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A landscape shape index-based sampling approach for land cover accuracy assessment

  • Research Paper
  • Special Topic: GlobeLand30 remote sensing mapping innovation and large data analysis
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Abstract

Spatial sampling design is one of the key steps in land cover accuracy assessment, and many traditional sampling approaches may not achieve credible spatial sampling due to the high spatial heterogeneity of land cover. This paper characterizes the spatial heterogeneity with three-level LSIs and determines the subsequent sample sizes and their spatial distributions. The three-level LSIs are rLSI in a region, cLSI for each land cover class and uLSI in each geographic sampling unit in the region. The rLSIs are used to derive appropriate sample sizes in the target regions. The cLSIs are used to assure that larger sample numbers are allocated to land cover classes with higher spatial heterogeneity. The uLSIs provide useful measures for selecting optimal geographic units in which sample sites will be located. This LSI-based sampling approach can derive the sample sizes and determine their distributions in an adaptive way according to the spatial heterogeneity. An experimental case study further demonstrates that the LSI-based sampling approach obtains more appropriate sample sizes for each region, sufficient sample numbers for rare classes, and optimal sample distributions in the geographical space.

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References

  • Ba H, Duffy C W, Ahouidi A D, Deh Y B, Diallo M Y, Tandia A, Conway D J. 2016. Widespread distribution of Plasmodium vivax malaria in Mauritania on the interface of the Maghreb and West Africa. Malar J, 15: 80

    Article  Google Scholar 

  • Ban Y, Gong P, Giri C. 2015. Global land cover mapping using Earth observation satellite data: Recent progresses and challenges. ISPRS-J Photogram Remote Sens, 103: 1–6

    Article  Google Scholar 

  • Brovelli M, Molinari M, Hussein E, Chen J, Li R. 2015. The first comprehensive accuracy assessment of GlobeLand30 at a national level: Methodology and results. Remote Sens, 7: 4191–4212

    Article  Google Scholar 

  • Cao X, Chen J, Chen L J, Liao A P, Sun F D, Li Y, Li L, Lin Z H, Pang Z G, Chen J, He C Y, Peng S. 2014. Preliminary analysis of spatiotemporal pattern of global land surface water. Sci China Earth Sci, 57: 2330–2339

    Article  Google Scholar 

  • Chen J, Ban Y F, Li S N. 2014a. China: Open access to Earth land-cover map. Nature, 514: 434–434

    Article  Google Scholar 

  • Chen J, Chen J, Liao A P, Cao X, Chen L J, Chen X H, He C Y, Han G, Peng S, Lu M, Zhang W W, Tong X H, Mills J. 2015. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS-J Photogramm Remote Sens, 103: 7–27

    Article  Google Scholar 

  • Chen J, Chen J, Liao A P, Chen L J, Chen X H, Peng S, Han G, Zhang H W, He C Y, Wu H, Lu M. 2014b. Concepts and key techniques for 30 m global land cover mapping. Acta Geod Carto Sin, 43: 551–557

    Google Scholar 

  • Chen J, Dowman I, Li S, Li Z, Madden M, Mills J, Paparoditis N, Rottensteiner F, Sester M, Toth C, Trinder J, Heipke C. 2016. Information from imagery: ISPRS scientific vision and research agenda. ISPRS-J Photogram Remote Sens, 115: 3–21

    Article  Google Scholar 

  • Chen X, Yamaguchi Y, Chen J. 2012. Weighted misclassification rate: A new measure of classification error designed for landscape pattern index. Remote Sens Lett, 3: 57–65

    Article  Google Scholar 

  • Congalton R, Gu J, Yadav K, Thenkabail P, Ozdogan M. 2014. Global land cover mapping: A review and uncertainty analysis. Remote Sens, 6: 12070–12093

    Article  Google Scholar 

  • Foody G M, Boyd D S. 2013. Using volunteered data in land cover map validation: Mapping west African forests. IEEE J Sel Top Appl Earth Observations Remote Sens, 6: 1305–1312

    Article  Google Scholar 

  • Foody G M. 2002. Status of land cover classification accuracy assessment. Remote Sens Environ, 80: 185–201

    Article  Google Scholar 

  • Foody G M. 2009. Sample size determination for image classification accuracy assessment and comparison. Int J Remote Sens, 30: 5273–5291

    Article  Google Scholar 

  • Friedl M A, McIver D K, Hodges J C F, Zhang X Y, Muchoney D, Strahler A H, Woodcock C E, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C. 2002. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens Environ, 83: 287–302

    Article  Google Scholar 

  • Gao L L, Li X H, Wang C P, Qiu Q Y, Cui S H, Zhao Q J. 2010. Survey site selection based on the spatial sampling theory: A case study in Xiamen Island. J Geo-Inform Sci, 48: 358–364

    Article  Google Scholar 

  • García-Mozo H, Oteros J A, Galán C. 2016. Impact of land cover changes and climate on the main airborne pollen types in Southern Spain. Sci Total Environ, 548-549: 221–228

    Article  Google Scholar 

  • Han G, Chen J, He C, Li S, Wu H, Liao A, Peng S. 2015. A web-based system for supporting global land cover data production. ISPRS-J Photogramm Remote Sens, 103: 66–80

    Article  Google Scholar 

  • Herold M, Mayaux P, Woodcock C E, Baccini A, Schmullius C. 2008. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sens Environ, 112: 2538–2556

    Article  Google Scholar 

  • Jin Y J, Du Z F, Jiang Y. 2015. Sampling Technique. 4th ed. Beijing: China Renmin University Press. 87

    Google Scholar 

  • Karim M, Mimura N. 2008. Impacts of climate change and sea-level rise on cyclonic storm surge floods in Bangladesh. Global Environ Change, 18: 490–500

    Article  Google Scholar 

  • Li D, Zhang J, Wu H. 2012. Spatial data quality and beyond. Int J Geogr Inf Sci, 26: 2277–2290

    Article  Google Scholar 

  • Li L F, Wang J F, Liu J Y. 2005. Optimal decision-making model of spatial sampling for survey of China’s land with remotely sensed data. Sci China Ser D-Earth Sci, 48: 752–764

    Article  Google Scholar 

  • Liao A P, Chen L J, Chen J, He C Y, Cao X, Chen J, Peng S, Sun F D, Gong P. 2014. High-resolution remote sensing mapping of global land water. Sci China Earth Sci, 57: 2305–2316

    Article  Google Scholar 

  • Mayaux P, Eva H, Gallego J, Strahler A H, Herold M, Agrawal S, Naumov S, De Miranda E E, Di Bella C M, Ordoyne C, Kopin Y, Roy P S. 2006. Validation of the global land cover 2000 map. IEEE Trans Geosci Remote Sens, 44: 1728–1739

    Article  Google Scholar 

  • Niu Z G, Gong P, Cheng X, Guo J H, Wang L, Huang H B, Shen S Q, Wu Y Z, Wang X F, Wang X W, Ying Q, Liang L, Zhang L N, Wang L, Yao Q, Yang Z Z, Guo Z Q, Dai Y J. 2009. Geographical characteristics of China’s wetlands derived from remotely sensed data. Sci China Ser D-Earth Sci, 52: 723–738

    Article  Google Scholar 

  • Olofsson P, Foody G M, Herold M, Stehman S V, Woodcock C E, Wulder M A. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ, 148: 42–57

    Article  Google Scholar 

  • Olofsson P, Stehman S V, Woodcock C E, Sulla-Menashe D, Sibley A M, Newell J D, Friedl M A, Herold M. 2012. A global land-cover validation data set, part I: Fundamental design principles. Int J Remote Sens, 33: 5768–5788

    Article  Google Scholar 

  • Patton D R. 1975. A diversity index for quantifying habitat ‘edge’. Wildlife Soc Bull, 3: 171–173

    Google Scholar 

  • Plexida S G, Sfougaris A I, Ispikoudis I P, Papanastasis V P. 2014. Selecting landscape metrics as indicators of spatial heterogeneity—A comparison among Greek landscapes. Int J Appl Earth Observ Geoinform, 26: 26–35

    Article  Google Scholar 

  • Pontius R G, Millones M. 2011. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens, 32: 4407–4429

    Article  Google Scholar 

  • Ran Y H, Li X. 2015. First comprehensive fine-resolution global land cover map in the world from China—Comments on global land cover map at 30-m resolution. Sci China Earth Sci, 58: 1677–1678

    Article  Google Scholar 

  • Ruiz-Colmenero M, Bienes R, Eldridge D J, Marques M J. 2013. Vegetation cover reduces erosion and enhances soil organic carbon in a vineyard in the central Spain. Catena, 104: 153–160

    Article  Google Scholar 

  • Rutledge D. 2003. Landscape Indices As Measures of the Effect of Fragmentation: Can Pattern Reflect Process. Wellington: Department of Conservation. 14

    Google Scholar 

  • Scepan J. 1999. Thematic validation of high-resolution global land-cover data sets. Photogramm Eng Remote Sens, 65: 1051–1060

    Google Scholar 

  • See L, Perger C, Hofer M, Weichselbaum J, Dresel C, Fritz S. 2015. LACOWiki: An open access online portal for land cover validation. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci, II-3/W5: 167–171

    Article  Google Scholar 

  • Smith J H, Stehman S V, Wickham J D. 2002. Impacts of patch size and land-cover heterogeneity on thematic image classification accuracy. Photogramm Eng Remote Sens, 68: 65–70

    Google Scholar 

  • Stehman S V. 2009. Sampling designs for accuracy assessment of land cover. Int J Remote Sens, 30: 5243–5272

    Article  Google Scholar 

  • Stehman S V. 2012. Impact of sample size allocation when using stratified random sampling to estimate accuracy and area of land-cover change. Remote Sens Lett, 3: 111–120

    Article  Google Scholar 

  • Stehman S V, Olofsson P, Woodcock C E, Herold M, Friedl M A. 2012. A global land-cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class. Int J Remote Sens, 33: 6975–6993

    Article  Google Scholar 

  • Strahler A H, Boschetti L, Foody G M, Friedl M A, Hansen M C, Herold M, Mayaux P, Morisette J T, Stehman S V, Woodcock C E. 2006. Global Land Cover Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps. Luxembourg: European Communities. 51

    Google Scholar 

  • Tong X H, Wang Z H, Xie H, Liang D, Jiang Z Q, Li J, Li J. 2011. Designing a two-rank acceptance sampling plan for quality inspection of geospatial data products. Comput Geosci, 37: 1570–1583

    Article  Google Scholar 

  • Wang J F, Christakos G, Hu M G. 2009a. Modeling spatial means of surfaces with stratified nonhomogeneity. IEEE Trans Geosci Remote Sens, 47: 4167–4174

    Article  Google Scholar 

  • Wang J F, Jiang C S, Hu M G, Cao Z D, Guo Y S, Li L F, Liu T J, Meng B. 2013. Design-based spatial sampling: Theory and implementation. Environ Modell Softw, 40: 280–288

    Article  Google Scholar 

  • Wang J F, Jiang C S, Li L F, Hu M G. 2009b. Spatial Sampling and Statistical Inference. Beijing: China science Publishing & Media Ltd. 21

    Google Scholar 

  • Wang J F, Stein A, Gao B B, Ge Y. 2012. A review of spatial sampling. Spatial Stat, 2: 1–14

    Article  Google Scholar 

  • Wang Z H, Tong X H, Liang D, Xie H. 2010. Sampling inspection schemes for continuous lot spatial data. J Tongji Univ, 38: 749–752

    Google Scholar 

  • Wu J G. 2001. Landscape Ecology: Pattern, Process, Scale and Hierarchy. 2nd ed. Beijing: High Education Press. 109

    Google Scholar 

  • Zhao Y, Gong P, Yu L, Hu L, Li X, Li C, Zhang H, Zheng Y, Wang J, Zhao Y, Cheng Q, Liu C, Liu S, Wang X. 2014. Towards a common validation sample set for global land-cover mapping. Int J Remote Sens, 35: 4795–4814

    Article  Google Scholar 

  • Zhou W, Cadenasso M, Schwarz K, Pickett S. 2014. Quantifying spatial heterogeneity in urban landscapes: Integrating visual interpretation and object-based classification. Remote Sens, 6: 3369–3386

    Article  Google Scholar 

  • Zhu Z L, Yang L M, Stehman S V, Czaplewski R L. 2000. Accuracy assessment for the US geological survey regional land-cover mapping program: New York and New Jersey region. Photogramm Eng Remote Sens, 66: 1425–1438

    Google Scholar 

  • Zimmerman P L, Housman I W, Perry C H, Chastain R A, Webb J B, Finco M V. 2013. An accuracy assessment of forest disturbance mapping in the western Great Lakes. Remote Sens Environ, 128: 176–185

    Article  Google Scholar 

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Acknowledgements

This work was supported by the International S & T Cooperation Program of China (Grant No. 2015DFA11360) and the National Natural Science Foundation of China (Grant No. 41231172).

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Authors and Affiliations

  1. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    Fei Chen & XiaoGuang Zhou

  2. National Geomatics Center of China, Beijing, 100830, China

    Jun Chen, Hao Wu, DongYang Hou, WeiWei Zhang, Jun Zhang & LiJun Chen

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  1. Fei Chen
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  2. Jun Chen
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Correspondence to Jun Chen.

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Chen, F., Chen, J., Wu, H. et al. A landscape shape index-based sampling approach for land cover accuracy assessment. Sci. China Earth Sci. 59, 2263–2274 (2016). https://doi.org/10.1007/s11430-015-5280-5

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  • DOI: https://doi.org/10.1007/s11430-015-5280-5

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