Spatial Characterization of NDVI-Based Vegetation Density in Smallholder Coffee Plantation on Mount Kawi’s Southern Slopes

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Published Nov 30, 2025
https://doi.org/10.55043/jaast.v9i4.494
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Vol. 9 No. 4 (2025): Journal of Applied Agricultural Science and Technology

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Dinna Hadi Sholikah
Universitas Pembangunan Nasional Veteran
https://orcid.org/0009-0005-4558-2571
Nabilla Putry Maharani
Universitas Brawijaya
Ramadhani Mahendra Kusuma
Universitas Pembangunan Nasional Veteran
https://orcid.org/0000-0001-8447-848X
Dewi Shasa Bella
Universitas Pembangunan Nasional Veteran
Yoga Gregorius Sembiring
Universitas Pembangunan Nasional Veteran
Fitri Wijayanti
Universitas Pembangunan Nasional Veteran
https://orcid.org/0000-0001-5708-2105
Soemarno Soemarno
Universitas Brawijaya
https://orcid.org/0000-0002-7058-6495

Abstract

Indonesia’s coffee cultivation covers 1.25 million hectares, predominantly managed by smallholders (98.14%). Malang Regency, a key production area in East Java, experienced a sharp yield decline from 29,728 tonnes (2021) to 14,151 tonnes (2022). This study investigates smallholder plantations in the Kletek sub-watershed, emphasising the role of shade vegetation in coffee growth. Shade density critically influences productivity and ecological resilience. To support sustainable management, vegetation cover is assessed via remote sensing using the Normalised Difference Vegetation Index (NDVI), enabling spatial analysis of canopy structure. This research aims to analyse the types of coffee shade trees on smallholder coffee farms. NDVI is used to distinguish differences in land cover, including coffee shade. The study employed a land survey using the grid method with 30 observation points. Spatial analysis involves spectral transformation of Sentinel-2A Harmonised imagery, while statistical analysis uses correlation tests. Smallholder coffee farms in the Kletek Sub-watershed feature shade plants such as lamtoro, mahogany, and banana trees. NDVI values across these plantations ranged from moderate (0.4–0.5) to very high (>0.6) vegetation density, showing a strong correlation with land cover conditions (r = 0.80). This confirms NDVI as an effective remote sensing tool for assessing shade vegetation, significantly influencing coffee productivity and ecological resilience. The findings support NDVI-based monitoring for precision agriculture and adaptive management, with scalable applications in sustainable land-use planning, agroforestry optimisation, and climate-resilient coffee cultivation in regions such as Malang Regency, where production has declined.

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Author Biographies

Dinna Hadi Sholikah, Universitas Pembangunan Nasional Veteran

Department of Agrotechnology, Faculty of Agriculture

Nabilla Putry Maharani, Universitas Brawijaya

Department of Soil Science, Agrotechnology Study Program, Faculty of Agriculture

Ramadhani Mahendra Kusuma, Universitas Pembangunan Nasional Veteran

Department of Agrotechnology, Faculty of Agriculture

Dewi Shasa Bella, Universitas Pembangunan Nasional Veteran

Department of Agrotechnology, Faculty of Agriculture

Yoga Gregorius Sembiring, Universitas Pembangunan Nasional Veteran

Department of Agrotechnology, Faculty of Agriculture

Fitri Wijayanti, Universitas Pembangunan Nasional Veteran

Department of Agrotechnology, Faculty of Agriculture

Soemarno Soemarno, Universitas Brawijaya

Department of Soil Science, Agrotechnology Study Program, Faculty of Agriculture

How to Cite
1.
Sholikah DH, Maharani NP, Kusuma RM, Bella DS, Sembiring YG, Wijayanti F, Soemarno S. Spatial Characterization of NDVI-Based Vegetation Density in Smallholder Coffee Plantation on Mount Kawi’s Southern Slopes. J. appl. agricultural sci. technol. [Internet]. 2025Nov.30 [cited 2025Dec.1];9(4):557-69. Available from: https://www.jaast.org/index.php/jaast/article/view/494

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