Thresholding Algorithm Optimization for Change Detection to Satellite Imagery

Vázquez-Jiménez, René; Ramos-Bernal, Rocío N.; Calcerrada, Raúl Romero; Arrogante-Funes, Patricia; Tizapa, Sulpicio Sánchez; Novillo, Carlos J.

doi:10.5772/intechopen.71002

Cited by 17 publications

(18 citation statements)

References 28 publications

(44 reference statements)

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“…Statistical methods refer to the use of the statistical parameters of the mean (µ) and the standard deviation (σ) to calculating a threshold by µ ± kσ, where k is an empirical parameter set by the user that can be adjusted [34]. In this method, the density function of the continuous change image is almost equal to the density function of the unmodified pixels and, in the determination of the threshold has statistically fixed [35].…”

Section: Methodsmentioning

confidence: 99%

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

et al. 2019

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Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

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Section: Methodsmentioning

confidence: 99%

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

et al. 2019

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“…The PCA is one of the techniques for determining variations of environmental parameters in the temporal dimension (Dalal et al 2010). The variations of environmental parameters in the temporal dimension can be examined on a pixel scale using PCA (Vázquez-Jiménez et al 2017). To model the variations of each particular parameter over a given time interval, the PCA model applied to its specific values over a time scale at a scale of each pixel (de Almeida et al 2015;Gaitani et al 2017;Hirosawa et al 1996;Wang et al 2010).…”

Section: Methodsmentioning

confidence: 99%

Spatioltemporal Modeling the Impact of Surface Characteristics Variations on Land Surface Temperature Variations: A Case Study of Samalghan Vally

Firozjaei

Makki

Lentschke

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

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“…PCA is one of the techniques for determining variations of environmental parameters in the temporal dimension [75][76][77]. The variations of environmental parameters in the temporal dimension can be examined on a pixel scale using PCA [78][79][80]. To model the variations of each particular parameter over a given time interval, a PCA model was applied to its specific values over a time scale at the pixel scale.…”

Section: Lst and Surface Biophysical Parametersmentioning

confidence: 99%

“…In contrast, a lower and more negative value of a pixel in PC1 indicates that the values of that pixel have been low and unchanged over time. A PC1 value close to zero indicates that changes in the values of the pixel have been high over time [78][79][80].…”

Section: Lst and Surface Biophysical Parametersmentioning

confidence: 99%

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

et al. 2019

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Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

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Thresholding Algorithm Optimization for Change Detection to Satellite Imagery

Cited by 17 publications

References 28 publications

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

Spatioltemporal Modeling the Impact of Surface Characteristics Variations on Land Surface Temperature Variations: A Case Study of Samalghan Vally

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

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