- Degraded land areas are defined as areas where biological or economic productivity has been reduced or lost due to pressures including land use and management practices. - Sustainable Development Goal 15 aims to protect and sustainably manage terrestrial ecosystems and combat land degradation by 2030. Target 15.3 focuses on restoring degraded land and achieving land degradation neutrality. - The UNCCD has developed a methodology using the SEPAL platform to calculate indicator 15.3.1, which measures the proportion of degraded land, using sub-indicators of trends in productivity, land cover, and soil organic carbon. SEPAL allows users to efficiently process satellite data to assess and monitor land degradation.

1. Degraded Land Areas
Degraded land areas, represented by 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” (UNCCD
1994, Article 1). Sustainable Development Goal (SDG) 15 aims to “Protect, restore and promote sustainable
use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land
degradation and halt biodiversity loss”. Each SDG has specific targets addressing different components, in this
case, of life on land. Target 15.3 aims to: “combat desertification, restore degraded land and soil, including land
affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world by 2030”.
Indicators will be used then to assess the progress of each SDG target. In the case of SDG 15.3 the progress
towards a land degradation-neutral world will be assessed using indicator 15.3.1: “proportion of land that is
degraded over a total land area.”
As the custodian agency for SDG 15.3, the United Nations Convention to Combat Desertification (UNCCD)
has developed a Good Practice Guidance (GPG) providing recommendations on how to calculate SDG
Indicator 15.3.1.
To assess the area degraded, SDG Indicator 15.3.1 uses information from 3 sub-indicators:
1. Productivity
2. Land cover
3. Soil organic carbon
Methodology
System for earth observations, data access, processing and analysis for land monitoring (SEPAL) is a cloud
computing-based platform for autonomous land monitoring using remotely sensed data developed by the
Forestry Department of the United Nations Food and Agriculture Organization (FAO). It is a combination of
Google Earth Engine and open-source software like ORFEO Toolbox, Python, Jupyter, GDAL, R, R Studio
Server, R Shiny Server, SNAP Toolkit, and OpenForis Geospatial Toolkit. It allows users to access powerful
cloud-computing resources to query, access and process satellite data quickly and efficiently for creating
advanced analyses. It can be accessed through www.sepal.io . There are several modules and analysis toolboxes
integrated in the SEPAL platform.
One of the modules of the SEPAL platform is SDG 15.3.1 Indicator
(https://docs.sepal.io/en/latest/modules/dwn/sdg_indicator.html), which measures the proportion of land
that is degraded over total land area. The methodology for SDG 15.3.1 module for GPG v1 (Good Practice
Guidance from United Nations Convention to Combat Desertification on SDG 15.3.1 issued in 2017 and
revised in 2021) was implemented in consultation with the trends.earth team and Conservation International.
The advantage of monitoring SDG 15.3.1 indicator in SEPAL over the trends.earth application is the access to
additional sensors and metrics for estimating the productivity. Furthermore, trends.earth application is used as
plugin in a third party GIS software i.e. QGIS whereas SEPAL is an independent cloud-based platform with
multiple functions including analysis and visualization components. The extent of land degradation for
reporting on SDG Indicator 15.3.1 is calculated as a binary - degraded/not degraded - quantification using its
three sub-indicators:
The extent of land degradation for reporting on SDG Indicator 15.3.1 is calculated as a binary - degraded/not
degraded - quantification using its three sub-indicators:
i. Trends in land productivity
ii. Trends in land cover, and

2. iii. Trends in carbon stocks (above and below ground), currently represented by soil organic carbon (SOC)
stocks.
Effectively, any significant reduction or negative change in one of the three sub-indicators is considered to
comprise land degradation. The sub-indicators are described below.
Sub-indicators
i. Productivity
Land productivity is the biological productive capacity of the land, the source of all the food, fiber and fuel that
sustains humans (United Nations Statistical Commission 2016). Net primary productivity (NPP) is the net
amount of carbon assimilated after photosynthesis and autotrophic respiration over a given period (Clark et al.
2001) and is typically represented in units such as kg/ha/yr. NPP is a variable time consuming and costly to
estimate, for that reason, remotely sensed information is used to derive the indicators of NPP.
One of the most used surrogates of NPP is the Normalized Difference Vegetation Index (NDVI), computed
using information from the red and near infrared portions of the electromagnetic spectrum. In SEPAL,
products from Landsat, MODIS, AVHRR and Sentinel are used to compute annual integrals of NDVI. These
annual integrals of NDVI are then used to compute each of the productivity matrices explained below.
a. Productivity trend
It measures the rate of change in primary productivity over time. The Mann–Kendall trend test is used to
describe the monotonic trend or trajectory (increasing or decreasing) of the productivity for a given time period.
The test statistics S is defined as:
S = ∑ 1
n−1
i=1
∑ 𝑠𝑔𝑛(𝑥𝑗
𝑛
𝑗=𝑖+1
− 𝑥𝑖 )
Where: xi and xj are the productivity of the sequence i and j years, n is the length of the period and
𝑠𝑔𝑛(𝑥𝑖 − 𝑥𝑗) = {
1 𝑖𝑓 𝑥𝑗 > 𝑥𝑖
0 𝑖𝑓 𝑥𝑗 = 𝑥𝑖
−1 𝑖𝑓 𝑥𝑗 < 𝑥𝑖
The variance of the S is determine using the following equation:
𝑉𝐴𝑅(𝑆) =
1
18
[𝑛(𝑛 − 1)(2𝑛 + 5) − 𝑇]
Then the z score can be obtained using the following equation
𝑧 𝑠𝑐𝑜𝑟𝑒 =
{
𝑆 − 1
√𝑉𝐴𝑅(𝑆)
𝑖𝑓 𝑆 > 0
0 𝑖𝑓 𝑆 = 0
𝑆 + 1
√𝑉𝐴𝑅(𝑆)
𝑖𝑓 𝑆 < 0
To identify the scale and direction of the trend a five-level scale is proposed:
• Z score < -1.96 = Degrading, as indicated by a significant decreasing trend
• Z score < -1.28 AND ≥ -1.96 = Potentially Degrading
• Z score ≥ -1.28 AND ≤ 1.28 = No significant change
• Z score > 1.28 AND ≤ 1.96 = Potentially Improving, or

3. • Z score > 1.96 = Improving, as indicated by a significant increasing trend
The area of the lowest negative z-score level (< -1.96) is considered as degraded and areas in other z-score
classes is as not degraded for calculating the sub-indicator.
Within a given ecosystem, primary productivity is affected by several factors, such as temperature, and the
availability of light, nutrients, and water. Of those, water availability is the most variable over time, and can have
very significant influences in the amount of plant tissue produced every year. When annual integrals of NDVI
are used to perform the trajectory analysis, it is important to interpret the results having historical precipitation
information as a context. Otherwise, declining productivity trends could be identified as human caused land
degradation when they are driven by regional patterns of changes in water availability.
SDG 15.3.1 indicator module allows the user to perform different types of analysis to separate the climatic
causes of the changes in primary productivity, from those which could be a consequence of human land use
decisions on the ground. The methods currently supported for climate corrections are Residual Trend Analysis
(RESTREND), Rain Use Efficiency (RUE) and Water Use Efficiency (WUE). For more details, please refer to
trends.earth documentation (https://trends.earth/en/v1.0.8/background/understanding_indicators15.html).
b. Productivity state
State represents the level of productivity in a land unit compared to the historical observations of productivity
for that land unit over time. The mean and standard deviations are calculated as follows:
𝜇 =
∑ 𝑥𝑛
𝑛−3
𝑛−15
13
𝜎 = √
∑ (𝑥𝑛 − 𝜇)2
𝑛−3
𝑛−15
13
Where, x is the productivity and n is the year of analysis. The mean productivity of the current period is given
as:
𝑥̅ =
∑ 𝑥𝑛
𝑛
𝑛−2
3
and the z score is given as
𝑧 =
𝑥̅ − 𝜇
𝜎
√3
The five level stats are as follows:
• Z score < -1.96 = Degraded, showing a significantly lower mean in the recent years compared to the
longer term
• Z score < -1.28 AND ≥ -1.96 = At risk of degrading
• Z score ≥ -1.28 AND ≤ 1.28 = No significant change
• Z score > 1.28 AND ≤ 1.96 = Potentially Improving
• Z score > 1.96 = Improving, as indicated by a significantly higher mean in recent years compared to
the longer term
The area of the lowest negative z-score level (< -1.96) is considered as degraded and areas in other z-score
classes is as not degraded for calculating the sub-indicator.

4. c. Productivity performance
Productivity Performance indicates the level of local plant productivity relative to other regions with similar
productivity potential. The maximum productivity index (NPPmax) value (90th percentile) observed within the
similar ecoregion is compared the observed productivity value (observed NPP). It is given as:
𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 =
𝑁𝑃𝑃𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑
𝑁𝑃𝑃𝑚𝑎𝑥
The pixels with an NPP (vegetation index) less than 0.5 of the NPPmax is considered as degraded.
Once all the matrices of the productivity are estimated, the sub indicator is calculated following the lookup
Table 4:
Table 1: Matrix table for assigning land degradation based on NPP
Trend State Performance Productivity
Degraded Degraded Degraded Degraded
Degraded Degraded Not degraded Degraded
Degraded Not degraded Degraded Degraded
Degraded Not degraded Not degraded Degraded
Not degraded Degraded Degraded Degraded
Not degraded Degraded Not degraded Not degraded
Not degraded Not degraded Degraded Not degraded
Not degraded Not degraded Not degraded Not degraded
Note: The combination of three matrices give the final degradation status. When z score of trend and state is lesser than
-1.96 and when performance matrix is less than 0.5, land is considered to be degraded, else not degraded (stable or
improved).
ii. Land cover
To assess changes in land cover, users need land cover maps covering the study area for the baseline and target
years. These maps need to be of acceptable accuracy and created in such a way which allows for valid
comparisons. SEPAL uses ESA CCI land cover maps as the default dataset, but local maps can also be used.
Both land cover maps are reclassified to the 7 land cover classes needed for reporting to the UNCCD (forest,
grassland, cropland, wetland, artificial area, bare land and water). A land cover transition analysis is then
conducted to identify which pixels remained in the same land cover class, and which ones changed. Based on
the local knowledge of the conditions in the study area and the land degradation process occurring there, the
table below is used to identify which transitions correspond to degradation (- sign), improvement (+ sign), or
no change in terms of land condition (zero). SEPAL will combine the information from the land cover maps
and the table of degradation typologies by land cover transition to compute the land cover sub-indicator as
shown in Table 5.

5. Table 2: Land degradation transition matrix for different land cover types
iii. Soil organic carbon
The third sub-indicator for monitoring land degradation as part of the SDG assessment quantifies changes in
soil organic carbon (SOC) over the reporting period. Changes in SOC are particularly difficult to assess for
several reasons, some of them being the high spatial variability of soil properties, the time and cost intensiveness
of conducting representative soil surveys and the lack of time series data on SOC for most regions of the world.
To address some of the limitations, a combined land cover/SOC method is used to estimate changes in SOC
and identify potentially degraded areas. The indicator is computed as follows:
The SOC reference values are obtained from SoilGrids 250m carbon stocks for the first 30 cm of the soil
profile as the reference values for calculation. The land cover maps are classified to the 7 land cover classes
needed for reporting to the UNCCD (forest, grassland, cropland, wetland, artificial area, bare land and water).
Ideally annual land cover maps are preferred, but at least land cover maps for the starting and end years are
needed.
The changes in C stocks for the reporting period are estimated using C conversion coefficients for changes in
land use, management and inputs recommended by the IPCC and the UNCCD. However, spatially explicit
information on management and C inputs is not available for most regions. As such, only land use conversion
coefficient can be applied for estimating changes in C stocks (using land cover as a proxy for land use). The
coefficients used were the result of a literature review performed by the UNCCD and are presented in the table
below. Those coefficients represent the proportion in C stocks after 20 years of land cover change. Changes in
SOC are better studied for land cover transitions involving agriculture, and for that reason there is a different
set of coefficients for each of the main global climatic regions: Temperate Dry (f = 0.80), Temperate Moist (f
= 0.69), Tropical Dry (f = 0.58), Tropical Moist (f = 0.48), and Tropical Montane (f = 0.64) as shown in Table
6. These values are mentioned in the Table 5.5 of Chapter 5 of IPCC (2006).
Table 3: Land use coefficients for different global climatic regions
The relative different in SOC between the baseline and the target period are computed and areas which
experienced a loss in SOC of 10% of more during the reporting period will be considered potentially degraded,
and areas experiencing a gain of 10% or more as potentially improved.

6. Table 7 shows the data needed for estimating land degradation. SEPAL module SDG 15.3.1 provides the
flexibility to use datasets of different sensors having different resolutions. One of the oldest and best-known
satellite missions is Landsat, which has been providing Earth surface data since 1972. The Landsat 8, launched
in 2013, which carries nine spectral bands from the Operational Land Imager and two bands from the thermal
infrared sensors. The multispectral bands have a resolution of 30m and include a newly introduced cirrus band
and a 15m panchromatic band. The thermal bands provide 100m resolution of data. Landsat 9 has been
launched in 2021. Sentinel 2 provides the higher resolution optical imageries of 10 m resolution since 2015 and
has 13 bands. Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra
(originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra MODIS and Aqua
MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands. MODIS is
playing a vital role in the development of validated, global, interactive Earth system models including products
like NDVI, NPP, EVI etc.
Table 4: Input data table for land degradation
Sub-indicator Variable Sensor/Dataset Spatial resolution Extent
Productivity
NDVI, EVI, MSVI2 Landsat 4,5,7,8 30 m Global
Sentinel 2 10 m Global
MODIS 250 m Global
Soil moisture MERRA 2 55 km Global
ERA I ~85 km Global
Precipitation GPCP v2.3 ~275 km Global
GPCC v7 ~110 km Global
CHIRPS 5 km 50N-50S
PERSIANN-CDR 25 km 60N-60S
Evaporation MODIS 1 km Global
Land cover ESA CCI 300 m Global
Soil taxonomic units SoilGrids-USDA 250 m Global
Land cover Land cover ESA CCI 300 m Global
Soil organic carbon Soil organic carbon SoilGrids-USDA 250 m Global
The user guide for calculating SDG 15.3.1 indicator can be accessed through the website
https://docs.sepal.io/en/latest/modules/dwn/sdg_indicator.html#land-cover.
The links to download the abovementioned data are as follows:
a. Landsat (https://earthexplorer.usgs.gov/)
b. Sentinel 2 ("EO Browser" https://apps.sentinel-hub.com/eo-browser/)
c. MODIS (https://ladsweb.modaps.eosdis.nasa.gov/)
d. MERRA 2 (https://disc.gsfc.nasa.gov/datasets?page=1&subject=Precipitation&project=MERRA-2)
e. ERA I (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land)
f. GPCP
g. GPCC (https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html)
h. CHIRPS (https://www.chc.ucsb.edu/data/chirps)
i. PERSIANN-CDR (http://chrsdata.eng.uci.edu/)
j. ESA CCI (http://maps.elie.ucl.ac.be/CCI/viewer/download.php)
k. SoilGrids (https://soilgrids.org/)