Improving litterfall production prediction in China under variable environmental conditions using machine learning algorithms

Geng, Aixin; Tu, Qingshi; Chen, Jiaxin; Wang, Weifeng; Yang, Hongqiang

doi:10.1016/j.jenvman.2022.114515

Cited by 13 publications

(6 citation statements)

References 44 publications

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“…In this study, Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R-squared (R 2 ) metrics are adopted to compare the predictive performance of machine learning models. We compare GPBoost with the XGBoost (Chen et al, 2018) and Mixed Effects Random Forest (MERF) model (Geng et al, 2022). As shown in Table 5, compared to the XGBoost and MERF models, GPBoost has lower RMSE and MSE values and higher R 2 values in all four periods, indicating that the GBPoost model has a much better prediction performance.…”

Section: Model Fitmentioning

confidence: 99%

Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications

Jia

et al. 2023

Transportation Research Part D: Transport and Environment

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Section: Model Fitmentioning

confidence: 99%

Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications

Jia

et al. 2023

Transportation Research Part D: Transport and Environment

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Section: Introductionmentioning

confidence: 99%

“…The return of plant litter to soil supplies a large proportion of nutrients, such as N and P required for plant growth, while their returning amounts (i.e. amounts returned to soils) are determined by their initial concentrations (Geng et al., 2022; Qin et al., 2019). Evidence suggests that litter production and nutrient return are important drivers of ecosystem processes, including nutrient cycling (Muqaddas & Lewis, 2020), soil and water conservation (Dunkerley, 2015), and soil fertility (Pandey et al., 2007).…”

Section: Introductionmentioning

confidence: 99%

Global spectra of plant litter carbon, nitrogen and phosphorus concentrations and returning amounts

Yuan,

Wu,

Peng

et al. 2024

Journal of Ecology

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Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood. We employed machine learning with a global database to quantitatively assess the global patterns and drivers of leaf litter initial C, N and P concentrations, as well as their returning amounts (i.e. amounts returned to soils). The medians of litter C, N and P concentrations were 46.7, 1.1, and 0.1%, respectively, and the medians of litter C, N and P returning amounts were 1.436, 0.038 and 0.004 Mg ha−1 year−1, respectively. Soil and climate emerged as the key predictors of leaf litter C, N and P concentrations. Predicted global maps showed that leaf litter N and P concentrations decreased with latitude, while C concentration exhibited an opposite pattern. Additionally, the returning amounts of leaf litter C, N and P all declined from the equator to the poles in both hemispheres. Synthesis: Our results provide a quantitative assessment of the global concentrations and returning amounts of leaf litter C, N and P, which showed new light on the role of leaf litter in global C and nutrients cycling.

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“…Recent methodological enhancements use improved geostatistical and machine learning methods on a wider set of geospatial covariates (Shen et al, 2019, Geng et al, 2022, Zhao et al, 2022 and use of remote sensing data in estimating litterfall (Wang et al, 2016, Hu et al, 2019, Shen et al, 2019. Recently there is spurt in studies reporting eld measurements and their statistical models (Wen and He, 2016; Shen et al, 2017, You et al, 2017, Jia et al, 2018Liu et al, 2019, Zhang et al, 2018, which however do not address the spatial variation at regional scale.…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of forest litterfall in Central Himalayas (India): Comparison of geospatial, remote sensing and data-driven estimates

Kaushik

2023

Preprint

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A comparison of multiple approaches for annual litterfall estimation and spatial assessment of forests was carried out for the state of Uttarakhand (Geog. Area = 54533 sq. km, Forest Area- 24652.32 sq. km) in Central Himalayas, India. Non-spatial approach used meta-analysis of published litterfall studies in Uttarakhand (29 studies with 115 measurements over sites/years) classified by forest types and area under forest types estimated by remote sensing by Forest Survey of India. The measured mean litterfall ranged from a high of 7.88 t/ha/yr for the sub-tropical broad- leaved forests to a low of 3.70 t/ha/yr in plantations. Spatial models of litterfall used a data-driven approach with 100 measurements and a random forest (RF) model that used bioclimate, elevation and forest type as covariates at a spatial grid of 1km resolution. This estimate was compared with published global (Li et al., 2019) and European (Neumann et al., 2018) spatial models. The total litterfall with five different forest-type area and estimated mean litterfall varied between 12.34 to 14.69 Mt/yr and with spatial allocation to forest type map estimated 14.02 Mt/yr litterfall. Data-driven spatial model using Random Forest approach estimated 13.305 Mt/yr of total litterfall. Use of spatial litterfall models developed for other study areas resulted in estimates that ranged from 9.11–15.81 Mt/yr. The study provides important insights towards developing a spatial gridded annual litterfall dataset for India and its use for studying the dynamics of forest carbon cycle.

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Improving litterfall production prediction in China under variable environmental conditions using machine learning algorithms

Cited by 13 publications

References 44 publications

Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications

Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications

Global spectra of plant litter carbon, nitrogen and phosphorus concentrations and returning amounts

Spatial Assessment of forest litterfall in Central Himalayas (India): Comparison of geospatial, remote sensing and data-driven estimates

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