ex2602_landsat_bndvi

Dates of Images: Post-Event: 5/6/2018; 5/15/2024 Pre-Event: 4/8/2018; 4/21/2024 Date of Next Image: Unknown Summary: NDVI: The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Suggested Use: NDVI: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_landsat_ndvi/ImageServer/WMSServer

This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).

Dates of Images: Dates: 04/20/2024 (pre-flood), 05/06/2024, 05/07/2024, 05/09/2024, 05/15/2024, 05/21/2024 Time Zone: GMT (local crossing time of ~1:00 pm) Summary: These are NDVI images from PACE OCI of the 2024 flooding in Brazil using data from the OCI LANDVI suite, where NDVI is one of the products delivered daily by the mission. The images show how the landscape changes from a pre-flood day (04/20/2024) through flood days ending at 05/21/2024 at ~1:00 pm local time. NDVI is a measure of approximate greenness and health in vegetation but can also be useful in distinguishing water extent and unhealthy vegetated areas, which can be see in these images throughout the flood. Suggested Use: NDVI is a normalized band ratio measuring approximate greenness and vegetation health. Higher positive values mean greener and abundant vegetation, values around 0 mean bare soil or rock, and lower negative values mean non-vegetated areas like water or clouds. These images show the transition of the area around the Jacui River from non-flooded conditions (high NDVI values) to flooded (NDVI values in the negative, indicating water presence). Clouds have been screened from these products, so low NDVI values can around the river can be identified as water. Satellite/Sensor/Resolution: Satellite: PACE, sensor: OCI, temporal resolution: daily, spatial resolution: gridded to 0.015 degrees from a Level 2 product in instrument swath (varying pixel size, 1.2 km at nadir).

Dates of Images: Post-Event: 5/6/2024 Pre-Event: 4/21/2024 Date of Next Image: Unknown Summary: Normalized Differential Vegetation Index (NDVI) maps are produced by converting pixel values to NDVI = (Red – NIR)/(Red + NIR), NIR indicates the near infrared band. The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. NDVI change was created by subtracting the cloud-masked post-event NDVI from the cloud-masked pre-event NDVI to show areas of potentially impacted vegetation as negative values (darker shades) and healthy/unimpacted vegetation in positive values (lighter shades). NDVI change can give insights to impacts from an event (like the scouring of vegetation from the riverbeds) but can also show changes in unrelated areas like the clearing of agriculture fields. This layer is a binary NDVI change polygon (threshold of areas where NDVI decreased by -0.25 or more) for May 6, 2024 compared to April 21, 2024. Suggested Use: NDVI change: Commonly used to distinguish healthy/active vegetation (positive values) from potentially unhealthy/impacted vegetation (negative values). This map shows a binary flag where NDVI decreased. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_sentinel2_bndvi/MapServer/WMSServer

Miscellaneous events are often the result of the cumulative impact of a combination of abiotic, insect and disease agents or events.  For example, Aspen decline where repeated infestations of Forest Tent Caterpillar, are combined with several seasons of prolonged drought. The Government of Ontario tracks forest damage events to help proactively manage the detrimental effects to our forests.   We monitor the threat and spread of invasive forest pest insect species in Ontario. The data is also important to the Forest Management Planning process in calculating timber volume loss within affected areas. Additional Documentation Forest Misc Damage Event - Documentation (Word) Forest Misc. Damage Event - Data Description (PDF) Status On going: data is being continually updated Maintenance and Update Frequency As needed: data is updated as deemed necessary Contact Calvin Julie, calvin.julie@ontario.ca Recommendations The boundaries for these events were coarsely mapped. This must be taken into account when comparing information from this data class to other NRVIS layers that have a higher spatial accuracy (e.g.:Forest Cover). Otherwise, misleading results will be derived whenever this information is subjected to analysis. This NRVIS data class accommodates history for events spanning several years which includes overlaps. Users should request and/or sort out individual events through the 'Year of Event' ,'Event Name' and 'Forest Damage Ranking' attributes in order to prevent clutter.