How can you design a remote sensing project that accounts for shadows?

1 Data sources and parameters

When designing a remote sensing project that accounts for shadows, the first step is to select the appropriate data sources and parameters that are suited to your objectives and area of interest. Factors like the spatial, spectral, temporal, and radiometric resolution of the data, as well as the sun angle, azimuth, and elevation at the time of acquisition should be taken into account. Additionally, you should consider cloud cover, atmospheric conditions, seasonality, availability, accessibility, and cost of the data. To ensure sufficient detail, contrast, and coverage of your features of interest while avoiding excessive shadows or noise, you should aim to choose data sources and parameters that provide high-resolution imagery with multiple bands acquired at low sun angles or near-nadir angles during clear and dry seasons.

2 Pre-processing techniques

The second step in designing a remote sensing project that accounts for shadows is to apply pre-processing techniques to enhance the image quality and prepare the data for further analysis. These techniques include radiometric correction, which adjusts pixel values to account for sensor characteristics, atmospheric effects, and illumination conditions; geometric correction, which eliminates distortions, errors, and gaps in the data; and image enhancement, which modifies contrast, brightness, color, and sharpness of the image to highlight features of interest and reduce noise. You should tailor the pre-processing techniques to your data sources, parameters, and objectives. For instance, dark object subtraction, atmospheric correction, or topographic correction may be used to reduce shadows on pixel values.

4 Ancillary data and information

The fourth step in designing a remote sensing project that accounts for shadows is to incorporate ancillary data and information to improve the interpretation and validation of your results. Ancillary data and information are additional sources of data and information that are relevant and complementary to your remote sensing data, such as digital elevation models (DEMs), land cover and land use maps, or field data and ground truthing. These sources can help you understand the topographic effects, identify features of interest, assess accuracy, and validate your results. Moreover, you should ensure that the ancillary data and information you use are reliable, relevant, and compatible with your remote sensing data. For instance, you may use DEMs to estimate the height and shape of the buildings and trees that cast shadows on the surface.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?