Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam

Dang, An T. N.; Nandy, Subrata; Srinet, Ritika; Luong, Nguyen Viet; Ghosh, Surajit; Kumar, Anil

doi:10.1016/j.ecoinf.2018.12.010

Cited by 123 publications

(108 citation statements)

References 51 publications

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“…The RF model is mainly applied to the fields of classification and regression. As a nonlinear modeling algorithm, the RF regression model is widely used in data mining [ 56 ], bioinformatics statistics [ 57 , 58 ], and other fields. Many studies have proved that RF regression has very high prediction accuracy, and it is less affected by noise and outliers, so it is not easy to overfit [ 59 ].…”

Section: Methodsmentioning

confidence: 99%

The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China

Wang

Zhou

Wang

et al. 2020

Sensors

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Nighttime lights (NTL) create a unique footprint left by human activities, which can reflect the economic index and demographic characteristics of a country or region to some extent. It is of great significance to explore the impact of land features related to social–economic indexes to NTL intensity in urban areas. At present, there are few studies on the impact factors of high-resolution NTL remote sensing data to analyze the influence of NTL intensity variation at a fine scale. In this paper, taking Changchun, China as a case study, we selected the new generation of high spatial resolution (0.92 m) and multispectral bands NTL image JL1-3B data to evaluate the relationship between NTL intensity and related land features such as the normalized difference vegetation index (NDVI), land use types and point of information (POI) at the parcel level, and combined Luojia 1-01 images for comparative analysis. After screening features by the Gini index, 17 variables were selected to establish the best random forest (RF) regression model for the Luojia 1-01 and JL1-3B data, corresponding to out-of-bag (oob) scores of 0.8304 and 0.9054, respectively. The impact of features on NTL was determined by calculating the features contribution. It was found that JL1-3B data perform better on a finer scale and provide more information. In addition, JL1-3B data are less affected by light overflow effect and saturation, and they could provide more accurate information at smaller parcels. Through the impact analysis of land features on the two kinds of NTL data, it is proven that JL1-3B images can be used to study effectively the relationship between NTL and human activities information. This paper aims to establish a regression model between the radiance of two types of NTL data and land features by RF algorithm, to further excavate the main land features that impact radiance according to the feature contribution, and compare the performance of two types of NTL data in regression. The study is expected to provide a reference to the further application of NTL data such as land feature inversion, artificial surface monitoring and evaluation, geographic information point estimation, information mining, etc., and a more comprehensive cognition of land feature impact to urban social–economic indexes from a unique perspective, which can be used to assist urban planning and related decision-making.

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Section: Methodsmentioning

confidence: 99%

The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China

Wang

Zhou

Wang

et al. 2020

Sensors

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“…A wide variety of vegetation indices that differ from each other in their transformation equations and required objectives were used in this study, as shown in ( Table 1). Such indices have also been suggested to have significant contributions to the overall AGB estimation, as in the studies of PLOS ONE [1,20,21,23]. Forty-two predictor variables were generated in Table 1 and the average values (based on the centers of sample plots and plot sizes) were used as an input database for the analysis workflow, as shown in Field survey dataset.…”

Section: Data Usedmentioning

confidence: 99%

“…Typically, there are three common ways to train and validate a model: (1) The hold-out method randomly divides the points in the training set into roughly 70% for training and 30% for validation, and (2) k-fold cross-validation (CV) randomly divides the training set into k equal folds. In this case, previous studies have shown that ten-folds is the optimal number for this method [23]. 3Leave p-out cross-validation with p equal to 1 is usually applied.…”

Section: Performance Assessmentmentioning

confidence: 99%

“…For example, the application of inversion of the PROSAIL model estimates AGB by using leaf dry matter content and leaf area index [22]. The combination of vegetation indices with radar data has also been investigated in several works [1,23]. From reviewing the literature, several types of research were found that employed machine learning models to estimate the AGB.…”

Section: Introductionmentioning

confidence: 99%

“…From reviewing the literature, several types of research were found that employed machine learning models to estimate the AGB. As examples, statistical and data-driven approaches have been proposed, such as multiple regression in the study of [13,24], geographically weighted regression [25], and support vector regression and random forest in [1,23,24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Challenges and Monitoring Methods of Forest Management Through Geospatial Application

Pandey¹,

Kumari²,

Dash³

et al. 2022

Advances in Remote Sensing for Forest Monitoring

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Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam

Cited by 123 publications

References 51 publications

The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China

The Impact Analysis of Land Features to JL1-3B Nighttime Light Data at Parcel Level: Illustrated by the Case of Changchun, China

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

Challenges and Monitoring Methods of Forest Management Through Geospatial Application

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