Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change

Wu, Chen; Chen, Yongfu; Peng, Changhui; Li, Zhaochen; Hong, Xin

doi:10.1016/j.jenvman.2018.12.090

Cited by 34 publications

(16 citation statements)

References 57 publications

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“…Previous scientists have been limited by severe saturation problems in optical images in grasslands with high vegetation cover when detecting canopy height [2,57]. We found that UAV LiDAR has the advantage of addressing optical sensors, which typically suffer from saturation problems [34,58].…”

Section: Discussionmentioning

confidence: 87%

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

et al. 2021

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The accurate estimation of grassland vegetation parameters at a high spatial resolution is important for the sustainable management of grassland areas. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) sensors with a single laser beam emission capability can rapidly detect grassland vegetation parameters, such as canopy height, fractional vegetation coverage (FVC) and aboveground biomass (AGB). However, there have been few reports on the ability to detect grassland vegetation parameters based on RIEGL VUX-1 UAV LiDAR (Riegl VUX-1) systems. In this paper, we investigated the ability of Riegl VUX-1 to model the AGB at a 0.1 m pixel resolution in the Hulun Buir grazing platform under different grazing intensities. The LiDAR-derived minimum, mean, and maximum canopy heights and FVC were used to estimate the AGB across the entire grazing platform. The flight height of the LiDAR-derived vegetation parameters was also analyzed. The following results were determined: (1) The Riegl VUX-1-derived AGB was predicted to range from 29 g/m2 to 563 g/m2 under different grazing conditions. (2) The LiDAR-derived maximum canopy height and FVC were the best predictors of grassland AGB (R2 = 0.54, root-mean-square error (RMSE) = 64.76 g/m2). (3) For different UAV flight altitudes from 40 m to 110 m, different flight heights showed no major effect on the derived canopy height. The LiDAR-derived canopy height decreased from 9.19 cm to 8.17 cm, and the standard deviation of the LiDAR-derived canopy height decreased from 3.31 cm to 2.35 cm with increasing UAV flight altitudes. These conclusions could be useful for estimating grasslands in smaller areas and serving as references for other remote sensing datasets for estimating grasslands in larger areas.

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Section: Discussionmentioning

confidence: 87%

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

et al. 2021

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“…For example, support vector machines (SVMs), artificial neural networks (ANNs), and generalized regression neural network (GRNN) have been applied to assess the impact of climate change on the above-ground biomass. However, due to the existence of systematic errors and the lack of consideration of time series, the uncertainty of the model is large [65]. Another study adopted an ANN method to determine the influence of climate drivers on sand-deposition in semi-arid regions [66].…”

Section: Discussionmentioning

confidence: 99%

A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

Ren

et al. 2020

Symmetry

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The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

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“…separately. It infers the ice sheets are of bigger sizes in the Ganga bowls rather than the additional two bowls [16].…”

Section: Drivers Of Climate Changementioning

confidence: 98%

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Malik¹,

Pande²,

Nishi³

et al. 2020

AIS

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Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn't happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentrate on utilizing ML and AI to determine as well as capitalize a great deal more on pre pre-existing simulations as well as info is suggested by us.

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Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change

Cited by 34 publications

References 57 publications

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

Using UAV LiDAR to Extract Vegetation Parameters of Inner Mongolian Grassland

A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

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