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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Land Use / Land Cover

Background | Methodology | Legend | Punjab-Sindh | KP-FATA | Baluchistan | Atlases

Methodology

Satellite Remote Sensing (SRS) offers a flexible, cost effective and an efficient means for monitoring and mapping natural resources and man-made infrastructure. The mapping methodology can be summarized in these sequential steps:

1. Image Acquisition and Pre-Processing

1.1 2010 SPOT-5 images were used to map the land cover of Pakistan. Images were analyzed with respect to cloud cover percentage and image quality, were geometrically corrected (orthorectification) to a UTM projection, pan-sharpened to 5 m spatial resolution, and finally aggregated in manageable mosaics (figure below) for the interpretation.

image mosaics

2. Image Processing and Interpretation

2.1 The pre-processed images were "segmented" with appropriate image processing software (Definiens) to generate spatially continuous and spectrally homogenous regions or objects. For land cover mapping, segmentation helps in developing cluster pixels that belong to same land cover class.

2.2 The segments were "interpreted" to assign a land cover class (labelling) from the LCCS based legend (see screenshot). The FAO tool - Mapping Device Change Analysis Tool (MADCAT) was used for the interpretation, adopting a 1:25,000 scale. Edge matching and topology check were applied to produce to final database.

2.3 A QA/QC process was applied to the database by two independent photo interpreters who were in charge to capture and correct various types of error in a cyclic approach.

3. Database Validation and Finalization

3.1 On completion of the interpretation phase, field surveys were conducted by SUPARCO officials to "validate" the image interpretation and to remove the ambiguities related to land cover classes based on detailed field surveys (example in figure below). For each survey point, the land cover types and the coordinates were recorded using GPS systems.

sindh lc classes

3.2 An overall QA/QC was performed at FAO, HQs for a final evaluation of the product. It was thoroughly reviewed and harmonized to create a consistent land cover database, minimizing differences from the subjectivity of different interpreters. Finally, detailed topology rules were applied to correct inconsistencies and to remove slivers or voids.

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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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