Tree Cover Loss ≠ Deforestation: Why Accurate Forest Mapping Matters in the Age of Sustainability Regulations?
- Gusi Ayu Putri Chandrika Sari
- Jun 17
- 7 min read
Updated: Jul 7
Editor’s note:
The Forest Accuracy Test is Koltiva’s commitment to geospatial integrity, spearheaded by KOLTIVA's Environmental Lead Roland Sinulingga. Designed to evaluate the accuracy of forest cover datasets, it ensures KoltiTrace MIS is powered by trusted data, supporting clients in meeting traceability and sustainability reporting requirements with confidence.
Executive Summary:
Tree cover loss does not always equate to deforestation. While deforestation refers specifically to the permanent, human-driven conversion of natural forests into non-forest land uses—such as commercial agriculture, urban expansion, or mining—tree cover loss is a broader term. It encompasses the complete removal of tree cover for any reason, including both natural events (like storms or fires) and human activities. This loss can be either temporary or permanent, unlike deforestation, which implies lasting land-use change (World Resources Institute, 2025).
In forest monitoring, not all maps offer the same level of accuracy. The Forest Accuracy Test is a rigorous method used to evaluate how accurately key forest attributes—like tree cover, species composition, area, and temporal changes—are captured. It applies grid-based sampling and statistical accuracy metrics to assess commonly used forest maps, including those from Global Forest Watch (GFW), Joint Research Centre (JRC), and Science Based Targets Network (SBTN) that have been used in Land Use Tracker (LUT) in KoltiTrace MIS.
The results have been integrated into KoltiTrace MIS, strengthening its geospatial intelligence features. This integration equips users with scientifically validated data, enabling more accurate traceability, improved regulatory compliance reporting, and data-driven sustainability decisions.
Since the start of the 21st century, the world has lost an estimated 517 million hectares of tree cover—about 13% of the planet’s total tree canopy as of 2000. Alarmingly, this loss is accelerating: from 13.4 million hectares in 2001 to nearly 29.6 million hectares in 2024 (World Resources Institute, 2025).
Yet amid this growing concern, one critical truth often goes overlooked: tree cover loss does not necessarily equal deforestation.
Tree Cover Loss ≠ Deforestation?
Defining what constitutes a forest—and, by extension, what qualifies as deforestation or forest degradation—is far more nuanced than it appears. Varying definitions, conflicting datasets, and differing interpretations of satellite imagery contribute to widespread confusion. Add to this the growing pressure from sustainability regulations like the EU Deforestation Regulation (EUDR), and the need for clarity becomes urgent.
Tree cover loss refers to the removal of tree canopy for any reason, whether human-caused or due to natural disturbances. This loss can be either temporary or permanent (World Resources Institute, n.d.). Deforestation, by contrast, is typically defined as the permanent, human-driven conversion of natural forests into non-forest land uses such as plantations, mining sites, or urban areas (World Resources Institute, 2025).
Table of Index:
Why Definitions Matter
Although often used interchangeably, tree cover loss and deforestation represent distinct ecological events—each with vastly different implications for land use, biodiversity, carbon emissions, and policy compliance.
For example, tree cover loss can result from timber harvesting, where trees are cut for wood production and the land is subsequently replanted or allowed to naturally regenerate (World Resources Institute, 2025).
In contrast, deforestation is the permanent conversion of forested land into non-forest uses, such as large-scale agriculture, urban development, or mining. This transformation leads to the loss of forest ecosystems and biodiversity, and significantly increases carbon emissions.
This distinction is especially critical for compliance frameworks like the EU Deforestation Regulation (EUDR), which require companies to track land-use changes with high precision. Misinterpreting temporary or non-deforestation-related tree loss as true deforestation can result in inaccurate reporting, reputational damage, and even legal consequences.
Koltiva’s Approach: Building Trust with Verifiable Data
The World Resources Institute emphasizes this difference: “The existence of tree cover does not always make a forest, tree cover loss does not always imply forest loss or deforestation, and tree cover gain does not always imply forest gain or restoration” (World Resources Institute, n.d.). This complexity presents a challenge for satellite-based monitoring systems, which often detect changes in tree canopy without identifying the underlying cause. As a result, users must evaluate data in context—distinguishing between managed forests, natural ecosystems, and commercial tree crops.
These distinctions have real-world implications. Ongoing efforts to enhance global-scale spatial data aim to improve how forest loss is monitored and interpreted. Many monitoring tools, including those used in traceability platforms, can detect tree canopy changes but often cannot determine the specific drivers behind them.
This multi-source approach reflects a critical reality: there is no single or universal “correct” map. Each dataset is built on different methodologies, assumptions, and definitions.
Recognizing that there is no one-size-fits-all solution, KoltiTrace MIS empowers users to select the most relevant dataset based on geography, land use type, and reporting requirements. It integrates multiple datasets—including those from the Joint Research Centre (JRC), Global Forest Watch (GFW), and the Science Based Targets Network (SBTN)—to provide contextualized insights into forest loss. These are presented alongside our proprietary dataset, which offers advanced forest cover and change detection capabilities.
As a result, selecting the most appropriate dataset becomes a strategic, context-dependent decision—one that directly influences compliance accuracy and the effectiveness of sustainability interventions.
At Koltiva, we believe that trust starts with data integrity and transparency. That’s why our geospatial expert, Roland Sinulingga, our Environmental Lead with 13 years of experience in Geographic Information Systems (GIS), launched the Forest Accuracy Test to answer a question frequently raised by many: “Which map can we trust to check deforestation accurately?”
The Forest Accuracy Test: Which Map Can We Trust?
In the world of forest monitoring, not all maps are created equal. The Forest Accuracy Test is a rigorous methodology developed to evaluate the precision with which forest attributes: such as tree cover, species composition, area, or change over time—are measured or mapped. These tests are essential for validating data from remote sensing, forest inventory software, and mapping databases, ensuring that forest management decisions are based on reliable information.
Led by our Environmental Lead, this Forest Accuracy Test serves three main purposes:
Assess the accuracy of available open-source datasets
The test evaluates how well each dataset reflects actual forest conditions on the ground. By comparing satellite-derived classifications against reference data, it identifies strengths and limitations in each source’s ability to detect forest cover and change.
Establish a priority ranking of datasets
Not all datasets perform equally across different landscapes. This test helps rank datasets based on their reliability, enabling users to prioritize those most suitable for specific geographies or regulatory needs.
Inform policy and decision-making
By providing a clear, evidence-based comparison, the test supports governments, companies, and sustainability professionals in selecting the most appropriate forest dataset for compliance, certification, or reporting. It helps answer a critical question: Which map should we trust?
How the Forest Accuracy Test Works?
Study Area
The analysis focused on the Sigi and Poso districts in Central Sulawesi, Indonesia. These two areas have distinct land cover characteristics. Poso features a heterogeneous landscape, combining mixed plantation forests and cocoa farms. In contrast, Sigi is more homogeneous, with limited areas of mixed plantations and cocoa, and notable degradation zones due to its overlap with Lore Lindu National Park.
Grid Sampling Approach
The research area was divided into a systematic 10-kilometers grid to ensure fair representation across different land types. This method avoids bias from land stratification, making it ideal for heterogeneous landscapes and allowing for “apple-to-apple” comparisons between datasets.
Confusion Matrix and Kappa Coefficient
Each dataset’s classification results were evaluated against reference data using a confusion matrix. The Kappa Coefficient was used to quantify agreement levels between datasets.
Kappa formula:
The interpretation of the Kappa Coefficient:
The test included all map sources integrated into the KoltiTrace MIS Land Use Tracker: JRC, GFW, and SBTN.
What We Found?
The image above shows several visualizations of forest cover datasets with high-resolution imagery. Among the tested datasets (JRC, GFW, and SBTN), Global Forest Watch’s 2020 forest cover dataset achieved a Kappa Coefficient of 0.849, indicating “almost perfect agreement.”
This finding scientifically supports our decision to use GFW as the foundational layer for our advanced dataset, which combines:
GFW forest cover data
Desktop verification tools
Historical land-use modeling using the CCDC algorithm (Continuous Change Detection and Classification). The CCDC method tracks spectral patterns over time, detecting even early-stage forest loss—helping users identify real deforestation rather than just changes in tree cover.
“While the dataset shows high accuracy, field validation is still necessary to confirm results. Looking ahead, integrating GIS, AI, and remote sensing technologies will further enhance our spatial analysis. Koltiva aims to develop its own forest detection algorithm for even greater precision,” suggest Roland based on this study.
What This Means for KoltiTrace Users?
With the Forest Accuracy Test results integrated into KoltiTrace MIS, our users gain:
✅ Confidence in data accuracy for compliance, certification, and reporting
✅ Flexibility in choosing datasets aligned with policy or sustainability goals
✅ Credibility through transparent, validated methodology
At Koltiva, we are committed to delivering precision-driven, trustworthy geospatial tools. With the data from the Forest Accuracy Test, clients can make confident land-cover decisions, backed by validated data and our commitment to transparency. This is just one of many steps as we continue to lead the way in digital traceability and sustainable sourcing.
Resources:
World Resources Institute. (n.d.). Key terms and definitions: Forests. WRI Research. https://www.wri.org/research/key-terms-and-definitions
Weisse, M., & Goldman, E. (2025). Forest loss. Global Forest Review. World Resources Institute. https://gfr.wri.org/forest-extent-indicators/forest-loss
Author: Gusi Ayu Putri Chandrika Sari, Social Media Officer at KOLTIVA
Subject Matter Experts: Roland Sinulingga, Environment Lead at KOLTIVA
Editor: Daniel A. Prasetyo, Head of Public Relations and Corporate Communications at KOLTIVA
About Expert:
Roland Sinulingga is a seasoned geo-information professional with over 13 years of experience in Geographic Information Systems (GIS) and remote sensing. His work spans across natural resource management, HCV/HCS assessments, plantation monitoring, spatial planning, and sustainability development. Roland has led and supported technical projects across Indonesia—from Aceh to Papua—as well as in Japan and the Netherlands. With deep expertise in spatial databases and earth observation, he brings a strong commitment to applying geospatial intelligence for sustainable land use and environmental protection.
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