Mountain Legacy Project Research Articles

Assessing the accuracy of georeferenced landcover data derived from oblique imagery using machine learning

AbstractRepeat photography offers distinctive insights into ecological change, with ground‐based oblique photographs often predating early aerial images by decades. However, the oblique angle of the photographs presents challenges for extracting and analyzing ecological information using traditional remote sensing approaches. Several innovative methods have been developed for analyzing repeat photographs, but none offer a comprehensive end‐to‐end workflow incorporating image classification and georeferencing to produce quantifiable landcover data. In this paper, we provide an overview of two new tools, an automated deep learning classifier and intuitive georeferencing tool, and describe how they are used to derive landcover data from 19 images associated with the Mountain Legacy Project, a research team that works with the world's largest collection of systematic high‐resolution historic mountain photographs. We then combined these data to produce a contemporary landcover map for a study area in Jasper National Park, Canada. We assessed georeferencing accuracy by calculating the root‐mean‐square error and mean displacement for a subset of the images, which was 4.6 and 3.7 m, respectively. Overall classification accuracy of the landcover map produced from oblique images was 68%, which was comparable to landcover data produced from aerial imagery using a conventional classification method. The new workflow advances the use of repeat photographs for yielding quantitative landcover data. It has several advantages over existing methods including the ability to produce quick and consistent image classifications with little human input, and accurately georeference and combine these data to generate landcover maps for large areas.

Repeat Oblique Photography Shows Terrain and Fire-Exposure Controls on Century-Scale Canopy Cover Change in the Alpine Treeline Ecotone

Alpine Treeline Ecotone (ATE), the typically gradual transition zone between closed canopy forest and alpine tundra vegetation in mountain regions, displays an elevational range that is generally constrained by thermal deficits. At landscape scales, precipitation and moisture regimes can suppress ATE elevation below thermal limits, causing variability in ATE position. Recent studies have investigated the relative effects of hydroclimatic variables on ATE position at multiple scales, but less attention has been given to interactions between hydroclimatic variables and disturbance agents, such as fire. Advances in monoplotting have enabled the extraction of canopy cover information from oblique photography. Using airborne lidar, and repeat photography from the Mountain Legacy Project, we observed canopy cover change in West Castle Watershed (Alberta, Canada; ~103 km2; 49.3° N, 114.4° W) over a 92-year period (1914–2006). Two wildfires, occurring 1934 and 1936, provided an opportunity to compare topographic patterns of mortality and succession in the ATE, while factoring by exposure to fire. Aspect was a strong predictor of mortality and succession. Fire-exposed areas accounted for 83.6% of all mortality, with 72.1% of mortality occurring on south- and east-facing slope aspects. Succession was balanced between fire-exposed and unburned areas, with 62.0% of all succession occurring on north- and east-facing slope aspects. The mean elevation increase in closed canopy forest (i.e., the lower boundary of ATE) on north- and east-facing undisturbed slopes was estimated to be 0.44 m per year, or ~44 m per century. The observed retardation of treeline advance on south-facing slopes is likely due to moisture limitation.

Assessing Fractional Cover in the Alpine Treeline Ecotone Using the WSL Monoplotting Tool and Airborne Lidar

ABSTRACTAs forest cover in mountain areas impacts headwater properties like habitat extent and downstream water resources, it is important to assess and understand the changes that occur across forest transition zones like the alpine treeline ecotone (ATE). Such changes occur slowly and manifest at decadal to century time scales; however, the present benchmark spatial resolution for land cover analysis from oblique repeat photographs, 100 m, is insufficient to analyze anticipated changes in ATE over a century-scale. In this research note, fractional cover classification of the ATE is achieved through oblique photography analysis with the WSL Monoplotting Tool. Seven oblique photographs of the West Castle Watershed (Alberta, Canada), collected by the Mountain Legacy Project, were gridded to a 20-m resolution and assigned canopy cover classes by manual interpretation. Four canopy cover classes (i.e., no cover, low vegetation, partial canopy, full canopy) were compared to lidar-derived fractional cover. The extraction of canopy cover information from oblique photography at a resolution of 20 m introduces the ability to assess and quantify changes in the ATE using century-scale oblique photographic records.

Exploring Landscape Change in Mountain Environments With the Mountain Legacy Online Image Analysis Toolkit

Since 1996, Mountain Legacy Project (MLP) researchers have been exploring change in Canada's mountain environments through the use of systematic repeat photography. With access to upwards of 120,000 systematic glass plate negatives from Canada's mountain west, the MLP field teams seek to stand where historic surveyors stood and accurately reshoot these images. The resulting image pairs are analyzed, catalogued, and made available for further research into landscape changes. In this article we suggest that repeat photography would fit well within the Future Earth research agenda. We go on to introduce the Image Analysis Toolkit (IAT), which provides interactive comparative image visualization and analytics for a wide variety of ecological, geological, fluvial, and human phenomena. The toolkit is based on insights from recent research on repeat photography and features the following: user-controlled ability to compare, overlay, classify, scale, fade, draw, and annotate images; production of comparative stat...

Extracting ecological information from oblique angle terrestrial landscape photographs: Performance evaluation of the WSL Monoplotting Tool

While aerial photography and satellite imagery are the usual data sources used in remote sensing, land based oblique photographs can also be used to measure ecological change. By using such historical photographs, the time frame for change detection can be extended into the late 1800s and early 1900s, predating the era of aerial imagery by decades. Recent advancements in computing power have enabled the development of techniques for georeferencing oblique angle photographs. The WSL Monoplotting Tool is a new piece of software that opens the door to analyzing such photographs by allowing for extraction of spatially referenced vector data from oblique photographs. A very large repeat photography collection based on the world's largest systematic collection of historical mountain topographic survey images, the Mountain Legacy Project, contains >6000 high resolution oblique image pairs showing landscape changes in the Rocky Mountains of Alberta between ca. 1900 – today. We used a subset of photographs from this collection to assess the accuracy and utility of the WSL Monoplotting Tool for georeferencing oblique photographs and measuring landscape change. We determined that the tool georeferenced objects to within less than 15 m of their real world 3D spatial location, and the displacement of the geographic center of over 121 control points was less than 3 m from the real world spatial location. Most of the error in individual object placement was due to the angle of viewing incidence with the ground (i.e., low angle/highly oblique angles resulted in greater horizontal error). Simple rules of control point selection are proposed to reduce georeferencing errors. We further demonstrate a method by which raster data can be rapidly extracted from an image pair to measure changes in vegetation cover over time. This new process permits the rapid evaluation of a large number of images to facilitate landscape scale analysis of oblique imagery.