Post on 20-Jan-2016
transcript
A Forest Cover Change Study Gone Bad
Lessons Learned(?) Measuring Changes in Forest Cover in
Madagascar
Ned HorningCenter for Biodiversity and Conservation
American Museum of Natural History
(horning@amnh.com)
Overview of the project
Goal:
Use available data to determine the rates of forest loss in the periphery of six protected areas in Madagascar to see if USAID interventions were having an impact.
Datasets:
• 1950 Forest maps (1:100,000) based on photography acquired in the late 1940s
• 1991/1992 black and white aerial photography 1:40,000
• 1993/1994 Landsat TM images
The issues
1. Land cover classes ill defined
2. Post-classification overlay used to derive results
3. Inappropriate data sets
4. Incorrect equations
5. Results indiscriminately modified
6. Accuracy not assessed
The land cover classes were not well defined
• Three classes were interpreted (primary forest, secondary forest, and other) and none were defined
• Primary and secondary forest classes can be very difficult to differentiate using satellite imagery
• Variations within the secondary class were severe since the image analysis was performed by several different groups without significant training
Typical land cover accuracy figures
• Forest/nonforest, water/no water, soil/vegetated: accuracies in the high 90%
• Conifer/hardwood: 80-90%
• Genus: 60-70%
• Species: 40-60%
• Bottom line: The greater the detail (precision) the lower the per class accuracy
Note: If including a Digital Elevation Model (DEM) in the classification accuracy typically improves by up to 10%
Recommendations
• Use the fewest number of classes that are practical - forest/non-forest in this case
• Clearly describe land cover classes before the analysis phase (an interpretation key can be helpful)
• Define classes that can be reliably interpreted using the available data
• Provide sufficient training
Used post-classification overlay to derive results
• Post-classification is the most common change detection method and is rarely the best choice, especially when the change-class of interest comprises a small percent of the entire area
• The errors from the individual layers are present in the final change image
• Error estimates for individual layers (dates) were not known
• Geometric registration errors were compounded since different data sets were compared using automated methods
The data sets and interpretation methods were not appropriate
• Different data set types were used for each time period and season of image acquisition varied significantly
• The original photos used to create the forest cover maps were available
• The 1991/1992 aerial photos were not orthorectified and were interpreted manually without a stereoscope or other suitable instrument
• Landsat TM data are not well suited for monitoring change in small areas, especially when the time interval is short and the terrain variation is significant
• The Landsat TM images were converted to 255-color index images
8 bits (0-255) 6 bits (0–63)
3 bits (0-7)
1 bit (0-1)
Recommendations
• Use aerial photos from the 1940’s and 1950’s in place of the forest cover maps
• Interpret aerial photos using appropriate techniques and if possible work with orthophotos
• If Landsat TM data are to be used the area of interest should be enlarged and/or the time interval between images/photos should be lengthened
• If possible select imagery from similar seasons
• Do not reduce the quantization of the Landsat TM images
• Take into account the desired accuracy and precision of the results when selecting data sets
The equation was incorrect
• The original formula for the 1991/92 – 1993/94 period was:
Change%=((forest T1-forest T2) * 100/(forest T2 * 3))
• The last part of the formula should have been (forest T1 * 3).
• The time period should not have been static (3).
Recommendations
• Calculate forest cover change like this:
For each target zone calculate:
• %forest loss = forest T1 – forest T2 / forest T1 * 100
• Annual forest loss = %forest loss / ((2nd date-1st date) / 365)
• Weighted %annual loss = annual forest loss * (forest T1 / sum of forest T1 for all zones)
Average forest loss for all areas:
average forest loss = sum of weighted % annual loss for all zones
The results were indiscriminately modified
• The non-forest to primary forest change class was often modified or eliminated
• The modifications were haphazard but biased
• The results happened to match the client’s perceived rate of deforestation
Recommendations
• Do not bias the results simply because they don’t appear to be correct
• Procedures for compiling data must be very clear and an effort should be made to verify the procedures are being followed
An accuracy assessment was not carried out
• The results were reviewed by several people but there was no attempt to assess the accuracy
• The project was rushed in order to produce results to meet a deadline
Recommendations
• Budget enough money to carry out an accuracy assessment
• Plan ahead to avoid the last minute rush
Consequences
• The rate of deforestation was overestimated by an order of magnitude
• The bottom line was that the results could not be used
• The client had to drop the deforestation indicator and reassessed the process used to monitor changes in forest cover
Lessons learned?
Extracted from a 2003 USAID Program Data Sheet:
“From 1993 - 2001, the rate of forest loss was 2.6% and 3.5% in USAID zones, compared to 6.7% loss in comparable non-intervention zones. “