Regional Variations in Temperatures

Shandas, Vivek; Makido, Yasuyo; Ferwati, M. Salim

doi:10.1007/978-3-030-26586-1_4

Cited by 1 publication

(1 citation statement)

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“…where ρ BLUE , ρ SWIR1 and ρ SWIR2 are the reflectance values at the blue visible, short-wave infrared 1, and short-wave infrared 2 bands, respectively, of the Landsat imagery data. In this study, albedo was estimated in a normalized form according to Reference [28]. The resulting values are bounded by a range between 0 and 2 indicating vegetation cover or barren land, respectively.…”

Section: Preparation Of Spectral Indicesmentioning

confidence: 99%

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

2020

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The main objective of this study was to explore the impact of various spectral indices on the performance of change vector analysis (CVA) for detecting the land cover changes on the island of Crete, Greece, between the last two decades (1999–2009 and 2009–2019). A set of such indices, namely, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), albedo, bare soil index (BSI), tasseled cap greenness (TCG), and tasseled cap brightness (TCB), representing both the vegetation and soil conditions of the study area, were estimated on Landsat satellite images captured in 1999, 2009, and 2019. Change vector analysis was then applied for five different index combinations resulting to the relative change outputs. The evaluation of these outputs was performed towards detailed land cover maps produced by supervised classification of the aforementioned images. The results from the two examined periods revealed that the five index combinations provided promising performance results in terms of kappa index (with a range of 0.60–0.69) and overall accuracy (with a range of 0.86–0.96). Moreover, among the different combinations, the use of NDVI and albedo were found to provide superior results against the other combinations.

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Section: Preparation Of Spectral Indicesmentioning

confidence: 99%