首页|New Findings Reported from Punjab Remote Sensing Centre Describe Advances in Mac hine Learning (Monitoring vegetation degradation using remote sensing and machin e learning over India-a multi-sensor, multi-temporal and multi-scale approach)
New Findings Reported from Punjab Remote Sensing Centre Describe Advances in Mac hine Learning (Monitoring vegetation degradation using remote sensing and machin e learning over India-a multi-sensor, multi-temporal and multi-scale approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Punjab, India, by NewsRx correspondents, research stated, "Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic vari ation and anthropogenic actions. Conservation of biodiversity is important becau se millions of people are directly and indirectly dependent on vegetation (fores t and crop) and its associated secondary products." The news journalists obtained a quote from the research from Punjab Remote Sensi ng Centre: "United Nations Sustainable Development Goals (SDGs) propose to quant ify the proportion of vegetation as a proportion of total land area of all count ries. Satellite images form as one of the main sources of accurate information t o capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degr adation. Open source Cloud computing system Google Earth Engine (GEE) was used t o systematically monitor vegetation degradation and evaluate the potential of mu ltiple satellite data with variable spatial resolutions. Hotspots were demarcate d using machine learning techniques to identify the greening and the browning ef fect of vegetation using coarse resolution Normalized Difference Vegetation Inde x (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipita tion with Station data (CHIRPS) for the period 2000-2022 were also used to find rainfall anomaly in the region."
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