DOI: 10.5061/dryad.hdr7sqvjz
title: Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

description: 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.
description: Input raster data all freely available online
 Original input raster data from: 
 Global Forest Change - https://glad.earthengine.app/view/global-forest-change  ALOS JAXA - https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm Processed with code at https://github.com/PatBall1/DeepForestcast Dataset includes: Input shapefiles for each study site. Input geotiff files (.tif) for each study site. Input PyTorch tensors (.pt) for each study site. Model weights (.pt) for trained networks (for testing and forecasting). Output deforestation forecasts for each study site as geotiffs (.tif).
description: Python (PyTorch) for processing tensors - https://pytorch.org/  QGIS to view raster and polygon data - https://www.qgis.org/