Agri Punjab, Agriculture Department Agri Sindh, Agriculture Department University of Maryland Pakistan Space and Upper Atmosphere Research Commission U.S. Department of Agriculture Food and Agriculture Organization of the United Nations
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Testing FAO's ECONET approach in monitoring land cover changes in Pakistan

Summary

Few years ago FAO developed and tested in several African countries the ECONET approach in monitoring land cover changes. It builds upon a systematic sampling of the area of interest against the traditional "wall-to-wall" mapping, with the advantage of reducing drastically time, costs and overall work for regularly monitoring the same area. The recent availability of Sentinel-2 data has suggested FAO and SUPARCO to test such approach in a pilot area of Pakistan.

Land cover data

Land-cover data and the relevant changes are of primary importance in satisfying the ever-increasing demand for reliable data to support studies and application such as natural resources evaluation and conservation, agriculture development, improvement of environmental policies. Data are essential for the comprehension and analysis of natural and human driven phenomena, including for example climatic change, land planning, disaster management and agriculture development monitoring.

Traditionally, land cover mapping is performed adopting the so called "wall-to-wall" approach, which means mapping the entire area, from sub-national to regional and global scale. This approach results in accurate and reliable data, but with time and costs that makes it not sustainable in many countries and areas of the world.

ECONET approach

The ECONET approach in monitoring land cover changes has been firstly proposed by FAO few years ago following the success of the Global Land Cover Network Programme. It is sample based and is a cost-effective solution to assess the land cover changes accuracy using high resolution Google Earth images: it has proved consistent in results versus a more traditional "wall-to-wall" mapping, but with the advantage of reducing drastically time, costs and overall work for implementation. With this sampling approach, statistical estimates can be derived while retaining the advantages of fine spatial detailed data. Systematic sampling has been successfully used for regional, continental, and global tree cover change assessments.

An ECONET based mapping and analysis has two major outcomes: 1) the production of very detailed information on land cover, based on photo-interpretation of a wide number of sample areas evenly distributed, and 2) the setup of a collaborative community involved to implement and spread a fully harmonized approach, supporting and making accessible reliable and comparative land cover data required by local, national and international activities. The ECONET approach heavily relies on the FAO's Land Cover Meta-Language (LCML) standard (ISO) and Land Cover Classification System (LCCS) tools.

Goals

  • Create a database for the description of land cover with detailed information to be used for a wide range of activities;
  • Serve as multi-statistical source of information at any level of detail or complexity. Using specific software any end-user will be able to extract statistical information for a given geographical area;
  • Work as a reference data-set for any future mapping activity;
  • Contribute to create a common shared awareness on the importance and value of natural resources.

Pakistan test

A pilot area of Pakistan is being tested in the application of the ECONET approach taking advantage of the Sentinel-2 mission data that are available freely at a medium high spatial resolution (10m). Few points:

  • The initiative in Pakistan is mainly for demonstration purpose;
  • Only agriculture is considered (limited number of classes);
  • Work done only at sub-country level (limited number of tiles);
  • Only local experts involved (no further interaction to increase the level of information);
  • Focus on change detection to monitor agriculture expansion.

Sample grid

The data production will be based on the photo-interpretation and analysis of very high resolution satellite imageries of 10 km x 10 km portion of land surface (tiles), distributed according to a fixed grid. The design of the sample grid has to ensure a sound statistical basis for the validity of the results and will follow basic zonation criteria: the tile density for Pakistan will be at ½ x ½ degree intersections.

econet sampling in Pakistan

Dot density per tile

A secondary grid inside the 10 by 10 sample tiles has been generated. Each dot (square size) has a size of 25 by 25 m and are sited at a distance of 250 m each. In total 1880 points are generated for each tile. A less dense dot grid is also generated at a distance of 500 m, dot size of 35 x 35 m, for a total number of dots of 480 per tile.

high and low dot density per tile

The analysis of the changes is carried out by using Google Earth high res images and recent Sentinel-2 images (10 m). With LCCS3, any land cover features can be described using a set of attributes (elements). Each polygon in the database is not indicated by a traditional class (as part of a predefined legend) but by a series of attributes (elements of LCML language). This will ensure that future analysis to verify change trends will be made on the same areas, thus permitting alid statistical estimation of changes.

sample Sentinel 2 false color image
Sentinel-2 image: false color composite (843)
of prevalent agriculture area (vegetation in red)

Legend

A preliminary legend of 22 classes of land cover has been generated based on LCML/LCCS "object oriented" standard language:

preliminary legend of 22 classes of land cover

Each category is systematically described both with a traditional text description (see table above) but also with a more accurate graphical schema that shows the combination of LCML elements (with their properties and characteristics) used to generate each of the land cover classes according to the rules defined in the ISO standard UML of the Land Cover Meta-Language. Here is an example of class description, in particular the Irrigated crop and Mango orchard:

Description: "Irrigated crop and Mango orchard"

LC class Schema:

LC class Schema

Screenshot of high res image (Google Earth) for the class above:

high res image of Irrigated crop and Mango orchard

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  ANNOUNCEMENT

Sep 2, 2015 - A new amendment (no. 3) was signed by USDA and FAO to formalize a no-cost extension of the project GCP/PAK/125/USA from Oct 1, 2015 to Sep 30, 2016.


  recent updates

Aug 8, 2016. E-bulletin 11, Apr-Jun 2016.


Aug 5, 2016. E-learning courses on agriculture monitoring and statistics.


Aug 5, 2016. FAO's ECONET approach and land cover change in Pakistan.


Aug 5, 2016. Training on Carbon Sequestration.


Aug 5, 2016. CRS Punjab bulletins Apr 2016.


Apr 22, 2016. Cotton Production & Analytics.


Mar 1, 2016. Testing Sentinel-2 images for crop monitoring in Pakistan.


Feb 20, 2016. Crop Information Portal training.


Jan 20, 2016. Sugarcane Production & Analytics.


Jan 20, 2016. Training on REDD+ Technology for Forest Department Officers of Punjab.


Oct 29, 2014. Agriculture Information System project: roll-out workshop.


Aug 13, 2015. RS/GIS Training in Forest Management, Lahore, 4-8 May 2015.


FAO Pakistan website >>>>

 

Forum: "Pakistan Agriculture Sector". >>>>

 

SUPARCO, the National Space Agency of Pakistan >>>>

 

Government of Sindh: Agriculture Department >>>>

 

Agri Punjab >>>>

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