Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

Zhuang, Qingwei; Shao, Zhenfeng; Gong, Jianya; Li, Deren; Huang, Xiao; Zhang, Ya; Xu, Xiaodi; Dang, Chaoya; Chen, Jinlong; Altan, Orhan; Wu, Shixin

doi:10.1016/j.jag.2022.103058

Cited by 20 publications

(12 citation statements)

References 128 publications

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“…At present, the evaluation methods of the LUCC response mechanism to carbon storage mainly include remote sensing estimation methods [5], empirical statistical models [6], and IPCC inventory approach [7,8]. Among them, the InVEST model can efficiently calculate and visualize carbon storage based on carbon pool data and has obvious advantages in reflecting the scale characteristics of carbon storage and the changes in spatiotemporal series [9].…”

Section: Introductionmentioning

confidence: 99%

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Chen²,

Li³

et al. 2023

Remote Sensing

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Studying the spatiotemporal distribution pattern of carbon storage, balancing land development and utilization with ecological protection, and promoting urban low-carbon sustainable development are important topics under China’s “dual carbon strategy” (Carbon emissions stabilize and harmonize with natural carbon absorption). However, existing research has paid little attention to the impact of land use changes under different spatial policies on the provincial-scale ecosystem carbon storage. In this study, we established a carbon density database for Liaoning Province and obtained the spatial and temporal distribution of carbon storage over the past 20 years. Then, based on 16 driving factors and multiple spatial policies in Liaoning Province, we predicted land use and land cover changes (LUCC) under three scenarios for 2050 and analyzed the spatiotemporal distribution characteristics and response mechanisms of carbon storage under different scenarios. The results showed that (1) LUCC directly affected carbon storage, with a 35.61% increase in construction land and a decrease in carbon storage of 0.51 Tg over the 20-year period. (2) From 2020 to 2050, the carbon storage varied significantly among the natural trend scenario (NTS), ecological restoration scenario (ERS), and economic priority scenario (EPS), with values of 2112.05 Tg, 2164.40 Tg, and 2105.90 Tg, respectively. Carbon storage in the ecological restoration scenario exhibited positive growth, mainly due to a substantial increase in forest area. (3) The spatial pattern of carbon storage in Liaoning Province was characterized by “low in the center, high in the east, and balanced in the west”. Therefore, Liaoning Province can consider rationally formulating and strictly implementing the spatial policy of ecological protection in the future land planning so as to control the disorderly growth of construction land, realize the growth of ecological land area, effectively enhance carbon storage, and ensure the realization of the goal of “dual carbon strategy”.

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Section: Introductionmentioning

confidence: 99%

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Chen²,

Li³

et al. 2023

Remote Sensing

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“…Vegetation carbon fluxes and carbon storage in cities are challenging to quantify due to limited data and lack of transferability from natural vegetation models (McHale et al, 2009;Zhuang et al, 2022). The average annual NPP across all species for the baseline irrigation was higher than field studies of natural forests (Malone et al, 2016;Alexander et al, 2023), which may reflect irrigation inputs as well as differences in soil and species adaptations in urban microclimates.…”

Section: Model Outcomesmentioning

confidence: 99%

Exploring potential trade-offs in outdoor water use reductions and urban tree ecosystem services during an extreme drought in Southern California

Torres,

Tague,

McFadden

2024

Front. Clim.

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In Southern California cities, urban trees play a vital role in alleviating heat waves through shade provision and evaporative cooling. Trees in arid to semi-arid regions may rely on irrigation, which is often the first municipal water use to be restricted during drought, causing further drought stress. Finding a balance between efficient water use and maintaining tree health will be crucial for long-term urban forestry and water resources management, as climate change will increase drought and extreme heat events. This study aimed to quantify how urban tree water and carbon fluxes are affected by irrigation reductions, and how that relationship changes with tree species and temperature. We used an ecohydrologic model that mechanistically simulates water, carbon, and energy cycling, parameterized for 5 common tree species in a semi-arid urban area. We simulated a range of irrigation reductions based on average outdoor water use data from the city for a recent extreme drought as well as with warmer temperatures. We then analyzed the response of model outcomes of plant carbon fluxes, leaf area index (LAI), and water use. Results show that reducing irrigation up to 25%, a comparable amount as the California state mandate in 2014, has minimal effects on tree primary productivity and water use efficiency. We found that transpiration was linearly related to irrigation input, which could lead to a short-term loss of evaporative cooling with irrigation reductions during drought. However, primary productivity and LAI had a nonlinear response to irrigation, indicating shade provision could be maintained throughout drought with partial irrigation reductions. Results varied across tree species, with some species showing greater sensitivity of productivity to both irrigation reductions and potentially warmer droughts. These results have implications for water resources management before and during drought, and for urban tree climate adaptation to future drought.

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“…With the gradual expansion of urban environments, many of the functions offered by urban forests, such as CO 2 emission reduction, air purification, PM2.5 absorption, urban rainfall, flood control, water quality improvement, noise reduction, and microclimate improvement, have received increasing attention [5]. Therefore, the study of urban forest AGC and its spatial and temporal distribution is of great significance to the construction of forested and low-carbon cities, which is a popular focus of domestic and international research [6,7].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China

Zhang

Zhong

et al. 2023

Remote Sensing

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The aboveground carbon storage (AGC) of urban forests is an important indicator reflecting the ecological function of urban forests. It is essential to monitor the AGC of urban forests and analyze their spatiotemporal distributions. Remote sensing is a technical tool that can be leveraged to accurately monitor forest AGC, whereas machine learning is an important algorithm for the accurate prediction of AGC. Therefore, in this study, single Landsat 8 (L) remote sensing data, single Sentinel-2 (S) remote sensing data, and combined Landsat 8 and Sentinel-2 (L + S) data are used as data sources. Four machine learning methods, support vector regression (SVR), random forest (RF), XGBoost (extreme gradient boosting), and CatBoost (categorical boosting), are used to predict forest AGC based on two phases of forest sample plots in Shanghai. We chose the optimal model to predict the AGC and simulate the spatiotemporal distribution. The study shows that both machine learning models based on separate Landsat 8 OLI and Sentinel-2 satellite remote sensing data can accurately predict the AGC and spatiotemporal distribution of the Shanghai urban forest. Nevertheless, the accuracy of the combined data (L + S) and CatBoost-integrated AGC models is higher than the others, with fitting and validation accuracy R2 values of 0.99 and 0.70, respectively. The RMSE was also smaller at 0.67 and 6.29 Mg/ha, respectively. The uncertainty of the AGC spatial distribution in the Shanghai urban forest derived from the CatBoost model prediction from the 2016–2019 data was small and consistent with the actual situation. Furthermore, the statistics showed that the AGC of the Shanghai forest increased from 24.90 Mg/ha in 2016 to 25.61 Mg/ha in 2019.

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Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

Cited by 20 publications

References 128 publications

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Using the InVEST-PLUS Model to Predict and Analyze the Pattern of Ecosystem Carbon storage in Liaoning Province, China

Exploring potential trade-offs in outdoor water use reductions and urban tree ecosystem services during an extreme drought in Southern California

Prediction of Urban Forest Aboveground Carbon Using Machine Learning Based on Landsat 8 and Sentinel-2: A Case Study of Shanghai, China

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