树高是监测森林状况的重要参数,摄影测量法具有低成本、灵活的特性,是树高采集的重要方法之一.作为一种被动遥感方式,传统的摄影测量方法往往需要数量较多,重叠率较高的图像数据,这与传统图像特征的稀疏性有关.为了提高图像数量受限条...树高是监测森林状况的重要参数,摄影测量法具有低成本、灵活的特性,是树高采集的重要方法之一.作为一种被动遥感方式,传统的摄影测量方法往往需要数量较多,重叠率较高的图像数据,这与传统图像特征的稀疏性有关.为了提高图像数量受限条件下的树高提取精度,提出将稀疏特征匹配和稠密像素匹配相结合,并使用对极约束过滤外点的方法,得到稠密且精度较高的匹配结果,并通过三维重建算法得到森林场景点云.该方法在少量图像的情况下就可以较为完整地重建森林场景并提取树高,将提取的树高与机载激光雷达(light detection and ranging,LiDAR)点云的结果进行对比,相关系数为0.91,最大误差为1.64 m.该算法只需要少量的重叠图像,这表明了该算法在处理高分辨率卫星图像方面具有一定潜力.展开更多
[目的/意义]猕猴桃果树生长重叠明显,树冠结构复杂,利用传统方式无法实现果树单木骨架提取与冠层预测,为对密集栽培的猕猴桃果园进行高效无损监测并获取果树生长参数,本研究利用冬季简单树形进行骨架提取,并集成深度学习与数学形态学方...[目的/意义]猕猴桃果树生长重叠明显,树冠结构复杂,利用传统方式无法实现果树单木骨架提取与冠层预测,为对密集栽培的猕猴桃果园进行高效无损监测并获取果树生长参数,本研究利用冬季简单树形进行骨架提取,并集成深度学习与数学形态学方法,提高单木骨架预测精度,提出了一种融合骨架信息的冠层分割方案。[方法方法]采用低成本无人机图像获取高分辨率数据支持,改进PSP-Net语义分割模型,引入数学形态学处理提取单木骨架并优化骨架连续性,以优化单木骨架为先验实现冠层分割。[结果与讨论]优化骨架提取精度可达95%以上,相较于传统方式精度提高约15.71%,像素准确率(Pixel Accuracy,PA)值达95.84%,平均交并比(Mean In-tersection over Union,MIo U)值达95.76%,冠层分割加权得分(Weighted F1 Score,WF1)达94.07%左右;而冠层预测像素准确率PA可达95%以上,冠层分割WF1达95.76%左右,与直接利用原始骨架相比,优化骨架提高了冠层分割的PA为13.2%,MIo U为10.9%,WF1为18.4%,显著改善了分割指标。[结论]该研究为高效监测猕猴桃园以获取果树数据提供了可靠技术支撑,并为高效、低成本的果园精细化管理提供了全新的技术方案,具有重要的应用前景。展开更多
Nitrogen(N)is an important mineral element in apple production.Rapid estimation of apple tree N status is helpful for achieving precise N management.The objective of this work was to explore partial least squares(PLS)...Nitrogen(N)is an important mineral element in apple production.Rapid estimation of apple tree N status is helpful for achieving precise N management.The objective of this work was to explore partial least squares(PLS)regression in dimensional reduction of spectral data and build the diagnostic model.The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer,and leaf total N content was obtained at the same time.The raw spectra were pretreated using Savitzky-Golay(SG)smoothing and a combination of SG and first-order derivative(SG_FD)or second-order derivative(SG_SD).The samples were divided into a calibration dataset and a prediction dataset using SPXY.Based on 4 factors of PLS regression,including latent variables(LVs),X-loading,variable importance in projection(VIP)and regression coefficients(RC),the 6 methods(LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02)were derived and used for variable extraction,based on which PLS model and ELM model were established.The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis.The amounts of variables extracted by LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02 were 6,11,18,305,26 and 88,respectively.The method of extracting variables with an RC threshold based on the minimum RMSEP(RC_02)could effectively avoid the omission of effective information.The RC_02 method was recommended for related research which required accurate wavelength information as a variable.The variable extraction method based on LVs generated an ELM model with a simple structure.The prediction results showed that the ELM model outperformed the PLS model.The PLS(LVs)_ELM model was the best;R2P,RMSEP and RPD were 0.837,2.393 and 2.220,respectively.展开更多
Forest data acquisition,which is of crucial importance for modeling global biogeochemical cycles and climate,makes a contribution to building the ecological Digital Earth(DE).Due to the complex calculations and large ...Forest data acquisition,which is of crucial importance for modeling global biogeochemical cycles and climate,makes a contribution to building the ecological Digital Earth(DE).Due to the complex calculations and large volumes of data associated with high-resolution images of large areas,accurate and effective extraction of individual tree crowns remains challenging.In this study,two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2,respectively,were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information(SMI)algorithm and tile computing technology(SMI-TCT).To evaluate the performance of the algorithm,four commonly used algorithms were also applied to extract the individual tree crowns.The overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms,with maximum extraction accuracies of 85.7%and 63.8%.Moreover,the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology.展开更多
森林冠层的三维重建研究能够更加直观反映森林空间结构,提高森林参数的测量精度。目前小光斑激光雷达已经广泛应用于林业研究中。为建立落叶松树冠三维形状模型,以长春净月潭实验区落叶松机载LiDAR(LiDAR,Light Detection And Ranging)...森林冠层的三维重建研究能够更加直观反映森林空间结构,提高森林参数的测量精度。目前小光斑激光雷达已经广泛应用于林业研究中。为建立落叶松树冠三维形状模型,以长春净月潭实验区落叶松机载LiDAR(LiDAR,Light Detection And Ranging)数据为基础,采用K-means算法提取建模参数。该算法以单木树冠顶点作为初始聚类中心,经过4次迭代估测出单木树高和单木树冠直径,通过与试验区的单木实测数据对比,进行相关性分析,得到估测树高和估测树冠与实测数据相关系数分别为0.892 4和0.769 0,经过验证,估测树高和估测树冠的精度为94.06%和82.21%。利用激光雷达提取出的单木坐标、树高、树冠和冠基高采用旋转抛物线方法重建森林尺度三维模型呈现森林结构。展开更多
文摘树高是监测森林状况的重要参数,摄影测量法具有低成本、灵活的特性,是树高采集的重要方法之一.作为一种被动遥感方式,传统的摄影测量方法往往需要数量较多,重叠率较高的图像数据,这与传统图像特征的稀疏性有关.为了提高图像数量受限条件下的树高提取精度,提出将稀疏特征匹配和稠密像素匹配相结合,并使用对极约束过滤外点的方法,得到稠密且精度较高的匹配结果,并通过三维重建算法得到森林场景点云.该方法在少量图像的情况下就可以较为完整地重建森林场景并提取树高,将提取的树高与机载激光雷达(light detection and ranging,LiDAR)点云的结果进行对比,相关系数为0.91,最大误差为1.64 m.该算法只需要少量的重叠图像,这表明了该算法在处理高分辨率卫星图像方面具有一定潜力.
文摘[目的/意义]猕猴桃果树生长重叠明显,树冠结构复杂,利用传统方式无法实现果树单木骨架提取与冠层预测,为对密集栽培的猕猴桃果园进行高效无损监测并获取果树生长参数,本研究利用冬季简单树形进行骨架提取,并集成深度学习与数学形态学方法,提高单木骨架预测精度,提出了一种融合骨架信息的冠层分割方案。[方法方法]采用低成本无人机图像获取高分辨率数据支持,改进PSP-Net语义分割模型,引入数学形态学处理提取单木骨架并优化骨架连续性,以优化单木骨架为先验实现冠层分割。[结果与讨论]优化骨架提取精度可达95%以上,相较于传统方式精度提高约15.71%,像素准确率(Pixel Accuracy,PA)值达95.84%,平均交并比(Mean In-tersection over Union,MIo U)值达95.76%,冠层分割加权得分(Weighted F1 Score,WF1)达94.07%左右;而冠层预测像素准确率PA可达95%以上,冠层分割WF1达95.76%左右,与直接利用原始骨架相比,优化骨架提高了冠层分割的PA为13.2%,MIo U为10.9%,WF1为18.4%,显著改善了分割指标。[结论]该研究为高效监测猕猴桃园以获取果树数据提供了可靠技术支撑,并为高效、低成本的果园精细化管理提供了全新的技术方案,具有重要的应用前景。
基金This work was supported by the National Key Research and Development Program of China(Grant No.2017YFD0201508).
文摘Nitrogen(N)is an important mineral element in apple production.Rapid estimation of apple tree N status is helpful for achieving precise N management.The objective of this work was to explore partial least squares(PLS)regression in dimensional reduction of spectral data and build the diagnostic model.The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer,and leaf total N content was obtained at the same time.The raw spectra were pretreated using Savitzky-Golay(SG)smoothing and a combination of SG and first-order derivative(SG_FD)or second-order derivative(SG_SD).The samples were divided into a calibration dataset and a prediction dataset using SPXY.Based on 4 factors of PLS regression,including latent variables(LVs),X-loading,variable importance in projection(VIP)and regression coefficients(RC),the 6 methods(LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02)were derived and used for variable extraction,based on which PLS model and ELM model were established.The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis.The amounts of variables extracted by LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02 were 6,11,18,305,26 and 88,respectively.The method of extracting variables with an RC threshold based on the minimum RMSEP(RC_02)could effectively avoid the omission of effective information.The RC_02 method was recommended for related research which required accurate wavelength information as a variable.The variable extraction method based on LVs generated an ELM model with a simple structure.The prediction results showed that the ELM model outperformed the PLS model.The PLS(LVs)_ELM model was the best;R2P,RMSEP and RPD were 0.837,2.393 and 2.220,respectively.
基金This study was jointly supported by the National Science and Technology Major Project Grant No.[30-Y20A01-9003-12/13]the State Key Fundamental Science Funds Grant No.[2010CB951503]+2 种基金National Key Basic Research Program Project Grant No.[2010CB434801]National Key Technology R&D Program of China Grant No.[2012BAH32B03]National Natural Science Foundation of China Grant No.[41101439].
文摘Forest data acquisition,which is of crucial importance for modeling global biogeochemical cycles and climate,makes a contribution to building the ecological Digital Earth(DE).Due to the complex calculations and large volumes of data associated with high-resolution images of large areas,accurate and effective extraction of individual tree crowns remains challenging.In this study,two GeoEye-1 panchromatic images of Beihai and Ningbo in China with areas of 5 and 25 km2,respectively,were used as experimental data to establish a novel method for the automatic extraction of individual tree crowns based on a self-adaptive mutual information(SMI)algorithm and tile computing technology(SMI-TCT).To evaluate the performance of the algorithm,four commonly used algorithms were also applied to extract the individual tree crowns.The overall accuracy of the proposed method for the two experimental areas was superior to that of the four other algorithms,with maximum extraction accuracies of 85.7%and 63.8%.Moreover,the results also indicated that the novel method was suitable for individual tree crowns extraction in sizeable areas because of the multithread parallel computing technology.
