The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field samplin...The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.展开更多
Cotton yield varies spatially within a field.The variability can be caused by various production inputs such as soil properties,water management,and fertilizer application.Airborne multispectral imaging is capable of ...Cotton yield varies spatially within a field.The variability can be caused by various production inputs such as soil properties,water management,and fertilizer application.Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on yield qualitatively and quantitatively in a timely and cost-effective fashion.A 10-ha cotton field with irrigation and non-irrigation 2×2 blocks was used in this study.Six nitrogen application treatments were randomized with two replications within each block.As plant canopy was closed,airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera.The images were processed to generate various vegetation indices.The vegetation indices were evaluated for the best performance to characterize yield.The effect of irrigation on vegetation indices was significant.Models for yield estimation were developed and verified by comparing the estimated and actual yields.Results indicated that ratio of vegetation index(RVI)had a close relationship with yield(R^(2)=0.47).Better yield estimation could be obtained using a model with RVI and soil electrical conductivity(EC)measurements of the field as explanatory variables(R^(2)=0.53).This research demonstrates the capability of aerial multispectral remote sensing in estimating cotton yield variation and considering soil properties and nitrogen.展开更多
A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases a...A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases are presented using the polar-orbiting satellite imageries.展开更多
The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into...The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.展开更多
LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and ...LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.展开更多
This paper introduces a sliding-window mean removal high pass filter by which background clutter of infrared multispectral image is obtained. The method of selecting the optimum size of the sliding-window is based on ...This paper introduces a sliding-window mean removal high pass filter by which background clutter of infrared multispectral image is obtained. The method of selecting the optimum size of the sliding-window is based on the skewness-kurtosis test. In the end, a multivariate Gaussian distribution mathematical expression of background clutter image is given.展开更多
Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the e...Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the exploitation of novel sensor data and imaging modes,big data and high-performance computing,advanced and automated analysis methods.This paper showcases the main research avenues in this field,with a focus on archaeological prospection and heritage site protection.Six demonstration use-cases with a wealth of heritage asset types(e.g.excavated and still buried archaeological features,standing monuments,natural reserves,burial mounds,paleo-channels)and respective scientific research objectives are presented:the Ostia-Portus area and the wider Province of Rome(Italy),the city of Wuhan and the Jiuzhaigou National Park(China),and the Siberian“Valley of the Kings”(Russia).Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite(e.g.Copernicus Sentinels and ESA Third Party Missions)and aerial(e.g.Unmanned Aerial Vehicles,UAV)platforms,as well as field-based evidence and ground truth,auxiliary topographic data,Digital Elevation Models(DEM),and monitoring data from geodetic campaigns and networks.The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes,identify threats to cultural heritage assets due to ground instability and urban development in large metropolises,and monitor post-disaster impacts in natural reserves.展开更多
Spartina alterniflora's robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground...Spartina alterniflora's robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground biomass at a fine scale is crucial for understanding its growth dynamics and managing its invasion. This study focuses on the coastal wetlands of central Jiangsu Province, China, utilizing multispectral unmanned aerial vehicle(UAV) data to map the distribution of Spartina alterniflora. Object-based image analysis(OBIA) combined with support vector machines(SVM) was employed for classification. Additionally, multiple regression models, including univariate, band-based, vegetation index(VI)-based, and multivariate linear regression models integrating both band and VI data, were developed to estimate biomass:(1) the Bands + VIs multiple linear regression model based on fresh weight exhibited the highest estimation accuracy;(2) the optimal model achieved R^(2) values of 0.81 and 0.82 at Dafeng and Tiaozini Nature Reserve,with RMSE values of 591.78 g/m^(2) and 337.62 g/m^(2), and MAE values of 576.82 g/m^(2) and 287.71 g/m^(2), respectively;and(3) the aboveground biomass of Spartina alterniflora primarily ranged from 994.60 g/m^(2) to 5 351.48 g/m^(2) at Dafeng and from 796.05 g/m^(2) to 1 994.02 g/m^(2) in Tiaozini Nature Reserve. These findings highlight the effectiveness of multispectral UAV technology for accurately estimating Spartina alterniflora biomass, providing a robust methodology for wetland vegetation monitoring and invasive species management.展开更多
In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more tra...In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.展开更多
Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper present...Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields.Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.Compared with traditional visual observations and ground measurements,remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.展开更多
Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in e...Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.展开更多
基金funded by the Key Research and Development Program of Shaanxi Province of China(2022NY-063)the Chinese Universities Scientific Fund(2452020018).
文摘The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.
文摘Cotton yield varies spatially within a field.The variability can be caused by various production inputs such as soil properties,water management,and fertilizer application.Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on yield qualitatively and quantitatively in a timely and cost-effective fashion.A 10-ha cotton field with irrigation and non-irrigation 2×2 blocks was used in this study.Six nitrogen application treatments were randomized with two replications within each block.As plant canopy was closed,airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera.The images were processed to generate various vegetation indices.The vegetation indices were evaluated for the best performance to characterize yield.The effect of irrigation on vegetation indices was significant.Models for yield estimation were developed and verified by comparing the estimated and actual yields.Results indicated that ratio of vegetation index(RVI)had a close relationship with yield(R^(2)=0.47).Better yield estimation could be obtained using a model with RVI and soil electrical conductivity(EC)measurements of the field as explanatory variables(R^(2)=0.53).This research demonstrates the capability of aerial multispectral remote sensing in estimating cotton yield variation and considering soil properties and nitrogen.
文摘A dynamic clustering method based on multispectral satellite imagery to identify the different features is described. The channel combinations selected are for the different purposes in classification. Several cases are presented using the polar-orbiting satellite imageries.
基金The National Natural Science Foundation of China under contract No.42001401the China Postdoctoral Science Foundation under contract No.2020M671431+1 种基金the Fundamental Research Funds for the Central Universities under contract No.0209-14380096the Guangxi Innovative Development Grand Grant under contract No.2018AA13005.
文摘The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.
基金This work forms part of a larger project titled“Salt Accumulation and Waterlogging Monitoring System(SAWMS)Development”which was initiated and funded by the Water Research Commission(WRC)of South Africa(contract number K5/2558//4)More information about this project is available in WRC Report No TT 782/18,titled SALT ACCUMULATION AND WATERLOGGING MONITORING SYSTEM(SAWMS)DEVELOPMENT(ISBN 978-0-6392-0084-2)+1 种基金available at www.wrc.org.za.This work was also supported by the National Research Foundation(grant number 112300)The authors would also like to thank www.linguafix.net for their language editing services.
文摘LiDAR data are becoming increasingly available,which has opened up many new applications.One such application is crop type mapping.Accurate crop type maps are critical for monitoring water use,estimating harvests and in precision agriculture.The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field-often informed by data collected during ground and aerial surveys.However,manual digitizing and labeling is time-consuming,expensive and subject to human error.Automated remote sensing methods is a cost-effective alternative,with machine learning gaining popularity for classifying crop types.This study evaluated the use of LiDAR data,Sentinel-2 imagery,aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area.Different combinations of the three datasets were evaluated along with ten machine learning.The classification results were interpreted by comparing overall accuracies,kappa,standard deviation and f-score.It was found that LiDAR data successfully differentiated between different crop types,with XGBoost providing the highest overall accuracy of 87.8%.Furthermore,the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data,with LiDAR obtaining a mean overall accuracy of 84.3%and Sentinel-2 a mean overall accuracy of 83.6%.However,the combination of all three datasets proved to be the most effective at differentiating between the crop types,with RF providing the highest overall accuracy of 94.4%.These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.
文摘This paper introduces a sliding-window mean removal high pass filter by which background clutter of infrared multispectral image is obtained. The method of selecting the optimum size of the sliding-window is based on the skewness-kurtosis test. In the end, a multivariate Gaussian distribution mathematical expression of background clutter image is given.
基金supported by the European Space Agency(ESA)and the National Remote Sensing Center(NRSCC)-Ministry of Science and Technology(MOST)of the P.R.China under[grant number 58113]ESA[contract number 4000135360/21/I-NB,grant numbers 190791 and PP0085498]+3 种基金the German Aerospace Center(DLR)[grant number MTH3764]the Italian Space Agency(ASI)[COSMO-SkyMed license WUHAN-CSK]Planet Labs PBC under the Education and Research Program[grant number 412519]the National Natural Science Foundation of China[grant number 42250610212].
文摘Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the exploitation of novel sensor data and imaging modes,big data and high-performance computing,advanced and automated analysis methods.This paper showcases the main research avenues in this field,with a focus on archaeological prospection and heritage site protection.Six demonstration use-cases with a wealth of heritage asset types(e.g.excavated and still buried archaeological features,standing monuments,natural reserves,burial mounds,paleo-channels)and respective scientific research objectives are presented:the Ostia-Portus area and the wider Province of Rome(Italy),the city of Wuhan and the Jiuzhaigou National Park(China),and the Siberian“Valley of the Kings”(Russia).Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite(e.g.Copernicus Sentinels and ESA Third Party Missions)and aerial(e.g.Unmanned Aerial Vehicles,UAV)platforms,as well as field-based evidence and ground truth,auxiliary topographic data,Digital Elevation Models(DEM),and monitoring data from geodetic campaigns and networks.The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes,identify threats to cultural heritage assets due to ground instability and urban development in large metropolises,and monitor post-disaster impacts in natural reserves.
基金Postgraduate Research&Practice Innovation Program of Jiangsu Province under contract Nos KYCX23_1067 and KYCX25_1231the National Natural Science Foundation of China under contract No.42476157。
文摘Spartina alterniflora's robust reproductive capacity has enabled it to spread rapidly, posing a serious threat to native ecosystems in China. Therefore, accurate quantification of Spartina alterniflora aboveground biomass at a fine scale is crucial for understanding its growth dynamics and managing its invasion. This study focuses on the coastal wetlands of central Jiangsu Province, China, utilizing multispectral unmanned aerial vehicle(UAV) data to map the distribution of Spartina alterniflora. Object-based image analysis(OBIA) combined with support vector machines(SVM) was employed for classification. Additionally, multiple regression models, including univariate, band-based, vegetation index(VI)-based, and multivariate linear regression models integrating both band and VI data, were developed to estimate biomass:(1) the Bands + VIs multiple linear regression model based on fresh weight exhibited the highest estimation accuracy;(2) the optimal model achieved R^(2) values of 0.81 and 0.82 at Dafeng and Tiaozini Nature Reserve,with RMSE values of 591.78 g/m^(2) and 337.62 g/m^(2), and MAE values of 576.82 g/m^(2) and 287.71 g/m^(2), respectively;and(3) the aboveground biomass of Spartina alterniflora primarily ranged from 994.60 g/m^(2) to 5 351.48 g/m^(2) at Dafeng and from 796.05 g/m^(2) to 1 994.02 g/m^(2) in Tiaozini Nature Reserve. These findings highlight the effectiveness of multispectral UAV technology for accurately estimating Spartina alterniflora biomass, providing a robust methodology for wetland vegetation monitoring and invasive species management.
基金the Spanish Directorate General for Scientific and Technical Research(Ministerio de Economía y Competitividad)[grant number CGL2013-46387-C2-2-R]Ruben Valbuena’s work is supported by an H2020 Marie Sklodowska Curie Actions entitled‘Classification of forest structural types with LIDAR remote sensing applied to study tree size-density scaling theories’[grant number LORENZLIDAR-658180].
文摘In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.
文摘Cotton defoliation and post-harvest destruction are important cultural practices for cotton production.Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality.This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies.Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies.Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments.Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction.Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields.Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields.Compared with traditional visual observations and ground measurements,remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.
基金Servizi Ecosistemici e Infrastrutture Verdi urbane e peri-urbane nell’area Metropolitana Romana:stima del contributo delle foreste naturali di Castelporziano nel miglioramento della qualitàdell’aria della cittàdi RomaAccademia Nazionale delle Scienze detta dei XL,in collaborazione con Segretariato Generale della Presidenza della Repubblica+1 种基金PRO-ICOS_MED Potenziamento della Rete di Osservazione ICOS-Italia nel Mediterraneo-Rafforzamento del capitale umano”funded by the Ministry of ResearchPNRR,Missione 4,Componente 2,Avviso 3264/2021,IR0000032-ITINERIS-Italian Integrated Environmental Research Infrastructures System CUP B53C22002150006。
文摘Background Leaf area index(LAI)is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration.For this reason,the LAI is often used as a key input parameter in ecosystem services’modeling,which is emerging as a critical tool for steering upcoming urban reforestation strategies.However,LAI field measures are extremely time-consuming and require remarkable economic and human resources.In this context,spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8,may represent a feasible and economic solution for estimating the LAI at the city scale.Nonetheless,as far as we know,only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems.To fill such a gap,we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI,using field measurements collected with the LI-COR LAI 2200c as a reference.We hypothesized that Sentinel-2 data,owing to their finer spatial and spectral resolution,perform better in estimating vegetation’s structural parameters compared to Landsat 8.Results We found that Landsat 8-derived models have,on average,a slightly better performance,with the best model(the one based on NDVI)showing an R^(2) of 0.55 and NRMSE of 14.74%,compared to R^(2) of 0.52 and NRMSE of 15.15%showed by the best Sentinel-2 model,which is based on the NBR.All models were affected by spectrum saturation for high LAI values(e.g.,above 5).Conclusion In Mediterranean ecosystems,Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season.Therefore,the uncertainty introduced using satellite-derived LAI in ecosystem services’assessments should be systematically accounted for.
