Indicator 15.3.1 Proportion of land that is degraded over total land area

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

Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world

Indicator 15.3.1: Proportion of land that is degraded over total land area

May 2024

2.4.1; 6.6.1; 11.3.1; 15.1.1; 15.2.1

Kenya National Bureau of Statistics

Kenya National Bureau of Statistics

Senior Manager Food Monitoring, Nutrition and Environment Statistics

Food Monitoring, Nutrition and Environment Statistics Division

Collect, compile, analyze and disseminate Environment and Natural Resources Statistics

+254-202-911-000 |+254-202-911-001

P. O BOX 30266-0100

DPS@KNBS.OR.KE

Definitions:

Land degradation is defined as the reduction or loss of the biological or economic productivity and complexity of rain fed cropland, irrigated cropland, or range, pasture, forest and woodlands resulting from a combination of pressures, including land use and management practices.

Land Degradation Neutrality (LDN) is defined as a state whereby the amount and quality of land resources necessary to support ecosystem functions and services and enhance food security remain stable or increase within specified temporal and spatial scales and ecosystems

Total land area is the total surface area of a country excluding the area covered by inland waters, like major rivers and lakes

Percent (%)

Not Applicable

Description:

National data on the three sub-indicators is and can be collected through existing sources (e.g., databases, maps, reports), including participatory inventories on land management systems as well as remote sensing data collected at the national level. Datasets that complement and support existing national indicators, data and information are likely to come from multiple sources, including statistics and estimated data for administrative or national boundaries, ground measurements, Earth observation and geospatial information. A comprehensive inventory of all data sources available for each sub-indicator is contained in the Good Practice Guidance for SDG Indicator 15.3.1.^[2]

The most accessible and widely used regional and global data sources for each of the sub-indicators are briefly described here.

1) Land cover and land cover change data are available in the:

(1) ESA-CCI-LC,^[3] containing annual land cover area data at 300 m spatial resolution for the period from 1992 to present, produced by the Catholic University of Louvain Geomatics as part of the Climate Change Initiative of the European Space Agency (ESA); or

(2) SEEA-MODIS,^[4] containing annual land cover area data at 500 m spatial resolution for the period 2001-2019, derived from the International Geosphere-Biosphere Programme (IGBP) type of the MODIS land cover dataset (MCD12Q1).

2) Land productivity data represented as vegetation indices (i.e., direct observations), and their derived products are considered the most independent and robust option for the analyses of land productivity, offering the longest consolidated time series and a broad range of operational data sets at different spatial scales. The most accurate and reliable datasets are available in the:

(1) MODIS data products,^[5] averaged at 250 m pixel resolution, integrated over each calendar year since 2000; and

(2) Copernicus Global Land Service products,^[6] averaged at 1 km pixel resolution and integrated over each calendar year since 1998.

3) Soil organic carbon stock data are available in the:

(1) Harmonized World Soil Database (HWSD), Version 1.2,^[7] the latest update being the current de facto standard soil grid with a spatial resolution of about 1 km;

(2) SoilGrids250m,^[8] a global 3D soil information system at 250m resolution containing spatial predictions for a selection of soil properties (at six standard depths) including SOC stock (t ha^-1);

(3) Global SOC Map, Version 1.0,^[9] which consists of national SOC maps, developed as 1 km soil grids, covering a depth of 0-30 cm.

In the absence of, to enhance, or as a complement to national data sources, good practice suggests that the data and information derived from global and regional data sets should be interpreted and validated by national authorities. The most common validation approach involves the use of national, sub-national or site-based indicators, data and information to assess the accuracy of the sub-indicators derived from these regional and global data sources. This could include a mixed-methods approach which makes use of multiple sources of information or combines quantitative and qualitative data, including the ground truthing of remotely sensed data using Google Earth images, field surveys or a combination of both.

Questionnare administered to the relevant Government bodies

Every 4 years

Data from the 2018 reporting period will be released by February 2019 and every four years thereafter.

Directorate of Resource Survey and Remote Sensing, Ministry of Agriculture

Kenya National Bureau of Statistics

Kenya National Bureau of Statistics is mandated to collect, compile, analyze, publish and disseminate official statistics for public use

In the last decade, there have been a number of global/regional targets and initiatives to halt and reverse land degradation and restore degraded land. Starting in 2010, these include the Aichi Biodiversity Targets, one of which aims to restore at least 15% of degraded ecosystems; the Bonn Challenge and its regional initiatives to restore more than 150 million hectares; and most recently the Sustainable Development Goals (SDGs), in particular SDG target 15.3.

For each of the sub-indicators, countries can access a wide range of data sources, including Earth observation and geospatial information, while at the same time ensuring national ownership.^[10] The use of the existing national reporting templates of the UNCCD,^[11] which include the indicator and sub-indicators, provides a practical and harmonized approach to reporting on this indicator beginning in 2018 and every four years thereafter.^[12] The quantitative assessments and corresponding mapping at the national level, as required by this indicator, would help countries to set policy and planning priorities among diverse land resource areas, in particular:

• to identify hotspots and plan actions of redress, including through the conservation, rehabilitation, restoration and sustainable management of land resources; and
• to address emerging pressures to help avoid future land degradation.

SDG indicator 15.3.1 is a binary -- degraded/not degraded -- quantification based on the analysis of available data that is validated and reported by national authorities. Reporting on the sub-indicators should be based primarily, and to the largest extent possible, on comparable and standardized national official data sources. To a certain extent, national data on the three sub-indicators is and can be collected through existing sources (e.g., databases, maps, reports), including participatory inventories on land management systems as well as remote sensing data collected at the national level.

Regional and global datasets derived from Earth observation and geospatial information 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 are spatially-explicit. Recognizing that the sub-indicators cannot fully capture the complexity of land degradation (i.e., its degree and drivers), countries are strongly encouraged to use other relevant national or sub-national indicators, data and information to strengthen their interpretation.

As regards slow changing variables, such as soil organic carbon stocks, reporting every four years may not be practical or offer reliable change detection for many countries. Nevertheless, this sub-indicator captures important data and information that will become more available in the future via improved measurements at the national level, such as those being facilitated by the FAO’s Global Soil Partnership and others.

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. The expectation is that the availability of high-resolution, locally-calibrated datasets will increase rapidly in the near future. National capacities to process, interpret and validate geospatial data still need to be enhanced in many countries; good practice guidance for the monitoring and the reporting of the sub-indicators in other processes will assist in this regard.

By analysing changes in the sub-indicators in the context of local assessments of climate, soil, land use and any other factors influencing land conditions, national authorities can determine which land units are to be classified as degraded, sum the total, and report on the indicator. A conceptual framework, endorsed by the UNCCD’s governing body in September 2017,^[13] underpins a universal methodology for deriving the indicator. The methodology helps countries to select the most appropriate datasets for the sub-indicators and determine national methods for estimating the indicator. In order to assist countries with monitoring and reporting, Good Practice Guidance for SDG Indicator 15.3.1^[14] has been developed by the UNCCD and its partners.

The indicator is derived from a binary classification of land condition (i.e., degraded or not degraded) based primarily, and to the largest extent possible, on comparable and standardized national official data sources. However, due to the nature of the indicator, Earth observation and geospatial information from regional and global data sources can play an important role in its derivation, subject to validation by national authorities.

Quantifying the indicator is based on the evaluation of changes in the sub-indicators in order to determine the extent of land that is degraded over total land area. The sub-indicators are few in number, complementary and non-additive components of land-based natural capital and sensitive to different degradation factors. As a result, the 1OAO principle is applied in the method of computation where changes in the sub-indicators are depicted as (i) positive or improving, (ii) negative or declining, or (iii) stable or unchanging. If one of the sub-indicators is negative (or stable when degraded in the baseline or previous monitoring year) for a particular land unit, then normally it would be considered as degraded subject to validation by national authorities.

The baseline year for the indicator is 2015 and its value (t₀) is derived from an initial quantification and assessment of time series data for the sub-indicators for each land unit during the period 2000-2015. Subsequent values for the indicator during each monitoring period (t_1-n) are derived from the quantification and assessment of changes in the sub-indicators as to whether there has been positive, negative or no change for each land unit relative to the baseline value. Although the indicator will be reported as a single figure quantifying the area of land that is degraded as a proportion of land area, it can be spatially disaggregated by land cover class or other policy‐relevant units.

As detailed in the Good Practice Guidance for SDG indicator 15.3.1, deriving the indicator for the baseline and subsequent monitoring years is done by summing all those areas where any changes in the sub-indicators are considered negative (or stable when degraded in the baseline or previous monitoring year) by national authorities. This involves the:

(1) assessment and evaluation of land cover and land cover changes;
(2) analysis of land productivity status and trends based on net primary production; and
(3) determination of carbon stock values and changes, with an initial assessment of soil organic carbon as the proxy.

It is good practice to assess change for interim and final reporting years in relation to the baseline year for each sub-indicator and then the indicator. This facilitates the spatial aggregation of the results from the sub-indicators for each land unit to determine the proportion of land that is degraded for the baseline and each monitoring year. Furthermore, it ensures that land classified as degraded will retain that status unless it has improved relative to the baseline or previous monitoring year.

Land degradation (or improvement) as compared to the baseline may be identified with reference to parameters describing the slope and confidence limits around the trends in the sub-indicators, or to the level or distribution of conditions in space and/or time as shown during the baseline period. The evaluation of changes in the sub-indicators may be determined using statistical significance tests or by interpretation of results in the context of local indicators, data and information. The method of computation for SDG indicator 15.3.1 is illustrated in Figure 2.

Figure 2: Steps to derive the indicator from the sub-indicators, where ND is not degraded and D is degraded.

The area degraded in the monitoring period t_n within land cover class i is estimated by summing all the area units within the land cover class determined to be degraded plus all area units that had previously been defined as degraded and that remain degraded, minus area units that have improved from a degraded to a non-degraded state:

(1)

Where:

is the total area degraded in the land cover class i in the year of monitoring n (ha);

is the area defined as degraded in the current monitoring year following 1OAO assessment of the sub-indicators (ha);

is the area previously defined as degraded which remains degraded in the monitoring year following the 1OAO assessment of the sub-indicators (ha);

is the area that has improved from a degraded to a non-degraded state following the 1OAO assessment of the sub-indicators (ha).

The proportion of land cover type i that is degraded is then given by:

(2)

Where

is the proportion of degraded land in that land cover type i in the monitoring period n;

is the total area degraded in the land cover type i in the year of monitoring n (ha);

is the total area of land cover type i within the national boundary (ha).

The total area of land that is degraded over total land area is the accumulation across all land cover classes within the monitoring period n is given by:

(3)

Where

is the total area degraded in the year of monitoring n (ha);

is the total area degraded in the land cover type i in the year of monitoring n.

The total proportion of land that is degraded over total land area is given by:

(4)

Where

is the proportion of land that is degraded over total land area;

is the total area degraded in the year of monitoring n (ha);

is the total area within the national boundary (ha).

The proportion is converted to a percentage value by multiplying by 100.

Once received, national reports will undergo a review process by the UNCCD and its partners to ensure the correct use of definitions and methodology as well as internal consistency. A comparison can be made with past assessments and other existing data sources. Regular contacts between the main reporting entity and UNCCD secretariat via a help desk system, and through regional, sub-regional, and national workshops, will form part of this review process, enable data adjustments when needed, and contribute to building national capacities. The data will then be aggregated at sub-regional, regional and global levels by the UNCCD and its partners.

By leveraging an already established reporting mechanism, this data flows and validation mechanism increases the efficiency with which UNCCD can gather data from countries. In addition, since the definitions and methodologies for reporting on SDG Indicator 15.3.1 are aligned with those adopted by the UNCCD, the reporting burden on countries and the need for harmonization/validation of the indicator values is reduced. Since national data and information to report on SDG Indicator 15.3.1 generally comes from outside the National Statistical Offices (NSOs), prior to submitting the data to the UN Statistical Division (UNSD), the UNCCD consults with the NSOs and requests them to review and validate the data submitted by their country as part of their national report. For those countries that have not submitted a national report, the UNCCD provides the NSOs with national estimates derived from global data sources for review and validation.

All data are provided to UNCCD by countries in the form of a national report following a standard reporting template,^[15] which includes the quantitative data for the indicator and sub-indicators as well as a qualitative assessment of indicator trends. The reporting template ensures that countries provide the full reference for original data sources as well as national definitions and methodology.

Detailed guidance on how to prepare the country reports and how to compute the indicator and sub-indicators is contained in the UNCCD reporting manual^[16] and in the Good Practice Guidance for SDG indicator 15.3.1,^[17] respectively.

The Kenya National Bureau of Statistics is ISO certified based on 9001:2015 Standard requirements. The processes of compilation, production, publication and dissemination of data, including quality control, are carried out following the methodological framework and standards established by the KNBS, in compliance with the Internationally acceptable standards

The KNBS adheres to Kenya Statistical Quality Assurance Framework (KesQAF) that underlines principles to be assured in managing the statistical production processes and output.

Data consistency and quality checks are conducted through Technical Working Groups (TWGs) before publication and dissemination.

The processes of compilation, production, publication and dissemination of data, including quality control are subjected to a set criteria and standards to ensure conformity.

Data is available for 2017, 2018, 2019, 2020, 2021, 2022 at National level

Not applicable

URL:

PRAIS 4 portal, data collection tool for SDG 15.3.1: https://reporting.unccd.int/

Trends.Earth, data calculation tool for SDG 15.3.1: https://trends.earth/docs/en/

References:

Di Gregorio, A. 2005. Land cover classification system (LCCS): classification concepts and user manual. Food and Agriculture Organization of the United Nations, Rome.

European Communities. (2013). Overall Approach to the Classification of Ecological Status and Ecological Potential, Guidance Document No 13. Luxembourg: European Union. https://circabc.europa.eu/sd/a/06480e87-27a6-41e6-b165-0581c2b046ad/Guidance%20No%2013%20-%20Classification%20of%20Ecological%20Status%20(WG%20A).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.

IPCC. 2006. IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and other Land Use. Prepared by the National Greenhouse Gas Inventories Programme: Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). IGES, Japan.

IPCC. 2019. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In: Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize, S., Osako, A., Pyrozhenko, Y., Shermanau, P., Federici, S. (eds). Intergovernmental Panel on Climate Change, Geneva, Switzerland.

Ivits and Cherlet. 2013. Land-productivity dynamics towards integrated assessment of land degradation at global scales. European Commission JRC Technical Report. https://publications.europa.eu/en/publication-detail/-/publication/1e2aceac-b20b-45ab-88d9-b3d187ae6375/language-en/format-PDF/source-49343336

Joint Research Centre of the European Commission. 2017. World Atlas of Desertification, 3^rd edition. JRC, Ispra. https://wad.jrc.ec.europa.eu/

Millennium Ecosystem Assessment. 2005. Ecosystems and human wellbeing: a framework for assessment. Island Press, Washington, DC.

Orr, B.J., Cowie, A.L., Castillo Sanchez, V.M., Chasek, P., Crossman, N.D., Erlewein, A., Louwagie, G., Maron, M., Metternicht, G.I., Minelli, S., Tengberg, A.E., Walter, S., Welton, S., 2017. Scientific Conceptual Framework for Land Degradation Neutrality. A Report of the Science Policy Interface. United Nations Convention to Combat Desertification (UNCCD), Bonn, Germany. https://www.unccd.int/publications/scientific-conceptual-framework-land-degradation-neutrality-report-science-policy

Running et al. 1999. MODIS Daily Photosynthesis (PSN) and Annual Net Primary Production (NPP) Product (MOD17): Algorithm Theoretical Basis Document https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod16.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. https://www.unccd.int/publications/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded-over-total-land

Smith, P., Fang, C., Dawson, J. J., & Moncrieff, J. B. 2008. Impact of global warming on soil organic carbon. Advances in agronomy, 97: 1-43.

United Nations Convention to Combat Desertification. 1994. Convention Text
http://www2.unccd.int/sites/default/files/relevant-links/2017-01/UNCCD_Convention_ENG_0.pdf