tag on yout theme's header.php Read the detailed step-by-step at https://humbertosilva.com/visual-composer-infinite-image-carousel/ */ // auxiliary code to create triggers for the add and remove class for later use (function($){ $.each(["addClass","removeClass"],function(i,methodname){ var oldmethod = $.fn[methodname]; $.fn[methodname] = function(){ oldmethod.apply( this, arguments ); this.trigger(methodname+"change"); return this; } }); })(jQuery); // main function for the infinite loop function vc_custominfiniteloop_init(vc_cil_element_id){ var vc_element = '#' + vc_cil_element_id; // because we're using this more than once let's create a variable for it window.maxItens = jQuery(vc_element).data('per-view'); // max visible items defined window.addedItens = 0; // auxiliary counter for added itens to the end // go to slides and duplicate them to the end to fill space jQuery(vc_element).find('.vc_carousel-slideline-inner').find('.vc_item').each(function(){ // we only need to duplicate the first visible images if (window.addedItens < window.maxItens) { if (window.addedItens == 0 ) { // the fisrt added slide will need a trigger so we know it ended and make it "restart" without animation jQuery(this).clone().addClass('vc_custominfiniteloop_restart').removeClass('vc_active').appendTo(jQuery(this).parent()); } else { jQuery(this).clone().removeClass('vc_active').appendTo(jQuery(this).parent()); } window.addedItens++; } }); // add the trigger so we know when to "restart" the animation without the knowing about it jQuery('.vc_custominfiniteloop_restart').bind('addClasschange', null, function(){ // navigate to the carousel element , I know, its ugly ... var vc_carousel = jQuery(this).parent().parent().parent().parent(); // first we temporarily change the animation speed to zero jQuery(vc_carousel).data('vc.carousel').transition_speed = 0; // make the slider go to the first slide without animation and because the fist set of images shown // are the same that are being shown now the slider is now "restarted" without that being visible jQuery(vc_carousel).data('vc.carousel').to(0); // allow the carousel to go to the first image and restore the original speed setTimeout("vc_cil_restore_transition_speed('"+jQuery(vc_carousel).prop('id')+"')",100); }); } // restore original speed setting of vc_carousel function vc_cil_restore_transition_speed(element_id){ // after inspecting the original source code the value of 600 is defined there so we put back the original here jQuery('#' + element_id).data('vc.carousel').transition_speed = 600; } // init jQuery(document).ready(function(){ // find all vc_carousel with the defined class and turn them into infine loop jQuery('.vc_custominfiniteloop').find('div[data-ride="vc_carousel"]').each(function(){ // allow time for the slider to be built on the page // because the slider is "long" we can wait a bit before adding images and events needed var vc_cil_element = jQuery(this).prop("id"); if (window.innerWidth <= 480) { // jQuery(vc_element).attr('data-per-view',1); jQuery('.vc_item').each(function(){ this.style.width = '25%' this.style.height = 'auto' }) } else { setTimeout("vc_custominfiniteloop_init('"+vc_cil_element+"')",2000); } }); }); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-TZHJ474'); var interval1 = setInterval(function(){ //console.log('ou no interval'); 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Título Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data
Autores Douglas C. Morton (a)
Ruth S. DeFries (b)
Jyoteshwar Nagol (a)
Carlos M. Souza Jr. (c)
Eric S. Kasischke (a)
George C. Hurtt (d)
Ralph Dubayah (a)
Vinculação dos autores (a) Department of Geography, 2181 LeFrak Hall, University of Maryland, College Park, MD 20742, United States
(b) Department of Ecology, Evolution, and Environmental Biology, Columbia University, New York, NY 10027, United States
Lamont-Doherty Earth Observatory, Palisades, NY 10964, United States
(c) Instituto do Homem e Meio Ambiente da Amazônia (IMAZON), Belém, Pará, Brazil
(d) Institute for the study of Earth, Oceans, and Space (EOS), University of New Hampshire, Durham, NH 03824, United States
Department of Natural Resources, University of New Hampshire, Durham, NH 03824, United States
Ano de publicação 2011
Meio de publicação Remote Sensing of Environment (Volume 115, Issue 7, 15 July 2011, Pages 1706-1720)
DOI (Digital Object Identifier) https://doi.org/10.1016/j.rse.2011.03.002

Abstract

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997–2004) and MODIS (2000–2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997–2002. MODIS data were suitable for mapping medium (50–500 ha) and large (> 500 ha) burn scars that ed for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km2) were an order of magnitude higher than during the 1997–1998 El Niño event (124 km2 and 39 km2, respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.


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