Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and Machine Learning Algorithms: A Case Study of the Dabie Mountain Region, China

Han, Haoshuang; Li, Bing

doi:10.3390/rs14010176

Cited by 35 publications

(26 citation statements)

References 65 publications

(108 reference statements)

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“…Meanwhile, Wang et al demonstrated the potential of the simultaneous use of HJ-1B and RadarSat-2 data to estimate AGB in the desert grasslands of Ningxia, China (R 2 = 0.71, RMSE = 14.20 kg/hm 2 ), which outperformed the AGB estimation based on the NDVI index of HJ-1B (R 2 = 0.27, RMSE = 20.58 kg/hm 2 ) [69]. Therefore, we argue that combining the strengths of optical remote sensing data and radar data enables better quantitative estimation of AGB of UGS, an inference that agrees with several studies [70][71][72][73].…”

Section: Discussionsupporting

confidence: 88%

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

Xiao

Chen

Zhang

et al. 2022

Forests

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High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

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Section: Discussionsupporting

confidence: 88%

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

Xiao

Chen

Zhang

et al. 2022

Forests

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“…One of the main challenges in AGB estimation methods and procedures is the ability to establish a reliable estimation protocol in new and complex ecosystems. Recent studies showed the strength and capabilities of various machine learning approaches to estimating AGB [69,16,70,18]. We suggest improving the current understanding and ML methods and using it in complex forests with a more generalized approach.…”

Section: Machine Learning Capabilities In Various Forest Ecosystemsmentioning

confidence: 94%

A Novel Carbon Stocking Estimation Through Continuous Catalog Learning

Haor,

Liven,

Barshai

et al. 2023

Preprint

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The role of forests as a significant mitigator of anthropogenic \coo emissions is integral to the global efforts to combat climate change. Precise monitoring of carbon sequestration is a high priority for governments and organizations striving to achieve a zero atmospheric carbon balance.The combination of remote sensing and machine learning has emerged as a powerful tool for estimating above-ground biomass (AGB) in diverse ecosystems.However, accurately measuring the carbon sequestrated by forests at various scales and forest types is still a technological and practical challenge carrying the risk of carbon overestimation, which may lead to wrong decision-making, flawed climate change mitigation actions, financial losses, and more.In this study, we propose a novel method to address this need using a catalog-based carbon stocking approach integrated within a continuous learning mechanism.Our method estimates forest carbon stocking based on high-resolution aerial LiDAR and multispectral imagery, offering valuable insights beyond the limitations of satellite-based imagery. Through the combination of unsupervised learning and a ground-based calibration procedure, we successfully delineated 10 distinct forest types within a vast area of mixed forest spanning 55,000 hectares.The calibrated carbon stocking estimation demonstrated superior accuracy compared to satellite-based analysis, as evidenced by rigorous cross-validation using an unprecedented dataset of 802 ground-surveyed plots.Employing a continual learning mechanism, the system can estimate carbon stocking on a 25x25m grid, enabling generalization across multiple forest types and scales of aggregation within a unified framework.This work serves as a starting point for further research to enhance the accuracy of carbon stocking monitoring and contribute to the momentum of carbon sequestration efforts.In addition to the scientific significance of this paper, we have made a notable contribution by providing access to a comprehensive dataset. This dataset encompasses high-density LiDAR point cloud data and multispectral imagery data, covering more than 13,000 acres and including samples from 1,725 individual trees.To our knowledge, this represents the most extensive combined aerial-ground dataset published in this field to date. Our objective in sharing this dataset is to facilitate ongoing research and set a benchmark for advancements in the domain of carbon stocking estimation.

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“…BP is a multilayer feed-forward network based on the error back propagation method, which is particularly good at handling non-linear and uncertain problems. The BP consists of input layers, implicit layers and output layers, and contains two stages: forward propagation and back propagation of the error [52]. The error is back-propagated through the implicit layer to the output layer, and apportioned to all units in each layer, until the error is eventually decreased to an acceptable level after continual training [53].…”

Section: Traditional Regression Methodsmentioning

confidence: 99%

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

et al. 2022

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Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

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Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and Machine Learning Algorithms: A Case Study of the Dabie Mountain Region, China

Cited by 35 publications

References 65 publications

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data

A Novel Carbon Stocking Estimation Through Continuous Catalog Learning

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

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