0 | 0 | 0 | 0 | 72 | 0.74 | ||
BuA | 0 | 2 | 0 | 70 | 0 | 0 | 0 | 72 | 0.97 | ||
ShL | 0 | 0 | 0 | 1 | 71 | 1 | 0 | 73 | 0.99 | ||
GrL | 0 | 0 | 0 | 0 | 0 | 72 | 0 | 72 | 1 | ||
BrnL | 3 | 0 | 0 | 0 | 0 | 0 | 69 | 72 | 0.96 | ||
Total | 84 | 82 | 53 | 71 | 72 | 73 | 69 | 504 | 0 | ||
PA | 0.86 | 0.82 | 1 | 1 | 1 | 0.99 | 1 | 0 | 0.94 | ||
(iv) | Reference Data | ||||||||||
LULC Cat. | Forest | AgrL | WB | BuA | ShL | GrL | BrnL | Total | UA | ||
LULC 2010 | Classified Data | Forest | 66 | 4 | 1 | 0 | 2 | 0 | 0 | 73 | 0.90 |
AgrL | 3 | 67 | 0 | 0 | 1 | 0 | 0 | 71 | 0.94 | ||
WB | 17 | 0 | 54 | 0 | 1 | 0 | 0 | 72 | 0.76 | ||
BuA | 1 | 2 | 1 | 68 | 1 | 0 | 0 | 73 | 0.93 | ||
ShL | 1 | 1 | 0 | 1 | 71 | 0 | 0 | 74 | 0.96 | ||
GrL | 0 | 0 | 0 | 0 | 0 | 71 | 0 | 71 | 1 | ||
BrnL | 0 | 0 | 0 | 0 | 0 | 0 | 70 | 70 | 1 | ||
Total | 88 | 74 | 56 | 69 | 76 | 71 | 70 | 504 | |||
PA | 0.77 | 0.91 | 0.96 | 1 | 0.93 | 1 | 1 | ||||
(v) | Reference Data | ||||||||||
LULC Cat. | Forest | AgrL | WB | BuA | ShL | GrL | BrnL | Total | UA | ||
LULC 2016 | Classified Data | Forest | 66 | 6 | 0 | 0 | 0 | 0 | 0 | 72 | 0.92 |
AgrL | 0 | 69 | 0 | 1 | 1 | 1 | 0 | 72 | 0.96 | ||
WB | 3 | 3 | 66 | 0 | 0 | 0 | 0 | 72 | 0.92 | ||
BuA | 0 | 0 | 0 | 72 | 0 | 0 | 0 | 72 | 1 | ||
ShL | 0 | 4 | 0 | 0 | 68 | 0 | 0 | 72 | 0.94 | ||
GrL | 0 | 5 | 0 | 1 | 0 | 66 | 0 | 72 | 0.92 | ||
BrnL | 12 | 0 | 0 | 0 | 0 | 0 | 60 | 72 | 0.83 | ||
Total | 81 | 87 | 66 | 74 | 69 | 67 | 60 | 504 | 0 | ||
PA | 0.81 | 0.79 | 1 | 0.97 | 0.99 | 0.99 | 1 | 0 | 0.93 | ||
(vi) | Reference Data | ||||||||||
Forest | AgrL | WB | BuA | ShL | GrL | BrnL | Total | UA | |||
LULC 2022 | Classified Data | FRST | 71 | 0 | 0 | 0 | 0 | 0 | 0 | 71 | 1 |
AGRL | 2 | 68 | 0 | 0 | 1 | 1 | 0 | 72 | 0.94 | ||
WB | 2 | 0 | 74 | 0 | 1 | 0 | 0 | 77 | 0.97 | ||
BUA | 0 | 10 | 0 | 58 | 2 | 1 | 0 | 71 | 0.82 | ||
SHL | 2 | 0 | 0 | 0 | 70 | 0 | 0 | 72 | 0.97 | ||
GL | 0 | 2 | 0 | 0 | 0 | 69 | 0 | 71 | 0.97 | ||
BL | 0 | 0 | 0 | 0 | 0 | 0 | 70 | 70 | 1 | ||
Total | 77 | 80 | 74 | 58 | 74 | 71 | 70 | 504 | |||
PA | 0.93 | 0.85 | 1 | 1 | 0.96 | 0.97 | 1 |
Year | Over All Accuracy (%) | Kappa Coefficient | Degree of Agreement (%) |
---|---|---|---|
1991 | 82 | 0.81698 | Almost perfect agreement |
1997 | 87 | 0.85296 | Almost perfect agreement |
2004 | 94 | 0.94348 | Almost perfect agreement |
2010 | 93 | 0.93179 | Almost perfect agreement |
2016 | 93 | 0.914352 | Almost perfect agreement |
2022 | 95 | 0.94825 | Almost perfect agreement |
1991 | 1997 | 2004 | 2010 | 2016 | 2022 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
LULC Cat. | Area (km2) | Area (%) | Area (km2) | Area (%) | Area (km2) | Area (%) | Area (km2) | Area (%) | Area (km2) | Area (%) | Area (km2) | Area (%) |
Forest | 5026 | 15 | 9372 | 27 | 6028 | 18 | 6483 | 19 | 9768 | 28 | 5004 | 15 |
AgrL | 12,032 | 36 | 15,736 | 46 | 18,275 | 53 | 21,219 | 62 | 19,536 | 57 | 22,147 | 65 |
WB | 729 | 2 | 567 | 2 | 257 | 1 | 652 | 2 | 281 | 1 | 277 | 1 |
BuA | 1776 | 5 | 2037 | 6 | 1812 | 5 | 1529 | 4 | 2025 | 6 | 2559 | 7 |
ShL | 6255 | 18 | 2476 | 7 | 4841 | 14 | 1632 | 5 | 1791 | 5 | 2398 | 7 |
GrL | 6629 | 19 | 2091 | 6 | 2903 | 8 | 981 | 3 | 772 | 2 | 859 | 3 |
BrnL | 1515.87 | 4 | 2007 | 6 | 172 | 1 | 1791 | 5 | 113 | 0 | 1044 | 3 |
1991–1997 | 1997–2004 | 2004–2010 | 2010–2016 | 2016–2022 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
LULC Cat. | Area (km2) | Change (%) | Area (km2) | Change (%) | Area (km2) | Change (%) | Area (km2) | Change (%) | Area (km2) | Change (%) |
Forest | 12,717 | 37 | −3344 | −10 | 456 | 1 | 3285 | 10 | −4764 | −14 |
AgL | 13,197 | 38 | 2539 | 7 | 2945 | 9 | −1684 | −5 | 2611 | 8 |
WB | 878 | 3 | −311 | −1 | 395 | 1 | 129 | 0 | 112 | 0 |
BuA | 263 | 1 | 1775 | 5 | −284 | −1 | 497 | 1 | 1544 | 5 |
ShL | 111 | 0 | 2365 | 7 | −3209 | −9 | 159 | 0 | 607 | 2 |
GrL | 1279 | 4 | 812 | 2 | −1922 | −6 | −209 | −1 | 87 | 0 |
BrnL | 3841 | 11 | −1835 | −5 | 1619 | 5 | −1678 | −5 | 931 | 3 |
Spatial Variables | Elev | Dmrd | SlpGt | DStr | Pdsty | DUC |
---|---|---|---|---|---|---|
Elevation (Elev) | 0.038 | 0.005 | 0.525 * | 0.037 | 0.109 | |
DistMainRoad (Dmrd) | 0.153 | −0.025 | 0.045 | 0.609 * | ||
Slope Gradient (SlpGt) | 0.084 | 0.026 | 0.059 | |||
DistStreams (DStr) | 0.031 | 0.016 | ||||
Population Density (Pdsty) | 0.072 | |||||
DistUrbanCenter (DUC) |
1991 | 1997 | |||||||
Forest | AgrL | WB | BuA | ShL | GL | BL | ||
Forest | 0.586 | 0.063 | 0.007 | 0.029 | 0.351 | 0.096 | 0.008 | |
AgrL | 0.014 | 0.603 | 0.023 | 0.016 | 0.141 | 0.199 | 0.004 | |
WB | 0.364 | 0.124 | 0.240 | 0.002 | 0.181 | 0.080 | 0.009 | |
BuA | 0.005 | 0.641 | 0.001 | 0.027 | 0.061 | 0.252 | 0.012 | |
ShL | 0.056 | 0.342 | 0.004 | 0.011 | 0.241 | 0.33 | 0.013 | |
GL | 0.012 | 0.492 | 0.001 | 0.149 | 0.039 | 0.259 | 0.043 | |
BL | 0.043 | 0.316 | 0.003 | 0.007 | 0.253 | 0.390 | 0.009 | |
1997 | 2004 | |||||||
Forest | AgrL | WB | BuA | ShL | GL | BL | ||
Forest | 0.472 | 0.352 | 0.221 | 0.003 | 0.034 | 0.004 | 0.023 | |
AgrL | 0.049 | 0.809 | 0.003 | 0.070 | 0.019 | 0.018 | 0.030 | |
WB | 0.435 | 0.462 | 0.052 | 0.018 | 0.015 | 0.003 | 0.014 | |
BuA | 0.007 | 0.854 | 0.003 | 0.081 | 0.004 | 0.042 | 0.009 | |
ShL | 0.373 | 0.389 | 0.016 | 0.010 | 0.113 | 0.022 | 0.076 | |
GL | 0.123 | 0.591 | 0.004 | 0.036 | 0.073 | 0.058 | 0.107 | |
BL | 0.078 | 0.714 | 0.007 | 0.045 | 0.056 | 0.241 | 0.073 | |
2004 | 2010 | |||||||
Forest | AgrL | WB | BuA | ShL | GL | BL | ||
Forest | 0.631 | 0.412 | 0.016 | 0.014 | 0.234 | 0.007 | 0.061 | |
AgrL | 0.137 | 0.619 | 0.008 | 0.061 | 0.078 | 0.037 | 0.059 | |
WB | 0.736 | 0.081 | 0.088 | 0.004 | 0.069 | 0.001 | 0.020 | |
BuA | 0.070 | 0.760 | 0.001 | 0.060 | 0.038 | 0.044 | 0.037 | |
ShL | 0.310 | 0.359 | 0.001 | 0.018 | 0.210 | 0.006 | 0.094 | |
GL | 0.115 | 0.703 | 0.001 | 0.010 | 0.070 | 0.065 | 0.035 | |
BL | 0.321 | 0.634 | 0.004 | 0.021 | 0.210 | 0.083 | 0.077 | |
2010 | 2016 | |||||||
Forest | AgrL | WB | BuA | ShL | GL | BL | ||
Forest | 0.633 | 0.271 | 0.007 | 0.011 | 0.067 | 0.007 | 0.003 | |
AgrL | 0.174 | 0.647 | 0.008 | 0.087 | 0.052 | 0.029 | 0.002 | |
WB | 0.757 | 0.115 | 0.083 | 0.004 | 0.034 | 0.003 | 0.005 | |
BuA | 0.051 | 0.756 | 0.002 | 0.132 | 0.036 | 0.023 | 0.000 | |
ShL | 0.448 | 0.445 | 0.002 | 0.012 | 0.074 | 0.014 | 0.004 | |
GL | 0.199 | 0.640 | 0.003 | 0.024 | 0.026 | 0.098 | 0.010 | |
BL | 0.277 | 0.578 | 0.006 | 0.031 | 0.076 | 0.019 | 0.013 | |
2016 | 2022 | |||||||
Forest | AgrL | WB | BuA | ShL | GL | BL | ||
Forest | 0.870 | 0.130 | 0 | 0 | 0 | 0 | 0 | |
AgrL | 0.142 | 0.858 | 0 | 0 | 0 | 0 | 0 | |
WB | 0.952 | 0.044 | 0.004 | 0 | 0 | 0 | 0 | |
BuA | 0.006 | 0.987 | 0 | 0.007 | 0 | 0 | 0 | |
ShL | 0.188 | 0.698 | 0 | 0 | 0.114 | 0 | 0 | |
GL | 0.069 | 0.916 | 0 | 0 | 0 | 0.015 | 0 | |
BL | 0.290 | 0.676 | 0 | 0 | 0 | 0 | 0.034 | |
2022 | 2037 | |||||||
Forest | AgrL | WB | BuA | ShL | GL | BL | ||
Forest | 0.999 | 0.001 | 0 | 0 | 0 | 0 | 0 | |
AgrL | 0.001 | 0.999 | 0 | 0 | 0 | 0 | 0 | |
WB | 0.002 | 0 | 0.998 | 0 | 0 | 0 | 0 | |
BuA | 0.000 | 0.000 | 0 | 1.000 | 0 | 0 | 0 | |
ShL | 0.026 | 0.004 | 0 | 0 | 0.970 | 0 | 0 | |
GL | 0.001 | 0.002 | 0 | 0 | 0 | 0.998 | 0 | |
BL | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
LULC Category | Actual | Simulated | ||
---|---|---|---|---|
Area (km2) | Area (%) | Area (km2) | Area (%) | |
Forest | 5004 | 13 | 4371 | 10 |
Agricultural land | 22,147 | 58 | 28,314 | 63 |
Waterbody | 893 | 2 | 938 | 2 |
Built-up area | 5569 | 15 | 7658 | 17 |
Shrubland | 2398 | 6 | 2149 | 5 |
Grassland | 859 | 2 | 678 | 2 |
Barren land | 1044 | 3 | 984 | 2 |
LULC2037 | LULC2052 | LULC 2067 | ||||
---|---|---|---|---|---|---|
LULC Cat. | Area (km2) | Area (%) | Area (km2) | Area (%) | Area (km2) | Area (%) |
Forest | 5139 | 15 | 4872 | 14 | 4328 | 13 |
Agricultural land | 22,146 | 64 | 21,273 | 62 | 22,353 | 65 |
Waterbody | 273 | 1 | 272 | 1 | 293 | 1 |
Built-up area | 1725 | 5 | 2749 | 8 | 2858 | 8 |
Shrubland | 1859 | 5 | 1763 | 5 | 1572 | 5 |
Grassland | 1787 | 5 | 1674 | 5 | 1561 | 5 |
Barren land | 1895 | 5 | 1684 | 5 | 1473 | 4 |
2022–2037 | 2037–2052 | 2052–2067 | ||||
---|---|---|---|---|---|---|
LULC Cat | km2 | % | km2 | % | km2 | % |
Forest | −16,492 | −48 | −267 | −1 | −544 | −2 |
Agricultural land | 21,631 | 63 | −873 | −3 | 1079 | 3 |
Waterbody | 515 | 2 | −1 | 0 | 21 | 0 |
Built-up area | 115 | 0 | 1024 | 3 | 109 | 0 |
Shrubland | 1967 | 6 | −96 | −0 | −191 | −1 |
Grassland | −107 | −0 | −113 | −0 | −113 | −0 |
Barren land | 1895 | 6 | −211 | −1 | −210 | −1 |
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Lukas, P.; Melesse, A.M.; Kenea, T.T. Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia. Remote Sens. 2023, 15, 1148. https://doi.org/10.3390/rs15041148
Lukas P, Melesse AM, Kenea TT. Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia. Remote Sensing. 2023; 15(4):1148. https://doi.org/10.3390/rs15041148
Chicago/Turabian StyleLukas, Paulos, Assefa M. Melesse, and Tadesse Tujuba Kenea. 2023. "Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia" Remote Sensing 15, no. 4: 1148. https://doi.org/10.3390/rs15041148