Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging

编号 030025101

推送时间 20200810

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging

来源期刊 INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

期 第251期

发表时间 20200608

关键词 Sugarcane;  Nitrogen;  Fertilizer;  UAV;  Drone;  LiDAR;  Multispectral;  Biomass;  Yield;  NDVI;  Fusion;  PCA; 

摘要 Unmanned Aerial Vehicle (UAV) platforms and associated sensing technologies are extensively utilized in precision agriculture. Using LiDAR and imaging sensors mounted on multirotor UAVs, we can observe fine-scale crop variations that can help improve fertilizer management and maximize yields. In this study we used UAV mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials with variable nitrogen (N) fertilization inputs in the Wet Tropics region of Australia. From six surveys performed at 42-day intervals, we monitored crop growth in terms of height, density and vegetation indices. In each survey period, we estimated a set of models to at-harvest biomass at fine scale (2m x 2m plots). We compared the predictive performance of models based on multispectral predictors only, LiDAR predictors only, a fusion of multispectral and LiDAR predictors, and a normalized difference vegetation index (NDVI) benchmark. We found that predictive performance peaked early in the season, at 100-142 days after the previous harvest (DAH), and declined closer to the harvest date. At peak performance (i.e. 142 DAH), the multispectral model performed slightly better ((R) over bar (2) = 0.57) than the LiDAR model ((R) over bar (2) = 0.52), with both outperforming NDVI benchmark ((R) over bar (2) = 0.34). This, however, flipped later in the season, with LiDAR performing slightly better than the multispectral imaging and NDVI benchmark. Interestingly, the fusion model did not perform significantly better than the multispectral model at 100-142 DAH, nor better than LiDAR in subsequent periods. We also estimated a model to predict contemporaneous leaf N content (%) using multispectral imagery, which demonstrated an (R) over bar (2) of 0.57. Our results are of particular interest to nutrient management programs aiming to deliver N fertilizer guidelines for sustainable sugarcane production, as both fine-scale biomass and leaf N content predictions are feasible when management interventions are still possible (i.e. as early as at 100 DAH).

服务人员 付贺龙

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