BEYOND THE GREEN ILLUSION: INTEGRATING INDIGENOUS KNOWLEDGE AND GEOSPATIAL EVIDENCE FOR SUSTAINABLE LANDSCAPE MANAGEMENT IN MATALE, SRI LANKA

Abstract

Vegetation greenness does not necessarily indicate ecological or structural stability in landscapes exposed to landslide risk. This study examines this apparent divergence in Matale District, Sri Lanka, by bringing a 30-year Landsat assessment into dialogue with Indigenous Knowledge from the Rattota Divisional Secretariat Division. The study investigates how increasing vegetation greenness may coexist with landscape conditions that remain vulnerable to instability, highlighting the limitations of interpreting vegetation change solely through remotely sensed indicators. An explanatory sequential mixed-methods approach was adopted. Landsat imagery from 1995, 2015, and 2025 was used to assess vegetation dynamics through the Normalized Difference Vegetation Index (NDVI), while topographic information and landslide susceptibility data were used to examine the spatial context of landscape vulnerability. Qualitative evidence was subsequently obtained from 12 long-term residents and Indigenous Knowledge holders in the Rattota Divisional Secretariat Division through semi-structured interviews and field observations, with the data analysed thematically. The results show an overall increase in mean NDVI from 0.304 in 1995 to 0.327 in 2015 and 0.350 in 2025. However, increased spectral greenness does not necessarily correspond with ecological or structural stability. Indigenous Knowledge provides additional insight into traditional vegetation, soil, slope, and water-management practices that contribute to landscape stability, while also indicating a decline or disconnection of such practices. The findings highlight a “Green Illusion,” whereby increased vegetation greenness may conceal changes in vegetation composition and the weakening of traditional land-stewardship practices. Integrating geospatial evidence with Indigenous Knowledge therefore provides a more context-sensitive understanding of landscape vulnerability and supports the potential for IK-weighted geospatial monitoring and sustainable landscape management in Matale District.