Spatial Pattern of Forest Carbon Storage in the Vertical and Horizontal Directions Based on HJ-CCD Remote Sensing Imagery

Luo, Kaisheng

doi:10.3390/rs11070788

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Forest AGC represents a more stable indicator of long-term carbon accumulation and is an essential attribute for reflecting the dynamics of forest ecosystems [8,9]. Although most studies indicate that estimating AGC in the spatial distribution of forest AGC and the challenge of reducing estimation uncertainty have gained widespread attention, especially the prevalent issues of overestimation and underestimation [49,50]. When the forest cover on the ground is too dense to be accurately distinguished by remote sensing methods, a data saturation phenomenon may occur.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

Wu,

Sun,

Jia

et al. 2023

Forests

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In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R2 value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies.

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Section: Introductionmentioning

confidence: 99%

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

Wu,

Sun,

Jia

et al. 2023

Forests

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“…The horizontal aspect of the forest ecosystem can be divided into above-ground layers: herbaceous layer, shrub layer, understory, and canopy (Boyle et al, 2016;Luo, 2019). There are different approaches for classifying the forest on the basis of canopy stratification, which provided a basis for conceptualizing forest structure in different ways (Parker & Brown, 2000).…”

Section: Introductionmentioning

confidence: 99%

Urban park layers: Spatial variation in plant community structure

et al. 2022

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Horizontal structure of natural plant communities attracted the attention of researchers for a long time, while the problem of horizontal structure of urban park plantations was not studied sufficiently. Species richness of different tiers of park plantation in the large industrial city of Dnipro (Ukraine) was revealed in this study. Also features of variation in the structure of plant communities at different spatial levels were revealed, the influence of park plantation canopy on the understory and herbaceous layer of the park. There were 30 plant species in the tree layer of the park plantation. The most common species were Robinia pseudoacacia L., Acer platanoides L., A. negundo L., Gleditsia triacanthos L., Aesculus hippocastanum L., Populus carolinensis Moench. The variance-to-mean ratio revealed that 13 tree species were randomly distributed throughout the park, and 14 species were aggregated. The number of occurrences of a given tree species per site and variance-to-mean ratio were positively correlated. The numerous tree species showed a tendency of aggregated distribution within the park. Sixteen plant species were found in the understory. Among them, the most abundant species were Acer platanoides L., A. negundo L., A. pseudoplatanus L., Sambucus nigra L., Robinia pseudoacacia L. Eight species were found to be randomly distributed over the park area, and eight species showed an aggregate distribution. The number of species encountered in the understory and variance-to-mean ratio were positively correlated. In the herbaceous stand, 99 plant species were found, of which Chelidonium majus L., Viola odorata L., Impatiens parviflora DC., Parthenocissus quinquefolia (L.) Planch., Geum urbanum L. predominated. The variance-to-mean ratio of all species was significantly less than unity, indicating regular spatial distribution. The values of alpha- and gamma-diversity of the plant community in separate layers are very different. The highest gamma diversity was found for the herbaceous stand, while the diversity of the tree stand and understory was significantly lower. Alpha biodiversity of the tree stand and the understory did not practically differ. Beta diversity values between the layers are very close, and beta diversity is practically equal for tree stand and herbaceous layer. Thus, we can assume that the mechanisms of species turnover for the plant communities of different layers are determined by the common causes. The spatial broad-scale component was able to explain 8.2% of community variation, the medium-scale component was able to explain 4.2% of community variation, and the fine-scale component was able to explain 0.7% of community variation. The understory is the most sensitive to the environmental factors, the herbaceous stand is somewhat less sensitive, and the tree stand is the least sensitive to the environmental factors. The environmental factors in this study are represented by a set of variables. The spatial variation of the stand is predominantly influenced by the factors of trophicity and moisture of the edaphotope. These same factors also act on the herbaceous stand and understory, but along with them are included the environmental variables, which are determined by the architectonics of the crown space and thus the light regime, which is regulated by the tree stand. It is important to note that the variation of the communities of the different layers of the park plantation is subject to spatial patterns. The herbaceous and understory variation is more spatially structured than the tree stand variation. The spatial patterns can arise as a result of the influence of spatially structured environmental factors and as a result of factors of a neutral nature. The latter aspect of variation is best described by the pure spatial component of community variation.

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“…Forests play a crucial role in the biosphere, as they mitigate carbon concentration in the atmosphere and control global warming [1][2][3]. Accurately mapping forest carbon density at different scales is of great significance for reducing greenhouse effects [4] and maintaining environmental sustainability [4,5]. Remote sensing technology has proved to be promising in estimating regional forest carbon density [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

et al. 2019

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Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

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Spatial Pattern of Forest Carbon Storage in the Vertical and Horizontal Directions Based on HJ-CCD Remote Sensing Imagery

Cited by 6 publications

References 40 publications

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

Urban park layers: Spatial variation in plant community structure

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

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