Skip to main content

A New Lake—Water Not Lava—On Kilauea. #Landsat #NASA #USGS #Earth

A New Lake—Water Not Lava—On Kilauea

Between 2010 and 2018, a large lava lake bubbled and spattered within Kilauea's summit caldera. Then in May 2018, as part of a broader eruption that poured lava from fissures to the east, the lake swiftly drained and part of the caldera floor collapsed. This left a hole nearly as deep as One World Trade Center.

But that was not all Pele had in store. For about a year, the much-deeper and wider Halema'uma'u crater was relatively quiet. But in July 2019, helicopter pilots began to notice water pooling into a pond in the lowest part of the crater. Water levels have risen steadily ever since. Today, the lake—now with a rusty brown sheen on its surface due to chemical reactions taking place in the water—has an area larger than five football fields combined and a maximum depth of roughly 30 meters (100 feet).

The sequence of satellite images above shows Halema'uma'u crater before the lava lake drained (left), after the caldera floor had collapsed (middle), and after water pooled on the crater floor for nine months (right). The Operational Land Imager (OLI) on Landsat 8 acquired all three natural-color images.

When the lava lake was present, it appeared in the southeast part of Halema'uma'u, though a crust of partially solidified lava on its surface made it appear gray from above. (The circular light gray area with a thin plume of volcanic emissions rising from it marks the location of the lake.) After the caldera collapse, the terrain surrounding the lake changed dramatically, including the formation of a new 140-meter cliff (thin dark line) north of the crater. In the final image, the pond on the summit appears small from Landsat's perspective (30 meters per pixel). The photograph below, taken on April 21, offers a better sense of scale.

The explanation for the new pond is simple. "We have a drill hole a little more than one kilometer south of the crater where we measure the level of the water table," explained Don Swanson, a volcanologist at the U.S. Geological Survey's Hawaiian Volcano Observatory. "We know that the crater floor dropped a little more than 70 meters below the water table in 2018. Any time that you punch a hole below the level of the water table, water is eventually going to come in and fill that hole."

Explaining what the new pond means for the volcano is where the story gets more complicated and interesting. One of the key factors that controls explosive volcanic eruptions is how much water and other gases get caught up within the magma. If magma has a lot of dissolved gases and steam, pressure builds and explosive eruptions can result. If not, lava tends to flow gently from fissures in the ground—as has been the case at Kilauea for the past 200 years.

Calm eruptions are the exception, not the norm. Over the past 2,500 years, Kilauea has erupted explosively about 60 percent of the time, noted Swanson. "We have been misled by how calm it has been. If this was 1720 rather than 2020, then we would we would not have seen a lava flow for more than 200 years, and we may have thought Kilauea was always an explosive volcano."

There are two scenarios that could lead to an explosive eruption. "In one case, magma could rise quickly up the conduit and intersect with the lake," said Swanson. "In the second, the crater floor could collapse and drop all of the water down to a zone where it would be quickly heated into steam."

But that does not mean the next eruption will be explosive. "The next eruption could happen slowly and the water could evaporate," he said. "We do not want to be alarmist, but we also need to point out to the public that there is an increasing possibility of explosive eruptions at Kilauea."

One thing is quite certain: geologists will be closely monitoring Kilauea and its new lake with every tool available, including seismometers, thermal cameras, drones, helicopter surveys, and satellites. "Is the volcano in the process of reverting back to an explosive period that may last for centuries?" said Swanson. "Or is this just a little blip, and we are going to return to quiet lava flows like we had during the 19th and 20th centuries? Only time will tell."

NASA Earth Observatory images by Joshua Stevens, using Landsat data from the U.S. Geological Survey. USGS photo by Matthew Patrick. Story by Adam Voiland.


#Landsat #NASA #USGS #Earth


....


Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://g.page/vineeshvc

Comments

Popular posts from this blog

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types  1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

RADIOMETRIC CORRECTION

  Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance. In other words, it corrects for sensor defects, illumination differences, and atmospheric effects. 1. Detector Response Calibration Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly. This includes: (a) De-Striping Problem: Sometimes images show light and dark vertical or horizontal stripes (banding). Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others. Common in early Landsat MSS data. Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker. Soluti...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...