Multi-temporal mapping of pantropical post-loss land cover using dense earth-observation time series and global pre-existing maps

Abstract

<p>Deforestation remains one of the largest contributors to global greenhouse emissions. Despite the efforts in monitoring forest change, there is still a lack of pan-tropical spatially-explicit data informing the subsequent land cover (LC) changes over deforested areas (also known as post-loss LC). Based on this premise, this research focuses on predicting post-loss LC over deforested areas as detected by Terra-i, an early warning system of pantropical forest change providing alerts every 16-days from 2004 to present at spatial resolution of 250 m. A supervised deep neural network model suited to extract spatio-temporal patterns from dense earth observation time series data was leveraged in this work by using 16-day MODIS images of 2015. The model was trained according to nine labelled datasets representing different number of LC classes and complexity. These datasets were generated from pre-existing global LC maps with a native spatial resolution ranging from 100 m to 500 m. The effectiveness of the trained models in producing accurate predictions of post-loss LC was assessed over the Amazon region, the largest continuous region of tropical forest in the world. A two-stage assessment approach was conducted to determine the most suitable labelled datasets to predict post-loss LC over Terra-i’s areas. For the first stage, traditional metrics for the assessment of the quality of LC thematic data — e.g. overall accuracy, per-class mapping accuracy, area (or quantity) disagreement and allocation disagreement — were computed according to the test partitions from the labelled datasets. A second stage consisted in using the trained models in 2015 to make predictions for all available years of MODIS satellite imagery, from 2001 to 2018, across seven representative areas distributed in the Amazon. The observed LC predictions were masked using annual aggregated data of Terra-i from 2004 to 2010. The post-LC data by trained model, which represents a given labelled dataset, was verified by i) visualising the temporal and spatial distribution of the most frequent subsequent LC changes; and ii) comparing with Mapbiomas Amazonia, a regional-tuned multi-temporal LC dataset from 2000 to 2017 for the whole Amazon. The results showed that one out of the nine labelled datasets allowed the supervised deep learning model to produce reasonable spatial predictions and classification accuracies (overall accuracy of 86.36±0.64, area disagreement of 5.34±0.39 and allocation disagreement of 8.31±0.64) according to the test partition data. Moreover, the trained model provided similar patterns of post-loss LC as informed by the Mapbiomas dataset. Due to the nature of the model (i.e. neural network) and input data (i.e. global), it is expected the model is scalable to other pantropical areas. The insights and products derived throughout this study are targeted to reduce current uncertainties and challenges in the calculation of global and regional drivers and impacts of deforestation in tropical forests and landscapes.</p>