Correspondence analysis for detecting land cover change

Çakır, Halil İbrahim; Khorram, Siamak; Nelson, Stacy A. C.

doi:10.1016/j.rse.2006.02.023

Cited by 82 publications

(46 citation statements)

References 9 publications

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“…In this study, we mainly measured the vegetation land covers, and we simply set the negative values to 0.0, and then the range of NDVI value is between 0.0 and +1.0. After the above processing, the areas of change can be identified through the subtraction of the NDVI image for one date from that of another [41]. The pre-hurricane NDVI image was subtracted from post-hurricane image by using map algebra, which is a cell-by-cell process.…”

Section: Vegetation Indices Calculationmentioning

confidence: 99%

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Smith

2018

Remote Sensing

104

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Abstract:As the worst natural disaster on record in Dominica and Puerto Rico, Hurricane Maria in September 2017 had a large impact on the vegetation of these islands. In this paper, multitemporal Landsat 8 OLI and Sentinel-2 data are used to investigate vegetation damage on Dominica and Puerto Rico by Hurricane Maria, and related influencing factors are analyzed. Moreover, the changes in the normalized difference vegetation index (NDVI) in the year 2017 are compared to reference years (2015 and 2016). The results show that (1) there is a sudden drop in NDVI values after Hurricane Maria's landfall (decreased about 0.2) which returns to near normal vegetation after 1.5 months; (2) different land cover types have different sensitivities to Hurricane Maria, whereby forest is the most sensitive type, then followed by wetland, built-up, and natural grassland; and (3) for Puerto Rico, the vegetation damage is highly correlated with distance from the storm center and elevation. For Dominica, where the whole island is within Hurricane Maria's radius of maximum wind, the vegetation damage has no obvious relationship to elevation or distance. The study provides insight into the sensitivity and recovery of vegetation after a major land-falling hurricane, and may lead to improved vegetation protection strategies.

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Section: Vegetation Indices Calculationmentioning

confidence: 99%

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Smith

2018

Remote Sensing

104

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“…In the resultant image, zero values indicated no change areas and the changed areas between the two dates had a negative or positive value, with the negative values representing decrease in vegetation and positive values indicating increase in vegetation. However, slight changes in the brightness values between the two dates occurred due to noises even after radiometric normalization and thus it was necessary to develop a set of threshold values to discriminate the real changes (Cakir et al, 2006).…”

Section: Ndvi Differencing and Post-classificationmentioning

confidence: 99%

Assessment of changes in urban green spaces of Mashad city using satellite data

Rafiee

Mahiny

Khorasani

2009

International Journal of Applied Earth Observation and Geoinfor

100

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“…Kappa analysis is a discrete multivariate analysis technique, which is widely used for accuracy analysis. It produces a statistical value of kappa (� � ) and shows the agreement or accuracy between the reference data and the classification maps that were produced by using remote sensing [1,4,5,7,8]. According to the evaluation criteria of the kappa coefficient that was defined by Landis and Koch [17], a � � value larger than 0.…”

Section: Methodsmentioning

confidence: 99%

Construction of a REDD Monitoring System Using GIS and RS Techniques

Lee

2012

International Journal of Advanced Robotic Systems

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This study aimed to construct a MRV system for the REDD regions. The system was based on the GIS and Remote Sensing techniques, and it developed a method which could detect the land cover change area as well as evaluate its accuracy. GIS, the Remote Sensing technique and the statistical modelling technique were merged, and then we monitored areas of deforestation among the REDD regions. This study compared deforestation from administrative information (GIS deforestation1) with deforestation (RS deforestation) extracted from satellite imagery by vegetation indices (NDVI, NBR, NDWI). The highest extraction accuracy that applies filtering to NDVI with a threshold of 1.5 showed reliable accuracy 35.47% with a k-value of 0.20. However, one of the reasons for the accuracy error was due to the difference between land-use change and land-cover change. The actual rate of land-cover change deforestation was 32% on the administrative information. The other reason was a 7.52% error extraction of the forest management area. Finally, considering the deforestation of the changed land-cover region (GIS deforestation) and RS deforestation, the highest accuracy was observed at the NDVI threshold value interval of 2 and showed an extraction rate and k̂ index of 61.2% and 0.23, respectively. In comparison with the case of applying GIS deforestation I, it showed 36.7% and 0.03 improvements. The MRV monitoring system based on the GIS and Remote Sensing techniques would enable the monitoring of larger size areas regularly and with high reliability.

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Correspondence analysis for detecting land cover change

Cited by 82 publications

References 9 publications

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Assessment of changes in urban green spaces of Mashad city using satellite data

Construction of a REDD Monitoring System Using GIS and RS Techniques

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