anchor.diff:          </div>
   
           <div class='source_summary'>
  -          <p class='credits'><strong>Citation:</strong> Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W.W. Wettengel, P. Hedao, and K. R. Kassem. 2001. “Terrestrial Ecoregions of the World: A New Map of Life on Earth.” BioScience 51, no. 11 (November): 933–38.</p>
  +          <p class='credits'><strong>Citation:</strong> Olson, D. M., E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell, E. C. Underwood, J. A. D’Amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks, T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W.W. Wettengel, P. Hedao, and K. R. Kassem. 2001. “Terrestrial Ecoregions of the World: A New Map of Life on Earth.” <em>BioScience</em> 51, no. 11 (November): 933–38.</p>
   
             <p class='read_more hidden'><em>Download from <a href='https://wri-01.cartodb.com/tables/ecoregions_biome/public' target='_blank'>CartoDB</a></em></p>
           </div>
  
positive.code:           <p>This dataset uses freely available satellite imagery, collected by the <a href='http://modis.gsfc.nasa.gov/about/' target='_blank'>Moderate Resolution Imaging Spectroradiometer (MODIS)</a>, which operates on NASA's Terra and Aqua (EOS PM) satellite platforms and views the entire Earth’s surface every 1 to 2 days. The images help to reveal notable changes in vegetation cover over time using an indicator that measures vegetation intensity called the <a href='http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php' target='_blank'>Normalized Difference Vegetation Index (NDVI)</a>. By applying an automated algorithm to this input and in combination with complementary data inputs for fire hotspots and global precipitation averages, areas where forest clearing is likely to have occurred are therefore identified. The algorithm also employs parallel processing in a remote server system the cloud that enables the rapid analysis of these very large data sets.</p>
  
            <h3 class='overview_title'>Explanation</h3>
            <p>Forest clearing alerts appear when and where new, large-scale clearing is likely to have occurred after 2005. Thus, the alerts should not be interpreted as an analysis of total forest cover loss area, but rather as an indication of an area that has a high probability of having experienced forest cover loss or disturbance over time. The system employs advanced statistical techniques to achieve the best fit to scientifically validate information on clearing, measured as a probability. On the GFW website, alerts appear only for 50 percent or higher probability. That is, alerts appear for a particular period when there has been significant clearing in the area during or before that period.</p>
  
            <h3 class='overview_title'>Temporal and spatial resolution</h3>
            <p>FORMA alerts are displayed on the GFW site as monthly data, and the site is updated each month. Users who download data for analysis will find that the underlying dataset is actually available at 16-day intervals, intervals that do not line up perfectly with calendar months. Data is displayed at a monthly resolution on GFW due to the monthly availability of precipitation data input. Users can manipulate the GFW time slider to view trends in clearing alerts from December 2005 to present.</p>
  
            <p>The alert system currently identifies 500 x 500 meter areas where clearing is statistically likely to have occurred. Alerts for a 250-meter resolution version are currently being developed on Google’s Earth Engine platform and will be available in 2014.</p>
  
            <h3 class='overview_title'>Geographic extent</h3>
            <p>FORMA alerts are currently available only for humid tropical forests (as defined by <a href='http://www.pnas.org/content/105/27/9439.full#ref-1' target='_blank'>Hansen et al. (2008)</a>, based on <a href='http://wolfweb.unr.edu/~ldyer/classes/396/olsonetal.pdf' target='_blank'>WWF’s terrestrial ecoregions</a>) spanning portions of 89 countries. The development team is working to incorporate additional data to extend the geographic coverage beyond the current extent. Currently FORMA data spans across portions of 89 countries. To visualize the geographic extent of the alerts on the map, switch on the “Humid Tropical Forest Biome” layer.</p>
  
            <h3 class='overview_title'>Data applications</h3>
            <p>The forest clearing alerts have been designed for quick identification of forest clearing as it emerges. This allows for rapid response and prioritization of scarce financial and human resources dedicated to forest conservation or sustainable forest management. Armed with this information, stakeholders can use preemptive methods such as on-the-ground visits or aerial inspection with high-resolution satellite imagery (less than 5 meter pixel resolution) to investigate suspected forest clearing areas.</p>
  
            <p>In addition, the alerts may be of value to a variety of researchers who study both temporal and spatial patterns related to forest clearing areas.</p>
  
            <p>Using the GFW platform, the alerts can be compared against other relevant data layers, such as protected areas and concessions boundaries, to evaluate the effectiveness of forest management practices across time and spatial extent.</p>
  
            <h3 class='overview_title'>Accuracy and validation</h3>
            <p>Inaccuracies are an inherent part of remote sensing analysis. FORMA Alerts appear in areas with a greater than 50% probability of deforestation, based on the algorithm described under methodology. However, persistent cloud cover is a continuous issue in the tropics, and extreme flooding can also produce unreliable remotely-sensed data that will result in forest clearing “false positives” (alerts where no actual tree cover loss has occurred). Furthermore, the alerting system cannot detect all forest cover loss, whether due to the small size of clearings, persistent cloud cover, or other explanations still being identified through GFW validation efforts.</p>
  
            <p>The major instances of false positives may occur as the following:</p>
  
            <ol class='bullets'>
              <li><strong>A random, 'speckled' distribution of alerts across an ecoregion, or complete filling of a small ecoregion.</strong> Caused by limited or sparse training data, particularly in small ecoregions, which makes it difficult to tune the model there. As a result, alerts cannot be reliably detected. In a normal ecoregion, alerts are usually clustered.</li>
  
              <li><strong>A rapid explosion of alerts over 1-3 months covering a relatively large area.</strong> Caused by a significant, persistent drop in detected vegetation levels due to persistent cloud cover along coastlines, in mountains, or elsewhere.</li>
  
              <li><strong>Alerts in water.</strong> Caused by shifting water bodies. These alerts should not necessarily be considered false positives, but rather ambiguous alerts requiring additional data for corroboration.</li>
            </ol>
  
            <p>The GFW team is working aggressively to address potential inaccuracies in the data through rigorous validation methods. Specifically, the GFW team is comparing the growing data set of historical alerts to other validated data sets, which are being used for similar applications.</p>
  
            <p>This paper demonstrates the spatial correlation of the alerts with the PRODES and DETER data sets, produced by the Brazilian Space Agency for the Amazon. The conclusions from the paper help to illustrate the potential pitfalls of the algorithm, along with its strengths. Through future refinement and proposed crowdsourcing efforts, the GFW team expects the data quality of the FORMA Alerts will continue to improve.</p>
  
            <h3 class='overview_title'>Additional Resources on FORMA</h3>
            <p><strong>Working Papers:</strong></p>
  
            <ul class='bullets'>
  
negative.code:       </div>
  
        <div class='source_coverage'>
          <p>The geographic coverage of Imazon SAD alerts is the Legal Amazon (political) excluding the Brazilian state of Maranhão.</p>
  
          <p class='read_more hidden'><em>Download data from <a href='http://wri-01.cartodb.com/tables/imazon_sad_geografic_extent/public' target='_blank'>CartoDB</a></em>
        </div>
  