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Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India’s aquaculture industry experienced exponential growth for more than one decade. In this study, we extract land-based aquaculture at the pond level for the entire coastal zone of India using large-volume time series Sentinel-1 synthetic-aperture radar (SAR) data at 10-m spatial resolution. Elevation and slope from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) data were used for masking inappropriate areas, whereas a coastline dataset was used to create a land/ocean mask. The pixel-wise temporal median was calculated from all available Sentinel-1 data to significantly reduce the amount of noise in the SAR data and to reduce confusions with temporary inundated rice fields. More than 3000 aquaculture pond vector samples were collected from high-resolution Google Earth imagery and used in an object-based image classification approach to exploit the characteristic shape information of aquaculture ponds. An open-source connected component segmentation algorithm was used for the extraction of the ponds based on the difference in backscatter intensity of inundated surfaces and shape metrics calculated from aquaculture samples as input parameters. This study, for the first time, provides spatial explicit information on aquaculture distribution at the pond level for the entire coastal zone of India. Quantitative spatial analyses were performed to identify the provincial dominance in aquaculture production, such as that revealed in Andhra Pradesh and Gujarat provinces. For accuracy assessment, 2000 random samples were generated based on a stratified random sampling method. The study demonstrates, with an overall accuracy of 0.89, the spatio-temporal transferability of the methodological framework and the high potential for a global-scale application.
Kumar Arun Prasad; Marco Ottinger; Chunzhu Wei; Patrick Leinenkugel. Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series. Remote Sensing 2019, 11, 357 .
AMA StyleKumar Arun Prasad, Marco Ottinger, Chunzhu Wei, Patrick Leinenkugel. Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series. Remote Sensing. 2019; 11 (3):357.
Chicago/Turabian StyleKumar Arun Prasad; Marco Ottinger; Chunzhu Wei; Patrick Leinenkugel. 2019. "Assessment of Coastal Aquaculture for India from Sentinel-1 SAR Time Series." Remote Sensing 11, no. 3: 357.
Impervious surface areas (ISA) are heavily influenced by urban structure and related structural features. We examined the effects of object-based impervious surface spatial pattern analysis on land surface temperature and population density in Guangzhou, China, in comparison to classic per-pixel analyses. An object-based support vector machine (SVM) and a linear spectral mixture analysis (LSMA) were integrated to estimate ISA fraction using images from the Chinese HJ-1B satellite for 2009 to 2011. The results revealed that the integrated object-based SVM-LSMA algorithm outperformed the traditional pixel-wise LSMA algorithm in classifying ISA fraction. More specifically, the object-based ISA spatial patterns extracted were more suitable than pixel-wise patterns for urban heat island (UHI) studies, in which the UHI areas (landscape surface temperature >37 °C) generally feature high ISA fraction values (ISA fraction >50%). In addition, the object-based spatial patterns enable us to quantify the relationship of ISA with population density (correlation coefficient >0.2 in general), with global human settlement density (correlation coefficient >0.2), and with night-time light map (correlation coefficient >0.4), and, whereas pixel-wise ISA did not yield significant correlations. These results indicate that object-based spatial patterns have a high potential for UHI detection and urbanization monitoring. Planning measures that aim to reduce the urbanization impacts and UHI intensities can be better supported.
Chunzhu Wei; Thomas Blaschke. Pixel-Wise vs. Object-Based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density. Urban Science 2018, 2, 2 .
AMA StyleChunzhu Wei, Thomas Blaschke. Pixel-Wise vs. Object-Based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density. Urban Science. 2018; 2 (1):2.
Chicago/Turabian StyleChunzhu Wei; Thomas Blaschke. 2018. "Pixel-Wise vs. Object-Based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density." Urban Science 2, no. 1: 2.
Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different spatial resolutions, as determined by the grid sizes used. Landsat satellite images of the Pearl River Delta (PRD) in China from the years 2000, 2005, 2010 and 2015 were hierarchically aggregated using different grid sizes. Statistical analyses of urbanization velocity derived using different spatial resolutions (or grid sizes) were used to investigate the relationships between socio-economic indicators and the velocity of urbanization for 27 large cities in PRD. The results revealed that those cities with above-average urbanization velocities remain unaffected by the spatial resolution (or grid-size), and the relationships between urbanization velocities and socio-economic indicators are independent of spatial resolution (or grid sizes) used. Moreover, empirical variogram models, the local variance model, and the geographical variance model all indicated that coarse resolution version (480 m) of Landsat images based on aggregated pixel yielded more appropriate results than the original fine resolution version (30 m), when identifying the characteristics of spatial autocorrelation and spatial structure variability of urbanization patterns and processes. The results conclude that the most appropriate spatial resolution for investigations into urbanization velocities is not always the highest resolution. The resulting patterns of urbanization velocities at different spatial resolutions can be used as a basis for studying the spatial heterogeneity of other datasets with variable spatial resolutions, especially for evaluating the capability of a multi-resolution dataset in reflecting spatial structure and spatial autocorrelation features in an urban environment.
Chunzhu Wei; Thomas Blaschke; Pavlos Kazakopoulos; Hannes Taubenböck; Dirk Tiede. Is Spatial Resolution Critical in Urbanization Velocity Analysis? Investigations in the Pearl River Delta. Remote Sensing 2017, 9, 80 .
AMA StyleChunzhu Wei, Thomas Blaschke, Pavlos Kazakopoulos, Hannes Taubenböck, Dirk Tiede. Is Spatial Resolution Critical in Urbanization Velocity Analysis? Investigations in the Pearl River Delta. Remote Sensing. 2017; 9 (1):80.
Chicago/Turabian StyleChunzhu Wei; Thomas Blaschke; Pavlos Kazakopoulos; Hannes Taubenböck; Dirk Tiede. 2017. "Is Spatial Resolution Critical in Urbanization Velocity Analysis? Investigations in the Pearl River Delta." Remote Sensing 9, no. 1: 80.
Urban planners and ecologists have long debated the relationship between the structure of urban landscapes and social activities. There have, however, been very few discussions as to whether any such relationships might depend on the scales of observation. This work applies a hierarchical zoning technique to data from the city of Quito, Ecuador, to examine how relationships between typical spatial landscape metrics and social indicators depend on zoning scales. Our results showed that the estimates of both landscape heterogeneity features and social indicators significantly depend on the zoning scale. The mean values of the typical landscape metrics and the social indicators all exhibited predictable responses to a changing zoning scale, suggesting a consistent and significant scaling relationship within the multiple zoning scales. Yet relationships between these pairs of variables remain notably invariant to scale. This quantitative demonstration of the scale-free nature of the relationship between landscape characteristics and social indicators furthers our understanding of the relationships between landscape structures and social aspects of urban spaces, including deprivation and public service accessibility. The relationships between social indicators and one typical landscape aggregation metric (represented as the percentage of like adjacencies) were nevertheless significantly dependent on scale, suggesting the importance of zoning scale decisions for analyzing the relationships between the social indicators and the landscape characteristics related with landscape adjacency. Aside from this typical landscape aggregation metric, the general invariance to the zoning scale of relationships between landscape structures and socioeconomic indicators in Quito suggests the importance of applying these scale-free relationships in understanding complex socio-ecological systems in other cities, which are shaped by the conflated influences of both anthropogenic and natural factors. Moreover, this scale-free behavior of landscape–social relationships challenges the traditional modifiable area unit problem, and provides mechanistic insight into the conflicts and compatibilities between human activities and human-induced land use change.
Chunzhu Wei; Mark Padgham; Pablo Cabrera-Barona; Thomas Blaschke. Scale-Free Relationships between Social and Landscape Factors in Urban Systems. Sustainability 2017, 9, 84 .
AMA StyleChunzhu Wei, Mark Padgham, Pablo Cabrera-Barona, Thomas Blaschke. Scale-Free Relationships between Social and Landscape Factors in Urban Systems. Sustainability. 2017; 9 (1):84.
Chicago/Turabian StyleChunzhu Wei; Mark Padgham; Pablo Cabrera-Barona; Thomas Blaschke. 2017. "Scale-Free Relationships between Social and Landscape Factors in Urban Systems." Sustainability 9, no. 1: 84.