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Dr. Matamyo Simwanda is a lecturer in the Department of Plant and Environmental Sciences, School of Natural Resources, Copperbelt University, Zambia. Dr. Simwanda teaches various courses including GIS & Spatial Analysis, Remote Sensing, Climate Change & Urban Development and Operations Research. He also has a lot of experience in conference presentations with a track of record peer-reviewed publications. His experience spanning over 12 years includes spatiotemporal analysis and modelling of landscapes, ecological assessments; environmental impact assessments and natural resources management and governance
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.
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 StyleManjula 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 StyleManjula 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.
The international statistics show that the global urban population will increase by up to 68% by 2050
Yuji Murayama; Matamyo Simwanda; Manjula Ranagalage. Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries. Sustainability 2021, 13, 3681 .
AMA StyleYuji Murayama, Matamyo Simwanda, Manjula Ranagalage. Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries. Sustainability. 2021; 13 (7):3681.
Chicago/Turabian StyleYuji Murayama; Matamyo Simwanda; Manjula Ranagalage. 2021. "Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries." Sustainability 13, no. 7: 3681.
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.
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 StyleMatamyo 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 StyleMatamyo 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.
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.
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 StyleVincent 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 StyleVincent 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.
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.
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 StyleManjula 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 StyleManjula 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.
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.
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 StyleManjula 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 StyleManjula 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.
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.
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 StyleDarius 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 StyleDarius 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.
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).
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 StyleDarius 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 StyleDarius 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.
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.
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 StyleMatamyo 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 StyleMatamyo 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.
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.
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 StyleManjula 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 StyleManjula 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.
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.
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 StyleManjula 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 StyleManjula 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.
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.
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 StyleMatamyo 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 StyleMatamyo 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.
Rapid urbanization in developing countries has been observed to be relatively high in the last two decades, especially in the Asian and African regions. Although many researchers have made efforts to improve the understanding of the urbanization trends of various cities in Asia and Africa, the absence of platforms where local stakeholders can visualize and obtain processed urbanization data for their specific needs or analysis, still remains a gap. In this paper, we present an Internet-based GIS platform called MEGA-WEB. The Platform was developed in view of the urban planning and management challenges in developing countries of Asia and Africa due to the limited availability of data resources, effective tools, and proficiency in data analysis. MEGA-WEB provides online access, visualization, spatial analysis, and data sharing services following a mashup framework of the MEGA-WEB Geo Web Services (GWS), with the third-party map services using HTML5/JavaScript techniques. Through the integration of GIS, remote sensing, geo-modelling, and Internet GIS, several indicators for analyzing urbanization are provided in MEGA-WEB to give diverse perspectives on the urbanization of not only the physical land surface condition, but also the relationships of population, energy use, and the environment. The design, architecture, system functions, and uses of MEGA-WEB are discussed in the paper. The MEGA-WEB project is aimed at contributing to sustainable urban development in developing countries of Asia and Africa.
Hao Gong; Matamyo Simwanda; Yuji Murayama. An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities. ISPRS International Journal of Geo-Information 2017, 6, 257 .
AMA StyleHao Gong, Matamyo Simwanda, Yuji Murayama. An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities. ISPRS International Journal of Geo-Information. 2017; 6 (8):257.
Chicago/Turabian StyleHao Gong; Matamyo Simwanda; Yuji Murayama. 2017. "An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities." ISPRS International Journal of Geo-Information 6, no. 8: 257.
For most sub-Saharan African (SSA) cities, in order to control the historically unplanned urban growth and stimulate sustainable future urban development, there is a need for accurate identification of the past and present urban land use (ULU). However, studies addressing ULU classification in SSA cities are lacking. In this study, we developed an integrated approach of remote sensing and Geographical Information System (GIS) techniques to classify ULU in the developing SSA city of Lusaka. First, we defined six ULU classes (i.e., unplanned high density residential; unplanned low density residential; planned medium-high density residential; planned low density residential; commercial and industrial; public institutions and service areas). ULU parcels, created using road networks as homogenous units separating ULU classes, were used to classify ULU. We utilised the combined detail of cadastral and land use data plus high-resolution Google Earth imagery to infer ULU and classify the parcels. For residential ULU, we also created density thresholds for accurate separation of the classes. We then used the classified ULU parcels for post-classification sorting of built-up pixels extracted from three Landsat TM/ETM+ imageries (1990, 2000, and 2010) into respective ULU classes. Three ULU maps were produced with overall accuracy values of 84.09% to 85.86%. The maps provide information that is relevant to urban planners and policy makers for sustainable future urban planning of Lusaka City. The study also provides an insight for ULU classification in SSA cities with complex urban landscapes similar to Lusaka.
Matamyo Simwanda; Yuji Murayama. Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia. ISPRS International Journal of Geo-Information 2017, 6, 102 .
AMA StyleMatamyo Simwanda, Yuji Murayama. Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia. ISPRS International Journal of Geo-Information. 2017; 6 (4):102.
Chicago/Turabian StyleMatamyo Simwanda; Yuji Murayama. 2017. "Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia." ISPRS International Journal of Geo-Information 6, no. 4: 102.