【Objective】 Malania oleifera is an endangered plant with high economic and ecological value. This study focuses on discovering the spatial distribution pattern of potential suitable areas of M. oleifera, and finding its main environmental affecting factors, laying a solid theory foundation for its conservation and utilization.【Method】 This study got 136 sampling points of M. oleifera by field investigations, specimens of the digital library, and the global biodiversity information network. Based on the ArcGIS geographic information system platform and the maximum entropy model (MaxEnt) with parameters of 20 common main environmental factors, the prediction model of M. oleifera potential suitable areas was built to simulate the M. oleifera distribution in Yunnan Province and Guangxi Zhuang Nationality Autonomous Prefecture (Guangxi).【Result】 The results showed that of M. oleifera is mainly distributed in the longitude of 104°-107°E and the latitude of 22°-26°N. The area under the curve (AUC) of potential suitable areas predicted by the MaxEnt model in this study were all over than 0.9. The top 4 environmental affecting factors and their contribution rates predicted by the MaxEnt model of potential suitable areas of M. oleifera were orderly listed: seasonal variation factors of temperature (contribution rate 39.6%), isothermal factors (contribution rate 16.7%), average daily temperature factors (contribution rate 13.7%), and annual temperature difference factors (contribution rate 11.5%).【Conclusion】 The southeast of Wenshan Zhuang and Miao Nationality Autonomous Prefecture of Yunnan and the west of Guangxi are the concentrated potential distribution suitable areas of M. oleifera, and temperature is the dominated affecting factor. Additionally, the MaxEnt model performs well for predicting potential suitable areas of M. oleifera both in accuracy and reliability. This study will provide an important basis for the conservation and utilization of M. oleifera resources, and an artificial breeding site selection.
【Objective】In this study, the Camellia oleifera base in Mingyue Village, Changsha County, was used as the study area to explore the feasibility of unmanned aerial vehicle (UAV) oblique photography technology for extracting C. oleifera crown volume and estimating the tree height and yield. 【Method】Remote sensing variables such as band reflectance, vegetation indices, texture factors, height characteristics, and crown parameters were extracted from UAV orthophotos and dense matched point clouds. The Kriging, inverse distance weighting, natural neighbor, and filtered triangulation methods were used to obtain the crown volume of C. oleifera. The multiple linear regression, random forest, and K-nearest-neighbor models were established to estimate the height and yield of C. oleifera, and the accuracy of the estimation results was evaluated using the crown volume obtained from the ground 3D laser point clouds and the actual measured height and yield of the sample plots as the measured values. 【Result】The filtered triangulation method was the most effective for obtaining the crown volume with an average relative error of 31.54%, which was better than 36.73% for the inverse distance weighting method, 37.04% for the Kriging method, and 38.54% for the natural neighbor method. The accuracy of all three estimation models for the tree height and yield was improved by using the crown volume as a characteristic variable in the modeling. The relative root mean squared errors (rRMSEs) for tree height were reduced by 3.77%, 0.78% and 0.64%, respectively. In addition, the rRMSEs for the yield were reduced by 1.32%, 0.34% and 0.16%, respectively. The multiple linear regression, random forest and K-nearest-neighbor models were compared, and the coefficients of determination of the random forest model were all better than those of the multiple linear regression and K-nearest-neighbor (the R2 of tree height was 0.78, 0.51 and 0.19, and the R2 of yield was 0.61, 0.48 and 0.24, respectively). There is no significant difference in the accuracy of using the estimated tree height and measured tree height to participate in yield modeling. 【Conclusion】The combination of crown volume and tree height participation modeling can effectively improve the accuracy of C. oleifera yield estimation, and the results of this study can provide a reference for conducting C. oleifera height and yield surveys using UAV remote sensing technology on a regional scale.
【Objective】Camellia oleifera, one of the four major woody oilseeds in the world, has a comprehensive utilization value. As a key rural industry in southern China, it plays an important role in the development of the agricultural economy. In recent years, with a vigorous support from the national government at all levels, the C. oleifera planting area has been expanding annually. Therefore, it is important to obtain information on the shapes of C. oleifera fruits quickly and accurately to determine the size distribution, guide fruit grading, and calculate rapid yield measurements. This paper proposes a batch extraction method for C. oleifera fruit shape feature parameters based on camera photography. 【Method】First, the picked C. oleifera fruits were placed on a background plate with a scale, using a camera to quickly obtain images of C. oleifera fruits and a correction of the geometric distortions produced by photography. Then, the Mask R-CNN model was used for a fast detection and counting of the C. oleifera fruits in the image. According to the generated mask, the number of pixels of the feature parameters (length of the long axis and short axis, area and perimeter) of the camellia fruits was counted using the elliptic fitting method. Finally, the image pixel size calculated with the help of the background plate scale was used to obtain the key parameters of the oil tea fruits, while the accuracy was verified using the actual measured values. 【Result】The average recognition accuracy and recall of the mask R-CNN model were 99.55% and 91.19%, respectively, and the F-measure value was 95.22%, showing that the method was suitable for describing C. oleifera fruit shape parameters statistically. The estimated accuracy of the area of the C. oleifera fruits was the highest with the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of 0.999 0, 10.75 mm2, and 14.88 mm2, followed by the perimeter and length of the long axis, and the lowest accuracy of length of the the short axis with R2, MAE, and RMSE of 0.864 7, 3.15 mm, and 3.74 mm, respectively. 【Conclusion】The method achieved a rapid and accurate counting of C. oleifera fruits shape feature parameters after picking and batch extraction. Thus, the technique can rapidly and accurately detect a large number of fruit feature parameters and provide a scientific basis for the C. oleifera fruit grading and rapid yield measurement.
