Mapping smallholder forest plantations in Andhra Pradesh, India using multitemporal harmonized landsat sentinel‐2 <scp>S10</scp> data

Williams, Paige T.; Wynne, Randolph H.; Thomas, Valerie A.; DeFries, Ruth

doi:10.1002/ldr.4027

Cited by 4 publications

(3 citation statements)

References 60 publications

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“…1, the model is initially trained. The study employs a statistical method in which the original data is randomly and centrally sampled, and the samples are replaced at the end of the sampling process (Williams et al, 2021;Tf et al, 2022). The original data set is assumed to have n samples so that each sampling probability is the same and is .…”

Section: Fig 1 Random Forest Regression Treementioning

confidence: 99%

Bridge Pier Displacement Prediction and Control in Subway Tunnel Construction

2024

EPPM-Journal

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As the scale of underground rail transit construction in urban areas continues to expand, the tunnel construction environment has become progressively more complex. In recent years, an emerging artificial intelligence (AI) method in the civil engineering field, called the Random Forest (RF) method, has been widely used. In the construction of Zhengzhou Metro Line 7, the RF method was used to predict and control the vertical displacement of the bridge pier pile foundation. Such displacement can indicate the deformation of the structure, particularly under long-term utilization and strenuous circumstances that could sink or lift the pier body. Moreover, the vertical displacement of the bridge pier can affect the stiffness and bearing capacity of the bridge, thus impacting driving safety and the bridge's service life. Therefore, the vertical displacement of bridge piers has become the main prediction and control indicator for research. In the Zhengzhou Metro Line 7 tunnel, the tunnel continuously passes through 78 bridge pier foundations, among which the pile foundations of 4 key bridge piers are less than 0.5 times the tunnel diameter with a clear distance planned for the tunnel. However, limitations such as surface traffic and environmental conditions prevent the reinforcement of the bridge pier foundation in advance. Therefore, determining and setting sensible shield construction parameters is crucial to effectively controlling the vertical displacement of these essential bridge piers. This project can serve as a model for future endeavors. The study combines Random Forest with Particle Swarm Optimization Algorithm (PSO) to upgrade the technology of shield tunneling through Pier 2, introduces the Bayesian principle for statistical analysis, and optimizes various main construction variables. Random Forest is an ensemble learning method based on decision trees, which has high flexibility and predictive performance. It can automatically filter out important features from a large number of input features, thereby establishing an effective prediction model. The primary research objective is to enhance tunnel construction by accurately predicting and controlling the vertical displacement of pier foundations. To achieve this objective, the study utilizes the PSO to optimize the parameters and structure of the RF model. By doing this, the model's ability to predict the pier's vertical displacement accuracy can be improved. By combining these two methods, the accuracy of the prediction model and the optimization effect of construction parameters can be improved. In addition, the reliability of the model is further improved by using the Bayesian principle for statistical analysis. The paper compares and evaluates the engineering data objectively, presenting the evaluation index and feature selection method. This approach is innovative and purposeful, aiming to enhance the predictive ability, construction efficiency, and quality. This method can provide support for decision-making and optimization of engineering projects an...

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Section: Fig 1 Random Forest Regression Treementioning

confidence: 99%

Bridge Pier Displacement Prediction and Control in Subway Tunnel Construction

2024

EPPM-Journal

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“…Their study showcased the applicability of EVI data for monitoring spatiotemporal changes in vegetation coverage, highlighting the role of remote sensing in assessing ecological health. A study by Williams et al (2021) focused on mapping smallholder forest plantations in India using multitemporal visible and near‐infrared (VNIR) bands from Sentinel‐2 multispectral instruments. They demonstrated the effectiveness of Sentinel‐2 VNIR bands and multitemporal data for accurately distinguishing forest plantations from natural forests, showcasing the potential of these remote sensing techniques.…”

Section: Remote Sensing and Geospatial Technologies For Land Use Cove...mentioning

confidence: 99%

Land use changes, degradation, and impact on ecosystem services in Asia and Southeast Asia

Vadrevu,

Gutman,

Tsuneo

et al. 2023

Land Degrad Dev

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“…Compared to the widely used Landsat satellite data, Sentinel-2 MSI data, freely available from the European Space Agency (ESA) since June 2015, has a higher spatial resolution of 10 m. This improves the detection of vegetation-soil mixed pixels and makes UGS highly separable from other urban surface objects, so it has increasingly become one of the most valuable data sources [30]. In addition, Sentinel-2 MSI data uniquely contains three bands within the red-edge range, which improves retrieval of vegetation canopy information [31,32]. However, the Sentinel-1 radar system, equipped with a C-band (wavelength ~6 cm) sensor, is superior for detecting ground objects without the hindrance of clouds and fog [33].…”

Section: Introductionmentioning

confidence: 99%

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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Mapping smallholder forest plantations in Andhra Pradesh, India using multitemporal harmonized landsat sentinel‐2 S10 data

Cited by 4 publications

References 60 publications

Bridge Pier Displacement Prediction and Control in Subway Tunnel Construction

Bridge Pier Displacement Prediction and Control in Subway Tunnel Construction

Land use changes, degradation, and impact on ecosystem services in Asia and Southeast Asia

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

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