# Presentation: 812092

## DISCover Validation: Lessons Learned

**Alan Strahler**
**GLC2000 Meeting**
**Ispra, March 2002**

## DISCover Validation in a Nutshell

**A random sample, stratified by class label, with 25 pixels selected globally per class, was drawn**
**Sampled pixels were overlain on SPOT or TM imagery**
**Expert regional photointerpreters labeled each pixel in a standard scheme**
**Accuracy was assessed on a per-class basis through a confusion matrix and weighted by area to give an overall accuracy of 67 percent**
**Individual class accuracies ranged from 40 to 100 percent**

## Lessons Learned

**Input Data and Mapping Methods**
**AVHRR data are severely limiting at 1-km resolution**
**New strategies are needed for dealing with uneven distribution of land cover reference data**
**It is unlikely that fully automated methods will ever be achieved**
**Land cover mapping projects should be data independent, using best data and methods available**

## Lessons Learned, Cont.

**Future Database Development Issues**
**Urban land-cover characterization improvements are required**
**Better methods are needed for wetlands**
**Improved consistency of forest cover classes is needed**
**Information content needs to go beyond conventional labels**
**Better understanding of the stability of the characteristics of land covers is needed**

## Lessons Learned, Cont.

**Core Validation**
**Validation of thematic accuracy of global land cover datasets is possible and practical**
**Not all class mappings met the target threshold of 85%**
**Accuracy of verification data amplifies the level of uncertainty on the final accuracies**
**Sample stratification for small classes can be problematic**
**Need to have the final map before distributing samples is problematic**

## Lessons Learned, Cont.

**Core Validation, Cont.**
**Small and fragmented classes are mapped with lower accuracy—legends need to consider this**
**For use of high-resolution imagery as the reference, more research is needed on impacts of misregistration, interpreter consistency, and quality of ancillary data**
**Use of expert interpreters was appropriate and beneficial**
**The ultimate value of careful validation and accuracy assessment needs to be documented by users**

## Conclusions

**The relationship between land cover and temporal-spectral data is too frequently ambiguous; different land cover types often have similar spatial, temporal, and spectral characteristics**
**Thus, land cover mapping at any scale yields imperfect results; the DISCover validation initiative is a first step toward developing a standard**
**Validation costs are not that great, especially considering the many uses of the land cover data**

## Conclusions, Cont.

***“******We have shown that, through cooperation and collaboration, the international remote sensing community can pull together to achieve... a validated land cover data set. We no longer should be satisfied using global maps of unknown accuracy whose thematic representations are, in essence, the ‘expert testimony’ of of their creators.  ...The need for current global terrestrial information continues to grow. The importance of the global perspective now has new significance as environmental information input to the policy making, policy development and policy implementation processes grows*****...*****” ****
***Estes, Belward, Loveland, Scepan, Strahler, Townshend and Justice, 1999, The way forward, *****Photogram. Engr. Remote Sens*****., 65:1088–1093.**

## Slide 9