为研究森林物候的差异性,以地面凋落叶为参考,使用3种类型数码相机(美国Stardot生产的NetCam、日本尼康生产的Coolpix和中国猎科生产的Ltl-5610)评估颜色指数时间序列和物候期与凋落叶的差异;评估数码相机的白平衡模式设置(晴天、阴天...为研究森林物候的差异性,以地面凋落叶为参考,使用3种类型数码相机(美国Stardot生产的NetCam、日本尼康生产的Coolpix和中国猎科生产的Ltl-5610)评估颜色指数时间序列和物候期与凋落叶的差异;评估数码相机的白平衡模式设置(晴天、阴天、自动)对采集图像的颜色指数和物候期提取的影响;对比设置为固定(或晴天)白平衡模式的NetCam、Coolpix和Ltl-5610之间以及Ltl-5610与华为智能手机之间观测冠层物候的差异。研究结果表明,1)3种相机的绿度指数均与凋落叶季节动态显著相关,且相机与凋落叶估计的物候期没有显著差异;但红度指数不能表征凋落叶动态,其估计的变色高峰日比凋落叶的凋落高峰日晚6~31 d;2)同一台华为智能手机在不同白平衡模式下的绿度指数估计的秋季物候期(叶衰老开始(start of fall,SOF)、落叶高峰(middle of fall,MOF)、落叶结束(end of fall,EOF))差异不显著,但固定或晴天模式估计值的不确定性更低;3)3种数码相机间绿度指数季节变化基本一致,6个关键物候期(展叶开始(start of spring,SOS)、展叶高峰(middle of spring,MOS)、展叶结束(peak of spring,POS)、SOF、MOF和EOF)和2种生长季长度(MOS-MOF和SOS-EOF)普遍差异不显著。MOS和MOF最为稳定,适合用于定义生长季的开始和结束。此研究结果证明低成本国产定时拍摄相机绿度指数监测森林物候的可行性,有助于实现更大覆盖面的物候联网观测,但使用红度指数表征秋季物候需慎重。展开更多
Video surveillance service, which receives live streams from IP cameras and forwards the streams to end users, has become one of the most popular services of video data center. The video data center focuses on minimiz...Video surveillance service, which receives live streams from IP cameras and forwards the streams to end users, has become one of the most popular services of video data center. The video data center focuses on minimizing the resource cost during resource provisioning for the service. However, little of the previous work comprehensively considers the bandwidth cost optimization of both upload and forwarding streams, and the capacity of the media server. In this paper, we propose an efficient resource scheduling approach for online multi-camera video forwarding, which tries to optimize the resource sharing of media servers and the networks together. Firstly, we not only provide a fine-grained resource usage model for media servers, but also evaluate the bandwidth cost of both upload and forwarding streams. Without loss of generality, we utilize two resource pricing models with different resource cost functions to evaluate the resource cost： the linear cost function and the non-linear cost functions. Then, we formulate the cost minimization problem as a constrained integer programming problem. For the linear resource cost function, the drift-plus-penalty optimization method is exploited in our approach. For non-linear resource cost functions, the approach employs a heuristic method to reduce both media server cost and bandwidth cost. The experimental results demonstrate that our approach obviously reduces the total resource costs on both media servers and networks simultaneously.展开更多
文摘为研究森林物候的差异性,以地面凋落叶为参考,使用3种类型数码相机(美国Stardot生产的NetCam、日本尼康生产的Coolpix和中国猎科生产的Ltl-5610)评估颜色指数时间序列和物候期与凋落叶的差异;评估数码相机的白平衡模式设置(晴天、阴天、自动)对采集图像的颜色指数和物候期提取的影响;对比设置为固定(或晴天)白平衡模式的NetCam、Coolpix和Ltl-5610之间以及Ltl-5610与华为智能手机之间观测冠层物候的差异。研究结果表明,1)3种相机的绿度指数均与凋落叶季节动态显著相关,且相机与凋落叶估计的物候期没有显著差异;但红度指数不能表征凋落叶动态,其估计的变色高峰日比凋落叶的凋落高峰日晚6~31 d;2)同一台华为智能手机在不同白平衡模式下的绿度指数估计的秋季物候期(叶衰老开始(start of fall,SOF)、落叶高峰(middle of fall,MOF)、落叶结束(end of fall,EOF))差异不显著,但固定或晴天模式估计值的不确定性更低;3)3种数码相机间绿度指数季节变化基本一致,6个关键物候期(展叶开始(start of spring,SOS)、展叶高峰(middle of spring,MOS)、展叶结束(peak of spring,POS)、SOF、MOF和EOF)和2种生长季长度(MOS-MOF和SOS-EOF)普遍差异不显著。MOS和MOF最为稳定,适合用于定义生长季的开始和结束。此研究结果证明低成本国产定时拍摄相机绿度指数监测森林物候的可行性,有助于实现更大覆盖面的物候联网观测,但使用红度指数表征秋季物候需慎重。
基金The research is supported by the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1501254, the National Natural Science Foundation of China under Grant No. 61332005, the Funds for Creative Research Groups of China under Grant No. 61421061, the Beijing Training Project for the Leading Talents in Science and Technology under Grant No. ljrc 201502, and the Cosponsored Project of Beijing Committee of Education.
文摘Video surveillance service, which receives live streams from IP cameras and forwards the streams to end users, has become one of the most popular services of video data center. The video data center focuses on minimizing the resource cost during resource provisioning for the service. However, little of the previous work comprehensively considers the bandwidth cost optimization of both upload and forwarding streams, and the capacity of the media server. In this paper, we propose an efficient resource scheduling approach for online multi-camera video forwarding, which tries to optimize the resource sharing of media servers and the networks together. Firstly, we not only provide a fine-grained resource usage model for media servers, but also evaluate the bandwidth cost of both upload and forwarding streams. Without loss of generality, we utilize two resource pricing models with different resource cost functions to evaluate the resource cost： the linear cost function and the non-linear cost functions. Then, we formulate the cost minimization problem as a constrained integer programming problem. For the linear resource cost function, the drift-plus-penalty optimization method is exploited in our approach. For non-linear resource cost functions, the approach employs a heuristic method to reduce both media server cost and bandwidth cost. The experimental results demonstrate that our approach obviously reduces the total resource costs on both media servers and networks simultaneously.
