Browsing by Subject "Remote sensing"

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    Open Access
    A fine-scale assessment of the ecosystem service-disservice dichotomy in the context of urban ecosystems affected by alien plant invasions
    (2019-10-28) Potgieter, Luke J; Gaertner, Mirijam; O’Farrell, Patrick J; Richardson, David M
    Abstract Background Natural resources within and around urban landscapes are under increasing pressure from ongoing urbanisation, and management efforts aimed at ensuring the sustainable provision of ecosystem services (ES) are an important response. Given the limited resources available for assessing urban ES in many cities, practical approaches for integrating ES in decision-making process are needed. Methods We apply remote sensing techniques (integrating LiDAR data with high-resolution multispectral imagery) and combined these with supplementary spatial data to develop a replicable approach for assessing the role of urban vegetation (including invasive alien plants) in providing ES and ecosystem disservices (EDS). We identify areas denoting potential management trade-offs based on the spatial distribution of ES and EDS using a local-scale case study in the city of Cape Town, South Africa. Situated within a global biodiversity hotspot, Cape Town must contend with widespread invasions of alien plants (especially trees and shrubs) along with complex socio-political challenges. This represents a useful system to examine the challenges in managing ES and EDS in the context of urban plant invasions. Results Areas of high ES provision (for example carbon sequestration, shade and visual amenity) are characterized by the presence of large trees. However, many of these areas also result in numerous EDS due to invasions of alien trees and shrubs – particularly along rivers, in wetlands and along the urban edge where tall alien trees have established and spread into the natural vegetation (for example increased water consumption, increased fire risk and reduced soil quality). This suggests significant trade-offs regarding the management of species and the ES and EDS they provide. Conclusions The approach applied here can be used to provide recommendations and to guide city planners and managers to fine-tune management interventions at local scales to maximise the provision of ES.
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    Open Access
    Detecting, Classifying and Locating Informal Settlements in Megacities such as Cape Town Using Sentinel-1 data
    (2026) Mgaga, Mfanafuthi; Paine, Stephen; Inggs, Michael
    The use of remote sensing is a method for generating land cover maps. This research utilizes radar remote sensing, specifically the Sentinel-1 sensor. A major advantage of radar remote sensing is its ability to penetrate clouds and vegetation, enabling data collection in all weather conditions. It also provides valuable information on surface roughness and moisture content, improving the accuracy and detail of land cover maps. The sensor was chosen for its ability to acquire data in any weather conditions, as well as during both day and night. Images were downloaded and processed in SNAP software. The objective of this study was to detect, classify, and map land use and cover, with a focus on informal settlements, using radar remote sensing and GIS techniques. The research had two main objectives: firstly, is to classify land use and land cover using supervised classification, and secondly to conduct an accuracy assessment using QGIS with relevant plugins. The methodology is outlined in Chapter 3, and implementation is detailed in Chapter 4. Results are discussed in Chapter 5, and the conclusion is presented in Chapter 6. Although there are multiple algorithms for supervised classification, random forest was chosen due to its ability to reduce overfitting in decision trees, improve accuracy, and handle both regression and classification problems. It works well with continuous and categorical values and automates missing data. Classified land cover categories included agricultural land, grassland, bare soil, forested areas, formal settlements, informal settlements, industrial areas and water bodies. While SNAP lacks built-in functionality for accuracy assessment, it can provide classification accuracy based on the classifier's threshold and replication of classes from training data. The overall classification accuracy was found to be 72.83% with a kappa coefficient of 0.65 when considering no-data pixels. Excluding no-data pixels improves accuracy to 83.24% with a kappa coefficient of 0.78. Informal settlements, the main class of interest, achieved a consumer accuracy of 91.8% and a producer accuracy of 90.7% Based on the results in Chapter 5, the research met its objectives. The results are valuable for city planners and decision-makers, particularly for rapid response and rescue during natural disasters like floods. It also aids in planning services for informal communities that may not otherwise receive attention due to their exclusion from city or municipal maps.
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    Indian Ocean Dipole and El Niño/Southern Oscillation impacts on regional chlorophyll anomalies in the Indian Ocean
    (2013) Currie, J C; Lengaigne, M; Vialard, J; Kaplan, D M; Aumont, O; Naqvi, S W A; Maury, O
    The Indian Ocean Dipole (IOD) and the El Niño/Southern Oscillation (ENSO) are independent climate modes, which frequently co-occur, driving significant interannual changes within the Indian Ocean. We use a four-decade hindcast from a coupled biophysical ocean general circulation model, to disentangle patterns of chlorophyll anomalies driven by these two climate modes. Comparisons with remotely sensed records show that the simulation competently reproduces the chlorophyll seasonal cycle, as well as open-ocean anomalies during the 1997/1998 ENSO and IOD event. Results suggest that anomalous surface and euphotic-layer chlorophyll blooms in the eastern equatorial Indian Ocean in fall, and southern Bay of Bengal in winter, are primarily related to IOD forcing. A negative influence of IOD on chlorophyll concentrations is shown in a region around the southern tip of India in fall. IOD also depresses depth-integrated chlorophyll in the 5–10° S thermocline ridge region, yet the signal is negligible in surface chlorophyll. The only investigated region where ENSO has a greater influence on chlorophyll than does IOD, is in the Somalia upwelling region, where it causes a decrease in fall and winter chlorophyll by reducing local upwelling winds. Yet unlike most other regions examined, the combined explanatory power of IOD and ENSO in predicting depth-integrated chlorophyll anomalies is relatively low in this region, suggestive that other drivers are important there. We show that the chlorophyll impact of climate indices is frequently asymmetric, with a general tendency for larger positive than negative chlorophyll anomalies. Our results suggest that ENSO and IOD cause significant and predictable regional re-organisation of chlorophyll via their influence on near-surface oceanography. Resolving the details of these effects should improve our understanding, and eventually gain predictability, of interannual changes in Indian Ocean productivity, fisheries, ecosystems and carbon budgets