内容来源：https://research.google/blog/forecasting-the-future-of-forests-with-ai-from-counting-losses-to-predicting-risk/

内容总结：

【科技前沿】谷歌研发团队发布全球首款AI森林砍伐风险预测系统

2025年11月5日，谷歌DeepMind与谷歌研究院联合研发团队正式推出全球首个基于深度学习的森林砍伐风险预警系统"ForestCast"。这项突破性技术标志着森林保护从传统的事后监测迈入智能预警新阶段。

森林作为地球生态基石，在碳储存、降雨调节、洪水防控及生物多样性保护方面具有不可替代的作用。然而全球森林正以惊人速度消失——去年全球热带雨林损失量达670万公顷，相当于每分钟失去18个足球场面积的森林，创下历史新高。

与传统依赖道路分布、人口密度等易过时数据的预测方式不同，该创新系统采用"纯卫星数据"分析模式，通过整合陆地卫星与哨兵2号卫星的实时影像，结合自主研发的视觉变换器模型，成功实现对任意区域森林风险的精准预测。令人惊讶的是，系统核心预测能力主要来源于"变化历史"数据——即通过分析每个像素点的历史砍伐记录形成动态轨迹图。

该系统已公开发布全套训练数据集，供全球科研机构验证与优化。研究显示，其预测精度不仅媲美传统方法，在多数区域甚至表现更优。这种标准化方案首次实现了全球不同森林区域的横向对比，且能随卫星数据更新持续迭代。

该技术将帮助政府机构精准投放保护资源，助力企业监控供应链 deforestation 风险，支持原住民社区优先守护高危林地。研究人员强调，这项预测的本质不是预判既定命运，而是通过提供前瞻性洞察，推动各方在关键窗口期采取保护行动，最终改变森林消失的命运轨迹。

目前该模型主要应用于拉丁美洲和非洲的热带雨林监测，未来将逐步扩展至温带与寒带森林区域，为全球生态保护提供统一的智能预警平台。

中文翻译：

用AI守护森林未来：从统计损失到预测风险
2025年11月5日
谷歌DeepMind研究科学家德鲁·普维斯与谷歌研究院高级项目经理夏洛特·斯坦顿 代表ForestCast团队联合撰文

我们正式推出首个基于深度学习的主动式毁林风险预测基准系统。
自然生态系统支撑着我们的气候、经济乃至生命存续。而森林作为自然体系中最强大的支柱之一，承担着固碳释氧、调节降雨、缓解洪涝、维系地球陆地生物多样性核心等重要功能。

尽管森林至关重要，全球森林面积仍以惊人速度持续减少。仅去年每分钟就有相当于18个足球场的热带森林消失，总计670万公顷——这一创纪录的数字达到前年损失量的两倍。当前，生境转化已成为陆地生物多样性面临的最大威胁。

多年来，卫星数据始终是监测森林流失的重要工具。近期我们与世界资源研究所合作，绘制出2000-2024年间导致森林消失的根本动因分布图——从农业垦殖、林木采伐到矿产开采与野火肆虐。这些分辨率达1平方公里的突破性图集，为各类森林保护措施提供了依据。但此类关键洞察仅能反映既成事实，现在我们需要向前展望。

我们欣然发布《ForestCast：基于深度学习的大规模毁林风险预测系统》，同时开放首个专门用于训练深度学习模型的公开基准数据集。从监测既成损失转向预测未来风险，这一转变具有革命性意义。既往风险评估方法依赖整合道路网络、人口密度等零散的基础图集，这些数据往往迅速过时。相比之下，我们开发的纯卫星数据解决方案兼具高效性与普适性，可全球统一应用，并能随数据更新持续优化。研究发现该方法精度可比肩甚至超越传统方案。为确保研究可复现、可拓展，我们完整公开所有输入数据、训练集与评估集作为基准数据集。

毁林预测的挑战根源
毁林本质上是受经济、政治、环境等多重因素驱动的人类活动过程。既源于牲畜养殖、棕榈油及大豆等商品生产扩张，也涉及野火、伐木、聚居区扩张、基础设施建设以及矿产能源开采。因此精准预测未来毁林的时空分布极具挑战。

当前前沿解决方案试图通过整合多源地理空间信息来应对——包括道路网络、经济指标、政策执行数据等。该方法在特定时段和区域取得了良好预测效果，但缺乏普适性：基础图集往往残缺不全、标准不一，需按区域单独整合。更严峻的是，这些输入数据易快速失效，且更新周期无法保障。

可扩展的卫星解决方案
为突破这些局限，我们构建了仅使用卫星数据的“纯卫星”模型。测试数据涵盖陆地卫星与哨兵2号卫星的原始影像，还包括我们称为“变化历程”的衍生数据——精准标识每个已毁林像元及其发生年份。模型训练与评估均基于卫星识别的毁林标签。

纯卫星方案实现了全球统一方法论，支持跨区域对比研究。同时具备未来适应性——卫星数据流将持续更新，使风险预测能与时俱进。为兼顾精度与扩展性，我们基于视觉Transformer架构开发定制模型。该模型通过接收整幅卫星像元图块捕捉景观空间背景与近期毁林动态，单次运算即可输出整图预测结果，实现大区域高效推算。

研究表明，新模型在精度上媲美或超越依赖专业数据的传统方法：既能准确预测不同图块间的毁林量级差异，又能精准识别图块内最可能消失的像元。令人惊讶的是，最关键的数据源竟是结构最简单的“变化历程”——仅凭此项输入的模型预测精度，与使用完整卫星数据的模型不相上下。回溯分析发现，变化历程虽数据量小，却凝聚着高密度信息：既包含区域毁林速率差异，又反映时空变化趋势，还能追踪林线推移动态。

为促进研究透明与可复现，我们公开全部训练与评估基准数据。这将助力机器学习社区验证成果、深化模型决策机制认知，最终推动毁林风险模型的优化与创新。我们的基准体系与论文还为全球推广提供清晰模板——从拉美非洲的热带雨林，到受畜牧与火灾威胁的温带寒带林区。

结语
土地利用变化（特别是热带毁林与林地转化）贡献了约10%的全球人为温室气体排放，并威胁着绝大多数陆地生物。毁林风险预测将成为关键工具，助力精准配置资源以遏制排放、守护自然。

这种风险预判能力使政府、企业与社区能在为时未晚时及早行动，而非被动应对既成损害。例如：
• 政府机构可向新兴毁林前沿社区提供保护激励措施
• 企业能主动管理供应链以消除毁林风险
• 原住民社区可将有限资源集中用于保护高危林地

因此，这类预测并非预判不可避免的未来，而是旨在改变未来走向的工具。我们的目标是向行动方提供信息支撑，帮助他们在关键时刻将资源投向生态脆弱区，守护高风险森林的存续。通过融合开放数据与前沿AI，我们正在锻造守护自然的强力新工具。

了解更多谷歌在AI与可持续发展领域的探索，请访问谷歌地球AI、谷歌地球引擎及AlphaEarth基金会项目。

致谢
本研究由谷歌DeepMind与谷歌研究院协同完成。
谷歌DeepMind团队：马特·奥弗兰、阿里安娜·曼齐尼、德鲁·普维斯、朱莉娅·哈斯、马克西姆·纽曼、梅兰妮·雷
谷歌研究院团队：夏洛特·斯坦顿、米开朗基罗·康塞尔瓦
特别感谢合作者吉拉·普拉布、申永仁、吕宽，以及彼得·巴蒂格利亚、周凯特的支持。

英文来源：

Forecasting the future of forests with AI: From counting losses to predicting risk
November 5, 2025
Drew Purves, Research Scientist, Google DeepMind, and Charlotte Stanton, Senior Program Manager, Google Research, on behalf of the ForestCast team
We introduce the first deep learning–powered benchmark for proactive deforestation risk forecasting.
Nature underpins our climate, our economies, and our very lives. And within nature, forests stand as one of the most powerful pillars — storing carbon, regulating rainfall, mitigating floods, and harboring the majority of the planet’s terrestrial biodiversity.
Yet, despite their critical importance, the world continues to lose forests at an alarming rate. Last year alone, we lost the equivalent of 18 soccer fields of tropical forest every minute, totaling 6.7 million hectares — a record high and double the amount lost the year before. Today, habitat conversion is the greatest threat to biodiversity on land.
For years, satellite data has been our essential tool for measuring this loss. More recently, in collaboration with the World Resources Institute, we helped map the underlying drivers of that loss — from agriculture and logging to mining and fire — for the years 2000–2024. These maps, which are at an unprecedented 1km2 resolution, provide a basis for a wide range of forest protection measures. However those insights, critical as they are, only look backward. Now, it's time to look ahead.
We're excited to announce the release of “ForestCast: Forecasting Deforestation Risk at Scale with Deep Learning”, along with the first publicly available benchmark dataset dedicated to training deep learning models to predict deforestation risk. This shift from merely monitoring what's already gone to forecasting what's at risk in the future changes the game. Previous approaches to risk have depended on assembling patchily-available input maps, such as roads and population density, which can quickly go out of date. By contrast, we have developed an efficient approach based on pure satellite data that can be applied consistently, in any region, and can be readily updated in the future when more data becomes available. We found that this approach could match or exceed the accuracy of previous approaches. To ensure the community can reproduce and build on our work, we are releasing all of the input, training, and evaluation data as a public benchmark dataset.
Why predicting deforestation is so difficult
Deforestation is fundamentally a human process driven by a complex web of economic, political, and environmental factors. It's fueled by commodity-driven expansion for products like cattle, palm oil, and soy, but also by wildfires, logging, the expansion of settlements and infrastructure, and the extraction of hard minerals and energy. Predicting the location and timing of future loss is therefore incredibly hard.
The current state-of-the-art approach tries to solve this by assembling specialized geospatial information on as many of those factors as possible: maps of roads, economic indicators, policy enforcement data, etc. This approach has provided accurate predictions for some regions at some times. However, it is not generally scalable because those input maps are often patchy, inconsistent, and need to be assembled separately for each region. This approach is also not future-proof, because the input maps tend to quickly go out of date, and there is no guarantee when, if ever, they may be refreshed.
A scalable satellite approach
To overcome these challenges, we adopt a “pure satellite” model, where the only inputs are derived from satellites. We tested raw satellite inputs from the Landsat and Sentinel 2 satellites. We also included a satellite-derived input we refer to as “change history”, which identifies each pixel that has already been deforested and provides a year for when that deforestation occurred. We trained and evaluated the model using satellite-derived labels of deforestation.
The pure satellite approach provides consistency, in that we can apply the exact same method anywhere on Earth, allowing for meaningful comparisons between different regions. It also makes our model future proof — these satellite data streams will continue for years to come, so we can repeat the method to give updated predictions of risk and examine how risk is changing through time.
To achieve accuracy and scalability, we developed a custom model based on vision transformers. The model receives a whole tile of satellite pixels as input, which is crucial to capture the spatial context of the landscape and recent deforestation (as captured in the change history). It then outputs a whole tile’s worth of predictions in one pass, which makes the model scalable to large regions.
We found that our model was able to reproduce, or exceed, the accuracy of methods based on specialized inputs (such as roads), accurately predicting tile-to-tile variation in the amount of deforestation, and, within tiles, accurately predicting which pixels were the most likely to become deforested next.
Surprisingly, we found that by far the most important satellite input was the simplest, the change history. So much so that a model receiving only this input could provide predictions with accuracy metrics indistinguishable from models using the full, raw satellite data. In retrospect we can see that the change history is a small, but highly information dense, model input — including information on tile-to-tile variation in recent deforestation rates, and how these are trending through time, and also capturing moving deforestation fronts within tiles.
To promote transparency and repeatability, we are releasing the training and evaluation data used in this work, as a benchmark. This allows the wider machine learning community to verify our results; to potentially extract deeper understanding of why the model makes certain predictions; and ultimately, to build and compare improved deforestation risk models.
Moreover, our benchmark and paper provide a clear template for scaling this approach globally — to model tropical deforestation across Latin America and Africa, and eventually, to temperate and boreal latitudes where forest loss is often driven by different dynamics, such as cattle ranching and fire.
Conclusion
Land-use change, especially tropical deforestation and forest conversion, is responsible for roughly 10% of global anthropogenic greenhouse-gas emissions and threatens the vast majority of the planet's terrestrial life. Forecasts of deforestation risk could be a vital tool for targeting resources where they can have the greatest impact in curbing those emissions and protecting nature.
This ability to anticipate risk allows governments, companies, and communities to act early, when there’s still time to prevent loss, rather than reacting to damage that’s already done. For example:

• A government agency can channel support and conservation incentives to communities in emerging deforestation frontiers.
• A company can proactively manage their supply chains to reduce and eliminate deforestation.
• An indigenous community can deploy scarce resources towards protecting their land at greatest risk.
  Thus, a forecast like this isn't a prediction of an unavoidable future. Instead, it's a tool designed to change a future outcome. The goal is to share this information with those who can act, helping them channel resources to the most vulnerable areas before it's too late, and empowering them to ensure these high-risk forests remain standing. By combining open data and advanced AI, we're forging a powerful new tool to safeguard nature.
  Learn more about our AI and sustainability efforts by checking out Google Earth AI, Google Earth Engine, and AlphaEarth Foundations.
  Acknowledgements
  This research was co-developed by Google Deepmind and Google Research.
  Google Deepmind: Matt Overlan, Arianna Manzini, Drew Purves, Julia Haas, Maxim Neumann, Mélanie Rey.
  Google Research: Charlotte Stanton, Michelangelo Conserva.
  We’d also like to thank our additional collaborators Kira Prabhu, Youngin Shin and Kuan Lu, as well as Peter Battaglia and Kat Chou for their support.