Report of the Meeting for Promotion of the Use of Big Data

No,01

June, 2021

Validation of SDG Indicator 15.4.2(Mountain Green Cover Index)

FUJIMURA Hidenori
HAMAMOTO Ko
ISHIDA Chu
KAWAKITA Shiro
MIZUNO Takayuki
SATO Aki-Hiro
TAKEUCHI Wataru

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(in alphabetical order)

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Geospatial Information Authority of Japan,
Ministry of Land, Infrastructure, Transport and Tourism
Japan Aerospace Exploration Agency
National Institute of Informatics
School of Data Science, Yokohama City University
Institute of Industrial Science, the University of Tokyo

The Report is a product of the results of surveys and research conducted byauthors in light of the discussion of Industry-Government-Academia
Partnership Meeting for Promotion of the Use of Big Data, and aims tocontribute to various measures for the development of oﬃcial statistics.
However, the content of and opinions expressed within the Report belong to
the individual authors and do not represent the oﬃcial views of the Ministryof Internal Aﬀairs and Communications.

Verification of SDG15.4.2 Mountain Green Cover Index (MGCI) (Draft)
June 16, 2021
Abstract
UN and National Statistical Offices (NSOs) are beginning to use Earth Observation (EO) and geospatial
information as a new data source to produce UN SDG indicators. The Food and Agriculture Organization (FAO)
has computed SDG 15.4.2 (Mountain Green Cover Index: MGCI) for countries using satellite-based global land
cover data and has requested countries to verify the estimations. In this report, the methodology to reproduce
FAO’s estimated values was confirmed and the indicator was estimated using the data that Japan owns. The
accuracy of the indicator was verified by comparing it with the FAO estimation. From this accuracy verification,
it was found that FAO’s estimation was overestimated for the Kapos mountain classes (Kapos 2, Kapos 3, andKapos 4). Its rationale is provided from the quantitative and qualitative point of view and its alternative
estimation together with statistical measures to verify and improve its accuracy are proposed.
Keywords: SDGs, Mountain Green Cover Index (MGCI), Earth observation data, geospatial information, land
cover, Kapos mountain classification, classification accuracy.
This paper summarizes the results of verification conducted by the Working Group on Validation of Methodsof Using Observation Data for SDG Indicators, which was launched under the Industry-Government-Academia
Partnership Meeting for Promotion of the Use of Big Data held by the Ministry of Internal Affairs andCommunications (MIC). We would like to express our gratitude to TSUMURA Akira, OGAWA Tomoaki, and
ARITA Chika of the MIC as well as TADONO Takeo, OCHIAI Osamu, and HARADA Mariko of the Japan Aerospace
Exploration Agency for their valuable advice. In addition, we would like to thank the Ministry of Foreign Affairs;
Ministry of Education, Culture, Sports, Science and Technology; Ministry of Agriculture, Forestry and Fisheries(MAFF); and Ministry of the Environment (MOE) as cooperating ministries. We would also like to express ourappreciation to the Biodiversity Center of Japan, Natural Environment Bureau, MOE for providing us withvegetation survey polygon data as well as the Statistics Department, Minister’s Secretariat, MAFF for providing
us with cropland lot data. Please note that the content of this paper and any opinions indicated herein belong
to the authors personally and do not represent the official views of their affiliated organizations or the MIC.
All possible errors are the authors’ own.

1

Contents
1. Introduction
1.1 Backgrounds and purpose of SDG15.4.2 verification
1.2 Process of verification
2. Outline of SDG15.4.2
2.1 Goal15, Target 15.4 and Indicator 15.4.2
2.2 Outline of SDG15.4.2
2.3 Data to be used
2.3.1

Mountain classification data

2.3.2

Land cover data

2.3.3

Administrative unit data

2.4 Methodology

3

2.4.1

Definition of Mountain Green Cover Index (MGCI)

2.4.2

Calculation method

Verification of FAO estimation
3.1 Estimation of MGCI by area
3.2 Estimation of MGCI by numbers of pixels

4

Estimation of SDG15.4.2 using alternative data that Japan has
4.1 Used data
4.1.1

Land cover data

4.1.2

Mountain classification data

4.1.3

Administrative unit data

4.2 Estimation of SDG15.4.2 using JAXA land cover data
4.3 Comparison with FAO estimation
5

Impact of sampling errors and classification errors on MGCI
5.1 Estimation of population proportion by sample proportion
5.2 Formulation of MGCI estimation error
5.3 MGCI estimation using JAXA high resolution land use and land cover map data
5.3.1

JAXA high resolution land use and land cover map

5.3.2

Verification of land cover classification (Confusion matrix)

5.4 Reduction of sampling errors of confusion matrix comparing with other data
6

Summary(Appendix)(Supplement)(Reference)2

1. Introduction
1.1 Backgrounds and purpose of SDG15.4.2 verification
The 2030 Agenda for Sustainable Development adopted at the UN Summit in September 2015 presents theresolution of international societies including the developing and developed countries to collaborate to solve
issues toward sustainable development with a basic concept of “Leave no one behind” and set the SDGs as itscore. Country leaders considered in agreeing to the agenda that it is necessary to promote sustainable
development with a strategy based upon evidence and data. For that reason, 169 targets and 242 (232 when
duplication is removed) indicators are set.
Article 76 of the Agenda2030 depicts the use of a wide range of data, including Earth observation andgeospatial information. It should be noted that Japan proposed to include Earth observation and it was
adopted in the negotiation of the text. Earth observation and geospatial information include satellite, aircraft,ships, ground-based observation data and model output data, which provide the possibility of monitoring at
local, national, regional, and global scales, and across sectors. There are increasing expectations for the
integration of Earth observation and geospatial information with statistical systems of UN and National
Statistical Offices (NSOs). Earth observation satellite data has features to be able to monitor wide areas
consistently and in repetition, but has inherent challenges that the data is huge and its analysis is complex. To
use Earth observation satellite data for the computation of SDG indicators, it is essential to verify its accuracy
and assess if it can be fit for the purpose.
The Inter-Agency Expert Group of SDG Indicators (IAEG-SDGs) for which the UN Statistical Division serves
as secretariat, is responsible for the global indicator framework. The Working Group on Geospatial Information(WGGI) studies the use of satellite data and geospatial information for the indicator calculation and provides
advices and guidance to the IAEG-SDGs. The Japan Space Exploration Agency (JAXA) is serving as co-chairs of
the SDGs initiative, EO4SDG, of the intergovernmental Group on Earth Observations (GEO), together with NASAand Mexico’s National Institute of Statistics and Geospatial Information (INEGI). JAXA was invited to the WGGI
together with NASA and ESA and has been participating in the study of the application of Earth observation
satellite data for the assessment of SDG indicators. In Japan, under “The Industry-Government-Academia
Partnership Meeting for Promotion of the Use of Big Data”[MIC Big Data meeting], “The Working Group on
Validation of Methods of Using Observation Data for SDG Indicators” was established to verify indicators based
upon Earth observation data and geospatial information in cooperation with the Transdisciplinary Federationof Science and Technology (a federation of 34 societies including the Japan Statistical Society, and the Remote
Sensing Society of Japan) and related ministries and agencies, to see if they can be authorized as data to be
used for the government’s SDG indicator calculation.
In 2020, FAO had calculated SDG15.4.2 of countries (June 29, 2020) using designated methodology and dataand had requested countries to verify it. In this article, the results of verifying the FAO estimation using FAO’s
methodology and designated data are reported. In the metadata, it is recommended to use the country’s own
land cover data if it is available. In this report, the high-resolution land use and land cover data produced by
JAXA and the Shuttle Radar Topography Mission (SRTM) data were used as alternative data to calculate3

SDG15.4.2. Fundamental Geospatial Data/high-resolution DEM（10m resolution）made available by the
Geospatial Information Authority of Japan (GSI) was used to verify the relevance of the SDG 15.4.2.
The purpose of SDG15.4.2 Mountain Green Cover Index (MGCI) is to assess the conservation of biodiversity
recognizing the difference of vegetation according to the mountain classification, by calculating the MGCI for
the mountain classes (Kapos mountain classification) considering their altitudes and slope ranges.
Particularly, in this work, a comparison between public values estimated by FAO and reproduced values
based upon data Japan owns was made. Quantitative assessment was made to see how much difference exists
between the two estimates and evaluation of the accuracy of FAO’s public values. In addition, if a large
difference is found in the FAO’s estimates from the states of Japan, its quantitative and qualitative rationales,
its corrected values, and methodologies are presented as the objective of this task.
1.2 Process of verification
As shown in Figure/Table 1(F/T-1), we introduced a workflow of the verification task consisting of seven steps
such as data preparation, data selection-collection, decision on calculation procedure, execution of calculation,
check and inspection of the calculation procedure, error estimation, and dissemination for calculating
indicators. For contentiously improving verification task quality of SDG15.4.2, we introduced redundancy foreach step by combining with other steps and repeating work. Our fundamental policy was to verify errors in
the indicator estimates and to evaluate the errors, by multiple tasks done by different members of staff withdifferent software programs.
Figure/Table-1(F/T-1) Concept of indicator calculation task

For the data preparation and the data selection-collection step, we performed data quality checks.
Specifically, we tracked data corruption during data transfer by comparing data analysis results in different
members and sites. In the decision of calculation procedure step, we compute indicators with different
calculation software programs. Throughout the documentation of calculation procedures, we confirmed the
reproducibility of the calculation or by comparing the calculation results of different software programs andalso verified the calculation errors.
For the decision of the calculation procedure, execution of calculation, check, and inspection of thecalculation procedure, and assessment of errors, we carefully introduced the following steps for the task.
In general, SDG indicators are calculated by integrating different data obtained from several different sources.
For data to be used for the estimation of the indicators, although the meaning of data is similar, there exist
some data sets provided by the different organizations with different measurement processes. For this reason,
depending upon the data to be used, there is a possibility of producing a difference in the estimated indicators.
This is an issue with data quality. To improve data reliability, we introduced data quality indicators quantitively
4defined and compared data with the same meaning from different data sources spatially. In particular, theKapos mountain classification data and land use/land cover data are used for the MGCI calculation. Based upon
the inferential statistics we computed the MGCI indicators by estimating population proportion from sample
proportion of vegetation pixels calculated from land use/land cover map for specified Kapos mountain class. It
is, therefore, important for the sake of assessing errors of the estimated value to estimate the sampling errors
deriving from the limited amount of data. By calculating 95% confidence interval using data with different data
quality from the theory of indicator calculation, we conducted a reliability assessment for each data used forthe indicator estimation based upon not only point estimation but also confidence interval.
In addition, it is known that land use/land cover data identified from satellite imageries with someclassification algorithms contain classification errors. The classification error can be quantitatively assessed byproducing a confusion matrix that compares automatic classification results with those derived fromclassification based upon validation. We developed a statistical method to correct the estimated population
proportion by using sample conditional probability obtained from the confusion matrix and we compute wecomputed the MGCI values with the developed correction method for reducing the influence of land coverclassification errors.
The volume of satellite data is enormous, and it is very difficult to make a visual inspection to find data
corruption when data corruption happens during data transfer. It is necessary to make sure that there is nodata corruption occurring by taking measures to check parity at data transfer (check by data file size and file
checksum) and data consistency check (comparison of estimation results at sending side and receiving side).
In particular, we accessed the JAXA land cover data FTP site from different organizations and shared satellite
data as tiles, we experienced that the number of tiles downloaded from the FTP site was smaller than thenumber of tiles to cover the whole of Japan (a tile is a basic unit of land cover data that covers 1 deg x 1degarea). Parity checks and data consistency checks were made repeatedly to cope with this issue. Throughout
the whole analysis, frequent data volume checks were checked and compared, by comparing the numbers of
pixels that belong to specific mountain classes before and after the data transfer, a consistency check was also
made. This is an issue of workflow and operations during the calculation, and it was confirmed from thisverification task that quality management of various process steps in counting, data management, andinformation system is just as important as data quality.
The indicator calculation requires the processing of multiple data with multiple computer programs andsoftware and involves a lot of human manual operations which can introduce errors in the calculation. Such
operational errors may strongly influence estimated values and their errors in the entire calculation results. To
reduce the influence of operational errors, automatic execution of the task by programable computer softwareand error detection by multiple checks are necessary. There can be issues with computer software: e.g., errors
in the algorithm of calculation software, software bugs, insufficient understanding of the application of
calculation algorithm. Hence, it is extremely important to enhance the understanding of operators of software,
software qualification, and prior verification using test data, capacity development of workers by training,seminar, and practices.
For the sake of count procedure check and inspection, and error evaluation, verification work was conducted5

incorporating the opinion of multiple verification task members and narrowing down areas to be verified at
the Observation Data Promotion and Verification Working Group. The calculation task was conducted bydifferent members and software in two control groups, independently checking errors in the calculation
process and the calculation program, thereby ensuring reliability in the verification task.
This guidance document for disseminating the indicator was prepared from the verification work reports
clarifying the quantitative results for the indicator verification and the indicator calculation procedure.
2. Outline of SDG15.4.2
2.1 Goal15, Target 15.4 and Indicator 15.4.2
Under Goal 15, target 15.4 aims at conservation of mountain ecosystems and set the indicator 15.4.2
Mountain Green Cover Index (MGCI) as follows; [MOFA Japan SDGs Action Platform]
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 15.4：
By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhancetheir capacity to provide benefits that are essential for sustainable development.
Indicator 15.4.2: Mountain Green Cover Index
2.2 Outline of SDG15.4.2 metadata
For calculation of SDG15.4.2 Mountain Green Cover Index (MGCI), the Kapos mountain classification data
and land cover data are necessary. The Kapos mountain classification data is produced from a digital elevation
model (DEM) data derived from satellite data and made accessible by FAO and USGS.
Land cover data are produced from US Landsat data, JAXA’s ALOS data, ESA’s Sentinel data, etc. SDG15.4.2
may vary depending on the characteristics and quality of data to be used for the calculation.
The UN Food and Agriculture Organization (FAO), Custodian Agency of SDG15.4.2, 6released the latest
metadata (February 14, 2021) which defines the methodology and data to be used for the indicator calculation[FAO HP].
The following section describes the data and methodology to be used for the indicator calculation.
2.3 Data to be used
2.3.1 Mountain classification data
F/T-2 shows altitude zones of the Kapos mountain classification and the ratio of the land areas of mountain6

classes for Japan. Kapos et al (2000) [Kapos(2000)] classified the mountain areas into 6 classes by altitude,
slope and Local Elevation Range (LER). The mountain classes Kapos 2 to Kapos 6 are applicable to Japan where
the altitude is lower than 4500m. Because of slope and LER requirements, flat areas in high altitude zone areexcluded from the mountain areas.
The Kapos mountain classification data can be downloaded from FAO Mountain Partnership HP [FAO Kapos]
and USGS site [USGS Kapos]. It should be noted that the reference to FAO’s Kapos mountain classification data
has been removed from the latest metadata.
F/T-2 Kapos mountain classification, its decisive factors, and the ratio of mountain classes in Japan
Mountain
Class
0
1
2
3
4
5
6

Description
Elevation < 300 meters
Elevation > 4.500 meters
Elevation 3.500–4.500 meters
Elevation 2.500–3.500 meters
Elevation 1.500–2.500 meters and slope > 2
Elevation 1.000–1.500 meters and slope > 5 or local
elevation range (LER 7 kilometers radius) > 300
metersElevation 300–1.000 meters and local elevation range(7 kilometers radius) > 300 meters

Ratio of mountain in
Japan (%)
54
0
0
0
2
6
38

In Japan, Kapos 2 area exists only at the summit of Mt. Fuji and Kapos 3 areas are high altitude mountainareas. The ratio of Kapos 2 area and Kapos 3 area are approximately 0. Area of Kapos 0 which is under 300m
altitude is 54%, ratios of Kapos 4, Kapos 5, and Kapos 6 areas are 2%, 6%, and 38% of the total land of Japan.
Two Kapos mountain classification data originated from satellite data analysis, one of FAO[FAO Kapos] andthe other of USGS[USGS Kapos], are compared in mountain areas of Kapos classification in Japan to see thedifference in their data characteristics. F/T-3 shows the ratio of areas of Kapos mountain classes against total
land area (Statistical reports on the land area by prefectures and municipalities in Japan (MLIT GSI)) as of July
1, 2020, 377,976.94km2[MLIT GSI land area report].
F/T-3 Areas of Kapos mountain classes and their ratios against the total land area of Japan
Kapos mountain
FAO Mountain (km2)
USGS K1class data (km2)classification
Kapos 2
1.94 (5.13 × 10-4 %)1.73 (4.57 × 10-4 %)Kapos 3309.67 (0.0819%)
314.56 (0.0832%)
Kapos 47431.89 (1.966%)
7443.57 （1.969%)
Kapos 5
Kapos 621970.27
143248.83(5.812%)(37.90%)21914.79
145037.73(5.798%)(38.37%)

F/T-3 shows that there exists a minor difference of data in the Kapos mountain classes made available
independently by FAO and USGS. The ratios of Kapos 2 and Kapos 3 areas indicate extremely small areas exist. The
Kapos 2 area is at the summit of Mt. Fuji with less than 2km2 and the Kapos 3 areas are rare places at high altitude
7

mountain summits which occupy only 310km2 approximately.
Examples of tile (1 deg x 1deg) of USGS’s Kapos mountain classification map are shown. F/T-4 are of Miyagi
Prefecture (N38, E140) and Kanagawa Prefecture (N35 E139). It shows areas of the higher Kapos mountain classsurrounded by those of lower mountain classes.
F/T-4 Examples of Kapos mountain classification maps provided by USGS: Miyagi Prefecture (left) and Kanagawa
Prefecture (right)

2.3.2

Land cover data

Land cover areas are categorized as green areas and non-green areas. FAO uses land cover time series
produced by the European Space Agency (ESA) under the Climate Change Initiative (CCI). The original CCI
classes are re-classified into six IPCC classes and further into binary green/non-green classes. Previously FAO
re-classified “Wetland” to non-green, but changed to green in the revised metadata (as of December 14, 2020).
F/T-5 ESA CCI land cover classification, IPCC classes and green/non-green categories
ESA CCI class
IPCC class
Green / Non green
1
50, 60, 61, 62, 70, 71, 72, 80,
ForestGreen81, 82, 90, 100

110, 120, 121, 122, 130, 140,

GrasslandGreen

10,11, 12, 20, 30, 40

CroplandGreen

160, 170, 180

Wetland

Green

190

Settlement

Non Green

150, 151, 152, 153, 200, 201,
202, 210, 220

Other land

Non Green

1 Please note, that here the term “Forest” refers to land cover, not necessarily land use.

8

2.3.3 Administrative unit data
Some different datasets are available for administrative unit data which defines the national border. The
following links are examples of global administrative unit data. Country data can be downloaded from FAO’s
Global Administrative Units Layer (GAUL) site.


FAO Global Administrative Units Layer (GAUL)
https://data.europa.eu/euodp/data/dataset/jrc-10112-10004



Divas-GIS https://www.diva-gis.org/gdataUN Second Administrative Level Boundaries (SALB) https://www.unsalb.org/

2.4 Methodology
2.4.1 Definition of Mountain Green Cover Index (MGCI)The Mountain Green Cover Index is defined as

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 =𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴

× 100,

where Mountain Green Cover area is defined as total areas covered by Cropland, Grassland, Forest andWetland land cover classes according to the latest metadata (February 14, 2021).
Initially, the metadata only included the definition of MGCI which calculates the ratio of areas of green coverand total mountain areas. The revised metadata (February 14, 2021) includes a computation method that isbased upon the number of pixels.
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 =𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
× 100
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑜𝑜𝑜𝑜 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝

In general, there exist several definitions for calculation methods of the area. The first one defines the areaon the reference ellipsoid (Earth Ellipsoid which is fitted the surface of Earth’s elevation 0m, namely geoid).
This area definition has the merit that it does not depend upon land topography, but it has an issue that it does
not represent the actual surface area of vegetation that grows on the land topography surface. The second
one defines the surface area as area. When the surface area definition is adopted, generally area depends
upon spatial resolution that observes the topographic ruggedness. The surface area simply increases if the
topography is monitored with higher resolution (land surface ruggedness exists at micron order). Therefore,
accurately calculating the surface area is not practical theoretically.
To avoid this issue of area definition, land cover classification by satellite image analysis can be considered
as geospatially uniform sampling survey, land cover classification pixels can be considered as samples for
population to estimate population proportion defining the MGCI, which is the methodology to use number ofpixels.

92.4.2

Calculation method

The indicator value can be calculated as follows:I.

The ESA CCI land cover classes are reclassified into six IPCC classes and Green/NonGreen cover map.

II.

The Kapos Elevation Ranges map is overlaid on top of the map resulting from step 1.

III.

The zonal histogram is calculated for each country and regional grouping in such a way that the
number of pixels belonging to green and non-green classes is counted within each elevation range.

IV.

The ratio (%) between the sum of the green pixels and the total number of pixels (green plus nongreen) falling within each Kapos is calculated to obtain MGCI values per each Kapos class.

V.

The same procedure is used to calculate the distribution of the land cover classes as defined by IPCC
within each elevation range.

3. Verification of FAO estimation
MGCI calculation results using ESA land cover data (300m resolution), Kapos mountain classification data
(250m resolution), and FAO’s Global Administrative Unit Layer data were shown for 2000, 2010, 2015 and 2018
together with the MGCI estimation by FAO as follows:
3.1 Estimation of MGCI by area
Procedure to calculate mountain green cover area and total mountain areas and MGCI using open and free
QGIS software is shown as follows:
Using GAUL data of Japan, data of Japan are clipped from ESA land cover data and Kapos mountainclassification data.
I.

Using QGIS raster calculator, produce mask image of Kapos mountain classification, Kapos 1 to
Kapos 6.

II.

Land cover data are overlaid with each mountain class mask image and land cover data of Kapos 1
to Kapos 6 are produced.

III.

Transform the raster image data to polygon data.

IV. Using QGIS GroupStats function, calculate areas of land covers data of Kapos 1 to Kapos 6.
V.

The same procedure is used to calculate the distribution of the land cover classes as defined by
IPCC within each elevation range.

10F/T-6 Example of MGCI calculation of the Kapos mountain class from land cover data (The figure
shows the case of Kapos 6)

Land cover data

Mountain class mask

Land cover for respective
mountain class

The Result of MGCI calculation based upon area are shown in F/T-7(a) change of re-classification of wetland from non-green to green category
In verifying the results of FAO’s MGCI estimation as of Oct 2020, it was found that the sum of the green-covercategories (Forest, Cropland, Grassland) did not match with the MGCI for respective Kapos classes and alsothat FAO calculated the MGCI with wetland included in the green-cover. FAO clarified that wetland is includedin the green category according to the IPCC standard and the inconsistency with the description of themetadata was identified.
When MGCI was re-calculated with wetland included in the green cover, a fairly good agreement with the
FAO estimation was confirmed. After that, FAO revised the metadata as of December 14 2020 in which wetland
was reclassified as green cover.
(b) change of application method of map projection
The UTM54 was used in the calculation of MGCI for Japan. However, UTM52/53/54 map projections shouldhave been used to cover the entire area of Japan. Comparison of the calculated areas between when UTM 54
was applied and when UTM52/53/54 were applied and the impact on the MGCI calculation was assessed asshown in Supplement A.3. The MGCI calculated with UTM52/53/54 map projections is shown in F/T-7.
3.2 Estimation of MGCI by number of pixels
In the MGCI calculation based upon the area in 3.1, from mask images produced with I. land cover data and
II. Kapos mountain classification data (Kapos 1 to Kapos 6), using QGIS zone histogram function, numbers of
pixels for land covers were calculated and the ratio between the number of mountain green cover pixels anda total numbers of mountain areas pixels was calculated (F/T-7 and Annex F/T-2). The difference from the FAO
estimation was within 0.1% and fairly good agreement with the FAO estimation was confirmed. From this, it
was considered possible to reproduce the FAO estimation using the data and methodology defined in the11metadata.
F/T-7 Summary of FAO estimation verification (left) and MGCI trend for Japan (right)

The SDG15.4.2 of Japan for 2015 is shown in F/T-8 from FAO’s disseminated MGCI estimation [FAO
SDG15.4.2 Data]. The Kapos 2 area (3500m-4500m) exists at the summit of Mt. Fuji and its area isapproximately 1.98km2. As reported in detail in Article 4, a report by the Ministry of the Environment [MOEreport] indicates seasonal variability of vegetation at the mountain summit during summer time (June toNovember). It seems that vegetation of several percent to approximately 10% when moss is included exists.
These are bare areas. For this, forest and cropland for the Kapos 2 area is 0 % and it may be relevant toestimate SDG15.4.2 for the Kapos 2 mountain class at several percent.

IPCC class
ForestCropland
Grassland
WetlandSettlement
Other land
MGCI(Overall)

F/T-8 Public values of SDG15.4.2 for Japan by FAO (2015)
Kapos mountain class
1
2
3
4
5
0.00
20.00
58.89
89.37
94.16
0.00
66.67
33.57
6.34
3.01
0.00
0.00
6.95
3.57
2.03
0.00
0.00
0.35
0.40
0.48
0.00
0.00
0.00
0.00
0.02
0.00
13.33
0.24
0.32
0.28
0.00
86.67
99.76
99.68
99.69

6
91.10
4.94
3.01
0.08
0.51
0.36
99.13

4. Estimation of SDG15.4.2 using alternative data that Japan has
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 MGCI values. In
this analysis, the high-resolution land use and land cover map data of JAXA (hereafter “JAXA land cover data”,
250m, 100m, 50m/30m resolutions) was used.
Matching the resolution of the JAXA land cover data, the Kapos mountain classification data accessible at12

FAO Mountain Partnership HP (Kapos-FAO, 430m resolution), USGS’s Kapos mountain classification data
(Kapos-USGS, 250m resolution) and Kapos mountain classification data which were produced by JAXA fromSRTM 90m DEM data (Kapos-SRTM, 90m resolution, see Supplement A.2) were used.
As for administrative unit data, FAO’s GAUL is not relevant for Japan since its northern island territories,
Ogasawara islands, Sakishima Islands, etc. are missing from the map. Here high-resolution coast line data
provided by GSI (resolution higher than 10m, see Supplement A.1) and Global Map (1km resolution) [Global
Map] and MLIT National Digital Information Administrative Area data [MLIT National Digital Information]
were used.
4.1 Used data：
4.1.1 Land cover data
JAXA land cover data (2006-2011, 2014-2016 and 2018-2020, 250m, 100m and 50m/30m resolution,
original resolution is 10m for 2006-2010 and 2018-2020, and 30m for 2014-2016) is used. F/T-9 shows JAXA
land cover data, its relation with the IPCC classification and binary green/non-green classification for Japan.
https://www.eorc.jaxa.jp/ALOS/lulc/lulc jindex.htm
F/T-9 JAXA land cover data, its relation with the IPCC classification and binary green/non-green classification
for Japan
JAXA class
IPCC class
Green/Non green(Digital number)
6,7,8,9,11*
ForestGreen
5
GrasslandGreen
3,4
CroplandGreenNot applicable
WetlandGreen
2
Settlement
Non green
Other
land
Non green
1，10，12＊＊11(Bamboo) and 12（Solar panels）are available only for 2018-2020
4.1.2

Mountain classification data

The Kapos-FAO mountain classification data (430m resolution) can be downloaded from FAO’s Mountain
Partnership HP [FAO Kapos]. Kapos-USGS mountain classification data (250m resolution) is made available by
USGS. For Kapos-SRTM mountain classification data (90m resolution), please see Supplement A.2.
4.1.3

Administrative unit data

Japan coastline data (higher than 10m resolution), Global Map (1km resolution) and MLIT Land Digital
Information administrative unit data were used. For the Japan coast line data, please see Supplement A.1.
4.2 Estimation of SDG 15.4.2 using JAXA land cover data
13

Among estimations disseminated by FAO, the MGCI for Kapos 2 mountain areas in Japan is 86.667% for 2000,
2015 and 2018. The estimation is very far from the scene of the Mt. Fuji’s summit. From the estimation of the
MOE Alpine zone observation site at Mt. Fuji summit, the estimation should be several percent in the summer
time and 0 for the winter season because the area is frozen.
FAO used the ESA CCI land cover data of the Mt. Fuji summit for computing MGCI estimates. It was foundthat there is no error in spatial matching, but land covers classes for the Kapos 2 area of Mt. Fuji summit arewater (3 pixels 7%), cropland or grassland (26 pixels, 66.6%), forest in mosaic (10 pixels, 25%). From this, land
cover classification of ESA CCI data is far from reality and is identified as the cause of the overestimation of
MGCI. In general, the Alpine zone above the forest limit is bare areas with no vegetation cover, and hence there
is concern that the FAO estimation based upon the binary green/non-green classification using ESA CCI land
cover data may be overestimated for Alpine areas generally.

5 Impact of sampling errors and classification errors on MGCIIn the methodology to compute the MGCI from the ratio of a number of green cover pixels to the total
number of pixels, land cover pixels as identified by satellite observations can be considered as samples. Then,the population proportion for the MGCI is estimated by sample proportion. Further, land cover data producedby automatic classification from satellite images contains classification errors and its accuracy is evaluated fromthe confusion matrix. The purpose of this section is to clarify a mathematical statistical formalization toestimate the MGCI value from samples and to show an error correction method and 95% confidence interval,
error assessment values of the MGCI empirically.
5.1 Estimation of population proportion by sample proportion
Suppose that the MGCI for each Kapos mountain classification is approximated as a ratio with the numberof green cover pixels to the total number of pixels in each Kapos mountain classification area, where land
classification grid data and Kapos mountain classification grid data are given as data products computed fromsatellite images. Then, sampled proportion 𝑝𝑝̂ 𝑖𝑖 for the MGCI of the i-th Kapos mountain class can be given by
the following equation,𝑝𝑝̂𝑖𝑖 =

𝑁𝑁𝑖𝑖 𝑋𝑋 (𝑡𝑡)
∑𝑡𝑡=1
𝑖𝑖

𝑁𝑁𝑖𝑖

,

where 𝑁𝑁𝑖𝑖 is the total number of pixels for the i-th Kapos mountain class areas and 𝑋𝑋𝑖𝑖 (𝑡𝑡) is green coverclassification at monitoring site t.

If monitoring site t located in the Kapos mountain class i is green, then one has Xi(t)=1, otherwise, Xi(t)=0.

Sample proportion 𝑝𝑝̂𝑖𝑖 is the estimation of population proportion 𝑝𝑝𝑖𝑖 for the i-th Kapos mountain class, where

population proportion is the true MGCI.

Furthermore, R(a|b) is defined as conditional probability of classified value (classified) a, which isrepresented as dichotomous variable a={1: green, 0: non-green} conditioning on real value (validated) b,
16

which is represented as dichotomous variable b={1: green, 0: non-green}. Suppose that Xi(t) is randomvariable as follows:
1 𝑤𝑤. 𝑝𝑝. 𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 )𝑋𝑋𝑖𝑖 = �
0 𝑤𝑤. 𝑝𝑝. 𝑅𝑅(0|0)(1 − 𝑝𝑝𝑖𝑖 ) + 𝑅𝑅(0|1)𝑝𝑝𝑖𝑖

This equation means that estimation errors in MGCI can be derived from two types of errors: sampling error
and classification error. R(a|b) can be computed as follows, where the confusion matrix is provided as in F/T13.

𝑅𝑅(0|0) =𝑅𝑅(0|1) =

𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇
, 𝑅𝑅(1|0) =𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇

𝑇𝑇𝑇𝑇
𝐹𝐹𝐹𝐹
, 𝑅𝑅(1|1) =𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹

where, TP, FP, TN, and FN are numbers of observations for the conditions matched in the 2 times 2 confusion
matrix for the green/non-green binary classification.
F/T-13 Confusion matrix for green/non-green binary classification
validated bclassified
a

a / b

Green(b=1)

Non green (b=0)

Green (a=1)

TPFP

Non green (a=0)

FN

TN

The following characteristics are introduced from the definition of conditional probability.
𝑅𝑅(𝑎𝑎|𝑏𝑏) =𝑝𝑝𝐴𝐴𝐴𝐴 (𝑎𝑎,𝑏𝑏),
𝑝𝑝𝐵𝐵 (𝑏𝑏)𝑝𝑝𝐴𝐴𝐴𝐴 (𝑎𝑎, 𝑏𝑏) = 𝑅𝑅(𝑎𝑎|𝑏𝑏)𝑝𝑝𝐵𝐵 (𝑏𝑏),𝑝𝑝𝐴𝐴 (𝑎𝑎) = ∑𝑏𝑏 𝑝𝑝𝐴𝐴𝐴𝐴 (𝑎𝑎, 𝑏𝑏) = ∑𝑏𝑏 𝑅𝑅(𝑎𝑎|𝑏𝑏)𝑝𝑝𝐵𝐵 (𝑏𝑏),𝑝𝑝𝐴𝐴 (1) = ∑𝑏𝑏=0,1 𝑅𝑅(1|𝑏𝑏)𝑝𝑝𝐵𝐵 (𝑏𝑏) = 𝑅𝑅(1|1)𝑝𝑝𝐵𝐵 (1) + 𝑅𝑅(1|0)𝑝𝑝𝐵𝐵 (0),𝑝𝑝𝐴𝐴 (0) = ∑𝑏𝑏=0,1 𝑅𝑅(0|𝑏𝑏)𝑝𝑝𝐵𝐵 (𝑏𝑏) = 𝑅𝑅(0|0)𝑝𝑝𝐵𝐵 (0) + 𝑅𝑅(0|1)𝑝𝑝𝐵𝐵 (1),where for the MGCI estimation for the i-th Kapos mountain class, we may set 𝑝𝑝𝐵𝐵 (1) = 𝑝𝑝𝑖𝑖 and 𝑝𝑝𝐵𝐵 (0) = 1 − 𝑝𝑝𝑖𝑖 .
5.2 Formulation of MGCI estimation error

Suppose that Xi(t) is the following the Bernoulli random variable.
17

1 𝑤𝑤. 𝑝𝑝. 𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 ),𝑋𝑋𝑖𝑖 = �
0 𝑤𝑤. 𝑝𝑝. 𝑅𝑅(0|0)(1 − 𝑝𝑝𝑖𝑖 ) + 𝑅𝑅(0|1)𝑝𝑝𝑖𝑖

where

𝑁𝑁

𝑖𝑖
𝑋𝑋𝑖𝑖 (𝑡𝑡).
𝑍𝑍𝑖𝑖 = ∑𝑡𝑡=1

Zi is sampled from the binominal distribution 𝐵𝐵(𝑁𝑁𝑖𝑖 , 𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0)(1 − 𝑝𝑝𝑖𝑖 )).

If Ni is large enough,then the binominal distribution can be approximated with normal distribution with mean 𝑁𝑁𝑖𝑖 (𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 +𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 )) and variance �𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 )�(𝑅𝑅(0|0)(1 − 𝑝𝑝𝑖𝑖 ) + 𝑅𝑅(0|1)𝑝𝑝𝑖𝑖 ). From this,sample proportion may be sampled from normal distribution with mean 𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 ) andvariance �𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 )�(𝑅𝑅(0|0)(1 − 𝑝𝑝𝑖𝑖 ) + 𝑅𝑅(0|1)𝑝𝑝𝑖𝑖 )/𝑁𝑁𝑖𝑖 . Thus, mean of the sample

proportion E[𝑝𝑝�𝚤𝚤 ] is computed as,E[𝑝𝑝�𝚤𝚤 ] = 𝑅𝑅(1|1)𝑝𝑝𝑖𝑖 + 𝑅𝑅(1|0) (1 − 𝑝𝑝𝑖𝑖 ).

From this equation, population proportion 𝑝𝑝𝑖𝑖 is corrected as follows:
𝑝𝑝𝑖𝑖 =

E[𝑔𝑔�𝚤𝚤 ] − 𝑅𝑅(1|0)
𝑅𝑅(1|1) − 𝑅𝑅(1|0)

Further, the confusion matrix is also the estimation of population proportion by sample proportion andcontains sampling error. Therefore, 95% confidence interval of each element of the confusion matrix can becalculated as follows:
𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
− 1.96��1 −� /(𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇) ≤ 𝑅𝑅(0|0)
𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇
≤

𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
+ 1.96��1 −� /(𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇)𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇

𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
− 1.96��1 −� /(𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇) ≤ 𝑅𝑅(1|0)
𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑁𝑁𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇
≤

𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
+ 1.96��1 −� /(𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇)𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇𝐹𝐹𝐹𝐹 + 𝑇𝑇𝑇𝑇

𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
− 1.96��1 −� /(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹) ≤ 𝑅𝑅(0|1)
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
≤

𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
𝐹𝐹𝐹𝐹
+ 1.96��1 −� /(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
18

𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
− 1.96��1 −� /(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹) ≤ 𝑅𝑅(1|1)
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
≤

𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇
+ 1.96��1 −� /(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹

By the way, it is known that error propagation formulas are given as follows;
•
•
•

•

Propagation of errors in addition(𝑎𝑎 ± 𝛿𝛿𝛿𝛿) + (𝑏𝑏 ± 𝛿𝛿𝛿𝛿) = (𝑎𝑎 + 𝑏𝑏) ± (𝛿𝛿𝛿𝛿 + 𝛿𝛿𝛿𝛿)Propagation of errors in subduction(𝑎𝑎 ± 𝛿𝛿𝛿𝛿) – (𝑏𝑏 ± 𝛿𝛿𝛿𝛿) = (𝑎𝑎 − 𝑏𝑏) ± (𝛿𝛿𝛿𝛿 + 𝛿𝛿𝛿𝛿)Propagation of errors in multiplication𝛿𝛿𝛿𝛿 𝛿𝛿𝛿𝛿
(𝑎𝑎 ± 𝛿𝛿𝛿𝛿)(𝑏𝑏 ± 𝛿𝛿𝛿𝛿) = 𝑎𝑎𝑎𝑎 �1 ± � + ��𝑏𝑏𝑎𝑎

Propagation of errors in division𝛿𝛿𝛿𝛿 𝛿𝛿𝛿𝛿
𝑎𝑎 ± 𝛿𝛿𝛿𝛿 𝑎𝑎= �1 ± � + ��𝑎𝑎𝑏𝑏𝑏𝑏 ± 𝛿𝛿𝛿𝛿 𝑏𝑏

Using these error propagation formulas, we have 95% confidence interval of,as:

𝑝𝑝𝑖𝑖 =

E[𝑔𝑔�𝚤𝚤 ] − 𝑅𝑅(1|0)
𝑅𝑅(1|1) − 𝑅𝑅(1|0)

𝑎𝑎−𝑏𝑏𝛿𝛿𝛿𝛿+𝛿𝛿𝛿𝛿
𝛿𝛿𝛿𝛿+𝛿𝛿𝛿𝛿
𝑎𝑎−𝑏𝑏𝛿𝛿𝛿𝛿+𝛿𝛿𝛿𝛿
𝛿𝛿𝛿𝛿+𝛿𝛿𝛿𝛿
�1 − �+�� ≤ 𝑝𝑝𝑖𝑖 ≤�1 + �
+
��
𝑎𝑎−𝑏𝑏
𝑐𝑐−𝑏𝑏
𝑎𝑎−𝑏𝑏
𝑐𝑐−𝑏𝑏
𝑐𝑐−𝑏𝑏
𝑐𝑐−𝑏𝑏

where we set
𝑎𝑎 = E[𝑝𝑝�𝚤𝚤 ], 𝑏𝑏 = 𝑅𝑅(1|0), 𝑐𝑐 = 𝑅𝑅(1|1)

and 𝛿𝛿𝛿𝛿, 𝛿𝛿𝛿𝛿, and 𝛿𝛿𝛿𝛿 are half of 95% confidence interval of E[𝑝𝑝�𝚤𝚤 ], 𝑅𝑅(1|0), and 𝑅𝑅(1|1) , respectively.

5.3 MGCI estimation the JAXA high-resolution land use and land cover map data
5.3.1 JAXA high-resolution land use and land cover map data

MGCIs are calculated at different spatial resolutions for each Kapos mountain class using JAXA highresolution land use and land cover map (2006-2011, 2014-2016, and 2018-2020) [JAXA] and their 95%
confidence intervals are estimated. JAXA land cover data has 10 classification categories for 2006-2011 and2014-2016 and 12 categories for 2018-2020 as shown in F/T-14.
19

F/T-14 Classification categories of JAXA high-resolution land use and land cover map (11 and 12 are only for
2018-2020)
Digital Number
1
2
3
4
5
6
7
8
9
10
11
12

Land cover

Water
Urban and built-up
Rice paddy
Crops
Grassland
Deciduous Broad-leaved Forest (DBF)
Deciduous Needle-leaved Forest (DNF)
Evergreen Broad-leaved Forest (EBF)
Evergreen Needle-leaved Forest (ENF)
Bare landBambooSolar panel

Green/Non green
Non green
Non green
Green
Green
Green
Green
Green
Green
Green
Non greenGreen
Non green

5.3.2 Verification of land cover data (Confusion matrix)
F/T-15 shows 2018-2020 JAXA land cover data (100m resolution) and locations of validation sites. Accuracy
verification is achieved by comparing the land cover of the validation site which is confirmed visually with theland cover classification of JAXA land cover map, automatically produced from the satellite image.
JAXA used validation information of approximately 2,700 sites which are independent of the training data,
for accuracy assessment of its high-resolution land use and land cover map data (ver21.03, 2018-2020, 10m
resolution) and confirmed an overall accuracy of 84.8% [JAXA ALOS ver21.03].
As for the land cover data, ver18.03, 2014-2016, 30m resolution, validation information of approximately
3000 sites were used and overall accuracy was 81.6% [JAXA ALOS ver18.03].
As for the land cover data, ver16.09, 2006-2011, 10m resolution, validation information of approximately
1400 sites were used and overall accuracy was 78.0% [JAXA ALOS ver16.09].
Validation sites in mountain areas were extracted from 2006-2011, 2014-2016, and 2018-2020 JAXA land
cover data, and confusion matrices of land cover classification in mountain areas at 250m, 100m, and50m/30m resolutions were produced. Results are shown in F/T-16 to F/T-18. It is seen that overall classification
accuracy in the mountain areas - which is 53-82% for 10 to 12 categories - improved significantly to 90 to 99%
for binary green/non-green re-classification and that theclassification accuracy also improved as data resolution ishigher.

F/T-15 2018-2020 JAXA land cover data (100mresolution) and validation sites (mountain validationsites: orange, non-mountain validation sites: yellow)

20

F/T-16 2018-2020 JAXA land cover data (100m, mountain area) confusion matrix
Validation

Classified

（％）

2

3

4

5

6

7

8

9

10

11

12 Total

1

27

0

0

0

0

0

0

0

0

1

0

0

28

96.4

2

0

9

0

1

0

0

0

0

0

0

0

6

16

56.3

3

0

0

4

5

0

0

0

0

0

1

0

1

11

36.4

4

0

1

0

14

0

0

0

0

0

1

0

1

17

82.4

5

0

0

0

4

40

1

2

0

0

2

1

1

51

78.4

6

0

0

1

1

2

118

25

1

6

0

2

4

160

73.8

7

1

0

0

0

1

2

124

0

0

2

0

0

130

95.4

8

0

0

0

0

0

0

0

7

1

0

2

0

10

70.0

9

0

0

0

0

0

11

5

1

98

0

7

1

123

79.7

10

3

0

0

1

0

0

0

0

0

38

0

0

42

90.5

11

0

0

0

0

0

0

0

0

1

0

11

0

12

91.7

12

0

0

0

0

0

0

0

0

0

0

0

11

11

100.0

25

611

Total

31

Producer's

87.1

accuracy

10

5
80.0

90.0

26
53.8

43
93.0

132
89.4

95

Non-green
Green
Total

156
79.5

Validation
Non-green
Classified

User's accuracy

1

9

106

77.8

45

92.5

23

84.4

47.8

44.0

82.0

Validation

Green

Total

2

Non-green

97

16

498

514

111

500

611

Classified

Non-green 0.855856
Green

Green

0.144144

0.996

1

1

Total

Total

0.004

Green
Non-green

F/T-17 2014-2016 JAXA land cover data (100m, mountain area) confusion matrix
Val dation
0
1

Classif ed

2

3

4

5

6

7

8

9

10 Total

0

0

0

0

0

0

0

0

0

0

0

0.0

34

0

0

0

0

0

0

0

0

0

34

100.0

（％）

2

0

7

0

0

0

1

0

0

0

4

12

58.3

3

1

0

9

4

0

0

0

0

0

2

16

56.3

4

0

0

0

23

0

1

0

0

0

2

26

88.5

5

0

0

0

3

63

6

2

0

1

2

77

81.8

6

1

0

1

0

8

125

38

2

7

1

183

68.3

7

0

0

0

3

1

35

169

1

7

1

217

77.9

8

0

0

0

0

0

5

2

15

12

0

34

44.1

9

0

0

0

0

0

27

11

13

102

2

155

65.8
88.6

10
Total
Producer's

0

3

0

1

1

0

0

0

0

39

44

36

10

10

34

73

200

222

31

129

53

798

94.4

accuracy

70.0

90.0

67.6

86.3

62.5

76.1

Validat on
Non-green
Classified

User's accuracy

187

Non-green
Green
Total

48.4

79.1

73.6

73.4

Validation

Green

3

Total

Non-green

90

12

696

708

99

699

798

Classified

Green

Non-green 0.878788 0.004292

Total

Green
Non-green

0.121212 0.995708

Green

1

Total

1

F/T-18 2006-2011 JAXA land cover data (100m, mountain area) confusion matrix
Val dation

Classified

3

4

5

6

7

8

9

10 Total

1

55

0

1

1

0

0

0

0

0

0

57

96.5

2

0

20

0

0

0

0

0

0

0

0

20

100.0

3

1

0

24

3

0

0

0

0

0

0

28

85.7

4

0

1

5

6

2

2

0

0

2

0

18

33.3

5

0

0

3

9

11

1

0

0

2

2

28

39.3

6

0

1

0

2

0

25

0

0

5

0

33

75.8

7

0

0

1

1

1

6

7

0

6

0

22

31.8

8

0

0

0

1

0

0

0

1

1

0

3

33.3

9

0

0

0

1

0

4

0

5

72

0

82

87.8

10

0

2

0

0

1

0

0

0

0

5

8

62.5

7

299

Total
accuracy

56
98.2

24
83.3

34
70.6

24
25.0

15
73.3

38
65.8

Validat on
Non-green
Non-green
Green
Total

（％）

2

Producer'sClassified

User's accuracy

1

82

7
100.0

6
16.7

88
81.8

71.4

75.3

Val dation

Green

3

Total

85

5

209

214

87

212

299

Non-green
Classified

Non-green
Green

Green

0.942529 0.014151

0.057471 0.985849
1

Total

21

1

Total

Green
Non-greenF/T-19 Classification accuracy of JAXA land cover data (mountain areas)
Year
Resolution
Number ofClassification accuracy
Green/Non-green
Verification sites(12/10 categories)*classification(%)
accuracy（%）
2018-2020
100m
611
82.0
99.6
2014-2016
250m
798
66.1
96.1
100m
798
73.4
98.1
30m
798
76.9
98.8
2006-2011
250m
299
53.7
90.6
100m
299
75.3
97.3
50m
299
79.6
97.7
*12 categories for 2018-2020 and 10 categories for 2014-2016 and 2006-2011
As shown in F/T-19, when MGCIs are calculated at 300m, 250m, 100m, 50m (2014-2016 and 2018-2020) and30m (2006-2011) resolutions, it was confirmed that the 95% confidence interval became narrower as thespatial resolution increased. As the highest-resolution Kapos mountain classification data, that of 90m
resolution was produced and became available [Kawakita], it was found that the highest MGCIs can beobtained when 100m resolution land cover data were used.
Using JAXA high-resolution land use and land cover map data of 100m resolution, the point estimation andthe interval estimation of MGCI for each Kapos mountain class were calculated as shown in F/T-20 to F/T-22.
F/T-20 MGCIs calculated using 2006-2011 JAXA land cover data (100m resolution)
Kapos
classification

MGCI

MGCI 95% lower limit

MGCI 95% upper limit

Kapos 1

FAO estimation
2015(reference)0

Kapos 20000.8667

Kapos 30.479932454

0.388758862

0.571106046

0.9976

Kapos 40.920235576

0.802742209

1.037728943

0.9969

Kapos 51.000198807

0.877558557

1.122837599

0.9971

Kapos 61.001861798

0.879194204

1.124529392

0.9931

Overall

0.997114

0.874775

1.119454

0.993822

F/T-21 MGCIs calculated using 2014-2016 JAXA land cover data (100m resolution)
Kapos
classification

MGCI

MGCI 95% lower limit

MGCI 95% upper limit

FAO estimation
2010(reference)

Kapos 10

Kapos 20000.8667

Kapos 30.324336795

0.219973983

0.428699608

0.9976

Kapos 40.964553709

0.814380131

1.114727287

0.9968

Kapos 50.99418406

0.84194352

1.146424599

0.9969

Kapos 6

0.991236695

0.839297171

1.143176218

0.9913Overall

0.989222

0.837443

1.141000

0.9923

F/T-22 MGCIs calculated using 2018-2020 JAXA land cover data (100m resolution)
Kapos
classification

MGCI

MGCI 95% lower limit

MGCI 95% upper limit

Kapos 1

FAO estimation
2010(reference)0

Kapos 20000.8667

Kapos 30.246453693

0.144044964

0.348862423

0.9976

Kapos 4

0.947168659

0.791180728

1.103156589

0.9969

Kapos 50.994111550

0.834573777

1.153649323

0.9969

Kapos 6

0.986879668

0.828002972

1.145756364

0.9931

Overall

0.984724

0.826035

1.143413

0.9923Among the estimated values in F/T-20 to F/T-22, MGCI values estimated by FAO for Kapos 5 and Kapos 6
mountain classes are in very good agreement with the estimation by Japan and calculated values of FAO are
within the 95% confidence interval. The estimated values for 2006 to 2011 are 0 in all cases for the Kapos 2
mountain class area. This is because JAXA high-resolution land cover classification data is of bare land (nongreen). This value is underestimated when compared with the field monitoring values from the MOE
monitoring site 1000 Alpine area survey report [MOE report]. Since the resolution of JAXA land cover data is
100 m (minimum resolution is 30m to 50m), the resolution seems insufficient to identify sparse vegetation
which only grows during summer time.
23

What needs to be discussed is the methodology to calculate MGCI. For this methodology to compute MGCI,
in reality, land cover, that has seasonal variability and density changes, is transformed to discrete land coverclassification and further into binary green/non-green categories - and then MGCI is calculated. Thismethodology has inherent discrete errors and when MGCI is small, it is believed that the errors become
tangible and cause underestimation.
For this, in the current methodology, the vegetation ratio which is continuous is transformed to binary values,
when MGCI is small and the sample size is small (corresponding mountain class area is small), the impact ofthe discrete errors caused by the methodology becomes non-negligible. The impact is not only for Kapos 2
mountain class area, but it should also be considered as an error factor for Kapos 3 and Kapos 4 mountain classzones where Japan has small areas. As a method to decrease the impact of the discrete errors, for example,
green/non-green weighting is set for continuous 0 to 1, MGCI can be estimated. The development of suchhighly accurate MGCI computation methodology which is expanded from the FAO metadata may need to befurther studied.
5.4 Reduction of sampling errors of confusion matrix comparing with other data
By comparing the land cover grid data with vegetation map polygons and parcel polygons of farm land,
verification of classification accuracy can be made. Particularly by comparing with other data, a sample size ofthe confusion matrix is increased and thereby improving estimation accuracy of classification errors, it can beshown that the confidence interval of MGCI can be decreased.
Here the vegetation survey (1/25000 scale) prefectural list shape file [MOE vegetation survey polygon data]
is used. Vegetation areas are presented as polygon data as a result of the MOE National Survey on the Natural
Environment [MOE National Survey on the Natural Environment]. F/T-23 is an example of a vegetation mapthat the Biodiversity Center of Japan, Natural Environment Bureau, MEO] provides.
F/T-23 Example of vegetation map (source: Biodiversity Center of Japan, Natural Environment Bureau, MOE)

24

Polygon data of the MOE vegetation map are compared with JAXA land cover grid data and a confusion
matrix between green/non-green of JAXA high-resolution land cover data and MOE vegetation map iscomputed, thereby increasing the sample size of the confusion matrix. For some mountain areas, using
vegetation map polygons and 2006-2011 100m resolution land cover data, the sample size of the confusion
matrix was increased and the point estimation and 95% confidence interval of conditional probability R(1|0)
and R(1|1) were computed. F/T-24 are the confusion matrix and point estimation and 95% confidence
interval of the conditional probabilities.
As shown in F/T-24, by increasing the number of validation sites, the conditional probability R(a|b) withdecreased estimation errors can be used to calculate the point estimation and 95% confidence interval of
MGCI for 2006-2011 100m resolution Kapos mountain classes. The estimations are shown in F/T-25. The 95%
confidence interval of MGCI in F/T-25 is smaller than that of F/T-20. This means that by increasing training
information to produce the confusion matrix, the confidence of MGCI evaluation value can be enhanced.
Among the 95% confidence interval estimation for 2006-2011 100m resolution JAXA land cover data for
Kapos mountain classes, the MGCI estimation by FAO are outside of those calculated by using the specific data
owned by Japan. From this, for those Kapos mountain classes (Kapos 2, Kapos 3 and Kapos 4) with small areasin Japan, it can be concluded that FAO estimation for SDG 15.4.2 is being proposed as overestimated.
Although this verification could not be completed for all cases with a schedule of the verification task,
personnel and budget, it could demonstrate that by preparing data increasing accurate training information
for land cover, it is technically feasible to decrease errors in the estimation, even if certain errors exist in
green/non-green classification.
F/T-24 Confusion matrix calculated by comparing the MOE vegetation polygon and JAXA high-resolution land
cover dataGreen

Non green

Green

98133

2562

Non green

767

3516

Lower limit of 95%
confidence interval

Upper limit of 95%
confidence interval

Half of 95% confidence
intervalMean
R(1|0)

0.42152

0.409105743

0.433934731

0.012414494

R(1|1)

0.992245

0.991697969

0.992791414

0.000546723

25

F/T-25 MGCI point estimation and 95% confidence interval for 2006-2011, using 100m resolution Kapos
mountain classes
FAO estimation 2010(reference)

2006-2011 JAXA land cover data
Kapos
classification

MGCI

MGCI 95% lower limit

MGCI 95% upper limit

Kapos 10

Kapos 20000.8667

Kapos 30.142818164

0.10970299

0.17593334

0.9976

Kapos 40.859043387

0.81684609

0.90124068

0.9969

Kapos 50.989115507

0.94467302

1.033558

0.9971

Kapos 6

0.99182182

0.94746112

1.03618252

0.9931

Overall

0.984099

0.939909

1.028290

0.9938

6．Summary
Following the data and methodology defined by the latest metadata, SDG15.4.2 (Mountain Green CoverIndex (MGCI)) was calculated in this verification task.
When MGCI was calculated using JAXA high-resolution land cover data, smaller estimations than thoseestimated by FAO were obtained. Using validation information independent from the training information, aconfusion matrix was produced and the accuracy of land cover classification was assessed. As the resolutionof used land cover data tends higher, classification accuracy is correspondingly higher and MGCI has a trend to
become smaller, suggesting that high-resolution data should be used. It is also the case with the used Kapos
mountain classification data.
SDG15.4.2 estimations were calculated considering the accuracy assessment of the land cover data at 100m
resolution which Japan has as follows:
・2006-2011

MGCI(overall) 0.984099 (0.939909 or more）

Kapos 2: 0 (it is almost 0 as it is frozen during winter time, but it could be several percent during summer time)
Kapos 3: 0.143 (0.110 or more and 0.176 or less；0 is relevant since the areas are frozen during winter time）
Kapos 4：0.859 (0.817 or more and 0.901 or less)
Kapos 5：0.989 (0.945 or more)
Kapos 6：0.992 (0.947 or more)
26

•

Kapos mountain classification data
For mountain classification in Japan, Kapos mountain classification data whose resolution is higher
than those provided by FAO and USGS (430m and 250m resolution respectively) is considerednecessary. For the verification task, 90m resolution Kapos mountain classification data was produced
from SRTM 90m data. The use of further higher-resolution data is advisable such as SRTM 30m dataor 10m DEM of GSI.

•

Administrative unit data
GAUL data of FAO does not include Japan’s northern island territories and Sakishima islands, Ioharchipelago of Ogasawara islands and may therefore not be most relevant for use by Japan. Highresolution coast line data was provided by GSI and the data was used to clip the Japanese island datafrom a global data set. However, the data resolution is very high and requires a long time to process;the Global Map data was used in the following data processing. Effective use of the high-resolution
coast line data provided by GSI is recommended.

MGCI estimation based upon areas of mountain areas and green cover and MGCI estimation based
upon a number of pixels for mountain area and green cover area were made.
For the methodology based upon area ratio, map projection to estimate the area is necessary anderrors at map projection ends and map projection transformation errors can occur
The methodology based upon a number of pixels does not require map projection in the geographic
coordinate system, therefore it features fewer errors, simpler data analysis, and shorter processing
time, and its use can be recommended.

29AnnexF/T-1 List of SDG15.4.2 (Mountain Green Cover Index) estimation cases

F/T-2 MGCI estimation by FAO(left), MGCI estimation based on the area using ESA-CCI data and Kapos-FAO
mountain classification data(center) and MGCI estimation based on number of pixels(right)
Calculation based on area（incl. wetland in green）

Estmat on by FAO
Year 2000
1

2

in green)

Year 2000

Kapos mountain class
IPCC class

Calculat on based on number of pixels (incl. wetland

Kapos mountain class1

2

Year 2000
Kapos mountain class

3

4

5

6

3

4

5

6

3

4

5

6

Forest

0.00

20.00

55.30

88.73

94.64

91.15

0.00

17.39

54.19

88.75

94.56

91.01

0.00

28.57

54.57

88.64

94.62

91.16

Cropland

0.00

66.67

35.57

6.17

2.55

5.09

0.00

78.26

44.44

9.39

4.53

8.35

0.00

1

62.86

2

44.10

9.45

4.45

8.20

Grassland

0.00

0.00

8.54

4.40

2.15

3.20

0.00

0.00

0.71

1.16

0.30

0.09

0.00

0.00

0.74

1.23

0.25

0.09

Wetland

0.00

0.00

0.35

0.39

0.39

0.07

0.00

0.00

0.33

0.41

0.39

0.07

0.00

0.00

0.34

0.41

0.41

0.07

Settlement

0.00

0.00

0.00

0.00

0.00

0.13

0.00

0.00

0.00

0.00

0.00

0.12

0.00

0.00

0.00

0.00

0.00

0.13

Other land

0.00

13.33

0.24

0.31

0.27

0.36

0.00

4.34

0.30

0.27

0.19

0.34

0.00

8.57

0.25

0.28

0.26

0.35

MGCI

0.00

86.67

99.76

99.69

99.72

99.52

0.00

95.65

99.67

99.71

99.78

99.52

0.00

91.43

99.75

99.72

99.73

99.52

MGCI all

99.55

99.57

Year 2010
Kapos mountain class
IPCC class
ForestCropland99.56

Year 2010

Year 2010

Kapos mountain class

Kapos mountain class

1

2

3

4

5

6

1

2

3

4

5

6

1

2

3

4

5

6

0.00

20.00

56.24

89.22

94.49

91.20

0.00

17.39

55.37

89.18

94.43

90.98

0.00

28.57

55.31

89.12

94.51

91.21

0.00

66.67

35.28

0.00

78.26

43.22

0.00

62.86

43.37

6.33

2.86

5.06

8.83

4.49

8.14

8.82

4.41

7.91

Grassland

0.00

0.00

7.89

3.72

1.93

2.98

0.00

0.00

0.71

1.22

0.43

0.11

0.00

0.00

0.74

1.31

0.36

0.11

Wetland

0.00

0.00

0.35

0.42

0.44

0.08

0.00

0.00

0.33

0.48

0.44

0.07

0.00

0.00

0.34

0.46

0.45

0.08
0.34

Settlement

0.00

0.00

0.00

0.00

0.01

0.33

0.00

0.00

0.00

0.00

0.00

0.34

0.00

0.00

0.00

0.00

0.00

Other land

0.00

13.33

0.24

0.31

0.28

0.36

0.00

4.34

0.30

0.26

0.18

0.34

0.00

8.57

0.25

0.28

0.27

0.35

MGCI

0.00

86.67

99.76

99.69

99.71

99.31

0.00

95.65

99.63

99.71

99.79

99.30

0.00

91.43

99.75

99.72

99.72

99.31

MGCI all

99.38

99.40

Year 2015

Year 2015

Kapos mountain class
IPCC class
Forest99.38
Year 2015

Kapos mountain class

Kapos mountain class

1

2

3

4

5

6

1

2

3

4

5

6

1

2

3

4

5

6

0.00

20.00

58.89

89.37

94.16

91.10

0.00

17.39

57.85

89.28

94.12

90.88

0.00

28.57

57.73

89.27

94.19

91.12

Cropland

0.00

66.67

33.57

6.34

3.01

4.94

0.00

78.26

40.74

8.76

4.74

8.05

0.00

62.86

40.94

8.67

4.66

7.81

Grassland

0.00

0.00

6.95

3.57

2.03

3.01

0.00

0.00

0.79

1.21

0.44

0.11

0.00

0.00

0.74

1.32

0.36

0.11

Wetland

0.00

0.00

0.35

0.40

0.48

0.08

0.00

0.00

0.30

0.47

0.47

0.00

0.00

0.34

0.44

0.49

0.08

Settlement

0.00

0.00

0.00

0.00

0.02

0.51

0.00

0.00

0.00

0.00

0.02

0.52

0.00

0.00

0.00

0.00

0.02

0.52

Other land

0.00

13.33

0.24

0.32

0.28

0.36

0.00

4.34

0.30

0.26

0.18

0.34

0.00

8.57

0.25

0.29

0.28

0.36

MGCI

0.00

86.67

99.76

99.68

99.69

99.13

0.00

95.65

99.68

99.72

99.77

99.12

0.00

91.43

99.75

99.71

99.71

99.12

MGCI all

99.23

99.23

Year 2018
Kapos mountain class
IPCC class

1

2

0.08

1

99.22

Year 2018Year 2018

Kapos mountain class

Kapos mountain class

3

4

5

6

3

4

5

6

3

4

5

6

Forest

0.00

20.00

59.31

88.97

93.98

90.96

0.00

17.39

57.97

88.96

93.97

90.82

0.00

28.57

58.03

88.90

94.02

90.98

Cropland

0.00

66.67

33.16

6.59

3.14

5.00

0.00

78.26

2

40.61

8.94

4.80

8.07

0.00

1

62.86

2

40.65

8.95

4.73

7.93

Grassland

0.00

0.00

6.95

3.70

2.08

3.09

0.00

0.00

0.79

1.34

0.51

0.12

0.00

0.00

0.74

1.41

0.46

0.12

Wetland

0.00

0.00

0.35

0.41

0.48

0.08

0.00

0.00

0.30

0.04

0.48

0.08

0.00

0.00

0.34

0.45

0.50

0.08

Settlement

0.00

0.00

0.00

0.00

0.02

0.51

0.00

0.00

0.00

0.00

0.01

0.53

0.00

0.00

0.00

0.00

0.02

0.52

Other land

0.00

13.33

0.24

0.32

0.29

0.36

0.00

4.34

0.30

0.27

0.19

0.34

0.00

8.57

0.25

0.29

0.28

0.36

MGCI

0.00

86.67

99.76

99.69

99.69

99.31

0.00

95.65

99.67

99.28

99.76

99.09

0.00

91.43

99.75

99.71

99.70

99.12

MGCI all

99.23

99.22

30

99.22

F/T-3 MGCI estimation based on number of pixels using JAXA land cover data (250m, 100m resolutions) and
Kapos-USGS mountain classification data (250m resolution)
250m resolution

2014-20162018-2020

Kapos mountain class

Kapos mountain class2006-2011
Kapos mountain class
3

4

5

6

Forest

(%)

2
0

46.06

83.95

92.15

93.17

0

Cropland

0

0.00

0.01

0.32

2.85

0

1.41

0.61

0.82

3.11

0.00

0.00

0.00

0.46

2.21

Grassland

0

3.43

7.69

6.32

2.91

0

12.37

9.01

5.04

4.69

0.00

3.57

6.58

2.09

2.55

Wetland

0

0.00

0.00

0.00

0.00

0

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Settlement

0

0.00

0.00

0.01

0.38

0

0.04

0.07

0.06

0.44

0.00

0.00

0.00

0.02

0.70

Other land

100

50.51

8.35

1.19

0.69

100

58.51

3.46

0.88

0.76

100.00

65.03

4.47

0.74

0.65

0

49.49

91.64

98.79

98.93

0

41.45

96.47

99.06

98.80

0.00

34.97

95.53

99.24

98.65

MGCI

2

3

4

5

6

2

3

4

5

6

27.67

86.85

93.20

91.00

0.00

31.40

88.95

96.69

93.89

98.5235

MGCI all98.6327

98.4819100ｍ resolution
2006-2011年
Kapos mountain class
3

4

5

6

Forest

(%)

2
0

46.29

82.80

91.39

92.18

0

Cropland

0

0.01

0.02

0.38

3.01

Grassland

0

4.89

8.34

6.80

3.56

Wetland

0

0.00

0.00

0.00

0.00

2014-2016年

2018-2020年

Kapos mountain class

Kapos mountain class

2

3

4

5

6

2

3

4

5

6

28.07

86.78

93.18

90.98

0.00

31.44

87.77

95.90

93.01

0

1.43

0.60

0.82

3.12

0.00

0.00

0.01

0.51

2.38

0

12.74

9.03

5.05

4.69

0.00

4.74

7.29

2.68

3.09

0

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00
0.80

Settlement

0

0.01

0.00

0.03

0.44

0

0.04

0.06

0.06

0.44

0.00

0.00

0.00

0.04

Other land

100

48.79

8.84

1.39

0.81

100

57.72

3.53

0.88

0.77

100.00

63.81

4.94

0.87

0.72

MGCI

0.00

51.20

91.16

98.58

98.75

0.00

42.24

96.41

99.06

98.79

0.00

36.19

95.06

99.09

98.48

98.3285

MGCI all98.6269

98.3018

F/T-4 MGCI estimation based on number of pixels using JAXA land cover data (100m, 50/30m resolutions)and Kapos-SRTM mountain classification data (90m resolution)
100m解像度

2006-2011年
Kapos mountain class2

Forest0

2014-2016年

2018-2020年

Kapos mountain class

Kapos mountain class

3

4

5

6

2

3

4

5

6

2

3

4

5

6

45.55

82.85

91.43

92.44

0.00

26.22

86.76

93.22

91.31

0.00

30.72

87.83

95.94

93.29

Cropland

0

0.01

0.02

0.37

2.86

0.00

1.43

0.60

0.81

2.94

0.00

0.00

0.01

0.50

2.24

Grassland

0

4.74

8.31

6.80

3.46

0.00

12.83

9.10

5.03

4.55

0.00

4.69

7 27

2.65

2.96

Wetland

0

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Settlement

0

0.01

0.00

0.03

0.45

0.00

0.02

0.06

0.06

0.44

0.00

0.00

0.00

0.04

0.80

Other land

100

49.69

8.82

1.37

0.80

100.00

59.49

3.47

0.88

0.75

100.00

64.59

4.90

0.86

0.72

0

50.30

91.18

98.60

98.76

0.00

40.48

96.47

99.06

98.80

0.00

35.41

95.10

99.10

98.48

MGCI

98.3170

MGCI all98.628250m解像度

30m解像度50ｍ解像度

2006-2011年
Kapos mountain class
Forest98.2987

2014-2016年

2018-2020年

Kapos mountain class

Kapos mountain class

2

3

4

5

6

2

3

4

5

6

2

3

4

5

6

0.00

45.94

82.15

90.93

91.86

0.00

26.92

86.79

93.21

91.31

0.00

31.00

87.16

95.47

92.90

Cropland

0.00

0.02

0.03

0.41

2.87

0.00

1.41

0.60

0.81

2.94

0.00

0.00

0.01

0.52

2 29

Grassland

0.00

5.77

8.81

7.14

3.93

0.00

12.81

9.07

5.04

4.55

0.00

5.41

7.67

3.01

3 20

Wetland

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Settlement

0.00

0.02

0.01

0.04

0.47

0.00

0.03

0.06

0.06

0.44

0.00

0.00

0.00

0.06

0.84

Other land

100.00

48.25

9.01

1.48

0.87

100.00

58.83

3.47

0.88

0.75

100.00

63.59

5.15

0.94

0.76

0.00

51.73

90.99

98.48

98.66

0.00

41.14

96.47

99.06

98.80

0.00

36.41

94.84

99.00

98.40

MGCI
MGCI all98.2183

98.629931

98.2071

F/T-5 Data used for SDG15.4.2 calculation
Land cover data

Kapos mountain classification data, Digital Elevation Model(DEM) data

Administrative unit data

F/T-6 Relationship among IPCC/ESA-CCI/JAXA land cover classification
IPCC land cover classificat on
1. Crop land

ESA-CCI land cover classification
Rainfed cropland

4

Crops

20

Irrigated cropland3

R ce paddy

3040
2. Forest

3. Grassland

6. Others

Shrubland

herbaceous cover) (<50%)
Mosaic natural vegetation (tree, shrub, herbaceous cover)(>50%) / cropland (< 50%)
Tree cover, broadleaved, evergreen, closed to open (>15%)

8

Evergreen Broad-leaved Forest (EBF)60, 61, 62

Tree cover, broadleaved, deciduous, closed to open (> 15%)

6

Deciduous Broad-leaved Forest (DBF)

70, 71, 72

Tree cover, needleleaved, evergreen, closed to open (> 15%)

9Evergreen Needle-leaved Forest (ENF)80, 81, 82

Tree cover, needleleaved, dec duous, closed to open (> 15%)

7

Deciduous Needle-leaved Forest(DNF)

90

Tree cover, mixed leaf type (broadleaved and needleleaved)

100

Mosaic tree and shrub (>50%) / herbaceous cover (< 50%)

160

Tree cover, flooded, fresh or brakish water

170

Tree cover, flooded, saline water11＊

Bamboo110

Mosaic herbaceous cover (>50%) / tree and shrub ((<50%)
5

Grassland

2

Urban and built-up

180

5. Settlement

Mosaic cropland (>50%) / natural vegetation (tree, shrub,

50

130
4. Wetland

JAXA high-resolution land cover classificat on

10, 11, 12

Grassland
Shrub or herbaceous cover, flooded, fresh-saline or brakish
water

190

Urban

120, 121, 122

Shrubland

Sparce vegetation 140

L chens and mosses

150, 151, 152, 153 Sparse vegetation (tree, shrub, herbaceous cover)
Bare area

200, 201, 202

Bare areas

10

Bare land

Water

210

Water

1

Water

12＊

Aolar panel＊only for ver21.03 (2018-2020）32

Supplement
A.1 Production of Japan’s coast line data
In Japan, the calculation of national land area has as long history as population statistics and theGeospatial Information Authority of Japan (GSI) releases the area survey results of prefectures, cities, towns,
and villages every year since 1960. The area survey provides basic data for calculating population densities
and local taxes.
The area survey can be accessed at the following URL:
https://www.gsi.go.jp/KOKUJYOHO/MENCHO-title.htm （in Japanese）
In calculating SDG indicators that require land area, such as SDG 15.4.2, it is relevant to use geospatial
information which has become the basis for the national land area calculation of Japan. From this point of view,for SDG15.4.2 calculation, GSI coastal line data was produced from the Fundamental Geospatial Data (FGD)
and provided to the Office of Statistics Commission, Ministry of Internal Affairs and Communication.
The above mentioned Fundamental Geospatial Data can be accessed at the following link:
https://fgd.gsi.go.jp/download/menu.php

33

A.2 Production of Kapos mountain classification data (90m resolution)
Altitude, slope, and Local Elevation Range (LER) were computed using the SRTM 90m data and with thehelp of these data, Kapos mountain classification data (Kapos-SRTM, 90m resolution) of Japan was produced.
The production process is shown in the following:
1) Download the 30m SRTM DEM data of Japan.
2) Divide national data to several regional and sub-regional data (three sub-regional data for Hokkaido,
regional data for Tohoku, Kanto, Chubu, etc.)
3) Resize the divided DEM data to 90 m data to decrease data volume4) Analyze if there is 300m altitude difference within 7km around a point (pixel) and if yes, set 1 and if no, set
0. Python and GPU(Graphics Processing Unit) were used.
5) Conduct the processing of 4) for divided DEM data
6) Merge LER maps produced in 5) and produce a national LER map
Altitude and slope can be calculated by QGIS directly from SRTM90m data, but LER requires calculation if
300m altitude difference exists within 7km radii for all the pixels – and this requires significant computing time.
For the analysis, a program written in Python was executed using Graphics Processing Unit (GPU) to speed up
the computing [Kawakita]. Half a month was necessary to compute LER for the entire area of Japan, Kapos
mountain classification data up to 90m resolution was produced. Since the highest resolution of the land cover
data is 50m/30m, it is advisable to use 30m resolution Kapos mountain classification data. It can be achievedby using hardware suitable for high-speed computing and improving computation algorithms, etc.

F/T-1 Production of Kapos mountain classification data from SRTM 90m data34

A.3 Impact of UTM coordinate applications to MGCI calculation
Using the National Digital Information administrative unit (polygon) data
(Japan Geodetic Datum 2011), land area calculations applying the UTM
coordinate system differently were compared. 0.5% difference in entire
national land area estimation appears between when UTM 54th band wasapplied to entire Japan and when different UTM coordinate systems wereapplied to regions.
F/T-2 Impact of UTM coordinate system application to land area estimation

Japan was divided into three regions and applications of UTM coordinates to the area estimation ofmountain and green cover were studied. 0.02% difference in MGCI estimation was found.
F/T-3 Impact of UTM coordinate system application to MGCI calculation

35

A.4 Handling of administrative unit data
FAO used the Global Administrative Unit Layer (GAUL) for country administrative unit data, but it does notinclude Japan northern territories, Ogasawara islands, and other small islands, and it seems unsuitable to use
for Japan.
High-resolution coast line data for Japan was provided by GSI and the data was used to clip Japanese datafrom the global data set. Because of the high resolution, it took a long processing time and after that, Global
Map data (1km resolution) was used. Although the Global Map data does not reflect the latest newly reclaimed
land and others, it may not be a big problem to analyze inland mountains. It is recommended to use the highresolution coast line data provided by GSI more effectively.
F/T-4 Administrative unit data

36

Reference[ESA] 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[ESA CCI] ESA CCI land cover data（300m resolution, Download CCI LC Products | ESA CCI Land cover website(esa-landcover-cci.org)[Kapos(2000)] Kapos, V., Rhind, J., Edwards, M., Prince, M., & Ravillous, C. (2000). Developing a map of theworld’s mountain forests. In M. F. Price , & N. Butt (Eds.), Forests in Sustainable Mountain Development: A Stateof-Knowledge Report for 2000 (pp. 4-9). Wallingford: CAB International.
[FAO HP] Sustainable Development Goals, Food and Agriculture Organization of the United Nations,
http://www.fao.org/sustainable-development-goals/indicators/1542/en/[FAO Kapos] FAO Mountain Partnership HP：
http://www.fao.org/mountain-partnership/our-work/focusareas/foodsecurity/2015-mountain-vulnerabilitystudy/en/[FAO SDG15.4.2 DATA] SDG15.4.2 estimation disseminated by FAO,
http://www.fao.org/sustainable-development-goals/indicators/1542/en/, latest access date: February 28,
2021[FAO Kapos DATA] Kapos mountain classification areas and percentage disseminated by FAO,
http://www.fao.org/fileadmin/templates/mountain partnership/doc/focusarea/foodsecurity/mountainarea/
Mountain areas - light.xlsx, latest access date: February 28, 2021
[FAO Metadata] FAO Metadata, https://unstats.un.org/sdgs/metadata/files/Metadata-15-04-02.pdf[JAXA] JAXA high-resolution land use and land cover map,
https://www.eorc.jaxa.jp/ALOS/a/en/dataset/lulc_e.htm[JAXA ALOS ver21.03] JAXA ALOS ver21.03,
https://www.eorc.jaxa.jp/ALOS/a/en/dataset/lulc/lulc_v2103_e.htm[JAXA ALOS ver18.03] JAXA ALOS ver18.03, https://www.eorc.jaxa.jp/ALOS/a/en/dataset/lulc/lulc_v1803_e.htm[JAXA ALOS ver16.09] JAXA ALOS ver16.09,
https://www.eorc.jaxa.jp/ALOS/a/en/dataset/lulc/lulc jpn e.htm
37

[SRTM] Space Shuttle Radar Mission
https://srtm.csi.cgiar.org[USGS Kapos] USGS Mountain Explorer
Global Mountain Explore Home：

Global Mountain Explorer Home (usgs.gov)

Kapos mountain classification visualization:
https://earthmap.org/?aoi=ar&boundary=level2&layers=%7B%22Kapos%22%3A%7B%22opacity%22%3A1%
7D%7D&map=%7B%22center%22%3A%7B%22lat%22%3A-40.43594909816739%2C%22lng%22%3A63.58954919773806%7D%2C%22zoom%22%3A4%2C%22type%22%3A%22roadmap%22%7D&scripts=%7B%
7D
Kapos classification（GME_K1, K2, K3 Datafiles） ：https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/[MOE National Survey on the Natural Environment] MOE National Survey on the Natural Environment,

Implementation Plan (in Japanese),
http://gis.biodic.go.jp/webgis/files/sc-012_sakusei_youryou_H31.pdf[MOE National Survey on the Natural Environment] Biodiversity Center of Japan, Natural Environment

Bureau, MOE, Vegetation survey list of prefectures (shape file), http://gis.biodic.go.jp/webgis/sc025.html?kind=vg67[MOE report] MOE monitoring site 1000 Alpine area survey 2008-2017 report (in Japanese)（Biodiversity Center of Japan, Natural Environment Bureau, MOE）
http://www.biodic.go.jp/moni1000/findings/reports/pdf/h20-h29 alpin zone.pdf[Kawakita] Local Elevation Range (LER) calculation code for Kapos mountain classification
https://github.com/shirokawakita/Kapos calculation example/blob/main/local elevation calculation example.ip
ynb[MOFA Japan SDGs Action Platform]
https://www.mofa.go.jp/mofaj/gaiko/oda/sdgs/statistics/goal15.html[MLIT National Digital Information]
https://nlftp.mlit.go.jp/ksj/index.html

38

[MLIT GSI land area report] Statistical reports on the land area by prefectures and municipalities in Japan(MLIT GIS), https://www.gsi.go.jp/KOKUJYOHO/MENCHO-title.htm[GSI Fundamental Geospatial Data] GSI Fundamental Geospatial Data,
https://fgd.gsi.go.jp/download/menu.php[MIC Big Data meeting] “The industry-government-academia partnership meeting for promotion of use of bigdata”,
https://www.soumu.go.jp/main_sosiki/kenkyu/big_data/index.html[Global Map]
https://www.gsi.go.jp/kankyochiri/gm_jpn.html

39