The aggregation index (AI) is a classical ecology calculation method, which has been widely used for measuring the aggregation level of spatial patterns within a landscape scale in landscape ecological studies. Howeve...The aggregation index (AI) is a classical ecology calculation method, which has been widely used for measuring the aggregation level of spatial patterns within a landscape scale in landscape ecological studies. However, it has certain limitions. For instance, identical results can be obtained by AI even when the shape and number of landscape patches are totally different in two landscape units. Furthermore, the value of AI approaches to 1 if the landscape patch is large enough. To solve these problems, a logical limitation of the original AI equation was revised firstly. Secondly, an improved AI-J was developed based on the awareness of the effects of spatial distribution characteristics of patches and changing spatial scale on AI operation. Finally, the accuracy of AI and AI-J results were evaluated through a case study of city green patches in Chengdu, P. R. China. The results show that the calculated result of AI-J is more precise than that of AI and AI-J can be used to compare a certain landscape class under different spatial scales.展开更多
Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents...Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. The study demonstrates the potential of this methodology by generating spatial layers at the landscape scale to inform on the state of rangeland ecosystems. The workflow showcases the power of remote sensing technology to map ecological states and addresses limitations in spatial coverage by integrating UAV and satellite data. By utilizing the bare ground LPI metric, which indicates the connectedness of bare ground, the methodology enables the classification of ecological states at a regional scale. This cost-effective approach potentially offers a standardized and reproducible method applicable across different sites and regions. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. The workflow serves as a blueprint for scaling up ecological states mapping in similar semi-arid rangelands. Further work should involve refining the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological states mapping.展开更多
城市用地空间扩张对生态环境的影响映射出人类社会活动和生态环境保护之间的交互作用,系统地研究城市空间无序蔓延所诱发的城市土地利用方式变化对城市生态环境的影响程度,对助推中国生态文明建设目标具有重要现实意义。为探究合肥市城...城市用地空间扩张对生态环境的影响映射出人类社会活动和生态环境保护之间的交互作用,系统地研究城市空间无序蔓延所诱发的城市土地利用方式变化对城市生态环境的影响程度,对助推中国生态文明建设目标具有重要现实意义。为探究合肥市城市扩张对生态安全格局的影响程度,综合运用生态遥感指数、最小累积阻力模型、电路理论和斑块生成土地利用模拟模型,构建合肥市生态安全格局,识别生态夹点和生态障碍点,再从模拟验证的基础上(总体精度为94.71%,Kappa系数为90.04%,Fom值为0.102),预测了2030—2040年的城市扩张,并根据预测结果探讨城市扩张对区域生态安全格局影响程度。研究发现:合肥市生态环境质量整体呈现南高中低的分布格局,识别出合肥市生态源地共计35处,源地间活跃生态廊道70条,非活跃廊道共17条,生态夹点290个,生态障碍点112个。2020—2040年合肥市城乡、工矿居民用地、林地、水域和未利用土地面积将不断增加,而耕地以及草地面积将持续减少。2020—2040年期间城镇建成区分别侵占了生态廊道、源地、夹点、障碍点面积为55.95、10.51、1.04、1.35 km 2。研究结果可为今后快速发展城市的生态环境治理和国土空间生态保护修复工作提供理论依据和技术参考。展开更多
基金Funded by the National 11th Five-Year Technology Based PlanTopic of China (No. 2006BAJ05A13)
文摘The aggregation index (AI) is a classical ecology calculation method, which has been widely used for measuring the aggregation level of spatial patterns within a landscape scale in landscape ecological studies. However, it has certain limitions. For instance, identical results can be obtained by AI even when the shape and number of landscape patches are totally different in two landscape units. Furthermore, the value of AI approaches to 1 if the landscape patch is large enough. To solve these problems, a logical limitation of the original AI equation was revised firstly. Secondly, an improved AI-J was developed based on the awareness of the effects of spatial distribution characteristics of patches and changing spatial scale on AI operation. Finally, the accuracy of AI and AI-J results were evaluated through a case study of city green patches in Chengdu, P. R. China. The results show that the calculated result of AI-J is more precise than that of AI and AI-J can be used to compare a certain landscape class under different spatial scales.
文摘Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. The study demonstrates the potential of this methodology by generating spatial layers at the landscape scale to inform on the state of rangeland ecosystems. The workflow showcases the power of remote sensing technology to map ecological states and addresses limitations in spatial coverage by integrating UAV and satellite data. By utilizing the bare ground LPI metric, which indicates the connectedness of bare ground, the methodology enables the classification of ecological states at a regional scale. This cost-effective approach potentially offers a standardized and reproducible method applicable across different sites and regions. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. The workflow serves as a blueprint for scaling up ecological states mapping in similar semi-arid rangelands. Further work should involve refining the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological states mapping.
文摘城市用地空间扩张对生态环境的影响映射出人类社会活动和生态环境保护之间的交互作用,系统地研究城市空间无序蔓延所诱发的城市土地利用方式变化对城市生态环境的影响程度,对助推中国生态文明建设目标具有重要现实意义。为探究合肥市城市扩张对生态安全格局的影响程度,综合运用生态遥感指数、最小累积阻力模型、电路理论和斑块生成土地利用模拟模型,构建合肥市生态安全格局,识别生态夹点和生态障碍点,再从模拟验证的基础上(总体精度为94.71%,Kappa系数为90.04%,Fom值为0.102),预测了2030—2040年的城市扩张,并根据预测结果探讨城市扩张对区域生态安全格局影响程度。研究发现:合肥市生态环境质量整体呈现南高中低的分布格局,识别出合肥市生态源地共计35处,源地间活跃生态廊道70条,非活跃廊道共17条,生态夹点290个,生态障碍点112个。2020—2040年合肥市城乡、工矿居民用地、林地、水域和未利用土地面积将不断增加,而耕地以及草地面积将持续减少。2020—2040年期间城镇建成区分别侵占了生态廊道、源地、夹点、障碍点面积为55.95、10.51、1.04、1.35 km 2。研究结果可为今后快速发展城市的生态环境治理和国土空间生态保护修复工作提供理论依据和技术参考。
