2篇
您的检索式：作者名="Christopher J.Post"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Developing a USLE cover and management factor(C)for forested regions of southern China显示文摘The Universal Soil Loss Equation model is often used to improve soil resource conservation by monitoring and forecasting soil erosion.This study tested a novel method to determine the cover and management factor(C)of this model by coupling the leaf area index(LAI)and soil basal respiration(SBR)to more accurately estimate a soil erosion map for a typical region with red soil in Hetian,Fujian Province,China.The spatial distribution of the LAI was obtained using the normalized difference vegetation index and was consistent with the LAI observed in the field(R^2=0.66).The spatial distribution of the SBR was obtained using the Carnegie-Ames-Stanford Approach model and verified by soil respiration field observations(R^2=0.51).Correlation analyses and regression models suggested that the LAI and SBR could reasonably reflect the structure of the forest canopy and understory vegetation,respectively.Finally,the C-factor was reconstructed using the proposed forest vegetation structure factor(Cs),which considers the effect of the forest canopy and shrub and litter layers on reducing rainfall erosion.The feasibility of this new method was thoroughly verified using runoff plots(R2=0.55).The results demonstrated that Cs may help local governments understand the vital role of the structure of the vegetation layer in limiting soil erosion and provide a more accurate large-scale quantification of the C-factor for soil erosion. | Conghui Li Lili Lin Zhenbang Hao Christopher J.Post Zhanghao Chen Jian Liu Kunyong Yu | 2020 | Frontiers of Earth Science2020，14，3： | 0 |
| 2 | The co-effect of image resolution and crown size on deep learning for individual tree detection and delineation显示文摘Individual tree detection and delineation(ITDD)is an important subject in forestry and urban forestry.This study represents the first research to propose the concept of crown resolution to comprehensively evaluate the co-effect of image resolution and crown size on deep learning.Six images with different resolutions were derived from a DJI Unmanned Aerial Vehicle(UAV),and 1344 manually delineated Chinese fir(Cunninghamia lanceolata(Lamb)Hook)tree crowns were used for six training and validation mask region-based convolutional neural network(Mask R-CNN)models,while additional 476 delineated tree crowns were reserved for testing.The overall detection accuracy,the influence of different crown sizes,and crown resolutions were calculated to evaluate model performance accuracy with different image resolutions for ITDD.Results show that the highest accuracy was achieved when the crown resolution was between 800 and 12800 pixels/tree.The accuracy of ITDD was impacted by crown resolution,and it was unable to effectively identify Chinese fir when the crown resolution was less than 25 pixels/tree or higher than 12800 pixels/tree.The study highlights crown resolution as a critical factor affecting ITDD and suggests selecting the appropriate resolution based on the target detected crown size. | Zhenbang Hao Lili Lin Christopher J.Post Elena A.Mikhailova Kunyong Yu Huirong Fang Jian Liu | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员