The destruction of the ecological system caused by urban expansion has led to the environmental deterioration,cities have become increasingly vulnerable.In this study,six districts and counties along the Yellow River ...The destruction of the ecological system caused by urban expansion has led to the environmental deterioration,cities have become increasingly vulnerable.In this study,six districts and counties along the Yellow River in Zhengzhou were selected as the study area.First,green infrastructure elements were extracted by morphological spatial pattern analysis.Then,outside the urban areas,we used connectivity analysis to evaluate the importance of core areas,adopted minimum cumulative resistance model to extract potential corridors,and identified the important corridors by using the gravity model.Finally,in the urban areas,we set up an evaluation system to assess the demands for ecosystem services.The results showed that:(1)Seven landscape types of green infrastructure be identified in study area.(2)There are 17 vital cores,136 potential corridors,and 24 vital corridors outside the urban areas.(3)The blocks with high demand for ecosystem services are mostly concentrated in the old blocks with dense populations and poor infrastructure,and there are 5 blocks with comprehensive high-demand.Based on identified importance for green infrastructure land space,and high-demand level for ecosystem services areas in this study,a green infrastructure net plan was proposed based on spatial conservation prioritisation.展开更多
Background: Remote sensing-based mapping of forest Ecosystem Service(ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize t...Background: Remote sensing-based mapping of forest Ecosystem Service(ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.Methods: This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.Results: Prediction models based on Airborne Laser Scanning(ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses(berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97%of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.Conclusions: Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m~2, i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.展开更多
Linear infrastructures(e.g.,roads,railways,pipelines,and powerlines)pose a serious threat to wildlife,due to the risk of wildlife-vehicle collisions(roadkills).The placement of mitigation measures,such as crossing str...Linear infrastructures(e.g.,roads,railways,pipelines,and powerlines)pose a serious threat to wildlife,due to the risk of wildlife-vehicle collisions(roadkills).The placement of mitigation measures,such as crossing structures,should consider species’life cycles and ecological requirements.Such an assessment would require data collection over large areas,which may be possible by employing citizen science.In this study,we aimed to identify spatiotemporal trends of roadkill occurrence using citizen science data from one of the most urbanized and biodiversityrich regions of Italy.Temporal trends were analyzed using generalized additive models,while landscape patterns were assessed by identifying significant thresholds over land cover gradients,related to increases in relative roadkill abundance,by employing threshold indicator taxa analysis.Our approach recorded a total of 529 roadkills,including 33 different species,comprising 13 mammal,10 bird,6 reptile,and 2 amphibian species.Statistical analysis indicated significant temporal trends for the red fox,the European hedgehog,the stone marten and the European badger,with peaks in roadkill occurrence between the winter and spring months.Relative roadkill abundance increased mostly in landscapes with anthropogenic land cover classes,such as complex cultivations,orchards,or urban surfaces.Our results allowed us to develop a map of potential roadkill risk that could assist in planning the placement of mitigation measures.Citizen science contributions from highly populated areas allowed data collection over a large area and a dense road network,and also directly led to the evaluation of management decisional options.展开更多
基金This work was supported by the National Natural Science Foundation of China[31600579]Henan Provincial Science and Technology Research Project[202102110234]Key Research Projects of Higher Education Institutions in Henan Province,China[21A220003].
文摘The destruction of the ecological system caused by urban expansion has led to the environmental deterioration,cities have become increasingly vulnerable.In this study,six districts and counties along the Yellow River in Zhengzhou were selected as the study area.First,green infrastructure elements were extracted by morphological spatial pattern analysis.Then,outside the urban areas,we used connectivity analysis to evaluate the importance of core areas,adopted minimum cumulative resistance model to extract potential corridors,and identified the important corridors by using the gravity model.Finally,in the urban areas,we set up an evaluation system to assess the demands for ecosystem services.The results showed that:(1)Seven landscape types of green infrastructure be identified in study area.(2)There are 17 vital cores,136 potential corridors,and 24 vital corridors outside the urban areas.(3)The blocks with high demand for ecosystem services are mostly concentrated in the old blocks with dense populations and poor infrastructure,and there are 5 blocks with comprehensive high-demand.Based on identified importance for green infrastructure land space,and high-demand level for ecosystem services areas in this study,a green infrastructure net plan was proposed based on spatial conservation prioritisation.
基金originally supported by the Research Funds of University of Helsinki
文摘Background: Remote sensing-based mapping of forest Ecosystem Service(ES) indicators has become increasingly popular. The resulting maps may enable to spatially assess the provisioning potential of ESs and prioritize the land use in subsequent decision analyses. However, the mapping is often based on readily available data, such as land cover maps and other publicly available databases, and ignoring the related uncertainties.Methods: This study tested the potential to improve the robustness of the decisions by means of local model fitting and uncertainty analysis. The quality of forest land use prioritization was evaluated under two different decision support models: either using the developed models deterministically or in corporation with the uncertainties of the models.Results: Prediction models based on Airborne Laser Scanning(ALS) data explained the variation in proxies of the suitability of forest plots for maintaining biodiversity, producing timber, storing carbon, or providing recreational uses(berry picking and visual amenity) with RMSEs of 15%–30%, depending on the ES. The RMSEs of the ALS-based predictions were 47%–97%of those derived from forest resource maps with a similar resolution. Due to applying a similar field calibration step on both of the data sources, the difference can be attributed to the better ability of ALS to explain the variation in the ES proxies.Conclusions: Despite the different accuracies, proxy values predicted by both the data sources could be used for a pixel-based prioritization of land use at a resolution of 250 m~2, i.e., in a considerably more detailed scale than required by current operational forest management. The uncertainty analysis indicated that maps of the ES provisioning potential should be prepared separately based on expected and extreme outcomes of the ES proxy models to fully describe the production possibilities of the landscape under the uncertainties in the models.
文摘Linear infrastructures(e.g.,roads,railways,pipelines,and powerlines)pose a serious threat to wildlife,due to the risk of wildlife-vehicle collisions(roadkills).The placement of mitigation measures,such as crossing structures,should consider species’life cycles and ecological requirements.Such an assessment would require data collection over large areas,which may be possible by employing citizen science.In this study,we aimed to identify spatiotemporal trends of roadkill occurrence using citizen science data from one of the most urbanized and biodiversityrich regions of Italy.Temporal trends were analyzed using generalized additive models,while landscape patterns were assessed by identifying significant thresholds over land cover gradients,related to increases in relative roadkill abundance,by employing threshold indicator taxa analysis.Our approach recorded a total of 529 roadkills,including 33 different species,comprising 13 mammal,10 bird,6 reptile,and 2 amphibian species.Statistical analysis indicated significant temporal trends for the red fox,the European hedgehog,the stone marten and the European badger,with peaks in roadkill occurrence between the winter and spring months.Relative roadkill abundance increased mostly in landscapes with anthropogenic land cover classes,such as complex cultivations,orchards,or urban surfaces.Our results allowed us to develop a map of potential roadkill risk that could assist in planning the placement of mitigation measures.Citizen science contributions from highly populated areas allowed data collection over a large area and a dense road network,and also directly led to the evaluation of management decisional options.
