Forests play a crucial role in ecosystems.This study focused on five common tree species in Northeast China:pine,elm,poplar,cedar,and ash.An improved YOLOv8n-based network structure was constructed,and a UAV image dat...Forests play a crucial role in ecosystems.This study focused on five common tree species in Northeast China:pine,elm,poplar,cedar,and ash.An improved YOLOv8n-based network structure was constructed,and a UAV image dataset was developed for analysis.The results showed that the improved YOLOv8 algorithm achieved a 4.9%increase in accuracy compared with the original version,and the average precision increased from 88.0%(original YOLOv8n)to 92.1%.展开更多
The monitoring of trees is crucial for the management of large areas of forest cultivations,but this process may be costly.However,remotely sensed data offers a solution to automate this process.In this work,we used t...The monitoring of trees is crucial for the management of large areas of forest cultivations,but this process may be costly.However,remotely sensed data offers a solution to automate this process.In this work,we used two neural network methods named You Only Look Once(YOLO)and Mask R-CNN to overcome the challenging tasks of counting,detecting,and segmenting high dimensional Red–Green–Blue(RGB)images taken from unmanned aerial vehicles(UAVs).We present a processing framework,which is suitable to generate accurate predictions for the aforementioned tasks using a reasonable amount of labeled data.We compared our method using forest stands of different ages and densities.For counting,YOLO overestimates 8.5%of the detected trees on average,whereas Mask R-CNN overestimates a 4.7%of the trees.For the detection task,YOLO obtains a precision of 0.72 and a recall of 0.68 on average,while Mask R-CNN obtains a precision of 0.82 and a recall of 0.80.In segmentation,YOLO overestimates a 13.5%of the predicted area on average,whereas Mask R-CNN overestimates a 9.2%.The proposed methods present a cost-effective solution for forest monitoring using RGB images and have been successfully used to monitor∼146,500 acres of pine cultivations.展开更多
The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Qu...The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.展开更多
In order to analyze the sequences of the internal transcribed spacer （ITS） including the 5.8 S ribosomal DNA （rDNA） of common dermatophytes, so as to obtain a rapid and accurate method to identify the species of d...In order to analyze the sequences of the internal transcribed spacer （ITS） including the 5.8 S ribosomal DNA （rDNA） of common dermatophytes, so as to obtain a rapid and accurate method to identify the species of dermatophytes and to establish the phylogenetic tree of these species to understand their relationship, 16 strains of dermatophytes were collected and preliminarily identified by morphological characteristics. General primers for fungi ITS1 and ITS4 were used to amplify the ITS rDNA of each strains with PCR. The PCR products after purification were sequenced directly and were analyzed through internet. In the results, 11 strains were identified by means of morphological features, among which 5 strains were Trichophyton, 5 strains were Microsporum and 1 was Epidermophytoa, which was consistent with the results by molecular biology. In the 5 unidentifiable strains, 1 strain was proved to be Chrysosporium by molecular biology. These strains studied could be divided into 3 different classes as indicated in the analysis of the phylogenetic tree of the sequences in ITS, which were quite different from those of morphological classification. It is evident from the above observations that the molecular method of analysis on the ITS sequences is a rapid, highly sensitive and accurate approach for the detection of dematophyte species, however, it still exhibits some limitations needing the supplementation with morphological identification.展开更多
This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate o...This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the imagery.To optimize tree counting and to minimize typical background noises from orchards(i.e.bare soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.展开更多
文摘Forests play a crucial role in ecosystems.This study focused on five common tree species in Northeast China:pine,elm,poplar,cedar,and ash.An improved YOLOv8n-based network structure was constructed,and a UAV image dataset was developed for analysis.The results showed that the improved YOLOv8 algorithm achieved a 4.9%increase in accuracy compared with the original version,and the average precision increased from 88.0%(original YOLOv8n)to 92.1%.
文摘The monitoring of trees is crucial for the management of large areas of forest cultivations,but this process may be costly.However,remotely sensed data offers a solution to automate this process.In this work,we used two neural network methods named You Only Look Once(YOLO)and Mask R-CNN to overcome the challenging tasks of counting,detecting,and segmenting high dimensional Red–Green–Blue(RGB)images taken from unmanned aerial vehicles(UAVs).We present a processing framework,which is suitable to generate accurate predictions for the aforementioned tasks using a reasonable amount of labeled data.We compared our method using forest stands of different ages and densities.For counting,YOLO overestimates 8.5%of the detected trees on average,whereas Mask R-CNN overestimates a 4.7%of the trees.For the detection task,YOLO obtains a precision of 0.72 and a recall of 0.68 on average,while Mask R-CNN obtains a precision of 0.82 and a recall of 0.80.In segmentation,YOLO overestimates a 13.5%of the predicted area on average,whereas Mask R-CNN overestimates a 9.2%.The proposed methods present a cost-effective solution for forest monitoring using RGB images and have been successfully used to monitor∼146,500 acres of pine cultivations.
文摘The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.
文摘In order to analyze the sequences of the internal transcribed spacer （ITS） including the 5.8 S ribosomal DNA （rDNA） of common dermatophytes, so as to obtain a rapid and accurate method to identify the species of dermatophytes and to establish the phylogenetic tree of these species to understand their relationship, 16 strains of dermatophytes were collected and preliminarily identified by morphological characteristics. General primers for fungi ITS1 and ITS4 were used to amplify the ITS rDNA of each strains with PCR. The PCR products after purification were sequenced directly and were analyzed through internet. In the results, 11 strains were identified by means of morphological features, among which 5 strains were Trichophyton, 5 strains were Microsporum and 1 was Epidermophytoa, which was consistent with the results by molecular biology. In the 5 unidentifiable strains, 1 strain was proved to be Chrysosporium by molecular biology. These strains studied could be divided into 3 different classes as indicated in the analysis of the phylogenetic tree of the sequences in ITS, which were quite different from those of morphological classification. It is evident from the above observations that the molecular method of analysis on the ITS sequences is a rapid, highly sensitive and accurate approach for the detection of dematophyte species, however, it still exhibits some limitations needing the supplementation with morphological identification.
文摘This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor data.Depending on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the imagery.To optimize tree counting and to minimize typical background noises from orchards(i.e.bare soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.
