Research on scale effect of domestic satellite land cover products considering patch area ratio

  • role: First author第一作者
  • Affiliation:

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • Email:ylgeng2018@126.com
  • Introduction:耿云龙,研究方向为自然资源监测与统计分析。E-mail:ylgeng2018@126.com
GENG Yunlong1,  
  • Affiliation:

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China

TANG Yuqi12,  
  • role: Corresponding author通信作者
  • Affiliation:

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • Email:ryan_yinzy@163.com
  • Introduction:尹芝勇,研究方向为目标识别与变化分析。E-mail:ryan_yinzy@163.com
YIN Zhiyong1*,  
  • Affiliation:

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China

ZOU Bin12,  
  • Affiliation:

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China

FENG Huihui12

Resümee

The optimal scale of land cover products varies by different applications. Consequently, it needs the scale conversion of land cover products from the base resolution to the objective one. However, the scale effects accompanying the scale conversion often lead to information distortion of land cover products. The state-of-art patch-level research on scale effect, which is vulnerable to patch classification accuracy, often classifies the patches of land cover products into different types. Consequently, a cross-type scale effect model (CSEM) with Patch Area Ratio (PAR) is proposed in this study.The CSEM is aimed to explore further the effect mechanism of Patch Morphological Indexes (PMIs) on scale conversion, predict the areas of land classes in land cover products with specific scales, and provide a rationality evaluation for the scale conversion. First, a candidate index library is defined with seven indexes to describe the patch morphology based on patch size, shape, edge complexity, and internal connectivity. Then, a set of PMIs is defined by information metric techniques. The scale is incorporated into the PMIs by mathematical transformation. Moreover, the CSEM with PAR is constructed by associating the PMIs with PAR, calculated according to the patch areas before and after scale conversion.In this study, a 30 m land cover product from domestic satellites is used with the support of a National Key Research and Development Program to construct and validate the proposed CSEM. According to the experimental results, the CSEM fits the relationship among Filling, logRsr, and PAR: the logRsr is the main factor affecting the value of PAR when logRsr > -0.05, whereas Filling is the main factor on PAR while logRsr < -0.05. The prediction results show that the maximum prediction errors for the four land-cover types of forest, cropland, grassland, and impervious surface on 12 converted scales, varying from 90 m to 750 m with an interval of 60 m, are 0.056, 0.051, 0.051, and 0.053; their average prediction errors are 0.036, 0.033, 0.034, and 0.035, respectively.The results confirm that the CESM explores the effects of Filling and logRsr on PAR and reveals the effect mechanism during the scale conversion, providing instruction for subsequent research of other indexes. The proposed CSEM has satisfactory prediction accuracy for various area types in land cover products with specific scales. It can also provide theoretical guidance for scale conversion and production of land cover products.

Schlüsselwort

land cover product;upscaling;scale effect;Patch area Ratio (PAR);Patch Morphology Index (PMI)

References

  1. 1.
    Bicheron P, Defourny P, Brockmann C, Schouten L, Vancutsem C, Huc M, Bontemps S, Leroy M, Achard F, Herold M, Ranera F and Arino O. 2011. GLOBCOVER - Products description and validation report. Foro Mundial de la Salud, 17(3): 285-287
  2. 2.
    Buyantuyev A and Wu J G. 2007. Effects of thematic resolution on landscape pattern analysis. Landscape Ecology, 22(1): 7-13
  3. 3.
    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 and Mills J. 2015. Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 7-27
  4. 4.
    de Fries R S, Hansen M, Townshend J R G and Sohlberg R. 1998. Global land cover classifications at 8 km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 19(16): 3141-3168
  5. 5.
    Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1): 168-182
  6. 6.
    Gong P, Liu H, Zhang M N, Li C C, Wang J, Huang H B, Clinton N, Ji LY, Li W Y, Bai Y Q, Chen B, Xu B, Zhu Z L, Yuan C, Suen H P, Guo J, Xu N, Li W J, Zhao Y Y, Yang J, Yu C Q, Wang X, Fu H H, Yu L, Dronova I, Hui F M, Cheng X, Shi X L, Xiao F J, Liu Q F and Song L C. 2019. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Science Bulletin, 64(6): 370-373
  7. 7.
    He H S, Ventura S J and Mladenoff D J. 2002. Effects of spatial aggregation approaches on classified satellite imagery. International Journal of Geographical Information Science, 16(1): 93-109
  8. 8.
    Li D R, Ding L and Shao Z F. 2016. Reflections on issues in national geographical conditions monitoring. Geomatics and Information Science of Wuhan University, 41(2): 143-147
  9. 9.
    Li X W and Wang Y T. 2013. Prospects on future developments of quantitative remote sensing. Acta Geographica Sinica, 68(9): 1163-1169
  10. 10.
    Li Z L. 2008. Multi-scale digital terrain modelling and analysis//Zhou Q M, Lees B and Tang G A, eds. Advances in Digital Terrain Analysis. Berlin, Heidelberg: Springer: 59-83
  11. 11.
    Li Z L, Wang J C, Tan S T and Xu Z. 2018. Scale in geo-information science: an overview of thirty-year development. Geomatics and Information Science of Wuhan University, 43(12): 2233-2242
  12. 12.
    Liu L Y. 2014. Simulation and correction of spatialscaling effects for leaf area index. Journal of Remote Sensing, 18(6): 1158-1168
  13. 13.
    Luan H J, Tian Q J, Gu X F, Yu T and Hu X L. 2013. Establishing continuous scaling of NDVI based on fractal theory and GEOEYE-1 image. Journal of Infrared and Millimeter Waves, 32(6): 538-544, 549
  14. 14.
    Luan H J, Tian Q J, Yu T, Gu X F, Huang Y, Hu X L and Yang Y J. 2015. Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system. Journal of Remote Sensing, 19(1): 116-125
  15. 15.
    McGarigal K. 2015. FRAGSTATS Help. Amherst: University of Massachusetts: 77-80
  16. 16.
    Muchoney D, Borak J, Chi H, Friedl M, Gopal S, Hodges J, Morrow N and Strahler A. 2000. Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing, 21(6/7): 1115-1138
  17. 17.
    Peterson G D. 2000. Scaling ecological dynamics: self-organization, hierarchical structure, and ecological resilience. Climatic Change, 44(3): 291-309
  18. 18.
    Riitters K, Wickham J, O'Neill R, Jones B and Smith E. 2000. Global-scale patterns of forest fragmentation. Conservation Ecology, 4(2): 3
  19. 19.
    Riitters K H, O'Neill R V, Hunsaker C T, Wickham J D, Yankee D H, Timmins S P, Jones K B and Jackson B L. 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecology, 10(1): 23-39
  20. 20.
    Tan S T, Xu Z, Ti P and Li Z L. 2018. Morphology-based modeling of aggregation effect on the patch area size for GlobeLand30 data. Transactions in GIS, 22(1): 98-118
  21. 21.
    Wu H, Jiang X G, Xi X H, Li C R and Li Z L. 2009. Comparison and analysis of two general scaling methods for remotely sensed information. Journal of Remote Sensing, 13(2): 183-189
  22. 22.
    Wu J G. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology, 19(2): 125-138
  23. 23.
    Xu Y H, Du B and Zhang L P. 2020. Beyond the patchwise classification: spectral-spatial fully convolutional networks for hyperspectral image classification. IEEE Transactions on Big Data, 6(3): 492-506
  24. 24.
    Xu Z Y, Hu Y F, Liu Y and Yan Y. 2012. A review on the accuracy analysis of spatial scaling data. Progress in Geography, 31(12): 1574-1582
  25. 25.
    Zhang Q N, Tan S T, Xu Z and Huang Z C. 2017. Applicability and simplification study of patch level landscape metrics based on GLC30. Remote Sensing for Land and Resources, 29(4): 98-105
  26. 26.
    Zhang X, Liu L Y, Chen X D, Xie S and Gao Y. 2019. Fine land-cover mapping in China using landsat datacube and an operational SPECLib-based approach. Remote Sensing, 11(9): 1056

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website