What question did this study set out to answer?

The research aims to assess and map the health status of tea plants using satellite imagery.

February 11, 2026

Utilization of Landsat 8 Level 2 Satellite Imagery for Identification of Tea Plant Health Using the Weighted Overlay Method (Case Study: Kertamanah Unit, Malabar Plantation, Bandung)

Puntos clave

The research aims to assess and map the health status of tea plants using satellite imagery.
Utilized Landsat 8 Level 2 satellite imagery to obtain NDVI, LST, and slope data.
Applied the weighted overlay method with specific weights for NDVI, LST, and DEM.
Processed NDVI and LST data using Google Earth Engine and visualized results in ArcMap 10.8.2.
Classified tea plant health into three categories: very healthy, quite healthy, and unhealthy.
Achieved a health map classification with high NDVI values and low surface temperatures indicating better plant health.
Validation of the classification showed an accuracy of 46.6% influenced by resolution and environmental factors.
The very healthy class was dominant in central to southern areas, with unhealthy plants in steep and peripheral areas.

Resumen

Monitoring tea plant health is important to maintain plantation productivity. This study aims to map the health status of tea plants in the Cinyiruan and Kertamanah divisions, Kertamanah Unit, Malabar Plantation owned by PT Perkebunan Nusantara 1 Regional 2 using Landsat 8 Level 2 satellite imagery. The parameters used were NDVI, LST, and slope from DEM. These parameters were integrated using the weighted overlay method with weights of 50% for NDVI, 30% for LST, and 20% for DEM in the Yielding Plant area. NDVI and LST processing were conducted in Google Earth Engine, while weighting and visualization were carried out in ArcMap 10.8.2. The resulting tea plant health map was classified into three classes: very healthy, quite healthy, and unhealthy. Validation using wet tea crop production data per-block for 2020–2024 produced an accuracy of 46.6%, influenced by the 30-meter image resolution and field variations such as crop age, rainfall, pests, and production records. Spatially, very healthy class dominated the central to southern areas, while unhealthy class was generally found in peripheral and steep slopes areas. These results indicate that high NDVI values, low surface temperatures, and gentle topography are closely related to better tea plant conditions.

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