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Deliverable 2.1

Essential Variables (EV) List

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 821918

enviroLENS – D2.1: Essential Variable (EV) List

Start date of project: 01-12-2018

Duration: 24 months

Deliverable number 2.1

Deliverable title Essential Variables (EV) List

Deliverable due date 31-05-2019

Lead beneficiary AUTH

Work package WP2

Deliverable type Report

Submission date: 31.05.2019

Revision: Version 1.0

Dissemination Level

PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium

CO Confidential, only for members of the consortium (including the Commission Services)

Project acronym: enviroLENS

Project title: Copernicus for environmental law enforcement support

Project number: 821918

Instrument: Horizon 2020

Call identifier: H2020-SPACE-2018

Topic DT-SPACE-01-EO-2018-2020

Type of action Innovation action


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Title:

Essential Variable (EV) List

Author(s)/Organisation(s):

Petros Patias/ AUTH

Natalia Verde/ AUTH

Giorgos Mallinis/AUTH

Maria Tasopoulou/ AUTH

Contributor(s):

Franziska Albrecht/ GeoVille

Mariza Pertovt/ Sinergise

Short Description:

Deliverable 2.1 includes a final list of selected Essential Variables (EVs) that will be compiled on the basis of current

capabilities of the EO-Toolset (see D5.1 EO-Toolset) and the perspective of available sensors to be added during the

project. Further the document details already available pre-processing capabilities and implemented biophysical

variables chosen by AUTH who has long standing expertise on EVs (as demonstrated through previous projects and as

described in scientific publications). Also, user requirements developed from the perspective of EO processing are

considered Finally, the EV list will be finalised based on the estimation of required work and availability of external open

source components.

Keywords:

Essential Variables, Earth Observation, modelling, EO toolset

History:

Version Author(s) Status Comment Date

0.1 Petros Patias Initial Draft 01.04.2019 0.2 Natalia Verde

Giorgos Mallinis Further Input 15.05.2019

0.3 Maria Tasopoulou Giorgos Mallinis

Finalization 30.05.2019

Review:

Version Reviewer Comment Date

1.0 Franziska Albrecht Final version, ready for submission 31.05.2019


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Table of Contents

1 Introduction .................................................................................................................................................... 9

2 Current and future capabilities of the EO-toolset ......................................................................................... 11

2.1 Available sensors .................................................................................................................................... 11

2.1.1 Sentinel-2 ......................................................................................................................................... 11

2.1.2 Landsat-8 .......................................................................................................................................... 11

2.1.3 Landsat-5 .......................................................................................................................................... 11

2.1.4 Landsat-7 .......................................................................................................................................... 12

2.1.5 Aqua/Terra MODIS ........................................................................................................................... 12

2.2 Sensors to be implemented in the future .............................................................................................. 13

2.3 Pre-processing capabilities ..................................................................................................................... 13

2.3.1 Atmospheric correction for Sentinel-2 data ..................................................................................... 13

2.3.2 Automated cloud and cloud shadow identification in Sentinel imagery .......................................... 13

2.4 Band colour composites ......................................................................................................................... 14

2.5 Spectral indices ...................................................................................................................................... 15

3 Essential variables ......................................................................................................................................... 16

3.1.1 Essential Climate Variables (ECV) ..................................................................................................... 16

3.1.2 Essential Biodiversity Variables (EBVs) ............................................................................................. 16

3.1.3 Essential Renewable Energies Variables (EREV) ............................................................................... 17

3.1.4 Essential Geodetic Variables (EGVs) and Essential Earth Rotation Variables (EERVs) ...................... 17

3.1.5 Essential Ocean Variables (OCVs) ..................................................................................................... 17

3.1.6 Essential Water Variables (EWVs) .................................................................................................... 18

3.1.7 Essential Sustainable Development Goals Variables (ESDGV) .......................................................... 18

3.1.8 Urban Essential Variables (UEVs) ..................................................................................................... 18

3.1.9 Essential Societal Variables (ESVs) ................................................................................................... 18

3.1.10 Essential Agriculture Variables (EAVs) ............................................................................................ 19

3.1.11 Essential Protected Area Variables (EPAVs) ................................................................................... 19

4 USE-CASE requirements and EVs linkages..................................................................................................... 20

4.1 Case 1: Environmental Impact Assessment Process in the Energy Projects ........................................... 20

4.2 Case 2: Force Majeure Events in the Energy Contracts .......................................................................... 20

4.3 Case 3: Oil and Gas Pipeline Monitoring For Safety and Environment ................................................... 21

4.4 Case 4: Forest Law Enforcement and Governance ................................................................................. 21


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4.5 Case 5: Infrastructure development in Protected Areas ........................................................................ 22

4.6 Case 6: Land-use change in the context of disaster risk reduction ......................................................... 22

5 Essential Variables modelling ........................................................................................................................ 23

5.1 Burned Area (ECV) and Burn Severity (ECV) modelling .......................................................................... 23

5.2 Built-up area ........................................................................................................................................... 25

5.3 Soil Sealing .............................................................................................................................................. 27

5.4 Land Cover .............................................................................................................................................. 29

6 Final Essential Variables list........................................................................................................................... 30

7 References .................................................................................................................................................... 31

Appendix .......................................................................................................................................................... 34

Spectral indices for the sensors available within the EO-Toolset ................................................................. 34

Sentinel-2 indices ...................................................................................................................................... 34

Landsat-8 spectral indices ......................................................................................................................... 39

MODIS spectral indices ............................................................................................................................. 40

Essential Variables list ............................................................................................................................... 42


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List of Figures

Figure 1: General Workflow for computing EVs for monitoring ...................................................................... 10

Figure 2: Cloud detection algorithm within the EO toolset based on Braaten et al. (2015) ............................ 13

Figure 3: Cloud detection algorithm within the EO toolset based on Hollstein et al. (2016) ........................... 14

Figure 4 :Spectral Response Curves of burned areas and of healthy vegetation, US Forest Service, 2018 ...... 20

Figure 5 Burned Area Index (BAI) and Normalized Burn Ratio (NBR) formulas indices, ((35) .......................... 23

Figure 6: Burned Area and Burn Severity workflow modelling ........................................................................ 24

Figure 7: Computation of NBR (middle image) and ΔNBR (right image) for the area of Alexandroupolis –

Greece (left image – orthorectified). Dark areas indicate burned areas. ......................................................... 25

Figure 8 Built Up area workflow modelling using EO ready data (spatial data from data repositories) ........... 26

Figure 9 Soil Sealing workflow modelling using satellite imagery .................................................................... 27

Figure 10: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2009-

2018 ................................................................................................................................................................. 28

Figure 11: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2015-

2018 ................................................................................................................................................................. 28

Figure 12: Computation of Land Cover using available spatial data and satellite imagery .............................. 29

List of Tables

Table 1: Readily available composites in EO-toolset for the Sentinel-2, Landsat and MODIS sensors ............ 14

Table 2 ΔNBR Burn Severity Categories, USGS FireMon program ................................................................... 21


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Abbreviations

ABT Aichi Biodiversity Targets

ARD Analysis-Ready-Data

BAI Burnt Area Index

EAV Essential Agriculture Variable

EBV Essential Biodiversity Variable

ECV Essential Climate Variable

EERV Essential Earth Rotation Variable

EERVs Essential Earth Rotation Variables

EGrVs Essential Gravimetric Variables

EGV Essential Geodetic Variable

EGVs Essential Geodetic Variables

EO Earth Observation

EOPs Earth orientation parameters

EOVs World Ocean Circulation Experiment

EPAV Essential Protected Area Variable

EREV Essential Renewable Energies Variable

ESDGV Essential Sustainable Development Goals Variable

ESV Essential Societal Variable

ETM+ Enhanced Thematic Mapper Plus

EVs Essential Variables

EWV Essential Water Variable

GBO4 Global Biodiversity Outlook

GEOGLAM GEO’s Global Agricultural Monitoring initiative

GEOGLOWS GEO-Global Water Sustainability

GGOS Global Geodetic Observing System

GOOS Global Ocean Observing System

IAG International Association of Geodesy

IGWCO Integrated Global Water Cycle Observations

IMBeR Integrated Marine Biosphere Research

IOC Intergovernmental Oceanographic Commission

LAI Leaf Area Index

LiDAR LIght Detection And Ranging

MBON Marine Biodiversity Observation Network

MODIS Moderate Resolution Imaging Spectroradiometer

MSS Multispectral Scanner

MSS Multispectral Scanner

NBR Normalized Burn Ratio

OBIS Ocean Biogeographic Information System

OCV Essential Ocean Variable

OLI Operational Land Imager

SDG Sustainable Development Goals


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TIRS Thermal Infrared Sensor

TM Thematic Mapper

UEV Urban Essential Variable

UNFCCC United Nation Convention on Climate Change

USGS United States Geological Survey

VHR Very High Resolution

WOCE World Ocean Circulation Experiment


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1 Introduction

The concept of Essential Variables (EVs) has emerged within the remote sensing community in recent years.

The EVs, having previously defined as a minimal set of variables that determine the system’s state and

development, have attracted considerable interest not only for remote sensing scientists, but from several,

diverse thematic groups and communities. The driving forces behind this evolution relates primary to the

need to support national to global monitoring, reporting, research, and forecasting of complex earth

systems. In addition, the necessity for an essential set of parameters that could be used for monitoring

progress towards the goals of different thematic communities as well as to the requirement to support

consistent, objective and temporal information provision for policy development and implementation is also

a critical issue for the development of EVs. Moreover, considering the availability of sensor data with similar

characteristics from different satellites orbiting around earth, there is a need to standardize the extracted

information independently of the observational platform and the processing algorithms and provide this

information in a more streamlined, comprehensible form to end-users not well acquainted with the remote

sensing technology and terminology.

Thus, integrated Earth Sensing, envisioned by early 1990’s (1) can be nowadays a reality, facilitating efficient,

reliable and affordable monitoring of our planet from global to local scales through the assimilation of

remote sensing observations and in situ measurements. In recent years alongside the growing societal and

environmental challenges and requirements for Earth’s monitoring at multiple spatial and temporal scales,

there has been a great improvement in EO development and research (sensor technology and image

processing). Spaceborne and airborne, EO multispectral, hyperspectral, thermal, LiDAR datasets

characterized by their synoptic view and extended spatial coverage collection capabilities, can offer the

potential to monitor Earth system and its components at local-to-global scales through appropriate

indicators and variables (2). Finally, in-situ instruments, which provide over limited geographic extent

accurate, high-frequency and high-quality measurements, are also crucial for providing ground-based

calibration and validation for the EO data.

Yet, even nowadays, challenges still exist since the growing and sometimes heterogeneous volume of raw EO

data available for local, regional, and national decision-making requires time-consuming processing

workflows for information extraction. These workflows incorporate significant costs and complexity, despite

hardware/software improvement and the availability of Analysis-Ready-Data (ARD) that facilitates faster and

more consistent analysis with lower user efforts. Furthermore, it shouldn’t be neglected that only in few

cases does the EO measure the actual variables of interest, but rather is used to infer or estimate variables

through expert-based selection of retrieval algorithms or models. Quite often it is also needed to integrate

diverse EO (and non-EO) data for Earth’s system monitoring, in order to meet multiple user requirements

across different scales, environmental settings and applications, generating non-standardized, fragmented

and heterogeneous information. In addition, EVs definition is an open process, which ideally should be based

in simultaneous top-down and bottom-up approach, trying to address both scientific questions and needs

and tackle societal benefit areas.


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Regardless of the EV Type and the specific theme, global requirements for characterizing a variable as

“Essential”, are that this variable is critical for characterizing a specific environmental system or process, it is

sensitive to detect change at different scales, it is technically feasible using scientifically understood methods

to observe or derive the variable on a global scale and it can be generated and archived on an affordable

way, relying on coordinated observing systems and proven current technology (3). Overall, an essential

requirement is that within each theme, a limited number of variables should be identified. This will not only

minimize technical and scientific requirements related to data formats, data processing, big data handling,

bias correction, data harmonization etc., but it will contribute to the cost-effectiveness of the framework,

allowing early detection of changes and improved communication between science, policy and the society

(4).

Concluding, the proposed methodology of identifying and computing an EV and the area of interest and the

reporting level of the monitored phenomenon are closely interdependent parameters. Specifically, by setting

the spatial level (i.e. global, regional, country level etc.) and the required time intervals of the monitoring, we

can define which EV can be used for the monitoring. In addition, the definition of the EV defines the

temporal and spectral analysis of the satellite imagery and in general the EO data products that will be

utilized for the computation of each EV.

The methodology proposed by the Laboratory of Photogrammetry and Remote Sensing of the Department

of Surveying Engineering of the Aristotle University of Thessaloniki (PE&RSLab), (5) suggests computing each

EV as a set of critical or complementary variables which are individual sub-parameters that when combined

can quantify the EV. Within that framework, one EV can potentially contribute to multiple case studies, and a

given observation can be linked to more than on EV (6). This can enable a potential reduction of the number

of observations required to deliver the EVs and thus monitoring a phenomenon (Figure 1).

Figure 1: General Workflow for computing EVs for monitoring


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2 Current and future capabilities of the EO-toolset

2.1 Available sensors

2.1.1 Sentinel-2

Dedicated to supplying data for Copernicus services, Sentinel-2 carries a multispectral imager with a swath of

290 km. The imager provides a versatile set of 13 spectral bands spanning from the visible and near infrared

to the shortwave infrared, featuring four spectral bands at 10 m, six bands at 20 m and three bands at 60 m

spatial resolution.

2.1.2 Landsat-8

The Landsat-8 satellite launched on February 11, 2013, orbits the Earth in a sun-synchronous, near-polar

orbit, at an altitude of 705 km (438 mi), inclined at 98.2 degrees, and circles the Earth every 99 minutes. The

satellite has a 16-day repeat cycle with an equatorial crossing time: 10:00 a.m. +/- 15 minutes. The Landsat-8

satellite carries 2 instruments namely, Operational Land Imager (OLI)- and Thermal Infrared Sensor (TIRS).

The Operational Land Imager includes eight “narrow” spectral bands with 30 m spatial resolution and a

“wider” panchromatic band with 15 m spatial resolution:

• Band 1 Coastal (0.43 - 0.45 µm) 30 m

• Band 2 Blue (0.450 - 0.51 µm) 30 m

• Band 3 Green (0.53 - 0.59 µm) 30 m

• Band 4 Red (0.64 - 0.67 µm) 30 m

• Band 5 Near-Infrared (0.85 - 0.88 µm) 30 m

• Band 6 SWIR 1(1.57 - 1.65 µm) 30 m

• Band 7 SWIR 2 (2.11 - 2.29 µm) 30 m

• Band 8 Panchromatic (PAN) (0.50 - 0.68 µm) 15 m

• Band 9 Cirrus (1.36 - 1.38 µm) 30 m

The Thermal Infrared Sensor (TIRS) includes two spectral bands with 100 m spatial resolution:

• Band 10 TIRS 1 (10.6 - 11.19 µm) 100 m

• Band 11 TIRS 2 (11.5 - 12.51 µm) 100 m

2.1.3 Landsat-5

Landsat-5 orbited the planet at an altitude of 705 km more than 150,000 times from 1984 to 2013 being the

longest-operating Earth-observing satellite sensor in history, transmitting over 2.5 million images of land

surface conditions around the world. The Landsat-5 satellite carried 2 instruments namely, Multispectral

Scanner (MSS) - and Thematic Mapper (TM). TM was an across-track mechanical scanner provided

multispectral images of Earth’s surface with 8-bit radiometric resolution and 30 m spatial resolution. The TM

included seven reflective spectral bands with 30 m spatial resolution and a thermal band with 120 meters

spatial resolution:


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• Band 1 Blue (0.45 - 0.52 µm) 30 m

• Band 2 Green (0.52 - 0.60 µm) 30 m

• Band 3 Red (0.63 - 0.69 µm) 30 m



• Band 6 Thermal (10.40 - 12.50 µm) 120 m

• Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m

2.1.4 Landsat-7

The Landsat-7 satellite orbits the Earth in a sun-synchronous, near-polar orbit, at an altitude of 705 km (438

mi), inclined at 98.2 degrees, and circles the Earth every 99 minutes. The satellite has a 16-day repeat cycle

with an equatorial crossing time: 10:00 a.m. +/- 15 minutes. Landsat-7 carries the Enhanced Thematic

Mapper Plus (ETM+) sensor, delivering 8-bit images with 256 grey levels.

The ETM+ includes seven reflective spectral bands (six “narrow” spectral bands with 30 m and a “wider”

panchromatic band with 15 meters spatial resolution) and a thermal band with 60 m spatial resolution:

• Band 1 Blue (0.45 - 0.52 µm) 30 m

• Band 2 Green (0.52 - 0.60 µm) 30 m

• Band 3 Red (0.63 - 0.69 µm) 30 m



• Band 6 Thermal (10.40 - 12.50 µm) 60 m Low Gain / High Gain

• Band 7 Mid-Infrared (2.08 - 2.35 µm) 30 m

• Band 8 Panchromatic (PAN) (0.52 - 0.90 µm) 15 m

2.1.5 Aqua/Terra MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra and

Aqua satellites at an orbit of 705 km. Both Terra- and Aqua-MODIS instruments view the entire surface of

the Earth every 1 to 2 days, acquiring data across 36 spectral MODIS bands from 0.4s to 14.4 μm. Available

on the EO-Toolset is the MCD43A4 product, providing the 500 meter reflectance data of the MODIS “land”

bands 1-7 adjusted using the bidirectional reflectance distribution function to model the values as if they

were collected from a nadir view.

The MCD43A4 product includes seven reflective spectral bands:

• Band 1 Red) (620–670 µm) 500 m

• Band 2 Near-Infrared (841–876 µm) 500 m

• Band 3 Blue (459–479 µm) 500 m

• Band 4 Green (545–565 µm) 500 m

• Band 5 SWIR-1 (1230–1250 µm) 500 m

• Band 6 SWIR-2 (1628–1652 µm) 500 m

• Band 7 MWIR (2105–2155 µm) 500 m


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2.2 Sensors to be implemented in the future

The EO-toolset includes currently a suite of sensors facilitating analysis and monitoring of Earth’s surface

from global to national scales. Yet, based on the preliminary identification of user needs of the law and

environmental communities, VHR data availability is a non-functional requirement. As such, very-high spatial

resolution sensors as described in detail D5.1, will be implemented within EO-toolset.

2.3 Pre-processing capabilities

2.3.1 Atmospheric correction for Sentinel-2 data

There are two atmospheric correction filters available, both of which use precomputed data:

• DOS1 being simpler and somewhat less accurate as well as available only for RGB bands.

• ATMCOR is the one based on Sen2cor and is available for all bands, except for band 10, but might

not be available for all tiles. In this case the system will use DOS1 calculation instead for those

tiles.

2.3.2 Automated cloud and cloud shadow identification in Sentinel imagery

Automated cloud and cloud shadow identification algorithms designed for Sentinel-2 satellite images

provides the means of including only clear-view pixels in image analysis and efficient cloud-free compositing.

Two algorithms are available for cloud detection within the EO-toolset

1. The modified Braaten, et al (7) cloud detection algorithm

2. The Hollstein et al. (8) algorithm for the detection of clouds, cirrus, snow, shadow, water and clear

sky pixels.

The first one (see Figure 2) is a modified version of the relatively simple cloud detection algorithm from

Braaten, et al (7) based on simple thresholding is available within EO-toolset using B11, B03 and B04:

Figure 2: Cloud detection algorithm within the EO toolset based on Braaten et al. (2015)

function index(x, y) {

return (x - y) / (x + y);

}

function clip(a) {

return Math.max(0, Math.min(1, a));

}

let bRatio = (B03 - 0.175) / (0.39 - 0.175);

let NGDR = index(B03, B04);

let gain = 2.5;

if (B11>0.1 && bRatio > 1) { //cloud

var v = 0.5*(bRatio - 1);

return [0.5*clip(B04), 0.5*clip(B03), 0.5*clip(B02) + v];

}

if (B11 > 0.1 && bRatio > 0 && NGDR>0) { //cloud

var v = 5 * Math.sqrt(bRatio * NGDR);

return [0.5 * clip(B04) + v, 0.5 * clip(B03), 0.5 * clip(B02)];

}

return [B04, B03, B02].map(a => gain * a);


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The second one is based on the individual bands, ratios and difference of bands considering several Sentinel-

2 bands (Figure 3).

Figure 3: Cloud detection algorithm within the EO toolset based on Hollstein et al. (2016)

2.4 Band colour composites

Any individual band of a multispectral digital image can be displayed as grayscale (panchromatic) image,

where the lowest-value pixels are displayed as black, the highest-value pixels are displayed as white, and

pixels with intermediate values are displayed in corresponding shades of grey (9).

Alternatively, true-color (or natural colour) and false-color composites work under the premise that a

computer screen will display no more than three image bands at a time, each matched to one of three

primary colour ramps: blue, green, and red. True colour composites depicts its features in natural colour

(similar to the human perception) while the false colour composites allow to visualize the wavelengths the

human eye does not see. Within the EO-toolset, several colour composites are available for each sensor (see

Table 1)

Table 1: Readily available composites in EO-toolset for the Sentinel-2, Landsat and MODIS sensors

Sentinel-2 Landsat-8 Landsat 5-7 MODIS

Natural color (B04,B03,B02) Natural color (B04, B03, B02)

Natural color (B03,B02,B01)

True Color (B01,B04,B03)

Color Infrared (vegetation) (B08,B04,B03)

Pansharpened natural color (B04,B03,B02)


False color (urban) (B12,B11,B04)


Short-wave Infrared (B07/05,B04,B02)

Agriculture (B11,B08,B02)

Vegetation Index (B08,B04)

Moisture Index (B8A - B11)


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Sentinel-2 Landsat-8 Landsat 5-7 MODIS

Geology (B12, B04,B02)

Bathymetric (B04,B03,B01)

Atmospheric penetration (B12,B11,B8A)

SWIR (B12,B8A,B04)

NDWI (B03,B08)

SWIR (B02,B11,B12)

2.5 Spectral indices

Several spectral indices have been implemented within the EO-Toolset for each sensor. A detailed reference

is given in the appendix (Appendix 7.1)


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3 Essential variables

The EV approach foresees the definition of a minimum set of variables that can be enough to decipher and

fully characterize the state, trend and future evolution of the Earth’s system in a cost-, time- and work-

efficient approach (10). The following sections include a detailed review of the all the recent progress in the

field of the EVs definition in different societal sectors and scientific disciplines.

3.1.1 Essential Climate Variables (ECV)

The first community that “introduced” the process of EVs identification was the weather and climate one. In

the 1990s, gaps in knowledge of climate and declining core observational networks in some countries,

emphasized the need of a limited set of critical variables that should be observed, providing empirical

evidence on status and evolution of climate, and guidance on mitigation and adaptation measures (10).

Accordingly, the ECV were defined as a ‘physical, chemical, or biological variable or a group of linked

variables that critically contributes to the characterization of Earth’s climate’ (11).

This definition was endorsed by the United Nation Convention on Climate Change (UNFCCC) and other

international bodies and programmes. ECVs datasets provide nowadays the empirical evidence needed to

understand and predict the evolution of climate, to guide mitigation and adaptation measures, to assess

risks and enable attribution of climatic events to underlying causes, and to underpin climate services. The

current ECVs list includes 50 variables in the three domains of Atmosphere, Ocean and Land (12).

3.1.2 Essential Biodiversity Variables (EBVs)

Essential Biodiversity Variables (EBVs) were proposed in 2013 by the biodiversity community to improve

harmonization of biodiversity data into meaningful metrics (4). Previously, it had been identified from the

Global Biodiversity Outlook (GBO4) monitoring progress towards Aichi Biodiversity Targets (ABT) (13), that

many of the indicators used for supporting the measurement of ABT, were characterized by insufficient data

standardization, global coverage, spatial resolution and long-time records (14).

The proposed EBVs have been grouped into six classes: genetic composition, species populations, species

traits, community composition, ecosystem structure, and ecosystem function (15). This concept has taken

root within wide segments of the theoretical and applied ecology communities. Furthermore, the idea

behind the original EBV concept was that at least one EBV per class should be monitored, while keeping the

set of EBVs limited is necessary to assure the usefulness of the EBV concept (16). Satellite-based EO as a

measurement tool for biodiversity-related indicators includes many proven advantages include synoptic view

of the Earth’s surface under constant conditions of solar illumination, consistent and systematic surface

observation, existence of multi-annual time series of observations, cost-effective for monitoring remote and

inaccessible areas. Therefore 14 out of the 22 candidate EBVs have a fully or partly remotely sensed

component and can be considered as Remote Sensing Essential Biodiversity Variables (RS-EBVs) (14)


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3.1.3 Essential Renewable Energies Variables (EREV)

Renewable Energy is a domain where no major dedicated EO network exists so far (17) while no formal

attempt by international bodies to validate EVs was undertaken (10). Renewable Energy Essential Variables

(RE-EVs) (12) or Essential Renewable Energies Variables (18) according to an earlier definition (12) are

variables that meet important requirements from RE stakeholders and are technical and economically

feasible for systematic observation and global implementation. Recently, RE-EVs were defined as a minimal

set of variables that determine the state of the energy system, being crucial for predicting its development,

and support metrics that measure its trajectory (18). Through GEO’s Energy Community of Practices (Energy

CoP) a provisional list of 18 variables has been identified (17).

3.1.4 Essential Geodetic Variables (EGVs) and Essential Earth Rotation Variables

(EERVs)

Efforts to identify essential variables have been initiated within the Global Geodetic Observing System

(GGOS) of the International Association of Geodesy (IAG) that provides the means for integrating ground-

and space-based geodetic and gravimetric observations. Modernizing the existing geodetic and gravimetric

infrastructure and homogenizing data processing are essential for consistent observations of Earth's time-

variable shape, rotation and gravity (19). Within this framework, IAG one of the eight semi-autonomous

Associations of the International Union of Geodesy and Geophysics, has prioritized the definition of the

respective essential variables and through its Bureau of Products and Standards established the Committee

on Essential Geodetic Variables (EGVs). The EGVs are observed variables that are crucial (essential) to

characterizing the geodetic properties of the Earth and that are key to sustainable geodetic observations

(19). Examples of EGVs might be the positions of reference objects, earth orientation parameters, ground-

and space-based gravity measurements (19). In parallel, the Committee on Essential Geodetic Variables also

aims to identify the Essential Gravimetric Variables (EGrVs) that can be used to enable a systematic

monitoring of the geodetic properties of the Earth (20). For the Earth’s rotation, the EERVs are proposed to

be the five Earth orientation parameters (EOPs), namely, the x- and y-components of polar motion (xp, yp),

the x- and y-components of nutation/precession (X,Y) and the spin parameter UT1 (21).

3.1.5 Essential Ocean Variables (OCVs)

Measurements of the status and trends of key indicators for the ocean and marine life are required for

ensuring their sustainable use and conservation (22). Observing status and trends of the global ocean dates

back to the 1980s and 1990s, when the World Ocean Circulation Experiment (WOCE) established an

essential set of observations, to meet regional sampling requirements (23). Essential Ocean Variables (EOVs)

are defined through the Global Ocean Observing System (GOOS), established under the Intergovernmental

Oceanographic Commission (IOC) and Marine Biodiversity Observation Network (MBON), in collaboration

with the Ocean Biogeographic Information System (OBIS), and the Integrated Marine Biosphere Research

(IMBeR) (24).


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Since EOVs are the Framework for Ocean Observing concept of the fundamental physical, biogeochemical,

and biological measurements needed for the scientific understanding of ocean phenomena and the

provision of applications in support of Societal Benefits (25), efforts are focused nowadays to the essential

biological and ecosystem variables to measure (26).

3.1.6 Essential Water Variables (EWVs)

The GEOSS Water Strategy Report submitted by the Integrated Global Water Cycle Observations (IGWCO)

Community of Practice (CoP) defined the EWVs as water variables/parameters that address specific user-

requirements for water management decisions and monitoring (27), including:

• Observational “monitoring” of key elements of the global and regional/local water cycle.

• Observations required by diagnostic and/or land surface/hydrological prediction models that are

used to generate derived products for the end-user communities

• Observational and model-derived variables and parameters required by users of water

data/information products as applied to various inter-disciplinary decision support systems and

tools

Based on these criteria, 10 primary and 9 supplementary variables were identified (27). The process of

further promoting and evolving the EWVs is currently supported by the GEO-Global Water Sustainability

(GEOGLOWS).

3.1.7 Essential Sustainable Development Goals Variables (ESDGV)

Reyes et al (28) after identifying the need of the global community to measure and monitor progress

towards Sustainable Development Goals (SDG), proposed for criteria for identifying Essential Variables for

the SDG domain. They suggested that the ESDGV should identify key features, processes and interactions are

critical for describing and projecting of social–ecological systems behaviour over time and space (capture

system essence, should support the transformative agenda of SDGs, based on knowledge about system

transformations and leverage points, should capture trade-offs or synergies between the SDGs or between

policy arenas requiring coordination and should be foundational and multipurpose.

3.1.8 Urban Essential Variables (UEVs)

UEVs are currently under development stage, mostly in the ERAPLANET project SMURBS (29). Global Urban

Observation and Information Initiative, included in the GEO 2012–2015 Implementation Plan, focused

among others in the identification of EO based essential urban variables and indicators for sustainable cities

(30).

3.1.9 Essential Societal Variables (ESVs)

Ehrilich et al. (31) introduced the term human societal system to indicate the integrated system that

combines human activities and earth system processes and the associated need to establish essential

variables to monitor this system and model human activities and the impact of climate induced hazards on


19

society. Societal variables are essential in the quantification of processes such as urbanization, global

displacement of land use, the looming land scarcity, dynamic of the food system and water cycle. In the

study of Ehrilich et al. (31) EO datasets are demonstrated for calculating global built-up area and global

population densities. Linkages of these two ESVs with SGDs and policies to mitigate risks were also explored.

3.1.10 Essential Agriculture Variables (EAVs)

The process for defining Essential Agriculture Variables (EAVs) is currently driven by GEO’s Global Agricultural

Monitoring initiative (GEOGLAM). EAVs are defined by GEOGLAM as the minimum set of variables needed

from their community to understand state and change in agricultural systems. The preliminary proposal is

formulated upon a tiered (hierarchical) approach that includes Core Agriculture Indicators, EAVs and

Supporting EVs from other themes (communities). The current tentative list of EAVs includes crop area, crop

type, crop condition, crop phenology, crop yield and crop management and agricultural practices.

3.1.11 Essential Protected Area Variables (EPAVs)

Within the ECOPOTENTIAL project1 an initiative was launched to identify essential variables through a

bottom-up approach. A three-tiered organization for the organization of the variables considering the

absolute or high-level of commonality regarding their use across diverse Protected Areas as well as their

technical dimensions was followed for the different storylines.

The EPAVs were identified using the following criteria (32):

• EVs must be observable and sensitive to change

• EVs can include core system variables

• EVs must be scalable

• EVs can include variables that are unique to a given system or ecological process

• EVs should be ecosystem agnostic

Within this community, a tentative list of 7 variables were identified including ecosystem extent and

fragmentation, precipitation, population abundance, taxonomic diversity, land use, land cover and net

primary productivity.

1 http://www.ecopotential-project.eu/

http://www.ecopotential-project.eu/


20

4 USE-CASE requirements and EVs linkages

4.1 Case 1: Environmental Impact Assessment Process in the Energy Projects

As the detailed definition of the context of the specific Use-Case is in progress, linkages with EVs will be

developed accordingly at a later stage.

4.2 Case 2: Force Majeure Events in the Energy Contracts

The aforementioned use cases focus on transnational agreements for the implementation of international

cross-border energy infrastructure projects. Based on the information requirements, two important EVs

were identified, providing information on potential damages in the natural environment. For instance, the

impacts of a devastating fire in a big-scale construction project on the human and natural ecosystem in the

area of interest can be quantified using the following Essential Variables:

1) ECV: Burned Area

The essential variable of the Burned Area can be quantified using EO data and more specific, by computing

the Normalized Burn Ratio (NBR) to highlight areas that have been burned.

2) ECV: Burn Severity

The NBR is also used to estimate burn severity. Namely, in order to quantify the burn severity, EO data are

collected for an area of interest, before and shortly after the fire. Thus, imagery collected before a fire will

have very high near-infrared band values and very low mid-infrared band values. On the contrary, the

corresponding imagery after the fire will have very low near-infrared band values and very high-mid infrared

band values. Higher ΔNBR indicate more severe damage. Areas with negative ΔNBR values may indicate

increased vegetation productivity following a fire (33), (Figure 4).

Figure 4 :Spectral Response Curves of burned areas and of healthy vegetation, US Forest Service, 2018


21

The general formula for the estimation of the Burn Severity is ΔNBR = estimation of the NBR before fire –

estimation of the NBR after fire. In addition, the United States Geological Survey (USGS) proposed a

classification table to interpret the burn severity using the following thresholds:

Table 2 ΔNBR Burn Severity Categories, USGS FireMon program

ΔNBR Burn Severity

<-0.25 High post-fire regrowth

-0.25 to -0.1 Low post-fire regrowth

-0.1 to +0.1 Unburned

0.1 to 0.27 Low-severity burn

0.27 to 0.44 Moderate-low severity burn

0.44 to 0.66 Moderate-high severity burn

> 0.66 to 1.30 High-severity burn

4.3 Case 3: Oil and Gas Pipeline Monitoring For Safety and Environment


developed accordingly at a later stage.

4.4 Case 4: Forest Law Enforcement and Governance

This use case will focus on illegal logging and deforestation monitoring in an Eastern European country.

Based on the information requirements, 5 EVs were identified as being more relevant to the specific case,

providing information on the forested extent within each area.

1) EBV: Ecosystem extent and fragmentation

Ecosystem extent refers to the location and geographic distribution of ecosystems across landscapes or in

the oceans, while ecosystem fragmentation refers to the spatial pattern and connectivity of ecosystem

occurrences on the landscape. Due to a lack of availability of time series data on ecosystem extent, and also

to the general lack of ecosystem maps in the first place, land cover is often used as a proxy for ecosystems.

Another approach to assessing change in ecosystem extent, which does not require use of a land cover

proxy, is to obtain a change map derived from image analysis of two images at different dates. The images

can be compared for changes in spectral properties, and without classifying the spectral signatures into

land cover classes, a change map can be produced which indicates where, on the ground, changes have

occurred (34).

2) ECV: Land cover

Land cover is defined as the observed (bio)-physical cover on the earth’s surface.

3) ECV: Leaf Area Index (LAI)

The LAI of a plant canopy or ecosystem, defined as one half the total green leaf area per unit horizontal

ground surface area, measures the area of leaf material present in the specified environment. LAI is defined


22

as the one-sided green leaf area per unit ground area in broadleaf canopies, or as the projected needle leaf

area per ground unit in needle canopies.

4) EUV: Vegetation canopy cover

Vegetation canopy cover is defined as the share of ground covered by the vertical projection of the canopy

and is commonly expressed as a percentage. In addition, the international definition of a forest is based on

canopy cover.

4.5 Case 5: Infrastructure development in Protected Areas

1) EUV: Built up area

Locations dominated by constructed surfaces or built environment, where dominancy implies >50%

coverage of pixels [16,20].

2) EUV: Soil sealing

Soil sealing can be defined as the destruction or covering of soils by buildings, constructions and layers of

completely or partly impermeable artificial material (asphalt, concrete, etc.).

3) ECV: Land cover

Land cover is defined as the observed (bio)-physical cover on the earth’s surface.

4.6 Case 6: Land-use change in the context of disaster risk reduction


developed accordingly.


23

5 Essential Variables modelling

5.1 Burned Area (ECV) and Burn Severity (ECV) modelling

The essential variable of the Burned Area can be quantified using EO data and more specific, by computing

the Burn Area Index (BAI) or the Normalized Burn Ratio (NBR) for large fire zones greater than 500 acres, to

highlight burned areas.

The Burn Area Index (BAI) is computed using analysis ready satellite data and more specific, by utilizing the

red and near-infrared bands of the image. In addition, the Normalized Burn Ratio (NBR) image, was originally

developed for use with Landsat Imagery (Landsat TM and ETM+) utilizing the near-infrared (NIR) and

shortwave-infrared (SWIR) bands of the image.

Figure 5 Burned Area Index (BAI) and Normalized Burn Ratio (NBR) formulas indices, ((35)

The creation of a pre-fire and a post-fire NBR image and the generation of their difference can result to the

estimation of the Burn Severity (ΔNBR). Darker pixels of the final image indicate burned areas, while the

proposed classification of the results (severity thresholds) can lead to an initial estimation of the burn

severity. Figure 6 represents and indicative workflow using analysis ready satellite data.


24

Figure 6: Burned Area and Burn Severity workflow modelling

Figure 7 represents the results of the aforementioned workflow in the area of Alexandroupolis – Greece.


25

Figure 7: Computation of NBR (middle image) and ΔNBR (right image) for the area of Alexandroupolis – Greece (left image – orthorectified). Dark areas indicate burned areas.

5.2 Built-up area

Built-up area is a key parameter for the description of urban areas. An accurate estimation of the built-up

area can quantify the changes in an urban tissue providing vital information about the effects of urbanization

on biodiversity and the sustainability of urban ecosystems. Built-up areas refer to urban areas dominated by

artificial surfaces and more specifically to areas where the dominancy of built environment is more than 50%

in cover by non-vegetated, human-constructed elements (roads, buildings, infrastructure, industrial facilities

and more). In addition, artificial areas within the administrative boundary of an urban area covered by

vegetation (e.g. parks, gardens and more), are also considered as part of the built-up area (36). EO data

provide a reliable, low cost and efficient source of defining built-up areas. Thus, the remote sensing

community has developed various methods for the extraction of built-up areas combining spectral and

spatial indexes, utilizing multi-sensor data such as Landsat or Sentinel Imagery and Radar (SAR) data (37).

An indicative workflow for the computation of built-up areas, utilizing already available spatial data from

various data repositories are as follow:


26

Figure 8 Built Up area workflow modelling using EO ready data (spatial data from data repositories)

Area Selection: The user will be able to select the exact location of the area of interest by providing the physical

address of a site (geocoding tool is needed) or by selecting a location on the map. In addition, in protected

landscapes the boundaries of a selected ecosystem can be provided by open access spatial data repositories

(e.g. Data repository of Natura20002 or Ramsar Site Information Service3, national data repositories and

more).

2 http://ftp.eea.europa.eu/www/natura2000/Natura2000_end2018_Shapefile.zip 3 https://rsis.ramsar.org/

http://ftp.eea.europa.eu/www/natura2000/Natura2000_end2018_Shapefile.zip

https://rsis.ramsar.org/


27

Set Time period: Select the time period and time intervals. The aforementioned selection will also define the

available satellite data that will be utilized for the process.

Image filtering and pre-processing: The process of image filtering and pre-processing is one of the most

crucial steps in each EV computation workflow. The available Satellite data as previously listed according to

their date of acquisition are further filtered to select the most suitable image for the process (i.e. selection of

the least cloudy image, radiometric enhancement of the selected image and more). Overall, the proposed

methodology suggests utilizing analysis ready data (e.g. Sentinel-2 BoA data (Bottomo of Atmosphere) or

Landsat Level-2 data and more). However, in cases where satellite imagery requires further pre-processing,

the proposed methodology also includes additional pre-processing algorithms (e.g. Landsat-7 correction due

to SLC (Scan Line Corrector) failure and more, atmospheric corrections).

5.3 Soil Sealing

Soil Sealing is defined as the destruction or covering of soils by buildings, constructions and layers of

completely or partly impermeable artificial material (asphalt, concrete, etc.). It can be computed using high

resolution satellite imagery (Figure 9).

Figure 9 Soil Sealing workflow modelling using satellite imagery


28

represents the results of the aforementioned workflow in the designated area of Velipoje –Albania

represents the results of the aforementioned workflow in the designated area of Velipoje –Albania.

Figure 10: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2009-2018

Figure 11: Computation of Soil Sealing for the designated area of Velipoje – Albania for the time period 2015-2018


29

5.4 Land Cover

Land Cover can be estimated using EO data from data repositories. In cases were ready available data are not

suitable for the area of interest; satellite imagery can also be utilized (Figure 12).

Figure 12: Computation of Land Cover using available spatial data and satellite imagery


30

6 Final Essential Variables list

The selection of the use cases has a profound influence in the identification of the EV list. We are still in the

course to define the cases to guarantee an optimal uptake of the enviroLENS tools and service.

The next steps will include bilateral meetings with the domain representative to design an implementation

plan for each of the show cases that details the software user requirements to investigate which software

can be employed to serve the user needs in the specific cases as well as the exact data needs and data

services.


31

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34

Appendix

Spectral indices for the sensors available within the EO-Toolset

Sentinel-2 indices

Abbreviation Name

ATSAVI Adjusted transformed soil-adjusted VI

AFRI1600 Aerosol free vegetation index 1600


Alteration

ARI Anthocyanin reflectance index

AVI Ashburn Vegetation Index

ARVI2 Atmospherically Resistant Vegetation Index 2

BWDRVI Blue-wide dynamic range vegetation index

BRI Browning Reflectance Index

CARI2 Chlorophyll Absorption Ratio Index 2

Chlgreen Chlorophyll Green

CIgreen Chlorophyll Index Green

CIrededge Chlorophyll IndexRedEdge

Chlred-edge Chlorophyll Red-Edge

CVI Chlorophyll vegetation index

CI Coloration Index

CTVI Corrected Transformed Vegetation Index

Datt1 Datt1

Datt4 Datt4

Datt6 Datt6

D678/500 Difference 678/500




GDVI Difference NIR/Green Green Difference Vegetation Index

DVIMSS Differenced Vegetation Index MSS

EVI Enhanced Vegetation Index

EVI2 Enhanced Vegetation Index 2

Fe2+ Ferric iron, Fe2+


Ferric Oxides

Ferrous iron

Ferrous Silicates

GVMI Global Vegetation Moisture Index

Gossan


35

Abbreviation Name

GARI Green atmospherically resistant vegetation index

GNDVI Green Normalized Difference Vegetation Index

GBNDVI Green-Blue NDVI

GRNDVI Green-Red NDVI

IPVI Infrared percentage vegetation index

I Intensity

Laterite

LCI Leaf Chlorophyll Index

Maccioni

MCARI/MTVI2 MCARI/MTVI2

MCARI/OSAVI MCARI/OSAVI

MVI Mid-infrared vegetation index

MGVI Misra Green Vegetation Index

MNSI Misra Non Such Index

MSBI Misra Soil Brightness Index

MYVI Misra Yellow Vegetation Index

mND680 mND680

MCARI Modified Chlorophyll Absorption in Reflectance Index

MCARI1 Modified Chlorophyll Absorption in Reflectance Index 1

MCARI2 Modified Chlorophyll Absorption in Reflectance Index 2

mNDVI Modified NDVI

mSR Modified Simple Ratio

MSR670 Modified Simple Ratio 670,800

MSAVI Modified Soil Adjusted Vegetation Index

MSAVIhyper Modified Soil Adjusted Vegetation Index hyper

MTVI1 Modified Triangular Vegetation Index 1

MTVI2 Modified Triangular Vegetation Index 2

Norm G Norm G

Norm NIR Norm NIR

Norm R Norm R

PPR Normalized Difference 550/450 Plant pigment ratio

PVR Normalized Difference 550/650 Photosynthetic vigour ratio

ND774/677 Normalized Difference 774/677

GNDVIhyper Normalized Difference 780/550 Green NDVI hyper




PSNDc2 Normalized Difference 800/470 Pigment specific normalised di€fference C2

PSNDc1 Normalized Difference 800/500 Pigment specific normalised di€fference C1

GNDVIhyper2 Normalized Difference 800/550 Green NDVI hyper 2

PSNDb1 Normalized Difference 800/650 Pigment specific normalised di€fference B1


36

Abbreviation Name

PSNDa1 Normalized Difference 800/675 Pigment specific normalised di€fference A1

NDII Normalized Difference 819/1600 NDII

NDII2 Normalized Difference 819/1649 NDII 2

NDMI Normalized Difference 820/1600 Normalized Difference Moisture Index


ND833/1649 Normalized Difference 833/1649 Infrared Index


SIWSI Normalized Difference 860/1640


NGRDI Normalized Difference Green/Red Normalized green red difference index, Visible Atmospherically Resistant Indices Green (VIgreen)

NDVI Normalized Difference MIR/NIR Normalized Difference Vegetation Index (in case of strong atmospheric disturbances)

BNDVI Normalized Difference NIR/Blue Blue-normalized difference vegetation index

GNDVI Normalized Difference NIR/Green Green NDVI

MNDVI Normalized Difference NIR/MIR Modified Normalized Difference Vegetation Index

NDVI Normalized Difference NIR/Red Normalized Difference Vegetation Index, Calibrated NDVI - CDVI

NDRE Normalized Difference NIR/Rededge Normalized Difference Red-Edge

NBR Normalized Difference NIR/SWIR Normalized Burn Ratio

RI Normalized Difference Red/Green Redness Index

NDSI Normalized Difference Salinity Index

NDVI690-710 Normalized Difference Vegetation Index 690-710

OSAVI Optimized Soil Adjusted Vegetation Index

PNDVI Pan NDVI

RDVI RDVI

RDVI2 RDVI2

Rededge1 Red edge 1

Rededge2 Red edge 2

RBNDVI Red-Blue NDVI

REP Red-Edge Position Linear Interpolation

Rre Reflectance at the inflexion point

RDVI Renormalized Difference Vegetation Index

IF Shape Index

MSI2 Simple Ratio 1599/819 Moisture Stress Index 2

MSI Simple Ratio 1600/820 Moisture Stress Index

TM5/TM7 Simple Ratio 1650/2218

SR440/740 Simple Ratio 440/740

BGI Simple Ratio 450/550 Blue green pigment index



DSWI-4 Simple Ratio 550/680 Disease-Water Stress Index 4



37

Abbreviation Name

GI Simple Ratio 554/677 Greenness Index

SR560/658 Simple Ratio 560/658 GRVIhyper

SR672/550 Simple Ratio 672/550 Datt5






SR700 Simple Ratio 700






PSSRc2 Simple Ratio 800/470 Pigment specific simple ratio C2

PSSRc1 Simple Ratio 800/500 Pigment specific simple ratio C1


PSSRb1 Simple Ratio 800/650 Pigment specific simple ratio B1

RVI Simple Ratio 800/670 Ratio Vegetation Index

PSSRa1 Simple Ratio 800/675 Pigment specific simple ratio A1

SR800/680 Simple Ratio 800/680 Pigment Specific Simple Ratio (Cholophyll a) (PSSRa)

SR801/550 Simple Ratio 801/550 NIR/Green

SR801/670 Simple Ratio 801/670 NIR/Red

PBI Simple Ratio 810/560 Plant biochemical index

SR833/1649 Simple Ratio 833/1649 MSIhyper


Datt2 Simple Ratio 850/710 Datt2



RDI Simple Ratio MIR/NIR Ratio Drought Index

SRMIR/Red Simple Ratio MIR/Red Eisenhydroxid-Index

SRNir/700-715 Simple Ratio NIR/700-715

GRVI Simple Ratio NIR/G Green Ratio Vegetation Index

SRNIR/MIR Simple Ratio NIR/MIR

DVI Simple Ratio NIR/RED Difference Vegetation Index, Vegetation Index Number (VIN)

RRI1 Simple Ratio NIR/Rededge RedEdge Ratio Index 1

IO Simple Ratio Red/Blue Iron Oxide

RGR Simple Ratio Red/Green Red-Green Ratio

SRRed/NIR Simple Ratio Red/NIR Ratio Vegetation-Index

SB1580 Single Band 1580



38

Abbreviation Name









BB3 Single Band 470 Blackburn3

SR495 Single Band 495









SB703 Single Band 703 Boochs






SAVI Soil Adjusted Vegetation Index

SARVI2 Soil and Atmospherically Resistant Vegetation Index 2

SAVI3 Soil and Atmospherically Resistant Vegetation Index 3

SBL Soil Background Line

Soil Composition Index

SLAVI Specific Leaf Area Vegetation Index

SIPI1 Structure Intensive Pigment Index 1

SIPI3 Structure Intensive Pigment Index 3

SBI Tasselled Cap - brightness

GVIMSS Tasselled Cap - Green Vegetation Index MSS

NSIMSS Tasselled Cap - Non Such Index MSS

SBIMSS Tasselled Cap - Soil Brightness Index MSS

GVI Tasselled Cap - vegetation

WET Tasselled Cap - wetness

YVIMSS Tasselled Cap - Yellow Vegetation Index MSS

TCARI/OSAVI TCARI/OSAVI

TCARI Transformed Chlorophyll Absorbtion Ratio


39

Abbreviation Name

TVI Transformed Vegetation Index

VI700 Vegetation Index 700

VARIgreen Visible Atmospherically Resistant Index Green

VARI700 Visible Atmospherically Resistant Indices 700

VARIrededge Visible Atmospherically Resistant Indices RedEdge

WDVI Weighted Difference Vegetation Index

WDRVI Wide Dynamic Range Vegetation Index

Landsat-8 spectral indices

Abbreviation

Name


Alteration

AVI Ashburn Vegetation Index

ARVI2 Atmospherically Resistant Vegetation Index 2

BWDRVI Blue-wide dynamic range vegetation index

Carbonate

CIgreen Chlorophyll Index Green

CVI Chlorophyll vegetation index

CI Coloration Index

CTVI Corrected Transformed Vegetation Index

GDVI Difference NIR/Green Green Difference Vegetation Index

DVIMSS Differenced Vegetation Index MSS

EVI Enhanced Vegetation Index

EVI2 Enhanced Vegetation Index 2



Ferric Oxides

Ferrous iron

Ferrous Silicates


Gossan


GBNDVI Green-Blue NDVI

GRNDVI Green-Red NDVI

IPVI Infrared percentage vegetation index

I Intensity

Laterite

MVI Mid-infrared vegetation index

MSAVI Modified Soil Adjusted Vegetation Index

Norm G Norm G

Norm NIR Norm NIR

Norm R Norm R

PPR Normalized Difference 550/450 Plant pigment ratio

PVR Normalized Difference 550/650 Photosynthetic vigour ratio



40

Abbreviation

Name

NGRDI Normalized Difference Green/Red Normalized green red difference index, Visible Atmospherically Resistant Indices Green (VIgreen)

BNDVI Normalized Difference NIR/Blue Blue-normalized difference vegetation index

GNDVI Normalized Difference NIR/Green Green NDVI



RI Normalized Difference Red/Green Redness Index


PNDVI Pan NDVI

RBNDVI Red-Blue NDVI

TM5/TM7 Simple Ratio 1650/2218

BGI Simple Ratio 450/550 Blue green pigment index





SRMIR/Red Simple Ratio MIR/Red Eisenhydroxid-Index

SI Simple Ratio MIR/SWIR Cley Mineral-Index, Salinity Index

GRVI Simple Ratio NIR/G Green Ratio Vegetation Index


DVI Simple Ratio NIR/RED Difference Vegetation Index, Vegetation Index Number (VIN)

IO Simple Ratio Red/Blue Iron Oxide

RGR Simple Ratio Red/Green Red-Green Ratio

SRRed/NIR Simple Ratio Red/NIR Ratio Vegetation-Index











SR495 Single Band 495






MODIS spectral indices

Abbreviation Name

Alteration



Ferric Oxides

Ferrous iron


41

Abbreviation Name

Ferrous Silicates


Gossan


Laterite

NDWI2 Normalized Difference 857/1241 Normalized Difference Water Index

NDWI Normalized Difference 860/1240 Normalized Difference Water Index





Silica 3


SRWI Simple Ratio 860/1240


SI Simple Ratio MIR/SWIR Cley Mineral-Index, Salinity Index











Soil Composition Index

VARIgreen Visible Atmospherically Resistant Index Green


42

Essential Variables list

Essential Variable Class/Category

Essential Variable Group

Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

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Ene

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Agr

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ltu

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He

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Wat

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Oce

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Pro

tect

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Are

as

Eart

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Genetic composition (EBV)

Co-ancestry

Allelic diversity

Population genetic differentiation

Breed and variety diversity

Species populations (EBV)

Species distribution

Population abundance

Population structure by age/size class

Species traits (EBV)

Phenology

Body mass

Natal dispersion distance

Migratory behavior

Demographic traits

Physiological traits

Community composition (EBV)

Taxonomic diversity

Species interactions

Ecosystem function (EBV)

Net primary productivity

Secondary productivity

Nutrient retention

Disturbance regime


43



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

h R

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n

Gra

vim

etr

ic

Soci

eta

l

Urb

an

Ecosystem structure (EBV)

Habitat structure

Ecosystem extent and fragmentation

Ecosystem composition by functional type

Atmosphere (ECV), Physical surface (EOV), Terrestrial (EREV)

Air temperature

Air Temperature (surface)

Atmosphere (ECV) Air Temperature (upper-air)

Atmosphere (ECV) Wind speed and direction

Wind speed and direction (surface)

Atmosphere (ECV) Wind speed and direction (upper-air)

Atmosphere (ECV), Terrestrial (EREV)

Atmospheric Pressure (surface)

Atmosphere (ECV), Primary (EWV), Terrestrial (EREV)

Precipitation

Atmosphere (ECV)

Water Vapour Water varour (surface)

Water varour (upper-air)

Lightning

Cloud properties

Surface Radiation Budget


44



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

h R

ota

tio

n

Gra

vim

etr

ic

Soci

eta

l

Urb

an

Earth Radiation budget

Carbon dioxide, Methane, and other long-lived greenhouse gases

Ozone and Aerosol, supported by their precursors Ozone

Atmosphere (ECV), Supplemental (EWV)

Aerosols

Atmosphere (ECV) Ozone and Aerosol Precursors

Ocean (ECV), Physical surface (EOV), Ocean (EREV)

Temperature (sea-surface, sub-surface, deep-sea) Sea Surface Temperature

Ocean (ECV), Physical sub-surface (EOV), Ocean (EREV)

Subsurface Temperature

Ocean (ECV), Physical surface (EOV)

Salinity Sea Surface Salinity

Ocean (ECV), Physical sub-surface (EOV)

Subsurface Salinity


45



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

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Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

h R

ota

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n

Gra

vim

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ic

Soci

eta

l

Urb

an


Sea Level


Sea State

Ocean (ECV) Sea Ice


Current Subsurface Currents


Surface Currents

Ocean (ECV) Surface Stress


Ocean Colour


Carbon dioxide partial pressure

Surface Carbon dioxide partial pressure


Subsurface Carbon dioxide partial pressure

Ocean (ECV) Ocean Surface Heat Flux


Ocean surface acidity


46



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

h R

ota

tio

n

Gra

vim

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ic

Soci

eta

l

Urb

an

Ocean (ECV)

Marine Habitat Properties

Phytoplankton/Plankton

Nutrients

Inorganic Carbon

Ocean (ECV), Biochemical (EOV)

Nitrous Oxide


Ocen Surface Oxygen


Tracers

Land (ECV), Primary (EWV)

River discharge


Water use


Groundwater


Lakes


Snow cover

Land (ECV) Ice Sheets and ice shelves

Land (ECV) Glaciers and ice caps


47



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

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n

Gra

vim

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Soci

eta

l

Urb

an

Land (ECV), Supplemental (EWV)

Permafrost

Land (ECV) Albedo

Land (ECV), Supplemental (EWV), Solar (EREV)

Land cover/land use

Land Cover

Supplemental (EWV), Solar (EREV)

Land Use

Land (ECV)

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

Leaf Area Index (LAI)

Above-ground biomass

Anthropogenic Greenhouse Gas Fluxes

Soil carbon

Fire disturbance

Latent and Sensible Heat fluxes

Land (ECV), Solar (EREV)

Land Surface Temperature

Land (ECV) , Terrestrial (EREV)

Humidity


Soil moisture


48



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

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n

Gra

vim

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ic

Soci

eta

l

Urb

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Primary (EWV) Soil temperature

All Global Numerical Weather Prediction (NWP) variables and others yet to be determined by WMO/GAW

Ocean (EREV)

Ocean, fixed and floating offshore wind, wave, tidal, currents, OTEC

Ocean Floor Type

Ocean Bathymetry

Sea current speed

Tidal (min, max, sea surface elevation)

Wave, height, direction, period

Terrestrial (EREV) Urbanization

Supplemental (EWV), Solar (EREV)

Elevation/topography and geological stratification

Solar (EREV)

Solar Surface Irradiance and its components (global, direct, diffuse)

Solar (EREV)

Sunshine duration (demand in energy)

Crop area


49



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

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Pro

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Are

as

Eart

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n

Gra

vim

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ic

Soci

eta

l

Urb

an

Crop type

Crop condition

Crop phenology

Crop yield (current and forecast)

Crop management and agricultural practices

Famine early warning

Short term forecasting of communicable diseases

Primary (EWV)

Evaporation and evapotranspiration

Runoff/streamflow

Lake reservoir levels and aquifer volumetric change

Water quality

Water use/demand (agriculture, hydrology, energy, urbanization)

Supplemental (EWV)

Water surface altimetry

Supplemental (EWV)

Atmospheric radiation budgets

Supplemental (EWV), Terrestrial (EREV)

Cloud Cover (for EREV means demand in energy)


50



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

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Pro

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Are

as

Eart

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n

Gra

vim

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Soci

eta

l

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Supplemental (EWV)

Surface meteorology

Physical surface (EOV)

Sea Level Pressure

Physical sub-surface (EOV)

Global Ocean Heat Content

Ocean Subsurface Acidity

Ocean Subsurface Tracers

Ocean Subsurface Oxygen

Biochemical (EOV)

Ocean Biochemical Oxygen

Macro Nutrients: NO3, PO4, Si, NH4, NO2

Carbonate System: DIC, Total Alkalinity, pCO2, pH

Transient Tracers: CFC-12, CFC11, SF6, tritium, 3He, 14C, 39Ar

Suspended particulates (POC, PON or POM) and PIC ++ laboratory, beam attenuation, backscatter, acidlabile, beam attenuation

Particulate Matter Export: POC export, CaCO3 export, BSi export


51



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

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tio

n

Gra

vim

etr

ic

Soci

eta

l

Urb

an

Carbon-13: 13C/12C of dissolved inorganic carbon

DOM (Dissolved organic matter), DOC, DON, DOP

Biology and Ecosystems (EOV)

Chlorophyll

Coral Cover

Mangrove Area

Harmful Algal Blooms (HABs)

Zooplankton (biomass/abundance)

Zooplankton (Krill)

Salt Marsh Area

Large marine vertebrates: abundance/distribution

Seagrass Area

Tags and Tracking of species of value/large marine vertebrates

Fish stocks

Bacteria concentration

x-component of polar motion (xp)

y-component of polar motion (yp)


52



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

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n

Gra

vim

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Soci

eta

l

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x-component of nutation/precession (X)

y-component of nutation/precession (Y)

spin parameter (UT1)

Level 0 (EGrV)

Reference Frames: e.g. Centerof Mass

Gravity Standards

Level 1 (EGrV)

Gravity Potential (Geoid)

Gravity Acceleration (1stderivative radial)

Deflections of the Vertical (1stderivatives horizontal)

Gravity Gradients (2ndderivatives)

Level 2 (EGrV)

Global Models (Mean and Time-variable)

Global Geoid (Mean)

Regional Geoid (Mean)

Level 3 (EGrV)

Mass Distribution in Earth System

Mass Transport in Earth System

Built-Up Area

Population Density


53



Essential Variable

Bio

div

ers

ity

Clim

ate

We

ath

er

Ene

rgy

Agr

icu

ltu

re

He

alth

Wat

er

Oce

ans

Pro

tect

ed

Are

as

Eart

h R

ota

tio

n

Gra

vim

etr

ic

Soci

eta

l

Urb

an

Vegetation canopy cover

Soil sealing

Area of natural habitats

Reclaimed waste deposits areas

Air quality

Actual evapotranspiration

Spatial structure of urban areas

Trend of total CO2 emissions

Temperature sum totals (for the active growing period)

Solar-energy potential

Urban runoff coefficient

Natural ground water discharge

Risk to soil and atmospheric drought


54

Documents

Deliverable 2 - WordPress.com · Deliverable 2.1 includes a final list of selected Essential Variables (EVs) that will be compiled on the basis of current capabilities of the EO-Toolset