Results from time-series analysis of Landsat images in characterizing global forest extent and change from 2000 through 2021. For additional information about these results, please see the associated journal article (Hansen et al., Science 2013).

The Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland, in partnership with Global Forest Watch (GFW), provides annually updated global-scale forest loss data, derived using Landsat time-series imagery. These data, available here, are a relative indicator of spatiotemporal trends in forest loss dynamics globally. However, inconsistencies exist due to the following factors:

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While the resulting map data are a largely viable relative indicator of trends, care must be taken when comparing change across any interval. Applying a temporal filter, for example a 3-year moving average, is often useful in discerning trends. However, definitive area estimation should not be made using pixels counts from the forest loss layers.

The Intergovernmental Panel on Climate Change (IPCC) provides guidance on reporting areal extent and change of land cover and land use, requiring the use of estimators that neither over or underestimate dynamics to the degree possible, and that have known uncertainties. The maps provided by GLAD do not have these properties. However, the maps can be leveraged to facilitate appropriate probability-based statistical methods in deriving statistically valid areas of forest extent and change. Specifically, the maps may be used as a stratifier in targeting forest extent and/or change by a probability sample. The team at GLAD has demonstrated such approaches using the GLAD forest loss data in sample-based area estimation (Tyukavina et al., ERL, 2018, Turubanova et al., ERL, 2019, and Potapov et al., RSE, 2019, among others).

This update of gross forest cover loss includes new 2021 loss-year and multispectral imagery layers. Relative to the version 1.0 product our method has been modified in numerous ways, and the new update should be seen as part of a transition to a future version 2.0 that is more consistent over the entire 2000-onward period. Key changes include:

You can also analyze these results directly in Google Earth Engine using the asset ID UMD/hansen/global_forest_change_2021_v1_9. If you are not yet an Earth Engine user, you may sign up here. To help you get started we have made an introductory tutorial showing examples of how to use this data to do a variety of things, including generating indices from annual Landsat composites and computing tree loss per year for regions of interest.

All vanilla farm maps have also been more or less redone as I have always thought they are not quite up to standard and they also now contain forests to some degree. I've also included my improved tree stumps in this map pack as the vanilla versions are kinda ugly.

The maps include lots of custom buildings and other props, random elements and multiple spawn point variations for both ufos and dropships. I've also added new forest tiles, (farm tileset), highway tiles (industrial), oasis tiles (desert) and walkable shipping container roofs with makeshift stairs to reach them (desert and industrial) and used them in several maps to give some additional variation in those tilesets.

Upon release, v1.09 was patched twice. The first patch, Version 1.09.1, was given out only as a series of public betas on the SRB2 Message Board. The second patch, Version 1.09.2, was released on January 7th, 2006, and added a few new editing features in addition to bugfixes. Mystic Realm v4.0 was released alongside v1.09, making use of its new features and adding a lot of new content.

Information on v1.09.3 and v1.09.4 is not included in this article, as they are considered significantly different from the original v1.09 release despite appearing to be patches of v1.09; for these versions, see Version 1.09.4.

Super Sonic received a proper set of sprites; previously Sonic had only been equipped with a Superman cape when turning Super. At this point, the standing sprites were used for spinning/jumping and floating; proper sprites for these animations would be added in v1.09.4. To reflect the addition of proper sprites, the Super Sonic music was changed from the Superman theme to a placeholder tune; the current Super Sonic music was added later in v1.09.2. Super Sonic's ability to run on water, which had been removed in Demo 4, was re-added. Players could also no longer turn Super with a shield.

The next version, v1.09.4, would allow the black Chaos Emerald to be obtained via another secret involving RedXVI, but prevented the player from turning into Hyper Sonic if cheats were used. The black Chaos Emerald and Hyper Sonic were later removed from the game entirely in v2.0.

The Host Game menu was revamped in v1.09. Previously, the level and the gametype could be chosen independently. The gametypes supported by a level were indicated in parentheses next to its name, e.g., "Greenflower Zone Act 1 (CR)" meant that the level supported Coop and Race. However, regardless of what gametypes a map supported, it could be loaded up in any gametype. For v1.09, the level list was moved to the center of the screen. Instead of always showing all levels, it now only showed the levels that supported the currently chosen gametype, preventing levels from being played in unsupported gametypes.

EAB has the ability to hurt professionals. Since it has been found, this tiny, invasive insect has killed millions of ash trees, created regulatory headaches and cost the forest products industries millions of dollars.

It is critical that all forest users partner together to control this and other invasive pests. Below is a list of the most frequently asked questions concerning the compliance agreement and quarantine currently in place in Missouri.

There is broad scientific consensus that halting forest loss is required to tackle the dual global crises of climate change and biodiversity loss1,2. Notwithstanding their provision of multiple services, the contribution of forests in removing CO2 from the atmosphere and storing it in a long-term carbon sink carries a particularly high social (economic) value3, and scientific research has produced increasingly sophisticated methods to assess this value4. In the global effort to reach net zero by 2050, combatting forest loss remains a feature of governmental and private decarbonization initiatives, for example, the Race to Zero Campaign5,6 and the UNFCCC-REDD+ mechanism7,8 (Reducing Emissions from Deforestation and Forest Degradation and the Role of Conservation, Sustainable Management of Forests and Enhancement of Forest Carbon Stocks in Developing Countries). Analysing and assessing trends in forest losses is therefore of global social relevance, while identifying factors underlying these trends is a precondition to control the drivers of forest losses.

The effectiveness of policies and private financial incentives to reduce forest losses (for example, the REDD+ initiatives9 or payments for ecosystem services10) remains highly debated11. Tracing back the impacts of domestic policies and other influential factors on forest loss is challenging12. Commonly cited country-scale factors associated with trends in forest loss include market-based factors, such as demand for agricultural commodities13 or minerals14; governance-related factors (for example, command-and-control policies15, the rollout of land titles for communities16 or pre-election promises17); factors associated with social conflicts and crises (for example, armed conflicts18 or COVID-1919); and climate anomalies, such as El Nio20.

To associate trends in forest losses with specific factors, counterfactuals are needed to show what losses would have occurred under a reference scenario that excludes these factors21. Reference scenarios are critically important to identify and demonstrate the additionality of achieved emission reductions22. Avoiding forest losses represents a nature-based solution (as an initiative to achieve societal goals by working with nature23) to combat climate change. Additionality means that observed emission reductions would not have occurred under business-as-usual conditions24. In the absence of government intervention, market forces can either exacerbate or mitigate trends in forest loss. Mitigation would happen when land managers become increasingly satisfied with the economic outcome of their current land-use allocation and stop clearing forest, or when decreasing commodity prices lead them to reduce agricultural expansion25. Both trends would cause non-additional emission reductions. So far, counterfactual studies have mostly been constructed from empirical forest loss data by applying quasi-experimental statistical models (Supplementary Table 1). However, these approaches usually focus on a specific policy instrument, the effects of which are difficult to isolate empirically.

Emission of CO2 has a warming effect that is approximately permanent and constant over time66. This emission is valued as the present value of all future marginal damages resulting from this permanent temperature increase starting with the year of the emission67. This value is known as the SCC46. Similarly, a permanent reduction of CO2 is valued at the SCC, that is, as the present value of an infinite stream of avoided marginal damages. A temporary emission reduction of CO2 by reducing forest losses is valued as the present value of a stream of avoided marginal damages over the duration of the avoided forest loss trend. The value of a temporary emission reduction is always positive because it temporarily avoids damages. This implies that immediate mitigation has high priority68. We consider the time-value of reduced or enhanced emissions resulting from changes in forest losses by using discount factors, which we apply to convert all benefits and costs to their present value (Methods equation (5)). The discount factors reflect issues such as income inequality aversion, positive growth in the economy and uncertainty about future growth. In principle, the discount factor could also reflect learning processes and technological change. 589ccfa754

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