Thematic validation of global land cover data sets-procedures and interpretation methods

Scepan, Joseph; Estes, J. E.

doi:10.1109/igarss.2001.976765

Cited by 12 publications

(12 citation statements)

References 4 publications

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“…To represent the land cover of Africa, 12 years (2001Africa, 12 years ( -2012 global vegetation classification scheme, shown to be the best among the five schemes, was selected for this analysis (Scepan & Estes, 2001;Friedl et al, 2010) (see figure 1). (Griffiths, 1971;United Nations, 2014).…”

Section: Modis Land Cover Type Datamentioning

confidence: 99%

Characterising the land surface phenology of Africa using 500 m MODIS EVI

2018

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Vegetation phenological studies at different spatial and temporal scales offer better understanding of the relationship between the global climate and the global distribution of biogeographical zones. These studies in the last few decades have focussed on characterising and understanding vegetation phenology and its drivers especially using satellite sensor data. Nevertheless, despite being home to 17% of the global forest cover, approximately 12% of the world's tropical mangroves, and a diverse range of vegetation types, Africa is one of the most poorly studied regions in the world. There has been no study characterising land surface phenology (LSP) of the major land cover types in the different geographical sub-regions in Africa, and only coarse spatial resolution datasets have been used for continental studies. Therefore, we aim to provide seasonal phenological pattern of Africa's vegetation and characterise the LSP of major land cover types in different geographical sub-regions in Africa at a medium spatial resolution of 500 m using MODIS EVI time-series data over a long temporal range of 15 years (2001-2015). The Discrete Fourier Transformation (DFT) technique was employed to smooth the time-series data and an inflection point-based method was used to extract phenological parameters such as start of season (SOS) and end of season (EOS). Homogeneous pixels from 12 years (2001-2012) MODIS land cover data (MODIS MCD12Q1) was used to describe, for the first time, the LSP of the major vegetation types in Africa. The results from this research characterise spatially and temporally the highly irregular and multi-annual variability of the vegetation phenology of Africa, and the maps and charts provide an improved representation of the LSP of Africa, which can serve as a pivot to filling other research gaps in the African continent.

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Section: Modis Land Cover Type Datamentioning

confidence: 99%

Characterising the land surface phenology of Africa using 500 m MODIS EVI

2018

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“…High resolution satellite data provide the only practical source of reference data for this global 500-sample-block dataset . Such use of relatively finer resolution satellite data as the basis of or as a component of global and large area land cover accuracy assessment has been established (Bontemps et al, 2012;De Fries, Hansen, Townshend, & Sohlberg, 1998;Desclee, Simonetti, Mayaux, & Achard, 2013;Mayaux et al, 2006;Montesano et al, 2009;Morisette et al, 2003;Petropoulos, Partsinevelos, & Mitraka, 2013;Raši et al, 2011;Scepan & Estes, 2001;Small & Lu, 2006). Our purpose in this article is to document the identification, collection and preprocessing of reference source-data, the reference map production, and the methods used in applying these high resolution thematic data to assess 30 m continuous field data layers characterizing global tree cover.…”

Section: Introductionmentioning

confidence: 98%

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

Pengra

Long

Dahal

et al. 2015

Remote Sensing of Environment

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“…Where Q and U is the total number of pixels and classes respectively. The minimum acceptable overall accuracy is 85% [21]. The overall accuracy and Kappa coefficient calculated from the parallel grid classification method is very high, but this classification method could not classify some pixels and these pixels seems as some black areas.…”

Section: Overall Accuracymentioning

confidence: 94%

RS-Based Assessment: Spatial-Temporal Changes in Water Basins of Tar and Havir Lakes

Ashinfar

Vahidi

Hatamzadeh

et al. 2023

IJECC

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In recent studies several factors such as landslides, volcanoes, glaciers, tectonic factors and karst forms such as flooded pulleys resulting of karsts development cause the formation of lakes in mountainous areas. The mountain lakes of Iran, including Tar and Havir lakes, are mainly formed by landslides. In this research investigation of water basins changes for (Tar and Hoyer lakes) by remote sensing method in the period from 2013 to 2022 have been done. It has been tried to select appropriate Landsat 8 satellite images in terms of cloud cover and image quality from the time when the lakes are full of water. The training and testing data were selected with the same distribution in the whole image to make the classifications more accurate. After estimate kappa coefficient and overall accuracy for all kind of supervised and unsupervised methods, the results obtained from the two classification methods of maximum likelihood and support vector machine with linear kernel from Tar and Havir lakes, the conclusion was that the classification method of support vector machine with linear kernel have better distinguish land and water areas and it is the best method for classifying water basins located in mountainous areas. The amount of water in Tar and Havir lakes had been increased from 2013 to 2017but in 2018, the water area of both lakes decreased significantly, and in 2019, the largest water area for the lakes was observed and calculated. In 2020, there is no big change in the water area of the two lakes, and from 2020 to 2022, the amount of water in the two lakes is decreasing.

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Thematic validation of global land cover data sets-procedures and interpretation methods

Cited by 12 publications

References 4 publications

Characterising the land surface phenology of Africa using 500 m MODIS EVI

Characterising the land surface phenology of Africa using 500 m MODIS EVI

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

RS-Based Assessment: Spatial-Temporal Changes in Water Basins of Tar and Havir Lakes

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