Considering time-lag effects can improve the accuracy of NPP simulation using a light use efficiency model

Li, Chuanhua; Liu, Yunfan; Zhu, Tongbin; Zhou, Min; Dou, Tianbao; Liu, Lihui; Wu, Xiaodong

doi:10.1007/s11442-023-2115-9

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…The temporal correlation between climate-induced effects on forests remains unclear. This is difficult to explain and quantify using known mechanisms [64,65]. Because of its gating mechanism, LSTM is better suited to processing and predicting a long-time series with relatively large intervals and delays [66,67].…”

Section: Estimation Accuracy and Interpretability Of The Hybrid Modelmentioning

confidence: 99%

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Qin

Zhu

et al. 2023

Forests

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Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.

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Section: Estimation Accuracy and Interpretability Of The Hybrid Modelmentioning

confidence: 99%

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Qin

Zhu

et al. 2023

Forests

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“…However, they are complex and have limited spatial generalization ability [14]. LUE models are designed to simulate the absorption and conversion ability of plants to solar radiation under varying climatic conditions [15][16][17]. Commonly used models include the Carnegie-Ames-Stanford Approach (CASA) model [18][19][20][21][22], the EC-LUE model [23], the GLO-PEM [24], and the MuSyQ-NPP model [25].…”

Section: Introductionmentioning

confidence: 99%

Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model

Lv,

Li,

Fang

et al. 2024

Remote Sensing

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Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m2·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.

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Elevation response of above-ground net primary productivity for Picea crassifolia to climate change in Qilian Mountains of Northwest China based on tree rings

Wu,

Jiao,

et al. 2024

J. Geogr. Sci.

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Considering time-lag effects can improve the accuracy of NPP simulation using a light use efficiency model

Cited by 6 publications

References 54 publications

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Disentangling the Influential Factors Driving NPP Decrease in Shandong Province: An Analysis from Time Series Evaluation Using MODIS and CASA Model

Elevation response of above-ground net primary productivity for Picea crassifolia to climate change in Qilian Mountains of Northwest China based on tree rings

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