Detecting settlement expansion in South Africa using a hyper-temporal SAR change detection approach

Kleynhans, W.; Salmon, B.P.; Olivier, J.C.

doi:10.1016/j.jag.2015.06.004

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…However, this study utilized only few classes for classifying the land use land cover, thus requiring additional research for evaluating the suitability of this approach in more complex urban environments. It is also worth noting that this study is one of only a few who exploited radar imagery for mapping slums (e.g., [98][99][100]). …”

Section: Image Texture Analysismentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High-and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.

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Section: Image Texture Analysismentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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“…In general, such data would allow due to its cost and computational efficiency to map deprived areas at regional and country scale. As an alternative, SAR data have been used, e.g., TerraSAR-X image [34][35][36] showing robust mapping results. Both data sources, optical and SAR, allow one to quantify the amount of roof coverage at settlement scale and could provide base data for the estimation of the deprived population [37].…”

Section: Suitable Remote Sensing Sensor Systems To Provide Localized mentioning

confidence: 99%

The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

Kuffer

Wang

Nagenborg

et al. 2018

IJGI

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The continuous increase in deprived living conditions in many cities of the Global South contradicts efforts to make cities inclusive, safe, resilient, and sustainable places. Using examples of Asian, African, and Latin American cities, this study shows the scope and limits of earth observation (EO)-based mapping of deprived living conditions in support of providing consistent global information for the SDG indicator 11.1.1 “proportion of urban population living in slums, informal settlements or inadequate housing”. At the technical level, we compare several EO-based methods and imagery for mapping deprived living conditions, discussing their ability to map such areas including differences in terms of accuracy and performance at the city scale. At the operational level, we compare available municipal maps showing identified deprived areas with the spatial extent of morphological mapped areas of deprived living conditions (using EO) at the city scale, discussing the reasons for inconsistencies between municipal and EO-based maps. We provide an outlook on how EO-based mapping of deprived living conditions could contribute to a global spatial information base to support targeting of deprived living conditions in support of the SDG Goal 11.1.1 indicator, when uncertainties and ethical considerations on data provision are well addressed.

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“…Hypertemporal datasets are univariate. It is rare to find researchers processing raw band data (for exceptions see [16,17]). The datasets processed are usually parameter estimates such as sea ice concentration estimates, or estimates of photosynthetic activity.…”

Section: Time Series Analysis (Tsa)-founded Approachesmentioning

confidence: 99%

“…Since 1995, a diverse range of applications have been developed which involved extracting meaningful information from hypertemporal data. These include landcover mapping [10,13], landuse mapping [14], change detection [15][16][17][18], ecosystem structure and species modelling [19][20][21][22][23], phenology mapping [24][25][26], gradient analysis [27][28][29], and data quality assessment [30]. To date, a limited range of ocean-focused hypertemporal studies have been conducted (Figure 3), hampered by validation challenges and limited knowledge on the partitioning of temporal patterns over the ocean's surface.…”

mentioning

confidence: 99%

From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

Scarrott

Cawkwell

Jessopp

et al. 2019

Water

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Increases in the temporal frequency of satellite-derived imagery mean a greater diversity of ocean surface features can be studied, modelled, and understood. The ongoing temporal data “explosion” is a valuable resource, having prompted the development of adapted and new methodologies to extract information from hypertemporal datasets. Current suitable methodologies for use in hypertemporal ocean surface studies include using pixel-centred measurement analyses (PMA), classification analyses (CLS), and principal components analyses (PCA). These require limited prior knowledge of the system being measured. Time-series analyses (TSA) are also promising, though they require more expert knowledge which may be unavailable. Full use of this resource by ocean and fisheries researchers is restrained by limitations in knowledge on the regional to sub-regional spatiotemporal characteristics of the ocean surface. To lay the foundations for more expert, knowledge-driven research, temporal signatures and temporal baselines need to be identified and quantified in large datasets. There is an opportunity for data-driven hypertemporal methodologies. This review examines nearly 25 years of advances in exploratory hypertemporal research, and how methodologies developed for terrestrial research should be adapted when tasked towards ocean applications. It highlights research gaps which impede methodology transfer, and suggests achievable research areas to be addressed as short-term priorities.

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Detecting settlement expansion in South Africa using a hyper-temporal SAR change detection approach

Cited by 14 publications

References 15 publications

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

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