Object-oriented crop classification based on UAV remote sensing imagery

UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.

The paleoclimatic environment reconstruction of Lop Nur in NW China in UAV spectroscopy

The change in the ecological environment in the arid core area is a critical issue in the context of global warming.To study the paleoclimate evolution,precise identification of minerals deposited in Asia’s arid hinterland,Lop Nur Salt Lake,NW China was conducted.The hyperspectral data of the salt crust was sampled to identify the species and content of sedimentary minerals,and the multispectral photos were used to reconstruct the salt crust morphology using the unmanned aerial vehicles platform.The SUnSAL(sparse unmixing by variable splitting and augmented Lagrangian)method was employed to inverse the sedimentary mineral components along the shoreline.The heterogeneity of salt and clay minerals in bright and dark ear-shaped strips was evaluated.The paleoclimatic environment associated with salt lake extinction was reconstructed by analyzing paleoclimate records of sediments,spectral reflectance and morphology of the salt crust.Results show that:(1)the variations in the micro-geomorphology of the salt crust are obviously the reason for the formation of bright and dark ear-shaped strips and the differences in the species and relative content of the sedimentary minerals are the microscopic reason.The high ratio of sedimentary salt minerals to clay minerals(RS/C)contributes to the high reflectivity,and the salt crust presents a bright texture.The low RS/C results in the low reflectivity,salt crust presents a dark texture;(2)the bright and dark ear-shaped strips represent warm-arid and cold-humid climates.The shape of the Lop Nur Lake shoreline evolved due to alternating warm-dry and cold-humid paleoclimate changes.

Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle

Recently near-ground remote sensing using unmanned aerial vehicles(UAV)witnessed wide applications in obtaining field information.In this research,four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July,2015 covering two experimental winter wheat fields,in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index(NDVI)with UAV’s RGB images based visible-band difference vegetation index(VDVI)and ground variables of the sampled grain protein contents.Firstly,through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution,the wheat canopy color changes could be intuitively and clearly monitored.Subsequently,by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values,the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season,reaching up to peak values at the same timing,and starting to decrease since then.The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7,2015,while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches.The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination.It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.