Using Spatial Data in Land Management Decisions
When it comes to measuring the environment, historically, data availability and resolution have been some of the biggest limitations. Now that we have been collecting high-resolution satellite and lidar data for many years, we have the ability to measure changes in the land over time. This isn’t a new feat; humans have been quantifying how land changes ever since we gained the tools to do so. With the advent of big data and capable GIS software such as Global Mapper, we have the power to use spatial data in land management decisions. We can measure changes of the past, such as ground erosion, vegetation growth, urban sprawl, and also map issues of the present, such as how water moves across the terrain.
Here are a few resources for using Global Mapper to assess and plan. This Industry Showcase links different resources across our website that can help you achieve specific goals. If you have any questions about your specific data or workflows, contact geohelp.bluemarblegeo.com.
Monitoring Forest Change
Raster imagery can provide valuable insight into the state and change of land cover. Using multiband satellite data collected worldwide through Landsat and other programs, image analysis methods in Global Mapper can create new layers of data depicting the land cover and vegetation health. In this example workflow, see how multispectral imagery can be processed in Global Mapper to measure how much forest cover was lost.
As an alternative to imagery, lidar data presents the ability to measure forest change more in-depth. With its ability to penetrate canopies, lidar can give a better estimate of vegetation growth, tree count, and crown estimations when compared to another dataset. Lidar processing and vegetation measurement can be done in Global Mapper. Classify the ground and vegetation points, then Global Mapper can estimate tree count and crown size. Here in this blog, you can find tips on classifying vegetation in lidar. Once classified, tree features can be extracted and counted using the Lidar Feature Extraction tool. A demonstration of that process can be found here.
If you have elevation data for the same place captured at different points in time, you can compare those datasets to measure erosion. Soil erosion is the loss of topsoil over time and is often caused by wind and/or rain. Erosion can be estimated by measuring the changes in elevation over time or by changes in terrain volume.
There are a few different ways to compare elevation layers in Global Mapper, including the Combine/Compare Terrain Layers tool that can compare raster layers and using Cloud Compare to compare point clouds. Despite the differences in the data structure, these tools have the same essential function; they subtract one data layer from another to generate a new layer. This new layer doesn’t represent elevation but the height difference between the two layers. The new ‘difference’ layer highlights only the changes, as shown in the image below. The red areas are areas where there are higher differences between the original layers.
You can highlight these areas by using the Raster Reclassify tool to turn all areas of concern into one color. To see this information out into the field, use the Vectorization tool to create area features from these areas that can be sent to a mobile device via Global Mapper Mobile.
Measuring Land Cover Change
Workflows in measuring land change can be taken a step further with the use of Land Use/Land Cover data (LULC). This freely available raster data divides the U.S. into different land cover types that can be used for geospatial analysis. By assigning a land cover type to parts of an image, LULC datasets are an easy way to process Landsat Imagery automatically without having to manually label pixel values. Forested areas are clearly delineated from range, from shrubland, and from urban areas, making changes in the size of these areas easier to see.
Download LULC from the National Land Cover Database, provided by the USGS, or stream it directly into Global Mapper from the online data tool under U.S. Data. You can choose to download multiple years for the same area to compare against each other. Use Global Mapper Raster Reclassify tool to isolate which cover type you are interested in, and assign codes to the types that can be analyzed in the Compare/Combine Terrain tool to quantity terrain change type.
Simply put, watershed analysis is the prediction of how water will move across the terrain based on elevation. The watershed analysis tool in Global Mapper has a wide variety of options, including marking areas where water could move across the terrain or demarcating watershed zones. This tool is great for predicting how water can move across existing landscapes to predict where it might flow from a single point to, for example, discover where spilled contaminants are likely to flow, or highlighting which areas flow into a river.
Combined with the Terrain Painting tool, which allows easy terrain manipulation, you can model terrain to predict how water would respond to dams or a specific elevation, or stream changes, or to map where water will flow across the landscape to plan where mitigation should be installed.
Another option for mapping watersheds is flood mapping. In Global Mapper, you can map where water levels are at typical high tide or delineate lake flood levels for parks on a dammed lake with a known highest flood level. More information on the flooding tool within Global Mapper can be found here: GeoTalks Express: Coastal Flood Analysis using Global Mapper
To learn more about mapping water movement across the terrain, check out this showcase on Global Mapper’s watershed analysis tool.
Climate Suitability Analysis for Invasives
For most invasive species, we have an idea of their ideal temperature range, food source and other details on their ideal habitat. By bringing this information into Global Mapper as different layers, you can highlight areas that have habitats best suited for the invasive, pointing you to areas most in need of remediation.
For example, climate change has allowed the western pine beetle to infest forests in the Western US at an alarming rate. An increase in the length of growing seasons, drought-weakened trees, and overly dense forests all contribute to the success of the infestation. These attributes are able to be loaded into Global Mapper as separate layers. By comparing areas that are seeing higher than average temperatures, lower than average rainfall, and high resolution imagery to estimate forest density, Global Mapper can be used to highlight areas at risk for an infestation to help target mitigation.
To learn more about Suitability analysis in Global Mapper, take a peek at this similar study done on Viticulture habitat HERE.