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Dr. DMSLB Dissanayake
Rajarata University of Sri Lanka, Department of Environmental Management, Faculty of Social Sciences

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Journal article
Published: 15 April 2021 in Remote Sensing
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The rapid and dominant urbanization in Asian cities has fueled concerns regarding the local and global efforts toward urban sustainability. Specifically, South Asian cities have been a topical issue concerning ecological and environmental threats due to their unplanned and haphazard urban development. However, comparative urbanization studies in South Asian cities remain uncommon. Therefore, in this study, we sought to comparatively examine the land use and land cover (LULC) dynamics and to detect the urbanization patterns of four rapidly developing South Asian lowland cities: Mumbai (India), Colombo (Sri Lanka), Karachi (Pakistan), and Dhaka (Bangladesh). Sentinel-2 (10 m) data and various geospatial approaches, including urban–rural gradient and grid-based methods, statistics, and urban landscape metric techniques, were used to facilitate the analysis. The study revealed that Mumbai, Karachi, and Dhaka had larger built-up landscapes compared to Colombo. Mumbai had the highest percentage of green spaces, followed by Colombo. Dhaka and Karachi had relatively small percentages of green spaces. Colombo and Dhaka had more croplands, which consistently increased along the urban–rural gradient compared to Mumbai and Karachi. Karachi showed that the only major land use was built-up, while most of the areas were left as open lands. On the other hand, Colombo’s urban setup was more fragmented than the other three cities. Mumbai and Karachi had larger patches of urban footprints compared to Colombo and Dhaka. Thus, this study provides vital information on the past land utilization priorities in the four cities, and comparatively proffers guidance on certain critical areas of focus for local, regional, and global future sustainable urban planning.

ACS Style

Manjula Ranagalage; Takehiro Morimoto; Matamyo Simwanda; Yuji Murayama. Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data. Remote Sensing 2021, 13, 1531 .

AMA Style

Manjula Ranagalage, Takehiro Morimoto, Matamyo Simwanda, Yuji Murayama. Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data. Remote Sensing. 2021; 13 (8):1531.

Chicago/Turabian Style

Manjula Ranagalage; Takehiro Morimoto; Matamyo Simwanda; Yuji Murayama. 2021. "Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data." Remote Sensing 13, no. 8: 1531.

Editorial
Published: 26 March 2021 in Sustainability
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The international statistics show that the global urban population will increase by up to 68% by 2050

ACS Style

Yuji Murayama; Matamyo Simwanda; Manjula Ranagalage. Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries. Sustainability 2021, 13, 3681 .

AMA Style

Yuji Murayama, Matamyo Simwanda, Manjula Ranagalage. Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries. Sustainability. 2021; 13 (7):3681.

Chicago/Turabian Style

Yuji Murayama; Matamyo Simwanda; Manjula Ranagalage. 2021. "Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries." Sustainability 13, no. 7: 3681.

Journal article
Published: 19 March 2021 in Sustainability
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For Sri Lanka, as an agricultural country, a methodical drought monitoring mechanism, including spatial and temporal variations, may significantly contribute to its agricultural sustainability. Investigating long-term meteorological and agricultural drought occurrences in Sri Lanka and assessing drought hazard at the district level are the main objectives of the study. Standardized Precipitation Index (SPI), Rainfall Anomaly Index (RAI), and Vegetation Health Index (VHI) were used as drought indicators to investigate the spatial and temporal distribution of agriculture and meteorological droughts. Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data from 1989 to 2019 was used to calculate SPI and RAI. MOD13A1 and MOD11A2 data from Moderate Resolution Imaging Spectroradiometer (MODIS) from 2001 to 2019, were used to generate the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). Agricultural drought monitoring was done using VHI and generated using the spatial integration of VCI and TCI. Thus, various spatial data analysis techniques were extensively employed for vector and raster data integration and analysis. A methodology has been developed for the drought declaration of the country using the VHI-derived drought area percentage. Accordingly, for a particular year, if the country-wide annual extreme and severe drought area percentage based on VHI drought classes is ≥30%, it can be declared as a drought year. Moreover, administrative districts of Sri Lanka were classified into four hazard classes, No drought, Low drought, Moderate drought, and High drought, using the natural-beak classification scheme for both agricultural and meteorological droughts. The findings of this study can be used effectively by the relevant decision-makers for drought risk management (DRM), resilience, sustainable agriculture, and policymaking.

ACS Style

Niranga Alahacoon; Mahesh Edirisinghe; Manjula Ranagalage. Satellite-Based Meteorological and Agricultural Drought Monitoring for Agricultural Sustainability in Sri Lanka. Sustainability 2021, 13, 3427 .

AMA Style

Niranga Alahacoon, Mahesh Edirisinghe, Manjula Ranagalage. Satellite-Based Meteorological and Agricultural Drought Monitoring for Agricultural Sustainability in Sri Lanka. Sustainability. 2021; 13 (6):3427.

Chicago/Turabian Style

Niranga Alahacoon; Mahesh Edirisinghe; Manjula Ranagalage. 2021. "Satellite-Based Meteorological and Agricultural Drought Monitoring for Agricultural Sustainability in Sri Lanka." Sustainability 13, no. 6: 3427.

Journal article
Published: 03 March 2021 in Remote Sensing
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Forecasting scenarios of future intra-urban land-use (intra-urban-LU) expansion can help to curb the historically unplanned urbanization in cities in sub-Saharan Africa (SSA) and promote urban sustainability. In this study, we applied the neural network–Markov model to simulate scenarios of future intra-urban-LU expansion in Lusaka city, Zambia. Data derived from remote sensing (RS) and geographic information system (GIS) techniques including urban-LU maps (from 2000, 2005, 2010, and 2015) and selected driver variables, were used to calibrate and validate the model. We then simulated urban-LU expansion for three scenarios (business as usual/status quo, environmental conservation and protection, and strategic urban planning) to explore alternatives for attaining urban sustainability by 2030. The results revealed that Lusaka had experienced rapid urban expansion dominated by informal settlements. Scenario analysis results suggest that a business-as-usual setup is perilous, as it signals an escalating problem of unplanned settlements. The environmental conservation and protection scenario is insufficient, as most of the green spaces and forests have been depleted. The strategic urban planning scenario has the potential for attaining urban sustainability, as it predicts sufficient control of unplanned settlement expansion and protection of green spaces and forests. The study proffers guidance for strategic policy directions and creating a planning vision.

ACS Style

Matamyo Simwanda; Yuji Murayama; Darius Phiri; Vincent Nyirenda; Manjula Ranagalage. Simulating Scenarios of Future Intra-Urban Land-Use Expansion Based on the Neural Network–Markov Model: A Case Study of Lusaka, Zambia. Remote Sensing 2021, 13, 942 .

AMA Style

Matamyo Simwanda, Yuji Murayama, Darius Phiri, Vincent Nyirenda, Manjula Ranagalage. Simulating Scenarios of Future Intra-Urban Land-Use Expansion Based on the Neural Network–Markov Model: A Case Study of Lusaka, Zambia. Remote Sensing. 2021; 13 (5):942.

Chicago/Turabian Style

Matamyo Simwanda; Yuji Murayama; Darius Phiri; Vincent Nyirenda; Manjula Ranagalage. 2021. "Simulating Scenarios of Future Intra-Urban Land-Use Expansion Based on the Neural Network–Markov Model: A Case Study of Lusaka, Zambia." Remote Sensing 13, no. 5: 942.

Journal article
Published: 29 September 2020 in ISPRS International Journal of Geo-Information
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In this study, a retrospective analysis of the relationship between the land use/land cover (LULC) change and associated surface urban heat island (SUHI) effect in fast-growing Greater Hefei between 1995 and 2016 was performed. Our results reveal the heterogeneous patterns of LULC change. The concentric buffer-based urban–rural gradient analysis reveals that most of the newly emerging developed land occurred within downtown Hefei. In contrast, in three suburban municipality/county jurisdictions, the overall area change in the non-developed land was much lower, but the net increase in developed land is remarkable. Meanwhile, the spatiotemporal patterns of SUHI are in good agreement with that of the developed land, as evidenced by the notable increase in SUHI intensity (SUHII) levels and SUHI spatial extent (SUHISE) in response to the rapid urban expansion, particularly along transportation corridors. In addition, partial least square regression (PLSR) models indicate that the buffer-based predictors/independent variables are significantly related to the responses (SUHII and SUHISE), explaining approximately 61.3% of the variance in the SUHII and 79.8% of the variance in the SUHISE, respectively. Furthermore, the relative strength of the independent variables in determining the relationship was quantitatively examined. The findings of this study provide clear evidence for decision making for sustainable land development and mitigation of the SUHI effect.

ACS Style

Ying-Ying Li; Yu Liu; Manjula Ranagalage; Hao Zhang; Rui Zhou. Examining Land Use/Land Cover Change and the Summertime Surface Urban Heat Island Effect in Fast-Growing Greater Hefei, China: Implications for Sustainable Land Development. ISPRS International Journal of Geo-Information 2020, 9, 568 .

AMA Style

Ying-Ying Li, Yu Liu, Manjula Ranagalage, Hao Zhang, Rui Zhou. Examining Land Use/Land Cover Change and the Summertime Surface Urban Heat Island Effect in Fast-Growing Greater Hefei, China: Implications for Sustainable Land Development. ISPRS International Journal of Geo-Information. 2020; 9 (10):568.

Chicago/Turabian Style

Ying-Ying Li; Yu Liu; Manjula Ranagalage; Hao Zhang; Rui Zhou. 2020. "Examining Land Use/Land Cover Change and the Summertime Surface Urban Heat Island Effect in Fast-Growing Greater Hefei, China: Implications for Sustainable Land Development." ISPRS International Journal of Geo-Information 9, no. 10: 568.

Journal article
Published: 23 September 2020 in Diversity
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Rodent assemblages have ecological importance in ecosystem functioning and protected area management. Our study examines the patterns of assemblages of rodents across four habitat types (i.e., Miombo woodland, Acacia woodland, grasslands and farmlands) in the savanna environment. Capture-mark-recapture (CMR) methods were applied for data collection across the Chembe Bird Sanctuary (CBS) landscape. The Non-metric Multi-Dimensional Scaling (NMDS) was used for exploratory data analysis, followed by Analysis of Variance (ANOVA) and Tukey–Kramer’s Honestly Significant Difference (HSD) post-hoc tests. The rodent assemblages in CBS significantly differed between the non-farmlands (i.e., Miombo woodland, Acacia woodland and grasslands) and farmlands. There were: (1) zero rodent diversity in farmlands, dominated completely by a pest species, M. natalensis; and (2) different rodent assemblages in three non-farmland habitat types. We suggest that rodent assemblages should be mediated by conservation planning and multi-stakeholder collaboration beyond the protected area boundaries to contribute to a working CBS landscape positively.

ACS Style

Vincent R. Nyirenda; Ngawo Namukonde; Matamyo Simwanda; Darius Phiri; Yuji Murayama; Manjula Ranagalage; Kaula Milimo. Rodent Assemblages in the Mosaic of Habitat Types in the Zambezian Bioregion. Diversity 2020, 12, 365 .

AMA Style

Vincent R. Nyirenda, Ngawo Namukonde, Matamyo Simwanda, Darius Phiri, Yuji Murayama, Manjula Ranagalage, Kaula Milimo. Rodent Assemblages in the Mosaic of Habitat Types in the Zambezian Bioregion. Diversity. 2020; 12 (10):365.

Chicago/Turabian Style

Vincent R. Nyirenda; Ngawo Namukonde; Matamyo Simwanda; Darius Phiri; Yuji Murayama; Manjula Ranagalage; Kaula Milimo. 2020. "Rodent Assemblages in the Mosaic of Habitat Types in the Zambezian Bioregion." Diversity 12, no. 10: 365.

Journal article
Published: 01 August 2020 in Forests
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Forest-cover change has become an important topic in global biodiversity conservation in recent decades because of the high rates of forest loss in different parts of the world, especially in the tropical region. While human interventions are the major cause, natural disasters also contribute to forest cover changes. During the past decades, several studies have been conducted to address different aspects of forest cover changes (e.g., drivers of deforestation, degradation, interventions) in different parts of the world. In Sri Lanka, increasing rates of forest loss have been recorded during the last 100 years on a regional basis, especially in the dry zone. However, Sri Lanka needs detailed studies that employ contemporary data and robust analytical tools to understand the patterns of forest cover changes and their drivers. The dry zone of Sri Lanka encompasses 59% of the total land area of the country, ergo, the most extensive forest cover. Our study analyzed forest cover dynamics and its drivers between 1992 and 2019. Our specific objectives included (i) producing a forest cover map for 2019, (ii) analyzing the spatiotemporal patterns of forest cover changes from 1992 to 2019, and (iii) determining the main driving forces. Landsat 8 images were used to develop forest-cover maps for 2019, and the rest of the forest cover maps (1992, 1999, and 2010) were obtained from the Forest Department of Sri Lanka. In this study, we found that the dry zone had undergone rapid forest loss (246,958.4 ha) during the past 27 years, which accounts for 8.0% of the net forest cover changes. From 2010 to 2019, the rates of forest loss were high, and this can be associated with the rapid infrastructure development of the country. The findings of this study can be used as a proxy to reform current forest policies and enhance the forest sustainability of the study area.

ACS Style

Manjula Ranagalage; M. H. J. P. Gunarathna; Thilina D. Surasinghe; Dmslb Dissanayake; Matamyo Simwanda; Yuji Murayama; Takehiro Morimoto; Darius Phiri; Vincent R. Nyirenda; K. T. Premakantha; Anura Sathurusinghe. Multi-Decadal Forest-Cover Dynamics in the Tropical Realm: Past Trends and Policy Insights for Forest Conservation in Dry Zone of Sri Lanka. Forests 2020, 11, 836 .

AMA Style

Manjula Ranagalage, M. H. J. P. Gunarathna, Thilina D. Surasinghe, Dmslb Dissanayake, Matamyo Simwanda, Yuji Murayama, Takehiro Morimoto, Darius Phiri, Vincent R. Nyirenda, K. T. Premakantha, Anura Sathurusinghe. Multi-Decadal Forest-Cover Dynamics in the Tropical Realm: Past Trends and Policy Insights for Forest Conservation in Dry Zone of Sri Lanka. Forests. 2020; 11 (8):836.

Chicago/Turabian Style

Manjula Ranagalage; M. H. J. P. Gunarathna; Thilina D. Surasinghe; Dmslb Dissanayake; Matamyo Simwanda; Yuji Murayama; Takehiro Morimoto; Darius Phiri; Vincent R. Nyirenda; K. T. Premakantha; Anura Sathurusinghe. 2020. "Multi-Decadal Forest-Cover Dynamics in the Tropical Realm: Past Trends and Policy Insights for Forest Conservation in Dry Zone of Sri Lanka." Forests 11, no. 8: 836.

Journal article
Published: 21 July 2020 in ISPRS International Journal of Geo-Information
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Changes in the urban landscape resulting from rapid urbanisation and climate change have the potential to increase land surface temperature (LST) and the incidence of the urban heat island (UHI). An increase in urban heat directly affects urban livelihoods and systems. This study investigated the spatiotemporal variation of the UHI in the Kurunegala urban area (KUA) of North-Western Province, Sri Lanka. The KUA is one of the most intensively developing economic and administrative capitals in Sri Lanka with an urban system that is facing climate vulnerabilities and challenges of extreme heat conditions. We examined the UHI formation for the period 1996–2019 and its impact on the urban-systems by exploring nature-based solutions (NBS). This study used annual median temperatures based on Landsat data from 1996 to 2019 using the Google Earth Engine (GEE). Various geospatial approaches, including spectral index-based land use/cover mapping (1996, 2009 and 2019), urban-rural gradient zones, UHI profile, statistics and grid-based analysis, were used to analyse the data. The results revealed that the mean LST increased by 5.5 °C between 1996 and 2019 mainly associated with the expansion pattern of impervious surfaces. The mean LST had a positive correlation with impervious surfaces and a negative correlation with the green spaces in all the three time-points. Impacts due to climate change, including positive temperature and negative rainfall anomalies, contributed to the increase in LST. The study recommends interactively applying NBS to addressing the UHI impacts with effective mitigation and adaptation measures for urban sustainability.

ACS Style

Manjula Ranagalage; Sujith S. Ratnayake; Dmslb Dissanayake; Lalit Kumar; Hasula Wickremasinghe; Jagathdeva Vidanagama; Hanna Cho; Susantha Udagedara; Keshav Kumar Jha; Matamyo Simwanda; Darius Phiri; Enc Perera; Priyantha Muthunayake. Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka. ISPRS International Journal of Geo-Information 2020, 9, 461 .

AMA Style

Manjula Ranagalage, Sujith S. Ratnayake, Dmslb Dissanayake, Lalit Kumar, Hasula Wickremasinghe, Jagathdeva Vidanagama, Hanna Cho, Susantha Udagedara, Keshav Kumar Jha, Matamyo Simwanda, Darius Phiri, Enc Perera, Priyantha Muthunayake. Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka. ISPRS International Journal of Geo-Information. 2020; 9 (7):461.

Chicago/Turabian Style

Manjula Ranagalage; Sujith S. Ratnayake; Dmslb Dissanayake; Lalit Kumar; Hasula Wickremasinghe; Jagathdeva Vidanagama; Hanna Cho; Susantha Udagedara; Keshav Kumar Jha; Matamyo Simwanda; Darius Phiri; Enc Perera; Priyantha Muthunayake. 2020. "Spatiotemporal Variation of Urban Heat Islands for Implementing Nature-Based Solutions: A Case Study of Kurunegala, Sri Lanka." ISPRS International Journal of Geo-Information 9, no. 7: 461.

Review
Published: 16 July 2020 in Remote Sensing
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The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

ACS Style

Darius Phiri; Matamyo Simwanda; Serajis Salekin; Vincent Nyirenda; Yuji Murayama; Manjula Ranagalage. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing 2020, 12, 2291 .

AMA Style

Darius Phiri, Matamyo Simwanda, Serajis Salekin, Vincent Nyirenda, Yuji Murayama, Manjula Ranagalage. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing. 2020; 12 (14):2291.

Chicago/Turabian Style

Darius Phiri; Matamyo Simwanda; Serajis Salekin; Vincent Nyirenda; Yuji Murayama; Manjula Ranagalage. 2020. "Sentinel-2 Data for Land Cover/Use Mapping: A Review." Remote Sensing 12, no. 14: 2291.

Original article
Published: 23 May 2020 in Modeling Earth Systems and Environment
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Landslides have become a frequent natural hazard and pressing severe environmental issues in Sri Lanka. The upward trend in high-intensity rainfall events, growing population, expansion of plantation, and lifelines increased the landslide risk of the country. Though developed countries adopted in risk assessment-based management, conversely, they rely on conventional landslide hazard assessment-based risk management. Therefore, this study is attempted to create a standardized landslide risk assessment framework, combining susceptibility and vulnerability. In the experimental design, landslide susceptibility was determined by nine (09) landslide causative factors, and fourteen (14) factors assessed for landslide vulnerability. Factors were prepared, standardized, and analyzed according to the level of contribution to susceptibility and vulnerability by using spatial multi-criteria evaluation method and entropy method under geographical information system. Spatial distribution of susceptibility and vulnerability were integrated to obtain the spatial distribution of risk. Analyses indicate that highly susceptible and high vulnerable areas are not demonstrated a high level of risk individually. However, a combination of them creates a high level of risk. The risk was classified into six classes, such as highest, high, moderate, low, lowest, and no risk. The highest-risk and high-risk zones of the area show 257 km2 (15%) and 21% (350 km2) of the total land area, respectively. Moderately risk zones take part 27% (446 km2). However, 22% (375 km2) of land area categorized as low or lowest risk and 15% (255 km2) under the no-risk. The study concluded that the developed framework is transparent and easy to update periodically by the local authorities. Hence, public policymakers can use the findings of this study to plan the future development of the region and the country. In contrast, risk assessment provides essential information to enhance national disaster risk reduction strategies.

ACS Style

E. N. C. Perera; D. T. Jayawardana; Manjula Ranagalage; D M S L B Dissanayake; H. M. D. S. Wijenayaka. Introduce a framework for landslide risk assessment using geospatial analysis: a case study from Kegalle District, Sri Lanka. Modeling Earth Systems and Environment 2020, 6, 2415 -2431.

AMA Style

E. N. C. Perera, D. T. Jayawardana, Manjula Ranagalage, D M S L B Dissanayake, H. M. D. S. Wijenayaka. Introduce a framework for landslide risk assessment using geospatial analysis: a case study from Kegalle District, Sri Lanka. Modeling Earth Systems and Environment. 2020; 6 (4):2415-2431.

Chicago/Turabian Style

E. N. C. Perera; D. T. Jayawardana; Manjula Ranagalage; D M S L B Dissanayake; H. M. D. S. Wijenayaka. 2020. "Introduce a framework for landslide risk assessment using geospatial analysis: a case study from Kegalle District, Sri Lanka." Modeling Earth Systems and Environment 6, no. 4: 2415-2431.

Journal article
Published: 19 May 2020 in ISPRS International Journal of Geo-Information
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Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine).

ACS Style

Darius Phiri; Matamyo Simwanda; Vincent Nyirenda; Yuji Murayama; Manjula Ranagalage. Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification. ISPRS International Journal of Geo-Information 2020, 9, 329 .

AMA Style

Darius Phiri, Matamyo Simwanda, Vincent Nyirenda, Yuji Murayama, Manjula Ranagalage. Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification. ISPRS International Journal of Geo-Information. 2020; 9 (5):329.

Chicago/Turabian Style

Darius Phiri; Matamyo Simwanda; Vincent Nyirenda; Yuji Murayama; Manjula Ranagalage. 2020. "Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification." ISPRS International Journal of Geo-Information 9, no. 5: 329.

Journal article
Published: 18 May 2020 in Climate
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This study investigated the spatiotemporal changes of land use land cover (LULC) and its impact on land surface temperature (LST) in the Galle Municipal Council area (GMCA), Sri Lanka. The same was achieved by employing the multi-temporal satellite data and geo-spatial techniques between 1996 and 2019. The post-classification change detection technique was employed to determine the temporal changes of LULC, and its results were utilized to assess the LST variation over the LULC changes. The results revealed that the area had undergone a drastic LULC transformation. It experienced 38% increase in the built-up area, while vegetation and non-built-up area declined by 26% and 12%, respectively. Rapid urban growth has had a significant effect on the LST, and the built-up area had the highest mean LST of 22.7 °C, 23.2 °C, and 26.3 °C for 1996, 2009, and 2019, correspondingly. The mean LST of the GMCA was 19.2 °C in 1996, 20.1 °C in 2009, and 22.4 °C in 2019. The land area with a temperature above 24 °C increased by 9% and 12% in 2009 and 2019, respectively. The highest LST variation (5.5 °C) was observed from newly added built-up area, which was also transferred from vegetation land. Meanwhile, the lowest mean LST difference was observed from newly added vegetation land. The results show that the mean annual LST increased by 3.2 °C in the last 22 years in GMCA. This study identified significant challenges for urban planners and respective administrative bodies to mitigate and control the negative effect of LST for the long livability of Galle City.

ACS Style

Dmslb Dissanayake. Land Use Change and Its Impacts on Land Surface Temperature in Galle City, Sri Lanka. Climate 2020, 8, 65 .

AMA Style

Dmslb Dissanayake. Land Use Change and Its Impacts on Land Surface Temperature in Galle City, Sri Lanka. Climate. 2020; 8 (5):65.

Chicago/Turabian Style

Dmslb Dissanayake. 2020. "Land Use Change and Its Impacts on Land Surface Temperature in Galle City, Sri Lanka." Climate 8, no. 5: 65.

Journal article
Published: 06 April 2020 in Sustainability
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The blooming of urban expansion has led to the improvement of urban life, but some of the negative externalities have affected the life quality of urban dwellers, both directly and indirectly. As a result of this, research related to the quality of life has gained much attention among multidisciplinary researchers around the world. A number of attempts have been made by previous researchers to identify, assess, quantify, and map quality of life or well-being under various kinds of perspectives. The objectives of this research were to create a life quality index (LQI) and identify the spatial distribution pattern of LQI in Kandy City, Sri Lanka. Multiple factors were decomposed, a hierarchy was constructed by the multi-criteria decision making (MCDM) method, and 13 factors were selected under two main criteria—environmental and socioeconomic. Pairwise comparison matrices were created, and the weight of each factor was determined by the analytic hierarchy process (AHP). Finally, gradient analysis was employed to examine the spatial distribution pattern of LQI from the city center to the periphery. The results show that socioeconomic factors affect the quality of life more strongly than environmental factors, and the most significant factor is transportation. The highest life quality zones (26% of the total area) were distributed around the city center, while the lowest zones represented only 9% of the whole area. As shown in the gradient analysis, more than 50% of the land in the first five kilometers from the city center comes under the highest life quality zone. This research will provide guidance for the residents and respective administrative bodies to make Kandy City a livable city. It the constructed model can be applied to any geographical area by conducting necessary data calibration.

ACS Style

Dmslb Dissanayake; Takehiro Morimoto; Yuji Murayama; Manjula Ranagalage; Enc Perera. Analysis of Life Quality in a Tropical Mountain City Using a Multi-Criteria Geospatial Technique: A Case Study of Kandy City, Sri Lanka. Sustainability 2020, 12, 2918 .

AMA Style

Dmslb Dissanayake, Takehiro Morimoto, Yuji Murayama, Manjula Ranagalage, Enc Perera. Analysis of Life Quality in a Tropical Mountain City Using a Multi-Criteria Geospatial Technique: A Case Study of Kandy City, Sri Lanka. Sustainability. 2020; 12 (7):2918.

Chicago/Turabian Style

Dmslb Dissanayake; Takehiro Morimoto; Yuji Murayama; Manjula Ranagalage; Enc Perera. 2020. "Analysis of Life Quality in a Tropical Mountain City Using a Multi-Criteria Geospatial Technique: A Case Study of Kandy City, Sri Lanka." Sustainability 12, no. 7: 2918.

Journal article
Published: 17 February 2020 in Agronomy
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As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM–Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.

ACS Style

M. H. J. P. Gunarathna; Kazuhito Sakai; M. K. N. Kumari; Manjula Ranagalage. A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models. Agronomy 2020, 10, 285 .

AMA Style

M. H. J. P. Gunarathna, Kazuhito Sakai, M. K. N. Kumari, Manjula Ranagalage. A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models. Agronomy. 2020; 10 (2):285.

Chicago/Turabian Style

M. H. J. P. Gunarathna; Kazuhito Sakai; M. K. N. Kumari; Manjula Ranagalage. 2020. "A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models." Agronomy 10, no. 2: 285.

Journal article
Published: 11 January 2020 in Land Use Policy
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Curbing the historically unplanned urban development in African cities crucially demands that the drivers of urban land use (urban-LU) changes are comprehended. However, this has become a complex decision problem for African urban planners and policy makers owing to the interconnections among urban-LU drivers and the complicated mixed development of planned and unplanned areas. Therefore, this study presents a new framework to model drivers of urban-LU changes in Lusaka, Zambia for the last 50 years using ground questionnaire surveys and the analytic network process (ANP). The study considers the growth of six urban-LUs, namely, unplanned high density residential (UHDR), unplanned low density residential (ULDR); planned medium-high density residential (PMHDR), planned low density residential (PLDR), commercial and industrial (CMI); and public institutions and service (PIS). The results revealed that socio-economic (55.11 %) and population (27.37 %) factors have been the major drivers of urban-LU changes while political factors (13.07 %) have also played a role. The role of biophysical factors (4.44 %) has been insignificant. The ANP model ranks UHDR (1st) and CMI (2nd) areas as the fastest-growing primarily driven by interactions amongst migration, economic opportunities, social services and land market. The growth of PMHDR, PIS and PLDR areas, ranked 3rd, 4th and 5th, respectively, has been largely driven by plans and policies and the political situation. The growth of ULDR areas is ranked (6th) as the lowest. The study discusses the urban planning and land use policy implications and suggests several strategies including strengthening of the local planning authority; improvement of the land tenure policies and delivery systems; establishment of satellite economic zones to decongest the city; investment in both green and blue infrastructure; and timely policy reviews.

ACS Style

Matamyo Simwanda; Yuji Murayama; Manjula Ranagalage. Modeling the drivers of urban land use changes in Lusaka, Zambia using multi-criteria evaluation: An analytic network process approach. Land Use Policy 2020, 92, 104441 .

AMA Style

Matamyo Simwanda, Yuji Murayama, Manjula Ranagalage. Modeling the drivers of urban land use changes in Lusaka, Zambia using multi-criteria evaluation: An analytic network process approach. Land Use Policy. 2020; 92 ():104441.

Chicago/Turabian Style

Matamyo Simwanda; Yuji Murayama; Manjula Ranagalage. 2020. "Modeling the drivers of urban land use changes in Lusaka, Zambia using multi-criteria evaluation: An analytic network process approach." Land Use Policy 92, no. : 104441.

Journal article
Published: 06 October 2019 in Sustainability
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Although urbanization has contributed to improving living conditions, it has had negative impacts on the natural environment in urbanized areas. Urbanization has changed the urban landscape and resulted in increasing land surface temperature (LST). Thus, studies related to LST in various urban environments have become popular. However, there are few LST studies focusing on mountain landscapes (i.e., hill stations). Therefore, this study investigated the changes in the landscape and their impacts on LST intensity (LSTI) in the tropical mountain city of Nuwara Eliya, Sri Lanka. The study utilized annual median temperatures extracted from Landsat data collected from 1996 to 2017 based on the Google Earth Engine (GEE) interface. The fractions of built-up (BL), forested (FL) and agricultural (AL) land, were calculated using land use and cover maps based on urban–rural zone (URZ) analysis. The urban–rural margin was demarcated based on the fractions of BL (<10%), and LSTI that were measured using the mean LST difference in the urban–rural zone. Besides, the mixture of land-use types was calculated using the AL/FL and BL/FL fraction ratios, and grid-based density analysis. The results revealed that the BL in all URZs rapidly developed, while AL decreased during the period 1996 to 2017. There was a minimal change in the forest area of the Nuwara Eliya owing to the government’s forest preservation policies. The mean temperature of the study area increased by 2.1 °C from 1996 to 2017. The magnitude of mean LST between urban–rural zones also increased from 1.0 °C (1996) to 3.5 °C (2017). The results also showed that mean LST was positively correlated with the increase and decrease of the BL/FL and AL/FL fraction ratios, respectively. The grid-based analysis showed an increasing, positive relationship between mean LST and density of BL. This indicated that BL density had been a crucial element in increasing LST in the study area. The results of this study will be a useful indicator to introduce improved landscape and urban planning in the future to minimize the negative impact of LST on urban sustainability.

ACS Style

Manjula Ranagalage; Yuji Murayama; Dmslb Dissanayake; Matamyo Simwanda. The Impacts of Landscape Changes on Annual Mean Land Surface Temperature in the Tropical Mountain City of Sri Lanka: A Case Study of Nuwara Eliya (1996–2017). Sustainability 2019, 11, 5517 .

AMA Style

Manjula Ranagalage, Yuji Murayama, Dmslb Dissanayake, Matamyo Simwanda. The Impacts of Landscape Changes on Annual Mean Land Surface Temperature in the Tropical Mountain City of Sri Lanka: A Case Study of Nuwara Eliya (1996–2017). Sustainability. 2019; 11 (19):5517.

Chicago/Turabian Style

Manjula Ranagalage; Yuji Murayama; Dmslb Dissanayake; Matamyo Simwanda. 2019. "The Impacts of Landscape Changes on Annual Mean Land Surface Temperature in the Tropical Mountain City of Sri Lanka: A Case Study of Nuwara Eliya (1996–2017)." Sustainability 11, no. 19: 5517.

Journal article
Published: 11 September 2019 in Climate
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The urban heat island (UHI) phenomenon is an important research topic in the scholarly community. There are only few research studies related to the UHI in the Seoul metropolitan area (SMA). Therefore, this study examined the impact of urbanization on the formation of UHI in the SMA as a geospatial study by using Landsat data from 1996, 2006, and 2017. For this purpose, we analyzed the relative variation of land surface temperature (LST) with changes of land use/land cover (LULC) rather than absolute values of LST using gradient, intensity, and directional analyses. It was observed that the impervious surface (IS) has expanded, and the UHI effect was more penetrating in the study area, with considerable loss of other LULC including green surfaces along with the rapid urbanization of the study area. In this study, we divided the IS into persistent IS (PIS) and newly added IS (NAIS). The spatial distribution of the IS, forest surface (FS), PIS, and NAIS was observed based on gradient zones (GZs). The results show that GZ1 recorded a difference of 6.0 °C when compared with the GZ109 in 2017. The results also show that the city center was warmer than the surrounding areas during the period of study. Results reveal that the mean LST has a strong significant positive relationship with a fraction of IS and PIS in 2006 and 2017. On other hand, the mean LST has a strong negative relationship with a fraction of FS and NAIS in the same time points. Relatively low temperatures were recorded in FS and NAIS in both time points. Further, it was proved that the local climate of the SMA and its surroundings had been affected by the UHI effect. Therefore, urban planners of the SMA should seriously consider the issue and plan to mitigate the effect by improving the green surfaces of the city. More greening-oriented concepts are recommended in both horizontal and vertical directions of the SMA, that can be used to control the negative impact associated with UHI. The overall outputs of the study could be used as a proxy indicator for the sustainability of the SMA and its surroundings.

ACS Style

Prabath Priyankara; Manjula Ranagalage; Dmslb Dissanayake; Takehiro Morimoto; Yuji Murayama. Spatial Process of Surface Urban Heat Island in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using Landsat Data (1996–2017). Climate 2019, 7, 110 .

AMA Style

Prabath Priyankara, Manjula Ranagalage, Dmslb Dissanayake, Takehiro Morimoto, Yuji Murayama. Spatial Process of Surface Urban Heat Island in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using Landsat Data (1996–2017). Climate. 2019; 7 (9):110.

Chicago/Turabian Style

Prabath Priyankara; Manjula Ranagalage; Dmslb Dissanayake; Takehiro Morimoto; Yuji Murayama. 2019. "Spatial Process of Surface Urban Heat Island in Rapidly Growing Seoul Metropolitan Area for Sustainable Urban Planning Using Landsat Data (1996–2017)." Climate 7, no. 9: 110.

Journal article
Published: 14 August 2019 in Climate
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An urban heat island (UHI) is a phenomenon that shows a higher temperature in urban areas compared to surrounding rural areas due to the impact of impervious surface (IS) density, and other anthropogenic activities including changes of land use/land cover (LULC). The purpose of this research is to examine the spatiotemporal land-use/land-cover changes and their impact on the surface UHI (SUHI) in Kandy City, Sri Lanka, using Landsat data and geospatial techniques. LULC classification was made by using a pixel-oriented supervised classification method, and LULC changes were computed by using a cross-cover comparison. The SUHI effect was discussed mainly through the variation of land-surface temperature (LST) over persistent IS and newly added IS. The study showed the dynamics of each LULC and its role in the SUHI. The results showed that IS areas expanded from 529 to 1514 ha (2.3% to 6.7% of the total land area) between 1996 and 2006, and to 5833 ha (23.9% of the total land area) in 2017, with an annual growth rate of 11.1% per year from 1996 to 2006 and 12.2% per year from 2006 to 2017. A gradually declining trend was observed in forest areas. Persistent IS reported the highest mean LST areas compared to newly added IS. The mean LST difference between persistent IS and newly added IS was 1.43 °C over the study period. This is because areas of persistent IS are typically surrounded by IS even in their neighborhoods, whereas areas of newly added IS occur at the edges of the city and are, therefore, cooled by the surrounding nonurban surfaces. This calls for appropriate green-oriented landscape-management methods to mitigate the impact of the SUHI in Kandy City. The findings of the study showed that LULC changes and their effect on the SUHI from 1996 to 2017 made a significant contribution to long records of change dynamics.

ACS Style

Dmslb Dissanayake; Takehiro Morimoto; Manjula Ranagalage; Yuji Murayama. Land-Use/Land-Cover Changes and Their Impact on Surface Urban Heat Islands: Case Study of Kandy City, Sri Lanka. Climate 2019, 7, 99 .

AMA Style

Dmslb Dissanayake, Takehiro Morimoto, Manjula Ranagalage, Yuji Murayama. Land-Use/Land-Cover Changes and Their Impact on Surface Urban Heat Islands: Case Study of Kandy City, Sri Lanka. Climate. 2019; 7 (8):99.

Chicago/Turabian Style

Dmslb Dissanayake; Takehiro Morimoto; Manjula Ranagalage; Yuji Murayama. 2019. "Land-Use/Land-Cover Changes and Their Impact on Surface Urban Heat Islands: Case Study of Kandy City, Sri Lanka." Climate 7, no. 8: 99.

Journal article
Published: 24 July 2019 in Remote Sensing
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Forecasting landscape changes is vital for developing and implementing sustainable urban planning. Presently, apart from lowland coastal cities, mountain cities (i.e., hill stations) are also facing the negative impacts of rapid urbanization due to their economic and social importance. However, few studies are addressing urban landscape changes in hill stations in Asia. This study aims to examine and forecast landscape changes in the rapidly urbanizing hill station of Nuwara Eliya, Sri Lanka. Landsat data and geospatial techniques including support vector machines, urban–rural gradient, and statistical analysis were used to map and examine the land use/land cover (LULC) change in Nuwara Eliya during the 1996–2006 and 2006–2017 periods. The multilayer perceptron neural network-Markov model was applied to simulate future LULC changes for 2027 and 2037. The results show that Nuwara Eliya has been directly affected by rapid urban development. During the past 21 years (1996–2017), built-up areas increased by 1791 ha while agricultural land declined by 1919 ha due to augmented urban development pressure. The pressure of urban development on forest land has been relatively low, mainly due to strict conservation government policies. The results further show that the observed landscape changes will continue in a similar pattern in the future, confirming a significant increase and decrease of built-up and agricultural land, respectively, from 2017 to 2037. The changes in agricultural land exhibit a strong negative relationship with the changes in built-up land along the urban–rural gradient (R2 were 0.86 in 1996–2006, and 0.93 in 2006–2017, respectively). The observed LULC changes could negatively affect the production of unique upcountry agricultural products such as exotic vegetables, fruits, cut flowers, and world-famous Ceylon tea. Further, unplanned development could cause several environmental issues. The study is important for understanding future LULC changes and suggesting necessary remedial measures to minimize possible undesirable environmental and socioeconomic impacts.

ACS Style

Manjula Ranagalage; Ruci Wang; M. H. J. P. Gunarathna; Dmslb Dissanayake; Yuji Murayama; Matamyo Simwanda. Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037). Remote Sensing 2019, 11, 1743 .

AMA Style

Manjula Ranagalage, Ruci Wang, M. H. J. P. Gunarathna, Dmslb Dissanayake, Yuji Murayama, Matamyo Simwanda. Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037). Remote Sensing. 2019; 11 (15):1743.

Chicago/Turabian Style

Manjula Ranagalage; Ruci Wang; M. H. J. P. Gunarathna; Dmslb Dissanayake; Yuji Murayama; Matamyo Simwanda. 2019. "Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)." Remote Sensing 11, no. 15: 1743.

Journal article
Published: 10 July 2019 in Remote Sensing
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Africa’s unprecedented, uncontrolled and unplanned urbanization has put many African cities under constant ecological and environmental threat. One of the critical ecological impacts of urbanization likely to adversely affect Africa’s urban dwellers is the urban heat island (UHI) effect. However, UHI studies in African cities remain uncommon. Therefore, this study attempts to examine the relationship between land surface temperature (LST) and the spatial patterns, composition and configuration of impervious surfaces/green spaces in four African cities, Lagos (Nigeria), Nairobi (Kenya), Addis Ababa (Ethiopia) and Lusaka (Zambia). Landsat OLI/TIRS data and various geospatial approaches, including urban–rural gradient, urban heat island intensity, statistics and urban landscape metrics-based techniques, were used to facilitate the analysis. The results show significantly strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban–rural gradients of the four African cities. The study also found high urban heat island intensities in the urban zones close (0 to 10 km) to the city center for all cities. Generally, cities with a higher percentage of the impervious surface were warmer by 3–4 °C and vice visa. This highlights the crucial mitigating effect of green spaces. We also found significant correlations between the mean LST and urban landscape metrics (patch density, size, shape, complexity and aggregation) of impervious surfaces (positive) and green spaces (negative). The study revealed that, although most African cities have relatively larger green space to impervious surface ratio with most green spaces located beyond the urban footprint, the UHI effect is still evident. We recommend that urban planners and policy makers should consider mitigating the UHI effect by restoring the urban ecosystems in the remaining open spaces in the urban area and further incorporate strategic combinations of impervious surfaces and green spaces in future urban and landscape planning.

ACS Style

Matamyo Simwanda; Manjula Ranagalage; Ronald C. Estoque; Yuji Murayama. Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities. Remote Sensing 2019, 11, 1645 .

AMA Style

Matamyo Simwanda, Manjula Ranagalage, Ronald C. Estoque, Yuji Murayama. Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities. Remote Sensing. 2019; 11 (14):1645.

Chicago/Turabian Style

Matamyo Simwanda; Manjula Ranagalage; Ronald C. Estoque; Yuji Murayama. 2019. "Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities." Remote Sensing 11, no. 14: 1645.