A comparative analysis of grid-based and object-based modeling approaches for poplar forest growing stock volume estimation in plain regions using airborne LIDAR data

Wang, Ruoqi; Li, Guiying; Lu, Yagang; Lu, Dengsheng

doi:10.1080/10095020.2023.2169199

Cited by 8 publications

(1 citation statement)

References 65 publications

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“…In this study, we calculated 19 height percentiles, from H 10 to H 95 at 5-intervals, and H 99 . To calculate the density variables, the point clouds were divided into 10 equal layers between the minimum and maximum heights within a given window size first, then, the density of the layer j (D j ) is the ratio of the number of points falling into the layer j (A j ) to the total number of points within the window (A all ), while j represents the layer number from 0 to 9, corresponding to the lowest to the highest layer [41].…”

Section: Dicranopteris Biomass Estimation With Handheld Laser Scannin...mentioning

confidence: 99%

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Li,

Wu,

et al. 2024

Remote Sensing

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Dicranopteris dichotoma is a pioneer herbaceous plant species that is tolerant to barrenness and drought. Mapping its biomass spatial distribution is valuable for understanding its important role in reducing soil erosion and restoring ecosystems. This research selected Luodihe watershed in Changting County, Fujian Province, China, where soil erosion has been a severe problem for a long time, as a case study to explore the method to estimate biomass, including total and aboveground biomass, through the integration of field measurements, handheld laser scanning (HLS), and airborne laser scanning (ALS) data. A stepwise regression model and an allometric equation form model were used to develop biomass estimation models based on Lidar-derived variables at typical areas and at a regional scale. The results indicate that at typical areas, both total and aboveground biomass were best estimated using an allometric equation form model when HLS-derived height and density variables were extracted from a window size of 6 m × 6 m, with the coefficients of determination (R2) of 0.64 and 0.58 and relative root mean square error (rRMSE) of 28.2% and 35.8%, respectively. When connecting HLS-estimated biomass with ALS-derived variables at a regional scale, total and aboveground biomass were effectively predicted with rRMSE values of 17.68% and 17.91%, respectively. The HLS data played an important role in linking field measurements and ALS data. This research provides a valuable method to map Dicranopteris biomass distribution using ALS data when other remotely sensed data cannot effectively estimate the understory vegetation biomass. The estimated biomass spatial pattern will be helpful to understand the role of Dicranopteris in reducing soil erosion and improving the degraded ecosystem.

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Section: Dicranopteris Biomass Estimation With Handheld Laser Scannin...mentioning

confidence: 99%

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Li,

Wu,

et al. 2024

Remote Sensing

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Individual tree detection and counting based on high-resolution imagery and the canopy height model data

Zhang,

Wang,

Mango

et al. 2024

Geo-spatial Information Science

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Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China

Chang,

Fan,

Wigneron

et al. 2024

Geo-spatial Information Science

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A comparative analysis of grid-based and object-based modeling approaches for poplar forest growing stock volume estimation in plain regions using airborne LIDAR data

Cited by 8 publications

References 65 publications

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Integration of Handheld and Airborne Lidar Data for Dicranopteris Dichotoma Biomass Estimation in a Subtropical Region of Fujian Province, China

Individual tree detection and counting based on high-resolution imagery and the canopy height model data

Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China

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