The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia
"> Figure 1
<p>Spatial location of the investigated watershed.</p> "> Figure 2
<p>Rainfall and temperature distribution in the study area.</p> "> Figure 3
<p>Urbanization development in the watershed in (<b>A</b>) 1988—891.23 ha, (<b>B</b>) 1998—1427.23 ha, (<b>C</b>) 2008—2744.28 ha, and (<b>D</b>) 2018—3963.60 ha.</p> "> Figure 4
<p>Agricultural expansion in the watershed in (<b>A</b>) 1988—44954.50 ha, (<b>B</b>) 1998—51892.20 ha, (<b>C</b>) 2008—58441.50 ha, and (<b>D</b>) 2018—59792.90 ha.</p> "> Figure 5
<p>Spatial distribution of soil erosion in 1988, 1998, 2008, and 2018.</p> "> Figure 6
<p>Spatial distribution maps of (<b>A</b>) the paved road system, (<b>B</b>) the unpaved road system, (<b>C</b>) water abstraction, (<b>D</b>) pollution, and (<b>E</b>) invasive species.</p> "> Figure 7
<p>land use/land cover (LULC) constancy, loss, and gain in the last 30 years.</p> "> Figure 8
<p>Threat types and their contribution to habitat degradation.</p> "> Figure 9
<p>Mapping and trend of habitat quality.</p> "> Figure 10
<p>(<b>A</b>,<b>B</b>) Habitat quality status vs. altitude and slope, respectively, (<b>C</b>) Habitat quality and normalized difference vegetation index (NDVI) in relation to years and (<b>D</b>) Habitat quality status in relation to years.</p> "> Figure 11
<p>The distribution of habitat impact factors and normality test.</p> "> Figure 12
<p>Pearson correlation of habitat quality (HQ: Habitat quality, LUI: Land-use intensity).</p> "> Figure 13
<p>Box-Cox power transformation of habitat quality impact factors.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methods
2.2.1. The InVEST Habitat Quality Model
2.2.2. Data Preparation and Input for the InVEST Model
2.2.3. Correlation Analysis of Habitat Quality and Impact Factors
2.2.4. Statistical Analyses
3. Results
3.1. Land Use/Cover Changes Associated with the Threats
3.2. The Threat Sources and Their Contribution to Habitat Degradation
3.3. Changes in Habitat Quality in the Watershed
3.4. The Impact Factors for Spatial Patterns of Habitat Quality
4. Discussion
4.1. Limitations of Uncertainty and Future Recommended Works
4.2. Implications of Habitat Quality Modeling
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Input Data | Description |
---|---|
Land use/land cover | A standard GIS raster dataset, with a numeric LULC code for each cell. The LULC raster should include the area of interest, as well as a buffer of the width of the greatest maximum threat distance. The raster should not contain any other data. The LULC codes must match the codes for the sensitivity of land cover types to each threat. |
Threat data | A CSV table of all threats needed to be considered in the model. The table contains information on each threat’s relative importance or weight and its impact across space. Each row is a degradation source. Each column contains a different attribute of each degradation source, and must be named as THREAT, MAX-DIST, WEIGHT, and DECAY (Table S5). |
Threat raster | GIS raster files with the distribution and intensity of each individual threat showing each of them affecting the habitat were prepared. However, the techniques applied for each threat raster varied according to the data types (threat raster data for soil erosion are different from pollution threat raster). Therefore, nine threat raster datasets were prepared (soil erosion, pollution, road (paved and unpaved), water abstraction, invasive species, agricultural expansion, urbanization, and population pressure). The threat maps should cover the area of interest and buffer of the width of the greatest maximum threat distance. Otherwise, locations near the edge of the area of interest may have inflated habitat quality scores. Each cell in the raster contains a value that indicates the density or presence of a threat within it. All threats should be measured in the same scale and units. |
Habitat types and sensitivity of each habitat to threats | A CSV table of LULC types contains information whether or not a habitat was identified (absence/presence of habitat) and their specific sensitivity to each threat (Table 6). Sensitivity values range from 0 to 1, where 0 represents no sensitivity to a threat and 1 represents the greatest sensitivity [48]. Sensitivity scores are determined from expert knowledge using the APH method. |
Half saturation constant (k) | The scaling parameter (or constant) of 0.5 was the default for the InVEST model. The InVEST model uses a half-saturation curve to convert habitat degradation scores to habitat quality scores [36]. It is determined as an inverse relationship between the degradation score and its habitat quality score. It helps with the visual representation of heterogeneity in quality across the landscape. |
Data Sampling Districts/Woredas | Sampling Village Names | KII | FGDa |
---|---|---|---|
Ezha | Ambeli | 6 | 2 (9) |
Shebraden | 6 | 2 (8) | |
Gumer | Derbo | 5 | 2 (10) |
Jembor | 7 | 2 (7) | |
Cheha | Dakune | 6 | 2 (8) |
Wrebeche | 6 | 2 (9) | |
Enemore | Gonchebet | 6 | 2 (10) |
Achewede | 6 | 2 (8) | |
Merb azernet | Kumishe | 7 | 2 (9) |
Bakenawet | 5 | 2 (10) | |
Geta | Abeke | 6 | 2 (10) |
Yafter | 6 | 2 (8) | |
Total | 72 | 12 (116) |
Image Types | Sensors | Spectral Bands Used | Pixel Resolution (Reflective Bands) | DATE of Acquisition |
---|---|---|---|---|
Landsat 5 | Thematic Mapper (TM) | Band 1–7 and 7 | 30 | 03 Dec 1988 and 15 Dec 1998 |
Landsat 7 | Enhanced Thematic Mapper Plus (ETM+) | Band 1–7 and 7 | 30 | 01 Jan 2008 and 16 Dec 2008 |
Landsat 8 | Operational Land Imager (OLI) | Band 2–7 | 30 | 4 Jan 2018 |
M_ Distance (km) | Weight | Threat | Decay |
---|---|---|---|
1 | 0.16 | Soil erosion (so) | exponential |
30 | 0.11 | Water abstraction (wa) | linear |
2 | 0.40 | Population (pop) | exponential |
7 | 0.31 | Urbanization (ur) | exponential |
1.5 | 0.24 | Paved road (pr) | linear |
0.5 | 0.23 | Unpaved road (unpr) | linear |
1 | 0.59 | Agriculture (agr) | linear |
20 | 0.25 | Pollution (pol) | exponential |
1 | 0.10 | Invasive species (Is) | linear |
Districts | 1988 | 1998 | 2008 | 2018 | 2018–1988 1 | 1988–2018 (%) | |
---|---|---|---|---|---|---|---|
Ezha | 255.76 | 269 | 289 | 296 | 40.2 | 16.4 | |
Gumer | 270 | 331 | 348 | 353 | 83.0 | 20.0 | |
Geta | 255.24 | 277 | 281 | 290 | 34.8 | 16.3 | |
Cheha | 217.44 | 231 | 243 | 255 | 37.6 | 14.0 | |
Enemor Ener | 266.06 | 276 | 287 | 298 | 31.9 | 16.7 | |
Merab Azernet | 248.37 | 282 | 291 | 296 | 47.6 | 16.5 | |
Average | 252.1 | 277.7 | 289.8 | 298.0 | 45.9 | 16.7 |
No | LULC | Habitat | so1 | wa2 | pop3 | ur4 | pr5 | unpr6 | agr7 | pol8 | Is9 |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | Bare land | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
2 | Built-up land | 0.00 | 0.30 | 0.00 | 0.60 | 0.30 | 0.10 | 0.00 | 0.40 | 0.60 | 0.20 |
3 | Shrubland | 1.00 | 0.00 | 0.10 | 0.40 | 0.20 | 0.20 | 0.30 | 0.70 | 0.20 | 0.00 |
4 | Cultivated land | 0.35 | 0.70 | 0.10 | 0.60 | 0.40 | 0.50 | 0.30 | 0.80 | 0.50 | 0.21 |
5 | Forest land | 1.00 | 0.10 | 0.10 | 0.50 | 0.60 | 0.50 | 0.40 | 0.80 | 0.20 | 0.12 |
6 | Grazing land | 0.50 | 0.10 | 0.10 | 0.40 | 0.20 | 0.20 | 0.40 | 0.80 | 0.20 | 0.14 |
7 | Water bodies | 1.00 | 0.00 | 0.40 | 0.30 | 0.40 | 0.20 | 0.20 | 0.60 | 0.20 | 0.21 |
8 | Woodland | 1.00 | 0.10 | 0.10 | 0.40 | 0.40 | 0.20 | 0.20 | 0.60 | 0.10 | 0.11 |
Bare Land | Built-Up Land | Shrubland | Cultivated Land | Forest Land | Grazing Land | Water Bodies | Woodland |
---|---|---|---|---|---|---|---|
0.06 | 0.94 | 0.13 | 0.55 | 0.11 | 0.24 | 0.12 | 0.14 |
LULC | 1988 | 1998 | 2008 | 2018 | Change (ha) | Change (%) |
---|---|---|---|---|---|---|
Bare land | 1517.49 | 1612.26 | 1804.59 | 2506.68 | 989.19 | 165.19 |
Urbanization | 891.23 | 1427.22 | 2744.28 | 3963.6 | 3377.43 | 444.73 |
Shrubland | 3245.22 | 2324.61 | 1816.02 | 2030.04 | −1215.18 | −62.55 |
Cultivated land | 44,954.5 | 51,892.2 | 57,442.5 | 59,792.9 | 14,838.4 | 133.01 |
Forests | 7353.72 | 5209.74 | 4466.79 | 4738.86 | −2614.86 | −64.44 |
Grazing land | 32,876.3 | 30,243 | 23,267.3 | 16,728.3 | −16,148 | −50.88 |
Water bodies | 252.18 | 254.34 | 280.98 | 242.01 | −10.17 | −95.97 |
Woodland | 18397 | 16,219.2 | 17,360.1 | 19,180.1 | 783.1 | 104.26 |
LULC Class | 1988 | 1998 | 2008 | 2018 | ||||
---|---|---|---|---|---|---|---|---|
Producer | User | Producer | User | Producer | User | Producer | User | |
Bare land | 83.56 | 93.23 | 86.34 | 92.03 | 87.01 | 94.20 | 97.23 | 99.95 |
Built-up | 84.67 | 89.12 | 91.17 | 90.34 | 91.90 | 92.12 | 94.90 | 98.45 |
Shrub land | 93.34 | 90.68 | 93.98 | 91.11 | 94.21 | 92.13 | 99.92 | 93.98 |
Cultivated land | 85.45 | 84.26 | 86.21 | 85.90 | 87.65 | 89.86 | 99.92 | 92.12 |
Forest land | 89.4 | 86.9 | 90.46 | 87.91 | 81.23 | 87.98 | 97.98 | 100 |
Grazing land | 91.23 | 90.67 | 92.34 | 91.87 | 93.63 | 94.53 | 99.97 | 96.45 |
Water body | 87.23 | 87.91 | 88.20 | 88.71 | 89.27 | 89.91 | 100 | 99.87 |
Woodland | 86.76 | 94.34 | 87.87 | 93.89 | 88.70 | 95.78 | 92.12 | 99.89 |
Overall accuracy | 85.23 | 88.45 | 92.21 | 95.30 | ||||
Kappa Coefficient | 0.88 | 0.89 | 0.92 | 0.93 |
Suitability Status | 1988 | 1998 | 2008 | 2018 | ||||
---|---|---|---|---|---|---|---|---|
Area (ha) | % | Area (ha) | % | Area (ha) | % | Area (ha) | % | |
Low | 29,295.60 | 26.83 | 41,489.40 | 38.00 | 56,774.90 | 52.00 | 72,263.20 | 66.19 |
Medium | 46,764.80 | 42.83 | 39,305.70 | 36.00 | 30,571.10 | 28.00 | 20,728.30 | 18.99 |
High | 33,122.10 | 30.34 | 27,387.50 | 25.08 | 21,836.50 | 20.00 | 16,191.00 | 14.83 |
LULC | 1988 | 1998 | 2008 | 2018 | Mean HQ |
---|---|---|---|---|---|
Bare land | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Built-up land | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Shrubland | 0.795 | 0.562 | 0.377 | 0.295 | 0.507 |
Cultivated land | 0.113 | 0.092 | 0.003 | 0.004 | 0.053 |
Forests | 0.997 | 0.811 | 0.702 | 0.599 | 0.777 |
Grazing land | 0.697 | 0.467 | 0.301 | 0.199 | 0.416 |
Water bodies | 0.499 | 0.296 | 0.100 | 0.090 | 0.246 |
Woodland | 0.648 | 0.529 | 0.485 | 0.203 | 0.401 |
Descriptive Statistics | HQ | Slope | NDVI | Altitude | Population | Land-Use Intensity |
---|---|---|---|---|---|---|
Min | 0.08 | 10.23 | 0.19 | 895 | 0 | 0.12 |
Max | 0.75 | 89.9 | 0.4 | 3662 | 307 | 0.95 |
Median | 0.3 | 41.23 | 0.23 | 2464 | 286 | 0.42 |
Sd | 0.15 | 2.46 | 0.02 | 431.86 | 42.38 | 0.17 |
Mean | 0.32 | 38.34 | 0.23 | 2409 | 276.6 | 0.407 |
Range | 0.67 | 79.99 | 0.11 | 1667 | 115 | 0.83 |
Normality test (P) | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
95% CI Mean | 0.29 to 0.34 | 52.65 to 58.77 | 0.23 to 0.24 | 2339.74 to 2479.10 | 275.33 to 284.61 | 0.38 to 0.44 |
Correlation Coefficient (r) | Impact Factors | ||||
---|---|---|---|---|---|
Population Density | Elevation | NDVI | Intensity of Land | Slope | |
2018 | −0.1c | −0.43b | −0.14c | −0.26b | −0.17c |
2008 | −0.09c | −0.43b | −0.13c | −0.25b | −0.17c |
1998 | −0.04a | −0.43b | −0.11c | −0.20c | −0.17c |
1988 | −0.01a | −0.43b | −0.01a | −0.01a | −0.17c |
Impact Factor | VIF1988 | VIF1998 | VIF2008 | VIF2018 |
---|---|---|---|---|
Population | 0.89 | 0.75 | 0.23 | 0.12 |
Slope | 0.32 | 0.32 | 0.32 | 0.32 |
Altitude | 0.10 | 0.10 | 0.10 | 0.10 |
Intensity of land use | 0.54 | 0.43 | 0.38 | 0.32 |
NDVI | 0.67 | 0.52 | 0.45 | 0.34 |
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Berta Aneseyee, A.; Noszczyk, T.; Soromessa, T.; Elias, E. The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia. Remote Sens. 2020, 12, 1103. https://doi.org/10.3390/rs12071103
Berta Aneseyee A, Noszczyk T, Soromessa T, Elias E. The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia. Remote Sensing. 2020; 12(7):1103. https://doi.org/10.3390/rs12071103
Chicago/Turabian StyleBerta Aneseyee, Abreham, Tomasz Noszczyk, Teshome Soromessa, and Eyasu Elias. 2020. "The InVEST Habitat Quality Model Associated with Land Use/Cover Changes: A Qualitative Case Study of the Winike Watershed in the Omo-Gibe Basin, Southwest Ethiopia" Remote Sensing 12, no. 7: 1103. https://doi.org/10.3390/rs12071103