Abstract
Land cover has been designated by the Global Climate Observing System (GCOS) as an Essential Climate Variable due to its integral role in many climate and environmental processes. Land cover and change affect regional precipitation patterns, surface energy balance, the carbon cycle and biodiversity. Accurate information on land cover and change is essential for climate change mitigation programs such as UN-REDD+. Still, uncertainties related to land change are large, in part due to the use of traditional land cover and change mapping techniques that use one or a few remotely sensed images, preventing a comprehensive analysis of ecosystem change processes. The opening of the Landsat archive and the initiation of the Copernicus Program have enabled analyses based on time series data, allowing the scientific community to explore global land cover dynamics in ways that were previously limited by data availability. One such method is the Continuous Change Detection and Classification algorithm (CCDC), which uses all available Landsat data to model temporal-spectral features that include seasonality, trends, and spectral variability. Until recently, the CCDC algorithm was restricted to academic environments due to computational requirements and complexity, preventing its use by local practitioners. The situation has changed with the recent implementation of CCDC in the Google Earth Engine, which enables analyses at global scales. What is still missing are tools that allow users to explore, analyze and process CCDC outputs in a simplified way. In this paper, we present a suite of free tools that facilitate interaction with CCDC outputs, including: (1) time series viewers of CCDC-generated time segments; (2) a spatial data viewer to explore CCDC model coefficients and derivatives, and visualize change information; (3) tools to create land cover and land cover change maps from CCDC outputs; (4) a tool for unbiased area estimation of key climate-related variables like deforestation extent; and (5) an API for accessing the functionality underlying these tools. We illustrate the usage of these tools at different locations with examples that explore Landsat time series and CCDC coefficients, and a land cover change mapping example in the Southeastern USA that includes area and accuracy estimates.
Highlights
Remote sensing of the land surface can contribute to our understanding of the climate system in a variety of ways
We focus on one of these Climate Essential Climate Variables (ECVs), land cover, with particular attention to change in land cover as it has been a primary driver of increases in atmospheric CO2
The different areas were selected to demonstrate how the tools can aid in the understanding of land change at different scales, and how they can be used in different geographical areas with varying levels of data coverage and drivers of change
Summary
Remote sensing of the land surface can contribute to our understanding of the climate system in a variety of ways. By the time the REDD+ program started, ∼12% of the annual carbon emissions were due to land use practices, including deforestation and forest degradation (Houghton et al, 2012; Goetz et al, 2015). The REDD+ Program, an abbreviation of 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, was designed to reduce greenhouse gas emissions from the land sector by providing economic incentives. The program stipulates that developing countries will receive economic compensation for measuring, verifying, and reporting on reductions of greenhouse gas emissions from one or more of the five REDD+ activities: (1) deforestation, (2) forest degradation; (3) conservation, (4) sustainable management, and (5) enhancement of forest carbon stocks (GFOI, 2016). Land cover change monitoring and reporting by participating countries is at the heart of the REDD+ Program
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