Indicator: 15.4.2

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0.a. Goal

Goal 15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss

0.b. Target

Target 15.4: By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development

0.c. Indicator

Indicator 15.4.2: (a) Mountain Green Cover Index and (b) proportion of degraded mountain land

0.d. Series

Primary series:

Mountain Green Cover Index (ER_MTN_GRNCVI)

Proportion of degraded mountain land (ER_MTN_DGRDP)

Supplementary series:

Mountain green cover area (square kilometres) (ER_MTN_GRNCOV)

Mountain area (square kilometres) (ER_MTN_TOTL)

Area of degraded mountain land (square kilometres) (ER_MTN_DGRDA)

0.e. Metadata update

2023-05-15

0.f. Related indicators

6.6.1, 15.1.1, 15.2.1, 15.3.1, 15.4.1

0.g. International organisations(s) responsible for global monitoring

Food and Agriculture Organization of the United Nations (FAO)

1.a. Organisation

Food and Agriculture Organization of the United Nations (FAO)

2.a. Definition and concepts

Definitions:

The indicator is composed of two sub-indicators to monitor progress towards the conservation of mountain ecosystems:

Sub-indicator 15.4.2a, Mountain Green Cover Index (MGCI), is designed to measure the extent and changes of green cover - i.e. forest, shrubs, trees, pasture land, cropland, etc. – in mountain areas. MGCI is defined as the percentage of green cover over the total surface of the mountain area of a given country and for given reporting year. The aim of the index is to monitor the evolution of green cover and thus assess the status of conservation of mountain ecosystems.

Sub-indicator 15.4.2b, Proportion of degraded mountain land, is designed to monitor the extent of degraded mountain land as a result of land cover change in a given country and for given reporting year. Similarly to sub-indicator ‘’trends in land cover” under SDG Indicator 15.3.1 (Sims et al. 2021), mountain ecosystem degradation and recovery is assessed based on the definition of land cover type transitions that indicate improving, stable or degrading conservation status. The definition of degradation adopted for the computation of this indicator is the one established Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES)^[1].

Concepts:

Mountain area is defined according to the UNEP-WCMC (2002) method. It defines total global mountain area as the sum of seven classes (commonly known as ‘Kapos mountain classes’), based on elevation, slope and local elevation ranges (Table 1).

Table 1. Global mountain classes as defined by UNEP-WCMC (2002)

Kapos Mountain Class	Description
Class 1	Elevation >= 4500 meters
Class 2	Elevation >= 3500 & < 4500 meters
Class 3	Elevation >= 2500 & < 3500 meters
Class 4	Elevation >= 1500 & < 2500 meters & slope >= 2 degrees
Class 5	Elevation>= 1000 & < 1500 meters & slope >= 5 degrees OR local (7 km radius) elevation range > 300 meters
Class 6	Elevation >= 300 & < 1000 meters & local (7 km radius) elevation range > 300 meters
Class 7	Inner isolated areas (<=25 Km2 in size) that do not meet criteria but surrounded by mountains

Prior to the methodological refinement of this indicator approved by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDG) in June 2022, the UNEP-WCMC classification was used to disaggregate the indicator by Kapos mountain classes. This is no longer the case, with Kapos mountain classes having been replaced by a bioclimatic belts (see section 2.c below).

Land cover refers to the observed physical cover of the Earth’s surface. It includes vegetation and man-made features as well as bare rock, bare soil and inland water surfaces (FAO-GTOS, 2009). The primary units for characterizing land cover are categories (e.g. Forest or Open Water). These categories must be defined following a standardized land cover classification in order to identify land cover changes consistently over time. Several global standards of land cover classifications have been developed by international initiatives for this purpose.

For the purposes of standardization and harmonization when reporting on SDG Indicator 15.4.2, this indicator has adapted the land cover classification established by the United Nations Statistical Commission’s System of Environmental and Economic Accounting (UN-SEEA) (UN Statistical Division, 2014) by selecting the most relevant SEEA classes for mountain ecosystems and aggregating all croplands classes (Table 2).

Table 2. Left: Land cover classification established by the UN-SEEA (Source: UN Statistical Division, 2014). Right: Adapted land cover classification for the computation and aggregate reporting on SDG Indicator 15.4.2.

Original UN – SEEA land cover classification (n=14)	SDG Indicator 15.4.2 land cover classification (n=10)
1 Artificial surfaces	1 Artificial surfaces
2 Herbaceous crops	2 Croplands
3 Woody crops
4 Multiple or layered crops
5 Grassland	3 Grasslands
6 Tree-covered areas	4 Tree-covered areas
7 Mangroves	Discarded. Not relevant for mountains
8 Shrub-covered areas	5 Shrub-covered areas
9 Shrubs and/or herbaceous vegetation, aquatic or regularly flooded	6 Shrubs and/or herbaceous vegetation, aquatic or regularly flooded
10 Sparsely natural vegetated areas	7 Sparsely natural vegetated areas
11 Terrestrial barren land	8 Terrestrial barren land
12 Permanent snow and glaciers	9 Permanent snow and glaciers
13 Inland water bodies	10 Inland water bodies
14 Coastal water bodies and intertidal areas	Discarded. Not relevant for mountains

Land cover serves different functions for SDG Indicator 15.4.2:

In sub-indicator 15.4.2a, land cover is used to categorize land into green and non-green cover areas. As showed in Table 3, green cover includes areas covered by both natural vegetation and vegetation resulting from anthropic activity. Non-green areas include non-vegetated areas such as bare land, water, permanent ice/snow, urban areas and sparsely vegetated areas. In addition, land cover is used to disaggregate the indicator into the 10 land cover classes included in Table 2, thus increasing the indicator’s policy relevance.

Table 3. Classification of SEEA land cover classes into green and non-green cover.

SEEA land cover classes	Green/Non-green
Croplands	Green
Grasslands	Green
Tree-covered areas	Green
Shrub-covered areas	Green
Shrubs and/or herbaceous vegetation, aquatic or regularly flooded	Green
Artificial surfaces	Non-green
Sparsely natural vegetated areas	Non-green
Terrestrial barren land	Non-green
Permanent snow and glaciers	Non-green
Inland water bodies	Non-green

In sub-indicator 15.4.2b, land cover is used to identify areas where changes in the type of land cover (land cover transitions) may indicate a decline or loss of biodiversity, mountain ecosystem functions or services that are considered desirable in a local or national context. A transition that indicates a decline or loss of biodiversity and mountain ecosystem services of the land is considered degradation. The definition of land cover transitions is documented in a transition matrix that specifies the land cover changes occurring in a given land unit (pixel) as being either degradation, improvement or neutral transitions.

¹

IPBES defines land degradation as “the many human-caused processes that drive the decline or loss in biodiversity, ecosystem functions or ecosystem services in any terrestrial and associated aquatic ecosystems” (IPBES, 2018) ↑

2.b. Unit of measure

Both sub-indicators will be expressed as proportions (percent) and area (KM2).

2.c. Classifications

This indicator uses two established classifications: (1) the simplified UN-SEEA land cover classification included in Table 2, and (2) the mountain bioclimatic belt classification established by Körner et al. (2011). The latter is used for data disaggregation only.

Körner et al. (2011) subdivides mountains vertically into seven bioclimatic belts based on average temperatures, therefore accounting for the latitudinal change in elevation of thermally similar areas in the world’s mountains. For the purposes of this indicator, these seven bioclimatic belts are aggregated into four (Nival, Alpine, Montane and Remaining mountain areas), as illustrated in Table 4.

Table 4. Mountain bioclimatic belts as defined by Körner et al. (2011) and reclassification for data disaggregation of SDG Indicator 15.4.2. Growing season is defined as the number of days between daily mean temperature exceeds 0.9 °C then falls below 0.9 °C

Bioclimatic belts	Growing season mean temperature	Growing season length	Bioclimatic belts adopted for SDG Indicator 15.4.2
Nival	< 3.5 °C	< 10 days	Nival
Upper alpine	< 3.5 °C	> 10 days & < 54 days	Alpine
Lower alpine	< 6.4°C	< 54 days
THE TREELINE
Upper montane	> 6.4°C & ≤ 10 °C	---	Montane
Lower montane	> 10 °C & ≤ 15 °C	---
Remaining mountain area with frost	> 15 °C	---	Remaining mountain areas
Remaining mountain area without frost	> 15 °C

3.a. Data sources

Land cover maps developed by appropriate national authorities will generally provide the most relevant data source to compute this indicator. However, in certain cases, such data may not be available. In those cases, various regional or global products provide a viable alternative.

The global default source of land cover data for this indicator is the European Space Agency Climate Change Initiative (ESA-CCI) Land Cover product (ESA, 2017). The ESA-CCI product consists of a series of annual Land Cover maps at 300 m resolution, providing 22 land cover classes based on 300m MERIS, 1km SPOT –VEGETATION, 1km PROBA –V and 1km AVHRR. The ESA CCI adheres to the FAO Land Cover Classification System (Santoro et al. 2015). Annual updates are currently available from 1992 to 2020. Additional years will be made available by the European Space Agency.

A global mountain area map sub-divided by bioclimatic belts has been developed by FAO and made available to national authorities to facilitate the compute this indicator. This map is the result of combining a global mountain area map developed from the Global Multi-Resolution Terrain Elevation Data (GMTED2010), following the UNEP-WCMC methodology (Ravilious et al. 2021) and a mountain bioclimatic belt map created by the Global Mountain Biodiversity Assessment^[2].

²

https://ilias.unibe.ch/goto.php?target=file_2171234 ↑

3.b. Data collection method

Data on both sub-indicators will be provided by National Statistics Office (NSO) SDG focal points to the FAO following a standard format every three years. This will include the original data and reference sources, and descriptions of how these have been used to derive sub-indicators values.

In addition, global estimates of both sub-indicators for all countries and territories having mountain areas will be computed by FAO using the above-mentioned global default data sources when national official data do not exist or are incomplete. In such cases, FAO shares country figures with NSO SDG focal points for their validation before publication, in accordance to the IAEG-SDG guidelines of Global Data Flows and Reporting.

3.c. Data collection calendar

SDG indicator 15.4.2 is updated every three years.

3.d. Data release calendar

March of every year, in line with the annual SDG reporting cycle.

3.e. Data providers

NSO SDG focal points will provide reports that include values for both sub-indicators, including the original data and reference sources, and descriptions of how these have been used to derive sub-indicators values. FAO will provide country-specific values for both sub-indicators when national official data do not exist or are incomplete, in consultation with concerned countries

3.f. Data compilers

Food and Agriculture Organization of the United Nations (FAO)

3.g. Institutional mandate

Article 1 of FAO’s constitution specifies that “The Organization shall collect, analyse, interpret, and disseminate information related to nutrition, food and agriculture.” In this regard, FAO collects national level data from member countries, which it then standardizes and disseminates through corporate statistical databases. FAO is the custodian UN agency for 21 SDG indicators, including 15.4.2.

4.a. Rationale

Mountain ecosystems are important biodiversity centres that provide valuable ecosystem services to upstream and downstream areas. Yet, mountains are very fragile and impacted easily by both natural and anthropogenic factors. These can include climate change, unplanned agricultural expansion, unplanned urbanization, timber extraction, recreational activities and natural hazards such as landslides and flooding. The degradation of mountain ecosystems such as loss of the glacial cover, mountain biodiversity and green cover will affect the ability of the ecosystem to supply water downstream. The loss of forest and vegetative cover will reduce the ability of the ecosystem to retain soil and prevent landslides and flooding downstream.

Therefore, monitoring mountain vegetation changes and its estimated impact in terms of ecosystem degradation and recovery provides information on the status of mountain ecosystems. Assessing the changes in land cover differentiated by bioclimatic belts is important in understanding the role that environmental factors, such as climate, play in explaining variations of mountain green cover across regions and helps to better interpret the direction of those changes.

4.b. Comment and limitations

The indicator can be calculated using freely available Earth Observation data and simple Geographic Information Science (GIS) operations that can be processed in free and open source software (FOSS) GIS. Regional and global land cover data derived from Earth observation can play an important role in the absence of, to complement, or to enhance national official data sources. These datasets can help validate and improve national statistics for greater accuracy by ensuring that the data

Recognizing that this indicator cannot fully capture the complexity of mountain ecosystems across the world, countries are strongly encouraged to use other relevant national or sub-national indicators, data and information to strengthen their interpretation, as well as taking into account the following limitations:

• Sub-indicator ‘’a’’ should be interpreted with care given that: 1) lack of green cover does not necessarily mean that a particular mountain area is degraded (i.e. areas of permanent snow and ice, scree slopes and natural sparsely vegetated areas above the tree line, 2) it does not capture significant drivers of change such as conversion of natural areas to cropland or pastureland, and 3) increase in green cover may due to impacts of climate change in mountain areas (i.e. increase in green cover due to snow and glacier retreat due to global warming).
• Because land cover refers to the naturally stable aspects of land and the structure of its key elements, transient aspects such as vegetation phenology, snow or flooding cannot be captured by land cover transitions as measured in sub-indicator 15.4.2b. In the context of SDG Target 15.4, this is particularly relevant for snow cover dynamics (snow cover duration within a year), which has been highlighted as a key impact of global warming in mountain ecosystems with direct impacts to water provision (Notarnicola, 2020).
• Decisions about which land cover transitions are linked to degradation processes would sometimes require information on the use of land, not only land cover. For example, the conversion of tree-covered areas to grassland may be a result of deforestation (change in land cover and land use) or just the result of certain management practices and natural disturbance (change in land cover only). The former could be identified as a negative transition, while the latter could be considered as stable or unchanging. The use of land use information would help to better characterize those changes in the context of sub-indicator “b’’.
• Both sub-indicators are not able to capture ecosystem degradation drivers that do not necessarily result in changes in land cover. Some examples of this include conversions of natural forests to intensively managed production systems such as plantation forests, orchards and oil palm plantations; conversion of natural and semi-natural grasslands to intensively used pastures, forest and grassland degradation or invasive species invasion, among others. However, the use of more detailed national land use maps may be able to overcome some of these gaps for sub-indicator 15.4.2b.
• While access to remote sensing imagery has improved dramatically in recent years, there is still a need for essential historical time series that is currently only available at coarse to medium resolution. Therefore, if countries have national land cover maps of higher spatial resolution and comparable or better quality, FAO advises using them, following the same methodology presented here, for the generation of the indicator’s values.
• Area estimations based on remote-sensing-derived land cover maps such as the ESA-CCI product via pixel counting may lead to biased area estimates due to map errors (Olofsson et al. 2014). Countries are encouraged to further refine those estimates by comparing them against reference datasets and applying bias corrections.

4.c. Method of computation

Sub-indicator 15.4.2a, Mountain Green Cover Index, is defined as:

$MGCI=\mathrm{\ }\frac{{Mountain\mathrm{\ }Green\mathrm{\ }Cover\mathrm{\ }Area}_n\mathrm{\ }}{Total\mathrm{\ }Mountain\mathrm{\ }Area}\times 100$

Where:

• Mountain Green Cover Area_n = Sum of areas (in km²) covered by (1) tree-covered areas, (2) croplands, (3) grasslands, (4) shrub-covered areas and (5) shrubs and/or herbaceous vegetation, aquatic or regularly flooded classes in the reporting period n.
• Total mountain area = Total area of mountains (in km²). In both the numerator and denominator, mountain area is defined according to UNEP-WCMC (2002).

Sub-indicator 15.4.2b, Proportion of degraded mountain area, is reported as a binary quantification (degraded/non-degraded) of the extent of degraded land over total mountain area, given by:

$Proportion\mathrm{\ }of\mathrm{\ }degraded\mathrm{\ }mountain\mathrm{\ }area=\frac{Degraded\mathrm{\ }mountain\mathrm{\ }area\mathrm{\ }n}{Total\mathrm{\ }mountain\mathrm{\ }area}\mathrm{\ }\times 100$

Where:

• Degraded mountain area_n = Total degraded mountain area (in km²) in the reporting period n. This is, the sum of the areas where land cover change is considered to constitute degradation from the baseline period. Degraded mountain land will be assessed based on the land cover transition matrix in Annex 1.
• Total mountain area = Total area of mountains (in km²). In both the numerator and denominator, mountain area is defined according to UNEP-WCMC (2002).

If the country/region has no mountain area, it will be assigned value NA.

4.d. Validation

Once received, national reported indicator values will undergo a review process by FAO to ensure the correct use of definitions and methodology as well as internal consistency.

For those countries that have not submitted national indicator values, FAO will provide the NSO SDG focal points with national estimates derived from global data sources for review and validation.

4.e. Adjustments

Not applicable

4.f. Treatment of missing values (i) at country level and (ii) at regional level

• At country level

For countries where data is not available or incomplete, FAO will provide default estimates derived from global data sources that would then be validated by national focal points.

• At regional and global levels

Not applicable, as the indicator has a universal coverage.

4.g. Regional aggregations

The indicator is aggregated to the regional and global level by, in the case of sub-indicator 15.4.2a, summing the spatial extent of green cover and total mountain area, and in the case of 15.4.2b, summing the spatial extent of degraded over total mountain area for all countries and territories reporting in a specific region or globally.

4.h. Methods and guidance available to countries for the compilation of the data at the national level

Detailed guidance and computation tools to support countries to compute the indicator and report its values using standardised reporting tables will be provided by FAO.

4.i. Quality management

FAO is responsible for the quality of the internal statistical processes used to compile the published datasets. The FAO Statistics Quality Assurance Framework (SQAF), available at: http://www.fao.org/docrep/019/i3664e/i3664e.pdf, provides the necessary principles, guidelines and tools to carry out quality assessments. FAO is performing an internal bi-annual survey (FAO Quality Assessment and Planning Survey) designed to gather information on all of FAO’s statistical activities, notably to assess the extent to which quality standards are being implemented with a view to increasing compliance with the quality dimensions of SQAF, documenting best practices and prepare quality improvement plans, where necessary. Domain-specific quality assurance activities are carried out systematically (e.g. quality reviews, self-assessments, compliance monitoring).

4.j. Quality assurance

Date reported by countries to FAO are subject to a rigorous review process to ensure correct use of definitions and methodology as well as internal consistency. A comparison is made with past assessments and other existing data sources. Regular contacts between national correspondents and FAO staff by e-mail form part of this review process.

4.k. Quality assessment

Quality of statistics produced and disseminated by the FAO is evaluated in terms of fitness for use i.e. the degree to which statistics meet the user’s requirements. The quality dimensions assessed are: Relevance; Accuracy and Reliability; Timeliness and Punctuality; Coherence and Comparability; Accessibility and Clarity. Quality dimensions definitions are provided in the FAO Statistical Quality Assurance Framework (SQAF), which provides the definition of quality and describes quality principles for statistical outputs; statistical processes; institutional environment (http://www.fao.org/docrep/019/i3664e/i3664e.pdf). The SQAF is based on the Fundamental Principles of Official Statistics and the Principles Governing International Statistical Activities (CCSA). Adherence to these principles ensures the quality of FAO statistical production processes and of statistical outputs. Regular quality assessments are conducted through the FAO Quality Assessment and Planning Survey (QAPS), a bi-annual survey designed to gather information on all of FAO’s statistical activities, which is used to assess the extent to which quality standards are being met with a view to increasing compliance with the SQAF, and to document best practices and provide guidance for improvement where necessary.

5. Data availability and disaggregation

Data availability:

The indicator is generated by geospatial data and therefore has universal coverage. Countries with no values on the global SDG database are either A) countries with no mountains where the indicator is not applicable (indicated as NA) or B) countries that have not validated FAO’s estimates and yet have not provided figures of their own.

Time series:

Country, regional and global figures are available since the year 2000.

For sub-indicator 15.4.2a, data is available for the years 2000, 2005, 2010, 2015 and 2018, and subsequently every three years.

For sub-indicator 15.4.2b, data is available for the reporting period 2000-2015 (baseline), 2018, and subsequently every three years.

Disaggregation:

In the global SDG database, both sub-indicators are disaggregated by mountain bioclimatic belts as defined by Körner et al. (2011) (see section 2c. Classifications). In addition, sub-indicator 15.4.2a is disaggregated by the 10 SEEA classes included in Table 2. Those values are reported both as proportions (percent) and area (in square kilometres).

6. Comparability/deviation from international standards

Sources of discrepancies:

The global default source of land cover data for this indicator, the ESA CCI Land Cover product, has been reported to have an overall accuracy of 73.2%. However, the accuracy estimate was calculated using the original 22 land cover classes. As the methodology presented here is based on use of aggregate classes, the accuracy can be expected to be higher for sub-indicator 15.4.2a non-disaggregated data. The accuracy of the global land cover products can vary regionally and by land cover type. For the same reason, the presented indicator values may differ from those derived using national land cover maps.

The reporting format will help to ensure that countries provide references for national data sources used, associated definitions and terminology as well as more detailed analysis of the data based on more detailed land cover classifications.

7. References and Documentation

ESA (2017) Land Cover CCI Product User Guide Version 2. Tech. Rep. Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

FAO-GTOS. (2009). Land Cover: Assessment of the status of the development of the standards for the Terrestrial Essential Climate Variables. Global Terrestrial Observing System, Rome.

IPBES (2018): Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. R. Scholes, L. Montanarella, A. Brainich, N. Barger, B. ten Brink, M. Cantele, B. Erasmus, J. Fisher, T. Gardner, T. G. Holland, F. Kohler, J. S. Kotiaho, G. Von Maltitz, G. Nangendo, R. Pandit, J. Parrotta, M. D. Potts, S. Prince, M. Sankaran and L. Willemen (eds.). IPBES secretariat, Bonn, Germany. 44 pages

Körner, C., Paulsen, J., & Spehn, E. (2011). A definition of mountains and their bioclimatic belts for global comparisons of biodiversity data. Alpine Botany, 121, 73-78.

Notarnicola, C. (2020) Hotspots of snow cover changes in global mountain regions over 2000-2018. Remote Sensing of Environment 243, 111781.

Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., Wulder, M. A. (2014): Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.

Ravilious, C., Tshwene-Mauchaza, B. and Kapos, V. (2021). Validation and implementation of the Kapos Mountain Classification: Assessing the impact of DEM resolution on the mapping of mountain classes following the Kapos methodology. UNEP-WCMC, Cambridge, UK.

Santoro, M., Kirches, G., Wevers, J., Boettcher, M., Brockmann, C., Lamarche, C., . . . Defourny, P. (2015). Land Cover CCI PRODUCT USER GUIDE VERSION 2.0. European Spatial Agency. European Spatial Agency. Retrieved from http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

Sims, N.C., Newnham, G.J., England, J.R., Guerschman, J., Cox, S.J.D., Roxburgh, S.H., Viscarra Rossel, R.A., Fritz, S. and Wheeler, I. (2021). Good Practice Guidance. SDG Indicator 15.3.1, Proportion of Land That Is Degraded Over Total Land Area. Version 2.0. United Nations Convention to Combat Desertification, Bonn, Germany

UN Statistical Division (2014). System of Environmental Economic Accounting 2012 — Central Framework. New York, USA.

UNEP-WCMC (2002). Mountain Watch: Environmental change and sustainable development in mountains. Cambridge, UK

Annex 1. Land cover transition matrix for the 10 SEEA classes. Land cover change processes are color coded as improvement (green), stable (yellow) or degradation (red). (Adapted from Sims et al. 2021).

		FINAL CLASS
		Artificial surfaces	Cropland	Grassland	Tree-covered areas	Shrub-covered areas	Herbaceous or shrub vegetation, aquatic or regularly flooded	Sparsely natural vegetated areas	Barren land	Permanent snow & glaciers	Water bodies
ORIGINAL CLASS	Artificial Surfaces	Stable	Agricultural Expansion	Vegetation establishment	Forest expansion	Vegetation establishment	Wetland establishment	Withdrawal of settlements	Withdrawal of settlements	Withdrawal of settlements	Withdrawal of settlements
	Cropland	Urban expansion	Stable	Withdrawal of agriculture	Forest expansion	Vegetation establishment	Wetland establishment	Vegetation loss	Vegetation loss	Glacier advance	Inundation
	Grassland	Urban expansion	Agricultural Expansion	Stable	Forest expansion	Woody encroachment	Wetland establishment	Vegetation loss	Vegetation loss	Glacier advance	Inundation
	Tree-covered areas	Deforestation	Deforestation	Deforestation	Stable	Vegetation loss	Inundation	Deforestation	Deforestation	Glacier advance	Inundation
	Shrub-covered areas	Urban expansion	Agricultural expansion	Vegetation loss	Forest expansion	Stable	Inundation	Vegetation loss	Vegetation loss	Glacier advance	Inundation
	Herbaceous or shrub vegetation, aquatic or regularly flooded	Wetland drainage	Wetland drainage	Wetland drainage	Wetland drainage	Woody encroachment	Stable	Wetland drainage	Wetland drainage	Glacier advance	Inundation
	Sparsely natural vegetated areas	Urban expansion	Agricultural expansion	Vegetation establishment	Forest expansion	Vegetation establishment	Wetland establishment	Stable	Vegetation loss	Glacier advance	Inundation
	Barren land	Urban expansion	Agricultural expansion	Vegetation establishment	Forest expansion	Vegetation establishment	Wetland establishment	Vegetation establishment	Stable	Glacier advance	Inundation
	Permanent snow & glaciers	Urban expansion	Agricultural expansion	Glacier retreat	Glacier retreat	Glacier retreat	Glacier melting	Glacier retreat	Glacier retreat	Stable	Glacier melting
	Water bodies	Urban expansion	Agricultural expansion	Lake dessication	Lake dessication	Lake dessication	Lake dessication	Lake dessication	Lake dessication	Glacier advance	Stable