Subtropical “Yambaru” forest, situated in the northern part of Okinawa Island of Japan, has a precious ecosystem inhabited by many endemic species. However, this region is also the center for forestry on Okinawa. Th...Subtropical “Yambaru” forest, situated in the northern part of Okinawa Island of Japan, has a precious ecosystem inhabited by many endemic species. However, this region is also the center for forestry on Okinawa. Therefore, sustainable forestry activities should take into consideration the natural environment. To contribute to sustainable forest management in the region, we conducted prediction of the site index at fine-scale resolution by using multiple regression analysis with easily calculated topographic factors. For the multiple regression analysis with site index as a dependent variable, three topographic factors (the effective relief, openness, and elevation) were adopted as independent variables. Approximately 68% of the variance was explained, and the effective relief was the variable with the greatest influence. This means that it is possible to predict forest productivity at a finer scale of resolution than ever before. For sustainable forest management of sites where environmental conservation and forestry are conflicting, it is useful to estimate the site index at the finest scale of resolution practically available in the field. It might be possible to improve estimation accuracy by examining further environmental factors in the future.展开更多
Multiscalar topography influence on soil distribution has a complex pattern that is related to overlay of pedological processes which occurred at different times, and these driving forces are correlated with many geom...Multiscalar topography influence on soil distribution has a complex pattern that is related to overlay of pedological processes which occurred at different times, and these driving forces are correlated with many geomorphologic scales. In this sense, the present study tested the hypothesis whether multiscale geomorphometric generalized covariables can improve pedometric modeling. To achieve this goal, this case study applied the Random Forest algorithm to a multiscale geomorphometric database to predict soil surface attributes. The study area is in phanerozoic sedimentary basins, in the Alter do Ch<span style="white-space:nowrap;">ã</span>o geological formation, Eastern Amazon, Brazil. The multiscale geomorphometric generalization was applied at general and specific geomorphometric covariables, producing groups for each scale combination. The modeling was run using Random Forest for A-horizon thickness, pH, silt and sand content. For model evaluation, visual analysis of digital maps, metrics of forest structures and effect of variables on prediction were used. For evaluation of soil textural classifications, the confusion matrix with a Kappa index, and the user’s and producer’s accuracies were employed. The geomorphometry generalization tends to smooth curvatures and produces identifiable geomorphic representations at sub-watershed and watershed levels. The forest structures and effect of variables on prediction are in agreement with pedological knowledge. The multiscale geomorphometric generalized covariables improved accuracy metrics of soil surface texture classification, with the Kappa Index going from 43% to 62%. Therefore, it can be argued that topography influences soil distribution at combined coarser spatial scales and is able to predict soil particle size contents in the studied watershed. Future development of the multiscale geomorphometric generalization framework could include generalization methods concerning preservation of features, landform classification adaptable at multiple scales.展开更多
This article details how forest soil moisture content (MC) and subsequent resistances to cone penetration (referred below as Cone Index, CI) vary by daily weather, season, topography, site and soil properties across e...This article details how forest soil moisture content (MC) and subsequent resistances to cone penetration (referred below as Cone Index, CI) vary by daily weather, season, topography, site and soil properties across eleven harvest blocks in northwestern New Brunswick. The MC- and CI-affecting soil variables refer to density, texture, organic matter content, coarse fragment content, and topographic position (i.e., elevation, and the seasonally affected cartographic depth-to-water (DTW) pattern). The harvest blocks were transect-sampled inside and outside their wood-forwarding tracks at varying times throughout the year. In detail, 61% of the pore-filled moisture content (MCPS) determinations inside and outside the tracks could be related to topographic position, coarse fragments, bulk density, and forest cover type specifications. In addition, 40% of the CI variations could be related to soil depth, MCPS, and block-specific cover type. Actual versus model-projected uncertainties amounted to ΔMCPS ≤ ± 15% and ΔCI ≤ ± 0.5 MPa, 8 times out of 10. Block-centered MC and CI projections were obtained through: 1) daily hydrological modelling using daily precipitation and air temperature weather-station records nearest each block, and 2) digitally mapped variations in soil properties, elevation, DTW and forest cover type, done at 10 m resolution.展开更多
This article describes how the cartographic depth-to-water (DTW) index in combination with other variables can be used to quantify, model and map the distribution of common forest floor bryophytes, at 1 m resolution. ...This article describes how the cartographic depth-to-water (DTW) index in combination with other variables can be used to quantify, model and map the distribution of common forest floor bryophytes, at 1 m resolution. This was done by way of a case study, using 12 terrain and climate representative locations across New Brunswick, Canada. The presence/absence by moss species was determined at each location along upland-to-wetland transects within >10-m spaced 1-m2 forest floor plots. It was found that Bazzania trilobata, Dicranum polysetum, Polytrichum commune, Hylocomium splendens, and Pleurozium schreberi had greater probabilities of occurrence in well-drained forested areas, whereas Sphagnum fuscum and Sphagnum girgensohnii dominated in low-lying wet areas. The presence/absence of each species was quantified by way of logistic regression analyses, using DTW, slope, canopy closure, forest litter depth, ecosite type (8 classes), nutrient regime (4 classes, poor to rich);vegetation type (deciduous, coniferous, mixed, and shrubs), and macro- and micro-topography (upland, wetland;mounds, pits) as predictor variables. Among these, log10DTW and forest litter depth were the most consistent predictor variables, followed by mound versus pit. For the mapping purpose, only log10DTW and already mapped classifications for upland versus wetland and vegetation type were used to predict the probability of occurrences for the most frequent moss species, namely, D. polysetum, P. schreberi and Sphagnum spp. The overall accuracy for doing this ranged from 67% to 83%, with false positives and negatives amounting to 18% to 42%. The overall classification accuracy exceeded the probability by chance alone at 76.8%, with the significance level reached at 75.3%. The average level of probability by chance alone was 60.3%.展开更多
Background： An examination of the distribution of ancient charcoal kiln sites in the forest landscape seems to be worthwhile, since general trends in the selection of suitable kiln site locations in the past might be...Background： An examination of the distribution of ancient charcoal kiln sites in the forest landscape seems to be worthwhile, since general trends in the selection of suitable kiln site locations in the past might become obvious. In this way forest landscape elements with a more intense usage by charcoal burning can be identified. By doing this, we can expect to gain information on the former condition and tree species composition of woodland. Investigations on the spatial distribution of charcoal kiln sites in relation to landscape attributes are sparse, however, probably due to the high on-site mapping effort. The outstanding suitability of LiDAR-derived digital terrain models （DTMs） for the detection of charcoal kiln sites has been recently proved. Hence, DTM-based surveys of charcoal kiln sites represent a promising attempt to fill this research gap. Methods： Based on DTM-based surveys, we analyzed the spatial distribution of charcoal kiln sites in two forest landscapes in the German federal state of Hesse： Reinhardswald and Kellerwald-Edersee National Park. In doing so, we considered the landscape attibutes ＂tree species composition＂, ＂water supply status＂, ＂nutrient supply status＂, ＂soil complex classes＂, ＂altitude＂, ＂exposition＂, and ＂inclination＂. Results： We found that charcoal kiln sites were established preferably on hillside locations that provided optimal growing and regeneration conditions for European beech （Fagus sylvatico） due to their acidic brown soils and sufficient water supply. These results are in line with instructions for the selection of appropriate kiln site locations, found in literature from the 18th to the 19th century. Conclusions： We conclude that there were well-stocked, beech-dominated deciduous forest stands in northern Hesse before 1800, particularly at poorly accessible hillside locations. These large stocks of beech wood were utilized by the governments of the different Hessian territories through the establishment of ironworks and hammer mills. Our argumentation is well in line with findings which underline that not all Hessian forests were overexploited in the 18th century. Frequently repeated complaints about ＂wood shortage＂ seemed to be more a political instrument than reality, not only in Hesse, but all over Europe. Consequently, a differentiated assessment of woodland conditions in proto-industrial times is strictly advised, even if contemporary sources draw a dark picture of the historic situation.展开更多
基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用Focus...基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用FocusS350/350 PLUS三维激光扫描仪获取3块不同树龄橡胶树的样地数据。然后,作为细化建模的重点,将枝干点云从原始树点中分离出来,再将其过度分割为若干点云簇,通过相邻点云簇判断是否有分枝以及动态确定骨架点间距,并将其运用在空间殖民算法以此来生成树的三维骨架点和骨架点连通性链表,根据连通链表结构自动识别树木中的主枝干和各个一级分枝,再通过广义圆柱体生成树干完成树木三维重建。最后,利用数字孪生技术对这3块不同树龄样地树木进行三维实景建模,使其穿越时空在同一空间中重现,以便更为直观地观察树木在生长过程中的形态变化。该算法得到的橡胶树胸径与实测值比对为,决定系数(R^(2))>0.91,均方根误差(root mean square Error,RMSE)<1.00 cm;主枝干与一级枝干的分枝角为,R^(2)>0.91,RMSE<2.93;一级枝干直径为,R^(2)>0.90,RMSE<1.41 cm;将3个树龄放在一起计算其生长参数,并与实测值进行对比,发现该算法同样适用于异龄林样地的各个生长参数计算。同时发现橡胶树的一级枝条的直径越大,其相对应的叶团簇体积就越大。运用人工智能的理论模型来处理林木的激光点云数据,旨在为森林的可视化以及树木骨架结构的智能化分析与处理等研究领域提供有价值的参考。展开更多
文摘Subtropical “Yambaru” forest, situated in the northern part of Okinawa Island of Japan, has a precious ecosystem inhabited by many endemic species. However, this region is also the center for forestry on Okinawa. Therefore, sustainable forestry activities should take into consideration the natural environment. To contribute to sustainable forest management in the region, we conducted prediction of the site index at fine-scale resolution by using multiple regression analysis with easily calculated topographic factors. For the multiple regression analysis with site index as a dependent variable, three topographic factors (the effective relief, openness, and elevation) were adopted as independent variables. Approximately 68% of the variance was explained, and the effective relief was the variable with the greatest influence. This means that it is possible to predict forest productivity at a finer scale of resolution than ever before. For sustainable forest management of sites where environmental conservation and forestry are conflicting, it is useful to estimate the site index at the finest scale of resolution practically available in the field. It might be possible to improve estimation accuracy by examining further environmental factors in the future.
文摘Multiscalar topography influence on soil distribution has a complex pattern that is related to overlay of pedological processes which occurred at different times, and these driving forces are correlated with many geomorphologic scales. In this sense, the present study tested the hypothesis whether multiscale geomorphometric generalized covariables can improve pedometric modeling. To achieve this goal, this case study applied the Random Forest algorithm to a multiscale geomorphometric database to predict soil surface attributes. The study area is in phanerozoic sedimentary basins, in the Alter do Ch<span style="white-space:nowrap;">ã</span>o geological formation, Eastern Amazon, Brazil. The multiscale geomorphometric generalization was applied at general and specific geomorphometric covariables, producing groups for each scale combination. The modeling was run using Random Forest for A-horizon thickness, pH, silt and sand content. For model evaluation, visual analysis of digital maps, metrics of forest structures and effect of variables on prediction were used. For evaluation of soil textural classifications, the confusion matrix with a Kappa index, and the user’s and producer’s accuracies were employed. The geomorphometry generalization tends to smooth curvatures and produces identifiable geomorphic representations at sub-watershed and watershed levels. The forest structures and effect of variables on prediction are in agreement with pedological knowledge. The multiscale geomorphometric generalized covariables improved accuracy metrics of soil surface texture classification, with the Kappa Index going from 43% to 62%. Therefore, it can be argued that topography influences soil distribution at combined coarser spatial scales and is able to predict soil particle size contents in the studied watershed. Future development of the multiscale geomorphometric generalization framework could include generalization methods concerning preservation of features, landform classification adaptable at multiple scales.
文摘This article details how forest soil moisture content (MC) and subsequent resistances to cone penetration (referred below as Cone Index, CI) vary by daily weather, season, topography, site and soil properties across eleven harvest blocks in northwestern New Brunswick. The MC- and CI-affecting soil variables refer to density, texture, organic matter content, coarse fragment content, and topographic position (i.e., elevation, and the seasonally affected cartographic depth-to-water (DTW) pattern). The harvest blocks were transect-sampled inside and outside their wood-forwarding tracks at varying times throughout the year. In detail, 61% of the pore-filled moisture content (MCPS) determinations inside and outside the tracks could be related to topographic position, coarse fragments, bulk density, and forest cover type specifications. In addition, 40% of the CI variations could be related to soil depth, MCPS, and block-specific cover type. Actual versus model-projected uncertainties amounted to ΔMCPS ≤ ± 15% and ΔCI ≤ ± 0.5 MPa, 8 times out of 10. Block-centered MC and CI projections were obtained through: 1) daily hydrological modelling using daily precipitation and air temperature weather-station records nearest each block, and 2) digitally mapped variations in soil properties, elevation, DTW and forest cover type, done at 10 m resolution.
文摘This article describes how the cartographic depth-to-water (DTW) index in combination with other variables can be used to quantify, model and map the distribution of common forest floor bryophytes, at 1 m resolution. This was done by way of a case study, using 12 terrain and climate representative locations across New Brunswick, Canada. The presence/absence by moss species was determined at each location along upland-to-wetland transects within >10-m spaced 1-m2 forest floor plots. It was found that Bazzania trilobata, Dicranum polysetum, Polytrichum commune, Hylocomium splendens, and Pleurozium schreberi had greater probabilities of occurrence in well-drained forested areas, whereas Sphagnum fuscum and Sphagnum girgensohnii dominated in low-lying wet areas. The presence/absence of each species was quantified by way of logistic regression analyses, using DTW, slope, canopy closure, forest litter depth, ecosite type (8 classes), nutrient regime (4 classes, poor to rich);vegetation type (deciduous, coniferous, mixed, and shrubs), and macro- and micro-topography (upland, wetland;mounds, pits) as predictor variables. Among these, log10DTW and forest litter depth were the most consistent predictor variables, followed by mound versus pit. For the mapping purpose, only log10DTW and already mapped classifications for upland versus wetland and vegetation type were used to predict the probability of occurrences for the most frequent moss species, namely, D. polysetum, P. schreberi and Sphagnum spp. The overall accuracy for doing this ranged from 67% to 83%, with false positives and negatives amounting to 18% to 42%. The overall classification accuracy exceeded the probability by chance alone at 76.8%, with the significance level reached at 75.3%. The average level of probability by chance alone was 60.3%.
文摘Background： An examination of the distribution of ancient charcoal kiln sites in the forest landscape seems to be worthwhile, since general trends in the selection of suitable kiln site locations in the past might become obvious. In this way forest landscape elements with a more intense usage by charcoal burning can be identified. By doing this, we can expect to gain information on the former condition and tree species composition of woodland. Investigations on the spatial distribution of charcoal kiln sites in relation to landscape attributes are sparse, however, probably due to the high on-site mapping effort. The outstanding suitability of LiDAR-derived digital terrain models （DTMs） for the detection of charcoal kiln sites has been recently proved. Hence, DTM-based surveys of charcoal kiln sites represent a promising attempt to fill this research gap. Methods： Based on DTM-based surveys, we analyzed the spatial distribution of charcoal kiln sites in two forest landscapes in the German federal state of Hesse： Reinhardswald and Kellerwald-Edersee National Park. In doing so, we considered the landscape attibutes ＂tree species composition＂, ＂water supply status＂, ＂nutrient supply status＂, ＂soil complex classes＂, ＂altitude＂, ＂exposition＂, and ＂inclination＂. Results： We found that charcoal kiln sites were established preferably on hillside locations that provided optimal growing and regeneration conditions for European beech （Fagus sylvatico） due to their acidic brown soils and sufficient water supply. These results are in line with instructions for the selection of appropriate kiln site locations, found in literature from the 18th to the 19th century. Conclusions： We conclude that there were well-stocked, beech-dominated deciduous forest stands in northern Hesse before 1800, particularly at poorly accessible hillside locations. These large stocks of beech wood were utilized by the governments of the different Hessian territories through the establishment of ironworks and hammer mills. Our argumentation is well in line with findings which underline that not all Hessian forests were overexploited in the 18th century. Frequently repeated complaints about ＂wood shortage＂ seemed to be more a political instrument than reality, not only in Hesse, but all over Europe. Consequently, a differentiated assessment of woodland conditions in proto-industrial times is strictly advised, even if contemporary sources draw a dark picture of the historic situation.
文摘基于激光雷达(Light Detection And Ranging,LiDAR)数据重建树体三维模型并精准获取林木空间枝干结构参数对林木性状评价、森林动态经营管理与可视化研究具有重要意义。为此提出一种基于骨架细化提取的树木模型重建方法。首先,采用FocusS350/350 PLUS三维激光扫描仪获取3块不同树龄橡胶树的样地数据。然后,作为细化建模的重点,将枝干点云从原始树点中分离出来,再将其过度分割为若干点云簇,通过相邻点云簇判断是否有分枝以及动态确定骨架点间距,并将其运用在空间殖民算法以此来生成树的三维骨架点和骨架点连通性链表,根据连通链表结构自动识别树木中的主枝干和各个一级分枝,再通过广义圆柱体生成树干完成树木三维重建。最后,利用数字孪生技术对这3块不同树龄样地树木进行三维实景建模,使其穿越时空在同一空间中重现,以便更为直观地观察树木在生长过程中的形态变化。该算法得到的橡胶树胸径与实测值比对为,决定系数(R^(2))>0.91,均方根误差(root mean square Error,RMSE)<1.00 cm;主枝干与一级枝干的分枝角为,R^(2)>0.91,RMSE<2.93;一级枝干直径为,R^(2)>0.90,RMSE<1.41 cm;将3个树龄放在一起计算其生长参数,并与实测值进行对比,发现该算法同样适用于异龄林样地的各个生长参数计算。同时发现橡胶树的一级枝条的直径越大,其相对应的叶团簇体积就越大。运用人工智能的理论模型来处理林木的激光点云数据,旨在为森林的可视化以及树木骨架结构的智能化分析与处理等研究领域提供有价值的参考。
