I recently took an online course on CLASlite offered by the Carnegie Institution for Science. CLASlite is a software designed to automatically detect deforestation, forest degradation and forest regrowth. It uses the AutoMCU algorithm to detect the percentage of various types of vegetation, allowing to automatically detect deforestation using Landsat images.
In one of my blog posts, I used Landsat image to see the Rockfall scar on Annapurna mountain. I detected the scar manually, but imagine if we could do this automatically. Manually delineating landslide takes a lot of time. We need a lot of historical landslide data to build better landslide hazard maps and landslide prediction models. Being able to automatically detect landslide would be very useful to the geo-disaster community.
A useful index used in remote sensing is the Normalized Difference Vegetation Index (NDVI). This is used to detect vegetations. Most of the hilly region in the Himalayan region is covered with vegetation. When a landslide occurs in such a vegetated area, the vegetation will be swept away, leaving fresh bare soil. Thus, could a change in NDVI be used as an indication of landslide?
Not necessarily. Deforestation would also have a similar effect in NDVI, thus it would be difficult to differentiate landslide from deforestation or another normal bare surface. However, there is a peculiar difference between a fresh landslide and existing normal bare surface. Fresh landslide generally consists of no vegetation, but an existing bare surface would contain some non-photosynthetic vegetations like chopped woods, dead leaves or other dry plans. This is when I thought AutoMCU can come in hand.
Unlike NDVI, AutoMCU outputs three levels of indicators: Bare Substrate (BS), Photosynthetic Vegetation (PV) and Non-photosynthetic Vegetation (NPV). NDVI can only detect photosynthetic vegetation, however AutoMCU can distinguish Bare Substrate from Non-photosynthetic Vegetation. I wanted to try this out myself, so I used the Landsat 8 images near the recent Jure landslide site in Sindhupalchowk, Nepal.
In one of my blog posts, I used Landsat image to see the Rockfall scar on Annapurna mountain. I detected the scar manually, but imagine if we could do this automatically. Manually delineating landslide takes a lot of time. We need a lot of historical landslide data to build better landslide hazard maps and landslide prediction models. Being able to automatically detect landslide would be very useful to the geo-disaster community.
A useful index used in remote sensing is the Normalized Difference Vegetation Index (NDVI). This is used to detect vegetations. Most of the hilly region in the Himalayan region is covered with vegetation. When a landslide occurs in such a vegetated area, the vegetation will be swept away, leaving fresh bare soil. Thus, could a change in NDVI be used as an indication of landslide?
Not necessarily. Deforestation would also have a similar effect in NDVI, thus it would be difficult to differentiate landslide from deforestation or another normal bare surface. However, there is a peculiar difference between a fresh landslide and existing normal bare surface. Fresh landslide generally consists of no vegetation, but an existing bare surface would contain some non-photosynthetic vegetations like chopped woods, dead leaves or other dry plans. This is when I thought AutoMCU can come in hand.
Unlike NDVI, AutoMCU outputs three levels of indicators: Bare Substrate (BS), Photosynthetic Vegetation (PV) and Non-photosynthetic Vegetation (NPV). NDVI can only detect photosynthetic vegetation, however AutoMCU can distinguish Bare Substrate from Non-photosynthetic Vegetation. I wanted to try this out myself, so I used the Landsat 8 images near the recent Jure landslide site in Sindhupalchowk, Nepal.
