Interactive Map . TCC density represents the estimated percent of a pixel that was covered by tree canopy in the year 2. For the tree cover loss data, TCC density therefore corresponds to the density of tree cover before loss occurred. For example, if you select 2. TCC density, you will only see tree cover loss pixels for which the original tree cover density was greater than 2. Adjustments to the minimum TCC density only affect the tree cover and tree cover loss data layers.

This feature does not pertain to Hansen/UMD/Google/USGS/NASA tree cover gain or to other GFW data layers or statistics. Tree cover gain is displayed with a set minimum TCC density greater than 5. The minimum TCC density cannot be changed independently for tree cover and tree cover loss.

A change made to one data layer will immediately take effect in the other. This feature is also available for statistics within the Country Profiles & Rankings. However, the adjustment made to the visualization and analysis through the map view will not be automatically reflected in other areas of the website. To adjust the minimum TCC density within the Country Profiles & Rankings pages, click on the settings icon. Loss of tree cover may occur for many reasons, including deforestation, fire, and logging within the course of sustainable forestry operations. In sustainably managed forests, the “loss” will eventually show up as “gain”, as young trees get large enough to achieve canopy closure.

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Function. Identifies areas of gross tree cover loss. RESOLUTION / SCALE3. The updates include new data for the target year and re- processed data for the previous two years (2. The re- processing increased the amount of change that could be detected, resulting in some changes in calculated tree cover loss for 2.

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Calculated tree cover loss for 2. The integrated use of the original 2. Version 1. 0) data and the updated 2.

Version 1. 1) data should be performed with caution. For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. As such, “loss” does not equate to deforestation. When zoomed out (< zoom level 1. Pixels with darker shading represent areas with a higher concentration of tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss.

There is no variation in pixel shading when the data is at full resolution (. The data were generated using multispectral satellite imagery from the Landsat 5 thematic mapper (TM), the Landsat 7 thematic mapper plus (ETM+), and the Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 6. Landsat 7 images for the 2. Landsat 5, 7, and 8 images for updates for the 2. The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss. Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale.

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Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human- induced. Update (Version 1. This data set has been updated twice since its creation, and now includes loss up to 2.

The analysis method has been modified in numerous ways, and the update should be seen as part of a transition to a future “version 2. Key changes include: The use of Landsat 8 data for 2. Landsat 5 data for 2. The reprocessing of data from the previous two years in measuring loss (2. Improved training data for calibrating the loss model. Improved per sensor quality assessment models to filter input data.

Improved input spectral features for building and applying the loss model. These changes lead to a different and improved detection of global tree cover loss. However, the years preceding 2.

It must also be noted that a full validation of the results incorporating Landsat 8 has not been undertaken. Such an analysis may reveal a more sensitive ability to detect and map forest disturbance using Landsat 8 data. If this is the case then there will be a more fundamental limitation to the consistency of this data set before and after the inclusion of Landsat 8 data. Validation of Landsat 8- incorporated loss detection is planned.

Some examples of improved change detection in the 2. Improved detection of boreal forest loss due to fire. Improved detection of smallholder rotation agricultural clearing in dry and humid tropical forests. Improved detection of selective logging.

These are examples of dynamics that may be differentially mapped over the 2. Version 1. 1. A version 2. The original version 1. Citation: Hansen, M. Turubanova, A. Tyukavina, D. Loveland, A. Kommareddy, A.

Justice, and J. Data available online from: http: //earthenginepartners. Hansen, M. Turubanova, A. Tyukavina, D. Loveland, A. Kommareddy, A. Justice, and J. Accessed through Global Forest Watch on . The year reported is the first year deforestation is identified by analysts, but this does not necessarily correspond to the year of deforestation if the landscape has been covered by clouds in previous years.

License. Creative Commons BY SA 3. Overview. The PRODES project monitors clear cut deforestation in the Brazilian Legal Amazon, and has produced annual deforestation rates for the region since 1. The Brazilian government uses these figures to establish public policy, including defining access to credit in the Amazon biome, establishing deforestation reduction goals, and soliciting funds to reduce deforestation. PRODES historically used Landsat 5 images, but now also incorporates imagery from Landsat 7 and 8, CBERS- 2, CBERS- 2.

B, Resourcesat- 1, and UK2- DMC. Spring (2015) Izle here. PRODES is operated by the National Institute of Space Research (INPE) in collaboration with the Ministry of the Environment (MMA) and the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA).

Since 2. 00. 2, all PRODES data is publicly available online. Input images for each of the 2. Landsat footprints that cover the Brazilian Amazon are selected based on their lack of cloud cover and their capture date. The PRODES system uses the seasonal year, starting on August 1st, to calculate annual deforestation, so images are selected as near to this date as possible (generally from July, August, and September). From 2. 00. 3 to 2. SPRING. These components were segmented and classified into the classes of forest, non- forest, deforestation in the target year, previous deforestation, clouds, and water, which are then manually corrected by experts. Starting in 2. 00.

Terra. Amazon platform. The platform allows the PRODES analysis to be more uniform and can incorporate imagery from a variety of satellites. As before, images are selected to be as cloud free as possible.

The images are then masked to exclude non- forest, previous deforestation, and water using the previous year’s analysis. Analysts then delineate deforested polygons in the intact forest of the previous year. More information on the methodology can be found on the PRODES website. This data set shows annual deforestation between 2. National Institute of Space Research (INPE). As such, “loss” does not equate to deforestation.

When zoomed out (< zoom level 1. Pixels with darker shading represent areas with a higher concentration of tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover gain. There is no variation in pixel shading when the data is at full resolution (. A stratified random sample (for no change, loss, and gain) of 1. Watch The Squeeze (2015) Online For Free more.

Overall accuracies for gain were over 9. However, since the overall accuracy calculations are positively skewed due to the high percentage of no change pixels, it is also important to assess the accuracy of the change predictions.