Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest reg...Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration.Early detection strategies are therefore necessary to limit invasive alien plant species’impacts,and remote sensing is one of the techniques for early invasion detection.Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images.Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images.The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area.Three machine learning classifiers(Support Vector Machines,Random Forest and Extreme Gradient Boosting)were applied to WorldView-3,GeoEye-1 and SPOT-7 satellite imagery.The Random Forest classifier performed well(Kappa=0.72).The SVM and XGBoost’s coefficient Kappa were 0.69 and 0.66,respectively.However,buckthorn distribution in understory was identified as the main limit to this approach,and LiDAR data could be used to improve buckthorn mapping in similar environments.展开更多
Agriculture is a primary activity in many countries,with wheat being a major cereal crop in India.Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dy...Agriculture is a primary activity in many countries,with wheat being a major cereal crop in India.Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dynamics,pricing,and trade.This study focuses on estimating wheat acreage and yield in Barwala block,Hisar district,Haryana,for the 2019-2020 Rabi season using remote sensing techniques.Multi-temporal satellite data capturing phenological stages of wheat(Seedling to Ripening)were processed using supervised classification with a maximum likelihood classifier in ERDAS Imagine.Wheat crop acreage was determined by overlaying ground truth points on the classified data.The estimated acreage showed a relative deviation of−1.07%compared to statistics from the Department of Agriculture(DoA),Haryana.Yield assessment employed a Semi-Physical model based on the Modified Monteith Model.Key parameters included Photosynthetically Active Radiation(PAR),fraction of PAR absorbed by wheat(fAPAR),light use efficiency,and water stress derived fromthe Land Surface Water Index(LSWI)using Sentinel-2 NIR and SWIR-1 bands.Net Primary Productivity(NPP)was computed for the wheat growth period,and grain yield was estimated using a harvest index obtained fromliterature.The estimated yield had a relative deviation of 9.3% from DoA data.The study demonstrates the potential ofmulti-temporal satellite imagery for accurate block-level wheat acreage and yield estimation,providing a valuable tool for agricultural planning and policy-making.展开更多
文摘Buckthorns(Glossy buckthorn,Frangula alnus and common buckthorn,Rhamnus cathartica)represent a threat to biodiversity.Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration.Early detection strategies are therefore necessary to limit invasive alien plant species’impacts,and remote sensing is one of the techniques for early invasion detection.Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images.Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images.The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area.Three machine learning classifiers(Support Vector Machines,Random Forest and Extreme Gradient Boosting)were applied to WorldView-3,GeoEye-1 and SPOT-7 satellite imagery.The Random Forest classifier performed well(Kappa=0.72).The SVM and XGBoost’s coefficient Kappa were 0.69 and 0.66,respectively.However,buckthorn distribution in understory was identified as the main limit to this approach,and LiDAR data could be used to improve buckthorn mapping in similar environments.
文摘Agriculture is a primary activity in many countries,with wheat being a major cereal crop in India.Accurate pre-harvest forecasts of crop acreage and production are critical for policymakers to address supply-demand dynamics,pricing,and trade.This study focuses on estimating wheat acreage and yield in Barwala block,Hisar district,Haryana,for the 2019-2020 Rabi season using remote sensing techniques.Multi-temporal satellite data capturing phenological stages of wheat(Seedling to Ripening)were processed using supervised classification with a maximum likelihood classifier in ERDAS Imagine.Wheat crop acreage was determined by overlaying ground truth points on the classified data.The estimated acreage showed a relative deviation of−1.07%compared to statistics from the Department of Agriculture(DoA),Haryana.Yield assessment employed a Semi-Physical model based on the Modified Monteith Model.Key parameters included Photosynthetically Active Radiation(PAR),fraction of PAR absorbed by wheat(fAPAR),light use efficiency,and water stress derived fromthe Land Surface Water Index(LSWI)using Sentinel-2 NIR and SWIR-1 bands.Net Primary Productivity(NPP)was computed for the wheat growth period,and grain yield was estimated using a harvest index obtained fromliterature.The estimated yield had a relative deviation of 9.3% from DoA data.The study demonstrates the potential ofmulti-temporal satellite imagery for accurate block-level wheat acreage and yield estimation,providing a valuable tool for agricultural planning and policy-making.
