Abstract
Aboveground biomass (AGB) models based on field-measured and remote-sensed data can help to understand and monitor ecosystems and evaluate the impacts of human activity. To create improved forest AGB models for use in an ecological rehabilitation area of Huainan, China, a suite of methods was used to evaluate a combination of the Chinese GaoFen-3 (GF-3) satellite’s synthetic aperture radar (SAR) data and vegetation indexes derived from the WorldView-3 satellite. Using vegetation indices and radar backscatter coefficients, a total of six modelling methods were applied to generate three AGB models, which included multivariable linear regression, linear, exponential, power, logarithmic, and growth functions. The results indicate that the observed root mean square errors (RMSE) of the best models, which included exponential functions based on the variables NDVI and HV, as well as their combination in a multivariable linear regression, were 43.74 Mg/ha, 30.87 Mg/ha, and 26.72 Mg/ha, respectively. The best model used multivariable linear regression with combined SAR and NDVI data (R2 = 0.861). The RMSEs were lowest for mixed forest, moderate for coniferous forest, and highest for broad-leaved forest. The results indicate that a combination of optical and microwave remote-sensing images can be used to effectively improve AGB estimation accuracy.
Highlights
Wetlands are important ecosystems that exist between terrestrial and aquatic systems [1, 2]. ey provide many ecosystem services such as regulation of the hydrological cycle, maintenance of water quality, conservation of biological diversity, and natural and socially beneficial services
To obtain Aboveground biomass (AGB) regression models from vegetation indices, the models based on an F threshold of 7.09 and significance level of 0.05 are shown in Table 3. e results indicate that the indexes of relative vigour index (RVI), difference vegetation index (DVI), and renormalized difference vegetation index (RDVI) produced relative root mean square errors (RMSE) of 34.80%, 31.9%, and 32.73%, respectively. e normalized difference vegetation index (NDVI)-based model was more relevant to AGB than the others
We proposed a collaborative observation based on World View high-resolution optical imaging and new synthetic aperture radar (SAR) data from the Gaofen-3 satellite. is was possible due to improvements in the ability to acquire high-resolution remote-sensing data and the expanded demand for refined observations. e proposed approach can improve the precision of biomass estimation and provide references for similar studies
Summary
Wetlands are important ecosystems that exist between terrestrial and aquatic systems [1, 2]. ey provide many ecosystem services such as regulation of the hydrological cycle, maintenance of water quality, conservation of biological diversity, and natural and socially beneficial services. Ey provide many ecosystem services such as regulation of the hydrological cycle, maintenance of water quality, conservation of biological diversity, and natural and socially beneficial services. As an important part of urban ecosystems, urban wetlands can improve the urban climate, enhance environmental quality, increase biodiversity, and conserve water [3,4,5]. They are strongly disturbed by human activities, such as excessive urban sprawl, and air and water pollution [6], in developing countries, where basic human needs have not been met. Biomass estimation and long-term biomass monitoring can be achieved using optical remote sensing, which obtains signals reflected from the forest canopy to extract vegetation parameters that have a significant response to biomass [11, 12]
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