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 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
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
The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since...The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture展开更多
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.
文摘The central concept of precision agriculture is to manage within-field soil and crop growth variability for more efficient use of farming inputs. Remote sensing has been an integral part of precision agriculture since the farming technology started developing in the mid to late 1980 s. Various types of remote sensors carried on groundbased platforms, manned aircraft, satellites, and more recently, unmanned aircraft have been used for precision agriculture applications. Original satellite sensors, such as Landsat and SPOT, have commonly been used for agricultural applications over large geographic areas since the 1970 s, but they have limited use for precision agriculture because of their relatively coarse spatial resolution and long revisit time. Recent developments in high resolution satellite sensors have significantly narrowed the gap in spatial resolution between satellite imagery and airborne imagery. Since the first high resolution satellite sensor IKONOS was launched in 1999, numerous commercial high resolution satellite sensors have become available. These imaging sensors not only provide images with high spatial resolution, but can also repeatedly view the same target area. The high revisit frequency and fast data turnaround time, combined with their relatively large aerial coverage, make high resolution satellite sensors attractive for many applications,including precision agriculture. This article will provide an overview of commercially available high resolution satellite sensors that have been used or have potential for precision agriculture. The applications of these sensors for precision agriculture are reviewed and application examples based on the studies conducted by the author and his collaborators are provided to illustrate how high resolution satellite imagery has been used for crop identification, crop yield variability mapping and pest management. Some challenges and future directions on the use of high resolution satellite sensors and other types of remote sensors for precision agriculture
