Development of forest aboveground biomass estimation, its problems and future solutions: A review

Ma, Taiyong; Zhang, Chao; Ji, Liping; Zuo, Zheng; Beckline, Mukete; Hu, Yang; Li, Xiaojuan; Xiao, Xiangming

doi:10.1016/j.ecolind.2024.111653

Cited by 7 publications

(3 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike high-resolution remote sensing images (Ma et al, 2023(Ma et al, , 2024, hyperspectral images (HSIs) organize data into threedimensional (3D) cubes with both spatial and spectral dimensions, providing intricate spectral "diagnostic" information (Ren et al, 2023). This unique capability, coupled with its inherent advantages such as wide coverage and short detection periods (Liu et al, 2014;Marinelli et al, 2019), holds immense potential for accurate crop change detection and the identification of various types of agricultural transformations (Song et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Wang,

Zhang,

Song

et al. 2024

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

Crop change detection study is the foundation of agricultural sustainability. The inherent high spectral resolution of hyperspectral images, combined with multi-temporal datasets, facilitates the detection of subtle changes. To enhance the accuracy and applicability of hyperspectral change detection in agricultural scenes, this paper introduces a fast hyperspectral change detection approach for agricultural crops based on low-rank matrix and morphological feature extraction (FLRaMF). The goal is to improve detection precision and computational efficiency of the change detection process. The method initially employs rapid low-rank matrix extraction to separate changing and non-changing pixels in the spectral domain. Subsequently, spatial information is introduced using attribute profiles, restricting spectral anomalies through hyperspectral morphology, which ultimately improves the detection results. This study utilized four hyperspectral change detection datasets in agricultural crop scenarios, optimizing and analyzing parameters. Experimental results and analysis indicate that the FLRaMF method can achieve higher detection accuracy with reduced computation cost in unsupervised, default parameter scenarios when performing agricultural crop change detection tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Wang,

Zhang,

Song

et al. 2024

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

show abstract

“…An essential parameter in this process is forest biomass, recognized as the essential climate variable. Aboveground biomass (AGB) in forests serves as a fundamental indicator of ecosystem productivity, biodiversity, and carbon storage (Ma et al, 2024). Although traditional methods such as site-level studies, national forest inventories, and regional synthesis have yielded significant insights, there is now widespread recognition of the need to address gaps in spatial carbon capture.…”

Section: Introductionmentioning

confidence: 99%

Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications

Dadhwal,

Nandy

2024

J Indian Soc Remote Sens

View full text Add to dashboard Cite

“…The advance in remote sensing technologies makes it possible to estimate biomass in a large area [13]. In the past four decades, many studies have been conducted to map forest or grass biomass at different scales using optical sensor or SAR data [13,14]. However, the signal saturation and radiometric data uncertainty caused by external environmental conditions resulted in poor biomass estimation using optical or SAR data alone [15,16].…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Development of forest aboveground biomass estimation, its problems and future solutions: A review

Cited by 7 publications

References 109 publications

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications

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

Contact Info

Product

Resources

About