Accuracy Assessment of the Globeland30 Dataset in Jiangxi Province

Ren, Huiqun; Cai, Guoyin; Zhao, Guo-hua; Li, Z.

doi:10.5194/isprs-archives-xlii-3-1481-2018

Cited by 7 publications

(10 citation statements)

References 8 publications

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“…According to Amler et al (2015) and Ren et al (2018) the K value is used to assess the accuracy of remote sensing data using Equation (1).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Assessing the Effects of Land Use/Land Cover Change on Groundwater Recharge in a Sudano-Sahelian Zone: Case of Koda Catchment, Mali, West Africa

Diancoumba¹,

Toure²,

Daou³

et al. 2022

GEP

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Groundwater is the main source of water in the studied area; therefore, it is significantly requested in all the activities of the inhabitants. These natural resources are affected by some drivers especially Land Use/Land Cover (LULC) and Climate Change. A Land Use/Land Cover (LULC) dynamics study is crucial for any global environmental change evaluation. For instance, for a given place, its change could affect considerably water cycle components. Therefore, the knowledge of the effects of LULC on groundwater recharge is then the key in water resources management system, in particular for the decision makers of the Koda Catchment where the scarcity of the water availability for agriculture is real. The spatiotemporal variation of the different units of LULC present in the catchment has been examined in this study. The Envi 4.5 Software coupled with ArcGIS using the Supervised Classification method, was applied to subset Landsat images from 1990 to 2016. Five (5) major LULC categories, cultivated land, bare land, herbaceous savannah, shrubby savannah and degraded savannah, were identified in the catchment. In a parallel direction, the groundwater recharge has been estimated through the conceptual Gardenia model for the same period 1990-2016. The results showed that the portion of cultivated land and bare land increased (14.9% and 23.5% respectively) while, the portion of savannah decreased: herbaceous savannah by 24.4%, degraded savannah by 10.32% and Shrubby Savannah by 3.6%.

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“…According to Amler et al (2015) and Ren et al (2018) the K value is used to assess the accuracy of remote sensing data using Equation (1).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Assessing the Effects of Land Use/Land Cover Change on Groundwater Recharge in a Sudano-Sahelian Zone: Case of Koda Catchment, Mali, West Africa

Diancoumba¹,

Toure²,

Daou³

et al. 2022

GEP

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“…This was followed by confidence intervals for area estimation and comparison of area estimation derived from map data to reference data (FAO-ONU, 2016). Many past studies have estimated the accuracy of the observed LULC map with a modeled one using a Kappa coefficient and overall accuracies (Chen et al, 2015;Franklin et Wulder, 2002;Lunetta et al, 2006;Ren et al, 2018). For accuracy assessment, 1,000 reference points were randomly taken from high-resolution (in meter) satellite images for the years 2010 and 2013 provided by Google Earth Pro (version 7.3).…”

Section: Pre-analysis and Harmonization Of Land Use And Land Cover Typementioning

confidence: 99%

“…This value is used to evaluate the accuracy of remote sensing data as following Eq. (5) (Amler et al, 2015;Ren et al, 2018;Sitthi et al, 2016)…”

Section: ̂=̂+mentioning

confidence: 99%

“…The analysis of Pontius and Millones (2011) discussed the limitations of comparing two maps using the Kappa coefficient and proposed a new methodology for comparison namely "quantify disagreement and allocation disagreement". Nevertheless, the Kappa coefficient is still considered as a vital tool for accuracy assessment measurement in a number of studies (Biondini et Kandus, 2006;Milad et al, 2017;Ren et al, 2018;Sitthi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Impact of the Accuracy of Land Cover Data Sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

Koubodana¹,

Diekkrüger²,

Näschen³

et al. 2019

Preprint

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The results reveal CILSS as the most accurate data set with a Kappa coefficient of 68% and an overall accuracy of 83%. CILSS data shows a decrease of savanna and forest whereas an increase of cropland over the period 1975 to 2013. The increase of cropland area of 30.97% from 1975 to 2013 can be related to the increase in population and their food demand, while the losses of forest area and the decrease of savanna are further amplified by using wood as energy sources and the lack of forest management. The three datasets were used to simulate future LULC changes using the Terrset Land Change Modeler. The validation of the model using CILSS data for 2013 showed a quality of 50.94%, it is only 40.04% for ESA and 20.13% for Globeland30. CILSS data was utilized to simulate the LULC distribution for the years 2020 and 2027 because of its satisfactory performances. The results show that a high spatial resolution is not a guarantee of high quality. The results of this study can be used for impact studies and to develop management strategies for mitigating negative effects of land use and land cover change.

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“…Land cover provides basic geospatial information for applications in the fields of global environmental change, natural resources management, carbon and nitrogen cycle, and ecological monitoring [1,2,3]. Because of the continuous earth surface scanning and the correspondingly long-term data archives, satellite remote sensing is proven as an effective way in mapping global land cover and measuring land cover dynamic change [4].…”

Section: Introductionmentioning

confidence: 99%

Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery

Ren

Cai

2019

Sensors

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Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth’s subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 m spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 m land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R2 is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NS is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.

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Accuracy Assessment of the Globeland30 Dataset in Jiangxi Province

Cited by 7 publications

References 8 publications

Assessing the Effects of Land Use/Land Cover Change on Groundwater Recharge in a Sudano-Sahelian Zone: Case of Koda Catchment, Mali, West Africa

Assessing the Effects of Land Use/Land Cover Change on Groundwater Recharge in a Sudano-Sahelian Zone: Case of Koda Catchment, Mali, West Africa

Impact of the Accuracy of Land Cover Data Sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery

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