Report on Documentation of Functional Specification, Web ...landconsult.de/mn/weidenbach_report_june2015.pdf · Report on Documentation of Functional Specification, Web Interface

Report on Documentation of Functional Specification, Web Interface and

Queries for NFI, MRV and M/I Online DB and Forest Atlas

Table of Content

1. Functional and Non-functional Specification and Web Interface for Online DBs and Forest Atlas .......... 4

1.1 Non-functional Specifications and Requirements ................................................................................... 4

1.1.1 System architecture ................................................................................................................... 4

1.1.2 Programmatical non-functional requirements .......................................................................... 6

1.2 Functional Specification and Requirements ............................................................................................ 7

1.2.1 The Spatial DB Extension PostGIS .............................................................................................. 9

1.2.2 SQL Language ........................................................................................................................... 10

1.3 Web Interfaces ...................................................................................................................................... 11

1.3.1 Definition of NFI Web Interface and Specification .................................................................. 11

1.3.2 Definition MRV Web Interface and Specification .................................................................... 12

1.3.3 Definition M/I Web Interface and Specification ...................................................................... 12

1.3.4 Definition FA WebGIS Web Interface and Specification .......................................................... 13

1.3.4.1 QGIS Server .......................................................................................................................... 13

1.3.4.2 The QGIS WebClient Interface ............................................................................................. 14

1.3.4.3 Other User Interfaces .......................................................................................................... 14

1.3.4.4 Definition of Relation between FA and Online DBs ............................................................. 15

2. Proposal of Content and Queries for NFI, MRV, M/I Online DBs and Forest Atlas WebGIS ................... 16

2.1 Proposal of Forest Characteristics to be queried from the NFI DB ....................................................... 16

2.2 Proposal of Content of MRV DB and Forest Carbon Parameters to be queried ................................... 17

2.2.1 MRV DB .................................................................................................................................... 17

2.2.2 Forest Area .............................................................................................................................. 20

2.3 Proposal of the Content of M/I DB and Socio Economic and REDD+ Safeguards Information ............. 21

2.3.1 REDD+ Safeguards ................................................................................................................... 21

2.3.2 M/I DB ...................................................................................................................................... 23

2.3.3 Proposed Geographical Data used for the M/I and the REDD+ Safeguards system ............... 24

2.3.3.1 Geographic Data on Local and Regional Scale .............................................................. 24

2.3.3.2 Geographic Data on National and Global Scale ............................................................ 25

2.3.3.3 Statistical Data for Mongolia ........................................................................................ 26

2.4 Proposal of GIS Datasets for Forest Atlas WebGIS ................................................................................ 27

3 Literature and Abbreviations ................................................................................................................... 28

3.1 Relevant Literature and Reports: .................................................................................................... 28

3.2 Abbreviations ................................................................................................................................... 29

Annex ............................................................................................................................................................... 32

ToR for Home Based Mission ...................................................................................................................... 32

Time Sheet June 2015.................................................................................................................................. 34

Description of Geodata from external Providers ............................................................................................ 36

Maps from Institute for Environmental Information in Ulaanbaatar ......................................................... 36

Canopy Heights ............................................................................................................................................ 38

Maps from University of Maryland´s Global Forest Change 2000-2013 program ...................................... 39

Tree cover gain .................................................................................................................................... 39

Tree Cover Loss .................................................................................................................................... 40

Map with Intact Forest Landscapes 2000/2013 .......................................................................................... 41

Maps from Climate Change Initiative (CCI) ................................................................................................. 43

Land Cover Maps ................................................................................................................................. 43

Meris Surface Reflectance ................................................................................................................... 44

Water Bodies ....................................................................................................................................... 44

Land Surface Seasonality products ...................................................................................................... 44

Vegetation greenness .......................................................................................................................... 44

Snow occurrence ................................................................................................................................. 45

Burned areas occurrence ..................................................................................................................... 45

User tool .............................................................................................................................................. 45

Copyright ............................................................................................................................................. 46

IUCN and UNEP Protected Areas ................................................................................................................. 47

Global Soil Dataset for Earth System Modeling .......................................................................................... 49

Available Global Layer from FAO: ................................................................................................................ 49

Atlas of Forest Landscape Restoration Opportunities................................................................................. 50

Current forest coverage....................................................................................................................... 50

Potential forest coverage .................................................................................................................... 50

Forest condition ................................................................................................................................... 51

Restoration opportunities ................................................................................................................... 51

Human pressure .................................................................................................................................. 51

Bonn Challenge ........................................................................................................................................ 52

Land Scan Data ............................................................................................................................................ 52

Open Weather Map ..................................................................................................................................... 53

1. FUNCTIONAL AND NON-FUNCTIONAL

SPECIFICATION AND WEB INTERFACE FOR

ONLINE DBS AND FOREST ATLAS

The clear understanding of the project design and architecture is an essential pre-condition for the

successful implementation and long-term maintenance of the envisaged technical components.

Therefore the terminology of functional and non-functional requirements or specifications is being

considered a helpful mean for better describing the characteristics of the planned DB and WebGIS.

The definition for a non-functional requirement is that it essentially specifies how the system

should behave and doing so it also specifies criteria that can be used to judge the operation of a

system. Another frequently used term for non-functional requirements are quality attributes or

constraints of a system.

In contrast, functional requirements or specification refer to the pure technical functions of a

system. In other words: functional requirements define what a system is supposed to do and

non-functional requirements define how a system is supposed to be.

An example for a non-functional requirement is sufficient network bandwidth for the DB server.

Whereas the need to provide the user with a function to select different DB records on a user

friendly interface is a functional requirement.

1.1 Non-functional Specifications and Requirements

1.1.1 System architecture

The most frequent non-functional requirements refer to all three DBs and the WebGIS application

similarly, because all components are technically interlinked and moreover are running on the same

Windows server under the responsibility of the FRDC (for technical server details see Weidenbach

2015).

Considering the full course of the system implementation and the future operation, maintenance and

further technical evolution of the system, there are two categories of non-functional system

qualities:

Operational qualities, such as security and usability, which are observable at run time.

Development qualities, such as testability, maintainability, extensibility and scalability, which are

related to the technical properties of the soft-/hardware system

There is a long list of non-functional requirements and technical IT specifications, more or less

important to host the system on the FRDC server. Trying to meet all of those requirements at this

initial phase of the project is not advisable because it would unnecessarily hamper the whole

process.

Instead, a cost-benefit equation for the IT planning should take into account that (a) the content of

the three DBs include no sensitive data, but mainly data that is meant for publication and (b) in the

opening phase of the system, there will be a limited number of interested public users, official

stakeholders and other professional users.

The following alphabetical list includes non-functional requirements, which - in terms of its

technical software capabilities – are met by the proposed and already installed

PostgreSQL/PostGIS/QGIS/Apache system on the FRDC Windows Server. Moreover some

requirements below refer to human and organisational capacities, which are necessary to manage

the system successfully. With special reference to the situation in Mongolia, the list has to be seen

as a mid-term challenge to optimise the system and adopt it for its future demands. In a way it is a

list of minimal and non-functional requirements that are important for the DB/FA system:

Accessibility: the system must be accessible 24h a day and 7 days a week

Accuracy: system accuracy of FA depends on input data and projection used, it must be

communicated to the user. Accuracy of statistical NFI data must be documented and notified.

Capacity: enough storage, CPU power and bandwidth, for present and forecasted number of

requests

Compatibility: the system is compatible to other common OS, such as Windows or Linux

Compliance: system and data is compliant with common software and data standards (OGC,

INSPIRE, DublinCore, etc)

Community: big and active open source user and developer community to support the system and

endure its live expectancy.

Configuration Management: the system must be configurable for persons without having

knowledge in any programming language. Preferably, remote configuration is possible.

Documentation: the system must have a comprehensive documentation, geo-data must be

documentable with metadata standards.

Effectiveness: resulting performance in relation to effort

Extensibility: adding and removing additional software features (plugins, extensions, API)

Integrity: QGIS and PostgreSQL online components are seamlessly capable of being integrated in

desktop applications.

Maintainability: a trained computer expert will be able to correct, re-configure or re-install the

system.

Regionalisation: it is possible to operate the system in different languages. The user interface

components support the locale of the system. The user interface language can be switched

between different languages. The WebGIS application (FA) can switch between different CRS

(Coordination Reference Systems)

Open source: 100% open source software with no commercial costly third party components

Open data: system can use freely available Open Geo-data, on HDD and/or via WMS/WFS/WCS

Services

Privacy: access to the system or parts of the systems data, can be limited to authorised users, if

required

Sufficient network bandwidth and connectivity to the Internet to guarantee a fast operation

Sufficient trained staff to manage and maintain the system

UPS, Backup and Recovery Routine to bridge voltage drop, restore lost data and recover the system

User Friendliness: The Graphical User Interface (GUI) must be effective and have a certain “fun

factor” to promote and facilitate the operation of the system

1.1.2 Programmatical non-functional requirements

From a programmatical point of view, the DB/FA system has to meet requirements that are driven

by the stakeholders and potential users. In such context, the system can be a start-up of a

comprehensive national Forest Information System that serves several interest groups in a versatile

way.

Based on its technical flexibility, the system can fulfil the requirements of different target groups as

summarised in the table below.

DB Target Groups and Major Benefits

International Climate

Convention, UNFCCC

and REDD+

National

Forest

Governance

Regional / local Forest

Administration and

Management level

Public, including

NGOs and local

Forest User groups

Forest

Research

Community

NFI x (1,9) x(2)

MRV x(3) x(4,9) x(7) x(7) x(4)

M/I x(5) x(4,9) x(5,6) x(5) x(2)

FA x(3,5) x(4,9) x(5,6,7) x(7,5) x(2,4)

Table 1: Target Groups and Major Benefits

(1) NFI Processing and Management in order to generate a national report on forest conditions and

carbon volume assessment (MEGD 2014).

(2) Profound data base for extended geo-statistical and ecological (niche) modelling and lthe

development of long-term monitoring systems.

(3) MRV system for reporting to UNFCCC and consulting CCC0

(4) Supporting MEGD, FRDC and NUM for planning a national Forest Strategy and new research activities

to stabilize the forest ecosystems and support sustainable forest management in the scope of expected

climatic changes in Mongolia (MEGD 2014).

(5) Supporting REDD+ Safeguards initiative and targets

(6) Supporting Forest Management Planning on regional and local level

(7) Publication of carbon stock information valid for national and forest region level

(9) Development of National Forest Monitoring System (NFMS) and supporting IMH´s activities to

develop ecological niche models (MEGD 2014).

1.2 Functional Specification and Requirements

A functional specification defines a function of a system and its components. In software

engineering a function is described as a set of inputs, the behaviour, and outputs. All functions of

the proposed online components are closely related to their desktop applications and the generic

and/or pre-processed data. Generally speaking, the technical specification of the implemented

desktop and server applications, namely the PostgreSQL DB with the PostgrSQL-Server and QGIS

with the QGIS Server including the Apache httpd server determine the technical capacity of the

proposed online components (Online DB and FA).

The following table includes the technical specification of the basic components needed for the

implementation of Online DB and FA. All components with the exception of the QGIS Server and

Web Client have been installed on the FRDC Windows Server in April 2015.

Name [Version] Important technical features

QGIS

[QGIS 2.8.1 via

OSGEO4W 64bit,

without QGIS

Mapserver und Client]

Web: http://qgis.org

Open source Geographic Information System, fully OGC compliant, runs on most

Unix platforms, Windows, and OS X. A mobile version for Android is available.

Use of PostgreSQL and PostGIS is integrated. Provides following OGC web

services: WMS, WMTS, WFS, WFS-T, WCS, WPS, needed to use and work with

external data served over the web. Global user and developer community,

multilingual documentation and GUI.

QGIS Server

[not yet installed]

Web: http://hub.qgis.org/wiki/quantum-gis/QGIS_Server_Tutorial

QGIS Server is an open source WMS 1.3, WFS 1.0., WFS-T 1.0, WCS 1.1.1 and SLD

1.0 implementation that supports advanced cartographic features for thematic

mapping.

QGIS Server uses QGIS as back end for the GIS logic and for map rendering and

since QGIS desktop and QGIS Server use the same visualization libraries, the

maps that are published on the web look the same as in the desktop GIS.

The QGIS Server is a FastCGI/CGI (Common Gateway Interface) application

written in C++ that works together with a web server (e.g., Apache or Lighttpd).

PostGIS

[PostGIS 2.1.7 for

Postgres 9.4. (64 bit),

installed via

Stackbuilder in

PGAdmin III]

Web: http://postgis.org/

PostGIS adds support for geographic objects to the PostgreSQL object-relational

database. In effect, PostGIS "spatially enables" the PostgreSQL server, allowing it

to be used as a backend spatial database for geographic information systems

(GIS), much like ESRI's SDE or Oracle's Spatial extension. PostGIS follows the

OpenGIS "Simple Features Specification for SQL" and has been certified as

compliant with the "Types and Functions" profile.

http://qgis.org/

http://hub.qgis.org/wiki/quantum-gis/QGIS_Server_Tutorial

http://postgis.org/

http://www.postgresql.org/

http://www.opengis.org/

http://www.opengis.org/docs/99-049.pdf

PostgreSQL

[Postgres 9.4.1-3 from

DB Enterprise (64bit) on

port 5432]

Web: http://www.postgresql.org/

PostgreSQL is a powerful, open source object-relational database system. It runs

on all major operating systems, including Windows, Linux and UNIX (AIX, BSD,

HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64).

PostgreSQL prides itself in standards compliance. Its SQL implementation

strongly conforms to the ANSI-SQL 2008 standard. It has full support for

subqueries (including sub-selects in the FROM clause), read-committed and

serializable transaction isolation levels. And while PostgreSQL has a fully

relational system catalogue which itself supports multiple schemas per database,

its catalogue is also accessible through the Information Schema as defined in the

SQL standard.

Data integrity features include (compound) primary keys, foreign keys with

restricting and cascading updates/deletes, check constraints, unique constraints,

and not null constraints.

Apache httpd Server

[Apache/PHP v. 2.4.12 –

5.5.24-1 on port 8080,

via PGAdmin III

Stackbuilder]

Web: http://httpd.apache.org/

The Apache HTTP Server, colloquially called Apache, is the world's most widely

used web server software. The Apache HTTP Server Project is a collaborative

software development effort aimed at creating a robust, commercial-grade,

feature-rich and freely available source code implementation of an HTTP (Web)

server.

Apache supports php, cgi and fcgi scripts that are used to program the DB and FA

web interface. The main configuration of Apache can be done within a text based

.conf file.

Table 2: Technical Features of DB/FA System. [Version installed on FRDC Server as per 23.04.15]

1.2.1 The Spatial DB Extension PostGIS

PostGIS is a very powerful spatial extension to the PostgreSQL Database System (and other

relational database systems). It works as a link between the DB and the GIS world. PostGIS

supports the manipulation and management of geo-data stored in a DB, even without using the

QGIS desktop application. Different from the native DB functions, PostGIS includes a lot of

geostatistical functionalities, which can play an essential role in the processing of the NFI.

With a PostGIS extension to the DB, one could do many of the GIS tasks even without having a

desktop GIS installed! This may sound confusing, but having this in mind, one may better

understand, that the FA is just another way to present spatial and tabular information of a DB on the

web. Vice versa, there is also an extension for QGIS Desktop to use PostGIS in the QGIS

application and perform all the DB queries in a familiar GIS environment, what makes the GIS user

http://www.postgresql.org/

http://httpd.apache.org/

to a DB analyser.

Finally, since the data and information which are related to the REDD+ targets are primarily

geographical data, one may tend to use rather the graphical GIS/PostGIS solution for data analysis

than using the tabular PostgreSQL/PostGIS option. At the end both ways are possible, last not least

because they use a common language, called SQL.

1.2.2 SQL Language

Both systems, the DB and the GIS/PostGIS require a common language to communicate and to

inter-act in order to analyse and manage the data of the NFI, MRV and M/I DB. The Structured

Query Language (SQL) is a special-purpose language designed for managing data held in a

relational database management system (RDBMS). As an ANSI and ISO standard, SQL is portable

to many different DB systems.

The SQL language is subdivided into several language elements, including:

1. Clauses, which are constituent components of statements and queries. (In some cases, these are

optional.)

2. Expressions, which can produce either scalar values, or tables consisting of columns and rows of

data

Figure 1: Structured Query Language

3. Predicates, which specify conditions that can be evaluated to SQL three-valued logic (3VL)

(true/false/unknown) or Boolean truth values and are used to limit the effects of statements and

queries, or to change program flow.

4. Queries, which retrieve the data based on specific criteria. This is an important element of SQL.

5. Statements, which may have a persistent effect on schemata and data, or may control transactions,

program flow, connections, sessions, or diagnostics.

6. SQL statements also include the semicolon (";") statement terminator. Though not required on

every platform, it is defined as a standard part of the SQL grammar.

7. Insignificant whitespace is generally ignored in SQL statements and queries, making it easier to

format SQL code for readability.

1.3 Web Interfaces

The web interface of the Online DB and the FA is meant to help the user to access, control and

visualise technical functions and manipulated data on the internet.

1.3.1 Definition of NFI Web Interface and Specification

The NFI Web Interface is a comprehensive DBMS and user interface for professional DB users. It

is based on PostgreSQL Server and the programming script PHP running on an httpd Apache Server

for Windows. On 23.04.15 the interface “phpPGAdmin v5.1-1” has been installed on th eFRDC

Windows Server together with PostgreSQL.

Following graphic illustrates the layout of the interface. Currently, on June/July 2015, there is a

demo version accessible via http://lcgis.de (password required).

http://lcgis.de/

1.3.2 Definition MRV Web Interface and Specification

The design of the MRV web interface shall focus the reporting tasks to the UNFCCC and support

the CCCO. The main function is the selection and tabular visualisation of information, which has

been generated from the NFI data records and already stored in several tables.

The design of the sample plot inventory, namely the distribution and density of the NFI units

(cluster of 3 plots) has been planned to produce statistically verified results for each of the 5 forest

regions (Ludwig 2014a).

So all forest statistics can be related either to all Mongolia or to one of the 5 regions. The layout of

the interface shall therefore choose at first place the area as a selector, (forest region 1-5 or all

Mongolia) at second place the forest parameter (tree species composition, wood and CO2 volume

etc.), at third place the unit (per ha or in total). The results will finally be displayed in a table.

The geographical visualisation in the FA could also be linked to the tabular query, even if the query

is limited to only 5 different reference areas.

1.3.3 Definition M/I Web Interface and Specification

Different from the MRV DB, the M/I DB includes primarily data with a changing geographical

reference and hence should be using the GIS/PostGIS approach and FA presentation. Consequently

the thematic information coming along with the vector and raster data shall be presented as attribute

Figure 2: NFI DB interface of PostgreSQL Server at http://lcgis.de (accessed on 19.06.15)

tables in the FA. An extra web interface to display the data is not necessarily helpful.

Figure 1: WebGIS Client with GUI for M/I Online DB showing attribute table and map legend

1.3.4 Definition FA WebGIS Web Interface and Specification

1.3.4.1 QGIS Server

As mentioned above, the geo-data and its attributes are streamed to the internet through the QGIS

Server. The GUI of the QGIS WebClient (QWC) is implemented on the top of the QGIS server and

it is only one of several methods to present the data on the web. The most important feature of this

configuration (QGIS/QGIS Server/QWC is the seamless and quick way to present data from the

desktop application on the internet. This is an important difference to the proposal made by Sonntag

(2014c). In fact it is a publication of the desktop project file (*.qgs) including the map layout

designed in the map composer.

The features of the QGIS Server include following OGC compliant web services that can be used in

versatile way to publish data or distribute it over the internet.

Web Map Service (WMS/WMTS Client)

Web Map Tile Service (WMS/WMTS Client)

Web Feature Service (WFS und WFS-T client)

http://docs.qgis.org/2.0/de/docs/user_manual/working_with_ogc/ogc_client_support.html





Web Feature Service – Transactional (WFS und WFS-T client with editing functionality)

Web Coverage Service (WCS Client)

Web Processing Service (WPS Client for remote data processing)

Simple Features for SQL (PostGIS Layer)

Geography Markup Language (GML)

1.3.4.2 The QGIS WebClient Interface

The Forest Atlas interface is a re-configured version of the QGIS WebClient (QWC), an open

source Java application which runs on all major web browsers. QWC supports the GIS Server and

hence also the QGIS Desktop application.

QWC is a WMS based webgis client that makes use of QGIS specific WMS extensions (e.g.

highlighting, printing, metadata, etc.). The QGIS webclient reads the configuration from the WMS

GetCapabilities command and builds the layer tree accordingly. It supports legend graphic, feature

info requests, printing and the creation of hyperlinks to selected views. The link can be submitted

via e.mail and hence supports the communication between different stakeholders.

The client builds on existing Web-GIS libraries OpenLayers and GeoExt, as well as ExtJS 3 for the

GUI widgets (see https://github.com/qgis/QGIS-Web-Client).

Manny settings of the QWC can be defined in special text based configuration files, including the

alternation of the CRS, behaviour of the system and search functions.

Changing of the GUI language (default is English) to e.g. Mongolian language works with a

separate text file which includes the terms used in the GUI in different languages.

Unlike other technical components, the QGIS WebClient is not yet installed on the FRDC server.

But a demo version of the QWC is currently, June/July 2015, running on http://lcgis.de

1.3.4.3 Other User Interfaces

There is also a mobile interface available that is designed for smart phones to work with the QGIS

Service.

Other GIS, such as ArcGIS, ArcPad or the mobile QGIS version for Android devices, can be used

to display, store and edit data from the OGC compliant services of the QGIS Mapserver (WMS,

WFS etc.). So in fact, each GIS Software and other web applications with WMS/WFS functionality

provide a separate interface to display data hosted on the QGIS server. If data protection is required,

user rights and data access can be managed by the Apache server.

A German demo of the Mobile QGIS Viewer for Smartphones is currently (June 2015) available at

http://188.138.88.141/wbvgis/ol3-mobile-viewer-master/





http://docs.qgis.org/2.0/de/docs/user_manual/working_with_vector/supported_data.html

http://lcgis.de/

1.3.4.4 Definition of Relation between FA and Online DBs

Data of a GIS, such as the Forest Atlas application, always include data with a geographical

reference, such as places, areas, etc.

A DB in general can include all kind of data and information, without a need for a geographical

reference, e.g. the volume of larches listed in a table.

The relation between the Forest Atlas WebGIS application and the proposed DBs is determined by

the information itself: FA can include only data with a geographical reference and vice versa only

DBs with geo-referenced data can be linked to the FA.

In other words, the FA is another way to visualize data of the DBs by showing the location of the

DB information.

All information without a reference to a well-defined location must be aggregated to a statistically

approved reference area in order to describe the quality of that specific area or point.

E.g.: the quality of measured trees can be listed in a DB, but not in the FA, because the trees have

no clearly defined location. Such trees first have to be aggregated to the next valid reference point

or area, i.e. the sample plots or the NFI Unit, which is represented by two geographical coordinates

(or three coordinates, if the elevation is included).

Any GIS system is only as good as the data that's in it. QGIS provides a complete set of tools that

give the flexibility to store, edit, and manage data in a way that fits with the planned workflow

including the streaming to the FA WebGis application.

The Geodata can be stored in

individual files, such as shape files or raster files

databases by making direct connections to various relational database management systems

(RDBMS), such as PostgreSQL

geodatabase, such as the PostGIS spatial extension. A geodatabase stores GIS data in a

central location for easy access and management. It can be leveraged in desktop, server, or

mobile environments. It sits on top of an RDBMS, such as SQL Server, Oracle, or

PostgreSQL, and supports all types of GIS data

a publicly accessible folder or “cloud”, i.e. on a special directory of the FRDC server, which

can be accessed through the internet by authorised users or all users. Finally the DB and

geodata, which shall be visible in the FA must be transferred to the FRDC Windows Server

(or any other Web Server) using the same folder and DB structure as that the data manager

or GIS operator has used before on his Desktop PC.

2. PROPOSAL OF CONTENT AND QUERIES FOR NFI,

MRV, M/I ONLINE DBS AND FOREST ATLAS

WEBGIS

2.1 Proposal of Forest Characteristics to be queried

from the NFI DB

First of all the parameters required for the MRV reporting must be queried from the NFI DB (see

next chapter). This includes the Biomass and CO2 calculation and its periodical changes for all

Mongolia.

As defined by the originally planned sample design, the information generated from the NFI Units

shall be suitable to assess the forest conditions in all Mongolia and in each forest region separately.

Regardless of possible statistical limitations that may arise due to changed sample plot locations or

problems with the correct forest area calculation or field work errors as reported by Ludwig (2014),

at this point it is assumed, that the NFI data is good enough to assess whole Mongolia and even

single forest regions in a statistically verified way.

Under this premise, all forest parameters recorded in the course of the NFI field work could be

aggregated and calculated for the NFI Units and for each or all forest regions and hence statistically

describing the character of a forest region or the forest conditions in all Mongolia and finally

suitable for a report on National Forest Conditions.

Here is a proposal for a list of possible results generated from the NFI data:

o mean coverage of ground vegetation

o -occurrence (extend) of grazing, fires and soil erosion

o condition of regeneration per species and size classes per ha

o mean number of trees > 2m, classified in tree species and height in total and per ha

o tree species composition classified per DBH class

o mean age of trees, categorised in tree species, DBH- and height classes

o quality of trees with DBH > 30 cm, classified per tree species

o mean DBH and distribution of DBH classes, per tree species

o basal area per tree species per ha

o mean health class per species and age category

o dead wood volume estimation categorised in logs and stumps, per ha and in total

o stocking volume per species, per DBH class and per ha The development of a reliable height curve function is essential to calculate the volume. According to Ludwig (2014), first tests in August 2014, based on a study of the Academy of Science, the Institute of Botany could then not produce the expected results.

Any profound statements on Biodiversity require a permanent monitoring during different seasons.

Records from the NFI may deliver helpful information on the temporal occurrence of certain

species but drawing conclusions to the condition of an entire forest region would be not serious.

If the aggregated NFI clusters can also deliver statistically verified results on other areas depend

very much on the distribution and density of the NFI Clusters falling in that specific area. This has

to be proved first, but as a proposal here are some possible applications of the NFI data to describe

some other forest characteristics:

Aggregating and processing the NFI data on new clusters of forest stand categories, such as

stands with a dominant tree species or stands on certain site locations (altitude, slope,

aspect) could deliver reliable results on smaller forest areas.

Aggregating the NFI record for each sample separately, could show differences within the

NFI unit and would also allow to refer the forest parameters to the original landform and soil

conditions. Three geo-referenced plots per NFI Unit increase the value of the data and in

particular it can be helpful to model certain parameters in a more detailed scale.

2.2 Proposal of Content of MRV DB and Forest

Carbon Parameters to be queried

2.2.1 MRV DB

The International Panel on Climate Change (IPCC) has published guidance which provides

methodologies to follow in order to achieve an inventory which is measurable, reportable and

verifiable (MRV). The UNFCCC Secretariat has adopted this guidance as the basis on which it

accepts GHG Inventory reports. (source: http://cdredd.org/). The main focus is on the national level

reporting to the UNFCCC, and the subsequent, anticipated accounting of valuable carbon credits for

the country as a whole.

http://cdredd.org/

Measurement

Refers to information on the area extent to which a human activity takes place in forests (activity

data – AD) with coefficients that quantify the emissions or removals per unit activity (emission

factors – EF). For REDD+ this translates into measurements of forest area and area change (AD)

and forest carbon stock and carbon stock changes (EF). Together, this information provides the

basis for compiling a greenhouse gases (GHGs) inventory. Countries may also be required to

measure safeguards indicators and other forest benefits, as conceived in MEGD (2014).

Reporting

Implies the compilation and availability of national data and statistics for information in the format

of a GHG inventory. Reporting requirements to the UNFCCC (National Communications) may

cover issues other than just those subject to measurement. The core elements of the national

communications are information on emissions and removals of GHGs and details of the activities a

country has undertaken to fulfil its commitments under UNFCCC.

Verification

Refers to the process of independently checking the accuracy and reliability of reported information

or the procedures used to generate information. This verification is done by a totally independent

and external review. The UNFCCC Secretariat through its experts will verify the data reported. The

verification of countries’ actions depends on 3 factors:

1) the degree to which reported data is capable of being verified

2) the actors conducting the verification and

3) the way in which verification is performed.

There are some remarkable key-issues in MRV for REDD+ published by the FAO (http://www.un-

redd.org):

Country driven

process: each country has to establish an autonomous MRV system. The national MRV system

is a crucial element of REDD+ implementation.

Learning-by-doing approach:

the development of an MRV system has to be based on in-country human resources being involved

in the MRV development process from the very beginning and gradually improving skills whilst

progressing towards its full implementation.

Safeguards:

the inclusion of the ‘REDD+ Safeguards’ in the monitoring system improves the consideration of

biodiversity, governance and the inclusion of local communities.

Consistency:

an MRV system should provide estimates that are consistent across years. Under certain

circumstances, estimates generated from different methodologies in different years can be

considered consistent if they have been calculated in a transparent manner.

Transparency:

all the data and the methodologies used in the MRV system should be clearly explained and

appropriately documented, so that anyone can verify their correctness.

Comparability:

estimates of emissions and removals should be comparable among different forest owners and

among Parties. For this purpose, forest owners/ Parties should follow the methodologies and

standard formats provided by the IPCC and agreed within the UNFCCC for compiling and

reporting inventories.

Conservativeness:

when completeness or accuracy of estimates cannot be achieved, the reduction of emissions should

not be overestimated, or at least the risk of overestimation should be minimized

The content of such national MRV DBs will be published in different ways, e.g. on the

Environmental Data Explorer of the UNEP (http://geodata.grid.unep.ch), where the national

UNFCCC reports, including emission rates of different GHGs from LULUCF are presented in

tables and maps..

Fig 2.: The UNEP Environmental Data Explorer illustrating the results of the national UNFCCC reports ((http://geodata.grid.unep.ch

http://geodata.grid.unep.ch/

Conclusion:

From the NFI, the national estimation of following parameters are required for the UNFCCC report

and consequently must be contained in the MRV DB:

Forest Area

Biomass

C02 Volume

C02 Balance from Forest Loss and Forest Gain

The estimation of the total biomass (and CO2 volume) from the NFI is based on suitable yield

models including regionalised allometric equations and the total size of the national forest area.

2.2.2 Forest Area

Next to the NFI Units, reliable information on the forested area is a most essential factor for the

calculation of the national CO2 balance and a basic element of the MRV DB.

Comparing visually the existing forest mask from Landsat Imagery for 2013 (Sonntag, 2014a) with

the freely available data of the global forest change program of the University of Maryland (Hansen

2013 and http://earthenginepartners.appspot.com/science-2013-global-forest as per June 2015) we

found the latter data set a more reliable basis for the CO2 balance calculations.

The scientific approach, using a series of multi-temporal cloud free Landsat images in combination

with MODIS and ICESat LiDAR data, including available Google and BING imagery, was planned

to deliver very detailed information on the forest canopy conditions. Each cell includes the value of

the crown cover percentage, starting from 0-100%. In order to meet certain criteria for what makes

a forest, such as areas with crown coverage of more than 10%, all cells with a value e.g. < 10%

could set to null to get the corresponding forest cover. And even if the maps were developed for a

global scale, its results for Mongolia are convincing, also because in the last updated version (2015,

Version 1.1), the algorithms to detect changes in boreal forests had been revised and improved

(Hansen et. Al 2014).

Another advantage of that data set is its long observation period that goes back to 2000. The annual

loss or gain of forest cover has been documented since. All layers are freely downloadable and

could be added to the MRV DB or the FA.

Currently the GiZ project team is producing a new forest mask for 2014, which shall deliver more

accurate results in order to replace the existing forest mask of 2013.

http://earthenginepartners.appspot.com/science-2013-global-forest

2.3 Proposal of the Content of M/I DB and Socio

Economic and REDD+ Safeguards Information

2.3.1 REDD+ Safeguards

Creating safeguards for reducing emissions from deforestation and forest degradation (REDD+) is a

mechanism that supports voluntary sustainable forest preservation. Safeguards are the answer to the

question how REDD projects would change the lives of those whose culture, survival and heritage

depend on the forests themselves.

The UNFCCC REDD+ Safeguards encompass environmental, social, carbon and governance

standards to be applied to all types of REDD-plus financing. These safeguards are the core

minimum performance requirements for REDD-plus projects. (http://reddplussafeguards.com).

The UN-REDD safeguards are officially laid out as “the Social and Environmental Principles and

Criteria” (SEPC).

The SEPC has seven principles and 20 criteria which encompass the following:

democratic governance and respect for stakeholder rights – complements the Cancun

safeguards of 1) having transparent and effective national forest governance structures with

respect to national legislation and sovereignty and 2) giving room for the participation of

indigenous peoples and forest-dependent communities in the REDD-plus projects.

promotion of sustainable livelihoods, protection of natural forests from degradation and

conservation of biodiversity – reflects the Cancun mandate that actions should be consistent

with the conservation of natural forests and biological diversity

protection of natural forests from degradation and/or conversion – supports the objective of

the Cancun agreements that reversals and risk emissions must be addressed. Reversals

happen when a decrease in emissions is annulled due to deforestation or disasters like fire or

pests.

SEPC also address the need to secure land tenure, empower women and vulnerable groups and

establish a grievance mechanism.

The SEPC also aims to guarantee that REDD-plus projects bring multiple benefits – aside from

monetary rewards, REDD-plus programs must improve the overall state of communities and

environmental resources.

Monitoring the effectiveness of the implementation of the REDD-plus safeguards, however,

presents a major challenge. In 2011 it was agreed that a Safeguards Information System (SIS)

should be established to ensure the “transparency, consistency, effectiveness and

comprehensiveness” of implementing REDD-plus safeguards (http://reddplussafeguards.com).

This idea is being reflected in Component 4b of Mongolia´s National REDD+ Readiness Roadmap

(MEGD 2014). The conception of a NFMS includes the design of an Information System for

http://reddplussafeguards.com/

Safeguards and the definition of Non-Carbon Benefits (NCB), such as an improved watershed

function. The following table summarizes potential parameters and indicators affecting the

monitoring of REDD+ multiple benefits (source MEGD 2014, page 128):

Parameters to be monitored Potential Indicators

Policy and governance Development of relevant policies, regulations and procedures for REDD+ implementation; Information in the public domain; Number of conflicts over the use of resources; A number and types of actors involved; Change in capacity and subjectivity of actors; and Law enforcement

Alignment of development plans

Area of forest under sustainable management; Area and number of people engaged in sustainable grazing practices; Forest area planted and species; Enforcement of EIA and land use plans; and REDD+ in district development plans;

Biodiversity

Endemic species – losses and gains; Degraded forest areas rehabilitated; Identification of key species that characterize ecosystem health; Protected areas: establishment of new ones and levels of encroachment of existing ones;

Poverty

Food security; Employment: gains or losses related to REDD+ activities; Natural resources use rights; Income: gains or losses; Technologies made available and accessible; Access to education and health; and Gender equity;

Environmental

Burnt forest area: number, extent and location; and Water regulation and provision linked to forests

Social

Conflict; Gender and changes in decision making as a result of REDD+ activities; and Local level institutions and decision making;

Private sector

Forest certification; and CSR linked to promoting/implementing REDD+ activities.

Table 3: Parameters and Indicators to monitor REDD+ multiple co-benfits (MEGD 2014)

Looking at all those targets and requirements the REDD+ safeguards shall fulfil, it is obvious that a

number of environmental information based on accurate geographical and socio-economic data is

needed to support and strengthen the safeguard mechanism.

Further down you find a list with a proposal of information to be added to the M/I and Safeguard

DB. Information that is considered to be important for the Safeguard system is labelled with

“[SG]”.

For a detailed data mining we recommend to consider the GiZ Project Report of Hampel (2010) and

the maps available from MEGD.

2.3.2 M/I DB

The main threats to forest biodiversity in Mongolia are (Jacob, 2011 cited in MEGD 2014):

fires;

pasture degradation and desertification;

hunting and wildlife trade;

industrial scale and artisanal mining;

climate change and water shortages;

animal disease and disease management;

deforestation and crop agriculture.

The monitoring of the Mongolian forests is a long-term and multi-scale task to control and

document the development of a nationwide multi-purpose and sustainable forest management. A

variety of different eco-services must be considered as well as economical aspects and the

protection and development of biodiversity. Biotic and anthropogenic impacts must be assessed,

such as the risk of fires (wild and man-made), insect calamities or the human pressure on forests.

Many of such impacts are due to the climatic change taking place on a regional level. Plans to

mitigate such negative impacts and to stabilize the forest eco-system must rely on profound

information of a comprehensive Information System.

Trade-offs between diverting stakeholder interests can be managed in a better way, if there is

sufficient information, available in due time and accessible to all parties involved.

Big data is not a technical problem anymore, new concepts for data distribution, better computer

performance and increasing network capacities help to manage complex data, no matter it is

mapped in national or local scale.

2.3.3 Proposed Geographical Data used for the M/I and the REDD+ Safeguards system

Many data sets we are proposing here is in a global or national scale, but always with a thematic issue relevant to the safeguards and M/I targets.

Data from international organisations are, of course, only relevant to the project if there is no other comparable and national data available.

The production of more detailed raw data (such as highly resoluted aerial photographs or satellite imagery) and of thematic maps in a detailed sacle (e.g. management plans in 1:10.000) remains a challenging task for the near future.

In addition data from several national and international research projects (e.g. Wyss 2007, Bayartsegtseg 2011, Lavrenko 1979 or Menzel 2011 and http://www.iwrm-momo.de) could complement the DB, even if it is often limited to local study areas only.

Generally, the below listed data is freely available, where that´s not the case the data is labelled as commercial. Information labelled with [SG] are relevant to the REDD+ Safeguards System.

The data attributes, i.e. information related to the extend of the area, are part of the data set and should always be used, if it is of relevance to the user.

2.3.3.1 Geographic Data on Local and Regional Scale

[SG] Relevant Maps from the MEGD, namely the Mongolian Information and Research Institute of

Meteorology, Hydrology and Environment (http://www.mne.mn/). It is assumed, that such maps

will be available via a Web Service (WMS, WCS, WFS) in due time, if not done already).

[SG] Polygons with Forest Cover 2014 (GiZ Project Team)

[SG] Polygons with Forest Change Maps 2000-2013 (Hansen et al 2013)

Polygons of sub-compartments with attributes of former forest inventories (FRDC)

Polygons of compartments with attributes (FRDC)

Points of NFI Units (cluster of 3 samples) with aggregated information

Location of each sample plot (derived from special GIS analysis) with aggregated information

[SG] Polygons of different Forest User Groups and concessionaires with attributes

[SG] Polygons of different private forestry entities with attributes

[SG] Polygon with Soum administrative areas with relevant attributes

[SG] Location of Soum centres with relevant attributes

[SG] Polygon with Aimag administrative areas with attributes (= National Forest Units)

[SG] Location of Aimag centres with relevant attributes

http://www.mne.mn/

[SG] Polygons with classified Special Protected Areas and points RAMSAR sites with attributes

[SG] Classified network of roads, railways and rivers (could include run-off simulations, see

Menzel 2011)

[SG] Polygons of water bodies

2.3.3.2 Geographic Data on National and Global Scale

Forest and Biodiversity Related Maps

[SG] Polygons of 5 forest regions

[SG] Polygons of eco-zones (Lavrenko, E.M. (ed), 1979, SDC 2011)

[SG] Polygons of Soil Units

Grid with Canopy Heights

[SG] Polygons with Intact Forests

[SG] Forest Fire Maps accessible through Global Forest Watch (http://earthwatch.unep.net) or

World Resources Institute (http://www.wri.org)

[SG] Global Soil Dataset for Earth System Modelling (available at:

http://globalchange.bnu.edu.cn/research/soilw)

Background Information

[SG] Polygon of Mongolia

[SG] Polygons of watersheds

[SG] Topographical Map of Mongolia

[SG] SRTM Radar Surface Modell, ground resolution: 90 m and 30 m (15 arc seconds), download

from http://topex.ucsd.edu

[SG] Aster Optical Surface Model (ground resolution 30 m), free for research

ALOS/Prism Optical Surface Modell (ground resolution: 5m), low costs for research

[SG] Corona US Spy Satellite, b/w images from the 60 – 70ies (ground resolution 2 m),

commercial, low costs

[SG] Google Maps background imagery, not printable (varying ground resolution)

[SG] BING Maps background imagery, not printable, (varying ground resolution), requires

Registration ID from Microsoft for WebGIS usage (not needed for Desktop application).

[SG] Open Street Map (http://www.openstreetmap.org/#map=13/50.4368/100.1613&layers=C)

http://earthwatch.unep.net/

http://www.wri.org/

Open Weather Map (http://openweathermap.org)

Maps of the Land Cover CCI Climate Research Data Package

(http://maps.elie.ucl.ac.be/CCI/viewer)

[SG] Protected Areas from the World Database on Protected Areas of IUCN and UNEP-WCMC

(http://protectplanet.net)

[SG] Names for locations in Mongolia (Geonames, http://www.geonames.org)

[SG] Maps from NASA´s Global Change Master Directory (http://gcmd.nasa.gov)

2.3.3.3 Statistical Data for Mongolia

Statistical Data, in particular such information that refers to a well-defined area (e.g. a Soum or

Aimag district) is particular important for REDD+ Safeguards and for assessing human and societal

factors which may have an impact on the forest management and protection. Information from

national and regional authorities has to be reviewed and if suitable, linked to the M/I Safeguard

system (MEGD 2014). Authorities of interest are those concerned with forestry, agriculture,

regional planning, nature conservation, water management, public infrastructure, education, etc.

Also NGOs could be a source for helpful information, maps and tables.

Online statistical data for all Mongolia is currently available from the UN through the ESCAP

Statistical Yearbook (http://www.unescap.org/stat/data/).

Fig. 3:UN ESCAP Statistical Yearbook with data for Mongolia

2.4 Proposal of GIS Datasets for Forest Atlas WebGIS

Basically the QGIS based WebGIS application can seamlessly present all QGIS Desktop projects

online, Consequently all geodata from the NFI, MRV and M/I Safeguards Dbs with a well defined

reference to any area, line or point including its attribute tables can be displayed in the WebGIS.

But since the term “Forest Atlas” implies a more general application with public information for

interested stakeholders, NGOs and citizen, we propose to use only such GIS datasets that can easily

be understood also by a layman.

Such information include:

Freely available global thematical maps and background data (fao geonetwork, google, open street

map etc.)

Freely available national data-(IMH, MEGD, FRDC)

Data of regional forest management units and forest user groups (FRDC)

Data of the M/I DB used for the REDD+ Safeguards, listet and labelled with [SG] in the previous

chapter

Geodata, which is not provided directly from the FRDC or GiZ REDD+ project, should be linked to

the Forest Atlas using the Web Service function (WMS or WFS). This guarantees, that the linked

external geodata is always being presented with its latest version, provided and updated by the

responsible authority.

In order to structure the information of the FA, it is recommended to group the data to several

thematic projects with a pre-defined layer content and layout. The thematic projects should be

described on an introducing webpage with a hyperlink to the FA. An example is currently

(June/July 2015) available at http://lcgis.de.

Figure 3: Description of Thematic Maps, linked to a pre-defined and corresponding FA application (http://lcgis.de, accessed 19th June 2015

3 LITERATURE AND ABBREVIATIONS

3.1 Relevant Literature and Reports:

Bayartsegtseg Baasan (2010): Entwicklung eines Inventurmodells als Grundlage einer nachhaltigen

Bewirtschaftung am Beispiel eines Waldgebiets im südwestlichen Teil des Khentii-Gebirges der Mongolei.

PhD Thesis University of Freiburg. Inlcudes English Summary. Accessed through https://www.freidok.uni-

freiburg.de/fedora/objects/freidok:8005/datastreams/FILE1/content on 19. June 2015)

GIZ-Project Team (2015): REDD+ National Forest Inventory in Mongolia. Project Inception Report – Draft.

Greenpeace, University of Maryland, World Resources Institute and Transparent World. 2014: Intact Forest

Landscapes: update and reduction in extent from 2000-2013. Accessed through Global Forest Watch on

19.06.2015. www.globalforestwatch.org

Hampel, N. (2010): GIS DATA CATALOGUE. Climate Change & Biodiversity 2010. GCI - Dr. Schindler Geo

Consult International GmbH & Co. KG

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V.

Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G.

Townshend (2013): High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342 (15

November): 850–53. Publication available from http://www.sciencemag.org/content/342/6160/850 and

data available online from: http://earthenginepartners.appspot.com/science-2013-global-forest.

Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V.

Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G.

Townshend. (2013): Hansen/UMD/Google/USGS/NASA Tree Cover Loss and Gain Area. University of

Maryland, Google, USGS, and NASA. Accessed through Global Forest Watch on 20th June 2015.

www.globalforestwatch.org.

Kießlich, N. (no year): Guideline for the Management of Geodata. GIZ Climate Change & Biodiversity

Program. GCI GmbH & Co. KG, Leipzig.

Lavrenko, E.M. (ed) (1979): Vegetation map of Mongolian People's Republic 1 : 1.500.000, 4 sheets, Moskva

Ludwig, R. (2014a): Multipurpose Forest Resources Inventory of Mongolia. Pre-Implementation Activities –

Training of Trainers for Field Implementation. DFS – Deutsche Forst Service GmbH.

Ludwig, R. (2014b): Multipurpose Forest Resources Inventory of Mongolia. Database Set-Up and Quality

Assurance Possibilities. DFS – Deutsche Forst Service GmbH.

Mongolian Ministry of Environment and Green Development – MEGD (2014): Mongolia´s National REDD+

Readiness Roadmap.

Menzel L., Hofmann, J., Ibisch, R. (2011): Untersuchung von Wasser- und Stoffflüssen als Grundlage für ein

Integriertes Wasserressource-Management im Kharaa-Einzugsgebiet, Mongolei. Studies of water and mass

fluxes tp provide a basis for an Integrated Water Ressource Managemenet (IWRM) in the catschment of the

River Kharaa in Mongolia. Universität Heidelberg. HW 55.2011, H.2. Accessed through

http://www.geog.uni-heidelberg.de/hydro/momo_en.html on 19.06.2015

Potapov, P., Laestadius,L. Minnemeyer S. (2011). Global map of human pressure on the world's forests.

World Resources Institute: Washington, DC. Online at www.wri.org/forest-restoration-atlas.

Potapov, P., A. Yaroshenko, S. Turubanova, M. Dubinin, L. Laestadius, C. Thies, D. Aksenov, A. Egorov, Y.

Yesipova, I. Glushkov, M. Karpachevski, A. Kostickova, A. Manisha, E. Tsybikova, and I. Zhuravleva. (2008):

Mapping the World’s Intact Forest Landscapes by Remote Sensing. Ecology and Society 13, no. 2: Art. 51.

www.ecologyandsociety.org/vol13/iss2/art51.

Sonntag, L (2014a): Creation of the forest mask 2013, elaboration of an environmental database and a

forest atlas (Revised final report). GCI - Dr. Schindler Geo Consult International GmbH & Co. KG

Sonntag, L (2014b):, Implementation of an online Forest Atlas. GCI - Dr. Schindler Geo Consult International

GmbH & Co. KG

Sonntag, L (2014c): Forest Atlas data import. GCI Dr. Schindler Geo Consult International GmbH & Co. KG

UNEP-WCMC, UNEP, and IUCN. World Database on Protected Areas. Accessed on 19.06.15.

www.protectedplanet.net.

Weidenbach, M. (2015): Mission Report April - May 2015. REDD+ National Forest Inventory. Proposed

Database and Forest Atlas WebGIS Development.

Wyss, D. (2007): Waldmanagement in der Mongolei: Anwendung von GIS- und Fernerkundungsmethoden

im Rahmen der Entwickungszusammenarbeit. Am Beispiel des Schutzgebietes Khan Khentii (Application of

remote sensing and GIS for sustainable forest management and capacity building. Taking as an example the

Khan Khentii Special Protected Area (KKSPA). PhD Thesis FU Berlin, Accessed through http://www.diss.fu-

berlin.de/diss/receive/FUDISS_thesis_000000002906 on 18. June 2015. English Summary available at:

http://www.diss.fu-berlin.de/diss/servlets/MCRFileNodeServlet/FUDISS_derivate_000000002906

/14_15_11_English.pdf?hosts=

Wyss et. al. (2007): Application of Remote Sensing and GIS for Sustainable Forest Management and

Capacity Building in Mongolia. Publication on PDF.

3.2 Abbreviations

ANSI American National Standards Institute

CCCO Climate Change Coordination Officeel

CRS Coordinate Reference System

DB Database

DBMS Database Management System

EIC Environmental Information Centre at Information and Research Institute of

Meteorology, Hydrology and Environment (http://www.eic.mn)

FA Forest Atlas Web GIS Application

FAO Food and Agriculture Organisation of the United Nations

FINNIDA Finnish International Development Agency

FMPL Forest Management Planning

FRDC Forest Research and Development Centre

FUG Forest User Group

GHG Greenhouse Gas

GUI Graphical User Interface

httpd Hypertext Transfer Protocol Daemon, such as Apache

IB-MAS Institute of Botany, Mongolian Academy of Sciences

IGE-MAS Institute of Geo-Ecology, Mongolian Academy of Sciences

IMH Information and Research Institute of Meteorology, Hydrology and Environment

(http://www.icc.mn)

IPCCC Intergovernmental Panel on Climate Change

ISO International Organisation for Standardisation

LULUCF Land Use and Land Use Change and Forestry

MEGD Ministry of Environment and Green Development

METT Management Effectiveness Tracking Tool

M/I Monitoring und Information

MIA Ministry of Industry and Agriculture

MRV Measurement, Reporting and Verification

NAMA Nationally Appropriate Mitigations Action

NFI National Forest Inventory

NFMS National Forest Monitoring System

NUM National University of Mongolia

OGC Open Geospatial Consortium, http://www.opengeospatial.org/

QGIS Open source Quantum Geographic Information System including Desktop

Application, Server and Web Client

QWC QGIS Web Client, Java online application

PostGis Spatial extension to an RDBMS

PostgreSQL Relational open source DBMS

RDBMS Relational Database Management System, such as PostgreSQL

REDD+ Reducing Emissions from Deforestation and Forest Degradation

R-PP Readiness Preparation Proposal

SDC Swiss Agency for Development and Cooperation

SPC UN REDD+ Safeguards Principals and Criteria

SIS UN REDD+ Safeguards Information System

SQL Structured Query Language used in PostgreSQL and PostGIS

SFM Sustainable Forest Management

SLD Styled Layer Descriptor

UNDP United Nations Development Programme

UNFCCC United Nations Framework Convention on Climate Change

UN-REDD United Nations Collaborative Programme on Reducing Emissions from

Deforestation and Forest Degradation in Developing Countries

UoA University of Agriculture

UST University of Science & Technology

WCS Web Coverage Service, function for remote access to raster data

WFS Web Feature Service, function for Online manipulation of geodatat

WMS Web Mapping Service, function for Online presentation and query of geodata

WPS Web Processing Service, function for remote data processing

Table 1 Abbreviations used

ANNEX

ToR for Home Based Mission

Time Sheet June 2015

TIME SHEET

Period from 14.06.2015 to 27.06.2015

NAME OF EXPERT: Markus Weidenbach

PROJECT TITLE: REDD+Nationale Waldinventur Mongolei

CONTRACT REF: GIZ Contract: 81174267 / ÖBf Contract 750100141210

Date „Working“ days*

Field/ Home

Summary of activity

01.06.2015

02.06.2015

03.06.2015

04.06.2015

05.06.2015

06.06.2015

07.06.2015

08.06.2015

09.06.2015

10.06.2015

11.06.2015

12.06.2015

13.06.2015

14.06.2015 1 H Defining web interface and specifications of the online MRV, M/I, NFI DBs and Forest Atlas WebGIS

15.06.2015 1 H Defining web interface and specifications of the online MRV, M/I, NFI DBs and Forest Atlas WebGIS

16.06.2015 1 H Defining relation between Forest Atlas DB and 3 individual DBs

17.06.2015 1 H Defining relation between Forest Atlas DB and 3 individual DBs

18.06.2015 1 H Proposing forest characteristics to be queried from NFI database

19.06.2015 1 H Proposing forest characteristics to be queried from NFI database

20.06.2015 1 H Proposing the content of MRV database and forest carbon parameters to be queried

21.06.2015 1 H Proposing the content of MRV database and forest carbon parameters to be queried

22.06.2015 1 H Proposing the content of M/I database and socio economic and REDD+ safeguard information to be queried

23.06.2015 1 H Proposing the content of M/I database and socio economic and REDD+

safeguard information to be queried

24.06.2015 1 H Proposing the GIS datasets for Forest Atlas WebGIS.

25.06.2015 1 H Report on documentation of functional specification, web interface and queries for NFI, MRV, M/I online databases and Forest Atlas WebGIS



28.06.2015

29.06.2015

30.06.2015

TOTAL 14 14

*1 = Work day; 0,5 = Half work day; H = Holiday; S = Sick leave

DESCRIPTION OF GEODATA FROM EXTERNAL

PROVIDERS Following is a list of REDD+ relevant geo-data, including free accessible maps from international

organisations that could be useful, unless national data with same thematic content is available.

Maps from Institute for Environmental Information in

Ulaanbaatar (accessed through http://eic.mn/ on 23.06.15):

Fig. 4: ECI RS products

Fig. 5: EIC Online Databases

Canopy Heights

Since tree heights are measured in the NFI already, the following paragraph is only a proposal for

an additional source of information that could be interesting to combine with the NFI Units. The

data is based on Satellite LiDAR measurements from 2005 and has yet not been verified for

Mongolia.

This base map depicts the highest points in the forest canopy. Its spatial resolution is 0.6 miles (1

km) and was validated against data from a network of nearly 70 ground sites around the world. It

was developed by a team of scientists from NASA’s Jet Propulsion Laboratory, the University of

Maryland and Woods Hole Research Center. The map was created using 2.5 million carefully

screened, globally distributed laser pulse measurements from space. The light detection and ranging

(Lidar) data were collected in 2005 by the Geoscience Laser Altimeter System instrument on

NASA’s Ice, Cloud, and land Elevation Satellite (ICESat).

Fig. 6: Source: www.nasa.gov/topics/earth/features/forest20120217.html

Maps from University of Maryland´s Global Forest

Change 2000-2013 program

Accessed through http://earthenginepartners.appspot.com/science-2013-global-forest on 19.06.15

Tree cover gain

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in

height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or

plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of

tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire,

disease, or storm damage. As such, “loss” does not equate to deforestation.

When zoomed out (< zoom level 13), pixels of gain are shaded according to the density of gain at

the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of

tree cover gain, whereas pixels with lighter shading indicate a lower concentration of tree cover

gain. There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).

This data set measures areas of tree cover gain across all global land (except Antarctica and other

Arctic islands) at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were

generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+)

sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a

cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30

meter pixels) in the satellite images were assembled and a supervised learning algorithm was then

applied to identify per pixel tree cover gain.

Fig. 7: Forest Cover (green areas) and Forest Loss (red areas) around Ulaanbaatar (http://www.globalforestwatch.org)

Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area

that previously had no tree cover. Tree cover gain may indicate a number of potential activities,

including natural forest growth or the crop rotation cycle of tree plantations.

Tree Cover Loss

This data layer was updated in January 2015 to extend the tree cover loss analysis to 2013. The

2013 data update included new Landsat 8 data (launched in February 2013) as well as re-processed

2010-2012 data from Landsat TM and ETM+, which increased the amount of change that could be

detected, resulting in some changes in calculated tree cover loss for 2011 (global increase of 6%)

and 2012 (increase of 22%). Calculated tree cover loss for 2001-2010 remains unchanged. The

integrated use of the original 2001-2012 (Version 1.0) data and the updated 2011–2013 data

(Version 1.1) should be performed with caution.

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in

height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or

plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of

tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire,

disease, or storm damage. As such, “loss” does not equate to deforestation.

When zoomed out (< zoom level 13), pixels of loss are shaded according to the density of loss at

the 30 x 30 meter scale. Pixels with darker shading represent areas with a higher concentration of

tree cover loss, whereas pixels with lighter shading indicate a lower concentration of tree cover loss.

There is no variation in pixel shading when the data is at full resolution (≥ zoom level 13).

This data set measures areas of tree cover loss across all global land (except Antarctica and other

Arctic islands) at approximately 30 × 30 meter resolution. The data were generated using

multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+), and Landsat 7

thematic mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) sensors. Over 1

million satellite images were processed and analysed, including over 600,000 Landsat 7 images for

the 2000-2012 interval, and approximately 400,000 Landsat 5,7 and 8 images for the 2010-2013

interval . The clear land surface observations in the satellite images were assembled and a

supervised learning algorithm was applied to identify per pixel tree cover loss.

Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover

canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including

forestry practices such as timber harvesting or deforestation (the conversion of natural forest to

other land uses), as well as natural causes such as disease or storm damage. Fire is another

widespread cause of tree cover loss, and can be either natural or human-induced.

2015 Update (Version 1.1)

This data set was recently updated and now includes a 2013 loss layer and revised layers for 2011

and 2012. The analysis method has been modified in numerous ways, and the update should be seen

as part of a transition to a future “version 2.0” of this data set that is more consistent over the entire

2001 and onward period. Key changes include:

The use of Landsat 8 data for 2013 and Landsat 5 data for 2010-2011

The reprocessing of data from 2011 to 2012 in measuring loss

Improved training data for calibrating the loss model

Improved per sensor quality assessment models to filter input data

Improved input spectral features for building and applying the loss model

These changes lead to a different and improved detection of global tree cover loss. However, the

years preceding 2011 have not yet been reprocessed with the revised analysis methods, and users

will notice inconsistencies between versions 1.0 (2001-2012) and 1.1 (2001-2013) as a result. It

must also be noted that a full validation of the results incorporating Landsat 8 has not been

undertaken. Such an analysis may reveal a more sensitive ability to detect and map forest

disturbance using Landsat 8 data. If this is the case then there will be a more fundamental limitation

to the consistency of this data set before and after the inclusion of Landsat 8 data. Validation of

Landsat 8-incorporated loss detection is planned.

Some examples of improved change detection in the 2011–2013 update include the following:

Improved detection of boreal forest loss due to fire

Improved detection of smallholder rotation agricultural clearing in dry and humid tropical

forests

Improved detection of selective logging

These are examples of dynamics that may be differentially mapped over the 2001-2013 period in

Version 1.1. A version 2.0 reprocessing of the 2001 and onward record is planned, but no delivery

date is yet confirmed.

Tree cover loss is not always deforestation

Loss of tree cover may occur for many reasons, including deforestation, fire, and logging within the course of sustainable forestry operations. In sustainably managed forests, the “loss” will eventually show up as “gain”, as young trees get large enough to achieve canopy closure.

Map with Intact Forest Landscapes 2000/2013

Another global Map illustrates the extend of intact forest landscape in Mongolia. In terms of forest

ecology it could be interesting to combine the detected areas in Mongolia with the NFI data.

The map identifies the world’s last remaining undisturbed forest areas, large enough to retain all

native biodiversity and showing no signs of human activity as of the year 2013 and reduction in

their extent from 2000-2013 (Potapov et. al. 2008).

Source: http://www.globalforestwatch.org and http://www.intactforests.org/

Function Identifies the world’s last remaining undisturbed forest areas, large enough to retain all

native biodiversity and showing no signs of human activity as of the year 2013 and reduction in

their extent from 2000-2013.

RESOLUTION / SCALE: Approximately 1:100,000

Geographic coverage: Global

Source data: Landsat TM/ETM+

Frequency of updates: 2014 update; 2006 original publication

Date of content: 2013

The world IFL map was created through visual interpretation of Landsat images by experts. The

map may contain inaccuracies due to limitations in the spatial resolution of the imagery and lack of

ancillary information about local land-use practices in some regions. In addition, the methodology

assumes that fire scars in proximity to roads or other infrastructure have been caused by humans,

and therefore constitute a form of significant human activity. This assumption could result in an

underestimation of IFL extent in the boreal ecozone. The attribution of forest fires to human

influence across boreal forest landscapes is disputed.

The Intact Forest Landscapes (IFL) data set identifies unbroken expanses of natural ecosystems

within the zone of forest extent that show no signs of significant human activity and are large

enough that all native biodiversity, including viable populations of wide-ranging species, could be

maintained. To map IFL areas, a set of criteria was developed and designed to be globally

applicable and easily replicable, the latter to allow for repeated assessments over time as well for

verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size,

and with a minimum width of 10 kilometers. These were then mapped and identified from Landsat

satellite imagery for the year 2013.

Changes in the extent of IFLs were identified by contrasting the map for 2013 with the analogous

map for 2000, adjusted for consistency. Areas identified as “reduction in extent” met the IFL

criteria in 2000, but no longer met the criteria in 2013. The main causes of change were industrial

activity such as logging and mining, and fragmentation due to infrastructure and new roads, and

fires assumed to be caused by humans.

This data can be used to assess forest intactness, alteration, and degradation at global and regional

scales. More information about the dataset and methodology is available on www.intactforests.org

Fig. 8: Maps with intact forest landscapes in Mongolia

Maps from Climate Change Initiative (CCI)

Include several datasets from Climate Research Data Package (CRDP)

Land Cover Maps

a 3-epoch series of global land cover maps at 300m spatial resolution, where each epoch covers a 5-

year period (2008-2012, 2003-2007, 1998-2002).

Each pixel value corresponds to the label of a land cover class defined using UN-LCCS classifiers.

For each epoch, the land cover map is delivered along with 4 quality flags which document the

reliability of the classification:

qualityflag1 pixel has been processed or not,

qualityflag2 pixel status as defined by the pre-processing,

qualityflag3 number of valid observations available to derive the classification,

qualityflag4 level of confidence in the product (ranging from 0 to 100).

Only major LC changes were detected at 1km spatial resolution and limited to a certain number of

classes (see CRDP user guide).

Meris Surface Reflectance

Given the amount and size of the MERIS surface reflectance archive (10 To), data transfer will be

made, on request, through your own disks. Please contact

[email protected]

The surface reflectance (SR) products consist of MERIS global time series covering the 2003-2012

period. The spectral content encompasses the 13 surface reflectance channels - the atmospheric

bands 11 and 15 being removed - and the spatial resolution is of 300 m for the FR and 1000 m for

the RR.

The time series are made of temporal syntheses obtained over a 7-day compositing period.

Water Bodies

Static map of stable open water bodies at 300m spatial resolution resulting from a land/water

classification based on Envisat ASAR, SRTM-SWBD and MERIS data. The water pixels of this

map correspond to the class "Water Bodies" of the CCI-LC Maps.

The product consists of 3 layers:

Map land/permanent water classification at 300m spatial resolution. Legend : 1-Land, 2-

Water,

NObsImsWS number of observations originating from the ASAR Wide Swath Mode +

Image Monitoring Mode imagery,

NObsImsGM number of observations originating from the ASAR global monitoring mode

imagery.

Land Surface Seasonality products

On a per pixel basis, these climatological variables reflect, along the year, the average trajectory and

the inter-annual variability of a land surface feature over the 1999-2012 period. They are built from

existing long-term global datasets with high temporal frequency and moderate spatial resolution

(500m-1km). They result from a compilation of 14 years of 7-day instantaneous observations into 1

temporarily aggregated profile depicting, along the year, the reference behaviour for the vegetation

greenness, the snow and the BA at global scale.

Vegetation greenness

The NDVI product describes globally the yearly reference dynamic of the vegetation greenness

characterizing the 1999-2012 period. It is derived from 1km SPOT-VEGETATION (VGT) data.

It is built from 14 years of SPOT-VGT daily top of canopy SR syntheses (S1 products) and of

related quality flags. It comprises 2 main series of measurements:

AggMean smoothed NDVI values corresponding to the mean NDVI over the 1999-2012

period. It gives the yearly reference dynamic of the vegetation greenness at a 7-day

frequency,

Std standard deviation of the mean NDVI over the 1999-2012 period. It represents the inter-

annual variability of the mean NDVI for each 7-day period.

In addition, 2 quality flags are provided at the pixel level:

NYearObs number of valid and cloud-free weekly composites contributing to each 7-day

period of the AggMean and Std series. It is a quality indicator of the mean and standard

deviation estimates

Status of the pixel; 0: invalid, 1: land , 2: water , 3: snow, 4: cloud , 5: filled ice

Each layer has a spatial resolution of 1km and a LAT/LONG WGS84 projection.

Snow occurrence

The snow product presents the frequency at which snow has been detected along the year, based on

observations over the 2000-2012 period. Data originate from the MODIS/Terra Snow Cover 8d L3

Global 500m SIN Grid Product (MOD10A2).

This seasonality product is composed of two series of 52 layers (1 per week):

AggOcc proportion of snow occurrence as detected over the 2000-2012 period on a 7-day

basis (ranging from 0 to 100). This describes the yearly reference dynamics of the snow

coverage at a 7-day frequency,


period of the AggOcc series. This is a quality indicator of the occurrence values.

Each layer has a spatial resolution of 500m and a LAT/LONG WGS84 projection.

Burned areas occurrence

The burned areas product presents the frequency at which burned areas have been detected along

the year, based on observations over the 2000-2012 period. Data currently originate from the

GFEDv3 dataset.

The burned areas product is composed of two series of 52 layers (1 per week):

AggOcc percentage of burned areas occurrence as detected over the 2000-2012 period on a

7-day basis (ranging from 0 to 100). This describes the yearly reference dynamics of the

burned areas presence at a 7-day frequency,


period of the AggOcc series. It is a quality indicator of the occurrence values.

Each layer has a spatial resolution of 500m and a LAT/LONG WGS84 projection.

User tool

Dedicated user tool for sub-setting, re-projecting and re-sampling the CCI-LC maps and seasonality

products in a way which is suitable to each climate model. This tool also allows converting the

LCCS legend to user-specific PFTs.

Functionalities:

Ranking of LC class by fractional area in target cell; first n entries of sorted list are written

to n bands (n is user parameter, called majority classes),

Fractional area of each LC class,

Fractional area of each PFT.

The files are packaged and compressed with the 7z file format.

For questions regarding one of these above-mentioned products, we invite you to contact us at

[email protected]

For general comments or questions about the CCI-LC project or the website, please contact us at

[email protected]

Copyright

Copyright notice: © ESA Climate Change Initiative - Land Cover project 2014

Fig. 9: ESAs LandCover CCI Data

IUCN and UNEP Protected Areas

Function: Displays areas that are legally protected according to various designations (e.g., national

parks, state reserves, and wildlife reserves) and managed to achieve conservation objectives

Geographic coverage: Global

Source data: The World Database on Protected Areas, which compiles protected area data from

governments, NGOs, and international secretariats

Frequency of updates: Monthly

Date of content : Varies by protected area

Protected area designations, such as “National Park,” can be applied differently in different

countries. Therefore, the associated IUCN category and its description of protection may also vary

by country.

Protected areas with no boundary data are displayed as brown dotted boxes, which represent the

reported protected area size. The box is centered around a single point location and the borders do

not indicate the real boundary of the protected area.

The World Database on Protected Areas (WDPA) is the most comprehensive global spatial data set

on marine and terrestrial protected areas available. Protected area data are provided via

protectedplanet.net, the online interface for the World Database on Protected Areas (WDPA). The

WDPA is a joint initiative of the IUCN and UNEP-WCMC to compile spatially referenced

information about protected areas.

IUCN Management Categories

Not all protected areas receive the same degree of protection. While some have strict guidelines

designed to preserve intact ecosystems, others allow for sustainable land use, often including

limited resource extraction. In addition, not all countries use the same terminology when

designating a protected area. Accordingly, the International Union for Conservation of Nature

defined universal management categories that stipulate the level of protection for most protected

areas.

As you click through protected areas in this layer, note the “legal designation” and the explanations

below to better understand the degree to which an area is protected.

Ia. Strict Nature Reserves. Protected areas designed to preserve biodiversity and all

geological features. Limited human use (e.g., scientific study, education) is allowed and

carefully monitored. Strict Nature Reserves are often used to understand the impact of

indirect human disturbance (e.g., burning fossil fuels) because of the area’s high level of

preservation. Other common designations: Biological Reserve, Botanical Reserve

Ib. Wilderness Areas. Protected areas managed to preserve ecosystem processes with

limited human use. Wilderness Areas cannot contain modern infrastructure (e.g., a visitor’s

center), but they allow for local indigenous groups to maintain subsistence lifestyles. These

areas are often established to restore disturbed environments. Other common designations:

Wilderness Reserve, Wildlife Area

II. National Parks. Protected areas designed to preserve large-scale ecosystems and support

human visitation. With conservation as a priority, these areas allow infrastructure and

contribute to the local economy by providing opportunities for environmental educational

and recreation. Other common designations: State Park, Class A Park, Park Reserve,

Provincial Park

III. National Monuments or Features. Areas established to protect a specific natural

feature (e.g., cave, grove) or human-made monument with significant historical, spiritual, or

environmental importance and the immediate surroundings. Accordingly, Natural

Monuments or Features are typically smaller in area and have high human impact resulting

from visitor traffic. Other common designations: Natural Features Reserve, Nature

Monument, Botanical Garden

IV. Habitat and Species Management Areas. Areas designed to conserve specific wildlife

populations and/or habitats. Habitat and Species Management Areas often exist within a

larger ecosystem or protected area and are carefully managed (e.g., through hunting

abatement or habitat restoration) to conserve a target species or habitat. Other common

designations: National Wildlife Refuge, State Wildlife Management Area, Faunal Reserve,

Zakaznik (Russia), Provincial Reserve, Wildlife Sanctuary

V. Protected Landscapes and Seascapes. Protected areas with ecological, biological, or

cultural importance that have been shaped by human use of the landscape. Protected

landscapes and seascapes typically cover entire bodies of land or ocean and allow for a

number of for-profit activities (e.g., ecotourism) in accordance with the region’s

management plan. Other common designations: National Forest, State Natural Area,

Environmental Protection Area, Protected Area, Quasi National Park (Japan), Nature

Reserve, State Natural Area

VI. Protected Areas with Sustainable Use of Natural Resources. Areas designed to

manage natural resources and uphold the livelihoods of surrounding communities. These

regions have a low level of human occupation, small-scale developments (i.e., not

industrial), and part of the landscape in its natural condition. Other common designations:

Wildlife Reserve, Biosphere Reserve, Forest Reserve, Protective Zone, National Forest,

Natural and National Reserves, Reserve, Multiple Use Reserve, Municipal Reserve

UNESCO-MAP Biosphere Reserves: areas under UNESCO’s Man and the Biosphere

Programme designated to “promote sustainable development based on local community

efforts and sound science.”

World Heritage Sites: areas considered to have “outstanding universal value” and meet at

least one of ten criteria, as described here.

Ramsar Sites—Wetlands of International Importance: wetlands that hold significant value

designated under the Ramsar Convention on Wetlands.

Global Soil Dataset for Earth System Modeling

Available Global Layer from FAO:

source: www.fao.org/geonetwork

Administrative and Political Boundaries

Agriculture and Livestock

Applied Ecology

Base Maps, Remote Sensing and Toponomy

Biological and Ecological Resources

Figure 4 Soil Map

Climate

Fisheries and Aquaculture

Forestry

Human Health

Hydrology and Water Resources

Infrastructures

Land Cover and Land Use

Population and Socio-Economic Indicators

Soils and Soil Resources

Topography

Atlas of Forest Landscape Restoration Opportunities

http://www.wri.org/resources/maps/atlas-forest-and-landscape-restoration-opportunities/data-info

Below, see the source information for each data layer presented in the Atlas of Forest Landscape

Restoration Opportunities:

Current forest coverage

The map of current forests shows the global extent of forest coverage, including categories of forest

density. It is based on a combination of two satellite-derived products: 1) a global forest map

derived from MODIS 250m data for the period 2000 to 2009 (South Dakota State University, 2011,

unpublished dataset), which was used to map the general extent of forests independent of canopy

density; and 2) a tree canopy density map derived from the MODIS vegetation continuous fields

(VCF) data (Hansen et al., 2003), which was used to separate classes of tree density (e.g. closed,

open, and woodland).

Potential forest coverage

The map of potential forests represents an estimate of where forests would grow under current

climate conditions and without human influence. The main source of data for defining potential

forest extent is the terrestrial ecoregions of the world (Olson et al., 2001). Each ecoregion was

classified as belonging to one of four categories: dense forests, open forests, woodlands, or non-

forest, depending on its description (including current and potential vegetation) and its proportion of

different forest types, with additional input from the following datasets: current forest extent (see

above); bioclimatic zoning and original forest cover extent (FAO, 1999; Bryant et al., 1997; Zomer

et al., 2008); and a forest distribution map produced by modeling based on global climate variables

and elevation.

Forest condition

A comparison of the maps of current and potential forests makes it possible to identify forest

condition, including areas of historical forest loss and degradation. There are four basic categories

for forest condition:

Intact: No forest conversion or degradation has taken place;

Fragmented/managed: Natural forests and woodlands that have experienced some level of

timber extraction (e.g., selectively logged forests or secondary forests) or are managed as

plantations.

Degraded: A reduction in the volume, tree canopy cover and biodiversity of forested areas;

Deforested: Formerly forested areas that have been converted to other non-forest land uses.

Restoration opportunities

Data on forest condition and current land use were used to derive the map of opportunities for

restoration on degraded lands. The land-use data sets include population density, urbanized or

industrial areas, and cropland distribution. Areas with high population density or those occupied by

intensively managed croplands were considered as having no or low forest restoration potential.

Areas with scattered cropland areas, pastures, agroforestry and all types of forest plantations were

considered as providing promising opportunities for restoration. Deforested and degraded forest

lands were divided into four categories, resulting in a map of restoration opportunity areas and other

former forest lands:

Wide-scale restoration: Less than 10 people per square kilometer and potential to support

closed forest.

Mosaic restoration: Moderate human pressure (between 10 and 100 people per square km).

Remote restoration: Very low human pressure (density of less than one person per square

km within a 500-km radius).

Forests without restoration needs: Intact forests.

Other former forest lands:

Agricultural land: Croplands with intensive usage for food production (Pittman, et al.,

2010).

Recent tropical deforestation: Loss of humid tropical forest between 2000 and 2005

(Hansen, et al., 2008).

Urban areas: Densely populated and industrialized areas (LandScan, 2005).

Human pressure

A map of land-use intensity (human pressure) was used to assess opportunities for restoration of

degraded lands as well as classify degraded lands according to suitability for different types of

restoration. Several separately-mapped land-use classes were combined to make the land use

intensity map, including population density, built-up areas, pasturelands, croplands and cultivated

areas. The resulting data were divided into the following three categories of human pressure:

High: Lands with high population density (more than 100 persons per square km),

croplands, and urban areas. These lands offer opportunities for protective restoration only

(e.g., buffering waterways near croplands; erosion prevention on steep slopes; and storm

water runoff mitigation).

Moderate: Lands with a rural population density between 10 and 100 persons per square

km. These lands offer opportunities for mosaic restoration.

Low: lands with a rural population density of less than 10 persons per square km. These

lands offer opportunities for wide-scale restoration.

Bonn Challenge

At the invitation of the German Government and IUCN, the Bonn Challenge was established at a

ministerial roundtable in September 2011 and calls for the restoration of 150 million hectares of

deforested and degraded lands worldwide by 2020. The map marks the general locations where

countries, regional organizations, businesses, and other entities have pledged to restore forests

toward meeting the Bonn Challenge.

Land Scan Data Source: http://web.ornl.gov/sci/landscan/landscan_documentation.shtml

Synthetic Map of Population Density and Pressure, such as the LandScan approach (existing maps

are only free for US Citizens ):

LandScan Documentation

Using an innovative approach with Geographic Information System and Remote Sensing, ORNL's

LandScanTM is the community standard for global population distribution. At approximately 1 km

Fig. 10: LandScan data for Mongolia with human pressure

resolution (30" X 30"), LandScan is the finest resolution global population distribution data

available and represents an ambient population (average over 24 hours). The LandScan algorithm,

an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-

variable dasymetric modeling approach to disaggregate census counts within an administrative

boundary. Since no single population distribution model can account for the differences in spatial

data availability, quality, scale, and accuracy as well as the differences in cultural settlement

practices, LandScan population distribution models are tailored to match the data conditions and

geographical nature of each individual country and region.

Open Weather Map OpenWeatherMap provides free weather data. However, if you need our specialists to be more

involved in your business we're ready to support! For higher level of availability and support see the

Developer and Professional support plans. For guaranteed high level of availability and API

customization please consider an Enterprise account.

http://openweathermap.org/maps

http://www.openweathermap.org/price_detailes

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