Incorporating private and working lands into protected area networks could mitigate the isolation state of protected areas(PAs) and improve the efficiency of conservation.But how to select patches of land for conserva...Incorporating private and working lands into protected area networks could mitigate the isolation state of protected areas(PAs) and improve the efficiency of conservation.But how to select patches of land for conservation is still a troublesome issue.In this study, the MaxEnt model and irreplaceability index were applied to guide marsh conservation in the Nenjiang River Basin, Northeast China.According to the high accuracy of the MaxEnt model predictions(i.e., the average AUC value = 0.933), the Wuyuer River and Zhalong marshes in the downstream reaches of Wuyuer River are the optimal habitat for the Red-crowned crane and migratory waterfowls.There are 22 marsh patches selected by the patch irreplaceability index for conservation, of which 12 patches had been included in the current network of protected areas.The other 10 patches of marsh(amounting to 1096 km^2) far from human disturbances with high NDVI(up to 0.8) and close distance to water(less than 100 m), which are excluded from the existing network of PAs, should be implemented conservation easement programs to improve the protection efficiency of conservation.Specifically, the marshes at Taha, Tangchi, and Lamadian should be given priority for conservation and restoration to reintroduce migratory waterfowls, as this would lessen the current isolation state of the Zhalong National Nature Reserve.展开更多
The lower reach of the Red River between Winnipeg and Lake Winnipeg is very prone to ice jam flooding. The one- dimensional ice jam model RIVICE was implemented for this reach to better understand the processes leadin...The lower reach of the Red River between Winnipeg and Lake Winnipeg is very prone to ice jam flooding. The one- dimensional ice jam model RIVICE was implemented for this reach to better understand the processes leading to such events and to provide a tool to evaluate strategies for ice jam mitigation. The most downstream portion of this river stretch flows through a delta and marsh system which poses challenges in modelling ice jams in such an area of low-lying topography and river banks. Solutions to overcome these challenges are discussed in this paper and results of one such solution using water abstractions from the main channel are also presented. Abstractions are inserted in the model to represent under-ice leakage from the main channel to side channel storage and diversions (up to 65% in the Red River delta) and spillage into the delta floodplain.展开更多
“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素...“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。展开更多
基金Under the auspices of National Key Research and Development Program of China(No.2016YFA0600401)the Key Research Program of Frontier Sciences from Chinese Academy of Sciences+1 种基金Fundamental Research Funds in Heilongjiang Provincial Universities(No.135209252,135309359)the Philosophy and Social Sciences Research Plan of Heilongjiang Province(No.16JLC01)
文摘Incorporating private and working lands into protected area networks could mitigate the isolation state of protected areas(PAs) and improve the efficiency of conservation.But how to select patches of land for conservation is still a troublesome issue.In this study, the MaxEnt model and irreplaceability index were applied to guide marsh conservation in the Nenjiang River Basin, Northeast China.According to the high accuracy of the MaxEnt model predictions(i.e., the average AUC value = 0.933), the Wuyuer River and Zhalong marshes in the downstream reaches of Wuyuer River are the optimal habitat for the Red-crowned crane and migratory waterfowls.There are 22 marsh patches selected by the patch irreplaceability index for conservation, of which 12 patches had been included in the current network of protected areas.The other 10 patches of marsh(amounting to 1096 km^2) far from human disturbances with high NDVI(up to 0.8) and close distance to water(less than 100 m), which are excluded from the existing network of PAs, should be implemented conservation easement programs to improve the protection efficiency of conservation.Specifically, the marshes at Taha, Tangchi, and Lamadian should be given priority for conservation and restoration to reintroduce migratory waterfowls, as this would lessen the current isolation state of the Zhalong National Nature Reserve.
文摘The lower reach of the Red River between Winnipeg and Lake Winnipeg is very prone to ice jam flooding. The one- dimensional ice jam model RIVICE was implemented for this reach to better understand the processes leading to such events and to provide a tool to evaluate strategies for ice jam mitigation. The most downstream portion of this river stretch flows through a delta and marsh system which poses challenges in modelling ice jams in such an area of low-lying topography and river banks. Solutions to overcome these challenges are discussed in this paper and results of one such solution using water abstractions from the main channel are also presented. Abstractions are inserted in the model to represent under-ice leakage from the main channel to side channel storage and diversions (up to 65% in the Red River delta) and spillage into the delta floodplain.
文摘“精灵圈”是海岸带盐沼植被生态系统中的一种“空间自组织”结构,对盐沼湿地的生产力、稳定性和恢复力有重要影响。无人机影像是实现“精灵圈”空间位置高精度识别及解译其时空演化趋势与规律的重要数据源,但“精灵圈”像素与背景像素在色彩信息和外形特征上差异较小,如何从二维影像中智能精准地识别“精灵圈”像素并对识别的单个像素形成个体“精灵圈”是目前的技术难点。本文提出了一种结合分割万物模型(Segment Anything Model,SAM)视觉分割模型与随机森林机器学习的无人机影像“精灵圈”分割及分类方法,实现了单个“精灵圈”的识别和提取。首先,通过构建索伦森-骰子系数(S?rensen-Dice coefficient,Dice)和交并比(Intersection over Union,IOU)评价指标,从SAM中筛选预训练模型并对其参数进行优化,实现全自动影像分割,得到无属性信息的分割掩码/分割类;然后,利用红、绿、蓝(RGB)三通道信息及空间二维坐标将分割掩码与原图像进行信息匹配,构造分割掩码的特征指标,并根据袋外数据(Out of Bag,OOB)误差减小及特征分布规律对特征进行分析和筛选;最后,利用筛选的特征对随机森林模型进行训练,实现“精灵圈”植被、普通植被和光滩的自动识别与分类。实验结果表明:本文方法“精灵圈”平均正确提取率96.1%,平均错误提取率为9.5%,为精准刻画“精灵圈”时空格局及海岸带无人机遥感图像处理提供了方法和技术支撑。
