Detecting medusahead (Taeniatherum caput-medusae (L.) Nevski) using high frequency, sequential, globally positioned digital images

Invasive plant species are expanding and transforming vegetative communities across Oregon and throughout the United States. Over the past three decades remote sensing, geographic information system (GIS), and Global Positioning System (GPS) technologies have been integrated to detect and map the distribution of noxious rangeland plants. This study developed low-cost protocols to detect and map Medusahead (Taeniatherum caput-medusae (L.) Nevski) weed infestations using GPS loggers to track aircraft/camera position, altitude, and bearing, as well as Aerial Image Positioning Tool software to geographically rectify and project each aerial image. We then mapped the extent of medusahead in target areas and evaluated patterns of infestation. Flying in a single engine fixed-wing aircraft, images were collected every five seconds, with a total of 10,362 images obtained. All of the aerial images were processed and, on average, 23.9 % of the area was classified as medusahead infested, with 76.1 % without infestation. Each image covered 215 ha (531 acres), with 60% overlap, at a cost of $ 0.54/km². Our study also employed mobile mapping technology to map medusahead on the ground by digitizing infestations using a laptop computer equipped with a GPS antenna and GIS software. Mobile mapping was also done from aircraft, but yielded coarser infestation maps, as the observation distance was greater. These maps covered the full study area. Aerial reconnaissance and mobile survey is cost effective, because thousands of digital images were collected, automatically positioned, and stored

Registration Of Very Time-Distant Aerial Images

In this paper we address the alignment of historical and present-day aerial photographs. Historical images refer to regions bombed during the second world war. In this regions the risk of unexploded bombs is still high, especially where the bombing were more frequent. The alignment is required to fill-in an unexploded bombs risk map. The task is challenging because a lot of features in the historical images are changed or missing (and vice versa). Moreover, in the historical images, bomb craters introduce large gray level variations so that it is difficult to extract features automatically. This work propose a semi-automatic application for images alignment in order to improve the accuracy and speed up alignment process