Journal: Landscape Ecology

Article Title: A novel method to assess spatio-temporal habitat availability for a generalist indicator species group in human-modified landscapes

doi: 10.1007/s10980-025-02124-x

Figure Lengend Snippet: a Map of the time-series clusters; b The resulting mean values for both the Available Habitat Area (AHA; orange) and Human Modification Density (HMD; blue) for the ten time-series clusters across the five dispersal distances. The shaded area around each line shows the standard deviation of the values across all the pixels that fall within each cluster for which the mean is the solid line. Insets in (a) show zoomed in regions showing significant visual patterns in clustering. The background topographic map is from Esri, TomTom, Garmin, FAO, NOAA.

Article Snippet: The background topographic map is from Esri, TomTom, Garmin, FAO, NOAA. ( ) When comparing the HMD to the AHA trajectories, the trends of AHA are not consistent with the trends in HMD.

Techniques: Modification, Standard Deviation

Journal: Landscape Ecology

Article Title: A novel method to assess spatio-temporal habitat availability for a generalist indicator species group in human-modified landscapes

doi: 10.1007/s10980-025-02124-x

Figure Lengend Snippet: a Map of the time-series clusters; b The resulting mean values for both the Available Habitat Area (AHA; orange) and Human Modification Density (HMD; blue) for the ten time-series clusters across the five dispersal distances. The shaded area around each line shows the standard deviation of the values across all the pixels that fall within each cluster for which the mean is the solid line. Insets in (a) show zoomed in regions showing significant visual patterns in clustering. The background topographic map is from Esri, TomTom, Garmin, FAO, NOAA.

Article Snippet: The background topographic map is from Esri, TomTom, Garmin, FAO, NOAA. ( ) When comparing the HMD to the AHA trajectories, the trends of AHA are not consistent with the trends in HMD.

Techniques: Modification, Standard Deviation

Journal: Environmental Science & Technology

Article Title: A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: A Case Study for California and Oklahoma

doi: 10.1021/acs.est.4c04413

Figure Lengend Snippet: (a) Portion of an HTMC map in Osage County, OK from 1973. Colored circles indicate O&G wells: purple = documented wells, blue = detected wells, and red = potential UOWs. The numbers in dark blue are unique IDs assigned to each detected well. Numbers in brown represent the distance in meters of the potential UOW to the closest documented well. (b) Zoomed satellite image of the area showing an oil rig 9.4 m away from the detected well symbol. (c) Portion of an HTMC map for Kern County, CA from 1953 with wells indicated as described in (a). (d) Satellite image of the same area of (c) showing no structure currently visible. (e) Zoomed-in historic aerial photo from 1956 showing the presence of a structure. (f) Magnetic survey (dots) showing signal compatible with the presence of an underground well. Maximum value of the anomaly being 6.5 m away from detected coordinates. The background is a 2D linear interpolation of the magnetic field from the dots on a color scale shown on the right, and is intended as a visual aid to visualize the magnetic field anomaly.

Article Snippet: In this study, we describe a semiautomated, transferable method developed for the CATALOG program to identify UOWs at regional scales in the US using the US Geological Survey (USGS) Historical Topographic Maps Collection (HTMC), a digital archive of about 190,000 georeferenced topographic maps published between 1884 and 2006.

Techniques: