#Normalized Difference Vegetation Index (NDVI)

[saxafimedianetwork]

Spatiotemporal Analysis of Agricultural Drought Severity and Hotspots in Somaliland

New #study reveals recurring #drought patterns in #Somaliland, with #Sool & #Togdheer regions most critically affected. Findings emphasize the need for targeted interventions & adaptive management strategies to build #Resilience in this country

Continue reading Spatiotemporal Analysis of Agricultural Drought Severity and Hotspots in Somaliland

[covid-safer-hotties]

Also preserved in our archive

Clean air and masking makes us all healthier.

By Hugo Francisco de Souza

In a recent study published in the journal Environmental Health Perspectives, researchers evaluated the relative risks of Long COVID following human exposure to several types of environmental pollutants. Their study leveraged data from the Catalonian COVICAT cohort (n = 2,853 Catalonian adults aged 40–65) and found that while air pollutant exposure (particularly particulate matter) increased Long COVID risk (PM2.5 RR = 1.14 [95% CI: 0.97–1.37], PM10 RR = 1.15 [95% CI: 0.98–1.39]), these associations were not statistically significant. Other environmental pollutants (noise, light, and green spaces) showed no such association.

Notably, study findings validate previous research suggesting the role of suspended particulate matter in COVID-19 severity and its indirect link to Long COVID risk through heightened initial infection severity, while highlighting subtle differences in Long COVID risk across sex, age, and education levels. It helps inform clinicians and policymakers of priorities when tackling this growing global public health concern.

Background Long COVID, also called ‘post-COVID-19 syndrome’ and ‘post-acute sequelae of COVID-19 (PASC)’ is an umbrella term for a group of symptoms that develop or persist for weeks, months, or even years following recovery from a coronavirus disease of 2019 (COVID-19) infection. Its symptoms vary substantially between patients and may include post-exertional malaise, fatigue, chest or muscle pain, and ‘brain fog’ (cognitive dysfunctions).

The duration and severity of Long COVID impart significant debilitation and socioeconomic stress to patients and their families, highlighting the need for identifying its risk factors and high-risk populations. Previous research has suggested that air pollution significantly contributes to COVID-19 infection severity and, in turn, Long COVID risk. However, such findings have often been limited by short study durations, reliance on self-reported data, and insufficient sample sizes. Furthermore, the impacts of other environmental pollutants remain unknown.

About the Study The present study aims to address present knowledge gaps by investigating the associations between urban environmental pollutant exposure and subsequent Long COVID risk. It leverages detailed, longitudinal self-reported online questionnaire data from the COVICAT cohort, a long-term prospective population-based cohort of Catalonian adults (40-65 years), supplemented with baseline medical data from the GCAT study (2019).

Study data was collected via three online questionnaires administered during the pandemic (2020, 2021, and 2023). Relevant data included sociodemographics (sex, age, education level) and clinical data (COVID-19 infection severity, body mass index [BMI], general health status, and vaccination details). Participants who remained unresponsive or provided incomplete data were excluded from statistical analyses.

The study further defined and investigated the prevalence and risk associations of ‘persistent Long COVID,’ symptoms which were reported in 2021 and persisted through 2023. Urban environmental pollutant exposure was assessed based on annual averages at participants' residential addresses. These included air (suspended particulate matter [PM2.5, PM10], nitrogen dioxide [NO2], and ozone [O3]), green space availability (including normalized difference vegetation index [NDVI]), nighttime road-traffic noise, and outdoor melanopic illuminance (blue light artificial light at night [ALAN]).

The association between each individual exposure and participant outcomes was evaluated using three mixed-effect robust Poisson regression models adjusted for sociodemographic, clinical, and infection severity factors. Statistical analyses included participants reporting COVID-19 infections but no subsequent Long COVID symptoms as controls.

Study Findings Out of the 2,853 participants included in the final dataset, 700 (24.5%) reported Long COVID symptoms, of which 153 (5%) reported persistent Long COVID. Women (n = 1,788, 27.6%) reported higher Long COVID prevalence than their male counterparts (n = 1,065, 19.4%). Education was found to be a significant determinant of Long COVID incidence, with university-educated participants (n = 1,557, 22.2%) demonstrating lower prevalence than those with only primary/lower education (n = 219, 29.2%).

Prior chronic disease substantially increased Long COVID risk (n = 1,013, 33.3%). COVID-19-associated clinical data revealed that infection severity and pre-infection vaccination administration were significant determinants, with the latter reducing Long COVID risk.

Notably, apart from suspended particulate matter exposure (PM2.5 RR = 1.14 [95% CI: 0.97–1.37], PM10 RR = 1.15 [95% CI: 0.98–1.39]), no other urban environmental pollutants were found to be associated with the heightened prevalence of Long COVID. These findings align with previous reports, albeit in a Catalonian-restricted sample cohort. Researchers speculate that particulate matter may influence Long COVID risk indirectly by increasing the severity of the initial infection.

Conclusions The present study investigates the impacts of several urban environmental pollutants (air, noise, green spaces, and light) on Long COVID or persistent Long COVID risk. Study findings reveal that suspended particulate matter increased the risk of Long COVID by exacerbating the severity of the initial COVID-19 infection. Women, participants with limited education, and those with pre-existing chronic diseases were found to demonstrate substantially higher Long COVID risk than their respective counterparts. Surprisingly, other evaluated environmental pollutants were not found to impact Long COVID incidence or prevalence.

“Further research focusing on Long COVID subtypes, symptom clusters, and potential mechanisms underlying observed associations will be crucial for enhancing our understanding of this complex condition,” the researchers concluded.

Journal reference: Saucy, A., Espinosa, A., Iraola-Guzmán, S., Castaño-Vinyals, G., Harding, B. N., Karachaliou, M., Ranzani, O., De Cid, R., Garcia-Aymerich, J., & Kogevinas, M. (2024). Environmental Exposures and Long COVID in a Prospective Population-Based Study in Catalonia (COVICAT Study). In Environmental Health Perspectives (Vol. 132, Issue 11). Environmental Health Perspectives, DOI – 10.1289/ehp15377, ehp.niehs.nih.gov/doi/10.1289/EHP15377

[spacetimewithstuartgary]

Satellites Reveal a Greener Antarctic Peninsula

The white, monochromatic vistas of Antarctica, a continent practically synonymous with ice, are becoming more verdant—at least around some of its edges. According to an analysis of 35 years of observations from Landsat satellites, the amount of vegetated land on the Antarctic Peninsula has increased more than tenfold since 1986.

As glaciers shrink and temperatures warm, plant life is finding more opportunity to move into this and other cold-climate regions. The expansion of greenery on the Antarctic Peninsula may signal a shift in its ecology, researchers say, raising questions about its future.

Using observations from Landsat 5 through Landsat 8, scientists determined that the area of vegetated land on the Antarctic Peninsula grew from 0.86 to 11.95 square kilometers (0.33 to 4.61 square miles) between 1986 and 2021. Notably, the expansion of green space accelerated starting in 2016. The research, published in Nature Geoscience in October 2024, was led by environmental scientist Tom Roland of the University of Exeter and remote sensing expert Olly Bartlett of the University of Hertfordshire.

Their results are encapsulated in the maps above. The panels show the amount of green on the peninsula’s ice-free land below 300 meters (1,000 feet) elevation at select years in the study period. The shade of each hexagon corresponds to the amount of land area where levels of plant greenness and density, as determined by the satellite-based Normalized Difference Vegetation Index (NDVI), were high enough to indicate the “almost certain” presence of vegetation. The NDVI values were derived from cloud-free Landsat observations from March of each year, which typically encompasses the end of the growing season. The maps show significant expansion in vegetation cover across the South Shetland Islands (at the top-left of each panel) and down the western side of the peninsula to nearly the southern limit for plant growth.

Past field studies on the Antarctic Peninsula have shown that mosses dominate its green areas, forming expansive “carpets,” like on Ardley Island in the photo below, and vertically accumulating “banks.” Both build up layers of new growth each year. In earlier work, Roland and colleagues carbon-dated core samples taken from moss banks along the western side of the peninsula. They discovered that the rates at which moss was accumulating had increased in the past 50 years, indicating an uptick in biological activity amid climatic changes.

These results got scientists wondering if vegetation was not only expanding its territory upward but also outward. So Roland, Bartlett, and their team tapped into the decades-long Landsat record. “Based on the core samples, we expected to see some greening,” Roland said, “but I don’t think we were expecting it on the scale that we reported here.”

The pace of greening was surprising, as well. “When we first ran the numbers, we were in disbelief,” Bartlett said. But the results, based on two different measures of greenness, kept coming out the same. “The rate itself is quite striking, especially in the last few years.”

The Antarctic Peninsula, jutting out into the Southern Ocean from West Antarctica, is one of the fastest warming places on Earth. Most of its glaciers are receding. The uptick in new vegetation in recent years also coincides with a decrease in Antarctic sea-ice extent, an increase in open water, and a strong positive phase in the Southern Annular Mode, which could be producing warmer, wetter conditions, the authors noted.

As plant life finds more favorable conditions for growth on the Antarctic Peninsula, questions about biodiversity in its unique habitats arise. “The narrative in these places has been dominated by glacial retreat,” Roland said. “We’re starting to think about what comes next, after ice recession.” Of particular concern is that where there’s moss, soil formation tends to follow, creating more opportunity for nonnative plants to find a foothold. When that happens, said Bartlett, “you’re looking at potentially an erosion of biodiversity.”

Antarctica hosts hundreds of native species of mosses, liverworts, lichens, and fungi, but only two native species of flowering plants. Human presence on the continent for tourism and research can result in the introduction of nonnative species (although seeds and spores can also arrive on the wind). Several cases of invasions have already been documented in the northern Antarctic Peninsula and nearby islands. “Biosecurity will become increasingly critical as temperature limitations on cold, high-latitude ecosystems decrease,” wrote the researchers.

Now, the scientists are keen to return to the field to understand the changes in finer detail. “We’re at the point that we’ve said the best we can say with the Landsat archives,” Roland said. Many questions remain as to what types of plant communities comprise these new green areas and what shifts in the structure and function of the Antarctic Peninsula’s ecosystems may already be underway. “We need to go to these places where we’re seeing the most distinctive changes and see what’s happening on the ground.”

NASA Earth Observatory maps by Michala Garrison, based on data from Roland, T.P., et al. (2024). Photo courtesy of Dan Charman (University of Exeter). Story by Lindsey Doermann.

[distinguishedconnoisseurgarden]

QGIS Course: Get Job-Ready with Practical GIS Training

In today's data-driven world, Geographic Information Systems (GIS) have become a crucial part of urban planning, environmental monitoring, disaster management, and much more. QGIS (Quantum GIS), a free and open-source platform, empowers professionals and learners with tools to visualize, manage, and analyze spatial data. Whether you're a student, engineer, environmentalist, or a researcher, a QGIS course can open doors to exciting opportunities across various industries.

Why Learn QGIS?

QGIS has become a go-to tool for both beginners and professionals due to its ease of use, robust community support, and constant updates. Here's why enrolling in a QGIS course makes sense:

Open-source and free: No licensing fees, which makes it ideal for individuals and startups.

Highly versatile: Supports vector, raster, and database formats.

Advanced features: Includes georeferencing, spatial analysis, plugin support, and 3D mapping.

Cross-industry applications: Useful in fields like agriculture, geology, city planning, and more.

What You Will Learn in a QGIS Course:

A comprehensive QGIS training program typically includes:

Introduction to GIS and spatial data types

Navigating the QGIS interface

Creating and managing shapefiles and layers

Georeferencing scanned maps

Performing spatial queries and analysis

Using plugins for advanced mapping

Creating interactive web maps

Real-world project-based learning

Courses may be structured for absolute beginners, intermediate users, or even advanced professionals, depending on your goals.

Case Studies:

Case Study 1: Mapping Urban Green Spaces in Bengaluru, India

Objective: To identify, measure, and map green cover in different wards of Bengaluru city.

Tools Used: QGIS, OpenStreetMap, and Sentinel satellite imagery

Process:

Satellite images were imported into QGIS and overlaid with city ward boundaries.

NDVI (Normalized Difference Vegetation Index) analysis was conducted using raster tools.

Green cover was calculated per ward and visualized on a thematic map.

Outcome:

The project revealed that only 14% of the city had consistent green cover.

Results were used by the city council to propose urban tree plantation drives in low-green areas.

Case Study 2: Disaster Risk Mapping in Nepal

Objective: To create a hazard map for earthquake-prone zones in Nepal.

Tools Used: QGIS, GPS survey data, DEM (Digital Elevation Models)

Process:

Field data was collected from various rural regions and imported into QGIS.

Slope and elevation maps were generated using DEM.

Layers were overlaid to identify regions at highest seismic risk.

Outcome:

Local authorities used the map to guide future construction projects and implement early-warning systems in critical zones.

Case Study 3: Agricultural Monitoring in Punjab

Objective: To monitor cropping patterns and improve irrigation efficiency in Punjab’s agricultural belt.

Tools Used: QGIS, Landsat imagery, Field survey data

Process:

Crop classification was done using supervised classification in QGIS.

Raster and vector data were combined to assess changes in crop types over time.

A report was generated to identify areas with over-irrigation.

Outcome:

Farmers received recommendations for crop rotation and water conservation.

The project helped reduce water usage by 18% over two years.

Career Opportunities After a QGIS Course:

Completing a QGIS certification course can lead to a variety of career paths, such as:

GIS Analyst

Remote Sensing Specialist

Urban Planner

Environmental Consultant

Agricultural GIS Expert

GIS Developer

With QGIS being recognized globally, your skills are applicable not just in India, but around the world.

Conclusion:

A QGIS course equips you with essential skills to analyze spatial data, contribute to real-world projects, and build a rewarding career. From urban development to environmental sustainability, GIS professionals are in high demand — and QGIS is your gateway to entering this dynamic field.

[lucyprincy]

2D Drone Mapping

Introduction

2D drone mapping is a modern surveying technique that uses drones equipped with high-resolution cameras to capture aerial images and generate detailed, georeferenced maps. This technology is widely used in agriculture, construction, urban planning, environmental monitoring, and disaster management. By providing fast, accurate, and cost-effective mapping solutions, 2D drone mapping has revolutionized traditional surveying methods.

How 2D Drone Mapping Works

1. Flight Planning

Before data collection, a flight plan is created using drone mapping software. Parameters such as altitude, camera angle, speed, and image overlap (typically 70-80%) are set to ensure complete area coverage.

2. Aerial Image Capture

The drone flies autonomously or manually over the designated area, capturing high-resolution images at regular intervals. The images overlap significantly to allow seamless stitching during processing.

3. Image Processing

After data collection, specialized software stitches the images together using photogrammetry techniques. The result is a detailed 2D orthomosaic map, which is georeferenced and corrected for distortions.

4. Analysis & Application

The final map is analyzed using Geographic Information System (GIS) software to extract valuable insights such as measurements, land classifications, and environmental changes.

Applications of 2D Drone Mapping

1. Agriculture

Farmers use 2D drone maps to monitor crop health, detect pests, and optimize irrigation. Vegetation indices like the Normalized Difference Vegetation Index (NDVI) help assess plant health and improve yield management.

2. Construction & Urban Planning

2D mapping assists engineers and urban planners in site planning, land surveying, and infrastructure development. It provides accurate topographic maps, helping in project visualization and progress tracking.

3. Environmental Monitoring

Drones help in tracking deforestation, coastal erosion, and climate change effects. Conservationists use 2D maps to analyze ecosystem changes and implement protective measures.

4. Disaster Management

Drones provide real-time maps for assessing damage caused by floods, earthquakes, or wildfires. Emergency responders use these maps to plan rescue operations and allocate resources efficiently.

5. Infrastructure & Utility Inspection

Power lines, pipelines, and roadways are mapped for maintenance and repair planning. Engineers use the maps to detect wear and tear, ensuring timely interventions.

Benefits of 2D Drone Mapping

Cost-Effective: Reduces the need for expensive manned surveys.

Time-Saving: Faster than traditional mapping methods.

High Accuracy: Provides precise, georeferenced data.

Enhanced Safety: Eliminates the need for manual surveying in hazardous areas.

Scalability: Suitable for small and large-scale projects.

Conclusion

2D drone mapping is transforming industries by offering a fast, accurate, and affordable solution for surveying and mapping. With advancements in drone technology and data processing software, its applications will continue to expand, making it an essential tool for data-driven decision-making across multiple sectors.

[aerosmart]

DJI Mavic 3 Multi Specteral

The DJI Mavic 3 Multi-Spectral is a specialized drone designed for precision agriculture, environmental monitoring, and land management. This powerful tool integrates multi-spectral imaging capabilities to provide detailed insights into crop health, land conditions, and environmental changes. Equipped with advanced sensors and AI technology, the Mavic 3 Multi-Spectral enables users to make data-driven decisions in agriculture and ecological research.

At the heart of the Mavic 3 Multi-Spectral is its multi-spectral camera system, which includes a 4K RGB camera and six individual multi-spectral sensors. These sensors capture data across different wavelengths of light, including Red, Green, Blue, Red Edge, Near-Infrared (NIR), and Shortwave Infrared (SWIR). This multi-spectral imaging allows users to generate vegetation index maps, such as NDVI (Normalized Difference Vegetation Index), which are crucial for assessing plant health, stress levels, and soil conditions.

The drone’s RTK module enables centimeter-level accuracy, making it ideal for high-precision mapping and surveying. This feature is especially useful in agriculture for creating detailed field maps that can guide irrigation, fertilization, and crop management practices. The Mavic 3 Multi-Spectral’s ability to collect accurate georeferenced data ensures that the resulting maps and analysis are reliable for decision-making.

In addition to its imaging capabilities, the Mavic 3 Multi-Spectral offers long flight times of up to 45 minutes, allowing for extensive coverage in a single mission. With its compact and foldable design, the drone is easy to transport and deploy in the field, making it a highly portable solution for agricultural professionals and researchers. Its Omnidirectional obstacle sensors also enhance flight safety, preventing collisions in complex environments.

Overall, the DJI Mavic 3 Multi Specteral is an exceptional tool for precision agriculture and environmental monitoring. By providing detailed, multi-spectral data with high accuracy, it helps farmers and land managers optimize their practices, improve crop yields, and monitor environmental changes. Whether used for large-scale agricultural management or small research projects, the Mavic 3 Multi-Spectral is a powerful, reliable drone that enhances operational efficiency and sustainability.

[servotechinc78]

Sustainable Farming Made Possible with Precision Technology

Sustainability in agriculture has become a pressing concern in the modern world. As populations grow and climate challenges intensify, the need for efficient, eco-friendly farming practices is more urgent than ever. Precision farming technology has emerged as a transformative solution, enabling farmers to meet the growing demand for food while minimizing environmental impact. This article explores how precision technology is making sustainable farming a reality.

What is Precision Farming Technology?

Precision farming technology refers to the use of advanced tools and techniques, such as GPS, sensors, drones, data analytics, and AI, to optimize agricultural practices. By collecting and analyzing data, farmers can make informed decisions to enhance efficiency, reduce waste, and maximize yields.

Unlike traditional farming methods, which often rely on generalized practices, precision farming focuses on specific areas within fields, tailoring inputs like water, fertilizers, and pesticides to the exact needs of crops. This targeted approach not only conserves resources but also reduces the environmental footprint of farming.

The Pillars of Sustainable Farming

Sustainable farming revolves around three key principles:

Environmental Conservation: Minimizing the use of harmful chemicals, preserving biodiversity, and reducing greenhouse gas emissions.

Economic Viability: Ensuring farming remains profitable through efficient use of resources.

Social Responsibility: Supporting communities and ensuring food security for future generations.

Precision technology directly addresses these pillars by integrating innovation with sustainability.

How Precision Technology Promotes Sustainable Farming

1. Optimized Resource Management

Precision farming technology enables the efficient use of vital resources like water, fertilizers, and energy. Tools such as soil moisture sensors and automated irrigation systems ensure that water is applied only where and when it is needed. This reduces wastage and prevents issues like waterlogging or drought stress in plants.

For example, Variable Rate Technology (VRT) allows farmers to apply fertilizers and pesticides in precise amounts, tailored to the specific needs of different parts of the field. This not only reduces chemical usage but also prevents runoff, protecting nearby water bodies from pollution.

2. Improved Soil Health

Healthy soil is the foundation of sustainable farming. Precision technology helps maintain and enhance soil quality through detailed soil mapping and monitoring. By understanding soil nutrient levels, pH, and organic matter, farmers can implement practices that prevent soil degradation and promote long-term fertility.

Additionally, techniques like no-till farming, supported by precision tools, help reduce soil erosion and maintain carbon sequestration, contributing to climate change mitigation.

3. Enhanced Crop Monitoring

Drones and satellite imagery provide real-time data on crop health, identifying issues like pest infestations, nutrient deficiencies, or diseases at an early stage. This allows for timely interventions, reducing crop losses and minimizing the need for excessive chemical applications.

For instance, Normalized Difference Vegetation Index (NDVI) technology can assess crop vigor and guide farmers in making data-driven decisions to enhance productivity while conserving resources.

4. Reduced Greenhouse Gas Emissions

By optimizing inputs and adopting efficient farming practices, precision technology significantly reduces greenhouse gas emissions associated with agriculture. For example, precise fertilizer application minimizes nitrous oxide emissions, a potent greenhouse gas.

Moreover, GPS-guided machinery reduces fuel consumption by enabling more efficient field operations, such as planting, harvesting, and tilling.

5. Increased Biodiversity

Precision farming allows for better land management, enabling farmers to set aside areas for natural habitats and biodiversity conservation. By reducing the overuse of chemicals and promoting precision in farming practices, ecosystems are less disrupted, fostering a balance between agriculture and nature.

Real-World Applications of Precision Farming Technology

Case Study 1: Precision Irrigation in California

In California, where water scarcity is a critical issue, farmers have adopted precision irrigation systems to conserve water. By using soil moisture sensors and weather data, they have reduced water usage by up to 30% while maintaining crop yields. This practice has not only saved resources but also improved sustainability in a region prone to droughts.

Case Study 2: Smart Farming in India

Smallholder farmers in India are leveraging mobile apps and sensors to monitor soil health and weather patterns. These tools provide recommendations on optimal planting times and fertilizer usage, helping farmers increase productivity while minimizing costs and environmental impact.

Case Study 3: Drones in Brazilian Agriculture

In Brazil, drones equipped with multispectral cameras are being used to monitor vast soybean fields. These drones identify areas affected by pests or diseases, enabling targeted interventions. This approach has reduced pesticide use by 20%, lowering costs and preserving the environment.

Challenges and Future Prospects

Challenges

While precision farming technology offers immense benefits, there are challenges to its widespread adoption:

High Initial Costs: The cost of acquiring and implementing precision tools can be prohibitive for small-scale farmers.

Technical Expertise: Farmers need training to effectively use and interpret data from precision tools.

Infrastructure Gaps: In developing regions, lack of access to reliable internet and electricity can hinder the adoption of precision farming.

Future Prospects

Despite these challenges, the future of precision farming is promising. Innovations such as AI-powered analytics, blockchain for supply chain transparency, and robotics are set to further revolutionize agriculture. Governments and organizations are also offering subsidies and training programs to encourage the adoption of precision technology.

As these technologies become more affordable and accessible, they will play a pivotal role in transforming global agriculture into a more sustainable and resilient system.

Conclusion

Precision farming technology is not just a tool for improving efficiency; it is a cornerstone of sustainable agriculture. By enabling farmers to optimize resource use, protect the environment, and enhance productivity, precision technology addresses the critical challenges of modern farming.

As the world grapples with climate change, population growth, and dwindling natural resources, adopting precision farming is no longer an option but a necessity. With continued innovation and support, precision technology holds the key to a sustainable and food-secure future.

[davidjohnstongisschool]

Remote Sensing: Revolutionizing Earth Observation Technologies

Remote sensing has emerged as a transformative technology in the field of Earth observation, enabling scientists, policymakers, and industries to monitor and understand our planet like never before. From tracking climate change to managing natural resources and responding to disasters, remote sensing has become an indispensable tool for addressing global challenges. This blog explores the fascinating world of remote sensing, its applications, and its potential to revolutionize how we observe and interact with Earth.

What Is Remote Sensing?

Remote sensing is the process of acquiring information about an object or phenomenon without making physical contact with it. This is typically achieved through sensors mounted on satellites, aircraft, drones, or ground-based platforms. These sensors capture data in various forms, such as images, electromagnetic waves, or thermal readings, which are then analyzed to extract valuable insights.

The two primary types of remote sensing are:

Passive Remote Sensing: This type relies on natural energy, such as sunlight, to illuminate the target. Sensors detect reflected or emitted radiation, commonly used in optical and infrared imaging.

Active Remote Sensing: Here, sensors emit their own energy (e.g., radar or LiDAR) and measure the response from the target. This method is ideal for capturing data in low-light or cloudy conditions.

Historical Evolution of Remote Sensing

The origins of remote sensing date back to the early 20th century, with the advent of aerial photography during World War I. However, the field truly gained momentum with the launch of satellites like Landsat-1 in 1972, marking the beginning of systematic Earth observation from space. Advances in sensor technology, computational power, and data processing have since propelled remote sensing into a new era, where real-time, high-resolution, and multispectral data are readily available.

Applications of Remote Sensing

Remote sensing has a wide range of applications across diverse fields, each contributing to a better understanding and management of Earth’s resources and systems. Let’s explore some of the most impactful uses:

1. Environmental Monitoring

Remote sensing plays a crucial role in tracking environmental changes, such as deforestation, desertification, and urban sprawl. High-resolution satellite imagery allows researchers to monitor land-use changes and assess their impact on ecosystems.

Example: NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) helps monitor vegetation health, ocean temperature, and atmospheric conditions, providing critical data for environmental research.

2. Climate Change Analysis

Understanding the complexities of climate change requires vast amounts of data, and remote sensing delivers just that. Satellite observations help track greenhouse gas emissions, melting glaciers, rising sea levels, and changes in weather patterns.

Example: The European Space Agency’s Sentinel-5P satellite measures air quality and monitors pollutants like carbon dioxide and methane, aiding climate policy development.

3. Agriculture and Food Security

Farmers and agricultural planners benefit immensely from remote sensing technologies, which provide insights into crop health, soil moisture, and weather conditions. Precision agriculture uses this data to optimize irrigation, fertilization, and pest control.

Example: NDVI (Normalized Difference Vegetation Index) maps generated from satellite imagery help farmers assess crop vigor and make informed decisions.

4. Disaster Management

Remote sensing is a lifeline during natural disasters, providing real-time data for emergency response and recovery. From tracking hurricanes to assessing earthquake damage, remote sensing technologies save lives and resources.

Example: Radar imagery from satellites like Sentinel-1 is used to monitor flood extents and support disaster relief efforts.

5. Urban Planning and Infrastructure

As cities expand, remote sensing helps urban planners design sustainable infrastructure. High-resolution imagery and 3D mapping support decision-making in transportation, zoning, and resource allocation.

Example: LiDAR data is often used to create detailed 3D models of urban areas, aiding in construction and development projects.

6. Oceanography

The vastness of oceans makes them challenging to study through conventional methods. Remote sensing fills this gap by providing data on sea surface temperature, chlorophyll concentration, and ocean currents.

Example: NOAA’s satellites monitor coral reef health and detect harmful algal blooms, contributing to marine conservation.

Cutting-Edge Technologies in Remote Sensing

The field of remote sensing is constantly evolving, driven by innovations in sensor technology, data processing, and machine learning. Here are some cutting-edge advancements:

1. Hyperspectral Imaging

Unlike traditional multispectral sensors, hyperspectral imaging captures data across hundreds of narrow spectral bands. This enables the detection of subtle differences in material composition and condition.

2. Synthetic Aperture Radar (SAR)

SAR is an advanced form of radar imaging that provides high-resolution data regardless of weather or lighting conditions. It is particularly useful for monitoring terrain changes and detecting oil spills.

3. Unmanned Aerial Vehicles (UAVs)

Drones equipped with remote sensing instruments offer unparalleled flexibility and accessibility. They are widely used for mapping small areas, inspecting infrastructure, and monitoring wildlife.

4. Artificial Intelligence and Machine Learning

AI and machine learning algorithms are transforming remote sensing by automating data analysis and pattern recognition. These technologies enhance the accuracy and efficiency of applications ranging from land classification to disaster prediction.

Challenges and Future Directions

Despite its many advantages, remote sensing faces several challenges that need to be addressed:

Data Overload: The sheer volume of data generated by modern sensors demands robust storage, processing, and analysis capabilities.

Cost and Accessibility: While satellite imagery has become more affordable, high-resolution data can still be expensive for many users.

Ethical Concerns: Issues related to privacy and surveillance need to be carefully managed as remote sensing becomes more pervasive.

Looking ahead, the future of remote sensing is incredibly promising. Advances in nanosatellites, quantum sensing, and edge computing are expected to make Earth observation more accessible, efficient, and precise. Collaborative efforts between governments, private companies, and academic institutions will play a key role in unlocking the full potential of remote sensing.

Conclusion

Remote sensing is undoubtedly revolutionizing Earth observation technologies, offering unparalleled insights into our planet’s dynamics. By enabling real-time monitoring and analysis, it empowers us to tackle pressing global challenges, from climate change to disaster management. As the technology continues to evolve, remote sensing will remain at the forefront of efforts to build a sustainable and resilient future for our planet. Whether through satellite constellations or AI-driven analytics, the possibilities are as vast as the Earth itself—and we are just beginning to explore them.

[kheti-buddy]

Satellite Data and Application of Remote Sensing in Agriculture

Satellite data and remote sensing have transformed agriculture by providing comprehensive, timely, and spatially explicit information across large areas. Here are several ways satellite data is applied in agriculture:

Crop Monitoring and Management:

Vegetation Indices: Satellites capture multispectral imagery to calculate vegetation indices like NDVI (Normalized Difference Vegetation Index). These indices indicate crop health, biomass, and photosynthetic activity. Farmers use this information to monitor crop growth stages, detect stress factors (such as water or nutrient deficiencies), and optimize management practices.

Crop Type Classification: Satellite imagery helps classify different crop types and monitor their distribution over agricultural landscapes. This information aids in crop rotation planning, land use management, and assessing agricultural productivity.

Precision Agriculture:

Variable Rate Application: Satellite data supports precision agriculture by identifying spatial variability in soil properties and crop conditions. Farmers can apply inputs such as fertilizers, pesticides, and water at variable rates across fields based on these insights. This targeted approach maximizes crop yield while minimizing input costs and environmental impact.

Yield Monitoring: Satellites provide data for yield estimation and prediction by monitoring vegetation growth patterns and biomass accumulation. This helps farmers and agricultural stakeholders anticipate production levels, plan logistics, and manage market expectations.

Weather and Climate Monitoring:

Weather Forecasting: Satellite data contributes to weather forecasting by monitoring atmospheric conditions, cloud cover, and precipitation patterns. Accurate weather forecasts enable farmers to plan field operations, irrigation schedules, and pest management activities more effectively.

Climate Change Impact Assessment: Long-term satellite observations help assess the impact of climate change on agriculture by tracking changes in temperature, precipitation, and vegetation cover. This information informs adaptation strategies and policy decisions aimed at building resilience in agricultural systems.

Disaster Monitoring and Response:

Early Warning Systems: Satellites play a crucial role in monitoring natural disasters such as floods, droughts, and wildfires. They provide real-time data on affected areas, enabling rapid response and disaster management efforts. This includes assessing crop damage, coordinating relief operations, and planning recovery measures.

Land Use Planning and Management:

Land Cover Mapping: Satellite imagery facilitates the mapping and monitoring of land cover changes, including deforestation, urbanization, and expansion of agricultural lands. This information supports land use planning, conservation efforts, and sustainable land management practices.

Soil Mapping and Analysis: Remote sensing data aids in soil mapping and analysis by identifying soil types, erosion patterns, and nutrient content across large geographical areas. This knowledge guides soil conservation practices, optimal crop selection, and land suitability assessments.

Overall, the application of remote sensing in agriculture provides farmers, researchers, and policymakers with valuable tools to enhance agricultural productivity, sustainability, and resilience in the face of environmental challenges and changing climate conditions. Integrating these insights into agricultural decision-making processes can lead to more efficient resource use, reduced risks, and improved livelihoods for farmers worldwide

[spacetimewithstuartgary]

Can we find floating vegetation on ocean planets?

Astronomical surveys have discovered nearly 6,000 exoplanets, including many habitable planets, which may harbor liquid water on their surfaces. The search for life on such planets is one of the most significant scientific endeavors of this century, with direct imaging observation projects currently under development.

On Earth-like planets, the characteristic reflectance spectrum of terrestrial vegetation, known as “vegetation red edge”, is considered as a key biosignature. However, ocean planets, with most of their surfaces covered by water, are unlikely to support terrestrial vegetation. To broaden the scope of life detection on ocean planets, this study examined the characteristics of reflectance spectra from floating plants and tested their detectability.

Results

The study investigated the reflectance spectra of floating plants across different scales, from individual leaves in laboratory settings to large-scale observation via satellite remote sensing of lake vegetation.

Although floating leaves exhibit considerable morphological variation among species, their general trend reveals a pronounced red edge, often comparable to or even exceeding that of terrestrial plants. This enhancement is attributed to air gaps in sponge tissue that provide buoyancy and specialized epidermal structures that offer water repellency. While floating leaves show slightly reduced reflectance when wet, they still display a more distinct red edge than submerged water plants (Figure 1).

However, on a larger scale, the red edge signature of floating vegetation weakens due to lower vegetation density and reduced leaf overlap on the water surface. Landscape-scale analyses using satellite remote sensing (Sentinel-2; ESA) with the Normalized Difference Vegetation Index (NDVI) flourishes in summer and disappears in winter, causing the NDVI to be relatively low when averaged over the year. Nevertheless, the fluctuation between minimum and maximum NDVI values is more pronounced for floating vegetation compared to forests. To further investigate this pattern, a large-scale survey of 148 lakes and marshes across Japan was conducted. The study revealed a characteristic seasonal NDVI variation, shifting from negative values in winter to positive values in summer (Figure 2). Importantly, while water suppresses the reflectance of floating vegetation, its own reflectance is even lower and remains stable. It enhances the detectability of seasonal NDVI fluctuations, which remain robust against atmospheric and cloud interference, suggesting that this method could be promising for detecting life on habitable exoplanets in the future.

Perspectives

If photosynthetic organisms, such as floating plants, exist universally on habitable exoplanets, then the scope of life exploration can be expanded to include ocean planets rather than being limited to Earth-like planets. It is important to understand the origin and evolutionary process of life as it coevolves with planetary environments to predict the morphology of organisms that may adapt to diverse planetary conditions. This study provides a foundation for future research on biosignatures, paving the way for the next generation of life-detection missions.

TOP IMAGE: An illustration depicting seasonal changes in floating vegetation and the resulting periodic changes in water surface reflectance. It is expected to become a novel indicator in searching for life on ocean planets. Credit Astrobiology Center

CENTRE IMAGE:  Reflectance comparison of different plant types. A submerged aquatic plant (Egeria densa, left panel) exhibits significantly lower reflectance than a terrestrial plant (Arabidopsis thaliana, right panel). A floating plant (Salvinia molesta, center panel) retains reflectance characteristics like the land plant, with a pronounced red edge. Credit Astrobiology Center

LOWER IMAGE: Seasonal variation of NDVI reflecting floating vegetation patterns. NDVI increases from spring to summer as floating vegetation flourishes and decreases from fall to winter. In winter when the floating vegetation disappears, NDVI values turn negative. Credit Astrobiology Center

[2023flywithavplinternational]

“Precision Farming: Algorithms for Agricultural Drones”

1. NDVI (Normalized Difference Vegetation Index): NDVI is used to assess and monitor plant health by measuring the difference between near-infrared and red light reflectance.

2. Object Detection and Classification: Identify and classify objects in the field, such as crops, weeds, or pests.

3. Crop Monitoring and Yield Prediction: Estimate crop yields and monitor crop growth over time.

4. Terrain and Topography Mapping: Create 3D maps of the terrain to identify elevation changes and drainage patterns.

5. Automated Path Planning: Optimize flight paths for efficient data collection.

6. Weed Detection and Management: Identify and manage weed infestations.

7. Soil Health Assessment: Assess soil properties, moisture content, and nutrient levels.

8. Pest and Disease Detection: Detect signs of pests and diseases in crops.

9. Weather and Climate Monitoring: Purpose: Monitor weather conditions and assess their impact on crop health.

10. Real-time Monitoring and Feedback: Provide real-time data for immediate decision-making.

These algorithms often work in conjunction, and the choice of algorithm depends on the specific goals of the agriculture drone mission. Combining these technologies can provide farmers with valuable insights for optimizing crop management and increasing overall efficiency.

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[laopiniononline]

HUB Ambiental UPLA y Municipalidad de Quilpué inician plan conjunto para reforzar protección de humedales.

Nueva publicación en https://ct2.cl/9F

HUB Ambiental UPLA y Municipalidad de Quilpué inician plan conjunto para reforzar protección de humedales.

Se realizarán muestreos de agua, suelo y vegetación, a fin de fortalecer la gobernanza y toma de decisiones con miras a la preservación de estos espacios.

En un esfuerzo mancomunado, la Municipalidad de Quilpué, la Corporación Municipal de la misma comuna y el HUB Ambiental de la Universidad de Playa Ancha, dieron inicio a una serie de acciones destinadas a reforzar la protección, conservación y preservación de los humedales en la zona, plan que también involucrará al área educacional.

Es así como, en una salida a terreno, investigadores del centro universitario realizaron las primeras mediciones en el Estero de Quilpué, a fin de determinar si existe, ya sea en el agua o el suelo, presencia de elementos contaminantes. Todo lo anterior, en el contexto de la implementación del Observatorio Ambiental Académico de la Región de Valparaíso, iniciativa a través de la cual el HUB Ambiental se adjudicó un Fondo de Innovación para la Competitividad Regional (FIC-R 2022) del Gobierno Regional.

Asimismo, participaron en esta instancia investigadores del Laboratorio de Teledetección Ambiental (TeleAmb), de la Facultad de Ciencias Naturales y Exactas de la casa de estudios e integrante del mismo centro de investigación, en el marco del proyecto Fondef “Plataforma de Monitoreo Satelital para la Gestión y Planificación de las Ciudades”, el cual tiene a la comuna de Quilpué como área piloto.

Contraparte técnica

Según manifiesta el encargado del Departamento de Gestión Ambiental Local de la Dirección Ambiental y de Sostenibilidad de la Municipalidad de Quilpué, Diego Peñaloza, “el HUB Ambiental es bien importante para lo que nosotros queremos realizar como municipio en el cuidado, conservación y preservación de los humedales. En este sentido, necesitamos el apoyo de entidades que puedan levantar datos para robustecer nuestra ordenanza y nuestras acciones de cara a su protección”.

La investigadora postdoctoral del centro y coordinadora del Observatorio Ambiental Académico, Dra. María José Díaz, explica que, cumpliendo el rol de contraparte técnica, buscan efectuar un levantamiento del estado de salud del humedal correspondiente al Estero de Quilpué, para lo cual llevaron a cabo estos primeros muestreos.

A estos esfuerzos se suma la contribución del Laboratorio TeleAmb que, a través del proyecto Fondef, ya ha levantado cinco indicadores, uno de los cuales tiene que ver con la vegetación. Se denomina Normalized Difference Vegetation Index (NDVI) y ya ha involucrado vuelos drones y levantamientos satelitales.

“Claramente, vemos que hay una asociación entre dónde están la mayor vegetación y las zonas más húmedas, en este caso la caja del estero, y lo que podemos hacer ahí es, básicamente, hacer un cruce del estado de la vegetación en conjunto con los resultados obtenidos en los muestreos de las aguas”, explica el geógrafo Marcelo Leguía, director del Fondef.

Transmisión de conocimiento

Complementando lo anterior, la Dra. María José Díaz destaca que uno de los ejes centrales del Observatorio Ambiental Académico es la vinculación con el territorio, abarcando no solamente al municipio, sino también los establecimientos educacionales de la comuna “que tengan alguna relación con los humedales, ya sea porque tengan ciertas prácticas de educación ambiental en sus establecimientos, o porque, de alguna forma, estén ubicados cerca de alguno de ellos”.

Esto se enlaza con un proyecto de la Corporación Municipal de Quilpué, denominado “Medioambientalización de la Educación”, el cual se relaciona con los ejes transversales de los establecimientos, con miras a fortalecer la educación medioambiental. En ese sentido, la encargada de Buen Vivir de la Corporación, Yasna Carpio, releva que “los humedales son considerados aulas abiertas, en donde se puede plasmar educación y aprendizajes significativos para los estudiantes”.

Es por eso que uno de los propósitos a corto plazo, es poder vincular al HUB Ambiental con el establecimiento más cercano al estero, el Colegio Ignacio Carrera Pinto. “La idea es integrarlos y, también, invitar a las organizaciones medioambientales que ya están trabajando en el establecimiento, para poder hacer una alianza transversal en el ámbito de la educación medioambiental”, agrega.

Esta acción va en directa relación con los planteamientos del Acuerdo de Escazú y la Dra. María José Díaz pone sobre relieve la diversidad de disciplinas complementarias con que cuenta el HUB Ambiental UPLA, además de la responsabilidad de “transmitir el conocimiento a la ciudadanía, de la forma más amigable para ellos, es decir, no quedarnos en los tecnicismos científicos, algo que pasa frecuentemente, para que ellos hagan uso de la información y entiendan las problemáticas socioambientales en las cuales estén vinculados”, puntualiza.

[andmaybegayer]

What else could you use a full-spectrum camera for?

The main thing that comes to mind is aerial plant survey for normalized difference vegetation index (NDVI). A full spectrum camera alone is really not very useful, it sees too much, you almost always end up pairing it with a filter. This is a filter that lets through two particular kinds of light, 1) the kind of visible light a plant absorbs for photosynthesis and 2) the kind of infrared light that a living plant cell will reflect to avoid overheating.

If you take those two values and compare them, you can see how well plant life is doing: it should be nearly black in 1), and nearly white in 2). If it's white in 1), it means the plant isn't photosynthesising, it is reflecting useful light, and if it's black in 2), it means the mechanisms that reflect or return that infrared light have failed and the plant is likely overheating and dying.

By using the difference between these values, you can easily rejected open earth, water and buildings (which will reflect both in fairly equal measure).

Normally you'd do this from space, but it's also sometimes used for aerial photographic survey of farmland, forests, etc. to identify disease outbreaks and soil problems. Farmers use it a lot.

Here's a shot of the plants under a circular watering system: the long red bar is the watering arm, so don't worry about that, but the bar itself has some clogged nozzles so it isn't watering evenly: you can see where those are in this image by the rings of red: the plants are underwatered and not photosynthesizing properly.

[quotesfromall]

In general, if there is much more reflected radiation in near-infrared wavelengths than in visible wavelengths, then the vegetation in that pixel is likely to be dense and may contain some type of forest. If there is very little difference in the intensity of visible and near-infrared wavelengths reflected, then the vegetation is probably sparse and may consist of grassland, tundra, or desert.

Measuring Vegetation