Background: Treeline dynamics have inevitable impacts on the forest treeline structure and composition. The present research sought to estimate treeline movement and structural shifts in response to recent warming in ...Background: Treeline dynamics have inevitable impacts on the forest treeline structure and composition. The present research sought to estimate treeline movement and structural shifts in response to recent warming in Cehennemdere, Turkey. After implementing an atmospheric correction, the geo-shifting of images was performed to match images together for a per pixel trend analysis. We developed a new approach based on the NDVI, LST(land surface temperature) data, air temperature data, and forest stand maps for a 43-year period. The forest treeline border was mapped on the forest stand maps for 1970, 1992, 2002, and 2013 to identify shifts in the treeline altitudes, and then profile statistics were calculated for each period. Twenty sample plots(10 × 10 pixels) were selected to estimatethe NDVI and LST shifts across the forest timberline using per-pixel trend analysis and non-parametric Spearman’s correlation analysis. In addition, the spatial and temporal shifts in treeline tree species were computed within the selected plots for four time periods on the forest stand maps to determine the pioneer tree species.Results: A statistically significant increasing trend in all climate variables was observed, with the highest slopein the monthly average mean July temperature(tau = 0.62, ρ < 0.00). The resultant forest stand maps showed a geographical expansion of the treeline in both the highest altitudes(22 m–45 m) and the lowest altitudes(20 m–105 m) from 1970 to 2013. The per pixel trend analysis indicated an increasing trend in the NDVI and LST values within the selected plots. Moreover, increases in the LST were highly correlated with increases in the NDVIbetween 1984 and 2017(r = 0.75, ρ < 0.05). Cedrus libani and Juniperus communis app. were two pioneer tree species that expanded and grew consistently on open lands, primarily on rocks and soil-covered areas, from 1970 to 2013.Conclusion: The present study il ustrated that forest treeline dynamics and treeline structural changes can be detected using two data sources. Additionally, the results will have a significant contribution to and implication for treeline movement studies and forest landscape change investigations attempting to project climate change impacts on tree species in response to climate warming. The results will assist forest managers in establishing some developmentaladaptation strategies for forest treeline ecotones.展开更多
The structural diversity in urban forests is highly important to protect biodiversity. In particular, fruit trees and bush species, cavity-bearing trees and coarse, woody debris provide habitats for animals to feed, n...The structural diversity in urban forests is highly important to protect biodiversity. In particular, fruit trees and bush species, cavity-bearing trees and coarse, woody debris provide habitats for animals to feed, nest and hide. Improper silvicultural practices, intensive recreational use and illegal harvesting lead to a decline in the structural diversity in forests within larger metropolitan cities. It is important to monitor the structural diversity at definite time intervals using effective technologies with a view to instituting the necessary conservation measures. The use of satellite images seems to be appropriate to this end. Here we aimed to identify the associations between the textural features derived from the satellite images with different spatial resolutions and the structural diversity indices in urban forest stands (Shannon-Wiener index, complexity index, dominance index and density of wildlife trees). RapidEye images with a spatial resolution of 5?m × 5?m, ASTER images with a spatial resolution of 15?m × 15?m and Landsat-8 ETM satellite images with a spatial resolution of 30?m × 30?m were used in this study. The first-order (standard deviation of gray levels) and second order (GLCM entropy, GLCM contrast and GLCM correlation) textural features were calculated from the satellite images. When associations between textural features in the images and the structural diversity indices were assessed using the Pearson correlation coefficient, very high associations were found between the image textural features and the diversity indices. The highest association was found between the standard deviation of gray levels (SDGL<sub>RAP</sub>) derived from RVI<sub>RAP</sub> of RapidEye image and the Shannon-Wiener index (H <sub>h</sub>) calculated on the basis of tree height (R <sup>2</sup>?=?0.64). The findings revealed that RapidEye satellite images with a spatial resolution of 5?m × 5?m are most suitable for estimating the structural diversity in urban forests.展开更多
Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics tha...Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics that afect the quality of the prediction model depends on window size.However,suitable window sizes are usually determined by trial and error.There are a limited number of published studies evaluating appropriate window sizes for diferent remote sensing data.This research investigated the efect of window size on predicting forest structural variables using airborne LiDAR data,digital aerial image and WorldView-3 satellite image.Results:In the WorldView-3 and digital aerial image,signifcant diferences were observed in the prediction accuracies of the structural variables according to diferent window sizes.For the estimation based on WorldView-3 in black pine stands,the optimal window sizes for stem number(N),volume(V),basal area(BA)and mean height(H)were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.In oak stands,the R^(2) values of each moving window size were almost identical for N and BA.The optimal window size was 400 m^(2) for V and 600 m^(2) for H.For the estimation based on aerial image in black pine stands,the 800 m^(2) window size was optimal for N and H,the 600 m^(2) window size was optimal for V and the 1000 m^(2) window size was optimal for BA.In the oak stands,the optimal window sizes for N,V,BA and H were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.The optimal window sizes may need to be scaled up or down to match the stand canopy components.In the LiDAR data,the R^(2) values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion:This study illustrated that the window size has an efect on the prediction accuracy in estimating forest structural variables based on remote sensing data.Moreover,the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.展开更多
文摘Background: Treeline dynamics have inevitable impacts on the forest treeline structure and composition. The present research sought to estimate treeline movement and structural shifts in response to recent warming in Cehennemdere, Turkey. After implementing an atmospheric correction, the geo-shifting of images was performed to match images together for a per pixel trend analysis. We developed a new approach based on the NDVI, LST(land surface temperature) data, air temperature data, and forest stand maps for a 43-year period. The forest treeline border was mapped on the forest stand maps for 1970, 1992, 2002, and 2013 to identify shifts in the treeline altitudes, and then profile statistics were calculated for each period. Twenty sample plots(10 × 10 pixels) were selected to estimatethe NDVI and LST shifts across the forest timberline using per-pixel trend analysis and non-parametric Spearman’s correlation analysis. In addition, the spatial and temporal shifts in treeline tree species were computed within the selected plots for four time periods on the forest stand maps to determine the pioneer tree species.Results: A statistically significant increasing trend in all climate variables was observed, with the highest slopein the monthly average mean July temperature(tau = 0.62, ρ < 0.00). The resultant forest stand maps showed a geographical expansion of the treeline in both the highest altitudes(22 m–45 m) and the lowest altitudes(20 m–105 m) from 1970 to 2013. The per pixel trend analysis indicated an increasing trend in the NDVI and LST values within the selected plots. Moreover, increases in the LST were highly correlated with increases in the NDVIbetween 1984 and 2017(r = 0.75, ρ < 0.05). Cedrus libani and Juniperus communis app. were two pioneer tree species that expanded and grew consistently on open lands, primarily on rocks and soil-covered areas, from 1970 to 2013.Conclusion: The present study il ustrated that forest treeline dynamics and treeline structural changes can be detected using two data sources. Additionally, the results will have a significant contribution to and implication for treeline movement studies and forest landscape change investigations attempting to project climate change impacts on tree species in response to climate warming. The results will assist forest managers in establishing some developmentaladaptation strategies for forest treeline ecotones.
基金supported by the Scientific and Technological Research Council of Turkey(TBTAK)under the project no.114O015
文摘The structural diversity in urban forests is highly important to protect biodiversity. In particular, fruit trees and bush species, cavity-bearing trees and coarse, woody debris provide habitats for animals to feed, nest and hide. Improper silvicultural practices, intensive recreational use and illegal harvesting lead to a decline in the structural diversity in forests within larger metropolitan cities. It is important to monitor the structural diversity at definite time intervals using effective technologies with a view to instituting the necessary conservation measures. The use of satellite images seems to be appropriate to this end. Here we aimed to identify the associations between the textural features derived from the satellite images with different spatial resolutions and the structural diversity indices in urban forest stands (Shannon-Wiener index, complexity index, dominance index and density of wildlife trees). RapidEye images with a spatial resolution of 5?m × 5?m, ASTER images with a spatial resolution of 15?m × 15?m and Landsat-8 ETM satellite images with a spatial resolution of 30?m × 30?m were used in this study. The first-order (standard deviation of gray levels) and second order (GLCM entropy, GLCM contrast and GLCM correlation) textural features were calculated from the satellite images. When associations between textural features in the images and the structural diversity indices were assessed using the Pearson correlation coefficient, very high associations were found between the image textural features and the diversity indices. The highest association was found between the standard deviation of gray levels (SDGL<sub>RAP</sub>) derived from RVI<sub>RAP</sub> of RapidEye image and the Shannon-Wiener index (H <sub>h</sub>) calculated on the basis of tree height (R <sup>2</sup>?=?0.64). The findings revealed that RapidEye satellite images with a spatial resolution of 5?m × 5?m are most suitable for estimating the structural diversity in urban forests.
文摘Background:Determining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data.Because the value of the reference laser and image metrics that afect the quality of the prediction model depends on window size.However,suitable window sizes are usually determined by trial and error.There are a limited number of published studies evaluating appropriate window sizes for diferent remote sensing data.This research investigated the efect of window size on predicting forest structural variables using airborne LiDAR data,digital aerial image and WorldView-3 satellite image.Results:In the WorldView-3 and digital aerial image,signifcant diferences were observed in the prediction accuracies of the structural variables according to diferent window sizes.For the estimation based on WorldView-3 in black pine stands,the optimal window sizes for stem number(N),volume(V),basal area(BA)and mean height(H)were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.In oak stands,the R^(2) values of each moving window size were almost identical for N and BA.The optimal window size was 400 m^(2) for V and 600 m^(2) for H.For the estimation based on aerial image in black pine stands,the 800 m^(2) window size was optimal for N and H,the 600 m^(2) window size was optimal for V and the 1000 m^(2) window size was optimal for BA.In the oak stands,the optimal window sizes for N,V,BA and H were determined as 1000 m^(2),100 m^(2),100 m^(2) and 600 m^(2),respectively.The optimal window sizes may need to be scaled up or down to match the stand canopy components.In the LiDAR data,the R^(2) values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion:This study illustrated that the window size has an efect on the prediction accuracy in estimating forest structural variables based on remote sensing data.Moreover,the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.
