Global land cover and its change are indispensable basic information for environmental change research

detection of national geographical conditions

and sustainable development. In 2014

the National Geomatics Center of China produced GlobeLand30

a remote sensing mapping product with the highest resolution (30 m) in the world. This data set exhibits high resolution and high accuracy

and thus

it can satisfy the cartographic requirement of 1∶250 000 and other smaller measuring scales as well as provide global data production and updating with significant data resources. When the difference between GlobeLand30 and vector data is considered

accurately and rapidly recognizing identical entities from these two types of data is highly important to update information as well as integrate and fuse multisource and multiscale spatial data. To overcome the shortcomings of the method for recognizing and matching GlobeLand30 and vector data

this study proposes a complex function based on a multilevel arc-height. Fourier shape descriptors can be obtained to measure shape similarity among area entities

and a comprehensive similarity measurement model can be established by integrating the location

size

direction

and shape of area entities. This study uses the proposed model to recognize and match GlobeLand30 and vector data as well as to measure the similarity of Globeland30 before and after simplification and smoothing. This method constructs a complex function based on multilevel chord length to describe the entire and detailed features of area entities by using the characteristics of their border

such as arc-height and central distance. After resampling the border of area entities

a shape descriptor with an independent and compact initial point on the border can be established through fast Fourier transform

which exhibits invariant properties in terms of translation

rotation

and scaling to measure shape similarity and diversity among area entities. Lastly

a comprehensive similarity measuring model is established by integrating the location

size

direction

and shape of area entities. GlobeLand30 and vector data are processed

and the similarity of the entities of these two types of data is calculated through the comprehensive similarity measuring model. Then

the specific entity is determined according to the set comprehensive spatial similarity threshold value. The rule for maintaining similarity by applying the proposed comprehensive similarity measuring model is discussed to measure the shape similarity and comprehensive similarity of Globeland30 data before and after applying different simplification and smoothing algorithms. This study selects the water data of Globeland30 (2010) as example and uses the proposed model to match them with another vector data after vectorization. Experimental results obtained a precision ratio of 100% and a recall ratio of 97.01%. The experiment

which is conducted to compare the method proposed in this study with others

shows that the method for describing tortuosity can only describe the entire

but not the detailed features. The description of similarity via central distance instead of Fourier shape descriptors increases the difference in similarity

which will result in omitting matching or other mistakes. The discussion regarding the effect of different arc-height levels proves that both matching precision ratio and recall ratio reach their maximum values when is set to 4 or 8. Computation complexity is positively related to the value of

and thus

matching speed will be lower if is set higher. Moreover

setting to 4 is better to achieve satisfying efficiency and accuracy. The point_remove or bend_simplify algorithms are applied to simplify different threshold values

whereas the peak or Bezier algorithm was selected for smoothing. The similarity measuring method is then applied to Globeland30 data before and after simplification and smoothing. From the results

we discuss the relation among similarity levels and threshold values. The findings of the experiment show that the two simplification algorithms can maintain approximately the same similarity when threshold values vary within a small range. However

variations outside the specific range will result in an evident difference that is reflected in the sharp corner phenomenon simplified by the point_remove algorithm. For the two smoothing algorithms

Bezier provides only one result

whereas the processing results of peak vary with different threshold values. 1) This method constructs the multilevel chord length complex function to describe the entire and detailed features of area entities by using the characteristics of their border

such as multilevel chord length

arc-height

and central distance. This complex function satisfies the demand for multilevel shape description by changing arc-height level

including describing the entire and detailed features of area entities. The Fourier transform of the multilevel chord length complex function solves the inconsistencies in border and initial numbers of points

thereby meeting the demand for invariance in terms of translation

rotation

and scaling. 2) A comprehensive similarity measuring model is established based on the multilevel arc-height Fourier shape description method by integrating the location

size

direction

and shape of area entities. Experiments prove that this model works efficiently when matching two types of data. This study discusses the rule of maintaining similarity through different simplification and smoothing algorithms and by applying the similarity measuring method to evaluate the similarity of Globeland30 data before and after simplification and smoothing. The study achieves good results. It uses the water data as example and focuses on matching the entities of Globeland30 with another vector data by applying the comprehensive similarity measuring model. Further studies will focus more on the problem of matching Globeland30 data with other vector data and applying the results to produce and update vector data

particularly of overseas regions worldwide.