L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia

Trisasongko, Bambang H.; Paull, David

doi:10.1080/10106049.2019.1573855

Cited by 13 publications

(7 citation statements)

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“…The maximum biomass from this regression model (145.40 Mg/ha) was similar to studies such as [9,65], reporting 63.70 and 65.10 Mg C/ha of carbon stock for rubber plantations with rotation periods of 38 and 25 years, respectively. However, this value was much higher than the value from Trisasongko's model-the maximum biomass of a 30-year-old rubber plantation was about 70 Mg/ha [37]. In addition to stand age, the planting density also affects biomass accumulation greatly [10].…”

Section: Biomass Estimation Using Stand Age and Remote Sensing Parametersmentioning

confidence: 75%

“…The correlation was higher than for the Horizontal transmitting with Horizontal receiving (HH) band, but it began to saturate when the biomass was about 50 Mg/ha. Trisaongko and Paull estimated the biomass of rubber plantations with a very high accuracy (R 2 = 0.98 and RMSE = 1.88 Mg/ha), indicating that the acquisition time of PALSAR-2 and the choice of AE model significantly affect the accuracy [37]. By summarizing this literature, two main approaches for estimating the biomass of rubber plantations on a large scale can be obtained: (1) establishing direct relationships between biomass and spectral/SAR bands, vegetation indices, and textural parameters and (2) obtaining proxy factors such as DBH from remote sensing data and then using them as the parameters of AE models.…”

Section: Introductionmentioning

confidence: 99%

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High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China

Chen

Yun

et al. 2020

Remote Sensing

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Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of difficulties in acquiring the Diameter at Breast Height (DBH) through remote sensing and the problem of biomass saturation. The stand age, which is closely related to the forest biomass, was proposed for biomass estimation in this study. A stand age map at an annual scale for Hainan Island, which is the second largest natural rubber production base in China, was generated using all Landsat and Sentinel-2 (LS2) data (1987–2017). Scatter plots and the correlation coefficient method were used to explore the relationship (e.g., biomass saturation) between rubber biomass and different LS2-based variables. Subsequently, a regression model fitted with the stand age (R2 = 0.96) and a Random Forest (RF) model parameterizing with LS2-based variables and/or the stand age were respectively employed to estimate rubber biomass for Hainan Island. The results show that rubber biomass was saturated around 65 Mg/ha with all LS2-based variables. The regression model estimated biomass accurately (R2 = 0.79 and Root Mean Square Error (RMSE) = 14.00 Mg/ha) and eliminated the saturation problem significantly. In addition to LS2-based variables, adding a stand age parameter to the RF models was found to significantly improve the prediction accuracy (R2 = 0.82–0.96 and RMSE = 4.08–10.59 Mg/ha, modeling using samples of different biomass sizes). However, all RF models overestimated the biomass of young plantations and underestimated the biomass of old plantations. A hybrid model integrating the optimal results of RF and regression models reduced estimation bias and generated the best performance (R2 = 0.83 and RMSE = 12.48 Mg/ha). The total rubber biomass of Hainan Island in 2017 was about 5.40 × 107 Mg. The northward and westward expansions after 2000 had great impact on the biomass distribution, leading to a higher biomass density for the inland coastal strip from south to northeast and a lower biomass density in the northern and western regions.

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Section: Biomass Estimation Using Stand Age and Remote Sensing Parametersmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China

Chen

Yun

et al. 2020

Remote Sensing

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show abstract

“…[21][22][23] Random forest (RF) and support vector machine (SVM) are two machine learning algorithms commonly applied to retrieve forest biophysical parameters using different remote sensing data. 21,23,24 In addition to these algorithms, gradient boosting decision tree algorithms, and more specifically the extreme gradient boost (XGB) algorithm, have demonstrated superior performance in estimating biophysical parameters of different vegetation types. 25,26 RF is a nonlinear and non-parametric ensemble decision-tree method 27 that provides flexible, robust, and accurate predictive capabilities for high dimensional datasets.…”

Section: Introductionmentioning

confidence: 99%

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

Diniz

Gama

Reis

et al. 2023

J. Appl. Rem. Sens.

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.Multifrequency synthetic aperture radar (SAR) data have been applied to discriminate subtle differences in the vegetation and to better characterize its structural properties, since each SAR frequency will interact with the different sections of the vegetation canopy. In this study, our main objective was to evaluate the use of multifrequency Sentinel-1 and ALOS-2/PALSAR-2 data for stem volume estimations in Eucalyptus sp. and Pinus sp. plantations using three different machine learning algorithms: random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGB). Different experiments were carried out using combinations of predictor variables derived from both SAR sensors: backscattering, polarimetric decompositions, and interferometry data, and field data considering specific models for Eucalyptus sp. and Pinus sp. and a generic model comprising all forest plantations data. The machine learning models using predictor variables derived from SAR data achieved moderately high accuracy to predict stem volume, mainly when SAR data were used in combination with stand age (Experiment iv). In the best prediction scenario (Experiment iv), the RF, SVR, and XGB models were able to explain 81.7%, 68.5%, and 81.8% [coefficient of variation (R2) values] of stem volume variability considering the generic models, respectively. Our results pointed out that the RF algorithm showed the best performance in predicting stem volume with significant good results and easier implementation in comparison with the other two algorithms (SVR and XGB).

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“…It has been assessed in various ecosystems, including tropical rural areas, 20 agricultural crops, 21 and plantations. 22 Competing algorithms include support vector machines (SVMs), 23 which were also implemented in previous publications. 20,22,24 A large number of machine learning choices suggests the need for more detailed experiments.…”

Section: Introductionmentioning

confidence: 99%

“…22 Competing algorithms include support vector machines (SVMs), 23 which were also implemented in previous publications. 20,22,24 A large number of machine learning choices suggests the need for more detailed experiments. This is important to clarify the robustness of competing algorithms, such as RF and SVM, as well as serving as a baseline for newly developed learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Rural land cover mapping using short-term Sentinel-2 temporal composites and machine learning

Trisasongko

Panuju

Karyati

et al. 2023

J. Appl. Rem. Sens.

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Multispectral sensors have been invaluable for land cover mapping. Nonetheless, cloud cover is frequent in many parts in the tropics, meaning that relying on single-date multispectral data can be problematic. The limited number of variables, i.e., only two, in conventional dual-polarized synthetic aperture radar data usually drives low classification accuracies. Researchers have explored an augmentation strategy that uses textural features, such as Haralick's Gray Level Co-occurrence Matrix and/or decomposition features. However, this also does not produce an acceptable outcome. Another approach involves short-term temporal stacking. This masks out atmospheric disturbance and means every pixel has a single value representing the object. Our research explored this last approach to identify woody vegetation in a rural area of Indonesia from Sentinel-2 data. Ten machine learning algorithms were explored to investigate limitations and opportunities. We investigated algorithms capable of reporting variable importance to indicate crucial spectral bands. This study indicated that highly important spectra include red edge and shortwave infrared bands and to a lesser extent, the red band. Part of these bands were confirmed by the variable importance. The spectral similarity between bands 8 and 8A could be seen as a drawback when both bands are employed. Variable importance indicated a negative impact due to this similarity, suggesting that only one should be used in the classification. Overall, classification accuracy was generally high (over 96%), except in the case of the benchmark, i.e., classification and regression trees. Highest accuracy (97.6%) was provided by C5.0 model, although the difference from random forests (RFs) was negligible. The RF was favored as it produced a better estimate of timber plantations. A few algorithms were unable to separate woody vegetation types, including the popular support vector machine. Successful separation was mainly achieved by ensemble tree learners, such as RFs and the variants of extreme gradient boosting.

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L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia

Cited by 13 publications

References 50 publications

High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China

High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China

Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data

Rural land cover mapping using short-term Sentinel-2 temporal composites and machine learning

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