Application of Spherical Statistics to Change Vector Analysis of Landsat Data Southern Appalachian Spruce–Fir Forests

Allen, Thomas R.; Kupfer, John A.

doi:10.1016/s0034-4257(00)00140-1

Cited by 67 publications

(44 citation statements)

References 18 publications

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“…The direction of change for n-dimensional multispectral data is difficult to calculate and interpret. The procedure most commonly used is the direction cosine method [5][6][7], but this generates the same number of output images that the input data and has a complex interpretation. This paper proposes a new alternative to calculate the direction of change using the angle between temporal vectors.…”

Section: Resultsmentioning

confidence: 99%

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

et al. 2011

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Abstract:The need to monitor the Earth's surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together. OPEN ACCESSRemote Sens. 2011, 3 2474

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Section: Resultsmentioning

confidence: 99%

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

et al. 2011

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“…Various previous studies that applied CVA as a change detection algorithm for vegetation and soil used the tasseled cap's brightness and greenness [35,38,[63][64][65][66][67]. It is likely that both biophysical spectral variables would end up with close results.…”

Section: Discussionmentioning

confidence: 99%

Spatio-Temporal Dynamics of Land-Use and Land-Cover in the Mu Us Sandy Land, China, Using the Change Vector Analysis Technique

Karnieli

Qin

et al. 2014

Remote Sensing

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Abstract:The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple consequences. Two biophysical variables, NDVI, positively correlated with vegetation cover, and albedo, positively correlated with cover of exposed sands, were computed from a time series of merged NOAA-AVHRR and MODIS images (1981 to 2010). Generally, throughout the study period, NDVI increased and albedo decreased. Improved understanding of spatial and temporal dynamics of these environmental processes was achieved by using the Change Vector Analysis (CVA) technique applied to NDVI and albedo data extracted from four sets of consecutive Landsat images, several years apart. Changes were detected for each time step, as well as over the entire period (1978 to 2007). Four categories of land cover were created-vegetation, exposed sands, water bodies and wetlands. The CVA's direction and magnitude enable detecting and quantifying finer changes compared to separate NDVI or albedo difference/ratio images OPEN ACCESS Remote Sens. 2014, 6 9317 and result in pixel-based maps of the change. Each of the four categories has a biophysical meaning that was validated in selected hot-spots, employing very high spatial resolution images (e.g., Ikonos). Selection of images, taking into account inter and intra annual variability of rainfall, enables differentiating between short-term conservancies (e.g., drought) and long-term alterations. NDVI and albedo, although comparable to tasseled cap's brightness and greenness indices, have the advantage of being computed using reflectance values extracted from various Landsat platforms since the early 1970s. It is shown that, over the entire study period, the majority of the Mu Us Sandy Land area remained unchanged. Part of the area (6%), mainly in the east, was under human-induced rehabilitation processes, in terms of increasing vegetation cover. In other areas (5.1%), bare sands were found to expand to the central-north and the southwest of the area.

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“…World War I provided a boost in the use of aerial photography. During that time, aerial photographs were used for two purposes -spying and mapping [1][2][3][4][5].…”

Section: Phase I (Up Till 1920): Initial Phasementioning

confidence: 99%

A Review- Remote Sensing

Waghmare¹,

Suryawanshi²

2017

IJERA

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Now-a-days the field of Remote Sensing and GIS has become exciting and glamorous with rapidly expanding opportunities. Many organizations spend large amounts of money on these fields. Here the question arises why these fields are so important in recent years. Two main reasons are there behind this. 1) Now-a-days scientists, researchers, students, and even common people are showing great interest for better understanding of our environment. By environment we mean the geographic space of their study area and the events that take place there. In other words, we have come to realize that geographic space along with the data describing it, is part of our everyday world; almost every decision we take is influenced or dictated by some fact of geography. 2) Advancement in sophisticated space technology (which can provide large volume of spatial data), along with declining costs of computer hardware and software (which can handle these data) has made Remote Sensing and G.I.S. affordable to not only complex environmental / spatial situation but also affordable to an increasingly wider audience.

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Application of Spherical Statistics to Change Vector Analysis of Landsat Data Southern Appalachian Spruce–Fir Forests

Cited by 67 publications

References 18 publications

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

Spatio-Temporal Dynamics of Land-Use and Land-Cover in the Mu Us Sandy Land, China, Using the Change Vector Analysis Technique

A Review- Remote Sensing

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