Exploring the role of remote sensing and IoT sensor technologies in measuring biodiversity

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The battle against climate change and biodiversity loss has a new ally: technology.

From earth-imaging satellites to the smallest sensors, technology is revolutionizing our understanding of the natural environment. The Internet of Things (IoT), an interconnected network of sensor-equipped devices, has also emerged as a player in providing timely and actionable information about the state of our ecosystems. The IoT and remote sensing approaches help land managers and ecologists use their time effectively by taking away some of the laborious day-to-day monitoring tasks and by providing an early warning of potential harms like disease or pests.


Sensors and in-situ monitoring

IoT sensors, when used for in-situ monitoring, have the potential to bolster efforts of field ecologists and land managers in collecting biodiversity information. Various types of IoT sensors measure a wide array of parameters, from temperature and soil moisture to wildlife movement. These sensors, working round the clock, provide continuous and on-the-ground monitoring, supplementing traditional field observations. All of the collected data can automatically sync with a central IoT hub that stores the information or uploads it to the cloud.

IoT sensors can exchange data among themselves and enable smart interactions with digital systems.

For example, the heath of a timber forest might be tracked using moisture sensors, tree girth-band sensors, plant-stress sensors, light sensors that measure canopy health, and thermometers that track temperature in the soil, understory, and canopy of trees. All this information could be collected automatically, gathered by the data hub, uploaded to the cloud, and presented to the land manager in an online platform that generates its own data insights. All a land manager would need to do is to check on the sensors when warnings or errors are reported by the hub via e-mail or text.

Although the use of IoT sensors is not yet widespread, their popularity is expected to skyrocket as more and more are deployed and supporting infrastructure is established. IoT Analytics estimates that by 2027, there will be around 29 billion connected IoT devices worldwide. These devices, working in diverse environments including natural ecosystems, will generate vast amounts of data, rendering it actionable when supported by a robust architecture comprising appropriate connectivity, analytics, and storage.


Remote sensing

Remote sensing refers to collecting data from a distance by measuring radiation (light) emitted or reflected from an area. The imagery obtained through remote sensing techniques has the capability to reveal habitat change over time, across diverse landscapes such as farmlands, woodlands, and urban areas. The information gathered from these multiple sources, when analyzed using AI, can provide powerful insights for decision-makers involved in addressing biodiversity concerns.

Each of the following remote sensing approaches can provide valuable information for biodiversity measurement. They can also be integrated to provide comprehensive and accurate data and insights. They can help scientists and conservationists to monitor ecosystems, detect changes, identify habitats, and understand species distributions without direct physical access to every location.

  • Satellite imagery: Satellite sensors capture data across wavelengths of light to assess land cover, vegetation health, and changes in ecosystems over time.
  • Aerial photography and drones: High-resolution cameras mounted on drones or aircraft capture detailed, localized images of landscapes, habitats, and species distributions.
  • LiDAR: Uses laser pulses to create precise 3D maps of terrain, vegetation structure, and canopy height. It aids in assessing forest structure, biomass estimation, and understanding vertical vegetation distribution, which are crucial for biodiversity measurement.
  • Hyperspectral imaging: This technology captures images across numerous narrow and contiguous spectral bands, providing detailed information about material composition, plant species, and ecosystem health by analyzing specific spectral signatures.

Radar imaging: Radar sensors on satellites can penetrate clouds and vegetation, offering information on terrain, biomass, and land cover. It’s particularly useful in tropical regions where cloud cover can hinder optical satellite imagery..

Remote sensing technologies are crucial in assessing biodiversity at local and regional scales. These technologies enable the mapping of biodiversity hotspots, based on spectral variation hypotheses. These hypotheses correlate variations in spectral heterogeneity captured by remote sensing with species alpha-diversity, offering insights into localized biodiversity.

Remote sensing, encompassing satellites and advanced imaging spectroscopy, can map spatial variations in species composition and diversity. For instance, the use of high-resolution remote sensing data collected via Unmanned Aerial Vehicles (UAVs) supplements or replaces part of the fieldwork required for biodiversity assessments. Spectral information obtained through remote sensing aids in predicting biodiversity patterns and understanding environmental drivers influencing ecosystems.

The integration of remote sensing technologies, IoT sensors, and AI holds tremendous promise in revolutionizing biodiversity measurements. These technological advancements offer a comprehensive understanding of ecosystems, paving the way for informed decision-making in the preservation and management of biodiversity.


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