Analysis of the Impact of Land-Use/Land-Cover Change on Land-Surface Temperature in the Villages within the Luki Biosphere Reserve
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection and Preprocessing
2.3. Land-Use/Land-Cover Classification
2.4. Estimation of Land Surface Temperature
2.5. Prediction of Land Use/Land Cover
2.5.1. Cellular Automata
2.5.2. Markov Chain Analysis
2.5.3. Implementation of CA–Markov Chain Model
- The computation land-use transition probability matrix and transition rules, using Markov chain analysis. The precedent state of each land-use/land-cover category was used to predict its future state. To predict land-use/land-cover change in 2038, we used the transition probabilities map between 2002 and 2020. Moreover, the land-use/land-cover transformation rules were provided by the transition probability matrices. These matrices provide the description of transition probability of each land-use/land-cover category into others. However, quantities of change in each land-use/land-cover categories to other categories were provided by the transition area matrices.
- Determining the CA filter. Here, several options of standard contiguity kernels were applied as neighborhoods to identify the suitable contiguity filter for the purpose of land-use/land-cover prediction. These are 7 × 7, 5 × 5 and 3 × 3 contiguity kernels. For this study, we selected the contiguity filter 5 × 5. This filter considers that the center of each pixel is surrounded by matrix space of 5 × 5 dimension in order to reflect the change in each pixel significantly.
- Determining the number of iterations and the time of starting point of CA. To identify the suitable iterations, we applied several iteration numbers, starting from 1 to 300. Ultimately, we selected four iterations to carry out the land-use/land-cover change prediction.
2.5.4. Model Validation
3. Results and Discussion
3.1. Land-Use/Land-Cover Change from 2002 to 2020
3.2. Spatiotemporal Distribution of LST and Its Variations with LULC Types
3.3. Variations of LSTs for Different Land Covers
3.4. Variation of LST Changes in Converted Land-Use/Land-Cover Areas
4. Discussions
4.1. Spatiotemporal Distribution of LST and its Variations with LULC Types
4.2. Impact of Land-Use/Land-Cover Change, Climate Change and Land-Surface Temperature in the Villages
Perception of Local Communities on the Impact of Land-Use/Land-Cover Change and Climate Change in the Region
- The present study revealed a major growth of built-up area in all the four villages, which was transformed from forest land, complex degraded and young secondary forest, fallow land and fields, and savannah.
- The highest and lowest land-surface temperature in built-up area and forest land, respectively, were determined for all the years under study. The spatial mean LST significantly increased by 2.28, 1.67, 1.49 and 0.93 °C for the Tsumba kituti, Kisavu, Kiobo and Kibuya villages, respectively.
- Continuous increase in the land-surface temperature was observed in all the land-use/land-cover categories over the years, except for the forest land class. This indicates the effect of the microclimate warming in the four villages. On the other hand, correlation analysis illustrates the gradual increase of land-surface temperature with the increase of built-up area.
- Changes in land use/land cover will continue to be experienced in all the villages (Figure 11 and Figure 12). In Tsumba kituti, the complex of degraded and young secondary forest, forest land and savannah will continue to decrease with 40.8, 36.94 and 16.3 hectares, respectively. Fallow land and field, built-up area will increase with 77.3 and 19.2 hectares, respectively. In Kiobo, forest land will continue to decrease with 19.74 hectares. Additionally, there will be an increase of 23.04, 1.65 and 0.49 hectares for fallow land and fields, complex of degraded and young secondary forest, and built-up area, respectively. In Kisavu, fallow land and fields, built-up area, complex of degraded and young secondary forest will experience an increase of 70.61, 5.54 and 10.63 hectares, respectively. The forest land will decrease by 85.28 hectares. Finally, in Kibuya, built-up and fallow land and fields will experience an increase of 0.75 and 4.04 hectares, respectively; while the forest land will decrease by 4.2 hectares. Consequently, this will impact the variation of land-surface temperature in the future.
- Land-use/land-cover change and climate-change-impacted land-surface temperature in the four villages. According to the perception of local communities, these changes can cause different environmental issues such as biodiversity loss, soil erosion, change in frequency and intensity of precipitation, seasonal variation and increase in temperature. Additionally, this change negatively impacts human health and crop production in the region.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Type | Name | Pixel Size | Wavelength | Description | Year |
---|---|---|---|---|---|
Landsat 7 ETM+ | B1 | 30 meters | 0.45–0.52 µm | Blue | 2002 |
B2 | 30 meters | 0.52–0.60 µm | Green | ||
B3 | 30 meters | 0.63–0.69 µm | Red | ||
B4 | 30 meters | 0.77–0.90 µm | Near infrared | ||
B5 | 30 meters | 1.55–1.75 µm | Shortwave infrared 1 | ||
B6 | 30 meters | 10.40–12.50 µm | Low-gain Thermal Infrared 1 | ||
B6 | 30 meters | 10.40–12.50 µm | High-gain Thermal Infrared 1 | ||
B7 | 30 meters | 2.08–2.35 µm | Shortwave infrared 2 | ||
B8 | 15 meters | 0.52–0.90 µm | Panchromatic | ||
Landsat 8 OLI/TIRS | B1 | 30 meters | 0.43–0.45 µm | Coastal aerosol | 2020 and 2017 |
B2 | 30 meters | 0.45–0.51 µm | Blue | ||
B3 | 30 meters | 0.53–0.59 µm | Green | ||
B4 | 30 meters | 0.64–0.67 µm | Red | ||
B5 | 30 meters | 0.85–0.88 µm | Near infrared | ||
B6 | 30 meters | 1.57–1.65 µm | Shortwave infrared 1 | ||
B7 | 30 meters | 2.11–2.29 µm | Shortwave infrared 2 | ||
B8 | 15 meters | 0.52–0.90 µm | Band 8 Panchromatic | ||
B9 | 15 meters | 1.36–1.38 µm | Cirrus | ||
B10 | 30 meters | 10.60–11.19 µm | Thermal infrared 1 | ||
B11 | 30 meters | 11.50–12.51 µm | Thermal infrared 2 |
Name of Component | CA–Markov Model | |||
---|---|---|---|---|
Tsumba kituti | Kisavu | Kibuya | Kiobo | |
Persistence simulated correctly | 96.11% | 95.82 | 96.62 | 96.91 |
Change simulated correctly | 0.81% | 0.79 | 0.89 | 1.11 |
Total agreement | 96.92% | 96.61 | 97.51% | 98.02% |
Change simulated as persistence | 1.72% | 1.81 | 1.41 | 1.34 |
Persistence simulated as change | 1.33% | 1.42 | 1.06 | 0.61 |
Change simulated as change to incorrect category | 0.03% | 0.16 | 0.02 | 0.03 |
Total disagreement | 3.60% | 3.39 | 2.49 | 1.98 |
Land-Use/Land-Cover Types | Mean LST in 2002 | Mean LST in 2020 | Change in °C | |
---|---|---|---|---|
Tsumba Kituti | Forest land to built-up area | 20.9 | 24.03 | 3.13 |
Complex secondary and degraded forest to built-up area | 20.34 | 23.96 | 3.62 | |
Complex secondary and degraded forest to fallow land and fields | 20.90 | 23.1 | 2.2 | |
Fallow land to built-up area | 20.87 | 23.99 | 3.12 | |
Forest land to fallow land and fields | 20.71 | 23.14 | 2.43 | |
Kisavu | Forest land to built-up area | 20.5 | 23.75 | 3.25 |
Complex secondary and degraded forest to built-up area | 20.61 | 23.52 | 2.91 | |
Complex secondary and degraded forest to fallow land and fields | 20.72 | 22.89 | 2.17 | |
Fallow land to built-up area | 20.23 | 24.12 | 3.89 | |
Forest land to fallow land and fields | 20.21 | 23.28 | 3.07 | |
Kiobo | Fallow land and fields to built-up area | 21.04 | 23.03 | 1.99 |
Forest land to built-up area | 20.75 | 22.98 | 2.23 | |
Forest land to fallow land and fields | 20.84 | 21.94 | 1.1 | |
Complex secondary and degraded forest to fallow land and fields | 20.92 | 22.1 | 1.18 | |
Kibuya | Fallow land and fields to built-up area | 21.94 | 24.68 | 2.74 |
Fallow land and fields to Forest lands | 21.29 | 21.39 | 0.1 | |
Forest land to built-up area | 20.97 | 24.61 | 3.64 | |
Forest land to fallow land and fields | 21.11 | 21.36 | 0.25 |
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Opelele Omeno, M.; Yu, Y.; Fan, W.; Lubalega, T.; Chen, C.; Kachaka Sudi Kaiko, C. Analysis of the Impact of Land-Use/Land-Cover Change on Land-Surface Temperature in the Villages within the Luki Biosphere Reserve. Sustainability 2021, 13, 11242. https://doi.org/10.3390/su132011242
Opelele Omeno M, Yu Y, Fan W, Lubalega T, Chen C, Kachaka Sudi Kaiko C. Analysis of the Impact of Land-Use/Land-Cover Change on Land-Surface Temperature in the Villages within the Luki Biosphere Reserve. Sustainability. 2021; 13(20):11242. https://doi.org/10.3390/su132011242
Chicago/Turabian StyleOpelele Omeno, Michel, Ying Yu, Wenyi Fan, Tolerant Lubalega, Chen Chen, and Claude Kachaka Sudi Kaiko. 2021. "Analysis of the Impact of Land-Use/Land-Cover Change on Land-Surface Temperature in the Villages within the Luki Biosphere Reserve" Sustainability 13, no. 20: 11242. https://doi.org/10.3390/su132011242