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Keywords = Winike watershed

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29 pages, 5588 KiB  
Article
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
by Abreham Berta Aneseyee, Tomasz Noszczyk, Teshome Soromessa and Eyasu Elias
Remote Sens. 2020, 12(7), 1103; https://doi.org/10.3390/rs12071103 - 30 Mar 2020
Cited by 180 | Viewed by 10909
Abstract
The contribution of biodiversity to the global economy, human survival, and welfare has been increasing significantly, but the anthropogenic pressure as a threat to the pristine habitat has followed. This study aims to identify habitat suitability, analyze the change in habitat quality from [...] Read more.
The contribution of biodiversity to the global economy, human survival, and welfare has been increasing significantly, but the anthropogenic pressure as a threat to the pristine habitat has followed. This study aims to identify habitat suitability, analyze the change in habitat quality from 1988 to 2018, and to investigate the correlation between impact factors and habitat quality. The InVEST habitat quality model was used to analyze the spatiotemporal change in habitat quality in individual land-use types in the Winike watershed. Remote sensing data were used to analyze the land use/land cover changes. Nine threat sources, their maximum distance of impact, mode of decay, and sensitivity to threats were also estimated for each land-use cover type. The analysis illustrates that habitat degradation in the watershed was continuously increasing over the last three decades (1988 to 2018). Each threat impact factor and habitat sensitivity have increased for the last 30 years. The most contributing factor of habitat degradation was the 25.41% agricultural expansion in 2018. Population density, land-use intensity, elevation, and slope were significantly correlated with the distribution of habitat quality. Habitat quality degradation in the watershed during the past three decades suggested that the conservation strategies applied in the watershed ecosystem were not effective. Therefore, this study helps decision makers, particularly regarding the lack of data on biodiversity. It further looks into the conflict between economic development and conservation of biodiversity. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Spatial location of the investigated watershed.</p>
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<p>Rainfall and temperature distribution in the study area.</p>
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<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>
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<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>
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<p>Spatial distribution of soil erosion in 1988, 1998, 2008, and 2018.</p>
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<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>
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<p>land use/land cover (LULC) constancy, loss, and gain in the last 30 years.</p>
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<p>Threat types and their contribution to habitat degradation.</p>
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<p>Mapping and trend of habitat quality.</p>
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<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>
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<p>The distribution of habitat impact factors and normality test.</p>
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<p>Pearson correlation of habitat quality (HQ: Habitat quality, LUI: Land-use intensity).</p>
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<p>Box-Cox power transformation of habitat quality impact factors.</p>
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