A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine

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.

Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy

“…Remote sensing (RS) data have a long history in mapping forest disturbances. Optical sensors, in particular, have been widely used to automatically map forest disturbances across the globe at different spatial scales through several different approaches, including (1) bi-temporal analyses of image pairs [9][10][11], (2) time series segmentation [12,13], and (3) near-real-time monitoring of forest disturbances [14,15]. Results indicate that simple bi-temporal methods are not suitable for large-scale monitoring because they depend too much on data availability and imply calibrations that are difficult to generalize [4].…”

Dense satellite data reveals landscape connectivity decline in the Andes-Amazon region

“…We flag deforestation when magnitude values are less than zero NDFI units. We produce intra-annual deforestation maps for the period 2000-2020, following Arévalo et al, (2020) processing tools; we then reclassify the Hansen et al (2013) tree cover dataset in 2000 as transformed (tree cover canopy < 50%) and non-transformed (tree canopy cover >50%). The non-transformed tree cover map in 2000 was updated using the deforestation events detected by CCDC for each conflict period.…”

A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018

“…Our underestimation of this browning trend may come from the global regression method (TSR) because several intra-annual NDVI differences were omitted. In future studies, the piecewise regression will be used and the breakpoints in the time series data will be detected with a time series analysis method, i.e., Breaks For Additive Seasonal and Trend (BFAST) [58][59][60], CCDC [61] or LandTrendr [62], which have been implemented into Google Earth Engine's platform [63][64][65]. Widespread active layer detachments and retrogressive thaw slumps exist in the low arctic tundra zone of Yamal, West Siberia [35].…”