Advanced ecosystem restoration: Blending phytoremediation with satellite-based imagery with remote sensing in the Himalayas of PIN Valley National Park, India

Abstract. Heavy metal pollution presents a formidable challenge to global ecosystems, threatening biodiversity, soil and water quality, and human health. In regions with ecological sensitivity or limited access, traditional remediation techniques often fall short due to their resource-intensive nature and potential environmental disturbance. In response, phytoremediation emerges as an innovative and sustainable solution. Advanced remote sensing techniques, spanning proximal, airborne, and space-borne data collection, enhance the prediction accuracy of contamination levels by correlating spectral reflectance data with metal concentrations. Proximal sensing, involving field-based in-situ laboratory samples, combined with satellite imagery insights of MODIS-TERRA/AQUA and Landsat-8, permits exhaustive coverage and detail, crucial for monitoring shifts in land use and surface cover. Despite challenges, such as spectral complexity and atmospheric variability, spectral data delineates metal-induced stress markers in vegetation, underscoring phytoremediation's potential. This study investigates phytoremediation's efficacy in Pin Valley National Park, Himachal Pradesh, India, focusing on species that exhibit significant metal-accumulating traits. We leveraged advanced remote sensing techniques, integrating data from Landsat-8, and MODIS, to comprehensively assess plant health and environmental quality over the period of 2022 for heavy metal contamination (Heavy Metal Index, Iron-Oxide Index, Hydrothermal Index) and earth observation on freely available datasets for the period 2010–2023. The core of this research lies in evaluating remote sensing (RS) as a non-invasive and cost-effective methodology for long-term monitoring of heavy metal contamination. Conventional site assessments are costly and time-consuming, often resulting in ecological disturbances— challenges that RS methodologies surmount efficiently. Using spectral indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), and Soil-Adjusted Vegetation Index (SAVI). NDVI and Land surface temperature (LST) as derived products from MODIS-Terra/Aqua dataset were compared for the 2019–2024 period. Our findings underscore the significant correlation between heavy metal concentration and vegetation stress, validating the utility of remote sensing in ecological management. By establishing a scalable and sustainable framework for monitoring phytoremediation efforts, this study not only confirms the practicality of phytoremediation in handling metal-contaminated soils but also advocates for its integration into broader environmental management protocols. The fusion of phytoremediation with remote sensing technology represents a pioneering step forward in ecological resilience, offering precise, actionable insights into the health of ecosystems beleaguered by soil-water pollution.