Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study wa...Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.展开更多
Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a bo...Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.展开更多
The objective of this research is to analyze the influences of light source incidence angle,fiber height,moisture content,and particle size on loamy mixed soil spectra.Nitrogen(N)content calibration and cross-validati...The objective of this research is to analyze the influences of light source incidence angle,fiber height,moisture content,and particle size on loamy mixed soil spectra.Nitrogen(N)content calibration and cross-validation models at different moisture contents and particle sizes were obtained using partial least squares(PLS)analysis.Spectral data were collected using a spectrophotometer.Fiber height of 100 mm and light source angle at 45°were chosen to obtain the sharpest spectra without apparent scattering effect.The results show that moisture content and particle size strongly influenced the absorbance of the spectra,and a better N prediction model was obtained when the particle sizes were in the ranges of 0.5-1.0,1.0-2.0 and 2.0-5.0 mm,with the correlation coefficients(r)of 0.819,0.815 and 0.818,and standard errors of prediction(SEP)of 2.29,2.41 and 2.42 mg/kg,respectively.Poor N prediction model was obtained when the soil was kept in its natural moisture content with r of 0.575 and SEP of 3.275 mg/kg,compared to the performance of dried soil samples with r of 0.815 and SEP of 2.425 mg/kg.展开更多
In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 ...In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 nm)and spatial(1620×841 pixels)information from sandy soil samples with variable SMC levels in the laboratory.Principal component analysis(PCA)transformation,K-means clustering,and several other image processing methods were performed to obtain a region of interest(ROI)of soil sample from the original HSI data.Then,256 optimal spectral wavelengths were selected from the average reflectance of the ROI,and 28 textural features were extracted using a gray-level co-occurrence matrix(GLCM).Data dimensionality reduction was conducted on both the spectral information and textural information by using a partial least square algorithm.Six latent variables(LVs)extracted from the spectral information,four LVs extracted from the textural information and fused data were used to build regression models with a three-layer BPNN,respectively.The results showed that all of the three calibration models achieved high prediction accuracy,particularly when using spectral information with R^(2)_(C)=0.9532 and RMSEC=0.0086.However,validation models demonstrate that predicting SMC using fused data is more effective than using spectral reflectance and textural features separately,with a R^(2)_(P)=0.9350 and RMSEP=0.0141,thus proving that the HSI technique is capable of detecting SMC.展开更多
Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the va...Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the variability of loess slope SWC through the change of resistivity.It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors.In this context,geoelectric and environmental factors such as soil temperature and SWC were monitored for three years.The prediction model of apparent resistivity and SWC was calibrated.The post processing of geoelectric data was introduced.In addition,the SWC collected by Time-Domain Reflectometry(TDR)was used to verify the feasibility of electrical resistivity tomography(ERT)data.The SWC variability in the process of rainfall,the evolution of four seasons,and the alternation of drying and wetting were evaluated.The research results show that:i)the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5-3.0 m depth.ii)The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring.iii)From 2017 to 2020,the SWC increased in August,and the soil became dry in January.iv)Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity.The outcome can provide the change information of SWC to a great extent without excavating boreholes.展开更多
Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral par...Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors.This study carried out research on the correlations among spectral parameters of the canopy of winter wheat,crop growth process,and soil water content,and finally constructed the soil water content prediction model with the growth days parameter.The results showed that the plant water content of winter wheat tended to decrease during the whole growth period.The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer.At different growth stages,even if the soil water content was the same,the plant water content and characteristic spectral reflectance were also different.Therefore,the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy.It is found that the determination coefficient(R^(2))of the models built during the whole growth period was greatly increased,ranging from 0.54 to 0.60.Then,the model built by OSAVI(Optimized Soil Adjusted Vegetation Index)and Rg/Rr,two of the highest precision characteristic spectral parameters,were selected for model validation.The correlation between OSAVI and soil water content,Rg/Rr,and soil water content were still significant(p<0.05).The R^(2),MAE,and RMSE validation models were 0.53 and 0.58,3.19 and 2.97,4.76 and 4.41,respectively,which was accurate enough to be applied in a large-area field.Furthermore,the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward.The research results could guide the agricultural production of winter wheat in northern China.展开更多
基金the National Key Research and Development Program of ChinaKey Projects for Strategic International Innovative Cooperation in Science and Technology(2018YFE0207800)+1 种基金Fundamental Research Funds for the Central Universities(2572019BA03)partly by the China Scholarship Council(CSC No.2016DFH417)。
文摘Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.
基金financially supported by the Special Fund for Forest Scientific Research in the Public Welfare(No.201404402)Fundamental Research Funds for the Central Universities(Nos.C2572014BA23 and 2572019BA03)。
文摘Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.
基金This study was supported by National Science and Technology Support Program(2006BAD10A09)863 National High-Tech Research and Development Plan(2007AA10Z210)+1 种基金the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,P.R.China.and Natural Science Foundation of China(Project No:30671213)Natural Science Foundation of Zhejiang Province(Project No:Y307119).
文摘The objective of this research is to analyze the influences of light source incidence angle,fiber height,moisture content,and particle size on loamy mixed soil spectra.Nitrogen(N)content calibration and cross-validation models at different moisture contents and particle sizes were obtained using partial least squares(PLS)analysis.Spectral data were collected using a spectrophotometer.Fiber height of 100 mm and light source angle at 45°were chosen to obtain the sharpest spectra without apparent scattering effect.The results show that moisture content and particle size strongly influenced the absorbance of the spectra,and a better N prediction model was obtained when the particle sizes were in the ranges of 0.5-1.0,1.0-2.0 and 2.0-5.0 mm,with the correlation coefficients(r)of 0.819,0.815 and 0.818,and standard errors of prediction(SEP)of 2.29,2.41 and 2.42 mg/kg,respectively.Poor N prediction model was obtained when the soil was kept in its natural moisture content with r of 0.575 and SEP of 3.275 mg/kg,compared to the performance of dried soil samples with r of 0.815 and SEP of 2.425 mg/kg.
基金This research was financially supported by International Science and Technology Cooperation Project of China Ministry of Agriculture(2015-Z44).
文摘In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 nm)and spatial(1620×841 pixels)information from sandy soil samples with variable SMC levels in the laboratory.Principal component analysis(PCA)transformation,K-means clustering,and several other image processing methods were performed to obtain a region of interest(ROI)of soil sample from the original HSI data.Then,256 optimal spectral wavelengths were selected from the average reflectance of the ROI,and 28 textural features were extracted using a gray-level co-occurrence matrix(GLCM).Data dimensionality reduction was conducted on both the spectral information and textural information by using a partial least square algorithm.Six latent variables(LVs)extracted from the spectral information,four LVs extracted from the textural information and fused data were used to build regression models with a three-layer BPNN,respectively.The results showed that all of the three calibration models achieved high prediction accuracy,particularly when using spectral information with R^(2)_(C)=0.9532 and RMSEC=0.0086.However,validation models demonstrate that predicting SMC using fused data is more effective than using spectral reflectance and textural features separately,with a R^(2)_(P)=0.9350 and RMSEP=0.0141,thus proving that the HSI technique is capable of detecting SMC.
基金supported by the National Natural Science Foundation of China(Grant Nos:42107209and 41530640)the National Key Research and Development Program of China(Grant No:2018YFC1504701)。
文摘Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the variability of loess slope SWC through the change of resistivity.It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors.In this context,geoelectric and environmental factors such as soil temperature and SWC were monitored for three years.The prediction model of apparent resistivity and SWC was calibrated.The post processing of geoelectric data was introduced.In addition,the SWC collected by Time-Domain Reflectometry(TDR)was used to verify the feasibility of electrical resistivity tomography(ERT)data.The SWC variability in the process of rainfall,the evolution of four seasons,and the alternation of drying and wetting were evaluated.The research results show that:i)the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5-3.0 m depth.ii)The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring.iii)From 2017 to 2020,the SWC increased in August,and the soil became dry in January.iv)Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity.The outcome can provide the change information of SWC to a great extent without excavating boreholes.
基金This study was financially supported by the National Natural Science Foundation of China No.31700640the National Key R&D Program of China(Grant No.2018YFC0407703)+3 种基金the Key R&D Projects of Ningxia Hui Autonomous Region(Grant No.2018BBF02022)the IWHR Research&Development Support Program(Grant No.ID0145B082017)Beijing Municipal Education Commission Innovative Transdisciplinary Program"Ecological Restoration Engineering"the National Key Laboratory Open Fund(Grant No.IWHR-SKL-KF201903).
文摘Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation.However,the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors.This study carried out research on the correlations among spectral parameters of the canopy of winter wheat,crop growth process,and soil water content,and finally constructed the soil water content prediction model with the growth days parameter.The results showed that the plant water content of winter wheat tended to decrease during the whole growth period.The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer.At different growth stages,even if the soil water content was the same,the plant water content and characteristic spectral reflectance were also different.Therefore,the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy.It is found that the determination coefficient(R^(2))of the models built during the whole growth period was greatly increased,ranging from 0.54 to 0.60.Then,the model built by OSAVI(Optimized Soil Adjusted Vegetation Index)and Rg/Rr,two of the highest precision characteristic spectral parameters,were selected for model validation.The correlation between OSAVI and soil water content,Rg/Rr,and soil water content were still significant(p<0.05).The R^(2),MAE,and RMSE validation models were 0.53 and 0.58,3.19 and 2.97,4.76 and 4.41,respectively,which was accurate enough to be applied in a large-area field.Furthermore,the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward.The research results could guide the agricultural production of winter wheat in northern China.