Adjusting Spectral Indices for Spectral Response Function Differences of Very High Spatial Resolution Sensors Simulated from Field Spectra

Automation of informal settlements detection using satellite imagery remains a challenging task in urban remote sensing. This is due to the fact that informal settlements vary in shape, size and spatial arrangement from one region to the other in some cases within a city. This paper investigated the methodology to detect informal settlements in a densely populated township by assessing informal settlement indicators observed from very high spatial resolution satellite imagery. We assessed twelve informal settlement indicators to determine the most effective indicators to distinguish between informal and informal classes. These indicators included the spectral indices first and second-order statistical measurements. In addition to the commonly used informal settlement indicators, we assessed the effectiveness of built-up area and iron cover. The GLCM textural measures performed poorly in separating informal and formal settlements compared to first-order statistics measurement and spectral indices. The built-up area index, coastal blue index and the first-order statistics mean measurements produced higher separability distance of informal and formal settlements. The iron index performed better in separating the two settlement types than the commonly used GLCM measure and NDVI. The proposed ruleset that uses the three features with the highest separability distance achieved producer and user accuracies of informal settlements of 95% and 82%, respectively. The results of this study will contribute towards developing methodologies to automatically detect informal settlements.

Download Full-text

Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles

Forests ◽

10.3390/f12020179 ◽

2021 ◽

Vol 12 (2) ◽

pp. 179

Author(s):

David Beltrán-Marcos ◽

Susana Suárez-Seoane ◽

José Manuel Fernández-Guisuraga ◽

Víctor Fernández-García ◽

Rayo Pinto ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Burn Severity ◽

Spectral Indices ◽

Aerial Vehicles ◽

Soil Burn Severity ◽

Debris Cover ◽

Very High Spatial Resolution ◽

Very High

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.

Download Full-text

High spatial resolution photo mosaicking for the monitoring of coralligenous reefs

Coral Reefs ◽

10.1007/s00338-021-02136-4 ◽

2021 ◽

Author(s):

E. Casoli ◽

D. Ventura ◽

G. Mancini ◽

D. S. Pace ◽

A. Belluscio ◽

...

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Low Cost ◽

Pixel Resolution ◽

Recording Apparatus ◽

Assemblage Composition ◽

Very High Spatial Resolution ◽

Vertical Walls ◽

Non Destructive ◽

Very High

AbstractCoralligenous reefs are characterized by large bathymetric and spatial distribution, as well as heterogeneity; in shallow environments, they develop mainly on vertical and sub-vertical rocky walls. Mainly diver-based techniques are carried out to gain detailed information on such habitats. Here, we propose a non-destructive and multi-purpose photo mosaicking method to study and monitor coralligenous reefs developing on vertical walls. High-pixel resolution images using three different commercial cameras were acquired on a 10 m2 reef, to compare the effectiveness of photomosaic method to the traditional photoquadrats technique in quantifying the coralligenous assemblage. Results showed very high spatial resolution and accuracy among the photomosaic acquired with different cameras and no significant differences with photoquadrats in assessing the assemblage composition. Despite the large difference in costs of each recording apparatus, little differences emerged from the assemblage characterization: through the analysis of the three photomosaics twelve taxa/morphological categories covered 97–99% of the sampled surface. Photo mosaicking represents a low-cost method that minimizes the time spent underwater by divers and capable of providing new opportunities for further studies on shallow coralligenous reefs.

Download Full-text

The Importance of Physical Quantities for the Analysis of Multitemporal and Multiangular Optical Very High Spatial Resolution Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2013.2295819 ◽

2014 ◽

Vol 52 (10) ◽

pp. 6241-6256 ◽

Cited By ~ 46

Author(s):

Fabio Pacifici ◽

Nathan Longbotham ◽

William J. Emery

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Very High Spatial Resolution ◽

Physical Quantities ◽

Very High

Download Full-text

The State of Radiology in 2006: Very High Spatial Resolution but No Visibility

Radiology ◽

10.1148/radiol.2411051910 ◽

2006 ◽

Vol 241 (1) ◽

pp. 11-16 ◽

Cited By ~ 24

Author(s):

Julie A. Ruiz ◽

Gary M. Glazer

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

The State ◽

Very High Spatial Resolution ◽

Very High

Download Full-text

Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

Remote Sensing ◽

10.3390/rs10111737 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1737 ◽

Cited By ~ 19

Author(s):

Jinchao Song ◽

Tao Lin ◽

Xinhu Li ◽

Alexander V. Prishchepov

Keyword(s):

Remote Sensing ◽

Land Use ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Images ◽

Remote Sensing Imagery ◽

Points Of Interest ◽

Very High Spatial Resolution ◽

Functional Zones ◽

Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

Download Full-text