Spatial heterogeneity of leaf area index of mixed spruce-fir-deciduous stands in northeast China

Dandan, 姚丹丹 YAO; Xiangdong, 雷相东 Lei; Li, 余黎 YU; Jun, 卢军 Lu; Liyong, 符利勇 Fu; Ruigang, 俞锐刚 YU

doi:10.5846/stxb201403300593

Acta Eco Sin

2015

DOI: 10.5846/stxb201403300593

|View full text |Cite

Spatial heterogeneity of leaf area index of mixed spruce-fir-deciduous stands in northeast China

姚丹丹 YAO Dandan¹,

雷相东 Lei Xiangdong²,

余黎 YU Li³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics

Yu,

Xu,

Shu

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR’s footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran’s I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran’s I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

show abstract

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics

Yu,

Xu,

Shu

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Spatial heterogeneity of leaf area index of mixed spruce-fir-deciduous stands in northeast China

Cited by 1 publication

References 0 publications

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics

Contact Info

Product

Resources

About