#Franklin rehab center rocky mounty series#

#Franklin rehab center rocky mounty series#

However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015–2020). In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. In addition, these are very strong assumptions in a citizen science project. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. This is helpful for analyzing past landscape conditions in historical photographs, monitoring decadal‐span landscape change and assessing habitat to model biodiversity through time.Īutomated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. Our work shows that terrestrial oblique photographs can be used to estimate landscape composition, particularly in mountain environments. We then evaluated land cover change from corresponding historical and repeat photographs and found that the landscape has homogenized over the past century via increased coniferous forest cover. We found that the two sources sampled the landscape in broadly similar ways, with near‐concordance for dominant land cover classes, yet that oblique photographs more frequently detected narrow landscape features and estimated higher proportions of rock compared to satellite imagery, possibly due to the higher spatial resolution of the oblique photographs, and to their angle of incidence against steep slopes. We compared landscape‐level composition estimates from both sources and regressed the land cover proportions from Landsat against the modern oblique images, hypothesizing a linear relationship for most classes. We classified 46 historical oblique photographs and their corresponding modern repeats using the same discrete land cover classes employed in a Landsat‐based map of the same area. We present a case study contrasting how these two types of imagery sample landscape composition and using repeat oblique photographs to evaluate long‐term land cover change in a remote region of the Canadian Rocky Mountains. Abstract While orthogonal (i.e., aerial or satellite) imagery has become the more conventional source of land cover data because it can yield spatially accurate land cover maps, terrestrial oblique photographs present a valuable, relatively untapped source of raw optical data for studies of land cover change.