A comparison on the use of different satellite multispectral data for the prediction of aboveground biomass

Naik, Parth; Dalponte, Michele; Bruzzone, Lorenzo

doi:10.1117/12.2572807

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…There are different ML-based models developed in the literature for prediction of forest AGB. The most fundamental modelling approach to AGB prediction is generalized linear regression that allows to relate model parameters to the response variable via a link function [75], [79], [80]. Furthermore, kernel based learners which include methods such as support vector machine [81]- [83] and Gaussian process regression [84], [85] have proven to be effective for the development of AGB estimation models.…”

Section: Introductionmentioning

confidence: 99%

Automated Machine Learning Driven Stacked Ensemble Modeling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data

Naik

Dalponte

Bruzzone

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Modelling and large-scale mapping of forest aboveground biomass (AGB) is a complicated, challenging and expensive task. There are considerable variations in forest characteristics that creates functional disparity for different models and needs comprehensive evaluation. Moreover, the human-bias involved in the process of modelling and evaluation affects the generalization of models at larger scales. In this paper, we present an automated machine learning (AutoML) framework for modelling, evaluation and stacking of multiple base models for AGB prediction. We incorporate a hyperparameter optimization procedure for automatic extraction of targeted features from multitemporal Sentinel-2 data that minimizes human-bias in the proposed modelling pipeline. We integrate the two independent frameworks for automatic feature extraction and automatic model ensembling and evaluation. The results suggest that the extracted target-oriented features have excessive contribution of red-edge and short-wave infrared spectrum. The feature importance scale indicates a dominant role of summer based features as compared to other seasons. The automated ensembling and evaluation framework produced a stacked ensemble of base models that outperformed individual base models in accurately predicting forest AGB. The stacked ensemble model delivered the best scores of R 2 cv = 0.71 and RMSE = 74.44 Mgha -1 . The other base models delivered R 2 cv and RMSE ranging between 0.38-0.66 and 81.27-109.44 Mg ha -1 respectively. The model evaluation metrics indicated that the stacked ensemble model was more resistant to outliers and achieved a better generalization. Thus, the proposed study demonstrated an effective automated modelling pipeline for predicting AGB by minimizing human-bias and deployable over large and diverse forest areas.

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Section: Introductionmentioning

confidence: 99%

Automated Machine Learning Driven Stacked Ensemble Modeling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data

Naik

Dalponte

Bruzzone

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Studies performed with satellite multispectral (MS) and Synthetic Aperture Radar (SAR) data used vegetation indices (e.g., NDVI, SAVI), vegetation biophysical features (e.g., leaf area index, chlorophyll concentration), textural features (e.g., entropy, variance) and SAR polarimetric indices for the prediction of forest AGB [18]- [20]. A prominent limitation identified with MS data was a high variation in response of the extracted features that depended on the time of acquisition and the spatio-spectral specifications [19], [21]. The main causes of such reduced prediction precision were linked to the seasonal variation of the spectral responses from the forest.…”

Section: Introductionmentioning

confidence: 99%

Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps

Naik¹,

Dalponte

Bruzzone

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Aboveground biomass (AGB) is an important forest attribute directly linked to the forest carbon pool. The use of satellite remote sensing (RS) data has increased for AGB prediction due to their large footprint and low-cost availability. However, they have been limited due to saturation effect that leads to low prediction precision. In this study, we propose an innovative and dynamic architecture based on generative neural network that extracts target oriented generative features for predicting forest AGB using satellite RS data. These features are more resilient to mixed forest types and geographical conditions as compared to the traditional features and models. The effectiveness of the proposed features was assessed by experiments performed using multispectral (MS), Synthetic aperture Radar (SAR) and combined dual-source (DS) datasets. The proposed model achieved best performance in terms of precision, model agreement and overfitting as compared to the other conventional models for all analyzed datasets. The t-distributed stochastic neighbor embedding (t-SNE) scatterplots of the generative features clearly show one dimension of the feature space associated with the target AGB. Feature importance analysis indicated that the produced generative features were more significant than the conventional analytical features. Also, the model provided a robust framework for homogeneous fusion of multi-sensor features from satellite RS data for predicting AGB.

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“…Since many different crops are grown in the vicinity of each other in large scale farming, the spectral properties of the target crop may overlap with those of the nearby crops, thereby resulting in faulty crop maps [8,9].The literature of crop mapping using multispectral remote sensing data establishes that using a single date imagery is a challenging task and yields a less accurate classification of crops. Temporal multispectral images have been effectively used for different applications of remote sensing and have delivered more accurate results for multiple studies as compared to single date images [10][11][12][13][14]. Similarly, multiple studies have developed methods based on temporal multispectral data in agricultural applications including specific crop identification [15,16].…”

Section: Introductionmentioning

confidence: 99%

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

et al. 2022

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This research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approach.

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A comparison on the use of different satellite multispectral data for the prediction of aboveground biomass

Cited by 6 publications

References 30 publications

Automated Machine Learning Driven Stacked Ensemble Modeling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data

Automated Machine Learning Driven Stacked Ensemble Modeling for Forest Aboveground Biomass Prediction Using Multitemporal Sentinel-2 Data

Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

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