Search

Search Results

• 1. [Article] Modeling and Mapping in Support of the Regional Conservational Strategy Framework

  Prior to November 2010, when The Intertwine Alliance launched the Regional Conservation Strategy (RCS) and Biodiversity Guide (RBG) efforts for the Portland-Vancouver metropolitan region, conservation ...

  Citation × Citation Citation Abstract Copy Title: Modeling and Mapping in Support of the Regional Conservational Strategy Framework Author: Burcsu, Theresa, Albo, Thomas, Bernert, Joseph, Dimiceli, Jennifer, Kagan, James S., Noone, Matthew D. Year: 2013 Prior to November 2010, when The Intertwine Alliance launched the Regional Conservation Strategy (RCS) and Biodiversity Guide (RBG) efforts for the Portland-Vancouver metropolitan region, conservation priorities in the metropolitan region were identified at a broad regional scale that generally excluded urban areas (e.g., state conservation strategies and Willamette Synthesis); were regional but based solely on expert opinion (e.g., Natural Features); and consisted of localized priorities that abruptly ended at jurisdiction boundaries. The goal of the RCS was to fill in the gaps between broad and local scales of information related to conservation priorities. RCS members envisioned a data-driven approach that could add a regional perspective to local efforts and facilitate cross-scale cooperation toward protecting remaining valuable habitat in the Portland-Vancouver metropolitan region. Also, RCS members expected that the product would complement rather than replace local knowledge, by validating what we know and expanding to areas we know less well. In June 2011, INR completed an initial proof-of-concept product describing high value conservation areas in the Portland-Vancouver region. The product demonstrated a methodology that enabled stakeholder involvement while also being data-driven. In September 2012, we completed a second version of this product that is reported on in this document. While the product is considered complete at this time, it is expected and hoped that the models and data will be updated and improved upon into the future as more and better information becomes available so that the product functions as a "living work" rather than a one-time snapshot in time. Several key products resulted from the project: the High Value Habitat data describing high value terrestrial habitat within the metropolitan region, the Riparian Habitat data describing high value habitat adjacent to streams and rivers, and the high spatial resolution land cover data set describing land cover at a 5 m spatial resolution. Title: Modeling and Mapping in Support of the Regional Conservational Strategy Framework Author: Burcsu, Theresa, Albo, Thomas, Bernert, Joseph, Dimiceli, Jennifer, Kagan, James S., Noone, Matthew D. Year: 2013 Prior to November 2010, when The Intertwine Alliance launched the Regional Conservation Strategy (RCS) and Biodiversity Guide (RBG) efforts for the Portland-Vancouver metropolitan region, conservation priorities in the metropolitan region were identified at a broad regional scale that generally excluded urban areas (e.g., state conservation strategies and Willamette Synthesis); were regional but based solely on expert opinion (e.g., Natural Features); and consisted of localized priorities that abruptly ended at jurisdiction boundaries. The goal of the RCS was to fill in the gaps between broad and local scales of information related to conservation priorities. RCS members envisioned a data-driven approach that could add a regional perspective to local efforts and facilitate cross-scale cooperation toward protecting remaining valuable habitat in the Portland-Vancouver metropolitan region. Also, RCS members expected that the product would complement rather than replace local knowledge, by validating what we know and expanding to areas we know less well. In June 2011, INR completed an initial proof-of-concept product describing high value conservation areas in the Portland-Vancouver region. The product demonstrated a methodology that enabled stakeholder involvement while also being data-driven. In September 2012, we completed a second version of this product that is reported on in this document. While the product is considered complete at this time, it is expected and hoped that the models and data will be updated and improved upon into the future as more and better information becomes available so that the product functions as a "living work" rather than a one-time snapshot in time. Several key products resulted from the project: the High Value Habitat data describing high value terrestrial habitat within the metropolitan region, the Riparian Habitat data describing high value habitat adjacent to streams and rivers, and the high spatial resolution land cover data set describing land cover at a 5 m spatial resolution. Close

• 2. [Article] Tree Cover Mapping for Assessing Greater Sage-Grouse Habitat in Eastern Oregon

  We used a predictive model to map canopy cover of vegetation over seven feet in height ("tall woody vegetation") at 30-meter resolution over nearly 29 million acres within and adjacent to the range of ...

  Citation × Citation Citation Abstract Copy Title: Tree Cover Mapping for Assessing Greater Sage-Grouse Habitat in Eastern Oregon Author: Nielsen, Eric M., Noone, Matthew D. Year: 2014 We used a predictive model to map canopy cover of vegetation over seven feet in height ("tall woody vegetation") at 30-meter resolution over nearly 29 million acres within and adjacent to the range of the greater sage-grouse in Oregon (Figure 1). Texture measures computed at various resolutions from color-infrared aerial photography provided the main source of predictor data used to produce the map. Canopy cover was treated as a categorical variable using six cover classes: absent (cover class C0), present at less than 4% (C1), 4 – 10% (C2), 10 – 20% (C3), 20 – 50% (C4), and 50% and over (C5). The map is referenced to conditions in the years 2011 and 2012. Although the specific target of the mapping was western juniper (Juniperus occidentalis), our reference data did not permit separating juniper from other tall woody vegetation during the predictive modeling process. The majority of the tall woody vegetation within the project area is western juniper. However, in high elevation regions, riparian, wetland, and residential areas, other vegetation is occasionally represented. The methodology discussed here produces raw modeled data. It is recommended that prior to use in most applications this raw tree cover product be additionally filtered or masked to eliminate false detections which often occur adjacent to agricultural areas and roads. For species-specific applications, an additional modeling phase is necessary to either eliminate tree cover detections likely to be species other than the target, or to model species importance values associated with each tree occurrence. Title: Tree Cover Mapping for Assessing Greater Sage-Grouse Habitat in Eastern Oregon Author: Nielsen, Eric M., Noone, Matthew D. Year: 2014 We used a predictive model to map canopy cover of vegetation over seven feet in height ("tall woody vegetation") at 30-meter resolution over nearly 29 million acres within and adjacent to the range of the greater sage-grouse in Oregon (Figure 1). Texture measures computed at various resolutions from color-infrared aerial photography provided the main source of predictor data used to produce the map. Canopy cover was treated as a categorical variable using six cover classes: absent (cover class C0), present at less than 4% (C1), 4 – 10% (C2), 10 – 20% (C3), 20 – 50% (C4), and 50% and over (C5). The map is referenced to conditions in the years 2011 and 2012. Although the specific target of the mapping was western juniper (Juniperus occidentalis), our reference data did not permit separating juniper from other tall woody vegetation during the predictive modeling process. The majority of the tall woody vegetation within the project area is western juniper. However, in high elevation regions, riparian, wetland, and residential areas, other vegetation is occasionally represented. The methodology discussed here produces raw modeled data. It is recommended that prior to use in most applications this raw tree cover product be additionally filtered or masked to eliminate false detections which often occur adjacent to agricultural areas and roads. For species-specific applications, an additional modeling phase is necessary to either eliminate tree cover detections likely to be species other than the target, or to model species importance values associated with each tree occurrence. Close