Despite the importance of Hyrcanian forests for biodiversity conservation, a few studies with biomass destruction has been done to predict biomass and carbon pools from this forest and there is a lack of knowledge in ...Despite the importance of Hyrcanian forests for biodiversity conservation, a few studies with biomass destruction has been done to predict biomass and carbon pools from this forest and there is a lack of knowledge in our country. Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications and forest management. In this paper, allometric biomass and leaf area equations were developed for three common Hyrcanian tree species, Oriental Beech (Fagus orientalis Lipsky), Hornbeam (Carpinus Betulus Lipsky) and Chestnut- leaved Oak (Quercus castaneifolia C. A. Mey). To evaluate and estimate the leaf biomass and leaf area index of Oriental Beech, Horbeam and Chestnut-leaved Oak, 21, 27 and 17 individuals were selected and felled down, respectively. Tree characteristics such as diameter at breast height, total height, crown length and perpendicular diameters were measured. Destructive sampling was applied for determination of leaf biomass and LAI. Allometric equations were calculated for estimation of leaf biomass and LAI using simple linear regression and nonlinear regression analysis. The equations were compared based on several modelling parameters. Model comparison and selection were based on R2, Akaike’s information criterion (AIC), prediction error sums of squares, model standard error estimate (SEE), ΔAIC, and a correction factor. Based on the results, the mean values of leaf area, leaf biomass and LAI for Oriental Beech were 53.05 cm<sup>2</sup>, 0.176 gr, 2.16, for Hornbeam were 27.2 cm<sup>2</sup>, 0.128 gr, 1.13 and for Chestnut-leaved Oak were 62.419 cm<sup>2</sup>, 0.401 gr, 2.26, respectively. The highest significant correlation for Oriental Beech was found between LAI and total height (R2</sup>adj</sub>= 0. 931), the highest significant correlation for Hornbeam was found between LAI and Dbh (R<sup>2</sup><sub>adj</sub> = 0. 956) and the highest significance for Chestnut-leaved Oak was found between LAI and SqrtDbh (R2</sup>adj</sub> = 0. 956). Also, the best equations were obtained by means of an exponential regression model for Oriental Beech, the Log-transformed regression model for Hornbeam and of a transformed regression model for Chestnut-leaved Oak.展开更多
针对南方丘陵地区针叶-阔叶混交林植被叶面积指数(leaf area index,LAI)反演精度低且研究较少的问题,本文提出了一种GLIBERTY-DSAIL耦合模型组合多元线性回归反演LAI的方法。本研究以GLIBERTY-DSAIL模型模拟光谱和植被实测高光谱为数据...针对南方丘陵地区针叶-阔叶混交林植被叶面积指数(leaf area index,LAI)反演精度低且研究较少的问题,本文提出了一种GLIBERTY-DSAIL耦合模型组合多元线性回归反演LAI的方法。本研究以GLIBERTY-DSAIL模型模拟光谱和植被实测高光谱为数据源,通过相关性分析,选取与LAI相关性高的植被指数作为反演因子,构建多元线性回归模型定量反演植被LAI并进行精度评定。结果表明:与LAI显著相关的RVI、DVI、GNDVI、MSAVI这4种植被指数作为反演因子,结合本文提出的组合模型反演LAI,模型预测决定系数R2为0.7086,均方根误差RMSE为0.3021,精度整体较高。该组合方法可较好地用于反演针叶-阔叶混交林植被LAI,为南方地区混交林LAI的研究提供新思路。展开更多
叶面积指数(Leaf Area Index,LAI)是植被生长状况的重要指标,反映了农田生态系统的生产力水平。以江苏省东台市水稻田为研究区,基于多时相高分一号WFV影像提取的水稻植被指数数据,结合样区同步测量的不同生长期水稻叶面积指数数据,利用...叶面积指数(Leaf Area Index,LAI)是植被生长状况的重要指标,反映了农田生态系统的生产力水平。以江苏省东台市水稻田为研究区,基于多时相高分一号WFV影像提取的水稻植被指数数据,结合样区同步测量的不同生长期水稻叶面积指数数据,利用随机森林算法构建研究区水稻LAI反演模型。研究结果表明:随机森林算法反演的研究区水稻LAI与实测验证值相关性较好,R2达到0.88,RMSE仅为1.03,能准确反映研究区水稻LAI生长季的变化趋势,不同时段LAI测量值与反演值相对误差均值为15%,且GF-1WFV影像对研究区水、路网的分辨能力较高,总体上适用于农田LAI的反演。展开更多
Groundwater is a key factor controlling the growth of vegetation in desert riparian systems. It is important to recognise how groundwater changes affect the riparian forest ecosystem. This information will not only he...Groundwater is a key factor controlling the growth of vegetation in desert riparian systems. It is important to recognise how groundwater changes affect the riparian forest ecosystem. This information will not only help us to understand the ecological and hydrological process of the riparian forest but also provide support for ecological recovery of riparian forests and water-resources management of arid inland river basins. This study aims to estimate the suitability of the Water Vegetation Energy and Solute Modelling(WAVES) model to simulate the Ejina Desert riparian forest ecosystem changes,China, to assess effects of groundwater-depth change on the canopy leaf area index(LAI) and water budgets, and to ascertain the suitable groundwater depth for preserving the stability and structure of desert riparian forest. Results demonstrated that the WAVES model can simulate changes to ecological and hydrological processes. The annual mean water consumption of a Tamarix chinensis riparian forest was less than that of a Populus euphratica riparian forest, and the canopy LAI of the desert riparian forest should increase as groundwater depth decreases. Groundwater changes could significantly influence water budgets for T. chinensis and P. euphratica riparian forests and show the positive and negative effects on vegetation growth and water budgets of riparian forests. Maintaining the annual mean groundwater depth at around 1.7-2.7 m is critical for healthy riparian forest growth. This study highlights the importance of considering groundwater-change impacts on desert riparian vegetation and water-balance applications in ecological restoration and efficient water-resource management in the Heihe River Basin.展开更多
文摘Despite the importance of Hyrcanian forests for biodiversity conservation, a few studies with biomass destruction has been done to predict biomass and carbon pools from this forest and there is a lack of knowledge in our country. Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications and forest management. In this paper, allometric biomass and leaf area equations were developed for three common Hyrcanian tree species, Oriental Beech (Fagus orientalis Lipsky), Hornbeam (Carpinus Betulus Lipsky) and Chestnut- leaved Oak (Quercus castaneifolia C. A. Mey). To evaluate and estimate the leaf biomass and leaf area index of Oriental Beech, Horbeam and Chestnut-leaved Oak, 21, 27 and 17 individuals were selected and felled down, respectively. Tree characteristics such as diameter at breast height, total height, crown length and perpendicular diameters were measured. Destructive sampling was applied for determination of leaf biomass and LAI. Allometric equations were calculated for estimation of leaf biomass and LAI using simple linear regression and nonlinear regression analysis. The equations were compared based on several modelling parameters. Model comparison and selection were based on R2, Akaike’s information criterion (AIC), prediction error sums of squares, model standard error estimate (SEE), ΔAIC, and a correction factor. Based on the results, the mean values of leaf area, leaf biomass and LAI for Oriental Beech were 53.05 cm<sup>2</sup>, 0.176 gr, 2.16, for Hornbeam were 27.2 cm<sup>2</sup>, 0.128 gr, 1.13 and for Chestnut-leaved Oak were 62.419 cm<sup>2</sup>, 0.401 gr, 2.26, respectively. The highest significant correlation for Oriental Beech was found between LAI and total height (R2</sup>adj</sub>= 0. 931), the highest significant correlation for Hornbeam was found between LAI and Dbh (R<sup>2</sup><sub>adj</sub> = 0. 956) and the highest significance for Chestnut-leaved Oak was found between LAI and SqrtDbh (R2</sup>adj</sub> = 0. 956). Also, the best equations were obtained by means of an exponential regression model for Oriental Beech, the Log-transformed regression model for Hornbeam and of a transformed regression model for Chestnut-leaved Oak.
文摘针对南方丘陵地区针叶-阔叶混交林植被叶面积指数(leaf area index,LAI)反演精度低且研究较少的问题,本文提出了一种GLIBERTY-DSAIL耦合模型组合多元线性回归反演LAI的方法。本研究以GLIBERTY-DSAIL模型模拟光谱和植被实测高光谱为数据源,通过相关性分析,选取与LAI相关性高的植被指数作为反演因子,构建多元线性回归模型定量反演植被LAI并进行精度评定。结果表明:与LAI显著相关的RVI、DVI、GNDVI、MSAVI这4种植被指数作为反演因子,结合本文提出的组合模型反演LAI,模型预测决定系数R2为0.7086,均方根误差RMSE为0.3021,精度整体较高。该组合方法可较好地用于反演针叶-阔叶混交林植被LAI,为南方地区混交林LAI的研究提供新思路。
文摘叶面积指数(Leaf Area Index,LAI)是植被生长状况的重要指标,反映了农田生态系统的生产力水平。以江苏省东台市水稻田为研究区,基于多时相高分一号WFV影像提取的水稻植被指数数据,结合样区同步测量的不同生长期水稻叶面积指数数据,利用随机森林算法构建研究区水稻LAI反演模型。研究结果表明:随机森林算法反演的研究区水稻LAI与实测验证值相关性较好,R2达到0.88,RMSE仅为1.03,能准确反映研究区水稻LAI生长季的变化趋势,不同时段LAI测量值与反演值相对误差均值为15%,且GF-1WFV影像对研究区水、路网的分辨能力较高,总体上适用于农田LAI的反演。
基金supported by the National Key Research and Development program (2016YFC0400908)the National Natural Science Foundation of China (Nos. 41101026, 31370466)the STS project of Chinese academy of sciences (29Y829731)
文摘Groundwater is a key factor controlling the growth of vegetation in desert riparian systems. It is important to recognise how groundwater changes affect the riparian forest ecosystem. This information will not only help us to understand the ecological and hydrological process of the riparian forest but also provide support for ecological recovery of riparian forests and water-resources management of arid inland river basins. This study aims to estimate the suitability of the Water Vegetation Energy and Solute Modelling(WAVES) model to simulate the Ejina Desert riparian forest ecosystem changes,China, to assess effects of groundwater-depth change on the canopy leaf area index(LAI) and water budgets, and to ascertain the suitable groundwater depth for preserving the stability and structure of desert riparian forest. Results demonstrated that the WAVES model can simulate changes to ecological and hydrological processes. The annual mean water consumption of a Tamarix chinensis riparian forest was less than that of a Populus euphratica riparian forest, and the canopy LAI of the desert riparian forest should increase as groundwater depth decreases. Groundwater changes could significantly influence water budgets for T. chinensis and P. euphratica riparian forests and show the positive and negative effects on vegetation growth and water budgets of riparian forests. Maintaining the annual mean groundwater depth at around 1.7-2.7 m is critical for healthy riparian forest growth. This study highlights the importance of considering groundwater-change impacts on desert riparian vegetation and water-balance applications in ecological restoration and efficient water-resource management in the Heihe River Basin.
