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		<description>India&#039;s environmental science and conservation news</description>
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					<title>Rethinking forest restoration beyond tree cover [Commentary]</title>
					<link>https://india.mongabay.com/2026/06/rethinking-forest-restoration-beyond-tree-cover-commentary/</link>
					<comments>https://india.mongabay.com/2026/06/rethinking-forest-restoration-beyond-tree-cover-commentary/?noamp=mobile#respond</comments>
					<pubDate>08 Jun 2026 12:19:59 +0000</pubDate>
											<dc:creator>
							<![CDATA[Dhanapal GovindarajuluJohan Oldekop]]>
						</dc:creator>
										<author>
						<![CDATA[Kundan Pandey]]>
					</author>
							<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[afforestation]]></category>
		<category><![CDATA[dryland conservation]]></category>
		<category><![CDATA[habitat degradation]]></category>
		<category><![CDATA[restoration]]></category>
		<category><![CDATA[tree plantation]]></category>
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											<reporting-project>
							<![CDATA[Beyond Protected Areas]]>
						</reporting-project>
					
											<locations>
							<![CDATA[India]]>
						</locations>
					
											<topic-tags>
							<![CDATA[Deforestation, Ecology, Environment, Forestry, Forests, Global Forest Watch, Habitat Loss, Plantations, Plants, Reforestation, and Trees]]>
						</topic-tags>
					
					
											<description>
							<![CDATA[Since its launch in 2011, the Bonn Challenge has mobilised commitments to restore more than 350 million hectares of degraded and deforested land by 2030, including India’s pledge to restore 26 million hectares. The International Union for Conservation of Nature (IUCN) defines Forest Landscape Restoration (FLR) as a process that seeks to regain ecological integrity [&#8230;]]]>
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																					<content:encoded>
							<![CDATA[Since its launch in 2011, the Bonn Challenge has mobilised commitments to restore more than 350 million hectares of degraded and deforested land by 2030, including India’s pledge to restore 26 million hectares. The International Union for Conservation of Nature (IUCN) defines Forest Landscape Restoration (FLR) as a process that seeks to regain ecological integrity while enhancing human well-being. In practice, this means that ecosystem restoration should improve biodiversity, wildlife habitats and corridors, watersheds, and other ecosystem services. It should also strengthen local livelihoods, particularly through the provision of non-timber forest products and support for pastoral communities. Yet in practice, restoration is often narrowly equated with tree planting. This is partly because plantations can sequester large amounts of carbon, making them attractive for climate mitigation targets. India, for example, has committed under the Paris Agreement to create an additional carbon sink of 3.5 to 4 billion tonnes through afforestation and the expansion of tree cover. Many tropical countries use globally available forest-cover products to monitor and report restoration progress. However, these global products come with limitations because they are generated using different satellite imagery, training data, classification algorithms, and forest definitions. For example, the Global Forest Watch dataset, derived primarily from Landsat imagery, can produce substantially different estimates of forest extent and change compared with GlobeLand30, which is generated using Landsat and China’s HJ-1 satellite imagery. A recent global study comparing ten global forest datasets found that these maps agreed on only 26% of the globally mapped forest area. The&hellip;This article was originally published on <a href="https://india.mongabay.com/2026/06/rethinking-forest-restoration-beyond-tree-cover-commentary/" data-wpel-link="internal">Mongabay</a>]]>
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														</item>
						<item>
					<title>Satellite data and AI identify deforestation drivers</title>
					<link>https://india.mongabay.com/2025/08/satellite-data-and-ai-identify-deforestation-drivers/</link>
					<comments>https://india.mongabay.com/2025/08/satellite-data-and-ai-identify-deforestation-drivers/?noamp=mobile#respond</comments>
					<pubDate>07 Aug 2025 14:19:24 +0000</pubDate>
											<dc:creator>
							<![CDATA[Sharmila Vaidyanathan]]>
						</dc:creator>
										<author>
						<![CDATA[Priyanka Shankar]]>
					</author>
							<category><![CDATA[Uncategorized]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[forest loss]]></category>
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					<guid isPermaLink="false">https://india.mongabay.com/?p=33993</guid>

											<reporting-project>
							<![CDATA[Beyond Protected Areas]]>
						</reporting-project>
					
											<locations>
							<![CDATA[Global]]>
						</locations>
					
											<topic-tags>
							<![CDATA[Deforestation, Forests, Global Forest Watch, Habitat Loss, Satellite Imagery, Technology, and Trees]]>
						</topic-tags>
					
					
											<description>
							<![CDATA[2024 was a record-breaking year for tropical forests globally. The world lost 18 football fields worth of tropical primary forests per minute in 2024, according to data gathered by the University of Maryland’s Global Land Analysis and Discovery (GLAD) Lab and shared on the World Resources Institute&#8217;s (WRI) Global Forest Watch platform. Along with greenhouse [&#8230;]]]>
						</description>
																					<content:encoded>
							<![CDATA[2024 was a record-breaking year for tropical forests globally. The world lost 18 football fields worth of tropical primary forests per minute in 2024, according to data gathered by the University of Maryland’s Global Land Analysis and Discovery (GLAD) Lab and shared on the World Resources Institute&#8217;s (WRI) Global Forest Watch platform. Along with greenhouse gas emissions of 3.1 gigatonnes, the deforestation also resulted in significant losses to biodiversity, ecosystem services and livelihoods, globally. The data revealed that forest fires were responsible for 49.5% of this loss. Understanding the drivers of forest loss is vital to devise strategies for forest conservation and restoration. Such data can also shed light on whether these factors contribute to forest loss or degradation, thus differentiating between permanent changes to forest land (forest loss) and a reduction in tree canopy or density (forest degradation). A new global dataset developed by the World Resources Institute, Google Deep Mind and The Sustainability Consortium determines the reasons for forest loss at one-kilometre spatial resolution. Using high-resolution satellite imagery and an artificial intelligence (AI) algorithm called ResNet, the researchers show the dominant drivers of tree cover loss between 2001 and 2022, distinguishing between seven drivers, including permanent agriculture, shifting cultivation, hard commodities, wildfires, logging, settlements and infrastructure, and other natural disturbances. The dataset reveals that permanent agriculture resulted in 34.8 ± 2.6% of global tree cover loss between 2001 and 2022. An 2010 image from Tengapani Reserve Project in Arunachal Pradesh. Image by Rohit Naniwadekar via Wikimedia Commons (CC&hellip;This article was originally published on <a href="https://india.mongabay.com/2025/08/satellite-data-and-ai-identify-deforestation-drivers/" data-wpel-link="internal">Mongabay</a>]]>
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