期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Dynamic Changes of Vegetation and Its Influences in Forest-grassland Ecotone of Ili Region of Xinjiang from the Concept of Ecological Environment
1
作者 Liping ZHANG Haiyan MA +2 位作者 Aihong FU Asiya Manlike ainiwan aimaier 《Asian Agricultural Research》 2025年第4期10-13,共4页
[Objectives] To analyze the dynamic changes of maximum vegetation coverage in Ili River Basin from 2006 to 2020,and to explore the vegetation change and its influencing factors in the forest-grassland ecotone of Ili r... [Objectives] To analyze the dynamic changes of maximum vegetation coverage in Ili River Basin from 2006 to 2020,and to explore the vegetation change and its influencing factors in the forest-grassland ecotone of Ili region.[Methods] The pixel dichotomy model was used to process the MODIS data and analyze the change of vegetation coverage in the Ili River Basin from 2006 to 2020.[Results] (i)The vegetation coverage in the Ili River Basin increases gradually from west to east,and fluctuates greatly between years.(ii)By monitoring the change rate of the maximum vegetation coverage,it is found that the vegetation coverage of the basin has experienced a process of first decline and then recovery in the past 15 years.(iii)In spatial distribution,vegetation coverage has improved in some regions,while it has deteriorated in others,which may be related to regional climate change and human activities.[Conclusions] The vegetation coverage in the Ili River Basin showed significant spatial and temporal differences during the study period,and its changes were affected by both natural and human factors. 展开更多
关键词 Ecological environment Ili region of Xinjiang Forest-grassland ecotone Vegetation dynamic changes
在线阅读 下载PDF
基于改进Faster-RCNN模型的无人机影像白喉乌头物种的检测 被引量:2
2
作者 梁俊欢 董峦 +5 位作者 孙宗玖 马海燕 艾尼玩·艾买尔 阿仁 阿斯娅·曼力克 郑逢令 《新疆农业大学学报》 CAS 2022年第4期323-329,共7页
伊犁地区的白喉乌头(Aconitum leucostomum)是危害草原生态和畜牧业安全最为严重的毒害草物种,为了实现精准快速检测白喉乌头,本研究用无人机航拍获取白喉乌头影像数据集,采用深度学习技术,在Faster-RCNN算法基础上,以VGG16为主干网络,... 伊犁地区的白喉乌头(Aconitum leucostomum)是危害草原生态和畜牧业安全最为严重的毒害草物种,为了实现精准快速检测白喉乌头,本研究用无人机航拍获取白喉乌头影像数据集,采用深度学习技术,在Faster-RCNN算法基础上,以VGG16为主干网络,根据领域知识和数据集特点优化锚框大小等超参数改善算法性能。通过优化Faster-RCNN的锚框大小和微调学习率后,模型在测试集上取得的平均准确率为67.24%,相对于基准模型提高了21.57%。 展开更多
关键词 白喉乌头 深度学习 卷积神经网络 Faster-RCNN
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部