MRV Carbon and Deforestation


Satellite Monitoring for Deforestation-free Cacao Supply Chains

September 21, 2023

Written by

Hyeonmin Kang

Over the last five decades, global cacao cultivation has doubled, exacerbating deforestation and resulting in the disappearance of about 15 million hectares of global forest cover.

Deforestation at Cacao del Peru Norte’s plantation in Tamshiyacu, Loreto, Peru. (EIA)

To minimize the deforestation footprint of cacao cultivation, deforestation and degradation-free supply chains have been implemented. Deforestation and degradation-free supply chains contain actions stemming from commitments that private sector actors have made to eliminate deforestation along their supply chains and operations.

To measure the impact of the commitments, deforestation along the supply chain needs to be tracked over time. Remote sensing technology can offer an unbiased constant and consistent data stream on how vegetation is changing in near real-time. The improved resolutions of map information based on Landsat and Sentinel imagery enable cacao sector stakeholders to assess deforestation risk better.

Also, it can allow farmers to achieve viable livelihoods without threatening the parks and reserves. With remote sensing science, the stakeholders can use the data to better and faster complete risk assessments and end deforestation and forest degradation in the global cocoa supply chain. Thanks to freely available radar and optical satellite imagery from the Copernicus and Landsat programs, it is now achievable to cover global supply chains in near real-time.

About the open source and free satellites.

1. Landsat

Landsat 8, June 28, 2018 - September 12, 2019 (

The Landsat satellites take 30m wide resolutions and have a revisit frequency of 8-16 days. Landsat 4 was launched on July 16, 1982, and the latest Landsat 9 was launched on September 27, 2021. With its long record of continuous measurement, these characteristics help develop land monitoring algorithms that take advantage of the seasonal and long-term trends in the data. Landsat 4 and 5 acquire data in 7 bands from two separate sensors: Thematic Mapper (TM) sensor and Multispectral Scanner (MSS). Landsat 7 carries the Enhanced Thematic Mapper Plus (ETM+) sensor and has 8 bands. Landsat 8 and 9 receive data in 11 bands from two separate sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS).  (More information:

Comparison of Landsat 7 and 8 bands with Sentinel-2 (Source:

2. Sentinel-2

The Sentinel-2 satellites are on a multi-spectral imaging mission, taking wide swath and high-resolution images. Its data became available on the 23rd of June 2015. It has 13 spectral bands ranging from visible to near and medium infrared with a spatial resolution between 10 m and 20 m with a revisit period between 5 and 10 days, involving a constellation of two twin satellites (Sentinel-2A and Sentinel-2B). (More information:

3. Sentinel-1

Sentinel-1 operates in C-band (5.6 cm) wavelength and provides the single or dual-polarization capability. The Sentinel-1 mission comprises a constellation of two satellites, Sentinel-1A and Sentinel-1B, which offers a 12-day (ground track) repeat cycle for one satellite, and a 6-day (ground track) repeat for two satellites. It observes in ascending (south-to-north) and descending (north-to-south) directions. (More information:

Problem of optical data for detecting deforestation

Sentinel-2 images over the Austrian Alps taken between July and August 2016 (© processed by EOX, contains modified Copernicus Sentinel data)

But it is very challenging to detect deforestation during rainy seasons and cloudy periods, using only optical images from satellites like Sentinel-2 or Landsat.

Sometimes, it could take months to get the cloud-free images.

But don't worry! There are some solutions to tackle this problem.

(Realistic) methods for detecting deforestation

Satellite images of Rondônia in western Brazil, taken in 1975 (left) and 2009 (right). (NASA, Images of Change)

First, we can apply bitemporal image comparison, which is used for the classic image change detection method. However, we do not use two single images but median composite images here. Of course, there are still some major drawbacks. For example, annual composite images will have significantly fewer clouds and haze, but you can detect the changes only annually. You can use the shorter term; there is a higher possibility that the composite images contain more clouds and haze effect.

Around 95% of the global cocoa production originates from smallholders who work on land plots of 1 to 3 hectares. One hectare means 10,000 m 2. So if the cocoa forestry has a square form, Sentinel-2 images will show deforestation with ten by ten pixels in true color composite, and Landsat-9 images will deliver it with only 3.3 by 3.3 pixels in the true-color composite. Of course, spatial resolution is not the only factor affecting deforestation monitoring, but it still significantly impacts change detection accuracy.

Second, time series analysis techniques can be applied to detect deforestation. Time series analysis is a popular and effective method for abrupt change detection. It uses repeat data in the locations (pixels) where the forest might have suffered disturbance at a specific time.

Time series analysis in remote sensing can be based on variables derived from the original data prior to analysis. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI), the enhanced vegetation index (EVI), the Leaf Area Index (LAI), the soil water index (SWI), and many other indices or feature space components, such as Landsat Tasseled Cap components are commonly used variables in time series analysis for forest monitoring. Thematic variables are variables, for example, land-cover and land-use information, that are derived from classification or regression approaches prior to time series analysis. Data sets from thematic variables are usually binary data sets with two classes (water/non-water, forest/non-forest).

Remote sensing time series analysis contains three components; trend, seasonal, and residual components. The trend component focuses on a long directional term and sometimes requires several decades. The remote sensing time series analysis of the forest's change dynamics contains a seasonal component (i.e., monthly, seasonal, annual, or multi-annual data) and short-term fluctuations.

Here I would like to mention one of the famous algorithms for time series analysis using landsat: LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery). This algorithm aims to temporally segment a time series into distinct periods representing stable conditions, disturbance periods, and recovery. LandTrendr was originally implemented in IDL(Interactive Data Language), but now it has been implemented to the GEE platform.

LandTrendr pixel time series segmentation. Image data is reduced to a single band or spectral index and then divided into a series of straight line segments by breakpoint (vertex) identification. Source: LT-GEE Guide (

As an example of time series analysis in remote sensing, it is impossible not to mention the "Global Forest Change" from the University of Maryland (, Hansen, et al., 2013), which is also implemented as a dataset in Google Earth Engine.

But time series analysis also has its challenges. Because it detects the changes within the complete long-term data sets involving seasonal variation, it still includes errors derived from geometric errors, sensor errors, atmospheric scattering, and cloud effects.

So, what can we do to avoid cloud effects?

Deforestation observed by Sentinel-1B (source: digital geography)
Deforestation observed by Sentinel-1B (source: digital geography)

Third, we can apply radar data, which has the advantage of all-weather capability for monitoring deforestation and degradation. Microwave remote sensors (radar) are essentially cloud-penetrating and can guarantee continuous monitoring through clouds. For tropical nations, this is particularly meaningful as constant cloud cover severely limits the availability of optical data (Flores, 2019).

But the problem is that the existing open source and free radar satellite is Sentinel-1, which operates in C-band. At the C-band wavelength, the radar signal scatters directly on the leaves at the top of the canopy without penetrating significantly through the foliage. Also, after deforestation, rough soil conditions and remnant debris can produce a strong backscatter.

Upcoming satellites for monitoring deforestation

NISAR (2023)

NASA Jet Propulsion Laboratory

NISAR radar instrument from NASA and ISRO provides all-weather, day and night imaging of nearly the entire land surface globally, considering ascending and descending orbits. NISAR utilizes two SAR instruments operating at different frequencies; L-band and S-band. Depending on the operating mode, NISAR’s orbiting radar can image at resolutions of 3-50 meters.

Sensitivity of SAR measurements to forest structure and penetration into the canopy at different wavelengths used for airborne or spaceborne remote sensing observations of the land surface. Credit: NASA SAR Handbook.

L-band is especially useful for mapping activities underneath canopies in dense forests due to its ability to penetrate vegetation covers. Longer wavelengths like L-band consequently penetrate through the forest canopy (since the small leaves are transparent) and interact with the larger structures such as the trunks and larger branches of trees. On the other hand, the shorter wavelength, such as C-band, is more sensitive to sparse and low biomass vegetation.

Comparison between ALOS-2 PALSAR-2 images (L-band) and Sentinel-1 images (C-band).

Backscatter at L-band shows the secondary growth after deforestation very vividly. Since the structure of leaves and small twigs in the top canopy layer of mature forests resemble that of secondary growth of forests, both provide similar backscatter responses at C-band frequency.


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About the author

Hyeonmin Kang

Chief Science Officer

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Hyeonmin is an experienced remote sensing scientist with a forestry sciences degree from the University of Göttingen and a degree in earth observation from the University of Würzburg, which is co-chaired by the German Aerospace Center (DLR). She has worked as a remote sensing scientist and geospatial developer in the areas of forest carbon modeling and deforestation monitoring with multispectral, Synthetic Aperture Radar, and LiDAR satellite data at WWF and the Leibniz Institute for Zoo and Wildlife Research (IZW).


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