Advancements in technology across nearly every sector have not overlooked agricultural management and production. Analyzing suitability and conditions for crops, optimizing resource use, and tracking variables in farming conditions are all extremely useful advancements in agricultural production. With a large farm to manage and many factors to consider across the land, the creation of maps and data in Global Mapper is an essential step in data-driven agricultural analysis.
Digital or smart farming takes advantage of new technologies and by incorporating GIS, allows farms to improve planning and productivity. From the use of GPS-enabled smart tractors and drones for data collection to mapped soil sampling and growth calculation, Global Mapper can help analyze the current status of a crop or field and plan for its future.
Freely available data allows users to easily load, view and interact with data covering the entire earth. The most prevalent form of easily accessible data is satellite imagery. A simple image for an area can be streamed into Global Mapper from the Online Sources dialog. Using an image source produced and hosted by the United States Department Agriculture (USDA), a map is begun in the workspace and the land cover, field bounds, and features can be viewed.
Imagery streamed directly into Global Mapper from the publicly available National Agricultural Imagery Program (NAIP) online source provides an easy starting point for map creation and data analysis.
For more advanced analysis and to further accentuate specific characteristics of the surface, multiband satellite imagery can be used. Multiple data collection programs use earth-orbiting satellites to capture data from the earth’s surface. Many of these satellites collect multiple bands of data for each area, recording how different wavelengths of electromagnetic radiation interact with the surface. Based on the various ways these wavelengths interact with different materials and land cover types, these datasets can be intelligently combined to create a library of images enhancing specific characteristics of the earth’s surface.
A natural color and false color composite image are created by combining different layers of satellite collected images. This false color composite image shows vegetated areas in green and bare earth areas in magenta.
The layers of data collected by the satellite programs can also be combined using a mathematical formula in the Raster Calculator to analyze the water or vegetation present in an area. The Raster Calculator applies a formula using corresponding pixels from multiple raster layers. In a Normalized Difference Vegetation Index (NDVI) calculation, the red and near infrared bands are used and the resulting image contains values from -1 to +1 with the higher values indicating the healthy vegetation in the area.
Using satellite datasets from two different times of the year, July and October, the calculated NDVI can be compared to show the change in crop cover across the farm area.
The data shown is collected and produced by the Landsat 8 program. The Landsat 8 satellite continuously orbits earth collecting data in a repeating pattern, covering each area of earth approximately every sixteen days. While some of the images contain high levels of cloud cover or weather interference, this repeated data collection allows satellite data analysis throughout the growing season and over multiple years in order to track progress and inform future decisions.
Creating Vector Map Features
To create a local farm map, point, line, and area features can be drawn directly in Global Mapper to expand the reference information beyond an image basemap. Simple polygons can be drawn to represent field areas or building footprints, and attributes can be added to better describe the soil and crop history for each field.
With areas created on the map, a legend is enabled to show the crop type to be grown in each field.
To map the extent of irrigation systems, features can be drawn representing existing or proposed infrastructure. For pivot point irrigation systems, the centerpoint can be marked and a range ring created showing the impacted or irrigated area. With fields and irrigated areas displayed in Global Mapper, the acreage of irrigated areas can be calculated.
Proposed or existing infrastructure, such as greenhouses and irrigation systems can be added to a map.
Field Data Collection
With agriculture management taking place very much outside on the ground, Global Mapper Mobile, a free mobile app, can help aid in outdoor data collection. Exporting a map containing reference data from Global Mapper desktop and sharing it to mobile phone or tablet, the map can be opened and interacted with in Global Mapper Mobile. The internal location services of the mobile device will be used to display the GPS location on the map and to record new features.
The GPS location of the user is displayed on top of the map data in Global Mapper Mobile.
In any agricultural endeavor, soil quality and nutrient content impacts the health and production yield of the crop. To verify or analyze soil nutrients and pH across the area of a field, Global Mapper Mobile can be used to record point features at the soil sample locations. To assist in this process, a Feature Template — a plan for a layer of data to be collected — can be created in Global Mapper and sent to Global Mapper Mobile for data collection. The use of a Feature Template helps to ensure all the desired information is recorded at every sample location using pick lists and pre-configured attributes.
In the field, a testing probe or test station can be used to check the pH, moisture level, and other aspects of the soil, and in Global Mapper Mobile on a phone or tablet a point feature at the GPS location of the sample can be created and the measured values added as attributes.
Using the internal location services of the mobile device, Global Mapper Mobile has options to create point, line, and area features, and even save captured photographs to the map.
Taking the collected data back into Global Mapper, the recorded points can be analyzed to show the distribution of data values across the field. The Voronoi diagram creates areas showing the closest soil sample for any given location in the field. Each area generated by this analysis inherits the attributes of the point located within the area.
A styled Voronoi diagram shows the pH value of the closest soil sample projected across the field.
Gridding a specific measured value across the field area interpolates the values to show the likely transitions in resources between the sample points. This interpolation method shows smooth, possibly more realistic, transitions between soil characteristics across the area.
With a specific attribute treated as an elevation value, the characteristic is gridded to create an interpolated raster layer showing the existing nitrogen compound levels distributed across the field.
Data collection and resource monitoring can only be taken so far and in general, they can only assess the current or former growing conditions. An essential part of agriculture is taking the aggregated data and utilizing it to make informed decisions for the upcoming season.
It has been shown how Global Mapper can be used to collect, generate, and display data, but creating a structure for decision making will help to make data driven decisions and plans for the future actions.
Creating a regular grid of polygons across a field and automatically adding attributes describing the approximate soil nutrient content from the interpolated raster layer, the fertilizer requirements per acre can be determined. The new attribute calculation takes into account the needs of the crop that will be planted, the crop history of the field, and the existing soil profile.
Using existing attributes as variables in the creation of a new attribute makes it easy to compute the fertilizer needed for the next season’s corn crop.
Breaking the field area into 1 acre areas and taking current conditions into account, the needed amounts of fertilizer across the field are calculated.
Using these newly calculated attribute values, a map can be made describing the amount of fertilizer needed across the field, and this information can be used with a smart tractor system to intelligently distribute the needed nutrients across the field. The use of Global Mapper along with smart agriculture infrastructure leads to more efficient and accurate distribution of resources.
Based on the needed row spacing of the equipment being used, the path for a tractor can be created in Global Mapper. Using the field bounds, lines are created to show the path the tractor will take. This fully georeferenced path can then be exported and used by a smart tractor as a guide to navigate through the field.
Using the feature creation options in Global Mapper the path for a smart tractor is planned.
Drone Data Collection
With the increased availability of GPS enabled drones, remotely captured images and videos of a farm area can be easily and frequently collected. This availability of frequent data collection creates excellent opportunities to view change in the conditions of the farm over time, and with change detection calculations and other analysis tools in Global Mapper, data can be created to make sure a farm is operating as successfully as possible. As the drone flies over the area it captures overlapping static images that are geotagged with the location of the camera where each image was captured. Global Mapper can display these individual images as Picture Points at the geotagged location, and can be used to display the individual images on a map.
To bring the set of individual images together, they can be processed with the Pixels to Points tool to create a single orthoimage and 3D outputs. A high resolution orthoimage of an agricultural area makes a great basemap for reference, and with the ability to collect and process multiple datasets throughout the season, visual changes can be tracked over time.
Taking static drone collected images, the Pixels to Points tool processes them into image and 3D outputs.
The 3D outputs created by the Pixels to Points tool are in the form of a point cloud and a mesh. While the mesh can be viewed in the 3D viewer and exported to many 3D formats, the primary point cloud output offers many opportunities for analysis. By classifying the point cloud into recognized classes — such as ground, buildings, and vegetation — Global Mapper’s point cloud processing tools can be used to create an accurate bare-earth terrain model that can subsequently be used for contour generation, volume calculation, and other terrain analysis procedures..
Classifying the generated point cloud helps to better understand and analyze the features represented in the data.
Using a minimum value method and only the ground classified points, a digital terrain model is generated in Global Mapper.
Since drone captured images can be collected frequently, capturing field areas after tilling, when they are bare, will result in the most accurate bare-earth terrain model. Using this as a baseline, subsequently generated point clouds can be compared to this bare-earth model to track crop and vegetation growth.
Using the point cloud derived surface mode and ground model, the difference in elevation from the ground to the vegetation is observed in a Path Profile view.
Taking the bare-earth, digital terrain model, and comparing it to the gridded surface model that includes vegetation provides a volume or growth measurement and a quantified map of elevation change, or crop height.
Subtracting the bare-earth elevation values from the corresponding surface, or vegetation growth values, a layer showing the height of vegetation growth over a field is generated.
A calculated total volume difference between the gridded layers can be used to estimate crop yield for the field.
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Beegle, Douglas. “Estimating Manure Application Rates.” Penn State Extension, The Pennsylvania State University, 12 July 2021, extension.psu.edu/estimating-manure-application-rates.
Hubbard, Allison. “The Ultimate Guide to Testing Soil PH.” Hanna Instruments Blog, Apr. 2017, blog.hannainst.com/soil-ph-testing.
Saiz-Rubio, Veronica & Rovira-Más, Francisco. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy. 10. 10.3390/agronomy10020207.
What Are the Best Landsat Spectral Bands for Use in My Research?, USGS, www.usgs.gov/faqs/what-are-best-landsat-spectral-bands-use-my-research?qt-news_science_products=0#qt-news_science_products.