I have been working on my summer project for about 2.5 weeks now. I will continue to work remotely until I head to Panama next week to be in the field for a month.  So far I have focused on two separate project, one is fire detection in the Azuero region (more specifically the Los Santos province) using satellite images and another is to set up an ESRI app for volunteers to be able to collect plant information in the field in a convenient manner.

The Azuero Earth Project wants to understand why locals burn in the region, the temporal and spatial distributions of the burns, if there are any patterns (for example clustering or proximity to certain features) that can be identified, in order to better plan their reforestation parcels.

The Global Forest Watch platform provides real-time fire data based on the FIRMS dataset created by NASA using MODIS and VIIRS data on a global scale.  However, the spatial resolution is very coarse, 1km for the MODIS dataset and 375m for the VIIRS dataset, so using data from higher spatial resolution sources would be beneficial to provide a more accurate picture of fire occurrences on a more regional scale.

It is my first time doing remote sensing work related to fires, so first I started going over literature to explore what are some data and methods that are commonly used for this purpose, and what specifically I should be detecting.

The burns occur during dry season, so from December to May each year in the peninsula.  Since next week I will be going to Panama to conduct social surveys with locals to understand their experience related to fires for this past season, I’ve decided to limit the timeline for fire detection to the period from December 2018 – May 2019.

Due to our need of higher spatial resolution images, I thought either Landsat OLI / ETM+ or Planet images would be good options.  Planet images are very high spatial resolution at about 3-5 meters, while Landsat images are at 30 meter resolution.  After acquiring some data, I realized that I do not have enough quota on my account to get Planet images (even if I get the monthly mosaic) over 6 months. Moreover, the spectral resolution of planet images is more limited as they come in RGB + near-infrared (NIR) bands.  For fire detection it is often better to use other bands such as the short-wave infrared (SWIR) or thermal bands depending on what specifically you are detecting (see below).  Thought I have decided to go with Landsat imagery, which has a lot more bands (11 in Landsat OLI, including SWIR and thermal bands) and which is freely available from USGS. A good alternative source for acquiring Landsat data is Google Earth Engine. I have decided to go with the latter and I acquired cloud-masked monthly mosaics for the Los Santos province and exported the images to my Google Drive so I would be able to process them locally using ENVI.

There are four types of spectral signals observed from space: direct radiation from the flame front (heat & light), aerosols (smoke), solid residue (char & ash) and altered vegetation structure (scar). The thermal signal of active fires is quite specific, however the biggest limitation of detecting active fires is their short-lived nature.  The spectral signal resulting from surface darkening due to charcoal deposition is quite a specific consequence of vegetation burning.  However, it is relatively short-lived. It is attenuated by wind scattering, or washed out by rain, in a period of a few days to a few weeks to some months after the fire.  The signal for scarred areas is less specific since partial or total vegetation removal may result from harvesting, grazing, windthrow, or defoliation by pathogenic agents.  However, the vegetation scar signal is more persistent, lasting from a few weeks in tropical savannas, to several years in boreal forests.  I have decided to detect burned areas since I was able to get data every two weeks to a month due to cloud cover in the region.

To detect burned area we can use the Normalized Burn Ratio (NBR).   The NBR was defined to highlight areas that have burned and to index the severity of a burn using Landsat imagery. The formula for the NBR is very similar to that of NDVI except that it uses the NIR band and the SWIR band.   The NIR and SWIR parts of the electromagnetic spectrum are a powerful combination of bands to use for this index given vegetation reflects strongly in the NIR region of the electromagnetic spectrum and weakly in the SWIR. Alternatively, it has been shown that a fire scar which contains scarred woody vegetation and earth will reflect more strongly in the SWIR part of the electromagnetic spectrum and beyond.

An alternative I could map burned areas using supervised classification, using algorithms such as Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM).  So far I have acquired NBR images for monthly mosaics of Los Santos using Google Earth Engine, and will use ENVI to explore the potential of using supervised classification for burned area detection instead of NBR.

Finally, a secondary project for which I have created an app for over the past couple weeks is a project involving plants related to local artisans. Although the Azuero peninsula of Panama was once largely tropical dry forest, it is now has less than 7% forest cover. The extreme pressure on natural ecosystems has affected the very tree and plant resources that sustain local artisans and their wellbeing. The AEP aims to revive and innovate upon existing artisanal traditions in the Azuero region while reaffirming the connection between artisan crafts and the natural environment through the formation of a local Eco-Artisans association, organization of workshops for artisan groups throughout the region, connection of local groups to key sale venues; and incorporating conservation of underlying natural resources into the fabric of Artisan group identity. I have created a plant collection app using ArcGIS Desktop, ArcGIS Online and GeoForms so researchers working on the plants project as well as peace corps volunteers would be able to collect plant data easily in the field.

I look forward to going to Pedasi next week to work with a group of motivated people on different projects and be live a new environment for a month!

It’s been a little over a month since the start of this project, and now’s a good time more than any to touch base and see if I’m still on course for this project’s goal. In summary, my project is to implement LiDAR into the Wisconsin Urban Forest Assessment program and measure for changes in accuracy for urban forest assessments. This month has been extremely informative and progressive as I build my knowledge and research robust methods, but my angst builds as I look forward towards developing my deliverable and tool.