If global policies intended to promote forest conservation continue to use the definition of "forest" adopted in 2001 by the United Nations Framework Convention on Climate Change (an area of >0.05-1 ha with >10-30% cover of plants >2-5 m tall at maturity), great quantities of carbon and other environmental values will be lost when natural forests are severely degraded or replaced by plantations but technically remain "forests." While a definition of "forest" that is globally acceptable and appropriate for monitoring using standard remote sensing options will necessarily be based on a small set of easily measured parameters, there are dangers when simple definitions are applied locally. At the very least, we recommend that natural forest be differentiated from plantations and that for defining "forest" the lower height limit defining "trees" be set at more than 5 m tall with the minimum cover of trees be set at more than 40%. These changes will help reduce greenhouse gas emissions from what is now termed forest "degradation" without increasing monitoring costs. Furthermore, these minor changes in the definition of "forest" will promote the switch from degradation to responsible forest management, which will help mitigate global warming while protecting biodiversity and contributing to sustainable development.

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As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.