Modelling Above Ground Biomass of Mangrove Forest Using Sentinel-1 Imagery

Argamosa, R. J. L.; Blanco, A. C.; Baloloy, A. B.; Candido, C. G.; Dumalag, John Bart Lovern Caboboy; Dimapilis, Lady Lee Carandang; Paringit, Enrico C.

doi:10.5194/isprs-annals-iv-3-13-2018

Cited by 24 publications

(19 citation statements)

References 6 publications

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“…This combination compromises interpretability. Principal Components (PC) analysis and Random Forest (RF) variable selection help in simplifying datasets with high dimensionality (King and Jackson, 1999;Genuer, Poggi and Malot, 2010;Argamosa et al, 2018). PC analysis summarizes variances of variables into uncorrelated dimensions producing different components with newly extracted information which are weighed using eigenvalues (King and Jackson, 1999).…”

Section: Dimensionality Reduction and Variable Selectionmentioning

confidence: 99%

“…On the other hand, RF designates variables with importance scores based on the increase in the mean of the error of a tree in a forest when the variable is employed during a regression or classification process (Genuer, Poggi and Malot, 2010). A high variable importance signifies a greater contribution to a model's accuracy while a low variable importance suggests that a variable has no significant contribution to the model regardless of the permutation applied (Argamosa et al, 2018).…”

Section: Dimensionality Reduction and Variable Selectionmentioning

confidence: 99%

“…This method has been effective as the noise variables tend to cluster together while significant variables appear as outliers (Sandri and Zuccolotto, 2006). On the other hand, Argamosa et al (2018) uses a different implementation of variable selection following Liaw and Wiener (2015). In their study, the variables were arranged by increasing variable importance scores and grouped into different regions based on the similarity of scores as defined by a relatively flat slope (Liaw and Wiener, 2015).…”

Section: Dimensionality Reduction and Variable Selectionmentioning

confidence: 99%

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Monitoring and Assessment of Agri-Urban Land Conversion Using Multi-Sensor Remote Sensing and Gis Techniques

Fargas

Narciso

Blanco

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Continuous agricultural land conversion poses threat to food security but this has not been monitored due to ineffectual policies. One of the Philippine provinces with a high rate of conversion is the rice-producing province of Cavite. To assess the spatiotemporal dynamics of agricultural land conversion in Cavite, this study aims to develop an operational methodology to produce Land Use and Land Cover (LULC) change maps using a multi-sensor remote sensing approach for decision making and planning. LULC maps were generated using Random Forest Classification of Landsat 8 and Sentinel-1 image collections. Spectral indices, combinations of radar polarizations (VV, VH), and their principal components were included to improve its accuracy. Conversion maps were generated by taking the bi-annual difference of LULC maps from 2016 to 2019. Accuracy was assessed using visual inspection with Google Earth Pro. Classification was carried out using single-sensor (optical or radar) and multi-sensor (optical and radar) approach in combination with three feature selection algorithms, namely, Sandri and Zuccolotto (2006), Liaw and Wiener (2015), Kursa and Rudnicki (2010). Multi-sensor and single sensor yielded similarly high overall accuracies (OA = 96%) with the exception of single-sensor radar approach (OA = 53%). Multi-sensor approaches exhibit high accuracies (Cumulative Accuracy = 91%) in detecting agricultural to built-up LULC change up to 5,000 square meters unlike single-sensor optical approach (Cumulative Accuracy = 76%). Among the multi-sensor approaches, the method of Liaw and Wiener (2015) remains to be superior as it only uses eight (8) variables.

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Section: Dimensionality Reduction and Variable Selectionmentioning

confidence: 99%

Section: Dimensionality Reduction and Variable Selectionmentioning

confidence: 99%

Section: Dimensionality Reduction and Variable Selectionmentioning

confidence: 99%

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Monitoring and Assessment of Agri-Urban Land Conversion Using Multi-Sensor Remote Sensing and Gis Techniques

Fargas

Narciso

Blanco

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Even though the limitation of the C-band in interacting with the more profound components of the forest was mentioned, few works have investigated the potential uses of Sentinel-1A with a variety of optical image indices [30,31]. This study is a continuation of research on the C-band from Sentinel-1A in mangrove forest estimation, in which the combination of polarizations was proposed, such as HH, HV, HH-HV, and HH/HV, as has been suggested in many studies [1,13,15].…”

Section: Data Usedmentioning

confidence: 99%

Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system

Pham¹,

Do²,

Pham

et al. 2020

PLoS ONE

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Background Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). Methods This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2). Results The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R 2 = 0.577 for AGB estimation.

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“…Those advantages make SAR images are valuable and important data sources in various application [15] . One application of SAR that widely used is forestry monitoring especially in Above Ground Biomass (AGB) estimation [1] [7] [18] [10] [21] . Above Ground Biomass (AGB) data is one of the key indicators in global carbon accounting to mitigate climate change and biodiversity loss.…”

Section: Introductionmentioning

confidence: 99%

Performance of various speckle filter methods in modelling forest aboveground biomass using Sentinel-1 data: case study of Barru Regency, South Sulawesi

Ananto

Putri

Hadi

et al. 2019

Sixth Geoinformation Science Symposium

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Noise in SAR imagery was produced due to different backscatter responses from the objects in the earth's surface. This resulted in a grainy image, usually known as "salt and pepper" noise, which reduces the capability to identify an object from radar imagery. Therefore, speckle filtering was conducted to decrease this noise from SAR imagery. This study aims to assess the performance of different types of speckle filters, especially when used to construct forest aboveground biomass (AGB) model from Sentinel-1 data in Barru Regency, South Sulawesi. There were 4 filters used in this study i.e. Frost, Gamma-MAP, Median, and Refined Lee. AGB model was constructed by using dual-polarization C-band SAR of Sentinel-1 data and ground inventory plots. 23 plots were collected in the field and the allometric equation was used to calculate the biomass value of the field survey data then cross-validation models were generated by using biomass value and backscatter data from VV and VH polarization. Quality control was performed by comparing the coefficient of determination (R 2) of those filters. The result shows that Frost filter, especially on VH polarization was chosen as the best-fit model to estimate the AGB based on the higher value of R 2 (0.3464158) and RMSE (33.5231). The result demonstrated the Frost filter as the best filter for retaining and/or enhancing the backscatter signal in Sentinel-1 data to be used in vegetation biophysical modelling.

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Modelling Above Ground Biomass of Mangrove Forest Using Sentinel-1 Imagery

Cited by 24 publications

References 6 publications

Monitoring and Assessment of Agri-Urban Land Conversion Using Multi-Sensor Remote Sensing and Gis Techniques

Monitoring and Assessment of Agri-Urban Land Conversion Using Multi-Sensor Remote Sensing and Gis Techniques

Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system

Performance of various speckle filter methods in modelling forest aboveground biomass using Sentinel-1 data: case study of Barru Regency, South Sulawesi

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