Usability of smartphone-based RGB vegetation indices for steppe rangeland inventory and monitoring

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-11-27 DOI:10.1007/s11119-024-10195-0

Onur İeri

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Abstract

Rapid rangeland monitoring is critical for implementing management actions effectively and therefore, various remote sensing methods are used for rangeland monitoring. Prices of high-resolution imagery and cloud problems could avoid practicing satellite based-methods. UAV- or ground-based high resolution RGB imagery suggested as an alternative to monitor rangelands. In this study, the performance of smartphone RGB imagery was evaluated over prediction of biomass yield and forage quality of steppe rangelands. Besides, the performance of a mobile application (Canopeo) over rangeland cover was evaluated. RGB band reflection values of smartphone images were determined using a simple open-source software, ImageJ. A total of thirteen different vegetation indices (eleven commonly used and two newly introduced) were estimated and their relations with ground data were evaluated over simple linear and quadratic regression models. AGB and DMY were predicted with moderate accuracy via the newly introduced modified blue-red-green index (MBRGI) (R² = 0.5 for AGB) and recently used normalized difference blue-red index (NDBRI) (R² = 0.46 for DMY) through quadratic regression models. Green leaf index (Gli), visible atmospheric resistant index (Vari), and red green blue vegetation index (RGBVI) gave better results for forage quality predictions among the other VI’s. Gli was an accurate predictor (R² = 0.78) of forage dry matter content. However, prediction performances of VI’s were low for CP (Vari, R² = 0.26), NDF, and ADF contents (RGBVI, R² = 0.31 and 0.37 respectively). Cover data of Canopeo highly correlated both with transect (R² = 0.99) and modified wheel loop (R² = 0.73) data. These results showed that Canopeo might be a useful tool for cover predictions and smartphone-based RGB imagery has good potential for managing rangeland in terms of yield and dry matter content but the accuracy of both yield and forage quality predictions still needs to be improved.

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基于智能手机的 RGB 植被指数在草原牧场清查和监测中的可用性

快速牧场监测对于有效实施管理行动至关重要，因此，各种遥感方法被用于牧场监测。高分辨率图像的价格和云层问题可能会避免使用基于卫星的方法。建议使用无人机或地面高分辨率 RGB 图像来监测牧场。本研究评估了智能手机 RGB 图像在预测草原生物量产量和牧草质量方面的性能。此外，还评估了移动应用程序（Canopeo）在牧场覆盖方面的性能。智能手机图像的 RGB 波段反射值是通过简单的开源软件 ImageJ 确定的。共估算了 13 种不同的植被指数（11 种常用指数和 2 种新引入指数），并通过简单的线性和二次回归模型评估了它们与地面数据的关系。通过二次回归模型，新引入的修正蓝-红-绿指数（MBRGI）（AGB 的 R2 = 0.5）和最近使用的归一化蓝-红差异指数（NDBRI）（DMY 的 R2 = 0.46）对 AGB 和 DMY 的预测具有中等准确性。绿叶指数（Gli）、可见光大气抗性指数（Vari）和红绿蓝植被指数（RGBVI）在其他植被指数中对牧草质量的预测结果较好。Gli 能准确预测牧草干物质含量（R2 = 0.78）。然而，VI 对 CP（Vari，R2 = 0.26）、NDF 和 ADF 含量（RGBVI，R2 分别 = 0.31 和 0.37）的预测性能较低。卡诺佩欧的覆盖度数据与横断面数据（R2 = 0.99）和改良轮环数据（R2 = 0.73）高度相关。这些结果表明，Canopeo 可能是一个有用的覆盖预测工具，基于智能手机的 RGB 图像在产量和干物质含量方面具有管理牧场的良好潜力，但产量和饲料质量预测的准确性仍有待提高。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.