Ecological Impact Patterns and Temporal Cycles of Green Tide Biomass in the Settlement Region: Based on Time-Series Remote Sensing and In Situ Data

Zhang, Guangzong; Niu, Lifeng; Wu, Mengquan; Kaufmann, Hermann; Li, Hanyu; He, Yufang; Chen, Bo

doi:10.1109/jstars.2023.3338979

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2023.3338979

|View full text |Cite

Ecological Impact Patterns and Temporal Cycles of Green Tide Biomass in the Settlement Region: Based on Time-Series Remote Sensing and In Situ Data

Guangzong Zhang,

Lifeng Niu,

Mengquan Wu

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 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method

Xu,

Yu,

Shu

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Estimation of forest biomass at regional scale based on GEDI spaceborne LiDAR data is of great significance for forest quality assessment and carbon cycle. To solve the problem of discontinuous data of GEDI footprints, this study mapped different echo indexes in the footprints to the surface by inverse distance weighted interpolation method, and verified the influence of different number of footprints on the interpolation results. Random forest algorithm was chosen to estimate the spruce-fir biomass combined with the parameters provided by GEDI and 138 spruce-fir sample plots in Shangri-La. The results show that: (1) By extracting different numbers of GEDI footprints and visualize it, the study revealed that a higher number of footprints correlates with a denser distribution and a more pronounced stripe phenomenon. (2) The prediction accuracy improves as the number of GEDI footprints decreases. The group with the highest R2, lowest RMSE and lowest MAE was the footprint extracted every 100 shots, and the footprint extracted every 10 shots had the worst prediction effect. (3) The biomass of spruce-fir inverted by random forest ranged from 51.33 t/hm2 to 179.83 t/hm2, with an average of 101.98 t/hm2. The total value was 3035.29 × 104 t/hm2. This study shows that the number and distribution of GEDI footprints will have a certain impact on the interpolation mapping to the surface information and presents a methodological reference for selecting the appropriate number of GEDI footprints to derive various vertical structure parameters of forest ecosystems.

show abstract

Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method

Xu,

Yu,

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.

Ecological Impact Patterns and Temporal Cycles of Green Tide Biomass in the Settlement Region: Based on Time-Series Remote Sensing and In Situ Data

Cited by 1 publication

References 81 publications

Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method

Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method

Contact Info

Product

Resources

About