Mangrove biomass estimation in Southwest Thailand using machine learning

Jachowski, Nicholas R.A.; Quak, M.; Friess, Daniel A.; Duangnamon, Decha; Webb, Edward L.; Ziegler, Alan D.

doi:10.1016/j.apgeog.2013.09.024

Cited by 118 publications

(71 citation statements)

References 44 publications

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“…Support vector machine is based on the structural risk minimization principle and isgood at solving practical problems involving small numbers of training samples, nonlinearity, high numbers of dimensions, and local minima; thus, SVM is considered to be a good alternative to ANN. Several studies have demonstrated that biomass estimation based on a SVM model can provide robust results [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Zhang

Xie

et al. 2015

Remote Sensing

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Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R 2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m 2 ), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R 2 = 0.77, RMSE = 17.22 g/m 2 ), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.

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Section: Introductionmentioning

confidence: 99%

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Zhang

Xie

et al. 2015

Remote Sensing

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“…Statistical models used in estimation of mangrove biomass mainly include multiple linear regression (MLR) [33] and machine learning [34] such as classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF). Machine learning has great potential for research due to fewer assumptions about the data and process as well as excellent performance in recent studies [35].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

et al. 2015

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Abstract:Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a "Weights" method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% OPEN ACCESSRemote Sens. 2015, 7 12193 lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation.

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“…Comparisons of multiple models can help discern the pros and cons of each, eventually point to the direction of the best performing model [48]. Two questions are addressed in this study: (1) Are WV2 imagery and the selected machine-learning algorithms suitable for developing LAI estimation models in mangrove forests with high spatial heterogeneity?…”

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confidence: 99%

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Zhu

Liu

et al. 2017

Remote Sensing

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Abstract:To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

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Mangrove biomass estimation in Southwest Thailand using machine learning

Cited by 118 publications

References 44 publications

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation

Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

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