A basic question with no consensus: Where are the forests? – Butler Nature

Global Forest Mapping and Its Implications for Sustainable Development Goals

Introduction

A fundamental question underpins numerous global environmental policies: Where exactly are the world’s forests? A recent study reveals that the answer varies significantly depending on the forest map consulted. These discrepancies have profound implications for climate targets, conservation priorities, and development spending, directly affecting the achievement of several Sustainable Development Goals (SDGs), including SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 1 (No Poverty).

Study Overview

Researchers Sarah Castle, Peter Newton, Johan Oldekop, Kathy Baylis, and Daniel Miller conducted a comparative analysis of ten widely used global forest datasets derived from satellite imagery. These datasets are critical for:

• Carbon accounting (SDG 13)
• Biodiversity assessments (SDG 15)
• Environmental governance and policy-making

However, the study found that these datasets rarely agree. Only about 26% of areas identified as forest by at least one dataset were classified as forest by all ten datasets. Even after harmonizing the spatial scale, agreement improved only modestly.

Causes of Discrepancies in Forest Mapping

1. Definitions of Forest: Variations in canopy cover thresholds lead to different classifications. For example:
   • A 10% canopy cover threshold includes savannas and open woodlands.
   • A 70% threshold captures only closed forests.
2. Resolution of Satellite Imagery: High-resolution images detect narrow riparian strips and small forest fragments that coarser data miss.
3. Technical Variations: Differences in sensors, algorithms, and training data further contribute to inconsistencies.

Geographical Patterns of Disagreement

The study highlights uneven patterns of disagreement across biomes:

• Moist Tropical Forests: Show relatively high consistency due to continuous tree cover.
• Dry Forests and Fragmented Landscapes: Exhibit much lower agreement, sometimes as low as 12% consensus, often in regions where conservation decisions are most contested.

Case Studies Demonstrating Practical Implications

1. Kenya: Forest carbon estimates vary widely from 2% to 37% of national biomass carbon depending on the dataset, complicating climate mitigation planning (SDG 13).
2. India: Estimates of forest-proximate people living in poverty range from 23 million to over 250 million based on different forest maps, impacting poverty alleviation strategies (SDG 1).
3. Brazil: Datasets tracking forest loss overlap on less than half of mapped deforestation affecting habitat for the endangered white-cheeked spider monkey, influencing biodiversity conservation efforts (SDG 15).

Implications for Environmental Governance and Sustainable Development

Satellite-derived forest maps are now central to environmental governance:

• Governments rely on them to report climate progress (SDG 13).
• Non-governmental organizations (NGOs) use them to target conservation interventions (SDG 15).
• Investors assess nature-related risks based on these datasets.

The study does not recommend a single “correct” dataset but emphasizes:

• Treating forest estimates as ranges rather than absolutes.
• Testing policy and research outcomes across multiple datasets.
• Improving standardization in forest mapping methodologies.

For effective forest management and to advance the SDGs, policymakers must first reach consensus on the fundamental question: Where are the world’s forests?

Reference

For the full article, see: Scientists can’t agree on where the world’s forests are

1. Sustainable Development Goals (SDGs) Addressed or Connected to the Issues Highlighted

1. SDG 13: Climate Action
   • The article discusses forest carbon estimates and their implications for climate mitigation planning.
2. SDG 15: Life on Land
   • Focus on forest mapping, biodiversity assessments, and conservation priorities.
   • Mentions endangered species habitat, e.g., white-cheeked spider monkey.
3. SDG 1: No Poverty
   • References forest-proximate people living in poverty, highlighting socio-economic dimensions.
4. SDG 17: Partnerships for the Goals
   • Emphasizes the need for improved standardization and collaboration among data providers and policymakers.

2. Specific Targets Under Those SDGs Identified Based on the Article’s Content

1. SDG 13: Climate Action
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.
   • Target 13.3: Improve education, awareness-raising, and human and institutional capacity on climate change mitigation.
2. SDG 15: Life on Land
   • Target 15.1: Ensure the conservation, restoration, and sustainable use of terrestrial ecosystems, including forests.
   • Target 15.2: Promote the implementation of sustainable management of all types of forests.
   • Target 15.5: Take urgent action to reduce the degradation of natural habitats and halt the loss of biodiversity.
3. SDG 1: No Poverty
   • Target 1.2: Reduce poverty in all its dimensions, including for forest-dependent populations.
4. SDG 17: Partnerships for the Goals
   • Target 17.18: Enhance capacity-building support to developing countries to increase significantly the availability of high-quality, timely and reliable data.

3. Indicators Mentioned or Implied in the Article to Measure Progress Towards the Identified Targets

1. Forest Area and Coverage Indicators
   • Percentage of land area covered by forests as measured by satellite-derived datasets.
   • Consistency/agreement percentage among different forest maps (e.g., only 26% agreement across datasets).
2. Carbon Stock Indicators
   • Estimates of forest carbon storage and biomass carbon at national and regional levels (e.g., Kenya’s forest carbon estimates ranging from 2% to 37%).
3. Biodiversity and Habitat Loss Indicators
   • Extent of forest loss affecting habitats of endangered species (e.g., white-cheeked spider monkey).
   • Overlap in mapped deforestation areas across datasets.
4. Socioeconomic Indicators
   • Number of forest-proximate people living in poverty (ranging from 23 million to 250 million depending on forest maps).
5. Data Quality and Standardization Indicators
   • Degree of agreement or divergence among different satellite forest datasets.
   • Use of multiple datasets to establish ranges rather than single estimates.

4. Table: SDGs, Targets and Indicators

Source: butlernature.com