Estimating Above Ground Biomass (AGB) and Tree Density using Sentinel-1 Data

Roy, Sambhunath; Mudi, Sujoy; Das, Pulakesh; Ghosh, Sanatan; Shit, Pravat Kumar; Bhunia, Gouri Shankar; Kim, John

doi:10.1007/978-3-030-56542-8_11

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…The obvious of backscattering energy in co-polarization modes can be observed between GF-3 ALOS-2 in evergreen forest (Chinese pine), and the gaps were reduced in deciduous (Larch) in the non-growing season (Figure 4). Moreover, features extracted from Sen 1 with VV polarization have more potential than those extracted from ALOS-2 and images in mapping FSV in deciduous forest in the non-growing season (Figure 5) inferred that polarization modes are the key factor in mapping FSV in deciduous without leaves [40,41].…”

Section: Polarization Response Of Deciduous and Evergreen Coniferous ...mentioning

confidence: 99%

“…Moreover, features extracted from Sentinel-1 with VV polarization have more potential than those extracted from ALOS-2 and GF-3 images in mapping FSV in deciduous forest in the non-growing season (Figure 5). It is inferred that polarization modes are the key factor in mapping FSV in deciduous forest without leaves [40,41].…”

Section: Polarization Response Of Deciduous and Evergreen Coniferous ...mentioning

confidence: 99%

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Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season

Zheng,

Long,

Zang

et al. 2023

Forests

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Polarimetric Synthetic Aperture Radar (PolSAR) images with dual polarization modes have great potential to map forest stock volume (FSV) by excellent penetration capabilities and distinct microwave scattering processes. However, the response of these SAR data to FSV is still uncertain in the non-growing season. To further interpret the response of FSV to different dual polarization SAR images, three types of dual polarization SAR images (GF-3, Sentinel-1, and ALOS-2) were initially acquired in coniferous planted forest in the non-growing season. Then, sensitivity between FSV and all alternative features extracted from each type of SAR image was analyzed to express the response of FSV to dual polarization SAR images with bands and polarization modes in the non-growing season in deciduous (Larch) and evergreen (Chinese pine) forests. Finally, mapped FSV using single and combined dual polarization images were derived by optimal feature sets and four machine learning models, respectively. The combined effects were also analyzed to clarify the difference of bands and polarization modes in deciduous and evergreen forests in the non-growing season. The results demonstrated that the backscattering energy from different sensors is significantly different in Chinese pine, and the difference is gradually reduced in Larch forests. It is also implied that the polarization mode is more important than penetration capability in mapping forest FSV in deciduous forest in the non-growing season. By comparing the accuracy of mapped FSV using single and combined images, combined images have more capability to improve the accuracy and reliability of mapped FSV. Meanwhile, it is confirmed that compensation effects with bands and polarization modes not only have great potential to delay the saturation phenomenon, but also have the capability to reduce errors caused by overestimation.

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Section: Polarization Response Of Deciduous and Evergreen Coniferous ...mentioning

confidence: 99%

Section: Polarization Response Of Deciduous and Evergreen Coniferous ...mentioning

confidence: 99%

Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season

Zheng,

Long,

Zang

et al. 2023

Forests

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“…There are mainly two methods used in field measurement to estimate the AGB, namely destructive (harvesting) and nondestructive methods. Although the destructive method is useful and accurate in developing equations for the assessment of aboveground biomass over larger areas, it is often constrained to few trees, being time consuming, difficult to implement, and expensive [16]. A nondestructive method is an alternative to estimate the AGB.…”

Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Moradi

Darvishsefat

Pourrahmati

et al. 2022

Forests

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Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

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“…The plant canopy height is an essential parameter that specifies the ecosystem structure, functional diversity, species regeneration and recruitment, natural and anthropogenic disturbances and management activities [1,2]. The canopy height is measured through field harvesting, wherein the satellite light detection and ranging (LiDAR) data can be supplemented and minimize the ground data collection efforts.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India

Ghosh¹,

Kumar

Das³

et al. 2022

Remote Sensing

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Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height <10 m, while 44.51% accounted for 10–20 m and 22.34% of forests demonstrated a higher canopy height (>20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists.

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Estimating Above Ground Biomass (AGB) and Tree Density using Sentinel-1 Data

Cited by 7 publications

References 35 publications

Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season

Interpreting the Response of Forest Stock Volume with Dual Polarization SAR Images in Boreal Coniferous Planted Forest in the Non-Growing Season

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India

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