Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Miettinen, Jukka; Carlier, Simon; Häme, Lauri; Mäkelä, Annikki; Minunno, Francesco; Penttilä, Juho; Pisl, Jan; Rasinmäki, Jussi; Rauste, Yrjö; Seitsonen, Lauri; Tian, Xianglin; Häme, Tuomas

doi:10.1080/01431161.2021.1998715

Cited by 12 publications

(19 citation statements)

References 37 publications

(42 reference statements)

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“…Observed experimental results are encouraging further investigations and are generally in line with other reported studies in boreal forest biome [3], [7], [48], [49]. Obtained accuracies are notably higher than in several other studies using Sentinel-1 data or Sentinel-2 datasets or their combinations, and compare well versus earlier multisensor EO data studies [3], [20], [48]–[52]. Several datasets with high potential for forest variable retrieval, such as e.g.…”

Section: Resultssupporting

confidence: 91%

“…In boreal region, studies on forest variable prediction using Sentinel-1 or Landsat data report prediction accuracies within the range of 35-60% rRMSE [3], [48], while proposed model utilizing Sentinel-1 time series data reached rRMSE as small as 18 %. Predictions obtained using traditional ML models were within the same accuracy range as in recently published studies using Sentinel-2 and Landsat [53], while predictions using different versions of LSTM models appeared more accurate.…”

Section: Resultsmentioning

confidence: 99%

“…T IMELY assessment and monitoring of forests forms basis for definition and implementation of preventive and corrective measures for sustainable forest management and forest restoration after disturbances [1]. Dynamics of forest structural variables provides information on forest status and forest changes and represents key information for forest management purposes [2], [3]. Traditional forest inventory variables, such as forest height, basal area, diameter at breast height and others can be used as inputs for biomass modelling.…”

Section: Introductionmentioning

confidence: 99%

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Boreal Forest Height Mapping using Sentinel-1 Time Series and improved LSTM model

et al. 2022

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Here, a novel semi-supervised Long Short-Term Memory (LSTM) model is developed and demonstrated for predicting forest tree height using time series of Sentinel-1 images. The model uses a Helix-Elapse (HE) projection approach to capture relationship between forest temporal patterns and Sentinel-1 time series, when the acquisition time intervals are irregular. A skip-link based LSTM block is introduced and a novel backbone network, Helix-LSTM, is proposed to retrieve temporal features at different receptive scales. Additionally, a novel semi-supervised strategy, Cross-Pseudo Regression, is employed to achieve better model performance. The developed model is compared versus basic LSTM model, attention-based bidirectional LSTM and several other established regression approaches used in forest variable mapping, demonstrating consistent improvement of forest height prediction accuracy. The study site is located in Central Finland and represents boreal forestland. At best, the achieved accuracy of forest height mapping was 28.3% rRMSE for pixel-level predictions, and 18.0% rRMSE on stand level. We expect that the developed model can also be used for modeling relationships between other forest variables and satellite image time series.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Boreal Forest Height Mapping using Sentinel-1 Time Series and improved LSTM model

et al. 2022

Preprint

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“…Obtained results compare favourably to previous studies on forest height prediction. In boreal region, reported forest height accuracies with Sentinel-2 and Landsat data were 35-60% RMSE [19], [49], while proposed DL model reached as small as 24 % RMSE on plot-level and 15.4% on standlevel. Obtained predictions with ML models and Sentinel-2 data are within the same accuracy range as in recent published studies using Sentinel-2 and Landsat [50], while our predictions using deep learning models are much more accurate.…”

Section: B Comparison To Similar Workmentioning

confidence: 90%

Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data

et al. 2022

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

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In this study, we introduce an improved semisupervised deep learning approach, and demonstrate its suitability for modeling the relationship between forest structural parameters and satellite remote sensing imagery and producing forest maps. The improved approach is based on a popular UNet model, modified and fine-tuned to improve the forest parameter prediction performance. Within the improved model, squeezeand-excitation blocks are embedded to re-calibrate the multisource features via retrieved channel-wise self-attention and a novel Cross-Pseudo Regression strategy is implemented to train the model in a semi-supervised way. The improvement imposes consistency learning on two perturbed network branches, generating regression pseudo-reference and expanding the dataset size. For demonstration, we used satellite synthetic aperture radar (SAR) Sentinel-1 and multispectral optical Sentinel-2 images as remote sensing data, complemented with reference data represented by forest tree height as one of the key forest structural variables. The study area is located in a boreal forestland in Central Finland. Proposed approach showed larger accuracy compared to traditional machine learning methods such as random forests and boosting trees, and baseline UNet model. Best accuracy figures for forest tree height were achieved with combined SAR and optical imagery and were as small as 24.1 % RMSE (root mean square error) on pixel-level and 15.4 % RMSE on forest stand level.

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“…To date, numerous methods and remotely sensed data combinations have been used for forest height estimation in boreal and temperate forests [5], [33], [37]- [39]. Reported accuracies for boreal forest height mapping range typically in the order of 30-40% rRMSE in these studies.…”

Section: B Comparison With Prior Studiesmentioning

confidence: 99%

Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Deep learning (DL) models are gaining popularity in forest variable prediction using Earth Observation images. However, in practical forest inventories, reference datasets are often represented by plot-or stand-level measurements, while high-quality representative wall-to-wall reference data for end-toend training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a "model transfer" (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. Multisource Earth Observation (EO) data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, JAXA ALOS-2 PALSAR-2 SAR mosaic and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the prediction of SeUNet achieved root mean squared error (RMSE) of 2.70 m and R 2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.

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Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Cited by 12 publications

References 37 publications

Boreal Forest Height Mapping using Sentinel-1 Time Series and improved LSTM model

Boreal Forest Height Mapping using Sentinel-1 Time Series and improved LSTM model

Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data

Deep Learning Models in Forest Mapping Using Multitemporal SAR and Optical Satellite Data

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