Multi-Index Image Differencing Method (MINDED) for Flood Extent Estimations

Satellite remote sensing data are often used to extract water surfaces related to extreme events like floods. This study presents the Multi INDEx Differencing (MINDED) method, an innovative procedure to estimate flood extents, aiming at improving the robustness of single water-related indices and threshold-based approaches. MINDED consists of a change detection approach integrating specific sensitivities of several indices. Moreover, the method also allows to quantify the uncertainty of the Overall flood map, based on both the agreement level of the stack of classifications and the weight of every index obtained from the literature. Assuming the lack of ground truths to be the most common condition in flood mapping, MINDED also integrates a procedure to reduce the subjectivity of thresholds extraction focused on the analysis of water-related indices frequency distribution. The results of the MINDED application to a case study using Landsat images are compared with an alternative change detection method using Sentinel-1A data, and demonstrate consistency with local fluvial flood records.

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Evaluating change detection techniques using remote sensing data: Case study New Administrative Capital Egypt

The Egyptian Journal of Remote Sensing and Space Science ◽

10.1016/j.ejrs.2021.03.001 ◽

2021 ◽

Author(s):

Ahmed Saber ◽

Ibrahim El-Sayed ◽

Mustafa Rabah ◽

Mohamed Selim

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Remote Sensing Data ◽

Detection Techniques ◽

Sensing Data

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Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

Journal of King Abdulaziz University-Engineering Sciences ◽

10.4197/eng.23-2.8 ◽

2012 ◽

Vol 23 (2) ◽

pp. 139-172

Author(s):

Abdullah Salman Alsalman Abdullah Salman Alsalman

Keyword(s):

Image Processing ◽

Change Detection ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Expansion ◽

Remote Sensing Data ◽

Principal Component ◽

Test Area ◽

Image Differencing ◽

Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

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