An analysis of settlement characterization in central Europe using SIR-B radar imagery

Henderson, Floyd M.

doi:10.1016/0034-4257(95)00103-8

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The performance gain from using shallower incidence angles was predominantly due to improved sensitivity. This agrees with earlier research that reported that a shallower incidence allows for a better discrimination of urban areas [57][58][59][60]. This is usually due to a combination of higher spatial resolutions achieved at larger incidence angles, as well as the increasing sensitivity to differences in surface roughness that occurs at larger incidence angles [61].…”

Section: Robustness Of Acf-based Classificationsupporting

confidence: 91%

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Kapp,

Kemp

2023

Geomatics

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Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions.

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Section: Robustness Of Acf-based Classificationsupporting

confidence: 91%

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Kapp,

Kemp

2023

Geomatics

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“…These parameters of SAR data are different from optical sensors (Henderson and Xia, 1997). Studies in Germany (Henderson, 1995), China (Lo, 1986), and the Ganges Plain (Imhoff et al, 1987) reported that settlements having a population of more than 1000 are generally recognizable from SAR imageries. The difficulty associated with identifying human settlement from radar data is that most SAR sensors obtain data at a single wavelength with fixed polarization.…”

Section: Delineating Human Settlementsmentioning

confidence: 98%

Remote sensing and GIS‐based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India

Sanyal

2005

Hydrological Processes

141

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Abstract:Flooding due to excessive rainfall in a short period of time is a frequent hazard in the flood plains of monsoon Asia. In late September 2000, a devastating flood stuck Gangetic West Bengal, India. This particular event has been selected for this study. Instead of following the conventional approach of flooded area delineation and overall damage estimation, this paper seeks to identify the rural settlements that are vulnerable to floods of a given magnitude. Vulnerability of a rural settlement is perceived as a function of two factors: the presence of deep flood water in and around the settlement and its proximity to an elevated area for temporary shelter during an extreme hydrological event. Landsat ETM C images acquired on 30 September 2000 have been used to identify the non-flooded areas within the flooded zone. Particular effort has been made to differentiate land from water under cloud shadow. ASTER digital elevation data have been used to assess accuracy and rectify the classified image. The presence of large numbers of trees around rural settlements made it particularly difficult to extract the flooded areas from their spectral signatures in the visible and infrared bands. ERS-1 synthetic aperture radar data are found particularly useful for extracting the settlement areas surrounded by trees. Finally, all information extracted from satellite imageries are imported into ArcGIS, and spatial analysis is carried out to identify the settlements vulnerable to river inundation.

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“…Advances in digital image processing techniques, in parallel with the emergence of new imaging satellites from the late 1980s and early 1990s onwards, represented a new boost for studies in this field in the following decades, and hence, research using satellite imagery for population estimates dealing with innovative methods and sensors has become more widespread (Langford and Unwinn 1994;Henderson 1995;Lo 1995;Henderson and Xia 1997;Harvey 2002;Faure et al 2003;Lo 2005;Wu, Qiu, and Wang 2005;Wu and Murray 2007;Hoque 2008). Such methods include dasymetric mapping and kernel techniques to build population density surfaces.…”

Section: Introductionmentioning

confidence: 99%

Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data

Tomás

Fonseca

Almeida

et al. 2015

International Journal of Remote Sensing

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This paper presents a methodological approach to estimation of urban population using the volume of single houses and high-rise residential buildings obtained from an IKONOS-2 ortho-image and light detection and raging (lidar) data. The estimates are directly executed at the finest scale level (i.e. the housing unit) and are then aggregated at the census district level for further validation with the aid of official data supplied by the local and federal governments. Unlike prior works, this study executes a thorough assessment of horizontal and elevation accuracy for the IKONOS-2 and lidar data used in the experiment. The methodological stages are threefold: the construction of a 3D city model, the accuracy assessment of the ortho-image and digital surface models (DSMs), and the quantification of urban population. The validation was accomplished by means of linear regression and associated hypothesis tests, considering the estimated population and the reference data. The results showed that there was a systematic underestimation of population. On average, the conducted estimates assessed 31 fewer inhabitants per district and lie 1.35% below the expected values given by the reference data. In spite of the observed underestimation, the estimated population can be regarded as equivalent to the population provided by the reference data at a 1% level of significance. ARTICLE HISTORY

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An analysis of settlement characterization in central Europe using SIR-B radar imagery

Cited by 8 publications

References 12 publications

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Remote sensing and GIS‐based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India

Urban population estimation based on residential buildings volume using IKONOS-2 images and lidar data

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