Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots

Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.

Landform classification based on landform geospatial structure-a case study on Loess Plateau of China

Landform classification,which is a key topic of geography,is of great significance to a wide range of fields including human construction,geological structure research,environmental governance,etc.Previous studies of landform classification generally paid attention to the topographic or texture information,whilst the watershed spatial structure has not been used.This study developed a new landform classification method based on watershed geospatial structure.Via abstracting the landform into the internal and marginal structure,we adopted the gully weighted complex network(GWCN)and watershed boundary profile(WBP)to simulate the watershed geospatial structure.Introducing various indices to quantitatively depict the watershed geospatial structure,we conducted the landform classification on the Northern Shaanxi of Loess Plateau with a watershed-based strategy and established the classification map.The classified landform distribution has significant spatial aggregation and clear regional boundaries.Classification accuracy reached 89%and the kappa coefficient reached 0.87%.Besides,the proposed method has a positive response to some similar and complex landforms.In general,the present study first utilized the watershed geospatial structure to conduct landform classification and is an efficient landform classification method with well accuracy and universality,offering additional insights for landform classification and mapping.