Earth Observations & Zones

Earth Observations & Zones

Use Decipher’s Earth Observations and Zones module to visualise and access advanced analysis and imagery including NDVI, NDRE, MSAVI and NDWI. Our unique partnership with Google Earth Engine and sourcing of real time data from Landsat and Sentinel means you’ll be working with the best data possible.

Earth Observations - Environmental Monitoring - Decipher - Wesfarmers
Monitoring from space, commonly known as Earth Observations, is transforming the way by which organisations and individuals are understanding and seeing the world around us. Since satellites first started orbitting the Earth, their monitoring programs are becoming increasingly ambitious and provide a means to access comprehensive imagery and insights into the Earths landscape at a large scale. Earth Observations data is being increasingly utilised across a variety of applications such as urban development and infrastrucutre, climate change, biodiversity and ecosystem sustainability, agriculture, water management and mineral resources management. Decipher, utilising Google Earth Engine (Google satellite images) in our cloud-based platform, is at the forefront of this new era of utilising satellite images and datasets, and are helping pave the way for additional research and increased utilisation and application in everyday use. Our library of available indices is constantly evolving to meet the demands of various customer requirements and our highly interactive interface allows users to apply it to other datasets and 'real life' scenarios. With results dating back to the late 1980's (Google Earth historical imagery) and being updated with every satellite flyover (Google Earth real time satellite images), users can benefit from accessing time series datasets of conditions pre and post project to better understand and demonstrate performance across selected satellite derived indices.

Bringing together powerful indices into one easy-to-use platform

Normalized Difference Vegetation Index (NDVI)

Normalized Difference Vegetation Index (NDVI) assists in determining vegetation health. It is a commonly used indices that is applied across a variety of functions such as biomass, drought monitoring, forest supply & leaf area index, agricultural production, fire zoning and rehabilitation works. NDVI is commonly used with NDRE for similar applications but targeted at earlier stages of plant growth.

How it works:

Chlorophyll is a key indicator of plant health, as it correlates to its ability to absorb light and photosynthesise. Chlorophyll strongly reflects near infrared (NIR) and absorbs visible light and light. NDVI measures the difference between difference between near-infrared (NIR) and red light to determine how the plant reflects light and therefore provide an indication of the state of health for that plant.

Works well with: NDMI, NDRE, NDWI, MSAVI

Satellites in use: The off-the-shelf solution Decipher offers utilises the Sentinel 2A and 2B satellites (S2A or S2B) depending on the coverage and date. Commercial satellite imagery can be acquired where preferred.

Resolution & Image Footprint (LxW): Sentinel 2A and 2B provide resolution of 10m x 10m and the image footprint is 100km x 100km.

Decipher - Earth Obs - NDVI

Normalized Difference Red Edge Index (NDRE)

Normalized Difference Red Edge’s (NDRE), similar to NDVI, assists in determining vegetation health. It is a commonly used indices that is applied across a variety of functions such as biomass, drought monitoring, forest supply & leaf area index, agricultural production, fire zoning and rehabilitation works. The key difference between NDVI and NDRE is that NDRE can provide better insight into permanent or later stage vegetation due to its ability to make more accurate readings where variance between intra-image readings is low.

How it works: Chlorophyll is a key indicator of plant health, as it correlates to its ability to absorb light and photosynthesise. Chlorophyll strongly reflects near infrared (NIR) and absorbs visible light and light. Chlorophyll, when looking at the top layer of plants or highly dense vegetation, reaches a maximum point in the red waveband that makes measuring variability difficult when applying NDVI as it becomes highly saturated. This is where Red Edge can be applied as it can also measure further down the canopy by utilising a waveband between the NIR band and the red band and is therefore less prone to the saturation affects presented to NDVI.

Works well with: NDVI, MSAVI, NDWI

Satellites in Use: The off-the-shelf solution Decipher offers utilises the Sentinel 2A and 2B satellites (S2A or S2B) depending on the coverage and date. Commercial satellite imagery can be acquired where preferred.

Resolution & Image Footprint (LxW): Sentinel 2A and 2B provide resolution of 10m x 10m and the image footprint is 100km x 100km.

Decipher - Earth Obs - NDRE

Modified Soil Adjusted Vegetation Index (MSAVI)

The Modified Soil Adjusted Vegetation Index (MSAVI) assists in determining vegetation health but specifically in areas where there is a large composition of bare soil. MSAVI is commonly used in conjunction with NDVI and NDRE for applications in forestry, agricultural production, mining and rehabilitation works.

How it works: MSAVI measures a ratio between Red and Near Infrared (NIR) and introduces a soil brightness correction factor. It is the same calculation as Soil Adjusted Vegetation Index (SAVI) however, the correction factor needs to be variable based on the amount of vegetation and therefore this modification is what makes up MSAVI. This approach enables soil brightness to be corrected more reliably.

Works well with: NDVI, RNDVI, SMI

Satellites in Use: The off-the-shelf solution Decipher offers utilises the Sentinel 2A and 2B satellites (S2A or S2B) depending on the coverage and date. Commercial satellite imagery can be acquired where preferred.

Resolution & Image Footprint (LxW): Sentinel 2A and 2B provide resolution of 10m x 10m and the image footprint is 100km x 100km.

Decipher - Earth Obs - MASVI

Normalized Difference Water Index (NDWI)

Normalised Difference Water Index (NDWI) relates to two applications of liquid water. Firstly, it is an indicator of vegetation health, specifically, moisture content. NDWI is used predominantly in drought monitoring within agriculture and rehabilitation works related activities as it assists in detection of water related stress on plants. Secondly, NDWI can assist in monitoring water content changes in water bodies and is therefore used across various industries to detect and measure water bodies, water changes and surface water analysis.

How it works: NDWI utilises near-infrared (NIR) and Short Wave Infrared (SWIR) bands. In the context of vegetation, NIR is not affected by water content, instead, dry matter and internal structure of leaves. SWIR reflectance however, signals changes in mesophyll structure of vegetation canopies and in vegetation water content. When united, SWIR & NIR can therefore derive a more reliable insight into vegetation water content.

In the context of water bodies, NDWI applies NIR and SWIR bands to measure the reflectance of water bodies in landscapes and determine turbidity and size.

Works well with: NDVI, MDWI, AWEI

Satellites in Use: The off-the-shelf solution Decipher offers utilises the Landsat satellite depending on the coverage and date. Commercial satel-lite imagery can be acquired where preferred.

Resolution & Image Footprint (LxW): Sentinel 2A and 2B provide resolution of 10m x 10m and the image footprint is 100km x 100km.

Decipher - Earth Obs - NDWI

Assess vegetation health and variability

Track vegetation health, performance and sustainability and identify areas of concern through Decipher's advanced indices including NDVI, NDRE and MSAVI

Remote offset site management

Harnessing remote sensing data and satellite derived indices allows users managing offset sites to access data remotely and reduce the need for in-person inspections

Advanced filtering & analysis

Filter satellite imagery by custom date range and seasons to extract time series data for points and areas comparison in a geospatial or chart manner for use in reporting

Management zones

Clustering algorithms automatically create zones based on outputs from a selected indices to highlight areas of low and high performing areas, all of which can be exported

Generate actions to address change & variability

Harnessing the action management tool, users can delegate tasks to responsible individuals, at specific locations, to address and manage trends occurring within the earth observations module

How Earth Observations works

1
Satellite Imagery
Our off-the-shelf product sources imagery from multiple satellites including Sentinel, Landsat 5, 7 and 8
2
Google Processing
Largescale, rapid geospatial processing of satellite imagery conducted in Google Earth Engine (GEE)
3
Data Cleanse
GEE outputs are cleansed to account for cloud cover and atmospheric conditions (including smoke, dust, angle of sun and more) to ensure optimal accuracy
4
Algorithm Selection
Users can select from a library of evolving algorithms such as NDVI, NDRE and MSAVI which interact with GEE
5
Analyse
Users are provided with a series of filters and tools to analyse the data including date range, cloud cover, image, seasons and colour scale
6
Visualise & Report
A built in algorithm allows for greater visualisation, timeseries charting of data and the ability to export and report
7
Real World Application
Make data-driven decisions to implement effective change and improvements to a project

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Modules this works well with