Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

Lovynska, Viktoriia; Buchavyi, Yuriy; Lakyda, Petro; Sytnyk, Svitlana; Gritzan, Yuriy; Sendziuk, Roman

doi:10.17221/28/2020-jfs

Cited by 4 publications

(7 citation statements)

References 31 publications

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“…Remote sensing methods have a long history of development for estimation of GSV [1,[4][5][6][7][8][9][10], and a number of approaches have evolved since the 1990s, exploiting spaceborne visible and near-infrared (VNIR) imagery, radar, and more recently the incorporation of airborne measurements from airborne laser scanners and UAV (unmanned aerial vehicle, commonly referred to as a 'drone') observations. The simplest approaches are based on multispectral analysis of freely-available VNIR imagery having a spatial resolution of the order of 10 m or coarser [11][12][13][14][15][16][17]. Useful enrichment of the available feature space has been demonstrated using multitemporal datasets [18][19][20][21], incorporating texture measures [14,22] and field-derived or satellite-derived three-dimensional information [23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

et al. 2021

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Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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Section: Introductionmentioning

confidence: 99%

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

et al. 2021

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“…Radar data cannot yet be used independently for estimating aboveground biomass and serve as supplementary data for optical data [ 79 , 90 , 95 , 97 , 117 ]; Sentinel-2 performs better than Landsat in mapping primary variables and attributes associated with biomass in forest stands [ 77 , 91 , 106 , 119 , 120 , 121 ]; Spatial resolution is crucial for biomass estimation [ 56 , 76 , 91 , 95 ]; The texture and extent of land-cover classes increase the accuracy of biomass estimation. They are useful only as supplementary data [ 79 , 81 , 83 ]; In general, very-high-resolution satellite data are highly useful for habitat mapping [ 111 , 112 ]; The best modeling algorithms are XG-Boost, random forest, and artificial neural network [ 83 , 91 , 92 , 95 , 96 ]. …”

Section: Discussionmentioning

confidence: 99%

“…The texture and extent of land-cover classes increase the accuracy of biomass estimation. They are useful only as supplementary data [ 79 , 81 , 83 ];…”

Section: Discussionmentioning

confidence: 99%

“…Vegetation indices are input data (predictors) for modeling, along with spectral bands. However, other variables, such as the digital elevation model [ 79 , 80 ], vegetation fraction [ 81 , 82 ], land cover [ 33 ], and GLCM [ 80 , 83 ], are also used in modeling. These predictors can contribute to improvements in modeling accuracy.…”

Section: Remote Sensing Of Ecosystem Functions In Researchmentioning

confidence: 99%

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Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review

Vyvlečka

Pechanec

2023

Sensors

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Keeping natural ecosystems and their functions in the proper condition is necessary. One of the best contactless monitoring methods is remote sensing, especially optical remote sensing, which is used for vegetation applications. In addition to satellite data, data from ground sensors are necessary for validation or training in ecosystem-function quantification. This article focuses on the ecosystem functions associated with aboveground-biomass production and storage. The study contains an overview of the remote-sensing methods used for ecosystem-function monitoring, especially methods for detecting primary variables linked to ecosystem functions. The related studies are summarized in multiple tables. Most studies use freely available Sentinel-2 or Landsat imagery, with Sentinel-2 mostly producing better results at larger scales and in areas with vegetation. The spatial resolution is a key factor that plays a significant role in the accuracy with which ecosystem functions are quantified. However, factors such as spectral bands, algorithm selection, and validation data are also important. In general, optical data are usable even without supplementary data.

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“…Although there are many publications on using ANN for the biomass estimation of grassland areas, only one or two studies have specifically applied the integration of Sentinel-2 and ANN algorithms for biomass estimation [67][68][69]. In Figure 5, the fitted line between the observed and values has a slope of 0.96 and an intercept of 0.18, showing that RF outperforms the SVR model.…”

Section: The Evaluation Of Machine Learning Algorithmsmentioning

confidence: 99%

Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques

Vahidi,

Shafian,

Thomas

et al. 2023

Remote Sensing

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Quantifying the forage biomass in pastoral systems can be used for enhancing farmers’ decision-making in precision management and optimizing livestock feeding systems. In this study, we assessed the feasibility of integrating Sentinel-1 and Sentinel-2 satellite imagery with machine learning techniques to estimate the aboveground biomass and forage quality of bale grazing and sacrificed grassland areas in Virginia. The workflow comprised two steps, each addressing specific objectives. Firstly, we analyzed the temporal variation in spectral and synthetic aperture radar (SAR) variables derived from Sentinel-1 and Sentinel-2 time series images. Subsequently, we evaluated the contribution of these variables with the estimation of grassland biomass using three machine learning algorithms, as follows: support vector regression (SVR), random forest (RF), and artificial neural network (ANN). The quantitative assessment of the models demonstrates that the ANN algorithm outperforms the other approaches when estimating pasture biomass. The developed ANN model achieved an R2 of 0.83 and RMSE of 6.68 kg/100 sq. meter. The evaluation of feature importance revealed that VV and VH polarizations play a significant role in the model, indicating the SAR sensor’s ability to perceive changes in plant structure during the growth period. Additionally, the blue, green, and NIR bands were identified as the most influential spectral variables in the model, underscoring the alterations in the spectrum of the pasture over time.

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Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

Cited by 4 publications

References 31 publications

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review

Estimation of Bale Grazing and Sacrificed Pasture Biomass through the Integration of Sentinel Satellite Images and Machine Learning Techniques

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