Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran

Ronoud, Ghasem; Fatehi, P; Darvishsefat, A A; Tomppo, Erkki; Praks, Jaan; Schaepman, Michael E.

doi:10.1080/07038992.2021.1968811

Cited by 16 publications

(8 citation statements)

References 84 publications

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“…Furthermore, the comparison between different imagery configurations-L8 + S2 outperforming S2, which in turn surpassed S8-demonstrates the benefit of integrating L8 and S2 datasets. This data integration not only improves AGB estimation accuracy but also emphasizes the additional insights gained from S2, particularly through its red-edge bands, which provide important constraints for vegetation monitoring insights [58].…”

Section: Variables Affecting Forest Agbmentioning

confidence: 85%

Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Wu,

Ou,

et al. 2024

Remote Sensing

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Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB,with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity , followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.

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Section: Variables Affecting Forest Agbmentioning

confidence: 85%

Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Wu,

Ou,

et al. 2024

Remote Sensing

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“…Another option would be using a leave-one-out cross-validation (LOOCV) procedure to improve the results [101]. Nevertheless, the approach described herein was commonly used in previous studies [102][103][104][105].…”

Section: Discussionmentioning

confidence: 99%

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Moradi

Darvishsefat

Pourrahmati

et al. 2022

Forests

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Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

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“…To address this, researchers may use sensors with a higher dynamic range to avoid optical saturation [45,47]. GPS positional errors can be a significant challenge when using remotely sensed data for biomass estimation, as they can affect the accuracy of the biomass estimates by misaligning the location of the remotely sensed data with the true location of the vegetation, leading to overestimation or underestimation of biomass [242,244].…”

Section: Challengesmentioning

confidence: 99%

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

et al. 2023

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Conventional measurement methods for above-ground biomass (AGB) are time-consuming, inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a promising solution, but a standardized procedure for UAS-based AGB estimation is lacking. This study reviews recent findings (2018–2022) on UAS applications for AGB estimation and develops a vegetation type-specific standard protocol. Analysis of 211 papers reveals the prevalence of rotary-wing UASs, especially quadcopters, in agricultural fields. Sensor selection varies by vegetation type, with LIDAR and RGB sensors in forests, and RGB, multispectral, and hyperspectral sensors in agricultural and grass fields. Flight altitudes and speeds depend on vegetation characteristics and sensor types, varying among crop groups. Ground control points (GCPs) needed for accurate AGB estimation differ based on vegetation type and topographic complexity. Optimal data collection during solar noon enhances accuracy, considering image quality, solar energy availability, and reduced atmospheric effects. Vegetation indices significantly affect AGB estimation in vertically growing crops, while their influence is comparatively less in forests, grasses, and horizontally growing crops. Plant height metrics differ across vegetation groups, with maximum height in forests and vertically growing crops, and central tendency metrics in grasses and horizontally growing crops. Linear regression and machine learning models perform similarly in forests, with machine learning outperforming in grasses; both yield comparable results for horizontally and vertically growing crops. Challenges include sensor limitations, environmental conditions, reflectance mixture, canopy complexity, water, cloud cover, dew, phenology, image artifacts, legal restrictions, computing power, battery capacity, optical saturation, and GPS errors. Addressing these requires careful sensor selection, timing, image processing, compliance with regulations, and overcoming technical limitations. Insights and guidelines provided enhance the precision and efficiency of UAS-based AGB estimation. Understanding vegetation requirements aids informed decisions on platform selection, sensor choice, flight parameters, and modeling approaches across different ecosystems. This study bridges the gap by providing a standardized protocol, facilitating widespread adoption of UAS technology for AGB estimation.

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Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran

Cited by 16 publications

References 84 publications

Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

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