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Tag: image processing Page 1 of 3

Classifying the dynamics of the forest using the time-series

In this post, we will use the datasets from UMD (Tree Canopy Cover [TCC] and LOSS layers) to calculate and classify the dynamics of the forest. The TCC is available from 1988 and LOSS layers are available from 1989. Here we will be calculating different forest dynamics. Use the final assemblage using this link or … CONTINUE READING

Generation of High-Resolution DSM Using UAV Images

Generating High-Resolution DSM often demands highly accurate data. Among the range of terrestrial and aerial methods available to produce such a dataset, this project tests the utility of images acquired by a fixed-wing, low-cost Unmanned Aerial Vehicle (UAV). The data processing of UAV images has been carried out using the algorithms ranging from classical photogrammetry … CONTINUE READING

Assessment of time since forest disturbance

In the assessment of time since forest disturbance, we define disturbance as the drop of TCC from =10% to 0%. Use following code or the link here.

Happy Earth Science 🙂… CONTINUE READING

Making Forest Rotation Map

Rotation of the forest is defined as the number of forest disturbance events for the year range, with disturbance defined as a drop of Tree Canopy Cover from >=10% to 0%, or the loss from 1 to 0.

The pseudo-code for the rotation map can be written as:

We travel time for 3 years and … CONTINUE READING

Calculate the amplitude of datasets over a time-series

With the Google Earth Engine, it’s very easy to calculate the amplitude of any time-series datasets. The amplitude in wave physics is defined as the distance between the high and the low peak as shown in the picture below. Similarly, for any raster datasets for a time series, the amplitude is the difference of the … CONTINUE READING

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