Climate change 2014 - Synthesis Report

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

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Change detection techniques

Techniques based on multi-temporal, multi-spectral, satellite-sensor-acquired data have demonstrated potential as a means to detect, identify, map and monitor ecosystem changes, irrespective of their causal agents. This review paper, which summarizes the methods and the results of digital change detection in the optical/infrared domain, has as its primary objective a synthesis of the state of the art today. It approaches digital change detection from three angles. First, the different perspectives from which the variability in ecosystems and the change events have been dealt with are summarized. Change detection between pairs of images (bi-temporal) as well as between time profiles of imagery derived indicators (temporal trajectories), and, where relevant, the appropriate choices for digital imagery acquisition timing and change interval length definition, are discussed. Second, pre-processing routines either to establish a more direct linkage between remote sensing data and biophysical phenomena, or to temporally mosaic imagery and extract time profiles, are reviewed. Third, the actual change detection methods themselves are categorized in an analytical framework and critically evaluated. Ultimately, the paper highlights how some of these methodological aspects are being fine-tuned as this review is being written, and we summarize the new developments that can be expected in the near future. The review highlights the high complementarity between different change detection methods.

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Digital change detection methods in ecosystem monitoring: a review

The merging of multisensor image data is becoming a widely used procedure because of the complementary nature of various data sets. Ideally, the method used to merge data sets with high-spatial and high-spectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper compares the results of three different methods used to merge the information contents of the Landsat Thermatic Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) panchromatic data. The comparison is based on spectral characteristics and is made using statistical, visual, and graphical analyses of the results

Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic

A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis

Analysis of variation of rainfall in space, time and amounts, and its attendant effect on the ecosystem is vital in arid and semi-arid environments where the resource is scare, highly variable and unpredictable. In this study, we analyse the characteristics of rainfall in the Sultanate of Oman using data recorded between 1977 and 2003. The data is divided into six geomorphic compartments to represent the various topographic regions in Oman. The average yearly rainfall varies from a low of 76.9 mm in the interior region to a high of 181.9 mm in the Dhofar Mountains, with an average of 117.4 mm for the whole country. Mann–Kendall statistics show a negative but insignificant rainfall trends for the datasets. In northern Oman, the main rainfall season occurs between December and April and that accounts for 57.8–82.9% of the annual rainfall. February and March record the highest rainfall accounting for 35.3 to 42% of the yearly rainfall. The Dhofar Mountains and surrounding areas in southern Oman are dominated by the khareef season in July–August, which produces 44.3 to 67.5% of the rainfall in that area. The number of days of light rainfall ( 100 mm rainfall per day) approximately every 50 years. Copyright © 2008 Royal Meteorological Society

Analysis of a 27-year rainfall data (1977–2003) in the Sultanate of Oman

Landsat Thematic Mapper (TM) images acquired on 17 February 1986, and 28 February, 1993, respectively, were used to demonstrate their usefulness for both surface/spectral mapping and temporal change detection in Kuwait City and environs. High pass spatial filters with a relatively large kernel (201 pixels by 201 pixels) were used to enhance high frequency information in both the bright desert and dark urban areas. The filtered results were edge enhanced to sharpen the local textural information. Colour composites were made for analyses using the TM bands-2, -3, -4 and-2, -4, -7. The two multi-temporal images were geometrically and radiometrically calibrated to each other and used as input to an automatic change detection procedure. The 'change image' composite, made from the individual change image results generated using TM bands -2, -4, and-7, detected and mapped temporal changes dealing with urban development, vegetation, coastal wetlands, and sand sheet surface differences caused by the large oil spil...

Andy Y. Kwarteng

Papers

Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis

Analysis of a 27-year rainfall data (1977–2003) in the Sultanate of Oman

Change detection study of Kuwait City and environs using multi-temporal Landsat Thematic Mapper data

Formation of fresh ground-water lenses in northern Kuwait

Multitemporal remote sensing data analysis of Kuwait's oil lakes