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Globally, the demand for timely land use/cover change information has increased over the past decades given the rapid pace of urbanization . The objective of this chapter was to analyze observed and simulated land use/cover changes between 1989 and 2030 in Dakar Metropolitan Area. The land use/cover maps for 1989, 1999, 2010, and 2014 indicated that built-up areas increased substantially over the study period. Generally, built-up expanded during the “1989–1999” and “2010–2014” epochs, while built-up expansion slowed down during the “1999–2010” epoch. Built-up growth in Dakar Metropolitan Area was characterized by a combination of sprawl and densification . Future land use/cover simulations (up to 2030) indicated that the current land use/cover change trends such as the increase in built-up areas and decrease in non-built-up areas as well as urban sprawl would continue to persist. The observed and simulated land use/cover changes provide a panoramic view of built-up expansion as well as a simulated urban growth scenario for Dakar Metropolitan Area. These results convey important insights about urban expansion, which could potentially be used to implement the “2035 Dakar Urban Masterplan.”
Courage Kamusoko. Dakar Metropolitan Area. The Life and Afterlife of Gay Neighborhoods 2017, 257 -273.
AMA StyleCourage Kamusoko. Dakar Metropolitan Area. The Life and Afterlife of Gay Neighborhoods. 2017; ():257-273.
Chicago/Turabian StyleCourage Kamusoko. 2017. "Dakar Metropolitan Area." The Life and Afterlife of Gay Neighborhoods , no. : 257-273.
Sustainable urban planning and development require reliable and timely land use/cover change information. The objective of this chapter was to analyze observed and simulated land use/cover changes between 1990 and 2030. Based on land use/cover maps for 1990, 2002, 2009, and 2014, built-up areas increased substantially, while non-built-up areas decreased over the study period. The land use/cover change analysis revealed significant built-up expansion for the “1990–2002” epoch. However, built-up expansion slowed down during the “2002–2009” and “2009–2014” epochs. The built-up growth pattern and the spatial/landscape metrics revealed that infill, extension, and leapfrog developments were occurring in the study area. Future land use/cover simulations (up to 2030) indicated that the current land use/cover change trends such as the increase in built-up areas and decrease in non-built-up areas would continue to persist unless sustainable urban development policies are implemented. The observed and simulated land use/cover changes provide a synoptic view of built-up expansion as well as a plausible future urban growth scenario for Harare Metropolitan Area. This could potentially assist decision-makers with general built-up change information, which can be used to guide strategic sustainable urban land use planning and development for Harare Metropolitan Area.
Courage Kamusoko; Enos Chikati. Harare Metropolitan Area. The Life and Afterlife of Gay Neighborhoods 2017, 347 -370.
AMA StyleCourage Kamusoko, Enos Chikati. Harare Metropolitan Area. The Life and Afterlife of Gay Neighborhoods. 2017; ():347-370.
Chicago/Turabian StyleCourage Kamusoko; Enos Chikati. 2017. "Harare Metropolitan Area." The Life and Afterlife of Gay Neighborhoods , no. : 347-370.
Remote sensing analysis and land change modeling provide valuable insights into urban land use/cover changes and growth processes at multiple spatial and temporal scales. This chapter briefly outlines the importance of remote sensing, and land change modeling for urbanization studies in selected countries in Africa and Asia. The methodological approaches discussed in this book showcase the potential of remote sensing and land change modeling analysis in order to improve understanding of urban growth in Africa and Asia. Given the complexity of urban growth processes globally, issues raised in this book will contribute to the improvement of future land use/cover change analysis and modeling, particularly in the developing country context. The geospatial analysis approach based on remote sensing and land change modeling provides a synoptic view of urbanization in Africa and Asia.
Courage Kamusoko. Importance of Remote Sensing and Land Change Modeling for Urbanization Studies. The Life and Afterlife of Gay Neighborhoods 2017, 3 -10.
AMA StyleCourage Kamusoko. Importance of Remote Sensing and Land Change Modeling for Urbanization Studies. The Life and Afterlife of Gay Neighborhoods. 2017; ():3-10.
Chicago/Turabian StyleCourage Kamusoko. 2017. "Importance of Remote Sensing and Land Change Modeling for Urbanization Studies." The Life and Afterlife of Gay Neighborhoods , no. : 3-10.
The objective of this chapter was to analyze observed and projected land use/cover changes between 1990 and 2030 in Bamako Metropolitan Area. The land use/cover change analysis revealed significant built-up expansion for the “1990–2000” and “2010–2014” epochs, while built-up expansion slowed down during the “2000–2010” epoch. Built-up growth in Bamako Metropolitan Area was characterized by a low-density urban sprawl moving outward from the urban core into the surrounding rural areas. Generally, vacant lands in the surrounding rural areas were converted to residential and urban land uses. Future land use/cover simulations (up to 2030) indicated that the current land use/cover change trends, such as the increase in built-up areas and decrease in non-built-up areas as well as low-density urban sprawl, would continue to persist. The observed and simulated land use/cover changes provide an overview of built-up expansion as well as a simulated urban growth scenario for Bamako Metropolitan Area. This could potentially assist decision-makers with general built-up change information that can be used to guide sustainable urban development in Bamako Metropolitan Area.
Courage Kamusoko. Bamako Metropolitan Area. The Life and Afterlife of Gay Neighborhoods 2017, 275 -291.
AMA StyleCourage Kamusoko. Bamako Metropolitan Area. The Life and Afterlife of Gay Neighborhoods. 2017; ():275-291.
Chicago/Turabian StyleCourage Kamusoko. 2017. "Bamako Metropolitan Area." The Life and Afterlife of Gay Neighborhoods , no. : 275-291.
Remote sensing , GIS, and land change models (LCMs) are critical for mapping urban land use/cover and simulating “what if” urban growth scenarios, particularly in developing countries experiencing rapid urbanization . The purpose of this chapter is to describe briefly the methodology used to produce land use/cover maps, and simulate land use/cover changes for selected metropolitan areas in Asia and Africa. Land use/cover maps were classified from Landsat imagery for 1990, 2000, 2010, and 2014 using the random forest (RF) classifier. Quantitative accuracy assessment was not conducted for the 1990 land use/cover maps due to lack of reference data. However, qualitative and quantitative accuracy assessment was performed for the 2000, 2010, and 2014 land use/cover maps based on Google Earth imagery. Overall land use/cover classification accuracy for all land use/cover maps ranged from 70 to 90%. Land use/cover changes were simulated based on the boosted regression trees-cellular automata (BRT-CA) and RF-CA LCMs. We evaluated the goodness-of-fit of transition potential maps, and validated the simulated land use/cover changes based on robust statistical measures. Generally, the BRT-CA and RF-CA LCMs for all metropolitan areas in Asia and Africa performed relatively well. In particular, the BRT-CA and RF-CA LCMs for metropolitan areas in Africa had the best performance. The modeling and simulation results presented in this chapter provide an initial exploration of BRT-CA and RF-CA LCMs in Asia and Africa. This chapter demonstrates the significance of robust calibration, validation , and simulation of spatial LCMs for all metropolitan areas in Asia and Africa.
Courage Kamusoko. Methodology. The Life and Afterlife of Gay Neighborhoods 2017, 11 -46.
AMA StyleCourage Kamusoko. Methodology. The Life and Afterlife of Gay Neighborhoods. 2017; ():11-46.
Chicago/Turabian StyleCourage Kamusoko. 2017. "Methodology." The Life and Afterlife of Gay Neighborhoods , no. : 11-46.
Sustainable urban planning and management require reliable land change models, which can be used to improve decision making. The objective of this study was to test a random forest-cellular automata (RF-CA) model, which combines random forest (RF) and cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and components of agreement and disagreement statistics were used to validate the RF-CA model. Furthermore, the RF-CA model was compared with support vector machine cellular automata (SVM-CA) and logistic regression cellular automata (LR-CA) models. Results show that the RF-CA model outperformed the SVM-CA and LR-CA models. The RF-CA model had a Kappa simulation (KSimulation) accuracy of 0.51 (with a figure of merit statistic of 47%), while SVM-CA and LR-CA models had a KSimulation accuracy of 0.39 and −0.22 (with figure of merit statistics of 39% and 6%), respectively. Generally, the RF-CA model was relatively accurate at allocating “non-built-up to built-up” changes as reflected by the correct “non-built-up to built-up” components of agreement of 15%. The performance of the RF-CA model was attributed to the relatively accurate RF transition potential maps. Therefore, this study highlights the potential of the RF-CA model for simulating urban growth.
Courage Kamusoko; Jonah Gamba. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model. ISPRS International Journal of Geo-Information 2015, 4, 447 -470.
AMA StyleCourage Kamusoko, Jonah Gamba. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model. ISPRS International Journal of Geo-Information. 2015; 4 (2):447-470.
Chicago/Turabian StyleCourage Kamusoko; Jonah Gamba. 2015. "Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model." ISPRS International Journal of Geo-Information 4, no. 2: 447-470.
Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT), random forests (RF), and support vector machines (SVM) in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I) variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem.
Courage Kamusoko; Jonah Gamba; Hitomi Murakami. Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers. Land 2014, 3, 524 -540.
AMA StyleCourage Kamusoko, Jonah Gamba, Hitomi Murakami. Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers. Land. 2014; 3 (2):524-540.
Chicago/Turabian StyleCourage Kamusoko; Jonah Gamba; Hitomi Murakami. 2014. "Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers." Land 3, no. 2: 524-540.
Future forest cover changes were simulated under the business-as-usual (BAU), pessimistic and optimistic scenarios using the Markov-cellular automata (MCA) model in Pakxeng district, Lao People’s Democratic Republic (PDR). The Markov chain analysis was used to compute transition probabilities from satellite-derived forest cover maps (1993, 1996, 2000 and 2004), while the “weights of evidence” procedure was used to generate transition potential (suitability) maps. Dynamic adjustments of transition probabilities and transition potential maps were implemented in a cellular automata (CA) model in order to simulate forest cover changes. The validation results revealed that unstocked forest and current forest classes were relatively well simulated, while the non-forest class was slightly underpredicted. The MCA simulations under the BAU and pessimistic scenarios indicated that current forest areas would decrease, whereas unstocked forest areas would increase in the future. In contrast, the MCA model projected that current forest areas would increase under the optimistic scenario if forestry laws are strictly enforced in the study area. The simulation scenarios observed in this study can be possibly used to understand implications of future forest cover changes on sustainable forest management in Pakxeng district.
Courage Kamusoko; Yukio Wada; Toru Furuya; Shunsuke Tomimura; Mitsuru Nasu; Khamma Homsysavath. Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management. Land 2013, 2, 1 -19.
AMA StyleCourage Kamusoko, Yukio Wada, Toru Furuya, Shunsuke Tomimura, Mitsuru Nasu, Khamma Homsysavath. Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management. Land. 2013; 2 (1):1-19.
Chicago/Turabian StyleCourage Kamusoko; Yukio Wada; Toru Furuya; Shunsuke Tomimura; Mitsuru Nasu; Khamma Homsysavath. 2013. "Simulating Future Forest Cover Changes in Pakxeng District, Lao People’s Democratic Republic (PDR): Implications for Sustainable Forest Management." Land 2, no. 1: 1-19.
Taking Harare metropolitan province in Zimbabwe as an example, we classified Landsat imagery (1984, 2002, 2008 and 2013) by using support vector machines (SVMs) and analyzed built-up and non-built-up changes. The overall classification accuracy for the four dates ranged from 89% to 95%, while the overall kappa varied from 86% to 93%. The results demonstrate that SVMs provide a cost-effective technique for mapping urban land use/cover by using mediumresolution satellite images such as Landsat. Based on land use/cover maps for 1984, 2002, 2008 and 2013, along with change analyses, built-up areas increased from 12.6% to 36.3% of the total land area, while non-built-up cover decreased from 87.3% to 63.4% between 1984 and 2013. The results revealed an urban growth process characterized by infill, extension and leapfrog developments. Given the dearth of spatial urban growth information in Harare metropolitan province, the land use/cover maps are valuable products that provide a synoptic view of built-up and non-built-up areas. Therefore, the land use/cover change maps could potentially assist decision-makers with up-to-date built-up and non-built-up information in order to guide strategic implementation of sustainable urban land use planning in Harare metropolitan province.
Courage Kamusoko; Jonah Gamba; Hitomi Murakami. Monitoring Urban Spatial Growth in Harare Metropolitan Province, Zimbabwe. Advances in Remote Sensing 2013, 02, 322 -331.
AMA StyleCourage Kamusoko, Jonah Gamba, Hitomi Murakami. Monitoring Urban Spatial Growth in Harare Metropolitan Province, Zimbabwe. Advances in Remote Sensing. 2013; 02 (04):322-331.
Chicago/Turabian StyleCourage Kamusoko; Jonah Gamba; Hitomi Murakami. 2013. "Monitoring Urban Spatial Growth in Harare Metropolitan Province, Zimbabwe." Advances in Remote Sensing 02, no. 04: 322-331.
Spatial simulation models are indispensable for modelling land use/cover changes (Wu and Webster 1998; Messina and Walsh 2001; Soares-Filho et al. 2002), deforestation and land degradation (Lambin 1994; Lambin 1997; Etter et al. 2006; Moreno et al. 2007), urban growth (Clarke et al. 1997; Couclelis 1989; Cheng and Masser 2004; Gar-On Yeh and Li 2009), climate change (Dale 1997) and hydrology (Matheussen et al. 2000). For land use/cover change studies, spatial simulation models are critical for understanding the driving forces of change, as well as to produce “what if” scenarios that can be used to gain insights into future land use/cover changes (Pijanowski et al. 2002; Eastman et al. 2005; Torrens 2006). Recently, the knowledge domain of spatial simulation modelling has advanced owing to the rapid developments in computer technology, coupled with the decrease in the cost of computer hardware. In addition, developments in geospatial, natural and social sciences concerning bottom-up, dynamic and flexible self-organising modelling systems, complemented by theories that emphasize the way in which decisions made locally give rise to global patterns, have enriched spatial simulation models (Tobler 1979; Wolfram 1984; Couclelis 1985; Engelen 1988; Wu and Webster 1998; Batty 1998). To date, numerous spatial simulation models have been developed and applied, particularly for land use/cover modelling (Clarke et al. 1997; Kaimowitz and Angelsen 1998; Messina and Walsh 2001; Soares-Filho et al. 2002; Walsh et al. 2006).
Courage Kamusoko. Markov–Cellular Automata in Geospatial Analysis. Progress in Geospatial Analysis 2012, 107 -124.
AMA StyleCourage Kamusoko. Markov–Cellular Automata in Geospatial Analysis. Progress in Geospatial Analysis. 2012; ():107-124.
Chicago/Turabian StyleCourage Kamusoko. 2012. "Markov–Cellular Automata in Geospatial Analysis." Progress in Geospatial Analysis , no. : 107-124.
Taking Luangprabang province in Lao Peoples’s Democratic Republic (PDR) as an example, we simulated future forest cover changes under the business-as-usual (BAU), pessimistic and optimistic scenarios based on the Markov-cellular automata (MCA) model. We computed transition probabilities from satellite-derived forest cover maps (1993 and 2000) using the Markov chains, while the “weights of evidence” technique was used to generate transition potential maps. The initial forest cover map (1993), the transition potential maps and the 1993–2000 transition probabilities were used to calibrate the model. Forest cover simulations were then performed from 1993 to 2007 at an annual time-step. The simulated forest cover map for 2007 was compared to the observed (actual) forest cover map for 2007 in order to test the accuracy of the model. Following the successful calibration and validation, future forest cover changes were simulated up to 2014 under different scenarios. The MCA simulations under the BAU and pessimistic scenarios projected that current forest areas would decrease, whereas unstocked forest areas would increase in the future. Conversely, the optimistic scenario projected that current forest areas would increase in the future if strict forestry laws enforcing conservation in protected forest areas are implemented. The three simulation scenarios provide a very good case study for simulating future forest cover changes at the subnational level (Luangprabang province). Thus, the future simulated forest cover changes can possibly be used as a guideline to set reference scenarios as well as undertake REDD/REDD+ preparedness activities within the study area.
Courage Kamusoko; Katsumata Oono; Akihiro Nakazawa; Yukio Wada; Ryuji Nakada; Takahiro Hosokawa; Shunsuke Tomimura; Toru Furuya; Akitaka Iwata; Hiromichi Moriike; Takashi Someya; Takashi Yamase; Mitsuru Nasu; Yoshitaka Gomi; Takio Sano; Takao Isobe; Khamma Homsysavath. Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR. Forests 2011, 2, 707 -729.
AMA StyleCourage Kamusoko, Katsumata Oono, Akihiro Nakazawa, Yukio Wada, Ryuji Nakada, Takahiro Hosokawa, Shunsuke Tomimura, Toru Furuya, Akitaka Iwata, Hiromichi Moriike, Takashi Someya, Takashi Yamase, Mitsuru Nasu, Yoshitaka Gomi, Takio Sano, Takao Isobe, Khamma Homsysavath. Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR. Forests. 2011; 2 (3):707-729.
Chicago/Turabian StyleCourage Kamusoko; Katsumata Oono; Akihiro Nakazawa; Yukio Wada; Ryuji Nakada; Takahiro Hosokawa; Shunsuke Tomimura; Toru Furuya; Akitaka Iwata; Hiromichi Moriike; Takashi Someya; Takashi Yamase; Mitsuru Nasu; Yoshitaka Gomi; Takio Sano; Takao Isobe; Khamma Homsysavath. 2011. "Spatial Simulation Modelling of Future Forest Cover Change Scenarios in Luangprabang Province, Lao PDR." Forests 2, no. 3: 707-729.