Abstract
Abstract. Biosphere Reserves are archetypal parts of natural and cultural landscapes encompassing over large area of different ecosystem, it represents bio-geographic zones of an region. Globally, the areas of biosphere reserve is shrinking and exploiting due to the extreme climatic condition, natural calamities and anthropogenic activities, which leads to environmental and land degradation. In this paper Nilgiri Biosphere Reserve (NBSR) area has been selected and it represents a biodiversity-rich ecosystem in the Western Ghats and includes two of the ten biogeographical provinces of India. Amongst the most insubstantial ecosystems in the world, the Nilgiri Biosphere Reserve is bearing the substance of climate change evident in increasingly unpredictable rainfall and higher temperatures during recent years. The region was mostly unscathed till two centuries ago, but has witnessed large-scale destruction ever since. In this scenario, a need of application of remote sensing and advance machine learning techniques to monitor environmental degradation and its ecosystem in NBSR is more essential. The objective of the present study is to develop satellite image classification techniques that can reliably to map forest cover and land use, and provide the basis for long-term monitoring. Advanced image classification techniques on the cloud-based platform Google Earth Engine (GEE) for mapping vegetation and land use types, and analyse their spatial distributions. To restore degraded ecosystems to their natural conditions through proper management and conservation practices. In order to understand the nature of environmental degradation and its ecosystem in Nilgiri Biosphere Reserve; following thematic criteria’s were grouped in to four major indicators such as Terrain Indicator (TI), Environmental Indicator (EI), Hydro-Meteorological Indicator (HMI) and Socio-Economic Indicator (SEI). The utilisation of remote sensing product of huge datasets and various data product in analysis and advanced machine learning algorithm through Google earth engine are indispensable. After extraction of all the thematic layers by using multi criteria decision and fuzzy linear member based weight and ranks were assigned and overlay in GIS environment at a common pixel size of 30 m. Based on the analysis the resultant layer has been classified into five environmental degraded classes i.e., very high, high, moderate, slight and no degradation. This study is help to identify the degradation and long term monitoring and suggest the appropriate conservation, management and policies, it is a time to implement and protect the Nilgiri biosphere reserves without hindering present stage of natural environment in a sustainable manner.
Highlights
Ecosystem is a biological community of all living creatures in province of biosphere
4.1.1 Assigning Factor Score: Assessment of environmental degradation successful extraction of all the thematic layers from Google Earth Engine (GEE); each layers were classified into benefit and non-benefit criteria’s. normalised the weight (Appendix 1a) to find the best score for the individual criterion shown in table 3
Cloud computing technology of Google Earth Engine (GEE) on one side and Entropy Techniques on the other side have been determined in computing the environmental degradation composed of elements like Terrain Indicator, Environmental Indicator, Hydro-Meteorological Indicator and Socio-Economic Indicator
Summary
Ecosystem is a biological community of all living creatures in province of biosphere. An environmental degradation persists for long time, which results in many problems are arising like impact of loss of biodiversity, migration, local climatic variations and human health. Nilgiris biosphere is under acute condition in depletion of resources and disruption in ecosystem cycle. The region is a biodiversity-rich ecosystem, in recent times the mining and exploitation of forest resources are highly dominated in the region. A drastic decline in the sholas and grasslands is one of the reasons for the recent water scarcity in the Nilgiri Biosphere Reserve. This seeks the essential need of monitoring the environmental degradation in Nilgiri biosphere.
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