Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain

Georgopoulos, Nikos; Sotiropoulos, Christos; Stefanidou, Alexandra; Gitas, Ioannis Z.

doi:10.3390/f13122157

Cited by 8 publications

(3 citation statements)

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“…As shown in Figure 5b, NDII emerged as the most critical variable, and we found that the third principal component factor (PCA3) contained more biomass-related information. Backscatter coefficient VH was found to be more suitable for AGB inversion compared to VV [70]. Features such as ARVI, b7, CON, DIS, DVI, HO, NDII, NDVI, OSAVI, PCA3, SAVI, SR, VH, and VH/VV contributed significantly, and will thus be used as input features for subsequent Tent_ASO_BP model development and AGB prediction.…”

Section: Variable Selection Resultsmentioning

confidence: 99%

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Chen,

Sun,

Zhang

et al. 2023

Remote Sensing

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Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1’s VV (vertical transmit–vertical receive) and VH (vertical transmit–horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys.

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Section: Variable Selection Resultsmentioning

confidence: 99%

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Chen,

Sun,

Zhang

et al. 2023

Remote Sensing

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“…Газрын дээрх биомасс (ABG) нь ойн нийт биомассын 80 орчим хувийг эзэлдэг ба ойн төлөв байдлыг илэрхийлэх гол үзүүлэлт юм [4,5].…”

Section: оршилunclassified

Estimation of forest above ground biomass using Sentinel-1 data

Altanchimeg,

Damdinsuren

2023

Mong. J. Geogr. Geoecol.

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Estimation of aboveground biomass is important for sustainable forest management and climate change mitigation. Traditional methods for estimating aboveground biomass rely on data collected from field measurements, which is spatially limited and also very expensive. Over the last years, remotely sensed (RS) datasets have been widely used for forest biomass assessment. So, the main aim of this research is to estimate and then map the above ground biomass (AGB) of forested site using modern radar satellite data. The study has the following objectives: I. Estimate the biomass of forest land, II. Estimating AGB using vegetation indices and Sentinel-1 satellite data recorded in the C-band with 5.55 cm wavelength. III. Conduct a comparative study of principal component analysis and random forest methods. As a test site, the area around Khangal sum of Bulgan Province was selected. In the study, the random forest method showed good results, and for Level 1 GRD data R2=0.823, RMSE=0.116 t/ha, while for Level 1 SLC data R2=0.815, RMSE=0.105 t/ha. Overall, sthe reseach indicated that it is possible to determine the AGB of forests in the temperate zone of Mongolia using radar satellite data. Sentinel-1 дагуулын мэдээ ашиглан ойн газрын дээрх биомассыг тооцоолох нь ХУРААНГУЙ: Газрын дээрх биомассыг тооцоолох нь ойн тогтвортой менежмент болон уур амьсгалын өөрчлөлтийг бууруулахад чухал үүрэгтэй. Газрын дээрх биомассыг тооцох уламжлалт аргууд нь хээрийн хэмжилтээр цуглуулсан өгөгдлийг ашиглан үнэлгээ хийх зарчимд тулгуурлах бөгөөд энэ нь орон зайн хувьд хязгаарлагдмал, өртөг өндөртэй юм. Орчин үед зайнаас тандсан мэдээг боловсруулан ойн биомассын үнэлгээнд ихээхэн ашиглаж байна. Энэхүү судалгааны ажил нь ойн газрын дээрх биомассыг сүүлийн үеийн радарын хиймэл дагуулын мэдээ ашиглан тооцоолж, улмаар зураглах үндсэн зорилготой. Тус зорилгын хүрээнд i) ойн газрын дээрх биомассыг тооцох, ii) Sentinel-1 дагуулын 5.55 см урттай радарын С-сувгийн мужид бүртгэгдсэн мэдээ болон ургамлын индексүүдийг ашиглан газрын дээрх биомассыг тооцоолох, iii) гол компонентын шинжилгээ болон санамсаргүй форестын аргыг харьцуулан судлах гэсэн зорилтуудыг дэвшүүлсэн. Судалгааны талбайгаар Булган аймгийн Хангал сум орчмын талбайг сонгон авсан. Судалгааны үр дүнгээс харахад санамсаргүй форестын арга сайн үр дүнг үзүүлж байсан бөгөөд Level 1 Ground Range Detected (GRD) мэдээний хувьд детерминацийн коэффициент (R2)=0.823, дундаж квадрат алдаа (RMSE)=0.116 тн га-1 байсан бол Level 1 Single Look Complex (SLC) мэдээний хувьд R2=0.815, RMSE=0.105 тн га-1 байлаа. Энэхүү судалгаа нь Монгол орны сэрүүн бүсийн ойн газрын дээрх биомассыг радарын мэдээ ашиглан тодорхойлох боломжтой гэдгийг баталж байна. Түлхүүр үгс: Газрын дээрх биомасс (ABG), Санамсаргүй форестын арга (RF), Гол компонентын шинжилгээ (PCA), Ургамлын индекс (VI)

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“…Compared to a process that does not consider or does not adequately consider topographic correction [38,61], radiative terrain correction for PolSAR data is a combination of three aspects of terrain correction [50,59,60,62,63], of which the most comprehensive radiometric terrain correction (RTC) process is one that includes all three aspects (POAC, ESAC, and AVEC) [43,64,65]. However, in terms of ESAC, the cross-sectional area of radar scattering is only applicable to a single scattering target (the target object is smaller than the irradiation range), and the scattered signal of the resolution unit for distributed scatterers (such as forests) is the result of coherent superposition of the scattered signal of each single scatterer, so the backscattering coefficient is commonly used to describe the scattering ability of ground objects based on statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Nie,

Hu,

et al. 2024

Remote Sensing

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Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

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Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain

Cited by 8 publications

References 84 publications

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Estimation of forest above ground biomass using Sentinel-1 data

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

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