SDG | Shaoqing Dai

Evaluating the Marginal Contribution of Remote Sensing for Forest Biomass Estimation When Inventory Data Exists

Mon, 14 Sep 2026 00:00:00 +0000

Figure 1. Workflow of the study. SA = stand age, CC = canopy cover, T = temperature, P = precipitation, ELE = elevation, SDE = soil depth, SVR = Support Vector Regression, RF = Random Forest, MLP = Multi-Layer Perceptron.

Understanding multi-perspective urban green space patterns under urban expansion: Evidence from Guangzhou

Thu, 21 May 2026 00:00:00 +0000

Figure 1. Research framework.

Assessing Variations in River Networks Under Urbanization Across Metropolitan Plains Using a Multi-Metric Approach

Sat, 04 Oct 2025 00:00:00 +0000

Agricultural socialized service system and its mechanism driving industrial upgrade: based on an empirical study on value chain theory(Chinese)

Tue, 02 Sep 2025 00:00:00 +0000

Identifying landscape patterns at different scales as driving factors for urban flooding

Fri, 23 May 2025 00:00:00 +0000

Long-Term Dynamic Monitoring and Driving Force Analysis of Eco-Environmental Quality in China

Thu, 14 Mar 2024 00:00:00 +0000

Local carbon emission zone construction in the highly urbanized regions: Application of residential and transport CO₂ emissions in Shanghai, China

Mon, 20 Nov 2023 00:00:00 +0000

Figure 1. The flowchart of the LCEZ construction. Step 1: Luojia1-01 night light image data is the proxy variable of the RTCE map. GWR is geographically weighted regression. Step 2: L is low. M is medium. H is high. the solid line is the three key factors. The dotted line means that n key factors can be selected in the future. The point is POI. the column is BF. the line is RD. A&B represents Dense&sparse forests. C&D represents Farmland&grassland&wetland. G represents the water body.

Quantifying urban mass gain and loss by a GIS-based material stocks and flows analysis

Tue, 08 Mar 2022 00:00:00 +0000

Figure 1 A framework combining stock-driven material flow analysis (MFA) and geographical information system (GIS) to simulate material metabolism. This improved framework highlights four typical activities that generate material flows, namely, construction, demolition, replacement, and maintenance

Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Tue, 30 Nov 2021 00:00:00 +0000

Figure 1 Framework for estimating (a–c) the machine learning models, (d) the P-BSHADE model, and the three models that combine machine learning with the P-BSHADE model ((a,d), (b,d) and (c,d)).

Figure 2 Prediction performance of the seven different models. (a) MAE and (b) MRE are presented as boxplots for each prediction method, with the median (black horizontal line in the box), interquartile range (25–75% in the box), range 5–95% (whiskers), and outliers (asterisks) labeled (S1 = SVM, S2 = RBF-ANN, S3 = RF, S4 = P-BSHADE, S5 = SVM & P-BSHADE, S6 = RBF-ANN & P-BSHADE, S7 = RF & P-BSHADE, S8 = allometric model, Allome = allometric model, ML = machine learning, and Sp Stats = spatial statistics). Histogram distributions of RMSE and nRMSE for each prediction method are presented in panels © and (d), respectively.

Figure 3 Improved accuracy assessment indexes of three combined machine learning and spatial statistical methods are revealed by comparison with three corresponding machine learning methods. Panels (a–d) show the MAE, MRE, RMSE, and nRMSE, respectively; S1 vs. S5 compares S5 with S1, S2 vs. S6 compares S6 with S2, and S3 vs. S7 compares S7 with S3 (S1 = SVM, S2 = RBF-ANN, S3 = RF, S5 = SVM & P-BSHADE, S6 = RBF-ANN & P-BSHADE, S7 = RF & P-BSHADE)

Forest AGB estimates at the plot level play a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level are plagued by numerous uncertainties. Improving the plot-level model of forest AGB is a key issue in producing accurate AGB maps. A variety of prediction models have been applied to make accurate AGB estimates, all of which have their own advantages and disadvantages. Different approaches complement the advantages of different models and may yield more accurate AGB estimates than would otherwise be produced by using a single method. The main goal of the current study was to determine whether combining machine learning with spatial statistics can improve plot-level AGB estimates.

This study explores the prediction performance of different AGB models, and the results show that the model combining the random forest and P-BSHADE models substantially improves the accuracy of the estimates of forest AGB. The results of this study suggest that combining machine learning with spatial statistics improves plot-level AGB estimates. The understanding gained here should help to improve AGB mapping in other regions and in different types of forests.

Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China

Sat, 24 Apr 2021 00:00:00 +0000

High spatial resolution mapping of steel resources accumulated above ground in mainland China: Past trends and future prospects

Tue, 02 Mar 2021 00:00:00 +0000

Figure 1 Comparisons between predicted and statistics-based steel stocks in 2015 (a), 2016 (b), 2017 (c), and 2018 (d). The gray shading represents 99% confidence intervals of the log-log regression

Figure 2 National coverage of in-use steel stocks in 2015 at 1 × 1 km grid level and an example (in a partial view) of modelling area (a), and stocks changes during 1995–2015 (b)

The direct and interactive impacts of hydrological factors on bacillary dysentery across different geographical regions in central China

Tue, 29 Dec 2020 00:00:00 +0000

Figure 1. The conceptual diagram illustrating the relationship between bacillary dysentery and hydrological factors.

Investigating the Uncertainties Propagation Analysis of CO₂ Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China

Mon, 30 Nov 2020 00:00:00 +0000

Graphical abstract

Highlights

A proposed workflow to analyze the uncertainties caused by gridded model and model input.

Fine-resolution (30 m) maps have a larger spatial variation in CO₂ emissions, which gives the fine-resolution maps a higher degree of uncertainty propagation.

This indicates a nonlinear change between the sum of the uncertainties for different sectors and the actual uncertainties in the gridded maps.

The nonlinear change can be explained by the “compensation of error” phenomenon.

Overview

Estimating CO₂ emissions at the urban scale, including both un-gridded (i.e., administrative unit maps) and gridded maps, cannot avoid the propagation of uncertainties from input to result, which highlights the importance of being aware of uncertainty estimation, especially in gridded maps due to its implications for the precision mitigation of CO₂ emissions.

To address the above mentioned issue, an analytic workflow was proposed to analyze the propagated uncertainties caused by the gridded model and the input for gridded CO₂ emission maps. The present workflow used four sub-modules based on Monte Carlo simulations and a bootstrap sampling method to analyze uncertainties, without other detailed open emission inventories. Two of the submodules obtained the corresponding uncertainty of each grid value, generated the uncertainty map caused by the gridded model, and the uncertainties for the sum of each cell (also referred to as propagated uncertainties caused by the model); the other submodules generated the uncertainty distribution maps based on the total emission estimations and spatial proxies (also referred to as propagated uncertainties caused by input).

Figure 1 Analytic workflow of the uncertainty propagation in gridded CO₂ emission maps. ALs, activity levels; CI, confidence interval; EFs, emission factors; GDP, gross domestic product; KS, Kolmogorov–Smirnov; LUT, look up table; MLE, maximum likelihood estimation; NTL, night time light; PDFs, probability distribution functions.

Results

We regarded the gridded maps of CO₂ emissions constructed in previous studies as a case study, and applied the workflow to estimate the uncertainties. The estimation of different uncertainties helps decision makers in formulating relevant policies. Uncertainties in total emission estimations aid the determination of emission reduction targets, the corresponding risks for cities and enterprises, and significant emission sources. Uncertainty maps could help to identify locations suitable for developing low-carbon communities. Fine-resolution (30 m) maps have a larger spatial variation in CO₂ emissions, which gives the fine-resolution maps a higher degree of uncertainty propagation. Furthermore, the uncertainties of gridded CO₂ emission maps, caused by inserting a random error into spatial proxies, were found to decrease after the gridding process. This can be explained by the “compensation of error” phenomenon, which may be attributed to the cancellation of the overestimated and underestimated values among the different sectors at the same grid. This indicates a nonlinear change between the sum of the uncertainties for different sectors and the actual uncertainties in the gridded maps.

U1

Figure 2 CDF curve of different CO₂ emissions at 30 m and 500 m resolution. The upper figures are at 30 m resolution, while the lower figures are at 500 m resolution. The red line is the distribution of the real values, while the blue line is the distribution of the simulated values. (a,e) are the total CO₂ emissions, (b,f) are the residential CO₂ emissions, (c,g) are the industrial CO₂ emissions, and (d,h) are the transport CO₂ emissions. KS., Kolmogorov–Smirnov; Sd, Standard deviation.

Figure 3 Uncertainty maps made by the gridded model of CO₂ emissions in Jinjiang city, at resolutions of 30 m (a–d) and 500 m (e–h). (a,e) show uncertainty maps of total CO₂ emissions. (b,f) show uncertainty maps of residential CO₂ emissions. (c,g) show uncertainty maps of industrial CO₂ emissions. (d,h) show uncertainty maps of transport CO₂ emissions.

U2

Figure 4 CO₂ emission population (statistic term) distributions of different sectors. The left figures are at 30 m resolution, while the right figures are at 500 m resolution. The red line is the distribution of the real values, and the blue line is the distribution of the simulated values. (a,e) are the total CO₂ emissions, (b,f) are the residential CO₂ emissions, (c,g) are the industrial CO₂ emissions, and (d,h) are the transport CO₂ emission.

U3

Figure 5 Map showing the uncertainty caused by activity levels. (a,b) represent the uncertainty maps of total CO₂ emissions at 30 m and 500 m resolution, respectively.

U4

Figure 6 Maps of uncertainty caused by the spatial proxies. (a,b) represent the maps of uncertainty of total CO₂ emissions at 30 m and 500 m resolutions.

Response of syntrophic bacterial and methanogenic archaeal communities in paddy soil to soil type and phenological period of rice growth

Sun, 09 Aug 2020 00:00:00 +0000

Figure 1. Distributions of syntrophs (SPOB and SBOB) and methanogens in the 10 paddy soils (a) at different rice growth stages (b).

Figure 2. Environmental drivers of syntrophic bacteria composition in the 10 paddy soils (a) at different rice growth stages (b). Pairwise comparisons of environmental parameters and bacterial communities are demonstrated, and the color gradient indicates Spearman’s correlation coefficients. Total syntrophs and SFAS were correlated to each environmental factor, as revealed by the partial Mantel tests. The edge color and width represent the statistical significance and Mantel’s R statistic, respectively.

Treatment of high-ash industrial sludge for producing improved char with low heavy metal toxicity

Sun, 21 Jun 2020 00:00:00 +0000

Figure 1. Pearson correlation between heavy metal speciation and characteristics of industrial sludge and char samples: (a) F1 + F2 fractions; (b) F3 fraction; © F4 fraction. The color (red to blue) indicates the change in correlation from negative to positive and the size of bubble shows the strength of correlation, which can be expressed by skew-symmetric numbers. Cross shows non-significant correlation between heavy metal and char property (P >  0.05). SA presents BET specific surface area.

A spatial database of CO₂ emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China

Fri, 21 Feb 2020 00:00:00 +0000

More fragmentized urban form more CO₂ emissions? A comprehensive relationship from the combination analysis across different scales

Thu, 03 Oct 2019 00:00:00 +0000

Highlights

Use of multi-source data and an analytical framework composed of 3 methods.

Exploration of the impact of 30-m versus 500-m spatial resolution.

Low fragmented mixed and industrial lands affect CO₂ emissions negatively.

Low fragmented residential and public lands affect CO₂ emissions positively.

Interactions of factors are weaker at a 30-m resolution than at a 500-m resolution.

Overview

Besides the industrial production, transportation, local weather, and fossil fuel use, urban form is the factor that affects energy-related CO₂ emissions from human activities secondarily. The major forms of human activity affected by urban form are traffic and residential energy consumption/use emissions. After a wide examination of the relationship between urban higher fragmentation and CO₂ emissions, scholars found fragmentation of land use areas (i.e. areas designated for human activities or purposes) is associated with greater CO₂ emissions. However, the evidences might be biased by the uncertainties from the spatial distribution of emissions (also referred to “downscaling”, or “representation” uncertainty), the delineation of urban fragmentation landscape, and the analysis method, which ignores the spatial heterogeneity. Zuo et al. (2019) published the study in the Journal of Cleaner Production, which compared the relationships between urban form fragmentation and CO₂ emissions in an urban system through the analytic framework composed of the Pearson correlation analysis, geographically weighted regression (GWR), and geographical detector methods with the use of multi-source data to construct the CO₂ emissions maps. The analytical framework can be applied to CO₂ emissions research in urban agglomerations, megacities, and small towns.

Figure 1 Analytical Framework

Results

In terms of the GWR analysis, the coarse resolution resulted in: 1) positive coefficients of fragmentation metric becoming negative, and 2) greater absolute values of negative coefficients. As to the results of Geographical detector, single factor impact powers and interactions among fragmentation factors showed a weakening effect at R30m, but a strengthening and weakening effect at R500m. However, there were common results observed in low-fragmented areas across different scales. That is, in low-fragmented mixed-function areas and industrial areas, the more fragmented the area was, the less the CO2 emission there would be. However, in low-fragmented residential, administrative and public service areas, the more fragmented the area was, the higher the CO₂ emission there would be. Therefore, the government should disperse the mixed function zones and industrial parcels with diverse types of land, and build the contiguous residential and public service land in the low fragmentation area of urban system.

Figure 2 Geographical detector results for fragmentation factors, PUA, and POID for CO₂ emissions from 4 sources.

The “Scale effect” has always been the hot topic in landscape ecology research. Although, there are many studies on the driving force of urban environmental problems by using landscape metrics. Most studies are carried out in regional, continental and global scales. However, in this study, we explored the relationship between the urban form fragmentation and CO₂ emissions in the city scale from the perspective of dual resolution sizes and summarized the research conclusions that can be used in urban internal management based on the analysis results of small towns. At the same time, the influence of scale effect and how to choose the appropriate scale were discussed in the discussion section. The rigorous discussion was also made on whether the research conclusion is applicable to medium and large cities.

A GIS-based high spatial resolution assessment of large-scale PV generation potential in China

Wed, 17 Apr 2019 00:00:00 +0000

Research on the effects of different policy tools on China’s emissions reduction innovation(Chinese)

Thu, 15 Feb 2018 00:00:00 +0000

Research on the effects of command-and-control and market-oriented policy tools on China’s energy conservation and emissions reduction innovation

Wed, 27 Dec 2017 00:00:00 +0000

The Simulation Analysis of Waterlogging and the Sponge City Planning Control of Central Urabn Area in Fuzhou City

Sun, 26 Jun 2016 00:00:00 +0000

The Analysis of Urban Spatial Development Pattern in Beijing Based on the Big Data of Government(Chinese)

Fri, 24 Jun 2016 00:00:00 +0000