Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China

Lou, Minghua; Zhang, Huiru; Lei, Xiangdong; Li, Chunming; Zang, Hao

doi:10.3390/f7020043

Cited by 9 publications

(6 citation statements)

References 53 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SLM is a formal representation of a spatial diffusion process which reflects the spatial dependence in the data and focuses on the assessment of the spatial dependence [32]. The SEM is developed based on the spatial autoregressive model with errors on the premise of no change for the explanatory variables [32,33], and it is assumed that the spatial autoregressive process occurs only in the error term, neither in the response variable nor in the predictor variables [64,97]. In short, although both the SEM and SLM models can explain the global spatial autocorrelation, they cannot deal with local spatial heterogeneity [33].…”

Section: Model Comparisonmentioning

confidence: 99%

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.

show abstract

Section: Model Comparisonmentioning

confidence: 99%

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…We applied GWR and ordinary least squares (OLS) regression to further analyze the relationship between the UBD and LST. The traditional regression model is based on the use of OLS to estimate the parameters and is usually compared as a benchmark model with an improved model [55][56][57]. OLS can be expressed as: We applied GWR and ordinary least squares (OLS) regression to further analyze the relationship between the UBD and LST.…”

Section: Geographically Weighted Regressionmentioning

confidence: 99%

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

et al. 2019

View full text Add to dashboard Cite

The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojia1-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat8 images, and carries out spatial correlation analysis on 0.5 × 0.5 km to 2 × 2 km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojia1-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model. Appl. Sci. 2019, 9, 5224 2 of 20 absorbing heat faster and the specific heat capacity being smaller gives rise to limited heat release. In this way, the average LST in the area gradually increases with the spatial distribution of the UHI area to become more continuous and concentrated, forming a high-temperature area centered on artificial buildings in the city [1,9,10]. However, our comprehensive literature research found that past studies focused on the analysis of seasonal, daily, and annual variations of the UHI, which revealed the temporal and spatial variation of the UHI, the distribution characteristics of the horizontal and vertical directions, etc., while there has been less quantitative analysis of the single factors of UHIs, especially discussion of the ubiquitous relationship between impervious surfaces and UHI in the city.As an integral part of the urban impervious surface percentage, the urban building density (UBD) (percentage of built area) can directly reflect the degree of intensification and land-use efficiency in cities to some extent [11]. It is also an essential comprehensive social indicator that can function as an urban planning layout measure [12,13], resource utilization efficiency measure [14,15], and assessment of the urban ecological environment and livability [16][17][18][19]. Some researchers have pointed out that UBD is a crucial indicator to reduce the UHI effect [20] and proved a positive correlation between UBD and LST or UHI [21][22][23]. Additionally, researchers have tried to use a variety of urban design factors to study the relationships of UHIs, including UBD information. For example, Yang studied the summer heat island intensity of three high-rise residential districts in Shanghai, based on the three indices of building layout, density, and greeni...

show abstract

“…It should be noted that for estimating BAPP based on NFI data, H instead of stand dominant height was applied in the whole calculation. This is because (a) stand dominant height was not measured in Chinese NFI data, especially for multi-aged and mixed forests; and (b) the correlation between H and stand dominant height is strong [27].…”

Section: Computational Conditions For Bappmentioning

confidence: 99%

A Basal Area Increment-Based Approach of Site Productivity Evaluation for Multi-Aged and Mixed Forests

Sharma

Zhu

et al. 2017

Forests

Self Cite

View full text Add to dashboard Cite

Accurate estimates of forest site productivity are essential for environmental planning and forest management. In this study, we developed a new productivity index, hereafter termed basal area potential productivity index (BAPP), to estimate site productivity for irregular and complex forests characterized by multi-aged, multi-species, and multi-layer stands. We presented the biological relevance of BAPP with its computational details. We also compared BAPP against basal area realized productivity (BARP) in order to verify the practicability and reliability of BAPP. Time-series data of the national forest inventory on 1912 permanent sample plots that were located in two main forest types and consisted of oak-dominated mixed forests and other broadleaf forests in northeast China were used to demonstrate the application of BAPP. The results showed that the value of BAPP for each sample plot was larger than or equal to the corresponding BARP value for each forest type. For appropriately managed stands with relatively better site conditions, the values of both BARP and BAPP were almost identical. The values of the difference between BAPP and BARP could therefore be used to effectively assess forest site productivity. Meanwhile, BAPP also provides much reliable and valuable information that can aid decision-making in forest management.

show abstract

Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China

Cited by 9 publications

References 53 publications

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

A Basal Area Increment-Based Approach of Site Productivity Evaluation for Multi-Aged and Mixed Forests

Contact Info

Product

Resources

About