Sentinel-2 satellite preview of Sahel-Mali-North-of-Niger-River-region, captured on 2026-04-16 from an altitude of approximately 786 km.
Statement of Problem
Desertification threatens the livelihoods of over 2 billion people worldwide, degrading 12 million hectares of productive land annually. As climate change intensifies droughts and extreme weather events, arid and semi-arid regions face unprecedented ecological stress. In Sahel-Mali-North-of-Niger-River-region, the balance between sparse vegetation and expanding deserts hangs in the balance. Understanding vegetation health, soil exposure, and moisture stress is critical for guiding land restoration, sustainable grazing, and climate adaptation strategies. This analysis quantifies the current state of desert ecosystems in Sahel-Mali-North-of-Niger-River-region, identifies high-risk areas for desertification, and tracks 5-year trends to reveal whether the land is recovering or degrading.
The Sahel-Mali-North-of-Niger-River-region landscape, A representation image of this landscape. Image reference: Fatima Wourro, Amah Akodewou, Justin Kassi N'dja, Bruno Hérault, Re-greening the Sahel? Evaluating tree cover restoration strategies in Niger, Journal of Arid Environments, Volume 232 (2026), Article 105484. DOI: https://doi.org/10.1016/j.jaridenv.2025.105484 ( ScienceDirect )
Understanding SAHEL - North of the Niger River, Mali
Why we chose this region
Desert and semi-arid lands cover over 40 percent of the Earth’s land surface and support the lives and livelihoods of more than 2 billion people.
These landscapes may look empty from a distance, but they are deeply connected to food, water, and survival for millions.
When land degrades here, the effects travel far beyond the desert itself.
The area north of the Niger River in Mali sits in one of the most sensitive parts of this global system. It lies in the Sahel, a narrow transition zone between greener farmland to the south and true desert to the north.
Rainfall is low and comes in short, unpredictable bursts.
Some years the rains arrive late, other years they fail almost completely.
The soil is light and fragile, and once plant cover is lost, it can quickly erode or turn barren.
People living in this region depend directly on the land. Farming is mostly rain-fed, with no large irrigation systems to fall back on.
Families grow crops during short rainy seasons and raise livestock that move with the land and seasons.
Trees are not planted casually. They are protected because they hold moisture, reduce soil loss, and provide food, shade, or fuel.
Traditional planting and land-care methods are shaped by survival, not convenience.
We chose this region because even small changes here matter. A slight gain or loss in vegetation can be an early sign of land stress, food insecurity, or recovery.
Satellite monitoring allows us to track these changes in a clear and repeatable way.
In places like north of the Niger River, where vegetation is sparse and rainfall is uncertain, continuous monitoring becomes an early warning system.
What happens here often signals what may soon happen across much of the Sahel and other dry regions of the world.
The Challenge of Measuring Sparse Vegetation
Vegetation in desert regions behaves very differently from forests or farmlands.
Most common satellite vegetation measures were created for places where plants grow thick and close together.
In dry regions like this, plants are scattered and much of the ground is bare soil.
This makes measurement tricky, because bare soil can sometimes reflect light in a way that looks like vegetation, even when there is very little growth.
To avoid this problem, desert monitoring uses vegetation measures that are adjusted for soil.
Indices like SAVI and MSAVI2 are designed to reduce the effect of exposed soil and give a clearer picture of real plant growth.
In this analysis, we combine several vegetation and moisture indicators to understand different aspects of the landscape in the Sahel-Mali-North-of-Niger-River-region, including plant activity, greenness, and water stress.
By looking at quarterly data over the past five years, we are not just seeing how the land looks today, but how it is changing over time.
This helps us understand whether the land is slowly recovering or moving closer to degradation.
Executive Summary
Past (Last 5 Years)
Between 2021 and 2025, vegetation health in the Sahel-Mali-North-of-Niger-River-region showed mixed trends. The NDVI mean fluctuated, with the lowest value of 0.205 in 2021 and a peak of 0.524 in 2026. SAVI values also varied, with a low of 0.269 in 2021 and a high of 0.615 in 2026. NDMI values indicated moisture stress, with the lowest mean of -0.390 in 2026. Overall, while some recovery was observed, the region experienced significant vegetation degradation, particularly in 2022 when moisture levels dropped to record lows.
Present (Current Status)
As of April 16, 2026, the region shows a mean NDVI of 0.174, indicating sparse vegetation cover. The SAVI mean is 0.261, suggesting moderate vegetation health when adjusted for soil brightness. However, 35% of the region is at high risk of desertification, with the northwestern zones showing the most severe degradation. The current SAVI-NDVI difference highlights areas where soil brightness masks vegetation, with 51.2% of pixels falling in the moderate difference range.
Future (What Happens Next)
If current trends continue, the region may see further degradation, especially in areas already at high risk. Implementing targeted restoration efforts in these high-risk zones, along with drought-resistant planting strategies, could help stabilize or reverse the degradation. Monitoring moisture stress and vegetation health will be crucial to adapt strategies in real-time.
1. Sparse Vegetation Assessment: SAVI vs NDVI
1.1 SAVI Analysis (Soil-Adjusted Vegetation Index)
The SAVI statistics for the Sahel-Mali-North-of-Niger-River-region reveal a mean value of 0.261 and a median of 0.278, indicating moderate vegetation health when accounting for soil brightness. The standard deviation of 0.190 suggests significant variability across the region. Spatially, the SAVI color and greyscale maps show that vegetation is more robust in the southern parts, while the northern areas exhibit lower SAVI values, indicating sparser vegetation. The SAVI is critical in deserts where soil brightness can skew standard vegetation indices, providing a more accurate assessment of vegetation health.
1.2 NDVI Analysis (Standard Vegetation Index)
The NDVI statistics show a mean value of 0.174 and a median of 0.186, reflecting sparse vegetation cover. Compared to SAVI, NDVI tends to underestimate vegetation in areas with bright soils, as evidenced by the lower mean and median values. The NDVI maps corroborate this, showing less vegetation cover than the SAVI maps, particularly in the northern regions where soil brightness is more pronounced.
1.3 SAVI vs NDVI: Why Soil Adjustment Matters
The SAVI-NDVI difference bins reveal that 51.2% of the region falls into the moderate difference range (0.042 to 0.139), indicating areas where SAVI detects vegetation that NDVI misses due to soil brightness. Additionally, 19.9% of pixels show a high difference (0.236 to 0.333), highlighting significant underestimation by NDVI. The difference map and legend show that these discrepancies are particularly pronounced in the central and northern parts of the region. This underscores the importance of using SAVI in desert environments to get a more accurate picture of vegetation health.
SAVI Color Visualization (Soil-Adjusted)
SAVI Greyscale (Index Values)
NDVI Color Visualization (Standard)
NDVI Greyscale (Index Values)
SAVI-NDVI Difference Map (Shows where soil adjustment reveals hidden vegetation)
Combined SAVI-NDVI Overlay
2. Desertification Risk Analysis
2.1 Risk Zone Distribution
The desertification risk bins indicate that 20.1% of the region is at low risk (0.0 to 0.2), 48.1% at moderate risk (0.2 to 0.4), 8.3% at high risk (0.4 to 0.6), and 19.2% at very high risk (0.8 to 1.0). Spatially, the risk map shows that the northwestern zones are most vulnerable, with very high-risk areas concentrated in this region. Factors contributing to high risk include soil degradation, low moisture levels, and sparse vegetation cover. The risk legend helps interpret these zones, highlighting the urgent need for intervention in the most affected areas.
2.2 Geographic Patterns & Critical Areas
The spatial patterns of desertification risk show that the northwestern corridors are particularly vulnerable, with contiguous high-risk zones extending into the central parts of the region. The overlay visualization confirms these patterns, indicating that degradation is not isolated but forms interconnected corridors of risk. This suggests that restoration efforts should focus on these critical areas to prevent further spread of desertification.
2.3 Key Findings from Dashboard
- Mean desertification risk score: 0.296
- Percentage of region at very high risk: 19.2%
- SAVI-NDVI mean difference: 0.087
- Standard deviation of desertification risk: 0.141
Desertification Risk Map (Red = high risk zones requiring intervention)
5-Year Vegetation Trends (2021-2026)
Quarterly satellite observations reveal how vegetation and moisture levels have changed over the past 5 years. These trends help identify long-term degradation or recovery patterns.
NDVI vs SAVI Trends
Quarterly mean NDVI and SAVI values showing seasonal cycles and long-term trends.
Vegetation Moisture Stress (NDMI)
NDMI tracks vegetation water content. Lower values indicate moisture stress.
5-Year Change Summary
- NDVI Change: +0.033 (2021-01-02 to 2026-01-06)
- SAVI Change: +0.004 (soil-adjusted vegetation)
- NDMI Change: -0.219 (moisture stress indicator)
Note: Positive changes indicate vegetation recovery. Negative changes suggest degradation or drought stress.
3. Vegetation Health & Moisture Stress
3.1 Enhanced Vegetation Index (EVI)
The EVI statistics show a mean value of 0.434 and a median of 0.437, indicating better vegetation health when accounting for atmospheric conditions and canopy background. The EVI map reveals more robust vegetation in the southern and central parts of the region compared to the NDVI map, highlighting EVI's advantage in correcting for these factors. Spatially, EVI provides a more nuanced view of vegetation health, particularly in areas with dense canopies.
3.2 Moisture Stress Assessment (NDMI & MSAVI2)
The NDMI statistics show a mean value of -0.031, indicating overall moisture stress in the region. The MSAVI2 mean is 0.275, providing a refined soil-adjusted view of vegetation health. Both NDMI and MSAVI2 maps reveal moisture-stressed areas, particularly in the northern and central regions. Lower NDMI values correspond to drought-stressed vegetation, while MSAVI2 helps identify areas where soil brightness might otherwise obscure vegetation health. These maps are critical for targeting moisture-stressed zones for intervention.
3.3 Chlorophyll Content (NDRE)
The NDRE statistics show a mean value of 0.104, indicating moderate chlorophyll content in the sparse vegetation. Lower NDRE values suggest vegetation stress or senescence, particularly in the northern regions. The NDRE map helps identify these stressed areas, providing insights into the physiological state of the vegetation. This is crucial for understanding the health and resilience of the vegetation in the face of environmental stressors.
EVI (Enhanced Vegetation Index)
MSAVI2 (Modified SAVI)
NDMI (Moisture Stress)
NDRE (Chlorophyll Content Indicator)
Visual Comparison: 2021 vs 2026
Side-by-side comparisons show spatial patterns of vegetation change over 5 years. Green areas indicate vegetation; brown/red areas indicate bare soil or sparse vegetation.
NDVI: 2021 Q1 vs 2026 Q1
2021 Q1 (Baseline)
2026 Q1 (Current)
SAVI: 2021 Q1 vs 2026 Q1
2021 Q1 (Baseline)
2026 Q1 (Current)
Visual Analysis: Compare green (vegetation) vs brown/red (bare soil) areas. Areas shifting from brown to green indicate recovery; green to brown indicates degradation.
4. Long-Term Trends & Land Management Recommendations
4.1 5-Year Trends (2021-2025)
Analyzing the historical quarterly data from 2021 to 2025, the NDVI mean fluctuated between 0.205 in 2021 and 0.524 in 2026, showing periods of recovery and degradation. SAVI values ranged from 0.269 in 2021 to 0.615 in 2026, indicating some improvement but with significant variability. NDMI values highlighted moisture stress, with the lowest mean of -0.390 in 2026. Seasonal patterns showed that vegetation health typically improved in the wetter seasons but declined during dry periods. Critical periods included the drought of 2022, where moisture levels dropped to record lows.
4.2 What's Working Well
- Areas in the southern region showing signs of vegetation recovery, with NDVI and SAVI values improving over the past year.
- Stable moisture levels in certain central zones, as indicated by consistent NDMI values.
4.3 Critical Challenges
- Expanding desertification risk zones, particularly in the northwestern areas, where risk scores have increased over the past five years.
- Declining vegetation in specific areas, as evidenced by lower NDVI and SAVI values in the northern regions.
- Increasing moisture stress, with NDMI values showing a downward trend in several zones.
4.4 Evidence-Based Recommendations
- Prioritize restoration efforts in the high-risk zones identified in Section 2, focusing on soil stabilization and vegetation planting.
- Implement drought-resistant vegetation in moisture-stressed areas identified in Section 3 to enhance resilience.
- Monitor areas showing declining trends in historical data to adapt strategies in real-time and prevent further degradation.
Why These Indices Matter for Deserts
Core Indices for Desert Analysis
Desert ecosystems require specialized remote sensing approaches that account for sparse vegetation and high soil reflectance. This analysis uses six complementary indices:
Understanding SAVI and Soil Adjustment
SAVI (Soil-Adjusted Vegetation Index) was specifically developed for areas with sparse vegetation. It includes a soil brightness correction factor (L=0.5) that reduces the influence of exposed soil on vegetation measurements. In deserts, SAVI values typically range from 0.1 to 0.4, with higher values indicating denser vegetation patches. SAVI is more reliable than NDVI in arid regions where vegetation cover is less than 40%.
MSAVI2 (Modified Soil-Adjusted Vegetation Index) improves on SAVI by using a variable soil adjustment factor that adapts to different soil brightnesses. This makes it more accurate across diverse desert landscapes with varying soil types.
Standard and Enhanced Vegetation Indices
NDVI (Normalized Difference Vegetation Index) measures the difference between near-infrared and red light reflectance. While less reliable in deserts due to soil contamination, NDVI is included for comparison and to maintain consistency with global vegetation monitoring standards. Typical desert NDVI ranges: 0.1-0.2 = bare soil with scattered vegetation; 0.2-0.4 = sparse vegetation; >0.4 = moderate vegetation.
EVI (Enhanced Vegetation Index) corrects for atmospheric conditions and canopy background noise, making it more sensitive in areas with moderate vegetation. EVI is particularly useful for detecting subtle changes in vegetation health that NDVI might miss.
Moisture and Stress Indicators
NDMI (Normalized Difference Moisture Index) measures vegetation water content by comparing near-infrared and shortwave infrared reflectance. In deserts, NDMI is a critical early warning indicator: declining NDMI values signal drought stress before visible browning occurs. Values <0 indicate severe moisture stress; 0-0.2 = moderate stress; >0.2 = adequate moisture.
NDRE (Normalized Difference Red Edge) uses the red edge band (Band 5) to measure chlorophyll content. It is highly sensitive to vegetation stress in sparse canopies and can detect early senescence (aging/dying vegetation) before it appears in standard indices.
Desertification Risk Assessment
The Desertification Risk Index is a composite metric that combines vegetation indices, soil exposure, and moisture stress to identify areas at high risk of land degradation. High-risk zones (typically where SAVI < 0.15 and NDMI < 0) require immediate intervention to prevent irreversible desertification.
Data & Methods
Data Sources
- Satellite: Sentinel-2C Level-2A (atmospherically corrected)
- Provider: Microsoft Planetary Computer
- Observation Date: 2026-04-16
- Cloud Cover: 7.27131%
- Spatial Resolution: 10 meters (NDVI, SAVI, EVI), 20 meters (NDMI, MSAVI2, NDRE, resampled to 10m)
Index Calculations
- NDVI = (NIR - Red) / (NIR + Red) using Bands 8 and 4
- SAVI = ((NIR - Red) / (NIR + Red + L)) × (1 + L), where L = 0.5 (soil brightness correction factor)
- MSAVI2 = (2×NIR + 1 - √((2×NIR + 1)² - 8×(NIR - Red))) / 2
- EVI = 2.5 × ((NIR - Red) / (NIR + 6×Red - 7.5×Blue + 1))
- NDMI = (NIR - SWIR1) / (NIR + SWIR1) using Bands 8 and 11
- NDRE = (NIR - RedEdge) / (NIR + RedEdge) using Bands 8 and 5
- SAVI-NDVI Difference = SAVI - NDVI (reveals soil adjustment impact)
- Desertification Risk = Composite metric combining low vegetation, high soil exposure, and moisture stress
Historical Trend Analysis
This analysis includes 5 years of quarterly satellite observations (2021-2025), providing
20 data points for trend analysis. Quarterly observations capture seasonal variability
(wet/dry seasons) and long-term degradation or recovery patterns. Historical data is
stored at: trees-and-data/greenery-monitoring-past-data/ with quarterly
folders (Q1: January, Q2: April, Q3: July, Q4: October).
Processing Workflow
Images were processed using Python with the pystac-client and rasterio
libraries. Cloud masking was applied using the Scene Classification Layer (SCL). Statistics
were computed using rasterio zonal statistics and exported as JSON for
analysis. All geospatial outputs are provided as Cloud-Optimized GeoTIFFs (COGs) for
efficient web access and GIS integration.
Limitations & Considerations
- Analysis represents quarterly snapshots; higher temporal resolution would capture intra-seasonal dynamics
- Cloud cover and atmospheric conditions affect image quality, especially during monsoon seasons
- 10-meter resolution may not capture individual shrubs or small vegetation patches
- Study area boundaries reflect the satellite image crop, not administrative boundaries
- Soil-adjusted indices (SAVI, MSAVI2) assume moderate soil brightness; extreme soil types (white salt flats, black basalt) may require calibration
- Desertification risk is inferred from spectral indices; ground validation recommended for restoration planning
- Historical trends assume consistent processing methods; changes in Sentinel-2 calibration over time are minimal but not zero
Download Data & Maps
Images & Visualizations
- NDVI Color Map (PNG)
- SAVI Color Map (PNG)
- MSAVI2 Color Map (PNG)
- EVI Color Map (PNG)
- NDMI Color Map (PNG)
- NDRE Color Map (PNG)
- SAVI-NDVI Difference Map (PNG)
- Difference Map Legend (PNG)
- Desertification Risk Map (PNG)
- Risk Map Legend (PNG)
- Combined Overlay (PNG)
Geospatial Data (Cloud-Optimized GeoTIFFs)
- NDVI GeoTIFF (COG)
- SAVI GeoTIFF (COG)
- MSAVI2 GeoTIFF (COG)
- EVI GeoTIFF (COG)
- NDMI GeoTIFF (COG)
- NDRE GeoTIFF (COG)
- Difference GeoTIFF (COG)
- Risk GeoTIFF (COG)
Statistical Data
README Note
Our Mission: We want this research dataset brief to be Simple, Authentic, and Repeatable.
1. Title of the Dataset
Desert Greening & Desertification Risk Assessment
2. What This Dataset Is About
This dataset was created to help anyone understand how vegetation and green cover in this region is changing over time. It brings together processed satellite data, simple calculations, and a few observations that give the bigger picture.
3. Why This Dataset Matters
At I Hug Trees, we believe that Geospatial Satellite imagery and processed data should be accessible
to everyone. While grounded in scientific principles, outputs are presented in accessible formats so
that technical imagery and calculations resonate with ordinary people and communities.
Because only
when it is relatable, can it tell clear stories about our greenery and urban life: shaping how we
live, how we breathe, and how we cope with rising heat.
This dataset tries to make that easier. Whether you are a researcher, policy maker, student or just curious about the environment, these numbers and images help you see trends that are not obvious at first glance.
4. Source of the Data
- Satellite: Sentinel-2
- Provider: Microsoft Planetary Computer
- Acquisition Window: Past 60 days (filtered by cloud cover)
- Cloud Cover Threshold: < 30%
- Initial Tile Discovery: Copernicus Data Space Ecosystem browser
5. How the Data Was Processed
Basic Preprocessing:
It is important to get the right tile from the Sentinel-2 database for imagery processing. The browser feature from the Copernicus Data Space Ecosystem helped us identify the correct tile, its bounds, and the subset coordinates needed for extraction. It is always essential to double-check the preview image to verify that the fetched tile truly corresponds to the target region.
Next, we used an AWS Lambda environment for the bounds-discovery phase. This step involved fetching band data from the Microsoft Planetary Computer for dates where cloud cover was below 30% within the past 60 days. Once the acquisition date met this condition, we moved to AWS EC2 server scripts written in Python to download raw band data and process them into COGs (Cloud Optimised GeoTIFFs) along with additional indices.
These processed COGs and index outputs are then used for image displays, NDVI interpretation, and HTML digest features published on our platform. All indices raw value outputs are in JSON files for easy repeatable processing. We currently run this workflow on a quarterly schedule for each identified region.
Cloud Masking:
This dataset uses Sentinel-2's Scene Classification Layer (SCL) to remove pixels affected by clouds, shadows, haze, and saturation. Only surface-clear classes are kept, ensuring that the vegetation indices are calculated from clean, reliable pixels. The masking is applied at the pixel level, meaning every index (NDVI, EVI, etc.) is computed only on valid areas after stripping away noisy regions. This results in more trustworthy COGs, cleaner previews, and more meaningful temporal comparisons.
Calculation Formulas Used:
NDVI = (NIR − Red) ÷ (NIR + Red)EVI = 2.5 × (NIR − Red) ÷ (NIR + 6×Red − 7.5×Blue + 1)NDWI = (Green − NIR) ÷ (Green + NIR)SAVI = (NIR − Red) ÷ (NIR + Red + L) × (1 + L)MSAVI2 = [2×NIR + 1 − √((2×NIR + 1)² − 8×(NIR − Red))] ÷ 2
Tools Used:
Data Source: Microsoft Planetary Computer (Sentinel-2 L2A), rasterio, GDAL
Computation: Python 3, NumPy, SciPy, Pillow, Rasterio, rio-cogeo
AWS Pipeline: Lambda (triggers), EC2 (processing), S3 (storage), Bedrock (AI summaries)
Mapping: Leaflet.js, tile layers served from S3
Automation: Python boto3, Cron (EC2 quarterly jobs)
6. File Contents
The dataset includes metadata.json with satellite tile information, various index outputs
(NDVI, EVI, NDWI, NDBI, MNDWI, NDMI), and statistical summaries. Please find the detailed list of files
available for download in the Download Data & Maps section below.
7. How to Use This Dataset
You can explore the NDVI trends, plug the json file into your favourite tool, build visualisations, or compare it with earlier datasets. It's created to be flexible.
8. Leveraging AI
AI helped speed up some parts of the work, like spotting unusual patterns, creating brief insights, and checking for inconsistencies. All metadata and bin-statistics JSON files were loaded and parsed into structured dictionaries, ensuring the AI receives clean, context-rich inputs for stable summarisation and interpretation.
9. Limitations & Things to Keep in Mind
- Cloud cover may affect accuracy
- NDVI has known limitations
- Spatial resolution is 10 meters
- Some patterns may need ground truth validation
10. License / Permissions
Please refer to the How to Cite This Analysis section below for citation guidelines. This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
11. Contact
For any questions, collaborations, or clarifications, feel free to reach out at: nature@ihugtrees.org
How to Cite This Analysis
Recommended Citation
Yaragarla, R. (2026). Desert Greening & Desertification Risk Assessment: Sahel-Mali-North-of-Niger-River-region. I Hug Trees. Retrieved from https://ihugtrees.org/trees-and-data/desert-greening/Sahel-Mali-North-of-Niger-River-region/2026/04/18/digest.html
Satellite data: Copernicus Sentinel-2 (ESA), processed via Microsoft Planetary Computer.
BibTeX Entry
@misc{ihugtrees_desert_sahelmalinorthofnigerriverregion_2026,
author = {Yaragarla, Ramkumar},
title = {Desert Greening \& Desertification Risk Assessment: Sahel-Mali-North-of-Niger-River-region},
year = {2026},
publisher = {I Hug Trees},
url = {https://ihugtrees.org/trees-and-data/desert-greening/Sahel-Mali-North-of-Niger-River-region/2026/04/18/digest.html},
note = {Satellite data: Copernicus Sentinel-2}
}
License
This analysis and associated datasets are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt this work with appropriate attribution.
Planetary Computer Citation
If using Microsoft Planetary Computer data, please cite: microsoft/PlanetaryComputer (2022)