AREA ESTIMATION OF MULTI-TEMPORAL GLOBAL IMPERVIOUS LAND COVER BASED ON STRATIFIED RANDOM SAMPLING

Abstract. Estimating area of impervious land cover is the most useful and one of the ecological assessment indexes of urban and regional environment. Global land cover maps are inevitably misclassified, which affects the quality and application of the data. Statistical approach for assessing the accuracy is critical to understand the global change information and area estimation is usually based on sample data with a probability-based estimator. However, research on evaluation of multi-temporal global impervious land cover maps has not been implemented. In this study, spatial characteristics of the data are considered to assess the thematic map accuracy with a two-stage stratified random sampling plan. The first stage of stratification is determined by the global urban ecoregion and the second one is determined by land cover classes. Additionally, sample size of both map stage and pixel stage are calculated using a probability sampling model. A response design is constructed for a per-pixel accuracy assessment and blind interpretation is implemented using sample pixels and its surrounding area. Our method is applied to the multi-temporal global impervious land cover maps between 2000 and 2010 with a time interval of 5 years and the estimated area in different epoch are listed in detail. The main contribution of our research is illustrating the details for calculating the proportion area of impervious land cover and corresponding confidence intervals based on the reference classification. The experimental results show that the increasing area of the impervious surface according to the sample unit shows good agreement with the urbanization and descriptive accuracy assessments by user’s, producer’s and overall accuracy are shown respectively.

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Utilizing the Global Land Cover 2000 reference dataset for a comparative accuracy assessment of 1 km global land cover maps

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-503-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 503-510 ◽

Cited By ~ 4

Author(s):

M. Schultz ◽

N. E. Tsendbazazr ◽

M. Herold ◽

M. Jung ◽

P. Mayaux ◽

...

Keyword(s):

Land Cover ◽

Climate Models ◽

Accuracy Assessment ◽

Careful Consideration ◽

Global Climate Models ◽

Reference Dataset ◽

Heterogeneous Landscape ◽

Global Land Cover ◽

Global Land ◽

Land Cover Maps

Many investigators use global land cover (GLC) maps for different purposes, such as an input for global climate models. The current GLC maps used for such purposes are based on different remote sensing data, methodologies and legends. Consequently, comparison of GLC maps is difficult and information about their relative utility is limited. The objective of this study is to analyse and compare the thematic accuracies of GLC maps (i.e., IGBP-DISCover, UMD, MODIS, GLC2000 and SYNMAP) at 1 km resolutions by (a) re-analysing the GLC2000 reference dataset, (b) applying a generalized GLC legend and (c) comparing their thematic accuracies at different homogeneity levels. The accuracy assessment was based on the GLC2000 reference dataset with 1253 samples that were visually interpreted. The legends of the GLC maps and the reference datasets were harmonized into 11 general land cover classes. There results show that the map accuracy estimates vary up to 10-16% depending on the homogeneity of the reference point (HRP) for all the GLC maps. An increase of the HRP resulted in higher overall accuracies but reduced accuracy confidence for the GLC maps due to less number of accountable samples. The overall accuracy of the SYNMAP was the highest at any HRP level followed by the GLC2000. The overall accuracies of the maps also varied by up to 10% depending on the definition of agreement between the reference and map categories in heterogeneous landscape. A careful consideration of heterogeneous landscape is therefore recommended for future accuracy assessments of land cover maps.

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Land-cover maps from partially cloudy multi-temporal image series: Optimal temporal sampling and cloud removal

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5652934 ◽

2010 ◽

Cited By ~ 6

Author(s):

Jordi Inglada ◽

Sebastien Garrigues

Keyword(s):

Land Cover ◽

Image Series ◽

Temporal Sampling ◽

Multi Temporal ◽

Cloud Removal ◽

Land Cover Maps

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Assessing the impacts of human uncertainty in the accuracy assessment of land-cover maps using linguistic scales and fuzzy intervals

International Journal of Remote Sensing ◽

10.1080/01431161.2015.1043034 ◽

2015 ◽

Vol 36 (10) ◽

pp. 2524-2547 ◽

Cited By ~ 1

Author(s):

Pedro Sarmento ◽

Cidália C. Fonte ◽

Joel Dinis ◽

Stephen V. Stehman ◽

Mário Caetano

Keyword(s):

Land Cover ◽

Accuracy Assessment ◽

Land Cover Maps

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Area estimation from discrete and continuous land cover maps

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6947555 ◽

2014 ◽

Author(s):

Rene R. Colditz ◽

Julian Equihua

Keyword(s):

Land Cover ◽

Area Estimation ◽

Land Cover Maps

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Combining Accuracy Assessment of Land-Cover Maps With Environmental Monitoring Programs

Monitoring Ecological Condition in the Western United States ◽

10.1007/978-94-011-4343-1_11 ◽

2000 ◽

pp. 115-126

Author(s):

Stephen V. Stehman ◽

Raymond L. Czaplewski ◽

Sarah M. Nusser ◽

Limin Yang ◽

Zhiliang Zhu

Keyword(s):

Land Cover ◽

Environmental Monitoring ◽

Accuracy Assessment ◽

Monitoring Programs ◽

Land Cover Maps

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REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b4-2021-175-2021 ◽

2021 ◽

Vol XLIII-B4-2021 ◽

pp. 175-182

Author(s):

G. Bratic ◽

A. Vavassori ◽

M. A. Brovelli

Keyword(s):

High Resolution ◽

Land Cover ◽

Spatial Resolution ◽

Accuracy Assessment ◽

General Information ◽

Tree Canopy ◽

Global Land Cover ◽

Tree Canopy Cover ◽

Global Land ◽

Land Cover Maps

Abstract. The land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions.

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Change detection analysis of reservoirs and lakes in Multi-Temporal Landsat-7 (ETM+) data over the Indian sub-continent during 2008-2018

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.6.20447 ◽

2018 ◽

Vol 7 (4.6) ◽

pp. 122

Author(s):

B. Chandrababu Naik ◽

Prof. B. Anuradha ◽

. .

Keyword(s):

Land Cover ◽

Change Detection ◽

Accuracy Assessment ◽

Kappa Statistics ◽

Detection Techniques ◽

Change Detection Analysis ◽

Detection Analysis ◽

Landsat 7 ◽

Multi Temporal ◽

Lakes And Reservoirs

Remote sensing change detection techniques are extensively used in numerous applications such as land cover monitoring, disaster monitoring, and urban sprawl. The main motive of this paper study the change detection analysis of Land Use / Land Cover (LULC) in different lakes and Reservoirs, such as Chilika Lake, Pulicat Lake, Vembanad Lake, Penna Reservoir, and Nagarjuna Sagar Reservoir located in the Indian subcontinent region. The analyses and changes are evaluated during period of 2008 - 2018 in multi-temporal Landsat-7 (ETM+) data. The major disadvantage in Landsat-7 for data acquired from satellite sensor, is that it includes strips (gaps) in an image. On May 31, 2003 the Scan-Line-Corrector (SLC) failed completely, due to 22% of pixel information lost in the Landsat-7 data. The focal analysis method is applied to the required image for removing all strips (gaps). Change detection using Image Differencing technique, maximum changed area and unchanged area detect the different Lakes and Reservoirs in the period of 2008-2018. The unsupervised classification is used to compute the accuracy assessment analysis. Excellent results are obtained by using accuracy assessment for different Lakes and Reservoirs from 2008 to 2018, with the overall accuracy of 91.59%, and overall kappa statistics of 0.9032. The percentage of a decreased area is more in 2018 as compared to 2008 and it concludes that the percentage of decreased area is more as compared to the percentage of increased area for acquired Landsat-7 data.

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