How can AI assist in restoring damaged ecosystems and supporting large-scale reforestation efforts? | Insights with M. Chairul Ridjal

Artificial intelligence is increasingly emerging as a powerful tool in ecosystem restoration and large-scale reforestation efforts. As landscapes degrade due to deforestation, climate stress, and unsustainable land use, the ability to monitor ecosystems in real time and allocate resources efficiently has become essential. AI provides this capability by processing satellite imagery, environmental datasets, and climatic patterns with far greater speed and precision than traditional field surveys. Machine learning models can detect soil degradation, biodiversity loss, illegal logging, and canopy decline long before these changes are visible to the human eye. Studies from regions such as central Malawi demonstrate the scale of AI’s impact: over a 20-year period, AI-assisted monitoring documented a 61.2 percent reduction in bare land and a 305.6 percent increase in tree cover through community-led restoration efforts. These results show how data-driven monitoring can capture recovery trends impossible to measure manually.

According to M. Chairul Ridjal from IPB University, AI’s role in restoration lies not only in mapping damage but also in preventing it. By analyzing large-scale environmental data, AI can forecast where degradation is likely to occur, enabling governments and NGOs to intervene before ecosystems collapse. This aligns with the broader shift in sustainability work toward predictive and preventive approaches rather than reactive ones. While Ridjal has not worked directly on AI tree-planting robotics or drone-based planting, he has observed how AI-powered remote sensing tools are increasingly being used for land-quality assessments and climate forecasting in sustainability initiatives. These tools provide critical insights into where restoration efforts would have the greatest ecological impact.

AI also accelerates processes that would otherwise take months. Automated monitoring can track regrowth, evaluate tree health, and identify environmental stressors across vast areas within minutes. In areas that are difficult or dangerous to reach, drone imaging combined with AI analysis allows for safe and efficient oversight. Emerging technologies even enable AI-guided drones to plant seeds in damaged or remote landscapes, speeding up the restoration process while reducing labor demands. This increased speed and precision could significantly enhance global reforestation efforts, especially in countries where manual monitoring is limited by funding or accessibility.

Yet, the use of AI in natural ecosystems comes with challenges. Remote areas often lack the data infrastructure necessary for high-resolution monitoring, resulting in gaps in accuracy. Some ecosystems require deep contextual knowledge, ecological, cultural, and historical, that AI alone cannot capture. Ridjal notes that AI tools risk misrepresenting or oversimplifying environmental conditions if they are used without considering local knowledge or indigenous perspectives. Restoration efforts that rely solely on algorithms may fail to recognize community needs, traditional land practices, or ecological nuances that are essential for long-term recovery.

Because of this, safeguards are necessary to ensure that restoration technologies support rather than disrupt communities and wildlife. Restoration planning must integrate local and Indigenous knowledge, involve communities in decision-making, and prioritize ecosystem integrity rather than speed. AI systems should also be transparent and interpretable so that stakeholders understand how decisions are made and can challenge or adjust them when necessary. Restoration technology must respect land rights, cultural practices, and biodiversity priorities to avoid causing unintended harm.

Ultimately, artificial intelligence has extraordinary potential to support global reforestation and ecological recovery. It can map damage faster, monitor restoration more accurately, and guide interventions with greater precision. But to realize these benefits, AI must be paired with ethical practices, community involvement, and ecological understanding. When used responsibly, AI becomes not just a tool for technical efficiency but a driver of more thoughtful, informed, and equitable restoration across ecosystems.