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Tropical Moist Forests product - Resources

Information, Resources and Frequently Asked Questions for Tropical Moist Forest dataset Users

Thanks for your interest in the Tropical Moist Forest dataset! Here you will find helpful resource material for using the data including technical documents, explanations, R and Google Earth Engine tutorials but also an FAQ section and contact information for the TMF team.

How to use the data

1. Data Users Guide
For a description of the datasets and details on how to use the data, an User Guide is available here.

2. How to use TMF data in GEE?
Tutorial and Java codes for using the Tropical Moist Forest Dataset in Google Earth Engine are available here.

Frequently Asked Questions

1. General

Q: How often is your dataset updated?
We are updating the TMF dataset every year. The update generally comes around the month of March-April. Note that the yearly update does not only add one year of additional data on forest cover changes but it also helps consolidating change trajectories over the past 3 years (mainly the separation between forest degradation and deforestation). We also constantly improve the products by correcting any sensor artefacts and by visually interpreting new areas of plantations (see note on updates integrated in the new version). Always make sure that you use the latest version of the TMF products.

2. TMF specifications

Before diving into this section, please make sure you have read in details the data users guide on the different TMF products and the GEE tutorial that will help you perform additional analyses

Q: Which minimum % threshold is used for defining forest cover area?
Forest area in the TMF dataset is not defined by any percentage of tree cover. Our study covers the tropical moist forests, which include all closed forests in the humid tropics with two main forest types: the tropical rain forest and the tropical moist deciduous forest (see Section “Study area and forest types” of Vancutsem et al. 2021). All the classes 1-69 of our transition map correspond to the TMF domain, i.e. the tropical moist forests that are currently undisturbed (classes 10-12) and the initially undisturbed TMFs (during our baseline period 1982-1989) and that have been disturbed over the past 32 years (1990-2021) (classes 20-69). Our mapping approach consists of observing the evolution of spectral signatures over time and of identifying potential disruption observations (i.e. detection of an absence of tree foliage cover within a Landsat pixel for a single-date observation) for each single-date image of the time series. The temporal sequence of those disruption observations at pixel scale was analyzed to first determine the initial extent of the TMF domain (period 1982-1989) and then to identify the change trajectories from this initial forest extent (from 1990 up to now).
Q: Does your undisturbed tropical moist forest correspond to a primary forest?
Indeed, there is uncertainty in the differentiation between deforestation and forest degradation during the past 3 years of analysis. Therefore, we have additional rules (only for the past 3 years) to distinguish these two processes combining the duration and intensity of the disturbance. Taking the example of a new clear-cut in 2019, this can be classified as degraded in version2019 of TMF if the intensity of the disturbance is low (less than 45 % of total valid observations of the year) but will be classified as deforested in version2021 if the disturbance lasted more than 900 days.
Q: Reading Vancutsem et al. 2021, I understood that deforestation is defined as disturbance in tree canopy > 2.5 years. Does it mean that a 1-year old clearcut would be classified as forest degradation till it is 2.5 years old? Conversely, does it mean that what is classified as deforested in 2021 might have been deforested in 2019 but was only classified as degraded in 2019 and 2020 because it was less than 2.5 year old?
Indeed, there is uncertainty in the differentiation between deforestation and forest degradation during the past 3 years of analysis. Therefore, we have additional rules (only for the past 3 years) to distinguish these two processes combining the duration and intensity of the disturbance. Taking the example of a new clear-cut in 2019, this can be classified as degraded in version2019 of TMF if the intensity of the disturbance is low (less than 45 % of total valid observations of the year) but will be classified as deforested in version2021 if the disturbance lasted more than 900 days.
Q : If a pixel is identified as regrowth for more than 20 years, wouldn’t it be better to reclassify it as forest ?
For the regrowth class (or secondary forest), we separate it from the other forest types (so-called undisturbed or degraded) because the historical trajectory is different (between a trajectory where no disturbance has been observed over the whole study period and a trajectory where deforestation and regrowth are observed). It may potentially be interesting to distinguish this in the framework of applications on carbon, biodiversity or other ecosystem services. It is therefore left to the user (depending on the definition of forest he/she is using) to decide whether or not to group these two types of forest trajectory together.
Q: Do you provide the tree commodities mask (shapefile) identified in the TMF product. Is this a shapefile that you share?
The commodities mask is a combination of external data sources (see data user guides for more details) which is updated every year with new visually interpreted areas of plantations. You can retrieve the commodity areas by selecting classes 81-86 from the Transition map - Sub types product.
Q: We noted in the TMF that Undisturbed mangroves have been identified using the Global Mangrove Watch dataset. Is there a particular reason that this dataset was chosen to serve as a baseline for the mapping of changes within mangrove forests? When mapping the deforested, degraded and disturbed mangroves, what spectral characteristics or techniques were used to specifically identify the changes within mangrove forests?

The GMW dataset is the first globally consistent and automated method for mapping mangroves (94% overall accuracy) at different periods. The GMW dataset covers the years 1996 and 2016 so we produced a maximum extent mask of the mangroves during the period 1996-2016.

The identification of changes within mangroves follows the same methodology as for non-mangrove forests (see in methods in article/technical report). Single-date classification of Landsat images were based on multispectral cluster analysis of 38326 sampled pixels that covered mangrove forests and their spectral attributes.

Q: What percent of natural forests in the humid tropics is tropical moist forest? In other terms, if we were to use only TMF data, how much natural forest we might miss?
The tropical moist forest represents 60% of the extent of tropical natural forests - based on FAO estimates (FAO, FRA 2015). See also Figure 8 in SOFO (FAO and UNEP. 2020. The State of the World’s Forests 2020. Forests, biodiversity and people. Rome.

3. Statistics questions

Q: How to extract statistics from the different TMF products?
We provide the statistics of annual forest cover changes for the period 1990-2021 at the country level for countries with more than 1 Mha TMF area in 1990 (see Section X). We also provide a GEE tutorial that explains how to extract statistics over your own study area.
Q: What are the meaning of the classes in the provided statistics?

The statistics provide the area (in Mha) of undisturbed forest, forest disturbances and forest regrowth. Forest disturbances are separated into forest degradation, deforestation and deforestation of forest regrowth. Regarding deforestation, we have added the sub classes of direct deforestation in order to separate deforestation of a degraded forest from deforestation of undisturbed forest. We have also added the conversion of forest due to plantation or to water expansion. Regarding forest degradation, we include the area of degradation not followed by deforestation to separate degradation that remains classified as degraded forest from degradation followed by deforestation in the following years.

The classes "Total degradation" and "Direct deforestation" have to be grouped to know all the disturbances that occur in one year (which leads to a conversion of the undisturbed forest into a degraded forest or deforested land). The "total deforestation" class represents all the forest areas (degraded or undisturbed) that have been converted into deforested land in one year.

Note that over the past 3 years, the discrimination between degradation and deforestation is not so robust compared to the historical estimations as 3 years are needed to confirm the type of disturbances. To consolidate our statistics over the past 3 years, we corrected the average proportion of disturbance types within total disturbances using the average proportion observed for the period 2007–2016.

4. Mapping accuracy questions

Q: Within the article by C. Vancutsem et al (2021) it was noted that, when the TMF data set was compared to the GFC (Global Forest Change) dataset there was an 83% discrepancy in changes within the mangroves. Between these two datasets, what are some of the suspected causes for the difference in undisturbed and degraded mangroves?

TMF data identify several forest cover change classes (degradation, deforestation, regrowth) which helps to nuance between a light (logging or fire) and a strong event (deforestation) whereas GFC includes all disturbance in losses. The number of disturbances per year is also provided as an indicator of the intensity of the disturbance. TMF may capture disturbances earlier (one year before) than GFC, particularly when the disturbance occurred at the end of the year because of the method (single-date vs composites). GFC data show some inconsistencies between the period 2000-2014 and the period 2015-2021, due to an enhanced Landsat processing algorithm that has “resulted in enhanced detection of loss— particularly from 2015 onwards” (GFW, 2021). Overall the main differences between TMF and GFC rely on different periods of analysis (2001-2020 for GFC) and single date classification for TMF which enables the detection of short duration events), differentiation between degradation, deforestation and regrowth.

We observed that most of the mangroves have a very low tree cover density (0-50%) on GFC, which could prevent the GFC approach’s ability to detect losses (as a minimum tree cover threshold is required).

Q: The overall confidence level of the TMF dataset was 95%; were the validation results from the sample plots additionally analyzed at the regional level? If so, what were the results for the Asia-Oceania region?
Continental-level results of the accuracy assessment of the single-date classification algorithm are available in table S3 of the supplementary materials of Vancutsem article. At continental level, overall accuracy is higher for Africa (94.6%) than for Latin America (91.0%) or Asia (89.0%).
Q: Do you know if there is much variation in accuracy from one country to the other?
The accuracy (presented in Vancutsem et al 2021) depends on the ability of our approach to detect a change from one single-date Landsat image. Consequently, accuracy depends mostly on the type of event observed (lower for shorter events, e.g. selective logging) and on the availability of valid observations (this meta-information is provided to the user as a proxy measure of confidence). Hence, our estimates of changes in the regions where the total number of valid observations is particularly low and/or the start year of the monitoring period is late, e.g., Gabon, Solomon Islands, and La Réunion, should be considered with lower confidence. However, considering the geographic completeness of Landsat 8 coverage after the year 2013, there is high confidence for the contemporary reported estimates.
Q: Given the reliance on Landsat imagery in the analysis, which a) improved over time, notably with the incorporation of LS8 and b) whose availability varied over time (e.g. decommissioning of Landsat 5 in 2011 and the launch of Landsat 8 in 2013), do you recommend using the TMF data in trend analysis?

Our methodology and algorithms for detecting the disturbances did not change over time and the accuracy assessment did not show significant differences in performance among sensors.

However, as mentioned in our paper (Section on "Known limitations and future improvements") the number of valid observations may vary over time and location, and this may have an impact on the detection of short-duration events. The risk of under-estimating the short-duration degradation mostly concerns the period of 2011-2013 (between the decommissioning of Landsat 5 and the launch of Landsat 8), and the full period before 2013 for Central and West Africa where there was low coverage of Landsat 4 and 5 data (unlike South America and Asia that were well covered). The meta-information provided with the TMF maps, i.e. the start of the monitoring period and the number of annual valid observations, allow the user to know when the monitoring period starts and estimate this risk of low coverage during the observation period in their specific region of interest.

One advantage of the TMF product is that we separate the different types of disturbances. Consequently, the user can analyze deforestation trends separately from long-duration and short-duration degradation trends and be more careful in interpreting changes for the short-duration degradation in risk areas.

However, to get unbiased estimates of change areas and related uncertainties (for trend analysis) it is recommended to combine any spatial wall to wall map with a sample-based reference dataset.

Citation guidelines

C. Vancutsem, F. Achard, J.-F. Pekel, G. Vieilledent, S. Carboni, D. Simonetti, J. Gallego, L.E.O.C. Aragão, R. Nasi. Long-term (1990-2019) monitoring of forest cover changes in the humid tropics. Science Advances 2021

If you are using the data as a layer in a published map, please include the following attribution text: 'Source: EC JRC’

Contact us

Write at if you have any feedback on the Tropical Moist Forest data or if you want to report any errors or issues you see. To submit an error or correction, please include at minimum the coordinates where you see the issue, a description and if available a screenshots of the issue. For any questions regarding the dataset itself, make sure you have checked out the how to use the data and the FAQ section first.