Creating Adaptation Strategies for Parks Using Enduring Features
Sector: Car parking • Location: United States of America
Source: Grants.gov
Data on sensitivity and adaptive capacity was incorporated at the landscape scale using remote sensing data. But, the assessment needs ground-truthing, using the substantial fine scale data available from NCRN monitoring plots. This will provide park managers with explicitly-defined sensitivity to stressors by assessing ecological integrity, and with improved understanding of existing adaptive cap
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Participants
Sponsoring Agency | Obfuscated Data |
Company | Obfuscated Data |
Status
Original status | archived |
Taiyo status | Obfuscated Data |
Taiyo last update | 00-00-0000 |
Available timestamps | 00-00-0000 |
Available timestamp type | Obfuscated Data |
Contact
Contact name | Obfuscated Data |
Phone | 0000000000 |
ObfuscatedData@email.com | |
Address | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Description
Description | Data on sensitivity and adaptive capacity was incorporated at the landscape scale using remote sensing data. But, the assessment needs ground-truthing, using the substantial fine scale data available from NCRN monitoring plots. This will provide park managers with explicitly-defined sensitivity to stressors by assessing ecological integrity, and with improved understanding of existing adaptive capacity measures that incorporate topographic complexity, local connectivity and forest patch size. The NCRN data will improve the modeling of forest response to larger landscape and regional scale exposures from stressors. We will use a spatial framework that assesses the systems ecological integrity over time and include other system stressors. The framework will use plot data from the parks’ vegetation classification and maps (113 plant communities, grouped into 24 ecological systems) and also 15 years of NCRN and other eastern I&M Networks monitoring data. |
Original sub-sector | Obfuscated |
Original Currency | USD |
Original budget | 000000000000000 |
Procurement method | Obfuscated Data |
Budget | 000000000000000 |
Location
Region | Obfuscated |
Country | Obfuscated |
State | Obfuscated Data |
County | Obfuscated |
Location | Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data |
Source
Source reliability | High |
Data quality score | 100% |
Source | Obfuscated Data |
URL | obfuscated_data,obfuscateddata.com |
More Details
Project Type | Obfuscated Data |
Article Published Date | Obfuscated Data |
