Data Lab Sandbox

umd_modis_burned_areas

created_on

2023-05-04T13:11:58.897347

updated_on

2024-10-04T16:27:47.748504

spatial_resolution

500

resolution_description

500m x 500m

geographic_coverage

Global

update_frequency

Monthly

scale

global

citation

Giglio, L. et al. (2018). “Monthly MODIS Burned Area Product (MCD64A1 v006).” Accessed on [date] from Global Forest Watch.

title

Global Burned Areas

source

Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., & Justice, C. O. (2018). The Collection 6 MODIS burned area mapping algorithm and product. *Remote sensing of environment*, 217, 72-85.

license

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

data_language

English

overview

The MODIS Burned Area data product uses a multi-stage algorithm to classify individual 500-m grid cells as either burned or unburned over a single calendar month. For a single MODIS tile, the change-detection algorithm 1) produces composite imagery that summarizes persistent changes in the time series of a burn-sensitive vegetation index, 2) uses spatial and temporal active-fire information to guide the statistical characterization of burn-related and non-burn-related change, and 3) estimates a probabilistic threshold to identify grid cells containing burned areas. One month of daily observations before and after the mapping period are required to accommodate the moving windows employed in the change-detection process, so three consecutive months of observations are required to map one calendar month of burning. In addition to detecting the burned area extent, the data product also approximates the day of burning in each month for each 500-m grid cell based on active fire detections.

function

Displays monthly burned area extent and date of burn based on differences in a burn-sensitive Vegetation Index derived from MODIS shortwave infrared surface reflectance bands

cautions

- Burned areas in cropland should generally be treated as low confidence and may be under-reported due to the inherent difficulty in mapping agricultural burning reliably [see Hall et al. 2016 for more information](https://doi.org/10.1016/j.rse.2016.07.022)<br>- The monthly products for August 2000 and June 2001 are heavily degraded due to extended Terra MODIS outages<br>- The Aqua MODIS satellite experienced a failure of about two weeks starting August 16, 2020; loss of data in Africa, eastern Asia, Indonesia, and Ocean a are not expected to significantly degrade the MCD64A1 burned are product in these regions since the Terra MODIS continued to function normally<br>- A global accuracy assessment for burned areas found an overall accuracy of 97%, commission error (i.e., false positive) of 24%, omission error (i.e., false negative) rate of 37%, producer’s accuracy of 63%, and user’s accuracy of 76%; temporal accuracy was 44% for same-day fire detection, and 68% within two days

key_restrictions

tags

Forest Change

why_added

learn_more

id

bb8c246c-5911-444c-b1da-c980098a13e0

Is downloadable?

Yes

Versions

v2021060
v20220129
v202207
v20221104