Derivation of Economic and Social Indicators for a Spatial Decision Support System to Evaluate the Impacts of Urban Development on Water Bodies in New Zealand

There is mounting evidence that urban development in New Zealand has contributed to poor water quality and ecological degradation of coastal and fresh water receiving waters. As a consequence, local governments have identified the need for improved methods to guide decision making to achieve improved outcomes for those receiving waters. This paper reports progress on a research programme to develop a catchmentscale spatial decision-support system (SDSS) that will aid evaluation of the impacts of urban development on attributes such as water and sediment quality; ecosystem health; and economic, social and cultural values. The SDSS aims to express indicators of impacts on these values within a sustainability indexing system in order to allow local governments to consider them holistically over planning timeframes of several decades. The SDSS will use a combination of deterministic and probabilistic methods to, firstly, estimate changes to environmental stressors such as contaminant loads from different land use and stormwater management scenarios and, secondly, use these results and information from a range of other sources to generate indicator values. This paper describes the project’s approach to the derivation of indicators of economic and social well being associated with the effects of urban storm water run-off on freshwater and estuarine receiving waters.Environmental Economics and Policy,

Modeling urban growth pattern for sustainable archaeological sites : a case study in Siem Reap, Cambodia

In this paper, the main goal is to understand the relationship between urban growth and physical factors in order to determine the potential area for future urban expansion. A policy is suggested that could effectively sustain the archaeological sites and to balance the land use between urban and non-urban areas in Siem Reap, Cambodia. Remote sensing is used to analyze land use maps of Siem Reap from 1993 to 2011. Results show that urban-built up area increased significantly which causes the forest land to reduce in the Siem Reap archaeological sites. In addition, Geographic Information System (GIS) is used to analyze urban growth in potential suitable sites. Geo-processing and logical functions are applied to detect and quantify the land use changes, especially urban changes. The percentage of urban area in each year is compared with the population density and road buffers by using Pearson correlation. It is shown that the increasing in urban area is related with population density and road network factors

A proposed methodology for understanding urban growth pattern : a case study in Siem Reap, Cambodia

In this paper, the main goal is to understand the relationship between urban growth and physical factors in order to determine the potential area for future urban expansion. A methodology is suggested for understanding urban growth pattern in Siem Reap which could effectively sustain archaeological sites and to balance the land use between urban and non-urban areas in Siem Reap, Cambodia. Remote sensing technique is used to analyze land use maps of Siem Reap from 1993 to 2011. Results show that urban-built up area increased significantly which causes the forest land to reduce steadily from 1993 to 2003 in the Siem Reap archaeological sites. In addition, Geographic Information System (GIS) is applied to analyze urban growth pattern. Geo-processing and logical functions are applied to detect and quantify the land use changes, especially urban changes. Two main factors are used to analyze the urban driving growth in Siem Reap, which are distance to road networks and population density. Pearson correlation statistics is applied to justify the relationship between the factors and urban area growth

Linking thermal variability and change to urban growth in Harare Metropolitan City using remotely sensed data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Urban growth, which involves Land Use and Land Cover Changes (LULCC), alters land surface thermal properties. Within the framework of rapid urban growth and global warming, land surface temperature (LST) and its elevation have potential significant socio-economic and environmental implications. Hence the main objectives of this study were to (i) map urban growth, (ii) link urban growth with indoor and outdoor thermal conditions and (iii) estimate implications of thermal trends on household energy consumption as well as predict future urban growth and temperature patterns in Harare Metropolitan, Zimbabwe. To achieve these objectives, broadband multi-spectral Landsat 5, 7 and 8, in-situ LULC observations, air temperature (Ta) and humidity data were integrated. LULC maps were obtained from multi-spectral remote sensing data and derived indices using the Support Vector Machine Algorithm, while LST were derived by applying single channel and split window algorithms. To improve remote sensing based urban growth mapping, a method of combining multi-spectral reflective data with thermal data and vegetation indices was tested. Vegetation indices were also combined with socio-demographic data to map the spatial distribution of heat vulnerability in Harare. Changes in outdoor human thermal discomfort in response to seasonal LULCC were evaluated, using the Discomfort Index (DI) derived parsimoniously from LST retrieved from Landsat 8 data. Responses of LST to long term urban growth were analysed for the period from 1984 to 2015. The implications of urban growth induced temperature changes on household air-conditioning energy demand were analysed using Landsat derived land surface temperature based Degree Days. Finally, the Cellular Automata Markov Chain (CAMC) analysis was used to predict future landscape transformation at 10-year time steps from 2015 to 2045. Results showed high overall accuracy of 89.33% and kappa index above 0.86 obtained, using Landsat 8 bands and indices. Similar results were observed when indices were used as stand-alone dataset (above 80%). Landsat 8 derived bio-physical surface properties and socio-demographic factors, showed that heat vulnerability was high in over 40% in densely built-up areas with low-income when compared to “leafy” suburbs. A strong spatial correlation (α = 0.61) between heat vulnerability and surface temperatures in the hot season was obtained, implying that LST is a good indicator of heat vulnerability in the area. LST based discomfort assessment approach retrieved DI with high accuracy as indicated by mean percentage error of less than 20% for each sub-season. Outdoor thermal discomfort was high in hot dry season (mean DI of 31oC), while the post rainy season was the most comfortable (mean DI of 19.9oC). During the hot season, thermal discomfort was very low in low density residential areas, which are characterised by forests and well maintained parks (DI ≤27oC). Long term changes results showed that high density residential areas increased by 92% between 1984 and 2016 at the expense of cooler green-spaces, which decreased by 75.5%, translating to a 1.98oC mean surface temperature increase. Due to surface alterations from urban growth between 1984 and 2015, LST increased by an average of 2.26oC and 4.10oC in the cool and hot season, respectively. This decreased potential indoor heating energy needed in the cool season by 1 degree day and increased indoor cooling energy during the hot season by 3 degree days. Spatial analysis showed that during the hot season, actual energy consumption was low in high temperature zones. This coincided with areas occupied by low income strata indicating that they do not afford as much energy and air conditioning facilities as expected. Besides quantifying and strongly relating with energy requirement, degree days provided a quantitative measure of heat vulnerability in Harare. Testing vegetation indices for predictive power showed that the Urban Index (UI) was comparatively the best predictor of future urban surface temperature (r = 0.98). The mean absolute percentage error of the UI derived temperature was 5.27% when tested against temperature derived from thermal band in October 2015. Using UI as predictor variable in CAMC analysis, we predicted that the low surface temperature class (18-28oC) will decrease in coverage, while the high temperature category (36-45oC) will increase in proportion covered from 42.5 to 58% of city, indicating further warming as the city continues to grow between 2015 and 2040. Overall, the findings of this study showed that LST, human thermal comfort and air-conditioning energy demand are strongly affected by seasonal and urban growth induced land cover changes. It can be observed that urban greenery and wetlands play a significant role of reducing LST and heat transfer between the surface and lower atmosphere and LST may continue unless effective mitigation strategies, such as effective vegetation cover spatial configuration are adopted. Limitations to the study included inadequate spatial and low temporal resolution of Landsat data, few in-situ observations of temperature and LULC classification which was area specific thus difficult for global comparison. Recommendations for future studies included data merging to improve spatial and temporal representation of remote sensing data, resource mobilization to increase urban weather station density and image classification into local climate zones which are of easy global interpretation and comparison

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Urban growth pattern identification : a case study in Siem Reap, Cambodia

The main purpose of this paper is to identify the pattern of urban growth from 1993 to 2011 in Siem reap town, Cambodia. Land use and land cover maps were generated from Landsat TM imageries from different years in order to extract the information related to urban sprawl. The settlement pattern theory, geographic pattern analysis and visualisation interpretation were used to detect the pattern of urban growth in Siem Reap. Result shows that from 1993 to 2011 the urban area grew significantly, about 102.51%. The development of core settlement areas in Siem Reap revealed to be concentrated along main roads and along the river in the past and still keeping the same trend in the present. The current pattern of urban settlement in Siem Reap was classified as clustered and linear, following the roads network

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics