The Nerd Side Of Life

Remote Sensors And Their Impact On Monitoring Of Environmental Data And Climate Intelligence

Monitoring the environmental data is critical now more than ever for protecting our health and the health of the planet. As the human population increases significantly and industrial development continues to grow, energy use surges and pollution is becoming a significant cause of climate change. Both world leaders, governing bodies, and scientists are increasingly dependent on advanced technology and data science to help quantify and prevent the impact of human activities on the environment. 

Despite the various advances in pollution control in the last few years, the Intergovernmental Panel on Climate Change’s 2021 climate change report provided the world a stark reminder that policy changes and innovations are still the need of the hour. The report urges that newer regulations and smarter systems need to be enforced expeditiously to stop the unprecedented changes in our environment. As each new year ushers in a string of record-breaking fires, floods, hurricanes, and an increase in global temperature, it is all the more urgent for us to employ monitoring of environmental data and control measures with the help of technologies and data science to gain complete visibility of our footprint. 

Technological innovation combined with data science has introduced real-time monitoring and climate intelligence, including the modern real-time sensor network, Geographic Information Systems (GIS), telemetry systems, machine learning techniques, the Internet of Things(IoT), predictive analytics, and other technologies to collect and statistically analyze environmental data. This real-time data plays a significant role in predicting and informing time-critical decisions related to climatic conditions with weather data. 

Real-time, accurate environmental data monitoring models and assessment technologies are now more sophisticated than ever

The technologies utilized for monitoring environmental data and climate intelligence include a combination of hardware, software, and statistical processes. Some of the most important of these technologies are: 

  1. Geographic Information Systems (GIS) 

GIS are software tools used to gather and analyze data connected to geographic locations and their connection to Earth’s human or natural activity. GIS mapping software uses spatial data to create maps and model layers of visual information that reveal patterns and relationships. Remote sensing data is extracted from GIS, and they help users get a defined picture of the climatic condition. 

  1. Remote Sensing 

Remote sensing refers to the acquisition of information or data related to a phenomenon or a territory without direct contact with it. Remote sensing techniques are used in a plethora of fields like geography, hydrology, ecology, meteorology, oceanography, glaciology, geology, and military scope, intelligence, commercial, economic, planning, and humanitarian applications. Of this long list, monitoring weather data via APIs take much higher precedence over others. 

Remote sensing helps improve the understanding of the climate system and its changes. The technique monitors the Earth’s surface, ocean, and atmosphere at various Spatio-temporal scales, thus allowing weather data observations and investigating climate-related processes, both long and short-term phenomena like deforestation or El Nino trends. Moreover, remote sensing is prominently used to collect information and data in inaccessible areas or dangerous disaster events. Some specific applications of remote sensing that is related to climate intelligence are 

(i) Natural resource management 

(ii) Farm management, for example, land use distribution (NDVI/EVI), land conservation and soil carbon stock, crop health

(iii) Forest fire detection and tactical fire-fighting operations in real-time with decision support systems, 

(iv) Better-informed forest and water management 

(v) Carbon stocks and related dynamics evaluations

(vi) Climate system dynamic simulation

(vii) Climate projections and meteorological reanalysis products improvements, widely used for climate change research studies.

In addition to monitoring environmental data, remote sensing can also be applied to improve early warning and preparedness to prevent climate disasters with weather APIs. The systems reinforce disaster risk management, specifically climate risk management. GIS and Remote Sensing use satellites for early warning and forecasting systems to provide climate intelligence that mitigates climate-related disaster risk or helps manage and prepare for it. 

The benefits and advantages of remote sensing in environmental data monitoring are enormous

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One significant benefit of the proliferation of climate intelligence via remote sensing is accountability. The ability of remote sensing techniques to conduct environmental monitoring and impact assessments throughout the remotest corners of the world is high and ensures corporate compliance and adheres to government regulations. 

Another major benefit of environmental data monitoring is improving disaster response and preparedness. Weather data can help disaster management by collecting data and facilitating innovative emergency management, such as disaster mapping, event simulations. Enormous volumes of real-time and historical weather data can also assist emergency management and disaster relief teams design proactive, protective strategies like early-fire detection and flood warning systems. 

Real-time monitoring of the environment has applications everywhere around us

The practical applications of environmental data monitoring are varied and vast. Wildfires, flooding, landslides, biodiversity, and pollutions are all issues that have increasing concern as the climate crisis is said to worsen in the next few years. With data science and analytics tools, real-time sensor data facilitates higher visibility and granular analysis of environmental phenomena. The most popular application to environmental data monitoring is air quality and weather with the help of weather APIs

Air quality data for monitoring is generated using specialized observational tools such as GIS models. The data originates from multiple environmental networks and is integrated into air dispersion models, which correlates the data with emissions, meteorological, and topographical data to detect the concentration of pollutants. 

Data analysts across the globe use environmental data monitoring solutions, tools, and capabilities for almost all purposes

Whether to measure air quality or understand the soil fertility, or even to run a weather-based marketing campaign with weather APIs, most of the environmental data monitoring solutions have the following features and capabilities: 

– Automated data collection tools 

– IoT visualization 

– Auditing and Validation Tools

– Multi-level security 

– Built-in GIS mapping capabilities 

– Mobile integrations 

– Advanced query tools 

– Analytics tools 

Ambee’s Outlook 

Being a pioneer in providing environmental data and weather API, Ambee constantly strives to give real-time data insights beyond the capacities of mainstream data providers. At Ambee, large datasets are made available in the form of simple REST APIs for everyone to use barrier-free. These environmental APIs, including weather API, air quality API, and Climate intelligence, allow anyone worldwide to cross-filter billions of location-based environmental data in less than a few seconds.

Author Bio: Ranjani A writer, nature lover and an environmental engineer. You can find me at Ambee’s office working with an amazing team to bring about positive change to our planet’s health. Being a big advocate of our environment, I write about technologies like AI, ML and Big data and its potential to save humankind from global warming.

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