Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

Fazzini, Paolo; Proia, Giuseppina De Felice; Adamo, Maria; Blonda, P.; Petracchini, Francesco; Forte, Luigi; Tarantino, Cristina

doi:10.3390/rs13122276

“…The main advantage is that the proposed variant is able to extract both spatial and spectral features from the satellite multi-spectral images using an end-to-end paradigm. In particular, a patch-wise approach was used [ 26 ], dividing each Sentinel-2 input and AGB-raster into non-overlapping patches of 16 × 16 pixels.…”

Section: Methodsmentioning

confidence: 99%

ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

Pascarella

¹

,

Giacco

²

,

Rigiroli³

et al. 2023

J. Imaging

7

0

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The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in situ observation becomes essential. To support this critical need, in this work, we introduce ReUse, a simple but effective deep learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The proposed method’s novelty is in using the public above-ground biomass (AGB) data from the European Space Agency’s Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise regressive UNet. The approach has been compared with two literature proposals using a private dataset and human-engineered features. The results show a more remarkable generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Square Error over the runner-up of 16.9 and 14.3 in the area of Vietnam, 4.7 and 5.1 in the area of Myanmar, 8.0 and 1.4 in the area of Central Europe, respectively. As a case study, we also report an analysis made for the Astroni area, a World Wildlife Fund (WWF) natural reserve struck by a large fire, producing predictions consistent with values found by experts in the field after in situ investigations. These results further support the use of such an approach for the early detection of AGB variations in urban and rural areas.

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“…The main advantage is that the proposed variant is able to extract both spatial and spectral features from the satellite multi-spectral images using an end-to-end paradigm. In particular, a patch-wise approach has been used [26] dividing each Sentinel-2 input and AGB-raster into non-overlapping patches of 16*16 pixels.…”

Section: Regressive Unetmentioning

confidence: 99%

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

Pascarella¹,

Giacco²,

Rigiroli³

et al. 2023

Preprint

4

0

View full text Add to dashboard Cite

United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program that requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in-situ observation becomes essential. To support this important need, in this work we introduce ReUse, a simple but effective deep-learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The novelty of the proposed method is in the use of the public above-ground biomass (AGB) data from the European Space Agency's Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise Regressive UNet. The approach has been compared to two literature proposals, using a private dataset and human-engineered features. The results show a greater generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Squared Error, respectively, of 16.9 and 14.3 in the area of Vietnam and 4.7 and 5.1 in the area of Myanmar over the runner-up. Finally, as a case study, we reported an analysis made for the Astroni area, a nature reserve located near the metropolitan area of Naples in southern Italy, struck by a large fire, producing predictions consistent with values found by experts in the field. These results further support the use of such an approach for the early detection of AGB variations, both in urban and rural areas.

show abstract

“…The advantage of this approach over classical approaches, such as [10] and [14], is that the extraction of both spatial and spectral features is incorporated into the same network in an end-to-end paradigm. A patch-wise approach was followed as in [18] so that each Sentinel-2 input and AGB-raster output were divided into many non-overlapping patches of 16*16. Fig.…”

Section: Regressive Unetmentioning

confidence: 99%

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

Pascarella¹,

Giacco²,

Rigiroli³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program that requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in-situ observation becomes essential. To support this important need, in this work we introduce ReUse, a simple but effective deep-learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The novelty of the proposed method is in the use of the public above-ground biomass (AGB) data from the European Space Agency's Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise Regressive UNet. The approach has been compared to two literature proposals, using a private dataset and human-engineered features. The results show a greater generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Squared Error, respectively, of 16.9 and 14.3 in the area of Vietnam and 4.7 and 5.1 in the area of Myanmar over the runner-up. Finally, as a case study, we reported an analysis made for the Astroni area, a nature reserve located near the metropolitan area of Naples in southern Italy, struck by a large fire, producing predictions consistent with values found by experts in the field. These results further support the use of such an approach for the early detection of AGB variations, both in urban and rural areas.

show abstract

Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

Cited by 9 publications

References 53 publications

ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

ReUse: REgressive Unet for Carbon Storage and above Ground Biomass Estimation

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