Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece
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 ind...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/2/319 |
| _version_ | 1828142947762176000 |
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| author | Christos Polykretis Manolis G. Grillakis Dimitrios D. Alexakis |
| author_facet | Christos Polykretis Manolis G. Grillakis Dimitrios D. Alexakis |
| author_sort | Christos Polykretis |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:49:05Z |
| format | Article |
| id | doaj.art-3f8572857d6944e1b565cfbd91557306 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:49:05Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3f8572857d6944e1b565cfbd915573062022-12-22T04:06:21ZengMDPI AGRemote Sensing2072-42922020-01-0112231910.3390/rs12020319rs12020319Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, GreeceChristos Polykretis0Manolis G. Grillakis1Dimitrios D. Alexakis2School of Environmental Engineering, Technical University of Crete, 73100 Chania, GreeceSchool of Environmental Engineering, Technical University of Crete, 73100 Chania, GreeceLab of Geophysical-Satellite Remote Sensing & Archaeo-environment, Institute for Mediterranean Studies, Foundation for Research and Technology-Hellas, 74100 Rethymno, GreeceThe 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.https://www.mdpi.com/2072-4292/12/2/319land coverchange vector analysisspectral indexlandsatcretegreece |
| spellingShingle | Christos Polykretis Manolis G. Grillakis Dimitrios D. Alexakis Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece Remote Sensing land cover change vector analysis spectral index landsat crete greece |
| title | Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece |
| title_full | Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece |
| title_fullStr | Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece |
| title_full_unstemmed | Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece |
| title_short | Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece |
| title_sort | exploring the impact of various spectral indices on land cover change detection using change vector analysis a case study of crete island greece |
| topic | land cover change vector analysis spectral index landsat crete greece |
| url | https://www.mdpi.com/2072-4292/12/2/319 |
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