Generation of multi-temporal landcover maps for three sites Application of automation enabling the scaling up to large regions In our study landscapes, we will explore the drivers and pressures affecting ecosystem service delivery through three distinct lenses: land cover analyses, in each landscape we will use remote sensing imagery (30 m LANDSAT TM and ETM+ imagery) available from 1980s onwards) to conduct analyses of current patterns of land use (including identification of the extent and distribution of remaining natural areas and agricultural production systems) and historical land use over the last 20-30 years, enabling the identification of changes in the type, extent and spatial arrangement of land use in each region. policy analyses 3) stakeholder and expert workshops.

Content Consultant responsible – Grady Harper (gradyharper@yahoo.com ) Multi-temporal forest cover and deforestation mapping expert OVERALL AIM create land cover and change maps for long-term stacks of landsat images, specifically bi-annual coverage from 1990-2010 Produce multi-temporal landcover maps for the three ASSETS sites Apply automation to enable scaling up of the work to large regions

Content Production of multi-temporal landcover maps for three sites Colombia (Landsat path/rows 4/61, 5/61, 5/60, 4/60?) Malawi (Landsat path/rows 167/71, 167/70?) Peru (Landsat path/rows 6/66, 7/66, 7/60?) source: http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp Questions that must be addressed Path/Rows to classify Landcover Classes Time intervals: annual, biannual, triannual Validation Level of automation Problems needing resolution

Satellite maps (path/rows) Colombia (4/61 & 5/61) Peru (6/66 & 7/66) Initial inputs Simon provided shapefiles For Colombia, Peru Conservation International already has some material (CIRCA 1990, 2000) Peru (Patricia Bejarano provided map) Malawi (167/71)

Procedure – challenges to address In Colombian or Peruvian Amazonia Natural non-forest is limited, it is primarily found due to shifting rivers, landslides (in the mountains), and wind storms. It is difficult to distinguish between natural non-forest due to shifting rivers, and anthropogenic non-forest (i.e. cropland) along rivers General Queries Is the reduced set of land-cover classes sufficient? How to separate natural non-forest from anthropogenic non-forest along rivers in Amazonia? Seasonally deciduous forest—does this occur in Malawi? If it does, what months is it with leaves, and what months without? What is necessary in terms of analyzing the temporal dynamics of shifting agriculture and forest regrowth? In relation to time intervals essential to have annual looks at the areas? low-cloud areas easier, high-cloud areas require a lot of processing Accuracy assessment To manage high-cloud areas: we would likely have to download multiple images per year, run them through an automated cloud filter and radiometric normalization, to merge them into a single image to be classified. It becomes quite a lot of processing, for annual looks over a period of 20 years.

Automation holy grail of satellite image classification when highly accurate spatially-explicit data (maps) are needed, a human element is necessary. Tool aternative use Alex Zvoleff’s R tools (http://azvoleff.com/) Look at others (with potential to build collaborations)

set of tools in R, assembled by Alex Zvoleff Postdoctoral Associate at Conservation International Tropical Ecology Assessment and Monitoring (TEAM) Network, CI, Arlington, VA The package is called teamlucc details on installation and use can be found at: http://azvoleff.com/articles/preprocessing-imagery-with-teamlucc/ Other packages that are necessary: ENVI & IDL slc-off gap fill used by two of the cloud removal algorithms in R, CLOUD_REMOVE & CLOUD_REMOVE_FAST

General Methodology (described on project proposal) Pre-processing 1. Image Search (low- or no- cloud images near target date(s)) 2. Image acquisition http://earthexplorer.usgs.gov/ https://espa.cr.usgs.gov/login/?next=/ 3. Image import 4. Geometric correction, 5. Crop image to remove edges where not all bands are present, and/or to the sub-area of the image. 6. Optional: Layer stacking of multiple dates, in a multi-date classification. Classification 7. Manually draw training sites on satellite image, run process, look for errors, draw more training sites. Iterative process until error is sufficiently low. Post-Processing 8. Mosaic classified images if it is multiple path/rows. 9. Filtering to remove noise. 10. Merge with other dates of the same path/row if it is a multi-date map Traditionally we mapped landcover change using landsat by doing supervised classifications of two-date images The advantage of this approach, versus classifying images individually and then comparing them to identify change, is that it eliminates a lot of 'false-change' problems. When two images of the same place are classified individually, they will always differ somewhat due to the individual nature of every image--sun angle, atmospheric conditions, seasonality, etc. So comparing them to look for change will give some change that is false. Fixing this is a matter of trying to get each of the classifications as 'correct', and therefore identical to each other, as possible. This is not an easy task, given the above variations mentioned, especially in complex landscapes and cloud conditions. Classifying the two dates together avoids this problem. New tools, and the new Landsat CDR-SR dataset, may make possible a more efficient landcover mapping method. (http://landsat.usgs.gov/CDR_ECV.php ) The new data is much more radiometrically consistent from image to image. Among the new tools: open-source, highly scriptable/automatable software; topographic correction; cloud removal; new approaches to extracting change information from series of single-date classifications that incorporate the probability info from the classifier. Putting it all together, the new approach is to train on a whole stack of images and apply the resulting model to classify each image in the stack. Then run a change detection routine on the stack of results. That last step is the key for me. We're very close now to being able to assess if we can get sufficiently accurate change detection with this approach. If so, it will represent a pretty significant advance from the way we used to do it.

work completed (implemented and tested) data acquisition, data pre-processing (involves) DEM acquisition and preprocessing Landsat CDR-SR ID & Acquisition one can’t just do an automated search for cloud-free images that meet the year and month criteria; one must visually inspect the images. Pre-Processing/Topo Correction (only necessary for those images requiring topographic correction, i.e. Malawi) Cloud Fill & Remove IDL is necessary for slc-off gap fill for two of the cloud removal algorithms in R, CLOUD_REMOVE & CLOUD_REMOVE_FAST (this is generally the best choice). auto_calc_predictors: to generate an image with texture layers, and DEM-derived layers image classification, extract_training_data Classify_image or team_classify? change detection accuracy assessment

Any questions Authors Grady Harper Alex Zoevlff (http://azvoleff.com/ Others Simon Willcock Miro Honzak Elena Pérez-Miñana Elena.perezminana@bc3research.org Any questions