Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contra...Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contrast,advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species.In this study,spectral signatures for stone pine（Pinus pinea L.） forests were collected using an advanced spectroradiometer ＂ASD FieldSpec 4 Hi-Res＂ with an accuracy of 1 nm.These spectral signatures are used to compare between different multispectral and hyperspectral satellite images.The comparison is based on processing satellite images： hyperspectral Hyperion,hyperspectral CHRIS-Proba,Advanced Land Imager（ALI）,and Landsat 8.Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed.In addition,a well-known hyperspectral image classification algorithm,spectral angle mapper（SAM）,has been improved to perform the classification process efficiently based on collected spectral signatures.The results show that the modified SAM is 9% more accurate than the conventional SAM.In addition,experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8（overall accuracy 82%,precision 93%,and Kappa coefficient 0.43 compared to 60,67%,and 0.035,respectively）.Similarly,Hyperion is better than ALI in mapping stone pine（overall accuracy 92%,precision 97%,and Kappa coefficient 0.74 compared to 52,56%,and -0.032,respectively）.展开更多
There are many crop yield estimation techniques which are used in countries around the world,but the most effective is the one based on remote sensing data and technologies.However,remote sensing data which are needed...There are many crop yield estimation techniques which are used in countries around the world,but the most effective is the one based on remote sensing data and technologies.However,remote sensing data which are needed to estimate crop yield is incomplete most of the time due to many obstacles such as climate conditions(percentage of cloud cover),and low temporal resolution.These problems reduce the effectiveness of the known crop yield estimation techniques and render them obsolete.There was many attempts to solve these problems by using high temporal resolution and low spatial resolution images.However,this type of images are suitable for very large homogeneous crop fields.To compensate for the lack of high spatial resolution satellite images,a new mathematical model is created.Based on the new mathematical model an intelligent system is implemented that includes the use of energy balance equation to improve the crop yield estimation.To verify the results of the intelligent system,several farmers are interviewed and information about their crops yield is collected.The comparison between the estimated crop yield and the actual production in different fields proves the high accuracy of the intelligent system.展开更多
基金funded by the Lebanese National Council for Scientific Research(Mapping Stone Pine Forests in Lebanon)
文摘Mapping forests is an important process in managing natural resources.At present,due to spectral resolution limitations,multispectral images do not give a complete separation between different forest species.In contrast,advances in remote sensing technologies have provided hyperspectral tools and images as a solution for the determination of species.In this study,spectral signatures for stone pine（Pinus pinea L.） forests were collected using an advanced spectroradiometer ＂ASD FieldSpec 4 Hi-Res＂ with an accuracy of 1 nm.These spectral signatures are used to compare between different multispectral and hyperspectral satellite images.The comparison is based on processing satellite images： hyperspectral Hyperion,hyperspectral CHRIS-Proba,Advanced Land Imager（ALI）,and Landsat 8.Enhancement and classification methods for hyperspectral and multispectral images are investigated and analyzed.In addition,a well-known hyperspectral image classification algorithm,spectral angle mapper（SAM）,has been improved to perform the classification process efficiently based on collected spectral signatures.The results show that the modified SAM is 9% more accurate than the conventional SAM.In addition,experiments indicate that the CHRIS-Proba image is more accurate than Landsat 8（overall accuracy 82%,precision 93%,and Kappa coefficient 0.43 compared to 60,67%,and 0.035,respectively）.Similarly,Hyperion is better than ALI in mapping stone pine（overall accuracy 92%,precision 97%,and Kappa coefficient 0.74 compared to 52,56%,and -0.032,respectively）.
基金The authors would like to thank CNRS for facilitating the achievement of this research as part of a project supported and financed by CEDRE 2018.
文摘There are many crop yield estimation techniques which are used in countries around the world,but the most effective is the one based on remote sensing data and technologies.However,remote sensing data which are needed to estimate crop yield is incomplete most of the time due to many obstacles such as climate conditions(percentage of cloud cover),and low temporal resolution.These problems reduce the effectiveness of the known crop yield estimation techniques and render them obsolete.There was many attempts to solve these problems by using high temporal resolution and low spatial resolution images.However,this type of images are suitable for very large homogeneous crop fields.To compensate for the lack of high spatial resolution satellite images,a new mathematical model is created.Based on the new mathematical model an intelligent system is implemented that includes the use of energy balance equation to improve the crop yield estimation.To verify the results of the intelligent system,several farmers are interviewed and information about their crops yield is collected.The comparison between the estimated crop yield and the actual production in different fields proves the high accuracy of the intelligent system.
