Anu Swatantran
Biographic Data
| ID | 7998574 |
|---|---|
| NAME | Anu Swatantran |
| GIVEN NAMES | Anu |
| FAMILY NAME | Swatantran |
| SIGNATURE | SWATANTRAN A |
| AFFILIATIONS | University of Maryland, College Park |
| ORCID | 0000-0003-0779-2779 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2019 |
| H-INDEX | 0 |
Beyond MRV
Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current …
High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA
Accurate estimation of forest aboveground biomass at high-resolution continues to remain a challenge and long-term goal for carbon monitoring and accounting systems. Here, we present an exhaustive evaluation and validation of a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms. We developed this framework over…
No prominent works on this page.
Beyond MRV
Forests are important ecosystems that are under increasing pressure from human use and environmental change, and have a significant ability to remove carbon dioxide from the atmosphere, and are therefore the focus of policy efforts aimed at reducing deforestation and degradation as well as increasing afforestation and reforestation for climate mitigation. Critical to these efforts is the accurate monitoring, reporting and verification of current …
High-resolution mapping of aboveground biomass for forest carbon monitoring system in the Tri-State region of Maryland, Pennsylvania and Delaware, USA
Accurate estimation of forest aboveground biomass at high-resolution continues to remain a challenge and long-term goal for carbon monitoring and accounting systems. Here, we present an exhaustive evaluation and validation of a robust, replicable and scalable framework that maps forest aboveground biomass over large areas at fine-resolution by linking airborne lidar and field data with machine learning algorithms. We developed this framework over…
Agroforestry (2 works) · Computer Science (2 works) · Ecology (2 works) · Environmental Science (2 works) · Forest management (2 works) · Forest Management and Policy (2 works) · Remote Sensing and LiDAR Applications (2 works) · Afforestation (1 works) · Biomass (ecology (1 works) · Canopy (1 works)