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Land Management

Land-use of Portland, OR, 2008. Landsat.
Image: Portland, OR, land use from Landsat data, 2008.

Land Management in-person and online trainings focus on accessing, interpreting, and processing NASA Earth Observation data for a variety of terrestrial applications. Topics include land cover mapping, conducting change detection, and processing vegetation indices such as the Normalized Difference Vegetation Index (NDVI). Trainings aid participants in the areas of conservation, animal movement, phenology, carbon monitoring, and near-shore land/ocean processes.

Courses are designed primarily for local, state, regional, and international land management agencies, NGOs, policy makers, and other applied science professionals.

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If you would like information on upcoming trainings please sign up for the listserv.


To partner with ARSET for training, or to request a topic, please visit training suggestions. For more information about land trainings and materials, contact Amber Jean McCullum or Juan Torres-Pérez.


+ What file type should I use for my remote sensing analysis?

Satellite data for land management applications are available in many different files types. One of the most commonly used file types are GeoTiffs, which is an easy-to-use geospatially referenced dataset. This can be imported easily into most geospatial software. Some geospatial data are available as hdf files. These are primarily used for storing large amounts of data and require tools for browsing and editing the data. For more information about these tools visit the National Center for Supercomputing Applications (NCSA) HDF website.

+ My study area is often covered by clouds. What can I do?

Clouds are a common issue for remote sensing of land characteristics. In general it is useful to use images that have 20% or less of could cover. However there are some processing steps you can take for cloud masking and removal. Depending on the image, there are algorithms you can apply to mask clouds. For MODIS, there is a cloud mask product. There is also a Could Mask User Guide available. For Landsat you can also conduct cloud masking with your geospatial software of choice. Here is a helpful tutorial for ENVI. For ArcGIS there is a Landsat Toolbox. There is also a provisional Landsat Spectral Reflectance Product that provides cloud free images

+ What is image classification and how can I do it?

Image classification techniques group pixels to represent land cover features based on their spectral properties. Image processing software is used to separate these different land cover types. There are two main types of image classification: unsupervised and supervised. Unsupervised classifications are conducted when the user manually identifies clusters of pixels based on their reflectance properties without the use of ground data. Supervised classifications are conducted when the user has an identified set of “training sites” where the land cover designation is known, and this information is used to classify the remainder of the image. There are many tools available for image classification available depending on the geospatial software of choice. Because image classification is best learned in a lab environment, we recommend you learn these advanced techniques in a remote sensing course.

+ Can you distinguish between different types of vegetation?

It depends. Using a multispectral sensor like Landsat, green vegetation looks very similar and it is often very difficult to distinguish between different vegetation types. However, there are various methods that may help. One is to use a hyperspectral imager such as AVIRIS, which uses an airborne platform. Hyperspectral imagery enables you to detect more subtle differences between vegetation types. Another common method is to use ancillary information such as elevation, slope and aspect with Landsat imagery to help distinguish vegetation types. Another very common method is to use a spectroradiometer in the field to collect specific information about the spectral reflectance of different vegetation types. This is typically used with hyperspectral imagery to precisely identify spectral signatures.