Indicator 15.4.2

Indicator Name, Target and Goal

Indicator 15.4.2: Mountain Green Cover Index

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

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

Definition and Rationale


The Green Cover Index is meant to measure the changes of the green vegetation in mountain areas – i.e. forest, shrubs, trees, pasture land, crop land, etc. – in order to monitor progress on the mountain target.

The index, will provide information on the changes in the vegetation cover and, as such, will provide an indication of the status of the conservation of mountain environments.


Mountains are defined according to the UNEP-WCMC classification that identifies them according to altitude, slope and local elevation range as described by Kapos et al. 2000:

Class 1: elevation > 4,500 meters

Class 2: elevation 3,500–4,500 meters

Class 3: elevation 2,500–3,500 meters

Class 4: elevation 1,500–2,500 meters and slope > 2

Class 5: elevation 1,000–1,500 meters and slope > 5 or local elevation range (LER 7 kilometer radius) > 300 meters

Rationale and Interpretation:

The scientific mountain community recognizes that there is a direct correlation between the green coverage of mountain areas and their state of health, and as a consequence their capacity of fulfilling their ecosystem roles. Monitoring mountain vegetation changes over time provides an adequate measure of the status of conservation of mountain ecosystems. Monitoring the mountain “Green Cover Index” over time can provide information on the forest, woody and vegetal cover in general. For instance, its reduction will be generally linked to overgrazing, land clearing, urbanization, forest exploitation, timber extraction, fuelwood collection, fire. Its increase will be due to vegetation growth possibly linked to land restoration, reforestation or afforestation programmes.

Unit of measure:

Percentage (%).


This indicator can be disaggregated by land cover class, including forest type, and elevation class.

Data Sources and Collection Method

Data sources:

The Land Cover Map 2015:

Mountainous areas link:

Data collection methods:

For determining land cover classes through the National Land Representation system (NLRS), the initial legend classes for land cover map 2015 were derived during a national workshop with experts from different organizations working in relevant fields. These initial classes were further refined during map development process to develop the final legend. Some of the classes were extended from the NLRS (e.g., bamboo forest, rubber plantation, etc.), while some other classes were merged (e.g., short and long rotation plantation into forest plantation) when differences among these classes were not discernible from the available satellite images by visual interpretation. The final land cover map has 33 classes.

For the development of land cover map 2015, multi-spectral ortho (Level 3) SPOT6/7 four band images of 6-meter spatial resolution with maximum 10% cloud coverage were primarily used for the whole country. An Object-Based Image Analysis approach (i.e., multi resolution segmentation algorithm) was adopted to create image objects using the green, red and near-infrared bands of SPOT imagery. Meaningful image segments were directly assigned with land cover code by visual interpretation. Image segments not corresponding well to geo-objects were manually edited (i.e., manual digitization by visual interpretation of satellite image) before assigning an appropriate land cover code. Seasonal variations in land and water features are common in Bangladesh. Landsat 8 and Sentinel 2 images from different seasons were taken into consideration in visually interpreting areas having a seasonal variation (especially agricultural classes). Field data were collected from 1,144 locations across the country to characterize the classes of the NLRS.

For collecting data on mountain area, SRTM elevation data was classified into elevation classes as shown in the Computational Methods.

Data collection calendar: 2015

Data release calendar: 2021

Data providers: Forest Department, Ministry of Environment, Forest and Climate Change

Data compilers: Forest Department, Ministry of Environment, Forest and Climate Change

Institutional mandate: Through the Statistical Act 2013, the Bangladesh Bureau of Statistics (BBS) is mandated to generate official statistics or provide guidance to other agencies for producing official statistics. Responsibilities for each ministry to support specific SDG indicators is outlined in the Mapping of Ministries by Targets in the implementation of SDGs aligning with 7th Five Year Plan (2016-20) document, which lists Ministry of Environment, Forest and Climate Change as the official Lead Ministry for this indicator. The Forest Department is the designated line agency within the Ministry for this indicator.

Method of Computation and Other Methodological Considerations

Computation Method:

The Mountain Green Cover Index (MGCI) is defined as:

𝑀𝐺𝐶𝐼 = (𝑀𝑜𝑢𝑛𝑡𝑎𝑖𝑛 𝐺𝑟𝑒𝑒𝑛 𝐶𝑜𝑣𝑒𝑟 𝐴𝑟𝑒𝑎 / 𝑇𝑜𝑡𝑎𝑙 𝑀𝑜𝑢𝑛𝑡𝑎𝑖𝑛 𝐴𝑟𝑒𝑎) × 100


Mountain Green Cover Area = (Area cover by Cropland + Area cover by Forest + Area cover by Grassland)

Total mountain area was determined based on the definitions of mountains in Kapos et al (2000) and the Shuttle Radar Topography Mission (SRTM) (resolution: 1 arc second or approximately 30 meter) data. Based on the elevation, two mountainous classes are identified in Bangladesh.

Mountains classes exists in BangladeshArea (ha)
Class 5: elevation 1,000–1,500 meters and slope > 5 or local elevation range (LER 7 kilometer radius) > 300 meters13
Class 6: elevation 300–1,000 meters and local elevation range (7 kilometer radius) > 300 meters157,479

Land cover classes and areas were determined from the 2015 Land Cover Map and then reclassified into Cropland and Forest. There were no Grasslands within the mountain areas. Specifically, Cropland area is comprised of Orchards and Other Plantations and Forest area was comprised of Bamboo Forest, Forest Plantation, Hill Forest, Rubber Plantation, and Shrub Dominated Forest Area as below.

Green cover Area (ha) %
Bamboo Forest 4.48 0.00
Forest Plantation 15.84 0.01
Hill Forest 99368.32 63.09
Orchards and Other Plantations 89.26 0.06
Rubber Plantation 2.69 0.00
Shrub Dominated Forest Area 51787.54 32.88
Sub total 151268 96.05
Other land cover    
Lake 44.28 0.03
Multiple Crop 0.05 0.00
Ponds 1.05 0.00
Rivers and Khals 59.17 0.04
Shifting Cultivation 5474.20 3.48
Single Crop 14.12 0.01
Rural Settlement 630.72 0.40
Sub total 6223.59 3.95
Total 157492 100

Comments and limitations: 

Method of computation: 


Quality checking of land cover attributes is completed using multiple approaches including a spatial topology check, an attribute check, a consistency check, expert judgment and field validation.

Quality Management:

Quality Assurance:

Quality Assessment:

Accuracy assessment for land cover classification analysis uses a pseudo-ground truth validation technique, with stratified random sampling by district and by land cover class. The most commonly used measures of accuracy (i.e., overall accuracy, user’s accuracy, producer’s accuracy) were estimated following the approach presented in Jalal et al. (2019).

The overall accuracy of the Land Cover Map 2016 was estimated at 89%. User’s accuracy ranged from 20% to 99% while producer’s accuracy ranged from 13% to 100%. The detail methodological process and results (including the accuracy, uncertainty and adjusted area estimates) of land cover 2015 are presented in Jalal et al. (2019).

Data Disaggregation

The indicator is disaggregated by mountain elevation class and land cover class.

Comparability/ deviations from international standards



Official SDG Metadata URL

Internationally agreed methodology and guideline URL

Other references

International Organization(s) for Global Monitoring

This document was prepared based on inputs from Food and Agricultural Organization (FAO).

For focal point information for this indicator, please visit