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
针对主动配电网(active distribution network,ADN)发生多故障时的快速恢复供电问题,在ADN分层控制技术以及"主动性"资源基础上,建立基于多代理系统(multi-agent system,MAS)的ADN多故障分区、分场景动态修复模型。当ADN发生...针对主动配电网(active distribution network,ADN)发生多故障时的快速恢复供电问题,在ADN分层控制技术以及"主动性"资源基础上,建立基于多代理系统(multi-agent system,MAS)的ADN多故障分区、分场景动态修复模型。当ADN发生多故障时,首先对非故障失电区域进行区域划分,利用子区域内DG(distributed generator)、光伏、储能系统以及主动负荷优先恢复重要负荷供电,并根据各区域内最大供电能力指标、最大主动调节能力指标和最大恢复能力指标选择各时段的恢复策略。在此基础上,基于MAS的自治性、协同性和并行计算能力,通过定义场景等级权重系数,以综合经济损失最少为目标,并采用离散化处理的细菌群体趋药性(discrete chemotaxis of bacterial population,DBCC)算法优化得到整个故障周期的最优修复策略。同时,考虑到故障修复过程中突发新故障的情况,通过多代理系统动态更新策略,尽快完成故障修复工作。以IEEE 69节点配电系统为例,验证了所提策略的可行性和有效性。展开更多
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.
文摘针对主动配电网(active distribution network,ADN)发生多故障时的快速恢复供电问题,在ADN分层控制技术以及"主动性"资源基础上,建立基于多代理系统(multi-agent system,MAS)的ADN多故障分区、分场景动态修复模型。当ADN发生多故障时,首先对非故障失电区域进行区域划分,利用子区域内DG(distributed generator)、光伏、储能系统以及主动负荷优先恢复重要负荷供电,并根据各区域内最大供电能力指标、最大主动调节能力指标和最大恢复能力指标选择各时段的恢复策略。在此基础上,基于MAS的自治性、协同性和并行计算能力,通过定义场景等级权重系数,以综合经济损失最少为目标,并采用离散化处理的细菌群体趋药性(discrete chemotaxis of bacterial population,DBCC)算法优化得到整个故障周期的最优修复策略。同时,考虑到故障修复过程中突发新故障的情况,通过多代理系统动态更新策略,尽快完成故障修复工作。以IEEE 69节点配电系统为例,验证了所提策略的可行性和有效性。
