Rice lodging identification relies on manual in situ assessment and often leads to a compensation dispute in agricultural disaster assessment. Therefore, this study proposes a comprehensive and efficient classification technique for agricultural lands that entails using unmanned aerial vehicle (UAV) imagery. In addition to spectral information, digital surface model (DSM) and texture information of the images was obtained through image-based modeling and texture analysis. Moreover, single feature probability (SFP) values were computed to evaluate the contribution of spectral and spatial hybrid image information to classification accuracy. The SFP results revealed that texture information was beneficial for the classification of rice and water, DSM information was valuable for lodging and tree classification, and the combination of texture and DSM information was helpful in distinguishing between artificial surface and bare land. Furthermore, a decision tree classification model incorporating SFP values yielded optimal results, with an accuracy of 96.17% and a Kappa value of 0.941, compared with that of a maximum likelihood classification model (90.76%). The rice lodging ratio in paddies at the study site was successfully identified, with three paddies being eligible for disaster relief. The study demonstrated that the proposed spatial and spectral hybrid image classification technology is a promising tool for rice lodging assessment.

Oyster racks identification relies on manual in situ assessment and often leads to a compensation dispute in aquacultural damage assessment. This study proposes an efficient classification method to identify oyster racks using unmanned aerial vehiclesw (UAVs) images. After image preprocessed by the morphology-based opening operation and mosaicing, the Canny edge detection algorithm, an edge line reparation algorithm, and the multiresolution segmentation techniques are applied to recognize the boundary of oyster racks and the number of oyster racks are further obtained. In this study, a case study was carried out in the District 23 of Dongshi fishing port on September 4, 2015 and October 5, 2015 to evaluate the influence of the Typhoon Dujuan. Based on the image identification, eight horizontal racks were destroyed by the storm surges and strong winds, but the raft-string racks were increased by 60, which were steered from neighbor districts. The damaged ratio of oyster racks was successfully identified and 38 racks were eligible for disaster relief. Comparing to the current manual in situ damage assessment process taking over one month, the proposed process provides a timely and quantitative assessment by efficiently identifying oyster racks on the UAV images within one week, which demonstrated that the proposed process is a promising way for the efficient damage assessment of oyster racks.