Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

1.    Tropical forests are subject to diverse
deforestation pressures while their conservation is essential to achieve
global climate goals. Predicting the location of deforestation is
challenging due to the complexity of the natural and human systems
involved but accurate and timely forecasts could enable effective planning
and on-the-ground enforcement practices to curb deforestation rates. New
computer vision technologies based on deep learning can be applied to the
increasing volume of Earth observation data to generate novel insights and
make predictions with unprecedented accuracy. 2.   
Here, we demonstrate the ability of deep convolutional neural networks
(CNNs) to learn spatiotemporal patterns of deforestation from a limited
set of freely available global data layers, including multispectral
satellite imagery, the Hansen maps of annual forest change (2001-2020) and
the ALOS PALSAR digital surface model, to forecast deforestation (2021).
We designed four model architectures, based on 2D CNNs, 3D CNNs, and
Convolutional Long Short-Term Memory (ConvLSTM) Recurrent Neural Networks
(RNNs), to produce spatial maps that indicate the risk to each forested
pixel (~30 m) in the landscape of becoming deforested within the next
year. They were trained and tested on data from two ~80,000 km2 tropical
forest regions in the Southern Peruvian Amazon. 3.   
The networks could predict the location of future forest loss to a high
degree of accuracy (F1 = 0.58-0.71). Our best performing model (3D CNN)
had the highest pixel-wise accuracy (F1 = 0.71) when validated on 2020
forest loss (2014-2019 training). Visual interpretation of the mapped
forecasts indicated that the network could automatically discern the
drivers of forest loss from the input data. For example, pixels around new
access routes (e.g. roads) were assigned high risk whereas this was not
the case for recent, concentrated natural loss events (e.g. remote
landslides). 4.    CNNs can harness limited time-series
data to predict near-future deforestation patterns, an important step in
harnessing the growing volume of satellite remote sensing data to curb
global deforestation. The modelling framework can be readily applied to
any tropical forest location and used by governments and conservation
organisations to prevent deforestation and plan protected areas.