Heteromorphism leaf area of garden trees was measured by CAD-vector method.It was showed that all the coefficients of variation were below 1% when measurement accuracy was relatively high.This method was fit for accur...Heteromorphism leaf area of garden trees was measured by CAD-vector method.It was showed that all the coefficients of variation were below 1% when measurement accuracy was relatively high.This method was fit for accuracy area measurement of abnormity leaf,pests and disease leaf,big leaf,small leaf and so on.展开更多
The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure...The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure was: (1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images; (2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated; (3) the relationship models between LAI and biomass were built and annual branch, stem, root and maximum leaf biomass over the landscape were simulated; (4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained. The simulation was based on spatial analysis module GRID in ArcInfo software. The method is also a kind of scaling method from patch scale to landscape scale. A case study of Changbai Mountain Nature Reserve was dissertated. Analysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.展开更多
Leaf area measurement is of great significance in plant growth process monitoring.It poses challenges to perform an unattended in-situ measurement,arising from quantifying the 3-dimensional pakchoi leaf surface.Conven...Leaf area measurement is of great significance in plant growth process monitoring.It poses challenges to perform an unattended in-situ measurement,arising from quantifying the 3-dimensional pakchoi leaf surface.Conventional non-destructive measurement techniques,which usually take its projection on the horizon plane of the leaf area,inevitably cause considerable measurement errors.In order to improve the measurement precision for leaf area,the exemplar pakchoi leaf was modeled as a complete or a piecewise spatial plane to approximate the actual leaf surface,and a machine-vision based ad hoc measuring platform was developed to conduct the in-situ measurement.First,the leaf image was captured by a stereo vision system and segmented via a semi-automatic process to obtain its projective area and spatial inclination angle.Second,pakchoi leaves were modeled as spatial surfaces regarding to their projected counterparts.Third,leaf areas were calculated according to the established planar spatial model,acquired inclination angles and projective areas.The experimental comparison among the lattice-based monotype method,projection method,and the model-based method,whose results are denoted as MA,PA,and EA respectively,showed that the proposed framework could simultaneously meet the accuracy and non-destructive measurement requirements.The constructed platform also provided a cost-effective semi-automatic measurement approach for continuously in-situ monitoring of pakchoi growth during its whole cultivation period.It is further suggested from the experimental results that the proposed methodology can offer a generic measurement solution to various kinds of plant physiological and ecological studies in future researches.展开更多
Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in e...Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.展开更多
文摘Heteromorphism leaf area of garden trees was measured by CAD-vector method.It was showed that all the coefficients of variation were below 1% when measurement accuracy was relatively high.This method was fit for accuracy area measurement of abnormity leaf,pests and disease leaf,big leaf,small leaf and so on.
基金One Hundred Talents Program of CAS No.CXIOG-C00-01+1 种基金 National Natural Science Foundation of China No.39970613
文摘The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure was: (1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images; (2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated; (3) the relationship models between LAI and biomass were built and annual branch, stem, root and maximum leaf biomass over the landscape were simulated; (4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained. The simulation was based on spatial analysis module GRID in ArcInfo software. The method is also a kind of scaling method from patch scale to landscape scale. A case study of Changbai Mountain Nature Reserve was dissertated. Analysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.
基金We acknowledge that the research is supported by the National High Technology Research and Development Program of China,under Grant No.2012AA1019 and No.2013AA102307.
文摘Leaf area measurement is of great significance in plant growth process monitoring.It poses challenges to perform an unattended in-situ measurement,arising from quantifying the 3-dimensional pakchoi leaf surface.Conventional non-destructive measurement techniques,which usually take its projection on the horizon plane of the leaf area,inevitably cause considerable measurement errors.In order to improve the measurement precision for leaf area,the exemplar pakchoi leaf was modeled as a complete or a piecewise spatial plane to approximate the actual leaf surface,and a machine-vision based ad hoc measuring platform was developed to conduct the in-situ measurement.First,the leaf image was captured by a stereo vision system and segmented via a semi-automatic process to obtain its projective area and spatial inclination angle.Second,pakchoi leaves were modeled as spatial surfaces regarding to their projected counterparts.Third,leaf areas were calculated according to the established planar spatial model,acquired inclination angles and projective areas.The experimental comparison among the lattice-based monotype method,projection method,and the model-based method,whose results are denoted as MA,PA,and EA respectively,showed that the proposed framework could simultaneously meet the accuracy and non-destructive measurement requirements.The constructed platform also provided a cost-effective semi-automatic measurement approach for continuously in-situ monitoring of pakchoi growth during its whole cultivation period.It is further suggested from the experimental results that the proposed methodology can offer a generic measurement solution to various kinds of plant physiological and ecological studies in future researches.
基金Servizi Ecosistemici e Infrastrutture Verdi urbane e peri-urbane nell’area Metropolitana Romana:stima del contributo delle foreste naturali di Castelporziano nel miglioramento della qualitàdell’aria della cittàdi RomaAccademia Nazionale delle Scienze detta dei XL,in collaborazione con Segretariato Generale della Presidenza della Repubblica+1 种基金PRO-ICOS_MED Potenziamento della Rete di Osservazione ICOS-Italia nel Mediterraneo-Rafforzamento del capitale umano”funded by the Ministry of ResearchPNRR,Missione 4,Componente 2,Avviso 3264/2021,IR0000032-ITINERIS-Italian Integrated Environmental Research Infrastructures System CUP B53C22002150006。
文摘Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.
