Approaches for mapping and monitoring arid rangelands with object-based image analysis and hyperspatial imagery

TitleApproaches for mapping and monitoring arid rangelands with object-based image analysis and hyperspatial imagery
Publication TypeConference Paper
Year of Publication2007
AuthorsLaliberte, Andrea S., Rango A.
Conference Name2nd Annual Symposium on Object-Based Image Analysis
Date PublishedJune 7-8, 2007
Conference LocationBerkeley, CA
ARIS Log Number220933
Keywordsanalysis, image, mapping, object-basaed
AbstractAt the USDA Agricultural Research Service Jornada Experimental Range (JER) in southern New Mexico, remote sensing research is focused on finding new methods for mapping and monitoring rangelands, and on relating ground-based surveys to remotely sensed information. This presentation will give an overview or recent research at JER involving object-based image analysis and hyperspatial imagery ranging from QuickBird satellite imagery, aerial photography, imagery acquired with unmanned aircraft, to ground-based plot photography. The QuickBird image was segmented at multiple scales to map shrubs at a fine scale and other vegetation communities at coarser scales. We were able to identify and map 87% of shrubs greater than 2 m2. A decision tree was used as an effective tool for sorting through the numerous input features and reducing them to a manageable rule set for classification. The overall accuracy was 80% for the optimal segmentation scale. In this case, we used ground-based plot photography as ground truth for the QuickBird remote sensing analysis. We compared image-based estimates of vegetation cover with line-point-intercept (LPI) measures for 50 plots (2.5 m x 3.5 m). The images were transformed from the RGB (red, green, blue) to the IHS (intensity, hue, saturation) color space. Object-based image analysis was used to classify the images into soil, shadow, green vegetation, and senescent vegetation using a masking approach and combination of membership rules and nearest neighbor classification. The correlation coefficients between LPI- and image-based estimates for the four classes ranged from 0.88 to 0.95. The object-based image approach was less labor and time intensive than the LPI method and has the potential to be incorporated into rangeland monitoring protocols. Our latest tool for rangeland monitoring is an unmanned aerial vehicle (UAV), capable of acquiring 5 cm resolution imagery from a 150 m above ground flying height. While the imagery presents some challenges for orthorectification and mosaicking, object-based image analysis has already proven to be highly successful. The very high-resolution imagery allows for identification of individual plants, patches, gaps, and patterns not previously possible, and will allow for assessment of rangeland health and ecosystem change at multiple scales. While this is a project in progress, initial mapping results indicate accuracies in the high 90% range. Based on this recent research, it is apparent that object-based image analysis will play a major part in future rangeland mapping and monitoring at multiple scales. It is also apparent that new tools in object-based image analysis will be needed, specifically in the area of object-based accuracy assessment.