{"version":"1.0","provider_name":"MAAP","provider_url":"https:\/\/www.maapprogram.org\/es\/","author_name":"dcadmin","author_url":"https:\/\/www.maapprogram.org\/es\/author\/dcadmin\/","title":"MAAP #65: Hotspots de Deforestaci\u00f3n del 2017, en la Amazon\u00eda Peruana - MAAP","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"cG41n6qGUJ\"><a href=\"https:\/\/www.maapprogram.org\/es\/hotpots-2017\/\">MAAP #65: Hotspots de Deforestaci\u00f3n del 2017, en la Amazon\u00eda Peruana<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.maapprogram.org\/es\/hotpots-2017\/embed\/#?secret=cG41n6qGUJ\" width=\"600\" height=\"338\" title=\"\u00abMAAP #65: Hotspots de Deforestaci\u00f3n del 2017, en la Amazon\u00eda Peruana\u00bb \u2014 MAAP\" data-secret=\"cG41n6qGUJ\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/www.maapprogram.org\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","description":"En el reporte anterior\u00a0MAAP #40,\u00a0destacamos la gran utilidad de combinar las alertas tempranas\u00a0GLAD* con un an\u00e1lisis de im\u00e1genes satelitales de alta resoluci\u00f3n (por ejemplo, de la empresa Planet), como parte de un\u00a0sistema integral\u00a0de\u00a0monitoreo de deforestaci\u00f3n en tiempo casi real. En el presente reporte, analizamos las alertas GLAD del 2017 (hasta 17 de julio) para identificar [&hellip;]","thumbnail_url":"https:\/\/www.maapprogram.org\/wp-content\/uploads\/2017\/08\/MAAP_Kernel_2017_O_v2.jpg"}