NDVI-Based Land Cover Classification Using Landsat 8: A Case Study from Bali
Monitoring land cover change is essential for regional planning, environmental protection, and agricultural management. As development pressures increase worldwide, remote sensing continues to offer a scalable and cost-effective way to assess how landscapes evolve over time.
A recent peer-reviewed study, Application of Normalized Difference Vegetation Index in Classifying Land Cover Change over Bangli Regency by Using Landsat 8 Imagery, demonstrates how NDVI-derived analysis can quantify measurable shifts in land use between 2015 and 2021. The research provides a clear example of how satellite-based vegetation indices are applied in real-world spatial analysis.
Study Overview: Land Cover Change in Bangli Regency (2015–2021)
The study focused on Bangli Regency in Bali, Indonesia, analyzing two Landsat 8 OLI Level 2 images captured in:
- June 2015
- October 2021
Using the red and near-infrared bands from Landsat 8, researchers calculated the Normalized Difference Vegetation Index (NDVI), a widely implemented vegetation index that helps differentiate vegetation density and land cover types.
A supervised classification method was then applied to categorize the landscape into six land cover classes:
- Water body
- Sand
- Dry land/soil
- Settlement
- Rice field
- Vegetation
Vegetation density was further divided into four categories based on NDVI thresholds. The overall classification accuracy achieved in the study was 86.54%, exceeding the commonly referenced 85% benchmark for land cover mapping.
What Changed Between 2015 and 2021?
The results reveal measurable shifts in land use over the six-year period.
Increases:
- Settlement area increased by 30.12%
Decreases:
- Sand: −14.14%
- Dry land/soil: −7.93%
- Rice fields: −8.63%
- Vegetation: −2.45%
- Water bodies: −1.62%
The authors attribute the growth in settlement area to population expansion and tourism development, particularly in southern Bangli. The corresponding decline in rice fields and vegetation highlights the direct impact of land conversion.
These findings underscore how NDVI-based classification can quantify urban expansion and agricultural change with measurable precision.
Implications for Remote Sensing Workflows
This case study reinforces the practical application of NDVI within broader remote sensing analytics. Landsat 8 OLI provides multispectral imagery at 30-meter spatial resolution, and NDVI relies specifically on surface reflectance in the red and near-infrared wavelengths.
Supervised classification in the study depended on:
- Defined NDVI thresholds
- Training samples
- Accuracy validation matrices
The ability to achieve over 86% overall accuracy demonstrates how structured classification approaches can produce reliable land cover maps when supported by appropriate spectral data and validation methods.
For organizations working in environmental monitoring, spatial planning, and geospatial analytics, this study provides a clear example of NDVI’s continued relevance in operational remote sensing workflows.
Learn more about how spectral data supports advanced Remote Sensing Applications across environmental and geospatial domains.
Relevance to Agriculture and Forestry Monitoring
NDVI remains one of the most widely implemented vegetation indices in agricultural and forestry analysis.
In this study:
- Rice fields were identified and quantified using NDVI-based classification.
- Vegetation density was segmented into low, moderate, and high classes.
- Land conversion from agricultural or vegetated areas to settlement was measured over time.
Vegetation density mapping supports practical use cases such as:
- Monitoring crop health and land productivity
- Assessing deforestation or forest degradation
- Tracking seasonal vegetation patterns
- Evaluating land suitability and management planning
For professionals operating in crop science, forestry management, and environmental planning, NDVI-based approaches continue to offer an efficient analytical framework.
Explore additional applications in Agriculture and Forestry Monitoring to see how spectral analysis supports land management decisions.
Accuracy, Validation, and Future Directions
Accuracy assessment is a critical component of any land cover classification study. The researchers evaluated their results using user accuracy, producer’s accuracy, and overall accuracy metrics.
The reported 86.54% overall accuracy indicates that the classification performance exceeded minimum recommended thresholds cited in remote sensing literature.
The authors also recommend future comparisons between Landsat 8 and Sentinel-2 imagery to further refine classification approaches and evaluate sensor-based differences in land cover detection.
Download the Full NDVI Land Cover Study
FAQs
What is NDVI land cover classification?
NDVI land cover classification uses red and near-infrared reflectance data to differentiate vegetation and non-vegetation areas in satellite imagery.
How accurate is Landsat 8 NDVI analysis?
In this study, supervised classification based on NDVI achieved 86.54% overall accuracy.
Why is NDVI used in agriculture and forestry?
NDVI measures vegetation density and health, making it useful for crop monitoring, forest assessment, and land use analysis.