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    A soil-landscape model of Blind River/Otuwhero, Marlborough : a case study
    (Massey University, 2024) Oliver, Matt
    Soils form in complex, four-dimensional, dynamic systems across landscapes. The role of a pedologist is to explore and describe the complexity and variability of soils within that landscape. The most common method used to document soils in the landscape is the soil map. Modern soil mapping has migrated to digital platforms where a much greater range of soil attribute information can be delivered at more appropriate scales. However, often soil mapping projects are carried out without specific reference to the landforms that the studied soils lie upon, in other words a soil-landscape model (S-LM) is often not included with the finished mapping outputs. The inclusion of an explicit soil-landscape model alongside a soil map is important for several reasons such as prediction of variability in soils across a landscape, reduction is survey costs, maintenance of an historic record and development of soil attribute mapping that allows interpolation of soil properties across the landscape rather than confining and defining soils into ‘crisp’ soil polygons. Map users who understand the S-LM can apply its principles across the wider landscape and to smaller landforms than would otherwise be captured on a broader-scale soil map. This study reviews soil-landscape modelling literature then combines Geographic Information System analysis with field work to establish a S-LM for the Blind River / Otuwhero region of Marlborough, New Zealand.
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    Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam
    (National Authority for Elsevier B V on behalf of Remote Sensing and Space Sciences, 2020-04-01) Ha TV; Tuohy M; Irwin M; Tuan PV
    Rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, Random Forest (RF) was employed in R programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of R programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning.
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    In support of sustainable densification in urban planning: a proposed framework for utilising CCTV for propagation of human energy from movement within urban spaces
    (Taylor and Francis Group, 2019-12-18) Jonescu E; Mercea T; Do K; Sutrisna M
    Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted–understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed–predictive spatial decision-making framework is proposed–facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.
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    Visitation Rate Analysis of Geoheritage Features from Earth Science Education Perspective Using Automated Landform Classification and Crowdsourcing: A Geoeducation Capacity Map of the Auckland Volcanic Field, New Zealand
    (MDPI (Basel, Switzerland), 2021-11-22) Németh B; Németh K; Procter JN; Jordá Pardo JF
    The increase in geoheritage studies has secured recognition globally regarding the importance of abiotic natural features. Prominent in geoheritage screening practices follows a multicriteria assessment framework; however, the complexity of interest in values often causes decision making to overlook geoeducation, one of the primary facets of geosystem services. Auckland volcanic field in New Zealand stretches through the whole area of metropolitan Auckland, which helps preserve volcanic cones and their cultural heritage around its central business district (CBD). They are important sites for developing tourist activities. Geoeducation is becoming a significant factor for tourists and others visiting geomorphological features, but it cannot be achieved without sound planning. This paper investigates the use of big data (FlickR), Geopreservation Inventory, and Geographic Information System for identifying geoeducation capacity of tourist attractions. Through landform classification using the Topographic Position Index and integrated with geological and the inventory data, the underpromoted important geoeducation sites can be mapped and added to the spatial database Auckland Council uses for urban planning. The use of the Geoeducation Capacity Map can help resolve conflicts between the multiple objectives that a bicultural, metropolitan city council need to tackle in the planning of upgrading open spaces while battling of growing demand for land.
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    An investigation of the application of remote sensing and geographic information systems for resource management in Westland, New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, New Zealand
    (Massey University, 1995) Brown, Leonard John; Brown, Leonard John
    Effective management of natural resources, and the side effects resulting from their use directly affects our environmental and economic wellbeing. This thesis was initiated to investigate the application of remote sensing, digital image processing and Geographic Information System (GIS) tools for natural resource management in Westland, New Zealand. From the multitude of potential applications, research was directed toward two issues: alluvial gold mining and indigenous forest management. This thesis focused on the use of personal computer (PC) applications. A study of alluvial gold mining operations utilised black-and-white aerial photography taken at five dates in the period 1943 to 1988. The photographs were digitised, registered to a common base-image, and classified for bare ground, scrub and trees. A cadastral plan was also digitised, registered to the digital imagery and used to extract specific land-tenure parcels. The classified imagery was processed in an independent-classification change detection to identify change in land-cover between the dates of aerial photography. The results demonstrated that digital image processing of black-and-white aerial photography could provide the quantitative and spatial land-cover information required for resource management in areas of alluvial gold mining. However, although the individual image classification accuracies exceeded 85%, error in the classifications generated areas of spurious change in the change detection imagery. Examination of subsequent change images revealed areas of land alternating between opposing change classes and indicated how a second, subsequent change image may be a useful tool to rapidly identify possible areas of spurious change. An investigation of satellite imagery and digital image processing for management of indigenous forests compared a supervised classification of SPOT multispectral (XS) and Landsat Thematic Mapper (TM) imagery with an existing vegetation map. The images were classified with a maximum likelihood algorithm, applying vegetation classes derived from the map. The Landsat TM image achieved a higher overall classification accuracy (75%) compared to the SPOT XS (53%), indicating a superior information content for vegetation discrimination in the Landsat TM imagery. However neither image could achieve sufficient accuracy to be used for updating the existing map. A second study of indigenous forestry applications investigated the use of integrated remote sensing and GIS analysis. A forest inventory comprising field-plots which recorded tree species and size-class information was interrogated within a GIS. The study illustrated how GIS tools could be used to rapidly identify and map field-plots that contained trees suitable for harvesting in sustained-yield logging operations. This information is a prerequisite for any sustained-yield logging and would have been unfeasible to obtain without a GIS. Two strategies were investigated for integrating the forest inventory with SPOT XS and Landsat TM imagery. The first approach applied a clustering procedure to generate the natural vegetative clusters within the forest inventory. A spectral signature for each plot was obtained by overlaying the plots on the digital imagery. A discriminant analysis was applied to determine whether the spectral information in the imagery could discriminate between the inventory clusters. The results revealed that this was not possible with overall classification accuracies of 39% and 48% for the SPOT XS and Landsat TM images respectively. The second approach reversed the procedure and applied an unsupervised image classification to identify the spectral classes present in each image. The vegetative composition of each image class was investigated by examining the forest inventory plots within each class. The results demonstrated that the number of trees in the sub-canopy showed the most variation between classes, with minimal differences attributed to the species composition. Analysis of the two approaches illustrated the difficulty of relating classifications derived from field survey and those from satellite imagery. While use of satellite imagery to map classes derived from field survey may result in disappointing results, an unsupervised approach provides a method to acquire an up-to-date, objective classification of the entire forest. The limitation is that the vegetation communities extracted from an unsupervised classification might well be different from those identified from analysis of forest inventory data and may not be of relevance to current resource management issues.