|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/crop-yield-estimation-using-remote-sensing-and-gis-arcgis/
课程评论：没有评论
**课程名称：** 运用遥感和GIS ArcGIS进行作物产量估算 **课程概述：** 本课程聚焦于利用遥感和地理信息系统（GIS）ArcGIS技术进行作物产量估算，以小麦为例，但方法通用。课程强调了遥感在作物识别、状况评估和产量估算中的重要作用。 **主要方法与技术：** * **基于NDVI的作物产量估算：** 将作物归一化差异植被指数（NDVI）与产量建立函数关系，利用ArcGIS中的机器学习方法，结合遥感数据的光谱、纹理和结构特征进行作物分类。 * **作物分类与NDVI指数评估：** 通过遥感数据进行作物分类，然后利用NDVI等植被指数评估作物健康状况。 * **产量估算模型：** 运用统计方法（如回归分析）和GIS建模，结合分类和建模数据以及实测记录，开发作物产量估算模型。本课程将重点介绍一个用于小麦产量估算的模型，该模型利用多种遥感数据。 * **模型验证：** 在课程中还将涵盖如何将开发的模型在邻近的研究区域进行验证，以确保其准确性和适用性。 **课程亮点：** * 在ArcGIS中运用机器学习进行作物分类，区分作物和天然植被。 * 使用最少量的在线可用观测数据开发模型。 * 进行作物NDVI分离。 * 开发作物产量模型。 * 通过GIS模型数据计算作物产量。 * 识别并计算低、高产量区域的面积。 * 计算区域总产量。 * 在另一研究区域验证开发的模型。 * 使用另一区域的开发模型来验证其他区域的产量和产量。 * 将模型转换为ArcGIS工具箱。 **先修知识要求：** * GIS基础 * Excel基础 **软件要求：** * ArcGIS（10.0至10.8版本均可） * Excel **总结：** 本课程全面介绍了如何利用遥感和GIS ArcGIS技术进行作物产量估算，包括作物识别、状况评估和产量预测。通过实际操作，学员将掌握关键建模和验证技术，从而提升农业资源管理和作物产量预测的准确性。
Crop yield estimation is a critical aspect of modern agriculture. In this course, the wheat crop is covered. The same method applies to all other crops. With the advent of remote sensing and GIS technologies, it has become possible to estimate crop yields using various methodologies. Remote sensing is a powerful tool that can be used to identify and classify different crops, assess crop conditions, and estimate crop yields. One of the most popular methods for crop identification using remote sensing is to relate crop NDVI as a function of yield. This method uses various spectral, textural and structural characteristics of crops to classify them using the machine learning method in ArcGIS. Another popular method for crop condition assessment using remote sensing is crop classification then relate to NDVI index. This method uses indices such as NDVI to assess the health of the crop. Both of these methods are widely used for crop identification and assessment. Crop yield estimation can also be done by using remote sensing data. Yield estimation using remote sensing is done by using statistical methods, such as regression analysis and modelling in GIS and excel, including classification and estimation. One popular method for estimating wheat yield is the crop yield estimation model using classified and modelled data with observed records, as shown in this course. This model uses various remote sensing data to estimate the wheat yield. It is also important to validate the developed model on another nearby study area. That validation of the developed model is also covered in this course. The identification of crops is an important step in estimating crop yields and managing agricultural resources. In summary, remote sensing and GIS technologies are widely used for crop identification, crop condition assessment, and crop yield estimation. They provide accurate and timely information that is critical for managing agricultural resources and increasing crop yields.Highlights:Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation The model was developed using the minimum observed data available onlineCrop NDVI separationCrop Yield model developmentCrop production calculation from GIS model dataIdentify the low and high-yield zones and area calculationCalculate the total production of the regionValidation of developed model on another study area Validate production and yield of other areas using a developed model of another areaConvert the model to the ArcGIS toolboxYou must know:Basics of GISBasics of ExcelSoftware Requirements: Any version of ArcGIS 10.0 to 10.8Excel
