Two New Earth Observation Satellites Launched

Artist's rendition of a satellite - paulfleet/123RF Stock Photo

Artist’s rendition of a satellite – paulfleet/123RF Stock Photo

Two new Earth observation satellites were launched last week from European Space Centre in Kourou in French Guyana, although you may only get to see the data from one. Ven”s and OPTSAT-3000 were put into sun synchronous orbits by Arianespace via its Vega launch vehicle on the 1st August. Both satellites were built by Israel’s state-owned Israel Aerospace Industries and carry instruments from Israel’s Elbit Systems.

Ven”s, or to give its full title of Vegetation and Environment monitoring on a New MicroSatellite, is a joint scientific collaboration between the Israeli Space Agency (ISA) and France’s CNES space agency.

Ven”s is focussed on environmental monitoring including climate, soil and topography. Its aim is to help improve the techniques and accuracy of global models, with a particular emphasis on understanding how environmental and human factors influence plant health. The satellite is equipped with the VEN”S Superspectral Camera (VSSC) that uses 12 narrow spectral bands in the Visible Near Infrared (VNIR) spectrum – ranging from 420nm wavelength up to 910 nm wavelength – to capture 12 simultaneous overlapping high resolution images which are then combined into a single image. The camera uses a pushbroom collection technique and has a spatial resolution of 5.3m and a swath size of 27.56 km.

Ven”s won’t have full global coverage; instead there are 110 areas of interest around the world that includes forests, croplands and nature reserves. With a two day revisit time, during which time it completes 29 orbits of the planet. This means every thirtieth image will be collected over the same place, at the same time and with the same angle. This will provide high resolution imagery more frequently than is currently available from existing EO satellites. The consistency of the place, time and angle will help researchers better assess fine-scale changes on the land to improve our understanding of the:

  • State of the soil,
  • vegetation growth,
  • detection of spreading disease or contamination,
  • snow cover and glacial movements; and
  • sediment movement in coastal estuaries

A specific software algorithm has been developed for the mission to work with the different wavelengths to remove clouds and aerosols from the satellite’s imagery, giving clear images of the planet irrespective of atmospheric conditions.

The second satellite launched was the OPTSAT-3000 which is an Italian controlled optical surveillance satellite, which will operate in conjunction with the COSMO-SkyMed radar satellites giving Italy’s Ministry of Defence independent autonomous national Earth observation capability across optical and radar imagery.

This is a military satellite and so some of the details are difficult to verify. As mentioned earlier the instrument was made by Elbit systems, and the camera used usually offers a spatial resolution of around 0.5 m. However, it has been reported that the resolution will be much closer to 0.3m because the satellite is in a very low earth orbit of a 450 km.

OPTSAT-3000 will collect high resolution imaging of the Earth, it’s not clear at this stage whether any of the imagery will be made available for commercial/scientific use or purchase, although it is worth noting that COSMOS-SkyMed images are sold.

Two more Earth observation satellites launched shows that our industry keeps on moving forward! We’re really interested, and in OPTSAT’s case hopeful, to see the imagery they produce.

Three Exciting Ways to Protect Forests With Remote Sensing

Forests cover one third of the Earth’s land mass and are home to more than 80% of the terrestrial species of animals, plants and insects. However, 13 million hectares of forest are destroyed each year. The United Nations International Day of Forests took place recently, on 21st March, to raise awareness of this vital resource.

Three remote sensing applications to help protect forests caught our eye recently:

Two scans show the difference between infected, on the right, and uninfected, on the left, patches of forest. Image Courtesy of University of Leiceste

Identifying Diseased Trees
In the March issue of Remote Sensing, researchers from the University of Leicester, (Barnes et al, 2017), published a paper entitled ‘Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands’. It describes how the researchers were able to identify individual trees affected by larch tree disease, also known as phytophthora ramorum.

This fungus-like disease can cause extensive damage, including the death, and diseased trees can be identified by defoliation and dieback. Airborne LiDAR surveys were undertaken by the company Bluesky at an average altitude of 1500 m, with a scan frequency of 66 Hz that gave a sensor range precision within 8 mm and elevation accuracy around 3–10 cm.

Remote sensing has been used to monitor forests for many years, but using it to identify individual trees is uncommon. The researchers in this project were able to successfully identify larch canopies partially or wholly defoliated by the disease in greater than 70% of cases. Whilst further development of the methodology will be needed, it is hoped that this will offer forest owners a better way of identifying diseased trees and enable them to respond more effectively to such outbreaks.

Monitoring Trees From Space
An interesting counterpoint to work of Barnes et al (2017) was published by the journal Forestry last month. The paper ‘Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications‘ written by Fassnacht et al (2017).

It describes work undertaken to compare the results of very high resolution optical satellite data with that of airborne LiDAR and hyperspectral data to provide support for forestry management. The team used WorldView-2 images, of a temperate mixed forest in Germany, with a 2m pixel size, alongside a LiDAR DTM with a 1 m pixel size. This data was then used to estimate tree species, forest stand density and biomass.

They found  good results for both forest stand density and biomass compared to other methods, and although the tree classification work did achieve over eighty percent, this was less than achieved by hyperspectral data over the same site; although differentiation of broadleaved and coniferous trees was almost perfect.

This work shows that whilst further work is needed, optical data has the potential to offer a number of benefits for forestry management.

Monitoring Illegal Logging
Through the International Partnership Programme the UK Space Agency is funding a consortium, led by Stevenson Astrosat Ltd, who will be using Earth Observation (EO) data to monitor, and reduce, illegal logging in Guatemala.

The issue has significant environmental and socioeconomic impacts to the country through deforestation and change of land use. The Guatemalan government have made significant efforts to combat the problem, however the area to be monitored is vast. This project will provide a centralised system using EO satellite data and Global Navigation Satellite Systems (GNSS) technology accessed via mobile phones or tablets to enable Guatemala’s National Institute of Forestry (INAB) to better track land management and identify cases of illegal logging.

The protection of our forests is critical to the future of the planet, and it’s clear that satellite remote sensing can play a much greater role in that work.

Goodbye to EO-1

Hyperspectral data of fields in South America classified using Principle Components Analysis. Data acquired by Hyperion. Image courtesy of NASA.

In contrast to our previous blog, this week’s is a celebration of the Earth Observing-1 (EO-1) satellite whose death will soon be upon us.

EO-1 was launched on the 21st November 2000 from Vandenberg Air Force Base, California. It has a polar sun-synchronous orbit at a height of 705 km, following the same orbital track as Landsat-7, but lagging one minute behind. It was put into this orbit to allow for a comparison with Landsat 7 images in addition to the evaluation of EO-1’s instruments.

It was the first in NASA’s New Millennium Program Earth Observing series, which had the aim of developing and testing advanced technology and land imaging instruments, particularly related to spatial, spectral and temporal characteristics not previously available.

EO-1 carries three main instruments:

  • Hyperion is an imaging spectrometer which collects data in 220 visible and infrared bands at 30 m spatial resolution with a 7.5 km x 100 km swath. Hyperion has offered a range of benefits to applications such as mining, geology, forestry, agriculture, and environmental management.
  • Advanced Land Imaging (ALI) is a multispectral imager capturing 9 bands at 30 m resolution, plus a panchromatic band at 10 m, with a swath width of 37 km. It has the same seven spectral bands as Landsat 7, although it collects data via a different method. ALI uses a pushbroom technique where the sensor acts like a broom head and collects data along a strip as if a broom was being pushed along the ground. Whereas Landsat operates a whiskbroom approach which involves several linear detectors (i.e., broom heads) perpendicular (at a right angle) to the direction of data collection. These detectors are stationary in the sensor and a mirror underneath sweeps the pixels from left to right reflecting the energy from the Earth into the detectors to collect the data.
  • Atmospheric Corrector (LAC) instrument allows the correction of imagery for atmospheric variability, primarily water vapour, by measuring the actual rate of atmospheric absorption, rather than using estimates.

The original EO-1 mission was only due to be in orbit only one year, but with a sixteen year lifetime it has surpassed all expectations. The extension of the one year mission was driven by the Earth observation user community who were very keen to continue with the data collection, and an agreement was reached with NASA to continue.

Psuedo-true colour hyperspectral data of fields in South America. Data acquired by Hyperion. Image courtesy of NASA.

All the data collect by both Hyperion and ALI is freely available through the USGS Centre for Earth Resources Observation and Science (EROS). At Pixalytics we’ve used Hyperion data for understanding the capabilities of hyperspectral data. The two images shown in the blog are a subset of a scene acquired over fields in South America, with image to the right is a pseudo-true colour composite stretched to show the in-field variability.

Whereas the image at the top is the hyperspectral data classified using a statistical procedure, called Principle Components Analysis (PCA), which extracts patterns from within the dataset. The first three derived uncorrelated variables, termed principle components, are shown as a colour composite.

Sadly, satellites cannot go on forever, and EO-1 is in its final few weeks of life. It stopped accepting data acquisition requests on the 6th January 2017, and will stop providing data by the end of February.

It has been a great satellite, and will be sadly missed.