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# Predicting Near-future Deforestation Across the Tropics Using Deep Learning: Insights from the Forest Foresight Project

To cite this article before publication: Laura Elena Elena Cué La Rosa et al 2025 *Environ. Res. Commun.* in press https://doi.org/10.1088/2515-7620/ae1f69

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# Predicting Near-future Deforestation Across the Tropics Using Deep Learning: Insights from the Forest Foresight Project

Laura Elena Cue La Rosa<fnref n="1" />,*, Jonas van Duijvenbode<fnref n="2" />, Zillah Calle<fnref n="2" />, Diego Marcos<fnref n="3" />, Jorn Dallinga<fnref n="2" />, Johannes Reiche<fnref n="1" />

**Abstract.** Tropical deforestation continues to threaten biodiversity, carbon storage, and climate regulation. While satellite-based near-real-time monitoring systems track deforestation across local to global scales, they only detect forest loss after it occurs. Proactively identifying areas at risk can help support timely mitigation efforts. As part of the Forest Foresight initiative led by the World Wide Fund for Nature (Netherlands), we developed a deep learning model to predict near-future deforestation risk. The model produces monthly risk maps at a 400-meter spatial resolution with a six-month prediction horizon, based on near-real-time deforestation alerts and various open-access geospatial datasets used as predictors. It was tested in 17 countries across humid tropical forests in South America, Africa, and Southeast Asia, and achieved an average F0.5 score of 64.8%. It demonstrates a modest performance gain over both a rule-based baseline model (4% global improvement) and an XGBoost decision forest model (1.4% improvement globally), as well as greater temporal and cross-country prediction stability. Prediction performance was highest in areas near recent deforestation and declined with increasing distance from these areas, highlighting the model's dependence on past deforestation patterns as the main limitation. The model is less effective in predicting new deforestation events in previously undisturbed forests, such as regions where new logging roads are being developed after the prediction date. To substantially improve prediction performance, it is essential to integrate frequently updated, region-specific, and novel data sources, particularly real-time indicators of human activity, such as mobile phone movements or economic signals

<footnote n="1">¹Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands</footnote>

<footnote n="2">²World Wide Fund for Nature, Zeist, The Netherlands</footnote>

<footnote n="3">³INRIA, University of Montpellier, Montpellier, France</footnote>

<footnote>*Author to whom any correspondence should be addressed.

laura.cuelarosa@wur.nl</footnote>
# 1 INTRODUCTION

2

that anticipate near-future deforestation. Our study demonstrates that scalable, fine-resolution deforestation prediction is operationally feasible and can empower forest actors to engage in proactive conservation.

## 1. Introduction

Tropical forests, known for their high biodiversity and carbon-storing capacity, play an essential role in regulating the Earth's climate and ecosystems. They are home to more than half of the world's terrestrial species (Lewis et al., 2015), providing critical habitats and providing vital ecosystem services. Intact tropical forests also act as significant carbon sinks (Pan et al., 2011). However, the balance between uptake and emissions is increasingly disrupted due to anthropogenic pressure from increasing deforestation and land degradation (Baccini et al., 2017). Despite international commitments, deforestation continues at a rate of approximately 10 million hectares per year, placing the 2030 Agenda for Sustainable Development Goals to halt deforestation out of reach for at least another 25 years if current trends persist (FAO, 2020). This highlights the critical importance of curbing deforestation, particularly in primary forests, which cannot be restored once lost.

Recognising the urgent need for forest conservation, several national and international frameworks have been established to address deforestation (Furumo and Lambin, 2021). One major initiative is the Reducing Emissions from Deforestation and Forest Degradation (REDD+) program under the United Nations Framework Convention on Climate Change (UNFCCC), which aims to provide financial incentives for forest preservation while aiming to support local communities (UNFCCC, 2008). A key outcome of the 26th UN Climate Change Conference of the Parties (COP26) was the Glasgow Leaders' Declaration on Forests and Land Use, where over 140 countries pledged to halt and reverse forest loss by 2030 (COP26).

To support these efforts, several satellite-based monitoring systems have been developed to detect deforestation in near-real-time, enabling governments and forest stakeholders to take timely law enforcement actions against illegal and unsustainable activities (Finer et al., 2018; Reiche et al., 2024). These systems range from regional-scale, such as the Brazilian NearReal-Time Deforestation Detection System (DETER and DETER-R) (Diniz et al., 2015; Doblas et al., 2022) and CESBIO/TropiSCO (Bouvet et al., 2018; Mermoz et al., 2021) in the Guianas and Southeast Asia,
# 1 INTRODUCTION

3

to continental-scale systems including RAdar for Detecting Deforestation (RADD) (Reiche et al., 2021), Global Land Analysis and Discovery (GLAD) (Hansen et al., 2016), and the JICA-JAXA Forest Early Warning System (JJ-FAST) (Watanabe et al., 2018, 2021). For instance, RADD uses cloud-penetrating radar from Sentinel-1 satellites to detect canopy disturbances at 10 m pixel spacing (Reiche et al., 2021), while GLAD-L and GLAD-S2 rely on optical imagery from Landsat (30m resolution) and Sentinel-2 (10 m resolution, limited to the Amazon Basin), respectively, to monitor changes in tree cover across tropical forests (Hansen et al., 2016). The Global Forest Watch (GFW) Integrated Alert system (Reiche et al., 2024; GFW, 2024) merges the RADD, GLAD-L, and GLAD-S2 alerts, and is hereafter referred to as Integrated Alerts. Using their complementarity leads to faster and more accurate detection of forest disturbances (Reiche et al., 2024).

Despite their wide application, near-real-time alert systems are inherently retrospective, detecting forest disturbances only after it has occurred. This limitation has led to growing interest in models that can predict where deforestation is likely to happen, enabling proactive intervention before irreversible damage. However, predicting deforestation remains challenging due to the complex interplay of political, biophysical, economic, and social drivers, along with inconsistent enforcement of forestry laws and governance (Harris et al., 2008; Rosa et al., 2013; Bax et al., 2016; Sales et al., 2017; Vijay et al., 2018; Jaffé et al., 2021).

Understanding the mechanisms of deforestation, including how it spreads, where it occurs, and what drives it, is essential for developing robust models to predict future deforestation. Deforestation typically follows a spatial and temporal pattern, meaning it does not occur randomly but often spreads from recently deforested areas. Consequently, the presence of deforestation in a region is a strong indicator of future forest loss in nearby areas (de Souza and De Marco Jr, 2014; Brito et al., 2019; Laurance et al., 2002). This pattern is often driven by activities such as logging, road construction, and mining, among others, which tend to expand over time.

Several approaches have been developed to predict deforestation, varying widely in terms of geography, methodology, predicted variable, and spatial and temporal resolution (see Table 1). Many studies focus on specific regions, particularly the Brazilian and Peruvian Amazon (Soares-Filho et al., 2006; Jaffé et al., 2021; Rosa et al., 2013; Bax et al., 2016; Sales et al., 2017; Vijay et al., 2018; de Souza and Junior, 2018; Domínguez et al., 2022; Ball et al., 2022; Dias et al., 2024; La Rosa et al., 2024), while relatively few target the tropical forests in Asia or Africa (Harris et al.,
# 1 INTRODUCTION 4

2008, 2017; Parsch et al., 2025). Recent efforts have produced global deforestation risk products. One example is the ForestAtRisk tool (Vieilledent et al., 2022), which provides spatially explicit deforestation probability maps based on machine learning models and publicly available global datasets. Although this product offers valuable insights, it primarily provides static risk assessments rather than regularly updated, near-term forecasts. Another ongoing initiative is led by Verra (Verra, 2024), developing a deforestation risk mapping and monitoring framework to support jurisdictional REDD+ methodologies.

In terms of prediction horizon (how far into the future the model predicts deforestation), most models generate annual predictions (Soares-Filho et al., 2006; Harris et al., 2008; Rosa et al., 2013; Aguiar et al., 2016; Bax et al., 2016; Harris et al., 2017; Sales et al., 2017; Vijay et al., 2018; Ball et al., 2022). Some studies extend their predictions, with a longer prediction horizon, ranging from 5 to 20 years (Parsch et al., 2025; Buřivalová et al., 2021; Mayfield et al., 2017). A recent study led by La Rosa et al. (2024) provides a near-future prediction horizon of two weeks, but the coarse spatial resolution of 25 km limits the applicability for targeted, site-specific interventions and poses challenges for effective law enforcement. Furthermore, the type of prediction varies: some models estimate deforestation risk (i.e., probability of occurrence), while others focus on intensity (i.e., the amount or extent of forest loss) (Table 1).

The methods used for deforestation prediction also vary widely, from traditional statistical and spatial models to newer approaches like machine learning and deep learning. The prediction frequency (how often a model produces predictions) also varies. While most studies provide one-time scientific results, few offer operational approaches that generate continuous or regularly updated predictions. However, to our knowledge, no existing studies combine near-future deforestation prediction (less than 1 year) with high-spatial detail and regular prediction frequency (e.g. monthly or sub-annual) at the pantropical level. This combination is important for operational applications to support timely and targeted responses.
# 1 INTRODUCTION

<table><caption>Table 1: Summary of key deforestation prediction studies.</caption><thead><tr><th>Study</th><th>Prediction horizon</th><th>Spatial scale</th><th>Response Variable</th><th>Method</th><th>Geography</th><th>Prediction Frequency</th></tr></thead><tbody><tr><td>Parsch et al. (2025)</td><td>20 yrs</td><td>10 km</td><td>Risk</td><td>Machine Learning</td><td>New Guinea</td><td>Not operational</td></tr><tr><td>Bufivalová et al. (2021)</td><td>18 yrs</td><td>Protected area</td><td>Intensity</td><td>Correlation</td><td>Global</td><td>Not operational</td></tr><tr><td>Vieilledent et al. (2022)</td><td>10 yrs</td><td>30 m</td><td>Risk</td><td>Spatial model</td><td>America, Africa, Asia</td><td>Not operational</td></tr><tr><td>Verra (2021)</td><td>6 yrs</td><td>100 m</td><td>Risk</td><td>Spatial model</td><td>Jurisdictional (REDD+)</td><td>Frequent updates</td></tr><tr><td>Mayfield et al. (2017)</td><td>5 yrs</td><td>500 m</td><td>Risk</td><td>Machine Learning</td><td>Mexico, Madagascar</td><td>Not operational</td></tr><tr><td>Mayfield et al. (2020)</td><td>5 yrs</td><td>500 m</td><td>Risk</td><td>Machine Learning</td><td>Mexico</td><td>Not operational</td></tr><tr><td>Jaffé et al. (2021)</td><td>1-3 yrs</td><td>5-40 km</td><td>Intensity</td><td>Bayesian</td><td>Brazil</td><td>Not operational</td></tr><tr><td>Soares-Filho et al. (2006)</td><td>1 yr</td><td>1 km</td><td>Risk</td><td>Spatial model</td><td>Amazon Basin</td><td>Not operational</td></tr><tr><td>Harris et al. (2008)</td><td>1 yr</td><td>250 m</td><td>Intensity</td><td>Spatial model</td><td>Kalimantan</td><td>Not operational</td></tr><tr><td>Rosa et al. (2013)</td><td>1 yr</td><td>5 km</td><td>Risk</td><td>Regression model</td><td>Brazil</td><td>Not operational</td></tr><tr><td>Aguiar et al. (2016)</td><td>1 yr</td><td>25 km</td><td>Intensity</td><td>Spatial model</td><td>Brazil</td><td>Not operational</td></tr><tr><td>Bax et al. (2016)</td><td>1 yr</td><td>9 km</td><td>Risk</td><td>Spatial model</td><td>Peru</td><td>Not operational</td></tr><tr><td>Harris et al. (2017)</td><td>1 yr</td><td>2.5 km</td><td>Intensity</td><td>Spatial model</td><td>Brazil, Indonesia, DRC</td><td>Not operational</td></tr><tr><td>Sales et al. (2017)</td><td>1 yr</td><td>1 km</td><td>Risk</td><td>Spatial model</td><td>Brazil</td><td>Not operational</td></tr><tr><td>Vijay et al. (2018)</td><td>1 yr</td><td>&lt;0.5 m</td><td>Risk</td><td>Machine Learning</td><td>Peru</td><td>Not operational</td></tr><tr><td>de Souza and Junior (2018)</td><td>1 yr</td><td>1 km</td><td>Risk</td><td>Machine Learning</td><td>Brazil</td><td>Biweekly</td></tr><tr><td>Saha et al. (2020)</td><td>1 yr</td><td>30 m</td><td>Intensity</td><td>Machine learning</td><td>Gumani River</td><td>Not operational</td></tr><tr><td>Dominguez et al. (2022)</td><td>1 yr</td><td>Municipality</td><td>Risk</td><td>Deep Learning</td><td>Brazil</td><td>Not operational</td></tr><tr><td>Ball et al. (2022)</td><td>1 yr</td><td>30 m</td><td>Intensity</td><td>Deep Learning</td><td>Peru</td><td>Not operational</td></tr><tr><td>Dias et al. (2024)</td><td>1 yr</td><td>Municipality</td><td>Intensity</td><td>Machine Learning</td><td>Brazil</td><td>Not operational</td></tr><tr><td>La Rosa et al. (2024)</td><td>15 days</td><td>25 km</td><td>Intensity</td><td>Machine Learning</td><td>Brazil</td><td>Biweekly</td></tr><tr><td>This study</td><td>6 months</td><td>400 m</td><td>Risk</td><td>Deep Learning</td><td>South America, Africa, Asia</td><td>Monthly</td></tr></tbody></table>
## 2 MATERIAL AND METHODS 6

To address this gap, Forest Foresight, an open-source and open-access initiative led by the World Wide Fund (WWF) for Nature (Netherlands), uses machine learning to predict near-future deforestation with monthly prediction frequency, a six-month prediction horizon and at 400 m resolution. These settings were selected to respond to user needs identified through the Forest Foresight project, providing sufficient spatial detail and prediction horizon for actionable interventions, such as patrol planning, while maintaining operational efficiency across large areas. High-risk areas are predicted based on near-real-time forest disturbance alerts (Integrated Alerts) and various open-access geospatial datasets. Forest Foresight has been operational since 2021 and is currently active in countries such as Gabon, Indonesia, Laos, Peru, Bolivia and Colombia, with the ambition to be active in the tropical forests of WWF's identified deforestation fronts (WWF). Local governments, authorities, and communities are actively involved and use the monthly deforestation predictions to verify threats and coordinate timely field interventions. When employing predictive models for operational use, users find it critical to reduce false positives in settings where on-the-ground resources are limited.

Here we (1) developed a deep-learning deforestation prediction model within the scope of Forest Foresight and (2) evaluated its performance against a baseline model and a standard machine learning approach across diverse tropical landscapes. We also (3) identified the limitations arising from the type and quality of the input data and future directions. Our approach differs from previous studies by combining high spatial detail (400 m), a near-future prediction horizon (six months), and regular, monthly predictions across the entire tropical belt. This work contributes to the advancements of predictive tools for forest conservation and outlines future directions for improving early-warning capabilities.

## 2. Material and methods

### 2.1. Study area

We focus on primary humid tropical forests (Turubanova et al., 2018) in South America, Africa, and Asia. In South America, the countries analysed include Ecuador, Peru, Venezuela, Colombia, Bolivia, Brazil (Brazilian Amazon region), Suriname, Guyana, and French Guiana. In Africa, we included Cameroon, Gabon, Equatorial Guinea, the Republic of the Congo, the Democratic Republic of the Congo
## 2 MATERIAL AND METHODS 7

(DRC), and the Central African Republic (CAR). In Asia, our study covers Malaysia, Indonesia, and Papua New Guinea (PNG). Alert system coverage also varies by region, with GLAD-L available in all countries, while GLAD-S2 is limited to South America and RADD is not available in Bolivia.

### 2.2. Geospatial input variables

We compiled a geospatial dataset of input variables encompassing socio-political, topographic, and biophysical characteristics across the pantropical belt. The input variables include both static and dynamic features, spanning monthly and annual temporal resolution, designed to reflect the complexities of deforestation dynamics. From Integrated Alerts, we derived spatial and temporal features such as deforestation extent, patch density, and time since deforestation. Other inputs include forest height, historical deforestation, initial forest cover, accessibility metrics (distance to roads and waterways), terrain (slope and elevation), and human activity indicators like population growth and nightlight intensity. Climate variables such as precipitation and fire alerts were also included to represent environmental factors. All variables were aggregated at a 400 × 400 m resolution. A complete list of variables, descriptions, and data sources is provided in Appendix A.

Analysis was conducted at both the individual country level and for continental groupings (Africa, Asia, and South America) to capture variation in deforestation dynamics across scales. A key component of the dataset is the 10 m Integrated Alerts system (Reiche et al., 2024; GFW, 2024), which merges three complementary alerts: RADD (Sentinel-1 radar-based, 10 m pixel spacing), GLAD-L (Landsat based), and GLAD-S2 (Sentinel 2 based, available only for South America). While referred to as “deforestation alerts”, these systems detect general disturbances in forest cover and do not explicitly distinguish between natural and anthropogenic drivers. For consistency, we use the term deforestation throughout this manuscript.

### 2.3. Pseudo-reference data

Pseudo-reference data used to assess the model's predictions are part of the Forest Foresight dataset and are sourced from the Integrated Alerts (Reiche et al., 2024; GFW, 2024). These alerts were aggregated within 400 × 400 m areas, each containing 1600 pixels at 10 × 10 m resolution (40 × 40). For each cell, monthly pseudo-reference data were calculated by counting the number of 10 m pixels detected as deforestation
## 2 MATERIAL AND METHODS 8

within the subsequent six months (the prediction horizon), resulting in values ranging from 0 (no alerts) to 1600 (all pixels deforested). For model training and validation, these values were converted into binary indicators at the grid-cell level: a value of 1 if any deforestation occurred within the six-month horizon, and 0 otherwise.

### 2.4. Predicting deforestation

We employed a deep learning model to predict deforestation and compared its performance against two benchmark approaches: an XGBoost model (Chen and Guestrin, 2016) and a baseline rule-based model. The baseline model operated a simple rule: if deforestation had occurred in the previous six months, it predicted future deforestation; otherwise, it predicted none. All models generated pixel-level deforestation probability estimates at the 400 × 400 m grid cell level. Predictions were made monthly, with a six-month prediction horizon; that is, at each monthly time step, the model predicted the probability of deforestation occurring within the next six months. Model evaluation was performed independently for each month of the testing period by comparing the model's predictions to the corresponding pseudo-reference for that month. The deep learning framework was based on the ResUNet architecture (Diakogiannis et al., 2020), which combines the encoder-decoder structure of U-Net (Ronneberger et al., 2015) with residual connections (He et al., 2016).

#### 2.4.1. Model description:
The ResUNet model used in this study is a fully convolutional neural network designed for pixel-wise prediction of deforestation probabilities. The architecture follows an encoder-decoder structure with four downsampling stages, using filter sizes of 16, 32, 64, and 128. This relatively small set of filters was selected to reduce the model's complexity, resulting in a lightweight network suitable for operational deployment. The network employs 2D spatial convolutions throughout, allowing it to capture spatial patterns across the input features. The encoder begins with an initial convolutional block that processes input image patches, followed by two residual convolutional blocks that progressively reduce spatial resolution while increasing feature depth. Each residual block consists of convolutional layers with 3×3 kernels, ReLU activations, and batch normalisation, combined with residual skip connections to facilitate gradient flow and improve training stability. A bridge block connects the encoder and decoder at the bottleneck.
## 2 MATERIAL AND METHODS 9

The decoder uses bilinear upsampling followed by residual convolutional blocks, with skip connections that add corresponding feature maps from the encoder to preserve both local and global spatial context. This design ensures that the output for each pixel is influenced by surrounding pixels rather than being based solely on individual pixel values. The output layer is a 1×1 convolution followed by a sigmoid activation function, generating pixel-wise probabilities. Model optimisation used a combination of Dice loss and Cross-Entropy loss functions with the RAdam optimiser (Liu et al., 2019), an initial learning rate of 0.01, a final learning rate of 0.00001, a weight decay of 1e-6, and a cosine decay learning rate schedule. Training was conducted over 100 epochs with a batch size of 128.

During training, we randomly sampled 64×64×35 (predictor variables) patches from the target region at any time step in the training dataset. This random sampling ensured diverse spatial and temporal coverage across the training period. Balanced data sampling was applied to mitigate class imbalance caused by the relatively small proportion of deforested pixels. The number of random samples per epoch was set to 50,000, ensuring that the model was exposed to a representative coverage of the entire training dataset over the course of training. Input features were normalised by their maximum values to reduce the influence of extreme values, which is particularly important given the heterogeneous nature of the data.

### 2.4.2. Experimental protocol:
Temporal split was used to divide the dataset into training and test subsets. The data were split temporally into a training period (January 2021 – December 2022) and an independent test period (June 2023 – May 2024), providing two years for training and one year for evaluation. Including December 2022 in training requires six months of follow-up pseudo-reference data (from December 2022 to May 2023). Accordingly, the first valid test prediction is June 2023 (see Figure 1). This temporal partitioning simulates a real-world setting in which models are trained on historical data and used to predict future deforestation. The spatial extent was kept the same across periods: all available grid cells within the target countries were used for training, while evaluation was performed on the same grid cells but at a later time period to ensure temporal independence. The training and test datasets consist of monthly input–pseudo-reference data pairs, where each input corresponds to the predictor variables for a given month and the pseudo-reference represents observed deforestation within the following six months. A total of 35 predictor variables were used (see Appendix A Table A1). Training
## 2 MATERIAL AND METHODS

<figure><img src="image_4.png" /></figure>

Figure 1: Overview of the training and testing periods. The model is trained on data from January 2021 to December 2022. The pseudo-reference data for December 2022 includes observed deforestation over the six-month prediction horizon (December 2022 to May 2023). The temporally independent test period spans from June 2023 to May 2024, during which the model produces monthly predictions with a six-month prediction horizon.

and evaluation were conducted region-wise, that is, one model for each geographic region (South America, Africa, and Asia).

A probability threshold for each model was selected based on the value that maximised the F0.5 score on the training set. This optimal threshold was then applied to the test set. The final thresholds were 0.87 for South America, 0.86 for Africa, and 0.83 for Asia (see Figure A2 in Appendix A). It is worth noting that the optimal thresholds for both the training and test sets are very close (Figure A2 and A3 in Appendix A), suggesting good generalisation capacity. Pixels with predicted probabilities above the selected threshold were classified as deforested, while those below were considered not deforested. The same procedure was followed for the XGBoost baseline model.

### 2.4.3. Evaluation metrics:
Model performance was evaluated using three metrics: user's accuracy, producer's accuracy, and the F0.5 score. User's accuracy (100% - false positive rate) measures the proportion of pixels predicted as deforestation that were indeed followed by actual deforestation in subsequent time steps. Producer's accuracy (also known as detection rate; 100% - false negative rate) measures the proportion of actual deforestation pixels that were correctly predicted by the model in advance. The F0.5 score combines both, placing greater emphasis on user's accuracy. Forest Foresight employs this metric because it aligns with the operational priority of minimising false positives, thereby reducing the operational cost and effort associated
3 RESULTS

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with investigating incorrect deforestation alerts. The F0.5 score is defined as

$$F0.5 = (1 + 0.5^2) \cdot \frac{\text{user's accuracy} \cdot \text{producer's accuracy}}{(0.5^2 \cdot \text{user's accuracy}) + \text{producer's accuracy}} \quad (1)$$

All evaluation metrics were calculated within the humid primary forest mask defined by Turubanova et al. (2018), ensuring consistency with the Integrated Alerts product, which detects disturbances only within the same forest extent.

3. Results

3.1. *Models' performance comparison*

Figure 2 presents pairwise model comparisons with diagonal kernel density estimate (KDE) panels showing score distributions by continent. For the F0.5 score, ResUNet leads in South America (61.7%) and remains competitive in Africa (68.8%) and Asia (64.6%), compared with the Baseline (56.3%, 65.0%, 63.0%) and XGBoost (58.8%, 68.4%, 65.3%) models. Globally, ResUNet outperformed the Baseline and XGBoost by 4.2% and 1.4%, respectively. KDEs highlight that ResUNet not only achieves higher performance in most regions but also exhibits narrower distributions in Asia and South America, reporting lower standard deviation from the mean. This indicates that ResUNet demonstrates greater temporal and cross-country stability, being less affected by seasonality.

The lower-triangle scatter plots offer direct model-to-model comparisons based on monthly metrics computed separately for each country. Points closely follow the identity line (red dashed), indicating strong correlations (r > 0.9). Nonetheless, Wilcoxon signed-rank tests (Bonferroni-corrected p-values) indicate that differences between models are statistically significant (p < 0.001), even when correlations are high.

A corresponding figure presenting the user's and producer's accuracy metrics is provided in Appendix A (Figure A1). ResUNet achieves the highest overall user's accuracy (71.8%) compared to XGBoost (68.3%) and the Baseline (63.6%). Conversely, ResUNet shows a lower producer's accuracy (49.3%) than the Baseline (56.0%) and XGBoost (52.9%), illustrating the trade-off associated with higher user's accuracy. However, in the target application, maximising user's accuracy is prioritised over producer's accuracy.
<figure><img src="image_5.png" /><figcaption>F0.5 Score</figcaption></figure>

Figure 2: F0.5 pairwise comparisons across models and continents. The diagonal panels show continent-specific and global KDEs, dashed global means ($\mu$), and mean $\pm$ sd in text boxes. The lower-triangle panels display model-pair scatter plots (per-country per-month results) with the identity line (red dashed), reporting the correlation coefficient (r) and Bonferroni-corrected Wilcoxon p-values for model differences.

Given its superior global performance, higher user's accuracy, and temporal robustness, subsequent analyses focus exclusively on the ResUNet model.

<figure><img src="image_6.png" /></figure>
# 3 RESULTS

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## 3.2. Model performance by country

Figure 3 depicts the F0.5 scores, along with user's accuracy and producer's accuracy, achieved by the ResUNet model at the individual country level. For the majority of countries, F0.5 scores are around or above 60%, indicating a reasonable balance between user's and producer's accuracy, with greater emphasis on user's accuracy. The highest performance was recorded for Cameroon, with an average score of 75.4%, followed by the Democratic Republic of the Congo (74.1%) and Equatorial Guinea (69.9%).

Overall, most countries in the Congo Basin exhibited good performance, with F0.5 scores exceeding 63%. Similarly, the model performed well across much of the Amazon Basin, with most countries achieving mean F0.5 scores above 60%. In Southeast Asia, Indonesia reported the highest performance, with an average F0.5 score of 65.8%, followed by Malaysia (64.1%) and PNG (63.8%). Notably, Venezuela recorded the lowest average F0.5 score of 56.8%.

<figure><img src="image_7.png" /><figcaption>Figure 3: Per-country model performance metrics (F0.5 score, user's accuracy, and producer's accuracy) for the test period (June 2023 to May 2024).</figcaption></figure>

User's accuracy remains consistently high, with average values exceeding 69%
<figure><img src="image_8.png" /></figure>

Figure 4: Producer's accuracy (i.e., deforestation detection rate) for the test period (June 2023 to May 2024), categorised by proximity to *past deforestation* (defined as deforestation occurring within the last six months). The right side of the plot shows the percentage of total pixels falling within each category.

for most countries, indicating relatively low false positives in the classification. In contrast, producer's accuracy shows greater variability, ranging from approximately 33% to 68%, reflecting inconsistencies in the model's ability to detect actual deforestation events. Producer's accuracy varied not only between different humid tropical forest ecosystems but also within them. For example, notable differences in producer's accuracy were observed between neighbouring countries such as Venezuela-Colombia and Guyana-French Guiana in the Amazon Basin, the CAR-Cameroon in the Congo Basin, and Malaysia-PNG in Southeast Asia.

### 3.3. Predicting new deforestation events

The comparable performance between the deep learning model and the rule-based baseline model suggests that the model's ability to predict future deforestation is closely linked to the spatial distribution of recent deforestation. To investigate this spatial dependency, Figure 4 shows the producer's accuracy (deforestation detection
## 3 RESULTS

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rates) for the test period as a function of proximity to areas deforested in the preceding six months, hereafter referred to as *past deforestation*. For a detailed illustration of how these distance-based categories were defined, we refer the reader to Appendix A Figure A4.

The Global category represents the overall detection rate across all pixels in the study area, with a median rate of approximately 50% and a mean of 49% (100% of the analysed data). The Within *past deforestation* category, which includes 400 × 400 m grid cells that experienced deforestation in the last six months, exhibits a substantially higher median producer's accuracy of 82% (mean: 79%). Overall, 57% of newly detected deforestation events occurred within previously deforested areas (400 x 400 m), highlighting strong spatial clustering of deforestation. In contrast, the Outside *past deforestation* category, which represents areas with no deforestation in the prior six months (43% of the data), shows a substantially lower median producer's accuracy of 7.4% (mean: 11.9%), indicating a highly skewed distribution influenced by a minority of high-accuracy detections. Producer's accuracy declines further with increasing distance from previously deforested areas. Specifically, areas located more than 0.4 km and 0.8 km from *past deforestation* exhibit progressively lower median detection rates of 2.2% and 0.9% (means: 5.1%, and 2.7%), respectively. It should be noted that 94% of all new deforestation events occur within 0.8 km of *past deforestation* (see the percentages in Figure 4). This dependence is also reflected by a feature importance analysis (see Appendix A, Figure A5), where variables derived from the Integrated Alerts dominate the model's predictive power, while environmental and socioeconomic factors play only a minor role.

### 3.4. Visual Assessment

Predicted deforestation is heavily concentrated in regions that experienced *past deforestation* within the preceding six months. Figure 5A displays the prediction map for June 2023 (predicting deforestation from June to November 2023), along with a detailed view of Suriname where road development and selective tree logging are ongoing (in panels A1-A3). The spatial overlay shows that a large proportion of prediction deforestation aligns with *past deforestation* (panels A1 and A2), leading to clusters of true positives and some false positive samples (panel A3). In contrast, regions without *past deforestation* (new road development) have received fewer predictions, and when deforestation does occur in these areas, it is often missed
by the model, resulting in numerous false negatives (panel A3).

<figure><img src="image_9.png" /></figure>

Figure 5: Predicted deforestation for June 2023 with a six-month prediction horizon (June to November 2023) for the Amazon Basin (A), Congo Basin (B), and Southeast Asia (C). Detailed examples depicting *past deforestation* (A1, B1, C1), predicted deforestation (A2, B2, C2) and prediction accuracy (A3, B3, C3) for example regions in Suriname, Gabon, and Malaysia. Planet imagery is shown in the background: Q3-2023 quarterly composites for the Amazon Basin, and Q2-2023 composites for Africa and Southeast Asia.

Figure 5B depicts similar dynamics in the Congo Basin. Panels B1 and B2 show *past deforestation* and predicted deforestation for June 2023 in Gabon, where road development is observed. Predictions again cluster in areas with a recent history of deforestation over the past six months, particularly around a mining operation. The corresponding error map (panel B3) shows many true positives aligned with *past*
deforestation within the mining region, but also reveals substantial false negatives in previously intact forest that experienced increased road development during the June to November 2023 period.

A similar pattern emerges in Southeast Asia (Figure 5C), focusing on an agricultural region in Malaysia. Part of this area shows deforestation related to agriculture in the preceding six months (panel C1), however, the model consequently fails to detect neighbouring regions affected by agricultural expansion in the prediction period June to November 2023 (panel C3). This results in a high rate of false negatives.

## 4. Discussion

### 4.1. Summary of Findings and Model Performance

In this study, we applied a fully convolutional neural network (ResUNet) to predict deforestation at monthly time intervals from June 2023 to May 2024, using a six-month prediction horizon. This model was trained on a set of static and monthly updated predictor variables derived from publicly available datasets and aggregated to a spatial resolution of 400 m. Unlike previous studies that have primarily focused on specific regions or countries (Buřivalová et al., 2021; Rosa et al., 2013; Parsch et al., 2025; Jaffé et al., 2021), our model was evaluated across 17 countries spanning the Amazon Basin, Congo Basin, and Southeast Asian rainforests, offering a more comprehensive assessment in diverse tropical contexts.

The overall performance of the ResUNet model demonstrates the advantages of deep convolutional architectures for deforestation prediction across diverse tropical regions, reporting competitive F0.5 scores relative to both the XGBoost and baseline models. The model's stability across continents and time periods further indicates a robust generalisation capability. Importantly, the higher user's accuracy achieved by ResUNet, despite a moderate reduction in producer's accuracy, highlights a trade-off that is often desirable in early-warning systems, where minimising false alarms is crucial for practical decision-making.

However, performance varied considerably among countries, suggesting high intra-regional variability despite geographical proximity. Recent reports indicate that countries such as the Democratic Republic of the Congo, Equatorial Guinea, Cameroon, and Indonesia experienced relatively stable forest loss rates between 2021
# 4 DISCUSSION

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and 2024 (University of Maryland and World Resources Institute, 2025). This temporal consistency between training (2021-2022) and evaluation (half 2023 to half 2024) likely improved model accuracy in these regions. In contrast, Bolivia, Venezuela, Brazil, and Guyana experienced a sharp rise in deforestation in 2024 according to University of Maryland and World Resources Institute (2025). This rise in forest loss was primarily caused by fire (Global Forest Review, Updated May 21, 2025), a seasonal event highly influenced by climate variability. These types of events can be particularly challenging for the model to capture, often leading to an increase in false negative rates and resulting in a greater variation in the producer's accuracy throughout the study period (Table 3). The opposite trend can also affect the model's performance. For example, PNG reported a sharp decline in deforestation in 2023 and 2024 compared to 2022 (Global Forest Review, Updated April 4, 2024), which was reflected with a drop in user's accuracy (higher false positive rates) compared to the other countries in Southeast Asia, while still reporting one of the highest producer's accuracy scores.

These results support previous findings that deforestation predictions are constrained by the spatial and temporal patterns observed in recent data, and that commission and omission errors are strongly influenced by both temporal dynamics and spatial variability (Rosa et al., 2013, 2014).

## 4.2. Limitations of Predicting Deforestation from the Past

Deforestation is often spatially clustered, with new clearing frequently occurring near previously disturbed areas. This pattern is well established (Rosa et al., 2013) and supported by numerous studies (Harris et al., 2008; Buřívalová et al., 2021; de Souza and Junior, 2018; La Rosa et al., 2024; Parsch et al., 2025; Mayfield et al., 2017). Our results confirm that approximately 94% of new deforestation events occurred within 0.8 km of prior deforestation. Predictive accuracy was substantially higher in these areas but declined sharply with increasing distance.

This performance reflects the model's reliance on recent deforestation patterns during training. Of the 13 dynamic predictors used in our model, 9 were derived from the Integrated Alerts, while only four were sourced from independent sources (fire alerts, precipitation, temperature, and nightlights). Additionally, key predictors such as road proximity were treated as static and sourced from OpenStreetMap (OpenStreetMap contributors, 2017).
# 4 DISCUSSION

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As a result, the model performs well in active frontier regions but struggles to predict deforestation in previously intact areas, particularly in places where deforestation is newly initiated, such as new logging roads and harvest operations that are often informal, rapidly evolving, and poorly mapped (Barber et al., 2014; Laurance et al., 2002; Saha et al., 2020; Bax et al., 2016; Perz et al., 2007). Although recent deforestation remains the strongest predictor, additional static variables provided additional spatial context and helped to reduce false positives (e.g. lower probability for deforestation at steep slopes). Previous studies (Table 1) have primarily relied on past deforestation as the main predictor, along with other static variables such as forest cover, distance to roads and rivers, elevation, slope, and protected areas, causing models to reproduce past trends rather than anticipate emerging deforestation frontiers.

Like many machine and deep learning models, our model is constrained by the assumption of temporal stationarity, meaning that the patterns learned from historical data will remain in future contexts. However, deforestation is shaped by dynamic socio-political and economic conditions, such as shifting enforcement capacity, market fluctuations, and political agendas (Jayathilake et al., 2021). Consequently, models trained on past data often fail to generalise to future periods. While spatial cross-validation within a single year can yield high classification metrics (e.g., Kappa > 88% in Saha et al. (2020)), the performance of such models drops significantly when applied across time. For example, Ball et al. (2022) documented a 20% decline in F1 score when switching from within-period evaluation to a one-year-ahead prediction. Their reported F1 scores of 62-71% in the Peruvian Amazon are comparable to our results, despite differences in region, spatial resolution, and data inputs, highlighting the importance of data quality and recency.

Nonetheless, the reported accuracy metrics must be interpreted with caution. Our model was evaluated using pseudo-reference data derived from the Integrated Alerts product, which detects canopy disturbances from satellite imagery but does not differentiate between deforestation and other forms of degradation (GFW, 2024). Therefore, the reported accuracy reflect the model's ability to predict future deforestation as captured by the Integrated Alerts, rather than confirmed deforestation verified through independent ground data.

In summary, our findings suggest that while deep learning models such as ResUNet offer some improvement over rule-based and traditional machine learning approaches, the gains remain modest and indicate that we may be approaching a
# 4 DISCUSSION

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performance plateau. This marginal improvement highlights a central limitation: models that rely heavily on past deforestation patterns struggle to predict new deforestation events that are spatially or causally disconnected from previous ones, particularly in highly dynamic or novel deforestation frontiers.

## 4.3. *Future Directions and the Need for Collaborative Approaches*

Moving forward, improving deforestation prediction will require expanded access to context-specific, up-to-date datasets, and stronger partnerships with local stakeholders, governments, and NGOs. These groups often hold essential spatial data, including land-use plans, infrastructure projects, enforcement records, and socioeconomic indicators that are unavailable in global datasets. Previous studies have shown that local context is critical to predictive performance. For example, sawmill proximity proved predictive in East Kalimantan (Harris et al., 2008), while embargo zones, rural credit flows, and political context have all been linked to deforestation risk (de Souza and Junior, 2018; Dias et al., 2024). These findings suggest that the inclusion of high-resolution, regionally specific variables and contextual information on socioeconomic drivers, infrastructure expansion, and land-use planning are crucial to improve early detection in intact forest regions. However, such datasets are often restricted by privacy laws or institutional access, making them difficult to integrate into large-scale, open-access predicting systems. Additionally, novel data sources such as mobile phone location data have shown promise in other domains (e.g., detecting illegal fishing vessels (Calderwood, 2022)), and could provide early indicators of human activities in forests. However, access to such data is highly restricted and will require transparent partnerships and responsible data governance frameworks. Community-based monitoring, where forest rangers or local communities record real-time observations using mobile devices, offers a promising way to anticipate near-future illegal forest activities at local scales (Cappello et al., 2022; Pratihast et al., 2012; Slough et al., 2021). While other modelling approaches, such as probabilistic networks, stochastic components, or active learning, could potentially enhance predictions in areas with no recent deforestation, these approaches are only effective if relevant data are available. Without indicators of near-future human activity, such as mobile phone data, financial flows, or other relevant proxies, the ability to reliably anticipate new deforestation events remains limited.
# 5 CONCLUSIONS

21

On the remote sensing side, developing a global dataset that classifies deforestation by its underlying drivers would significantly enhance model interpretability and enable driver-specific predictions. Future work should prioritize integrating machine learning with local expertise and novel datasets that anticipate deforestation to enhance predictive accuracy. Such integration would also make deforestation prediction systems more interpretable and actionable on-the-ground enforcement activities and decision-making. However, the successful adoption of systems such as *Forest Foresight* ultimately depends on institutional capacity and political will. Without adequate training, integration support, and effective enforcement, these tools alone cannot halt deforestation.

## 5. Conclusions

This study highlights the potential and limitations of deep learning models to predict near-future deforestation across tropical regions at a spatial resolution of 400 m. By combining open-access geospatial data with a ResUNet model, predictions were generated for 17 countries spanning the Amazon Basin, Congo Basin, and Southeast Asia, offering a consistent benchmark for deforestation prediction applications. Our results show that while the model achieves promising overall performance, it is strongly influenced by recent spatial and temporal deforestation patterns, and its accuracy decreases in areas experiencing new deforestation within previously intact forest. Furthermore, the model's dependence on a limited set of alert-based predictors and static variables (e.g., road networks) constrains its ability to generalise, especially in regions where deforestation is just beginning. Despite these limitations, the Forest Foresight model presents a scalable and transparent predicting system that can support operational planning and resource allocation for enforcement activities to avoid deforestation, particularly in regions where monitoring capacity is low and large forest areas cannot be continuously patrolled. Future progress will depend on free access to context-specific, up-to-date, and often locally held datasets, along with stronger collaborations with local actors, and governments. Incorporating local expertise and data will be critical for advancing model accuracy and usability.
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## Acknowledgment

This project received funding from the Open Domain Science project Forest Carbon Crime (Project Number: OCENW.M.21.203) of the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), and the World Wide Fund for Nature (WWF) Netherlands. Planet imagery was provided through the Planet Education & Research program. We thank the two anonymous reviewers for their constructive feedback.

## Data availability

The data used in this study is openly accessible via the Downloading Forest Foresight data - Forest Foresight - Confluence page.

## References

Aguiar, A.P.D., Vieira, I.C.G., Assis, T.O., Dalla-Nora, E.L., Toledo, P.M., Oliveira Santos-Junior, R.A., Batistella, M., Coelho, A.S., Savaget, E.K., Aragão, L.E.O.C., et al., 2016. Land use change emission scenarios: anticipating a forest transition process in the brazilian amazon. Global change biology 22, 1821–1840.

Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., Houghton, R.A., 2017. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234.

Ball, J.G., Petrova, K., Coomes, D.A., Flaxman, S., 2022. Using deep convolutional neural networks to forecast spatial patterns of amazonian deforestation. Methods in ecology and evolution 13, 2622–2634.

Barber, C.P., Cochrane, M.A., Souza Jr, C.M., Laurance, W.F., 2014. Roads, deforestation, and the mitigating effect of protected areas in the amazon. Biological conservation 177, 203–209.

Bax, V., Francesconi, W., Quintero, M., 2016. Spatial modeling of deforestation processes in the central peruvian amazon. Journal for nature conservation 29, 79–88.

Bouvet, A., Mermoz, S., Ballère, M., Koleck, T., Le Toan, T., 2018. Use of the sar shadowing effect for deforestation detection with sentinel-1 time series. Remote Sensing 10, 1250.
Brito, B., Barreto, P., Brandão, A., Baima, S., Gomes, P.H., 2019. Stimulus for land grabbing and deforestation in the brazilian amazon. Environmental Research Letters 14, 064018.

Buřivalová, Z., Hart, S.J., Radeloff, V.C., Srinivasan, U., 2021. Early warning sign of forest loss in protected areas. Current Biology 31, 4620–4626.

Calderwood, J., 2022. Smartphone application use in commercial wild capture fisheries. Reviews in Fish Biology and Fisheries 32, 1063–1083.

Cappello, C., Pratihast, A.K., Pérez Ojeda del Arco, A., Reiche, J., De Sy, V., Herold, M., Vivanco Vicencio, R.E., Castillo Soto, D., 2022. Alert-driven community-based forest monitoring: a case of the peruvian amazon. Remote Sensing 14, 4284.

Carioli, A., Schiavina, M., Freire, S., MacManus, K., 2023. GHS-POP R2023A - GHS population grid multitemporal (1975-2030). URL: http://data.europa.eu /89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5 B-4A22-8F40-C41DA8332CFE. [Dataset].

Čengić, M., Steinmann, Z.J., Defourny, P., Doelman, J.C., Lamarche, C., Stehfest, E., Schipper, A.M., Huijbregts, M.A., 2023. Global maps of agricultural expansion potential at a 300 m resolution. Land 12, 579.

Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794.

COP26, Glasgow, ., . Glasgow leaders’ declaration on forests and land use. URL: https://ukcop26.org/glasgow-leaders-declaration-on-forests-and-lan d-use.

Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C., 2020. Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing 162, 94–114.

Dias, F., Suhadolnik, N., Camargo, H., Da Silva, S., 2024. Predicting the pulse of the amazon: Machine learning insights into deforestation dynamics. Journal of Environmental Management 362, 121359.

Diniz, C.G., de Almeida Souza, A.A., Santos, D.C., Dias, M.C., Da Luz, N.C., De Moraes, D.R.V., Maia, J.S., Gomes, A.R., da Silva Narvaes, I., Valeriano, D.M., et al., 2015. Deter-b: The new amazon near real-time deforestation detection
REFERENCES

24

system. IEEE Journal of selected topics in applied earth observations and remote sensing 8, 3619–3628.

Doblas, J., Reis, M.S., Belluzzo, A.P., Quadros, C.B., Moraes, D.R., Almeida, C.A., Maurano, L.E., Carvalho, A.F., Sant'Anna, S.J., Shimabukuro, Y.E., 2022. Determination of an operational near-real time tropical forest disturbance warning system based on sentinel-1 time series analysis. Remote Sensing 14, 3658.

Dominguez, D., del Villar, L.d.J., Pantoja, O., González-Rodríguez, M., 2022. Forecasting amazon rain-forest deforestation using a hybrid machine learning model. Sustainability 14, 691.

FAO, 2020. Global forest resources assessment 2020: Main report.

Finer, M., Novoa, S., Weisse, M.J., Petersen, R., Mascaro, J., Souto, T., Stearns, F., Martinez, R.G., 2018. Combating deforestation: From satellite to intervention. Science 360, 1303–1305.

Furumo, P.R., Lambin, E.F., 2021. Policy sequencing to reduce tropical deforestation. Global Sustainability 4, e24.

GFW, 2024. Explore gfw's new integrated deforestation alerts. URL: https://www.globalforestwatch.org/blog/data/integrated-deforestation-alerts/.

Global Forest Review, Updated April 4, 2024. Tropical forest loss drops steeply in brazil and colombia, but high rates persist overall. https://research.wri.org/gfr/latest-analysis-deforestation-trends.

Global Forest Review, Updated May 21, 2025. Fires drove record-breaking tropical forest loss in 2024. url: https://research.wri.org/gfr/latest-analysis-deforestation-trends/.

Hansen, M.C., Krylov, A., Tyukavina, A., Potapov, P.V., Turubanova, S., Zutta, B., Ifo, S., Margono, B., Stolle, F., Moore, R., 2016. Humid tropical forest disturbance alerts using landsat data. Environmental Research Letters 11, 034008. doi:10.1088/1748-9326/11/3/034008.

Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., et al., 2013. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853.

Harris, N.L., Goldman, E., Gabris, C., Nordling, J., Minnemeyer, S., Ansari, S., Lippmann, M., Bennett, L., Raad, M., Hansen, M., et al., 2017. Using spatial
statistics to identify emerging hot spots of forest loss. Environmental Research Letters 12, 024012.

Harris, N.L., Petrova, S., Stolle, F., Brown, S., 2008. Identifying optimal areas for redd intervention: East kalimantan, Indonesia as a casestudy. Environmental Research Letters 3, 035006.

He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.

Jaffé, R., Nunes, S., Dos Santos, J.F., Gastauer, M., Giannini, T.C., Nascimento Jr, W., Sales, M., Souza, C.M., Souza-Filho, P.W., Fletcher, R.J., 2021. Forecasting deforestation in the brazilian amazon to prioritize conservation efforts. Environmental Research Letters 16, 084034.

Jayathilake, H.M., Prescott, G.W., Carrasco, L.R., Rao, M., Symes, W.S., 2021. Drivers of deforestation and degradation for 28 tropical conservation landscapes. Ambio 50, 215–228.

La Rosa, L.E.C., Ferrari, F., Bezerra, F.G.S., de Souza, R.A., Maretto, R.V., de Aguiar, A.P.D., Vinhas, L., Happ, P.N., Feitosa, R.Q., 2024. Xgboost and multitemporal deter data for deforestation forecasting. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 10, 193–198.

Laurance, W.F., Albernaz, A.K., Schroth, G., Fearnside, P.M., Bergen, S., Venticinque, E.M., Da Costa, C., 2002. Predictors of deforestation in the brazilian amazon. Journal of biogeography 29, 737–748.

Lewis, S.L., Edwards, D.P., Galbraith, D., 2015. Increasing human dominance of tropical forests. Science 349, 827–832.

Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J., 2019. On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265.

Mayfield, H., Smith, C., Gallagher, M., Hockings, M., 2017. Use of freely available datasets and machine learning methods in predicting deforestation. Environmental Modelling & Software 87, 17–28.

Mayfield, H.J., Smith, C., Gallagher, M., Hockings, M., 2020. Considerations for selecting a machine learning technique for predicting deforestation. Environmental Modelling & Software 131, 104741.
REFERENCES

26

Mermoz, S., Bouvet, A., Koleck, T., Ballère, M., Le Toan, T., 2021. Continuous detection of forest loss in vietnam, laos, and cambodia using sentinel-1 data. Remote Sensing 13, 4877.

OpenStreetMap contributors, 2017. Planet dump retrieved from https://planet.osm.org . https://www.openstreetmap.org.

Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., et al., 2011. A large and persistent carbon sink in the world's forests. science 333, 988-993.

Parsch, C., Wagner, B., Engert, J.E., Panjaitan, R., Laurance, W.F., Nitschke, C.R., Kreft, H., 2025. Forecasting deforestation and carbon loss across new guinea using machine learning and cellular automata. Science of The Total Environment 970, 178864.

Perz, S.G., Caldas, M.M., Arima, E., Walker, R.J., 2007. Unofficial road building in the amazon: socioeconomic and biophysical explanations. Development and change 38, 529-551.

Potapov, P., Turubanova, S., Hansen, M.C., Tyukavina, A., Zalles, V., Khan, A., Song, X.P., Pickens, A., Shen, Q., Cortez, J., 2022. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3, 19-28.

Pratihast, A.K., Herold, M., Avitabile, V., De Bruin, S., Bartholomeus, H., Souza Jr, C.M., Ribbe, L., 2012. Mobile devices for community-based redd+ monitoring: a case study for central vietnam. Sensors 13, 21-38.

Reiche, J., Balling, J., Pickens, A.H., Masolele, R.N., Berger, A., Weisse, M.J., Mannarino, D., Gou, Y., Slagter, B., Donchyts, G., et al., 2024. Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence. Environmental Research Letters 19, 054011.

Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Odongo-Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., et al., 2021. Forest disturbance alerts for the congo basin using sentinel-1. Environmental Research Letters 16, 024005.

Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation, in: Medical image computing and computer-
assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, Springer. pp. 234-241.

Rosa, I.M., Ahmed, S.E., Ewers, R.M., 2014. The transparency, reliability and utility of tropical rainforest land-use and land-cover change models. Global change biology 20, 1707-1722.

Rosa, I.M., Purves, D., Souza Jr, C., Ewers, R.M., 2013. Predictive modelling of contagious deforestation in the brazilian amazon. PloS one 8, e77231.

Saha, S., Saha, M., Mukherjee, K., Arabameri, A., Ngo, P.T.T., Paul, G.C., 2020. Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, reptaree: A case study at the gumani river basin, india. Science of the Total Environment 730, 139197.

Sales, M., De Bruin, S., Herold, M., Kyriakidis, P., Souza Jr, C., 2017. A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction. Spatial statistics 21, 304-318.

Slough, T., Kopas, J., Urpelainen, J., 2021. Satellite-based deforestation alerts with training and incentives for patrolling facilitate community monitoring in the peruvian amazon. Proceedings of the National Academy of Sciences 118, e2015171118.

Soares-Filho, B.S., Nepstad, D.C., Curran, L.M., Cerqueira, G.C., Garcia, R.A., Ramos, C.A., Voll, E., McDonald, A., Lefebvre, P., Schlesinger, P., 2006. Modelling conservation in the amazon basin. Nature 440, 520-523.

de Souza, R.A., De Marco Jr, P., 2014. The use of species distribution models to predict the spatial distribution of deforestation in the western brazilian amazon. Ecological Modelling 291, 250-259.

de Souza, R.A., Junior, P.D.M., 2018. Improved spatial model for amazonian deforestation: An empirical assessment and spatial bias analysis. Ecological modelling 387, 1-9.

Turubanova, S., Potapov, P.V., Tyukavina, A., Hansen, M.C., 2018. Ongoing primary forest loss in brazil, democratic republic of the congo, and indonesia. Environmental Research Letters 13, 074028. URL: https://dx.doi.org/10.1088/1748-9326/aacd1c, doi:10.1088/1748-9326/aacd1c.

UNFCCC, C., 2008. Report of the conference of the parties on its thirteenth session, held in bali from 3 to 15 december 2007, in: Addendum. Part Two: Action taken
REFERENCES

28

by the Conference of the Parties at its thirteenth session Decisions adopted by the
Conference of the Parties.

University of Maryland, World Resources Institute, 2025. Global primary forest loss. https://www.globalforestwatch.org/. Accessed through Global Forest Watch on October 6, 2025.

Verra, 2024. Update on deforestation risk map development for verra's new redd methodology. https://verra.org/update-on-deforestation-risk-map-dev elopment-for-verras-new-redd-methodology/.

Vieilledent, G., Vancutsem, C., Bourgoin, C., Ploton, P., Verley, P., Achard, F., 2022. Spatial scenario of tropical deforestation and carbon emissions for the 21st century. BioRxiv, 2022-03.

Vijay, V., Reid, C.D., Finer, M., Jenkins, C.N., Pimm, S.L., 2018. Deforestation risks posed by oil palm expansion in the peruvian amazon. Environmental Research Letters 13, 114010.

Watanabe, M., Koyama, C.N., Hayashi, M., Nagatani, I., Shimada, M., 2018. Early-stage deforestation detection in the tropics with l-band sar. IEEE journal of selected topics in applied earth observations and remote sensing 11, 2127-2133.

Watanabe, M., Koyama, C.N., Hayashi, M., Nagatani, I., Tadono, T., Shimada, M., 2021. Refined algorithm for forest early warning system with alos-2/palsar-2 scansar data in tropical forest regions. Remote Sensing of Environment 265, 112643.
REFERENCES

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Appendix A. Supplementary Material

<table><caption>Table A1: Feature Descriptions. Source: Forest Foresight.</caption><thead><tr><td>Name</td><td>Description</td><td>Periodicity</td><td>Source</td></tr></thead><tbody><tr><td>groundtruth6mbin</td><td>binary pseudo-reference of oncoming six months</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>lastsixmonths</td><td>deforestation last six months, sum of the underlying 1600 10 x 10 m pixels that have been disturbed in the last 6 months</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>lastthreemonths</td><td>sum of the underlying 1600 10 x 10 m pixels that have been disturbed in the last 3 months</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>lastmonth</td><td>sum of the underlying 1600 10 x 10 m pixels that have been disturbed in the previous month</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>patchdensity</td><td>disturbance of the last six months is processed by giving every group of disturbance pixels a unique value. The amount of unique values within the 400 m pixel represents the amount of disturbance patches</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>smoothedsixmonths</td><td>smoothed version of layer lastsixmonths, the further away from the aggregated 400 m pixel the pixel in question is, the lower the value gets</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>smoothedtotal</td><td>smoothed version of layer totaldeforestation (the total number of disturbed pixels according to the Integrated Alerts), the further away from the aggregated 400 m pixel the pixel in question is, the lower the value gets</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>timesinceloss</td><td>this is classified as the highest value (representing latest moment of disturbance) since 1-1-2015 divided by the current date</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>previoussameseason</td><td>sum of the underlying 1600 pixels that have been disturbed 6-12 months to account for seasonality</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>totallossalerts</td><td>total amount of the underlying 1600 pixels disturbed according to the GFW alerts since the alerts started</td><td>monthly</td><td>Integrated Alerts, Reiche et al. (2024) (GFW)</td></tr><tr><td>nightlights</td><td>cloud-corrected nighttime activity (visible light/SWIR radiation at night) where the values represent the amount of radiation.</td><td>monthly</td><td>NASA VIIRS Day-Night band (DNB) (VIIRS)</td></tr><tr><td>firealerts</td><td>number of forest fires within the 400 m pixels in the last six months, points are rasterized with the sum of all points within the pixel</td><td>monthly</td><td>Fire Information for Resource Management System (FIRMS)</td></tr></tbody></table>

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<table><caption>Table A1 – continued from previous page</caption><thead><tr><td>Name</td><td>Description</td><td>Periodicity</td><td>Source</td></tr></thead><tbody><tr><td>precipitation</td><td>averaged (median) over several projection models within the framework of CMIP6 resampled to 400 m pixel. The actual values represent the average (mean) amount of expected precipitation in the next 6 months</td><td>monthly</td><td>Copernicus Climate Change Service (CMIP6)</td></tr><tr><td>temperature</td><td>averaged (median) over several projection models within the framework of CMIP6 resampled to 400 m pixels. The actual values represent the average temperature in the next 6 months</td><td>monthly</td><td>Copernicus Climate Change Service (CMIP6)</td></tr><tr><td>losslastyear</td><td>sum of the underlying pixels from the GLAD deforestation previous year</td><td>annual</td><td>Hansen et al. (2013) (GLAD)</td></tr><tr><td>closenesstoroads</td><td>closeness to roads, the lines are simplified on 10 m, the distance is then converted by the formula 255-20*log distanc+1</td><td>annual</td><td>OpenStreetMap</td></tr><tr><td>closenesstowaterways</td><td>closeness to waterways, the lines are simplified on 10 m, the distance is then converted by the formula 255-20*log (distance+1)</td><td>static</td><td>OpenStreetMap</td></tr><tr><td>historicloss</td><td>total number of pixels being disturbed between 2001 and 2018 according to the yearly report</td><td>static</td><td>Hansen et al. (2013) (GLAD)</td></tr><tr><td>initialforestcover</td><td>sum of the underlying pixels in the 400 m pixel, where every underlying pixel is a fraction of canopy cover, pixels deforested between 2001 and 2024 are set to 0</td><td>static</td><td>Hansen et al. (2013) (GLAD)</td></tr><tr><td>forestheight</td><td>average forest height in meters within the pixel, where 0 means no forest</td><td>static</td><td>Forest height, Potapov et al. (2022) (GLAD)</td></tr><tr><td>elevation</td><td>average elevation of the SRTM rasters when aggregated to the 400 m pixel level in meters above sea level</td><td>static</td><td>Shuttle Radar Topography Mission (SRTM)</td></tr><tr><td>slope</td><td>Slope is calculated using the terrain function in Terra and then taken the average of to aggregate</td><td>static</td><td>Shuttle Radar Topography Mission (SRTM)</td></tr><tr><td>populationcurrent</td><td>GHS population grid dataset that is first reprojected to the 400 m pixel resolution with the max function and then a gradual filter of 25 pixels is applied to smooth it over a larger area</td><td>static</td><td>Carioli et al. (2023) (POP2025 (GHS-POP))</td></tr><tr><td>populationincrease</td><td>difference between POP2030 (the expected population in 2030, see source GHS-POP) and POP2020 (the known population according to census in 2020, see source GHS-POP) dataset that is first reprojected to the 400 m pixel resolution with the max function and then a gradual filter of 25 pixels is applied to smooth it over a larger area.</td><td>static</td><td>Carioli et al. (2023) (POP2025 (GHS-POP))</td></tr></tbody></table>

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Accepted for publication [2021-04-20]
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<table><caption>Table A1 – continued from previous page</caption><thead><tr><td>Name</td><td>Description</td><td>Periodicity</td><td>Source</td></tr></thead><tbody><tr><td>wetlands</td><td>aggregated to 400 m and reclassified from categorical values to 1/0 for wetland/no wetland by taking any value above 0</td><td>static</td><td>World Wide Fund for Nature /Center for Environmental Systems Research/University of Kassel/(GLWD)</td></tr><tr><td>peatland</td><td>aggregated to 400 m and represents the average depth of the peat soil in meters with a maximum of 15 meters</td><td>static</td><td>CIFOR-ICRAF (Global Peatland Initiative)</td></tr><tr><td>croplandcapacity100p</td><td>agricultural conversion capacity (cropland only) was reprojected to 400 m by using a nearest neighbor resampling</td><td>static</td><td>Cengić et al. (2023) (RIBES)</td></tr><tr><td>croplandcapacitybelow50p</td><td>agricultural conversion capacity (less than 50 percent cropland cover) data was reprojected to 400 m by using a nearest neighbor resampling.</td><td>static</td><td>Cengić et al. (2023) (RIBES)</td></tr><tr><td>croplandcapacityover50p</td><td>agricultural conversion capacity (more than 50 percent cropland cover) data was reprojected to 400x400m by using a nearest neighbor resampling.</td><td>static</td><td>Cengić et al. (2023) (RIBES)</td></tr><tr><td>landpercentage</td><td>percentage landcover in values from 0 (no land) to 255 (100% landcover of the pixel)</td><td>static</td><td>Database of Global Administrative Areas (GADM)</td></tr><tr><td>wdpa</td><td>protected areas (WDPA) data was intersected with land area, then simplified to 400 m and rasterized, a value of 1 means that the entire area is protected area, 0 means no protected area within the pixel</td><td>static</td><td>UN Environment Programme World Conservation Monitoring Centre (WDPA)</td></tr><tr><td>catexcap</td><td>cation exchange capacity at 0-5cm data was aggregated to 400 m by using the mode of the underlying pixels (most occurring), the cation exchange capacity in mmol(c)/kg is a useful indicator for soil fertility, a proxy for deforestation for agriculture</td><td>static</td><td>International Soil Reference and Information Centre (ISRIC)</td></tr><tr><td>month</td><td>the number of the month</td><td>static</td><td>autogenerated</td></tr><tr><td>sinmonth</td><td>sine value of the day of the year, between -1 and 1</td><td>static</td><td>autogenerated</td></tr><tr><td>xy</td><td>autogenerated in the preprocessing and represents the latitude and longitude per pixel</td><td>static</td><td>autogenerated</td></tr></tbody></table>

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<figure><img src="image_10.png" /></figure>

Figure A1: User's and producer's accuracy pairwise comparisons across models and continents. The diagonal panels show continent-specific and global KDEs, dashed means ($\mu$), and mean ± sd in text boxes. The lower-triangle panels display model-pair scatter plots (per-country per-month results) with the identity line (red dashed), reporting the correlation coefficient (r) and Bonferroni-corrected Wilcoxon p-values for model differences.

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<figure><img src="image_11.png" /><figcaption>Figure A2: Assessment metrics as a function of the probability threshold for each geographical region for the train set. The red dot indicates the threshold that maximises the F0.5 score.</figcaption></figure>
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<figure><img src="image_12.png" /><figcaption>Figure A3: Assessment metrics as a function of the probability threshold for each geographical region for the test set. The red dot indicates the threshold that maximises the F0.5 score.</figcaption></figure>

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<figure><img src="image_13.png" /></figure>
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<figure><img src="image_14.png" /></figure>

Figure A4: Schematic of spatial categorization relative to *past deforestation*. This diagram illustrates how pixels were classified based on their proximity to areas deforested in the previous six months (*past deforestation*). The central dark cell represents pixels classified as “Within *past deforestation*”. Surrounding pixels indicate increasing distances: “Outside *past deforestation*” (within 0.4km or more from *past deforestation*), “More than 0.4km”, and “More than 0.8km” from *past deforestation*.
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<figure><img src="image_15.png" /></figure>

Figure A5: Global feature importance derived from the Guided Grad-CAM XAI method. Bars show mean absolute attributions for variables derived from the Integrated Alerts (red) and other data sources (grey). The x-axis represents the global average attribution, where higher values indicate stronger influence on the model, and the y-axis lists individual features. Integrated alert-based variables (e.g., totallossalerts, previoussameseason, patchdensity, timesinceLoss) dominate the ranking, indicating that recent and cumulative forest loss alerts are the primary drivers of model predictions. In contrast, other environmental and socioeconomic data show much lower attribution values.