Online deforestation detection
Emiliano Diaz

TL;DR
This paper adapts a satellite-based deforestation detection algorithm from Landsat to MODIS data, enabling continuous, large-scale forest monitoring with two proposed methodological approaches.
Contribution
It introduces an adaptation of the CMFDA algorithm for MODIS data, including two new approaches for deforestation detection without relying on the original forest index.
Findings
Successful adaptation of the CMFDA algorithm to MODIS data
Development of two alternative deforestation detection approaches
Potential for large-scale, continuous forest monitoring
Abstract
Deforestation detection using satellite images can make an important contribution to forest management. Current approaches can be broadly divided into those that compare two images taken at similar periods of the year and those that monitor changes by using multiple images taken during the growing season. The CMFDA algorithm described in Zhu et al. (2012) is an algorithm that builds on the latter category by implementing a year-long, continuous, time-series based approach to monitoring images. This algorithm was developed for 30m resolution, 16-day frequency reflectance data from the Landsat satellite. In this work we adapt the algorithm to 1km, 16-day frequency reflectance data from the modis sensor aboard the Terra satellite. The CMFDA algorithm is composed of two submodels which are fitted on a pixel-by-pixel basis. The first estimates the amount of surface reflectance as a function…
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Taxonomy
TopicsRemote Sensing in Agriculture · Remote Sensing and LiDAR Applications · Species Distribution and Climate Change
