Modeling biomass transport on single lane forest roads and monitoring GPS accuracy for vehicle tracking under different forest canopy conditions

The transportation of wood and biomass resources from landing and other collection locations to processing and distribution sites is a substantial cost within the wood supply chain. These high costs provide a basis for research aimed at improving biomass transportation planning decisions and potentially reducing biomass transportation costs. Chip vans have been identified to be the most cost-efficient mode of transporting biomass provided the roads are suitable for the trucks which are generally built for highway use. Research to develop chip van performance simulation models for travel time prediction could potentially reduce biomass transportation costs by improving transportation planning decisions. GPS technology has the ability to record information such as location (longitude, latitude and elevation), movement (speed, heading) and travel time which makes it an attractive tool for data collection to develop, test and validate vehicle simulation models. In spite of several studies investigating the accuracy and performance of GPS under different forest conditions, the reliability of GPS receiver measurements for moving vehicles under forest canopy and in mountainous terrain has not been examined. This dissertation includes two manuscripts. One manuscript presents a Chip Van Travel Time Prediction Simulation Model (CHIP-VAN) that was developed using data collected by GPS receivers to track and monitor chip vans. The vans were exclusively used for transporting chipped (ground) biomass from forest operation sites in western Oregon. The other manuscript examines the accuracy and reliability of GPS for vehicle tracking under different forest canopy conditions and mountainous terrain. The model, CHIP-VAN, is developed based on the maximum limiting speeds on each road segment as limited by road grade, stopping sight distance (SSD) and road alignment as well as modeling the driver's behavior as these road conditions change. A two pass simulation was used in the model; the first pass simulation calculates the maximum limiting speeds on each road segment and the second pass simulates the driver's behavior and calculates the travel time. To emulate the driver's behavior, four cases that determine whether a driver will accelerate, decelerate or continue at current speed, were developed. The model has been tested for validation using the data collected for the study. The validation tests suggest that the model is appropriate for predicting travel time for chip vans on single lane forest roads with acceptable accuracy. The findings in the second study demonstrate that the GPS tracking accuracy of vehicles on forested roads are clearly influenced by the composition of the surrounding canopy, with the strongest influence being from heavy forest canopy cover. Accuracy is generally improved in areas with less forest canopy. The study concludes that the consumer-grade GPS receiver measurements determined are acceptable for tracking and improving biomass transport from forest supply locations to distribution and processing centers. The analysis of the range of accuracies found for vehicles operating within heavy forest canopy cover demonstrates that the accuracies are probably acceptable for many forest transportation monitoring and planning applications, including the mapping of forest road locations and other forest transportation operations. It is expected that the CHIP-VAN model and GPS accuracy studies will aid forest transportation managers in decision making and transportation planning in biomass operations. Most importantly it is hoped that the results of this research will increase transportation management planning efficiency for biomass and lead to improved methods for developing biomass cost assessment

ザンビアのルサカにおける土地利用変化の空間分析とモデリング : 急速に都市発展するアフリカのサブ・サハラの都市を事例に

筑波大学 (University of Tsukuba)201

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

AbstractFor 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

An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities

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

〈Original Papers〉Validating ALOS PRISM DSM-derived surface feature height: Implications for urban volume estimation

Urban volume, such as urban built volume (UBV), can be used as a proxy indicator for measuring the intensity and spatial pattern of urban development, and for characterizing social structure, intensity of economic activity, levels of economic supremacy, and levels of resource consumption. Urban volume estimation requires two basic input data: (1) urban footprint (built footprint for UBV and green footprint for urban green volume (UGV)); and (2) height data for urban features (herein called surface feature height (SFH)). A digital surface model (DSM) and a digital terrain model (DTM) can be used to extract SFH, i.e., by subtracting the DTM from the DSM. Light Detection and Ranging (LiDAR) data are often used to generate DSMs and DTMs. However, the availability of LiDAR data remains limited. The recent release of ALOS World 3D topographic data provides an alternative data source for DSMs and potentially for DTMs. However, the potential of ALOS PRISM DSM for deriving SFH has not been rigorously assessed, especially at the micro level. In this study, we validated six sets of 5 m ALOS PRISM DSM-derived SFH data across six test sites (Tokyo (Japan), Beijing (China), Shanghai (China), Surabaya (Indonesia), Tsukuba (Japan), and Lusaka (Zambia)). We described the grid-based method used to derive a DTM from a DSM and how this method was applied. We then validated the derived SFH data through comparison with recorded building height (RBH) data. Across the six test sites, the root-mean-square error (RMSE) of the ALOS PRISM DSM-derived SFH data ranged from 7 m (Tsukuba) (approximately 2 building floors) to 81 m (Beijing) (approximately 27 building floors). The ALOS PRISM DSM-derived SFH data for lower buildings (e.g., RBH 100 m) and larger and denser cities (Tokyo, Beijing and Shanghai). Factors that may have influenced the validation results were considered, as were the implications of the findings on urban volume estimation

Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities

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

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

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

An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities

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